[ { "id": "YoEVLhBBsQnvRmqe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.015, 0.372, 0.034, 0.557]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 0 }, { "id": "nKaPh882qRLM1TAh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first item in the legend?" }, { "from": "gpt", "value": "[0.753, 0.478, 0.985, 0.664]" } ], "id_raw": 0 }, { "id": "jhYs4EIv5Qmu1CmD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.385, 0.943, 0.426, 0.975]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 0 }, { "id": "FCrJryXykNggHxvL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_0.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Rural_Population'}[[0.067, 0.705, 0.744, 0.791]] above or below {the line for 'Urban_Infrastructure'}[[0.067, 0.206, 0.744, 0.550]]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 0 }, { "id": "vi44sq2rPQT20isX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_0.png", "conversations": [ { "from": "human", "value": "\nFind the coordinates for the point for 'Rural_Infrastructure' and 'Urban_Infrastructure' in Year 2001. Which of the two is higher?" }, { "from": "gpt", "value": "{Rural_Infrastructure}[0.135, 0.705], {Urban_Infrastructure}[0.135, 0.533]. Rural_Infrastructure is higher." } ], "id_raw": 0 }, { "id": "cBq48VAVGoyu1n8D", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Rural_Population' in 2010}[0.744, 0.791] above or below {the point for 'Urban_Population' in 2010}[0.744, 0.413]" }, { "from": "gpt", "value": "Below." } ], "id_raw": 0 }, { "id": "wm93ABskFnsYGE1a", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content found in [0.753, 0.478, 0.985, 0.664]." }, { "from": "gpt", "value": "This area contains the legend items: {Rural_Population}[0.806, 0.489, 0.944, 0.520], {Urban_Population}[0.806, 0.534, 0.952, 0.564], {Rural_Infrastructure}[0.806, 0.578, 0.971, 0.608], and {Urban_Infrastructure}[0.806, 0.622, 0.978, 0.653], each color-coded." } ], "id_raw": 0 }, { "id": "BaqN815lNCSpP4Yp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_0.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects located in the region [0.067, 0.034, 0.744, 0.895]?" }, { "from": "gpt", "value": "This region contains four overlapping lines, each representing a data series (Rural_Population, Urban_Population, Rural_Infrastructure, Urban_Infrastructure), and the associated data points plotted on the chart." } ], "id_raw": 0 }, { "id": "fv3QkGrMFsfyoUXc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content of the region between coordinates [0.067, 0.722] and [0.744, 0.791]." }, { "from": "gpt", "value": "This region consists mainly of the line named 'Rural_Population' plotted using blue stars showing changes from Year 2000 to 2010." } ], "id_raw": 0 }, { "id": "bpoWm8DU6iXpwaCc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.144, 0.034, 0.785]?" }, { "from": "gpt", "value": "Energy Use and Resource Consumption." } ], "id_raw": 1 }, { "id": "ZUy2O1SDWmrR58aK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the line representing 'Renewable Energy' in the chart?" }, { "from": "gpt", "value": "[0.175, 0.161, 0.911, 0.893]" } ], "id_raw": 1 }, { "id": "6wTKhghzajWbkReN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_1.png", "conversations": [ { "from": "human", "value": "\nWhat does {the point}[0.911, 0.161] stand for?" }, { "from": "gpt", "value": "The data of 'Renewable Energy' in 2021." } ], "id_raw": 1 }, { "id": "W9kyPYjJYzN34JHi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_1.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Coal'}[0.175, 0.537, 0.911, 0.624] predominantly above or below {the line for 'Oil'}[0.175, 0.467, 0.911, 0.628]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1 }, { "id": "6FZRJzdq520RCR3K", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Renewable Energy'}[0.175, 0.161, 0.911, 0.893] have a higher or lower ending point compared to {the line for 'Nuclear Energy'}[0.175, 0.686, 0.911, 0.884]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1 }, { "id": "WeiDIds9vbW3ZHaX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_1.png", "conversations": [ { "from": "human", "value": "\nIn 1971, is the use of {Natural Gas}[0.175, 0.679] more or less than {Oil}[0.175, 0.628]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 1 }, { "id": "ysV7GvWaJYuE4XRE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.110, 0.048, 0.325, 0.320]." }, { "from": "gpt", "value": "The region encompasses the chart's legend, listing the different energy sources: Coal, Oil, Natural Gas, Nuclear Energy, Hydro Power, Renewable Energy \u2013 each with distinct markers." } ], "id_raw": 1 }, { "id": "P3LKfGmp5Rb7Bji9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.523, 0.943, 0.564, 0.975]." }, { "from": "gpt", "value": "The area contains the label 'Year' for the x-axis." } ], "id_raw": 1 }, { "id": "b6ikWZgv9mDQv79I", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.894, 0.911, 0.929, 0.934]?" }, { "from": "gpt", "value": "There is an x-axis tick label '2021' within this region." } ], "id_raw": 1 }, { "id": "QESVi2NFqZdjeoMq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at these coordinates [0.082, 0.377, 0.098, 0.633]?" }, { "from": "gpt", "value": "It's the Y-axis label, which represents 'Observation Value'." } ], "id_raw": 2 }, { "id": "QqsrgTfMVv9nCyQE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the x-axis tick label present at the coordinates [0.287, 0.904, 0.316, 0.924]?" }, { "from": "gpt", "value": "The tick label at this coordinate is '2002'." } ], "id_raw": 2 }, { "id": "IBSQWxnUDXfRlTjR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the handle mark in the legend, representing 'Consistent Expansion'}[0.134, 0.147, 0.169, 0.159]?" }, { "from": "gpt", "value": "The handle mark color is #1f77b4." } ], "id_raw": 2 }, { "id": "k8DAWcR7qaiiXArF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the line representing 'Consistent Expansion'}[0.160, 0.237, 0.865, 0.649] have a higher or lower starting point compared to {the line for 'Gradual Recovery'}[0.160, 0.155, 0.865, 0.546]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 2 }, { "id": "3tJwzglTRB25I53R", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_2.png", "conversations": [ { "from": "human", "value": "\nWhat line ends at a higher value in 2010, {Fluctuating Growth}[0.160, 0.155, 0.865, 0.608] or {Slight Decrease}[0.160, 0.505, 0.865, 0.855]?" }, { "from": "gpt", "value": "Fluctuating Growth." } ], "id_raw": 2 }, { "id": "Y5Jeld10uYGDSzra", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_2.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Gradual Recovery'}[0.160, 0.155, 0.865, 0.546] above or below {the line for 'Fluctuating Growth'}[0.160, 0.155, 0.865, 0.608] at 2006?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 2 }, { "id": "179sCRZtFz9Jkseb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents within the region [0.132, 0.132, 0.331, 0.290]?" }, { "from": "gpt", "value": "This is the legend of the chart, containing handles and labels for 'Consistent Expansion', 'Gradual Recovery', 'Fluctuating Growth', and 'Slight Decrease', each associated with a colored handle." } ], "id_raw": 2 }, { "id": "CNQBw71MhWyfEaen", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_2.png", "conversations": [ { "from": "human", "value": "\nTell me about the contents located at these coordinates [0.495, 0.932, 0.530, 0.959]." }, { "from": "gpt", "value": "This region contains the x-axis label, which displays 'Year'." } ], "id_raw": 2 }, { "id": "EcIOL3GFrVoQPG4b", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_2.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the region between [0.1, 0.127, 0.3, 0.147]?" }, { "from": "gpt", "value": "The y-axis tick label '40' is located here, at {coordinates}[0.102, 0.127, 0.117, 0.147]." } ], "id_raw": 2 }, { "id": "agDPI4XdtcT6WWK5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_3.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at the coordinates [0.744, 0.533, 0.909, 0.563]?" }, { "from": "gpt", "value": "Legend item \"Water Quality Index\"." } ], "id_raw": 3 }, { "id": "CkA9KuhhQxUKNpBO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the value at {the x-coordinate}[0.880, 0.911, 0.897, 0.934] on the x-axis?" }, { "from": "gpt", "value": "60." } ], "id_raw": 3 }, { "id": "P7LCCsGV2kdyzFun", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_3.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {the \"Natural Areas %\" line}[0.083, 1.562, 0.888, 1.606] in the legend?" }, { "from": "gpt", "value": "Purple." } ], "id_raw": 3 }, { "id": "U4ZyHNyaqOT7Ls5T", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_3.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Air Pollution Particles Index'}[0.083, 0.034, 0.888, 0.799] higher or lower than {the 'Natural Areas %'}[0.083, 1.562, 0.888, 1.606] at x=60?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 3 }, { "id": "O4e1R00t9EGQ3svA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_3.png", "conversations": [ { "from": "human", "value": "\nAre {the \"Life Expectancy at Birth\" values}[0.083, 0.241, 0.888, 0.262] generally increasing or decreasing?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 3 }, { "id": "1VQPUkntSFuQKX5J", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_3.png", "conversations": [ { "from": "human", "value": "\nWhich label is represented by the {color \"#1f77b4\"}[0.692, 0.494, 0.736, 0.512] in the legend?" }, { "from": "gpt", "value": "Life Expectancy at Birth." } ], "id_raw": 3 }, { "id": "1XtQsyqFxB7cjVWh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region from [0.691, 0.478, 0.977, 0.662]?" }, { "from": "gpt", "value": "This area contains the legend for the chart. It includes four indicators: \"Life Expectancy at Birth\", \"Water Quality Index\", \"Air Pollution Particles Index\", and \"Natural Areas %\". Each indicator is represented with a unique color and marker." } ], "id_raw": 3 }, { "id": "5pnDEy2I7Z977sgw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.040, 0.121, 0.057, 0.144]." }, { "from": "gpt", "value": "Within this region, there is a y-axis label '85'." } ], "id_raw": 3 }, { "id": "dP2uxMx9SetlbHJ4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.244, 0.187, 0.888, 0.244]?" }, { "from": "gpt", "value": "This region contains points from the Water Quality Index line with values for x=20 to x=60." } ], "id_raw": 3 }, { "id": "T7FSF9J8UGQHgMuS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_4.png", "conversations": [ { "from": "human", "value": "\nWhat kind of information is expressed by the object with coordinates [0.454, 0.943, 0.496, 0.975] in the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 4 }, { "id": "SJeHBmb7BB8Cs4sj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the location of the second Y-axis label?" }, { "from": "gpt", "value": "[0.049, 0.790, 0.092, 0.813]" } ], "id_raw": 4 }, { "id": "kh0N1Dav7x7Da3xd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the second line graph}[0.177, 0.536, 0.773, 0.632]?" }, { "from": "gpt", "value": "Asia." } ], "id_raw": 4 }, { "id": "ZQe3usqvrDAo4x1q", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point at year 2002 of the Africa line graph} [0.326, 0.818] higher or lower than {the corresponding point at year 2002 on the Asia line graph} [0.326, 0.613]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 4 }, { "id": "uQk76FkZHMXoZHZZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_4.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Africa'} [[0.177, 0.823], [0.251, 0.822] , [0.326, 0.818], [0.400, 0.816], [0.475, 0.808], [0.550, 0.804], [0.624, 0.794], [0.699, 0.785], [0.773, 0.795]] generally above or below {the line for 'America'} [[0.177, 0.751], [0.251, 0.747] , [0.326, 0.785], [0.400, 0.761], [0.475, 0.751], [0.550, 0.665], [0.624, 0.646], [0.699, 0.574], [0.773, 0.636]]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 4 }, { "id": "d0Alx6l43vdou5mW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_4.png", "conversations": [ { "from": "human", "value": "\nIn the year 2007, which of the continents had a higher market value, Africa or America?" }, { "from": "gpt", "value": "America." } ], "id_raw": 4 }, { "id": "rU9LD1iuVx5ekkhV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the coordinates [0.856, 0.478, 0.985, 0.662]." }, { "from": "gpt", "value": "This region contains the chart legend, with labels for the line graphs of 'Global', 'Asia', 'Africa', and 'America'. Each of these labels has a colour-coded symbol associated with it." } ], "id_raw": 4 }, { "id": "tUdgz72LzyGeqdy3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_4.png", "conversations": [ { "from": "human", "value": "\nWhat's in the area [0.049, 0.694, 0.092, 0.718] of the figure?" }, { "from": "gpt", "value": "The Y-axis tick label '40000' is located in this area." } ], "id_raw": 4 }, { "id": "aQHei6oiEHHlL4yK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the region denoted by [0.159, 0.911, 0.194, 0.934]?" }, { "from": "gpt", "value": "This region contains the first x-axis tick label '2000'." } ], "id_raw": 4 }, { "id": "V51zcLMiiBCfdMEY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the text located at coordinates [0.028, 0.279, 0.047, 0.299]?" }, { "from": "gpt", "value": "The text is a Y-axis tick label \"120\"." } ], "id_raw": 5 }, { "id": "hb3D5JE8q4ppas1U", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label shown in the chart?" }, { "from": "gpt", "value": "The x-axis label is \"Year\" based on its positioning {at}[0.507, 0.951, 0.536, 0.979]." } ], "id_raw": 5 }, { "id": "9389IjQxCz6pC6ww", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the first line plotted in the chart?" }, { "from": "gpt", "value": "The color of the first line plotted is #1f77b4 which was for {New Drug Approvals}[0.119, 0.754, 0.924, 0.832]." } ], "id_raw": 5 }, { "id": "5kbL88nCwqChrvaF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_5.png", "conversations": [ { "from": "human", "value": "\nWhich line represented by the markers 'o' and 'x' is on the top at the x-axis label 2010?" }, { "from": "gpt", "value": "The line represented by the marker 'o', {New Drug Approvals}[0.441, 0.806], is on top of the line represented by the marker 'x', {Patents Filed}[0.441, -4.023], at the x-axis label 2010." } ], "id_raw": 5 }, { "id": "Vu1MaV4uEiTgzLaD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest point on the line representing Biotech Investment {coordinates}[0.119, 0.102, 0.924, 0.753]?" }, { "from": "gpt", "value": "The highest point on the line is at {2000}[0.119, 0.753]." } ], "id_raw": 5 }, { "id": "nTHZkYIgTTqHj8Xr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_5.png", "conversations": [ { "from": "human", "value": "\nIs {the New Drug Approvals line}[0.119, 0.754, 0.924, 0.832] above or below the {Biotech Investment line}[0.119, 0.102, 0.924, 0.753] in 2020?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 5 }, { "id": "8KWCYKBBnjSFADyr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the coordinates [0.060, 0.033, 0.313, 0.191]." }, { "from": "gpt", "value": "This area contains the legend for the chart. The items listed include {New Drug Approvals}[0.098, 0.043, 0.220, 0.069], {Patents Filed}[0.098, 0.080, 0.174, 0.106], {Market Size}[0.098, 0.118, 0.261, 0.144], and {Biotech Investment}[0.098, 0.156, 0.308, 0.181]. Each of these items is associated with a unique color and marker." } ], "id_raw": 5 }, { "id": "vGv3hUc2NEdfvmVI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_5.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within the coordinates [0.106, 0.923, 0.937, 0.943]?" }, { "from": "gpt", "value": "This area contains tick labels of the x-axis for the years 2000 at {coordinates}[0.106, 0.923, 0.131, 0.943], 2005 at {coordinates}[0.267, 0.923, 0.292, 0.943], 2010 at {coordinates}[0.428, 0.923, 0.453, 0.943], 2015 at {coordinates}[0.589, 0.923, 0.614, 0.943], 2020 at {coordinates}[0.750, 0.923, 0.775, 0.943], and 2025 at {coordinates}[0.912, 0.923, 0.937, 0.943]." } ], "id_raw": 5 }, { "id": "Sv1wCf7IQF3jgA4g", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_5.png", "conversations": [ { "from": "human", "value": "\nCan you portray the details seen within the coordinates [0.098, 0.043, 0.308, 0.181]?" }, { "from": "gpt", "value": "This rectangular region encapsulates all the text labels attached to the legend handles. These labels include {New Drug Approvals}[0.098, 0.043, 0.220, 0.069], {Patents Filed}[0.098, 0.080, 0.174, 0.106], {Market Size}[0.098, 0.118, 0.261, 0.144], and {Biotech Investment}[0.098, 0.156, 0.308, 0.181]." } ], "id_raw": 5 }, { "id": "xsnWuSGWyM87dFJr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_6.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the line labeled 'Modern'}[0.130, 0.158, 0.942, 0.381]?" }, { "from": "gpt", "value": "#008000." } ], "id_raw": 6 }, { "id": "2l3fwn4YQSpSdtdI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the value associated with the second tick on {the y-axis}[0.015, 0.394, 0.034, 0.539]?" }, { "from": "gpt", "value": "$10^{-5}$." } ], "id_raw": 6 }, { "id": "kxGDmy6Jv8Z81XyS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_6.png", "conversations": [ { "from": "human", "value": "\nWhat represents {the point coordinates} at [0.444, 0.299] on the chart?" }, { "from": "gpt", "value": "Data point on the line labeled 'Modern'." } ], "id_raw": 6 }, { "id": "l4IL0ib2JJuRrv2v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_6.png", "conversations": [ { "from": "human", "value": "\nIs {the traditional line point at '30'}[0.628, 0.470] higher or lower than {the modern line point at '30'}[0.628, 0.236]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 6 }, { "id": "VIizc0z75iskI0ds", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_6.png", "conversations": [ { "from": "human", "value": "\nWhich line begins at a higher position on the chart, {traditional}[0.130, 0.723, 0.942, 0.381] or {modern}[0.130, 0.381, 0.942, 0.158]?" }, { "from": "gpt", "value": "Traditional." } ], "id_raw": 6 }, { "id": "j2TSYN3EhvxAkTNk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the point of the Traditional line at x = 40}[0.758, 0.432] occur above or below {the Modern line's starting position}[0.130, 0.381]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 6 }, { "id": "Bxn1pfdKZiXA94xA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_6.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.015, 0.394, 0.139, 0.934]?" }, { "from": "gpt", "value": "This region contains the y-axis label 'Precision' styled in black color at [0.015, 0.394, 0.034, 0.539] and several y-axis tick labels with tick coordinates ranging from [0.083, 0.895] to [0.083, 0.038]." } ], "id_raw": 6 }, { "id": "C5sVMvvbmoaSsRT9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content located at [0.830, 0.480, 0.977, 0.576]." }, { "from": "gpt", "value": "This area is occupied by the chart's legend, which includes 'Traditional' with coordinates [0.884, 0.491, 0.970, 0.521] and 'Modern' with coordinates [0.884, 0.535, 0.946, 0.565], each associated with color markers [0.831, 0.496, 0.876, 0.514] and [0.831, 0.540, 0.876, 0.558] respectively." } ], "id_raw": 6 }, { "id": "91vpWF3TC7ZkznSG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_6.png", "conversations": [ { "from": "human", "value": "\nDescribe what is included in the region [0.122, 0.911, 0.951, 0.934]." }, { "from": "gpt", "value": "This region encloses the x-axis labels from '10' at [0.122, 0.911, 0.139, 0.934] up to '60' at [0.933, 0.911, 0.951, 0.934], each associated with their bottom tick coordinates from [0.130, 0.895] to [0.942, 0.895]." } ], "id_raw": 6 }, { "id": "PSUO5YHeYjHZzXa2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the box with coordinates [0.892, 0.547, 0.965, 0.577]?" }, { "from": "gpt", "value": "The label 'Unstable' in the legend." } ], "id_raw": 7 }, { "id": "QWneA1xVvS2eWaxF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_7.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the y-axis label is at the coordinates [0.015, 0.247, 0.034, 0.533]?" }, { "from": "gpt", "value": "Values (log scale)." } ], "id_raw": 7 }, { "id": "fhGddhqQaYZ0yMXH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.050, 0.772, 0.128, 0.901]?" }, { "from": "gpt", "value": "Activity_Level." } ], "id_raw": 7 }, { "id": "jda2hUSH8xMrMZcI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Erratic'}[0.537, 0.343] higher or lower than {the point for 'Standard'}[0.537, 0.418]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 7 }, { "id": "vDGstNylwKLr9zs2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_7.png", "conversations": [ { "from": "human", "value": "\nAt Social_Interaction, is {the line for 'Decrease'}[0.537, 0.261] above or below {the line for 'Unstable'}[0.537, 0.584]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 7 }, { "id": "3Wl0NpjRVAO5YFgx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_7.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at Activity_Level, {the line for 'Decrease'}[0.089, 0.263] or {the line for 'Standard'}[0.089, 0.306]?" }, { "from": "gpt", "value": "Standard." } ], "id_raw": 7 }, { "id": "XUY9n40aEo9vcjWw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the region [0.839, 0.404, 0.977, 0.588]?" }, { "from": "gpt", "value": "The chart legend is located within these coordinates. It contains four entries: {Standard}[0.892, 0.415, 0.967, 0.445], {Erratic}[0.892, 0.459, 0.946, 0.489], {Decrease}[0.892, 0.503, 0.970, 0.533], and {Unstable}[0.892, 0.547, 0.965, 0.577], each with an associated marker." } ], "id_raw": 7 }, { "id": "rePT11G5mNThbcLv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_7.png", "conversations": [ { "from": "human", "value": "\nCan you tell me about the region at [0.040, 0.293, 0.079, 0.324]?" }, { "from": "gpt", "value": "This region contains the y-axis tick label for the value of {10^-6}[0.040, 0.293, 0.079, 0.324]." } ], "id_raw": 7 }, { "id": "WxBMUw2207flcui8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_7.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area [0.040, 0.181, 0.079, 0.212]?" }, { "from": "gpt", "value": "The y-axis tick label for {10^-4}[0.040, 0.181, 0.079, 0.212] is located within this area." } ], "id_raw": 7 }, { "id": "GIw04bmXzyStj7MA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the feature represented by {the handle colored '#1f77b4'}[0.815, 0.494, 0.860, 0.512]?" }, { "from": "gpt", "value": "Standard." } ], "id_raw": 9 }, { "id": "9boWb2ieZPtvDryd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the value displayed by {the point} at [0.465, 0.670] on the 'Hypothetical' line?" }, { "from": "gpt", "value": "1.569." } ], "id_raw": 9 }, { "id": "82TNOamM0B98XcKl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_9.png", "conversations": [ { "from": "human", "value": "\nWhat represents {the legend item} with label at [0.868, 0.621, 0.973, 0.651]?" }, { "from": "gpt", "value": "Hypothetical." } ], "id_raw": 9 }, { "id": "oyjO8OaLb7XIV7eb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_9.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Standard' line point} at [0.202, 0.260] have higher or lower value than {the 'Constrained' line point} at [0.202, 0.772]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 9 }, { "id": "kT52rHy6oQwx5hqh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_9.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Experimental' line point} at [0.596, 0.603] above or below {the 'Hypothetical' line point} at [0.596, 0.694]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 9 }, { "id": "g9jAQZhxeHUSnVFG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_9.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at {the x=30 point}, 'Standard' or 'Experimental'?" }, { "from": "gpt", "value": "Standard." } ], "id_raw": 9 }, { "id": "aZ4OlIHcnZYTsnpm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the region [0.814, 0.478, 0.985, 0.662]?" }, { "from": "gpt", "value": "This region contains the legend of the chart, including labels and corresponding handles for 'Standard', 'Constrained', 'Experimental', and 'Hypothetical'." } ], "id_raw": 9 }, { "id": "WOxOn9j0k31pokGV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.058, 0.025, 0.048, 0.909]." }, { "from": "gpt", "value": "This vertical area includes the y-axis tick labels ranging from '0' to '6'." } ], "id_raw": 9 }, { "id": "d8yASj4DgIxcLieE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_9.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area [0.465, 0.481, 0.465, 0.670]?" }, { "from": "gpt", "value": "In this region, points from the 'Standard', 'Experimental', and 'Hypothetical' lines intersect the x=40 tick label." } ], "id_raw": 9 }, { "id": "DkO70zJNve1DtALT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_10.png", "conversations": [ { "from": "human", "value": "\nWhat represents the volume of transactions in {Regular Cryptocurrency}[0.109, 0.093, 0.943, 0.790] on the chart?" }, { "from": "gpt", "value": "Blue line with circle markers." } ], "id_raw": 10 }, { "id": "K2MsQy80mag65GZ8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.303, 0.034, 0.667]?" }, { "from": "gpt", "value": "Transactions Volume (billions)." } ], "id_raw": 10 }, { "id": "r3D9reotBMAMpVfg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the year represented by {the x-axis tick}[0.759, 0.933, 0.794, 0.951]?" }, { "from": "gpt", "value": "2019." } ], "id_raw": 10 }, { "id": "xgJfmMKChcOwEB8U", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_10.png", "conversations": [ { "from": "human", "value": "\nWhich trendline has a higher value in 2017, the {Regular Cryptocurrency line}[0.109, 0.093, 0.943, 0.790] or the {Blockchain Transactions line}[0.109, 0.494, 0.943, 0.572]?" }, { "from": "gpt", "value": "Blockchain Transactions line." } ], "id_raw": 10 }, { "id": "m06m3QfpPzSXlqS7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_10.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Stablecoin}[0.109, 0.441, 0.943, 0.877] in 2020 higher or lower than its value in 2015?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 10 }, { "id": "a4MZAdmIeZ8iiNQ2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the trend of {Traditional Currency}[0.109, 0.842, 0.943, 0.877] from 2015 to 2020?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 10 }, { "id": "EQ5GylebIwuQlbOo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_10.png", "conversations": [ { "from": "human", "value": "\nWhat features are located in the region with coordinates [0.067, 0.049, 0.985, 0.921]?" }, { "from": "gpt", "value": "This region contains the main chart area, which includes four trend lines representing different types of currency, each marked with different color and line style. The labels for the year are located along the x-axis, and the labels for the transactions volume are located along the y-axis." } ], "id_raw": 10 }, { "id": "yjZoHL36R83SFDZU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region with coordinates [0.074, 0.058, 0.290, 0.168]." }, { "from": "gpt", "value": "This region defines the legend of the chart, which contains 4 items, each representing a type of currency trend: {Regular Cryptocurrency}[0.118, 0.065, 0.283, 0.082], {Stablecoin}[0.118, 0.091, 0.191, 0.108], {Traditional Currency}[0.118, 0.117, 0.258, 0.135], and {Blockchain Transactions}[0.118, 0.143, 0.285, 0.161]." } ], "id_raw": 10 }, { "id": "gMnAMIGoY2sFFgEJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the region [0.067, 0.049, 0.949, 0.973]?" }, { "from": "gpt", "value": "This region contains the entire chart, including all of the labels, trend lines, axes, and the legend." } ], "id_raw": 10 }, { "id": "DbxQDwASFfoWYDPi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.015, 0.289, 0.034, 0.626] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 11 }, { "id": "Up8nfD0WS9oXM1hM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_11.png", "conversations": [ { "from": "human", "value": "\nWhere can I find {age 40} in the x-axis tick labels?" }, { "from": "gpt", "value": "[0.334, 0.907, 0.351, 0.930]." } ], "id_raw": 11 }, { "id": "kmsh5ygzUoM2pwdi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend item at [0.753, 0.619, 0.797, 0.637] represent?" }, { "from": "gpt", "value": "Aging." } ], "id_raw": 11 }, { "id": "BMvW4WQ0jrMrCFgM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_11.png", "conversations": [ { "from": "human", "value": "\nWhich is at a higher point in the graph at age 80, {Preventable Stable}[0.575, 0.757] or {Preventable Growing}[0.575, 0.390]?" }, { "from": "gpt", "value": "Preventable Stable." } ], "id_raw": 11 }, { "id": "AwCarS9GAc3qme2C", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_11.png", "conversations": [ { "from": "human", "value": "\nAt the age of 120, is the {Eradicated} line[0.711, 0.060] above or below the {Aging} line[0.711, 0.425]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 11 }, { "id": "DCQXghNVc864YOug", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_11.png", "conversations": [ { "from": "human", "value": "\nAt age 100, is {Preventable Growing}[0.650, 0.335] above or below {Preventable Stable}[0.650, 0.661]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 11 }, { "id": "hVSigp4XK1Oqn7oM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the content found in the region [0.752, 0.472, 0.985, 0.656]?" }, { "from": "gpt", "value": "This region contains the legend for the four lines: {Eradicated}[0.753, 0.487], {Preventable Growing}[0.753, 0.531], {Preventable Stable}[0.753, 0.575], {Aging}[0.753, 0.619]." } ], "id_raw": 11 }, { "id": "w8KQFL0TVmcyPe84", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area spanning [0.357, 0.943, 0.461, 0.975]." }, { "from": "gpt", "value": "This area houses the x-axis label \"Age Group\"." } ], "id_raw": 11 }, { "id": "6QoiY5gCAWHgddkf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is contained within the region [0.101, 0.907, 0.119, 0.930]?" }, { "from": "gpt", "value": "This area contains the x-axis tick label for the age group '20'." } ], "id_raw": 11 }, { "id": "FM5SqByvM2Dm8sts", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.817, 0.059, 0.935, 0.089] in the legend?" }, { "from": "gpt", "value": "It's legend label, Stable Growth." } ], "id_raw": 12 }, { "id": "GSZVXOCAPBhRN4yZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the {xlabel} at [0.498, 0.943, 0.559, 0.975] of the chart?" }, { "from": "gpt", "value": "Month." } ], "id_raw": 12 }, { "id": "zEuBBERTyDdtl9JF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_12.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.079, 0.142] in the plot?" }, { "from": "gpt", "value": "The first data point for the \"Stable Growth\"." } ], "id_raw": 12 }, { "id": "7tzsDRtAMIVg9QT3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_12.png", "conversations": [ { "from": "human", "value": "\nIs {the x marker representing 'Erratic Decline'}[0.154, 0.397] above or below {the o marker representing 'Stable Growth'}[0.154, 0.166] in February?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 12 }, { "id": "zTDbICT3HtZfRNDL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_12.png", "conversations": [ { "from": "human", "value": "\nIs {the triangle marker for 'Unstable Behavior'}[0.683, 0.325] above or below the {square marker for 'Minimal Change'}[0.683, 1.133] in September?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 12 }, { "id": "3d7naDrvC7PGTLfw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_12.png", "conversations": [ { "from": "human", "value": "\nIn October, which shows a higher value, {Stable Growth}[0.758, 0.362] or {Erratic Decline}[0.758, 0.718]?" }, { "from": "gpt", "value": "Erratic Decline." } ], "id_raw": 12 }, { "id": "1bWWfLETxCEdlVrZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within region [0.764, 0.048, 0.977, 0.232]." }, { "from": "gpt", "value": "It's the area of the legend which includes the labels 'Stable Growth', 'Erratic Decline', 'Minimal Change', 'Unstable Behavior' along with their corresponding color markers." } ], "id_raw": 12 }, { "id": "WS8rBsGXC2F3I3lw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the region [0.817, 0.191, 0.970, 0.221]." }, { "from": "gpt", "value": "It contains the last text in the legend, the label 'Unstable Behavior'." } ], "id_raw": 12 }, { "id": "rXYvMEbbu7yqRXcE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_12.png", "conversations": [ { "from": "human", "value": "\nMention the objects within [0.365, 0.911, 0.395, 0.934]." }, { "from": "gpt", "value": "It's the x-axis tick label for 'May'." } ], "id_raw": 12 }, { "id": "gc9ywe1Htx4d4aOD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.015, 0.271, 0.034, 0.649]?" }, { "from": "gpt", "value": "Y-axis title." } ], "id_raw": 13 }, { "id": "DeaHLMlBy3LVhZOs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_13.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the coordinates of the last item in the legend?" }, { "from": "gpt", "value": "[0.736, 0.621, 0.780, 0.639]." } ], "id_raw": 13 }, { "id": "boivaZi9U5Wa929b", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_13.png", "conversations": [ { "from": "human", "value": "\nWhat data value does {the x-axis label}[0.933, 0.911, 0.951, 0.934] represent?" }, { "from": "gpt", "value": "60." } ], "id_raw": 13 }, { "id": "Eyw7t4Lnj72RV37f", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_13.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Anxiety Level at N=60'}[0.942, 0.359] higher or lower than {the point for 'Self-esteem Level at N=60'}[0.942, 0.359]?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 13 }, { "id": "4KwB1WRyqCYzSYO5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_13.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Depression Level'}[0.136, 0.226, 0.942, 0.226] above or below the line for {the line for 'Self-esteem Level'}[0.136, 0.159, 0.942, 0.359]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 13 }, { "id": "vHmjhUU1TLn0UNht", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_13.png", "conversations": [ { "from": "human", "value": "\nWhich variable shows a higher value for N=30, Anxiety Level or Life Satisfaction Level?" }, { "from": "gpt", "value": "Anxiety Level." } ], "id_raw": 13 }, { "id": "VQ2nyxXq0enzAz2V", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.735, 0.474, 0.977, 0.658]." }, { "from": "gpt", "value": "The area contains the legend of the chart, which includes four variables - {Anxiety Level}[0.788, 0.485, 0.900, 0.515], {Depression Level}[0.788, 0.529, 0.930, 0.559], {Self-esteem Level}[0.788, 0.573, 0.936, 0.603] and {Life Satisfaction Level}[0.788, 0.616, 0.970, 0.646], each labeled with a distinctive marker and color." } ], "id_raw": 13 }, { "id": "oluy70F5oeGBGMzl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.136, 0.025, 0.942, 0.359]." }, { "from": "gpt", "value": "This area represents the trend lines for 'Anxiety Level', 'Depression Level', 'Self-esteem Level' and 'Life Satisfaction Level' against the variable 'N' ranging from 10 to 60." } ], "id_raw": 13 }, { "id": "weFFQCJsSkhXPWxV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects can you see within coordinates [0.136, 0.159, 0.942, 0.359]?" }, { "from": "gpt", "value": "Inside this bounding box, we have points from the trend lines of three variables: 'Anxiety Level', 'Self-esteem Level', and 'Life Satisfaction Level', ranging from 'N'=10 to 'N'=60." } ], "id_raw": 13 }, { "id": "Xs8h5u1vXVj43hX5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object represented by the handle at coordinates [0.885, 0.041, 0.922, 0.055] in the 'sublegend'?" }, { "from": "gpt", "value": "It's the handle representing the color for 'Stock A'." } ], "id_raw": 14 }, { "id": "x99xGGOyctwdIIEQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the Y-axis label at [0.012, 0.396, 0.028, 0.544] indicate?" }, { "from": "gpt", "value": "The Y-axis label indicates 'Stock Prices'." } ], "id_raw": 14 }, { "id": "Llxkooxg7PwNqGwQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.351, 0.933, 0.358, 0.951]?" }, { "from": "gpt", "value": "5." } ], "id_raw": 14 }, { "id": "wqrv7UtOpMix0opa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Stock C'}[0.739, 0.438] above or below {the point for 'Stock A'}[0.739, 0.249] in the 11th month?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 14 }, { "id": "eayvAkK3KAUC5ij4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_14.png", "conversations": [ { "from": "human", "value": "\nIn the tenth month, is {the point for 'Stock B'}[0.675, 0.154] above or below {the point for 'Stock D'}[0.675, 0.722]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 14 }, { "id": "WRAC1jsyoyxXG0OM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_14.png", "conversations": [ { "from": "human", "value": "\nWhich stock has a higher value in the last month, {Stock A}[0.803, 0.217] or {Stock B}[0.803, 0.060]?" }, { "from": "gpt", "value": "Stock B." } ], "id_raw": 14 }, { "id": "C76Dj5U4Xbv5cCrh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.884, 0.029, 0.988, 0.167]." }, { "from": "gpt", "value": "This region contains the legend of the chart with indicators of four different stocks: {Stock A}[0.885, 0.041, 0.922, 0.055], {Stock B}[0.885, 0.074, 0.922, 0.088], {Stock C}[0.885, 0.107, 0.922, 0.120], and {Stock D}[0.885, 0.140, 0.922, 0.153]. Each indicator includes a color handle and a label representing a specific stock." } ], "id_raw": 14 }, { "id": "Lu21CXCVqj8raUDd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area within the coordinates [0.098, 0.217, 0.803, 0.564]?" }, { "from": "gpt", "value": "This is the line representing the 'Stock A' trend across the months. Its points range from month 1 to month 12, showing a mostly increasing trend." } ], "id_raw": 14 }, { "id": "hp3oqL5kMhwrJstW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.033, 0.053, 0.055, 0.070]?" }, { "from": "gpt", "value": "It contains only one object. This is the Y-axis label '180'." } ], "id_raw": 14 }, { "id": "5BmcW10LJU7oKxwe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the x-axis label in the chart?" }, { "from": "gpt", "value": "[0.492, 0.957, 0.557, 0.981]" } ], "id_raw": 15 }, { "id": "jtHTiI8CzAKmfab9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis} at [0.012, 0.381, 0.028, 0.553]?" }, { "from": "gpt", "value": "Popularity (%)." } ], "id_raw": 15 }, { "id": "ngv5FwHFvOyxwEaZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the identify of the object at [-1081.205, 0.113, 0.945, 0.530] in the chart?" }, { "from": "gpt", "value": "It is the Standard line." } ], "id_raw": 15 }, { "id": "EO5J5oltW7X4QnLB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_15.png", "conversations": [ { "from": "human", "value": "\nWhich cuisine popularity is higher in France {Standard}[[-1081.205, 0.530]] or {Vegetarian Popularity}[-1081.205, 0.578]?" }, { "from": "gpt", "value": "Vegetarian Popularity." } ], "id_raw": 15 }, { "id": "y3TVg6PWhgHRtvmI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_15.png", "conversations": [ { "from": "human", "value": "\nIn China, is {Standard}[0.430, 0.295] higher than {Street Food Popularity}[0.430, 0.165]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 15 }, { "id": "58Ki4tn1M0O0oOoF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_15.png", "conversations": [ { "from": "human", "value": "\nIn Thailand, is {Fine Dining Popularity}[0.945, 0.309] higher or lower than {Vegetarian Popularity}[0.945, 0.146]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 15 }, { "id": "Dp8bIsiOQqpIQJzc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.777, 0.029, 0.981, 0.167] on the chart?" }, { "from": "gpt", "value": "This region contains the legend of the chart, representing different line'd color keys such as Standard [0.822, 0.038, 0.885, 0.060], Vegetarian Popularity [0.822, 0.070, 0.970, 0.093], Street Food Popularity [0.822, 0.103, 0.975, 0.126] and Fine Dining Popularity[0.822, 0.136, 0.973, 0.159]." } ], "id_raw": 15 }, { "id": "WXSc9bMp3rxdTA9L", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.062, 0.019, 0.987, 0.915]." }, { "from": "gpt", "value": "This region is the main plotting area of the line chart, depicting the popularity of different cuisines of different countries It includes data points for various cuisine popularity categories such as Standard, Vegetarian Popularity, Street Food Popularity, and Fine Dining Popularity." } ], "id_raw": 15 }, { "id": "oJaP03EVFhS8TuR7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_15.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [-0.991, 0.927, -0.963, 0.951]?" }, { "from": "gpt", "value": "The x-axis tick label '$\\\\mathdefault{10^{-1}}$' is located within these coordinates." } ], "id_raw": 15 }, { "id": "5jeW0Ke3kblYnG83", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the coordinates [0.015, 0.415, 0.034, 0.504]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 16 }, { "id": "dYESMqtHRoHISNbN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the text in the second item of the legend with the coordinates [0.760, 0.423, 0.978, 0.453]?" }, { "from": "gpt", "value": "Inconsistent Development." } ], "id_raw": 16 }, { "id": "jtQKEpdP50KMz0oK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_16.png", "conversations": [ { "from": "human", "value": "\nWhat value does the Y-axis show at the point [0.085, 0.361]?" }, { "from": "gpt", "value": "1200." } ], "id_raw": 16 }, { "id": "XPXPLEQ8lZz5G5ZO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_16.png", "conversations": [ { "from": "human", "value": "\nCompared to the {Inconsistent Development line}[0.136, 0.262, 0.647, 0.361], is the {Rapid Expansion line}[0.136, 0.065, 0.647, 0.756] above or below?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 16 }, { "id": "t5we2MMspgC7SAWW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_16.png", "conversations": [ { "from": "human", "value": "\nAre the values of {Inconsistent Development}[0.136, 0.262, 0.647, 0.361] greater or less than the values for {Inexorable Decline}[0.136, 0.559, 0.647, 0.756] in 2020?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 16 }, { "id": "z25BbXvYnJibfPFS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_16.png", "conversations": [ { "from": "human", "value": "\nIn 2010, does the {Inexorable Decline line}[0.136, 0.559, 0.647, 0.756] have a higher or lower value than the {Rapid Expansion line}[0.136, 0.065, 0.647, 0.756]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 16 }, { "id": "7hmxWwwghoBQoNAY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.707, 0.368, 0.985, 0.552]?" }, { "from": "gpt", "value": "This region includes the legend of the chart, showing four items namely {Inexorable Decline}[0.760, 0.379, 0.915, 0.409], {Inconsistent Development}[0.760, 0.423, 0.978, 0.453], {Rapid Expansion}[0.760, 0.467, 0.898, 0.497], and {Dramatic Decrease}[0.760, 0.511, 0.920, 0.541], each with an associated color marker located to their left respectively." } ], "id_raw": 16 }, { "id": "mgZ5L6H4RO3GNQip", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_16.png", "conversations": [ { "from": "human", "value": "\nPlease detail the area of interest [0.371, 0.943, 0.412, 0.975]." }, { "from": "gpt", "value": "The X-axis label \"Year\" is located within these coordinates." } ], "id_raw": 16 }, { "id": "YHdeI7FVGcB56nIC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_16.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the space [0.040, 0.549, 0.075, 0.573]?" }, { "from": "gpt", "value": "This area is occupied by the Y-axis label '1000'." } ], "id_raw": 16 }, { "id": "s8YuEJYvStXU0zfW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.015, 0.369, 0.034, 0.551] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 17 }, { "id": "Ah4A1eG8PBq02Oig", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the line marker} at [0.837, 0.621, 0.882, 0.639]?" }, { "from": "gpt", "value": "Frightened." } ], "id_raw": 17 }, { "id": "0U0hjSKno4Dr5Dga", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_17.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.717, 0.911, 0.735, 0.934]?" }, { "from": "gpt", "value": "50." } ], "id_raw": 17 }, { "id": "L1qPrpybH1xpjBsc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_17.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Relaxed'}[0.793, 0.453] above or below {the point of 'Anxious'}[0.793, 0.433] at 'N = 60'?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 17 }, { "id": "2OMAzKmYnKqllnVM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_17.png", "conversations": [ { "from": "human", "value": "\nWhich emotion experienced a decrease from 'N = 10' to 'N = 60', 'Happy' or 'Frightened'?" }, { "from": "gpt", "value": "Frightened." } ], "id_raw": 17 }, { "id": "6A87lEQytdwU7mjC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_17.png", "conversations": [ { "from": "human", "value": "\nComparing the 'Anxious' and 'Happy' lines, which one is generally higher?" }, { "from": "gpt", "value": "Happy." } ], "id_raw": 17 }, { "id": "WrJhHEfYUddiS0Iz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_17.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.836, 0.474, 0.985, 0.658]?" }, { "from": "gpt", "value": "It is the chart's legend listing the 'Happy', 'Anxious', 'Relaxed', and 'Frightened' trends, complete with their respective colored markers: {Happy}[0.890, 0.485, 0.943, 0.515], {Anxious}[0.890, 0.529, 0.955, 0.559], {Relaxed}[0.890, 0.573, 0.956, 0.603], and {Frightened}[0.890, 0.616, 0.978, 0.646]." } ], "id_raw": 17 }, { "id": "EfoJQFfvLoO55rdf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_17.png", "conversations": [ { "from": "human", "value": "\nCould you describe the items between these coordinates [0.128, 0.165, 0.793, 0.226]?" }, { "from": "gpt", "value": "Sure, this area demonstrates the 'Frightened' trend line. It is represented by triangle markers and includes points corresponding to the values at different 'N' levels." } ], "id_raw": 17 }, { "id": "K6RTgP1fKZR0Zqqo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area defined by [0.446, 0.943, 0.465, 0.975]?" }, { "from": "gpt", "value": "This region houses the x-axis label, which reads 'N'." } ], "id_raw": 17 }, { "id": "BNhuN6sahfuIqBzs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the graph at coordinates [0.383, 0.943, 0.494, 0.975]?" }, { "from": "gpt", "value": "Parameters." } ], "id_raw": 18 }, { "id": "tZ3qy6xj4xBGhz26", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second item in the legend?" }, { "from": "gpt", "value": "[0.868, 0.533, 0.968, 0.563]." } ], "id_raw": 18 }, { "id": "svW45LSfatzhVBQt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.040, 0.541, 0.062, 0.565]?" }, { "from": "gpt", "value": "0.4." } ], "id_raw": 18 }, { "id": "WHBQeKKiCSfGS5MW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_18.png", "conversations": [ { "from": "human", "value": "\nIs {the Standard line point for 'ResponseTime'}[0.773, 0.464] higher or lower than {the Constrained line point for 'ResponseTime'}[0.773, 0.765] ?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 18 }, { "id": "B5P3A2H0TldzK1Md", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_18.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Experimental'}[0.105, 0.206, 0.773, 0.636] have a positive or negative slope compared to {the line for 'Hypothetical'}[0.105, 0.077, 0.773, 0.679]?" }, { "from": "gpt", "value": "Negative." } ], "id_raw": 18 }, { "id": "ocskAImHhHpQFztO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_18.png", "conversations": [ { "from": "human", "value": "\nWhich parameter has a higher value for Constrained line, 'NoOfQueries' or 'ResponseTime'?" }, { "from": "gpt", "value": "'NoOfQueries'." } ], "id_raw": 18 }, { "id": "zQmERJiOh6We2kYm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.814, 0.478, 0.985, 0.662]." }, { "from": "gpt", "value": "The area contains the legend items: {Standard}[0.868, 0.489, 0.943, 0.519], {Constrained}[0.868, 0.533, 0.968, 0.563], {Experimental}[0.868, 0.577, 0.978, 0.607] and {Hypothetical}[0.868, 0.621, 0.973, 0.651]. Each items is paired with a color coded marker." } ], "id_raw": 18 }, { "id": "zsmKsWKWhbRGL6rL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.071, 0.034, 0.806, 0.895]." }, { "from": "gpt", "value": "This is the main plotting area of the chart where 4 lines named Standard, Constrained, Experimental, and Hypothetical are plotted against two parameters named NoOfQueries and ResponseTime. The visible grids, x-axis, and y-axis labels are also within this area." } ], "id_raw": 18 }, { "id": "Migwk62nVRfLM4kb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_18.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the area [0.105, 0.077, 0.773, 0.679]?" }, { "from": "gpt", "value": "There are 4 objects: a point on the {Standard line}[0.773, 0.464], a point on the {Constrained line}[0.773, 0.765], a point on the {Experimental line}[0.773, 0.636], and a point on the {Hypothetical line}[0.773, 0.679]." } ], "id_raw": 18 }, { "id": "8xxo6P31sfwEYl10", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.228, 0.034, 0.531]?" }, { "from": "gpt", "value": "Number of Patents." } ], "id_raw": 19 }, { "id": "g3CwX1Krx0CAljht", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the fifth x-axis label}[0.569, 0.750, 0.625, 0.842] referring to?" }, { "from": "gpt", "value": "Electrical." } ], "id_raw": 19 }, { "id": "hmnRAmtdYfeDaGVX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_1_19.png", "conversations": [ { "from": "human", "value": "\nWhat does {the point on the line chart}[0.713, 0.236] represent?" }, { "from": "gpt", "value": "Number of patents in the 'Space' field." } ], "id_raw": 19 }, { "id": "UxM75p28A03t9iQP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_19.png", "conversations": [ { "from": "human", "value": "\nIs {the point 'Mechanical'}[0.481, 0.630] above or below {the point 'Electrical'}[0.597, 0.558] on the line chart?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 19 }, { "id": "F6YUqMws1WoRlD1i", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_19.png", "conversations": [ { "from": "human", "value": "\nComparing {the point 'NanoTech'}[0.365, 0.487] and {the point 'Electronics'}[0.944, 0.057], which one has a higher number of patents?" }, { "from": "gpt", "value": "Electronics." } ], "id_raw": 19 }, { "id": "jkBBUp0MyLjqFkr1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_1_19.png", "conversations": [ { "from": "human", "value": "\nWhich research field has more patents, {Point 'BioTech'}[0.250, 0.701] or {Point 'Artificial Intelligence'}[0.829, 0.343]?" }, { "from": "gpt", "value": "BioTech." } ], "id_raw": 19 }, { "id": "Png1VjDLWT4OFFBO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in [0.101, 0.039, 0.317, 0.091]." }, { "from": "gpt", "value": "The area contains the legend for the line chart with 'Number of Patents' written in coordinates {text_coordinates}[0.155, 0.050, 0.310, 0.080]. It is coupled with a colored marker." } ], "id_raw": 19 }, { "id": "MDXkaC5hsG1l8rRi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_19.png", "conversations": [ { "from": "human", "value": "\nDescribe what is in the region [0.449, 0.750, 0.514, 0.858]." }, { "from": "gpt", "value": "The region includes the 'Mechanical' label on the x-axis." } ], "id_raw": 19 }, { "id": "Ifcaplz1xW0EpKTc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_1_19.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the area [0.769, 0.738, 0.888, 0.935]?" }, { "from": "gpt", "value": "The x-axis label 'Artificial Intelligence' is located within this region." } ], "id_raw": 19 }, { "id": "XK5SI09ciQIXqjf7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_0.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is used for the line representing 'Run 1' in the subplot titled 'Minimal decline'?" }, { "from": "gpt", "value": "Circle." } ], "id_raw": 20 }, { "id": "P8dVIvn66pmSsMVJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of {the y-axis label} in the 'Marginal decline' subplot?" }, { "from": "gpt", "value": "[0.330, 0.109, 0.345, 0.312]." } ], "id_raw": 20 }, { "id": "Sxb38K6H2BVWJgRL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_0.png", "conversations": [ { "from": "human", "value": "\nWhat color corresponds to {the legend item 'Run 2'}[0.401, 0.113, 0.444, 0.136] in 'Marginal decline' subplot?" }, { "from": "gpt", "value": "#f08080." } ], "id_raw": 20 }, { "id": "VmUKoPICVuqUQUcq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_0.png", "conversations": [ { "from": "human", "value": "\nIs {the value labeled '72'}[0.080, 0.176] higher or lower than {the value labeled '56'}[0.080, 0.344] in 'Minimal decline' subplot?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 20 }, { "id": "we41X0Ky3cycRvGt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the first point}[0.735, 0.344] have a higher or lower value than {the fifth point}[0.955, 0.122] on 'Run 1' line in 'Downward trend' subplot?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 20 }, { "id": "E6NRF27OFwUAouuY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_0.png", "conversations": [ { "from": "human", "value": "\nWhich color represents a higher value at {the coordinate}[0.462, 0.233] in 'Marginal decline' subplot, #dd5145 or #f08080?" }, { "from": "gpt", "value": "#f08080." } ], "id_raw": 20 }, { "id": "cCW0UyyBcuytEHzs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects included in the region [0.407, 0.358, 0.628, 0.358]." }, { "from": "gpt", "value": "The x-axis labels of the 'Marginal decline' subplot, which include 'Phase 2', 'Phase 3', 'Phase 4', 'Phase 5' with their respective tick marks positioned lower than other items caught in the top view." } ], "id_raw": 20 }, { "id": "MXAj8hSMpPo7nb3e", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.120, 0.083, 0.160, 0.136] reveal?" }, { "from": "gpt", "value": "This area shows the legend information for 'Run 1' and 'Run 2' in the 'Gradual decline' subplot including their labels and corresponding handles colored in #67a9cf and #dd5145 respectively." } ], "id_raw": 20 }, { "id": "vxSAuTObybx5csUm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within [0.730, 0.064, 0.966, 0.145]?" }, { "from": "gpt", "value": "The area contains the legend for 'Downward trend' subplot displaying two runs represented by handles colored in #faa74b and #8034a0." } ], "id_raw": 20 }, { "id": "q7eJjs3skegl53kC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object at coordinates [0.024, 0.143, 0.038, 0.300] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 32 }, { "id": "36zaN9iLPfymTl1L", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.073, 0.397, 0.108, 0.455]?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 32 }, { "id": "zesHT0pYeNg0VZkA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_12.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.073, 0.884, 0.108, 0.942]?" }, { "from": "gpt", "value": "x-axis tick label for the year 2010." } ], "id_raw": 32 }, { "id": "HsLZ7EjEuZQp5K89", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_12.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher, {the point for 'Run 1'}[0.237, 0.138] in the first subplot or {the point for 'Run 2'}[0.200, 0.357] in the second subplot?" }, { "from": "gpt", "value": "Run 1." } ], "id_raw": 32 }, { "id": "MIH2Svu6HUSr5zIu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_12.png", "conversations": [ { "from": "human", "value": "\nIs {the Y-axis label}[0.024, 0.143, 0.038, 0.300] to the left of {the X-axis label}[0.186, 0.464, 0.215, 0.488]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 32 }, { "id": "KP8kBu9djdZVuuku", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the point for 'Year 2014' in Run 1}[0.894, 0.115] in the third subplot have a higher value than {the point for 'Year 2015' in Run 1}[0.930, 0.112]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 32 }, { "id": "tP2M4EePiuHwDVH8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_12.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained within [0.450, 0.025, 0.607, 0.055] in the chart?" }, { "from": "gpt", "value": "This region displays the subplot title, specifically \"Category Y Growth,\" with the text in black." } ], "id_raw": 32 }, { "id": "KCp4Lb2AmKKpoByd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the area enclosed by [0.188, 0.561, 0.213, 0.616]." }, { "from": "gpt", "value": "This region is a part of the legend in the fourth subplot, containing 'Run 1' and 'Run 2' labels and markers colored in #67a9cf and #dd5145 respectively." } ], "id_raw": 32 }, { "id": "ur2PjzeeqDjM04om", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_12.png", "conversations": [ { "from": "human", "value": "\nExplain what is present within [0.450, 0.512, 0.608, 0.952]." }, { "from": "gpt", "value": "This larger region includes axes labeled 'Year' and 'Trade Volume', numerical axis markers for years from 2010 to 2016, and y-axis values from \u2212250 up to 1500, along with the plot lines for 'Run 1' and 'Run 2' depicting trade volume growth in Category N." } ], "id_raw": 32 }, { "id": "baWFZRRKBOwU6pop", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_13.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {Run 1 in the legend for Household Goods}[0.785, 0.083, 0.825, 0.104]?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 33 }, { "id": "l3KqVS4fTtG699sR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label of the x-axis}[0.835, 0.464, 0.872, 0.487] in the Luxe Items sales chart?" }, { "from": "gpt", "value": "Runs." } ], "id_raw": 33 }, { "id": "zvO7iP4gj2Bw0oET", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-axis label}[0.704, 0.556, 0.723, 0.580] closest to Run 2 in the Luxe Items subplot?" }, { "from": "gpt", "value": "33." } ], "id_raw": 33 }, { "id": "iufpX1E2H0FgbnMG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_13.png", "conversations": [ { "from": "human", "value": "\nIs {the marker for Run 1 at point 1}[0.412, 0.849] higher or lower than {the marker for Run 2 at point 1}[0.412, 0.849] in the Necessities Sales subplot?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 33 }, { "id": "3WH4hj0cqqcoDVu6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_13.png", "conversations": [ { "from": "human", "value": "\nWhich run marker is colored pink for {Lux Items}[0.785, 0.570, 0.825, 0.592]?" }, { "from": "gpt", "value": "Run 1." } ], "id_raw": 33 }, { "id": "Epxisha6Jb1CAfiT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_13.png", "conversations": [ { "from": "human", "value": "\nIs {the value represented by the first tick mark on the y-axis}[0.401, 0.905] greater than or less than {the value represented by the second tick mark on the same axis}[0.401, 0.849] in the Necessities subplot?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 33 }, { "id": "qq7GI4d9iko3IQ2n", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the bounds [0.069, 0.064, 0.307, 0.375]." }, { "from": "gpt", "value": "This region encloses the axes for the Books Sales subplot, displaying a light pink background, grids in gray dashed lines, and labels for sales and runs." } ], "id_raw": 33 }, { "id": "jHF2BjufAlwJ5iLa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_13.png", "conversations": [ { "from": "human", "value": "\nWhat items are located within the region [0.059, 0.879, 0.318, 0.942] in the Accessories Sales subplot?" }, { "from": "gpt", "value": "This region contains x-axis tick labels 'Run 1' and 'Run 2', specially formatted with a 45-degree rotation in black color." } ], "id_raw": 33 }, { "id": "gctz2YLWxugYLX6w", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be described in the area [0.734, 0.551, 0.973, 0.863]?" }, { "from": "gpt", "value": "This area encompasses the axes of the Luxe Items Sales subplot showing a light pink backdrop, gray dashed line grids, vertical sales labels, horizontal runs labels, and coordinate points for two sales runs." } ], "id_raw": 33 }, { "id": "Aebi5CqR9fSujxAx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object at coordinates [0.348, 0.125, 0.362, 0.278] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 35 }, { "id": "fLjqcZEoQqRSEgiL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_15.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.117, 0.584, 0.188, 0.610] represent?" }, { "from": "gpt", "value": "Market label text." } ], "id_raw": 35 }, { "id": "n2VUPMsXW0ovz7t2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_15.png", "conversations": [ { "from": "human", "value": "\nAt the coordinates [0.081, 0.327], what specific element can be found?" }, { "from": "gpt", "value": "X-axis tick." } ], "id_raw": 35 }, { "id": "tpVL8Dv3hv6XrhTR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_15.png", "conversations": [ { "from": "human", "value": "\nIs {the point at 'Period 1'}[0.081, 0.327] higher on the graph than {the point at 'Period 2'}[0.960, 0.573] in the {Cryptocurrency sub-plot}[0.736, 0.562, 0.970, 0.812]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 35 }, { "id": "wYVPIWIPkshjl1dI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_15.png", "conversations": [ { "from": "human", "value": "\nComparatively, is the value at {Period 1}[0.414, 0.573] in the {Commodities sub-plot}[0.403, 0.562, 0.638, 0.812] higher than the value at {Period 2}[0.627, 0.800]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 35 }, { "id": "2QBY8Qnx6reicvQY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_15.png", "conversations": [ { "from": "human", "value": "\nWhich is positioned lower, the end point of the {Forex Market line}[0.081, 0.088, 0.294, 0.315] or the {Bond Market line}[0.747, 0.088, 0.960, 0.315] at 'Period 2'?" }, { "from": "gpt", "value": "Bond Market." } ], "id_raw": 35 }, { "id": "WS19H4l3YYrUNPHA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.722, 0.831, 0.985, 0.931]." }, { "from": "gpt", "value": "The area contains x-axis tick labels for Period 1 and Period 2, located at {72.2% width, 83.1% height, 77.2% width, 93.1% height} and {93.5% width, 83.1% height, 98.5% width, 93.1% height} respectively in the Cryptocurrency sub-plot, aligned at a 45 degree angle." } ], "id_raw": 35 }, { "id": "L94b0rQ4a8SSTABf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.067, 0.077, 0.295, 0.327]." }, { "from": "gpt", "value": "This section of the chart holds the area that plots the 'Forex Market' assets with specific colors assigned to the plotted lines indicated with corresponding points for 'Period 1' and 'Period 2'." } ], "id_raw": 35 }, { "id": "GerFSfyQCglnooNf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_15.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the specifications of region [0.776, 0.574, 0.885, 0.620]?" }, { "from": "gpt", "value": "This region houses the legend for the Cryptocurrency subplot, including the label and color handle for market labeling." } ], "id_raw": 35 }, { "id": "kpb0RFbj3uCvmxhU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_17.png", "conversations": [ { "from": "human", "value": "\nWhat is shown by {the color in the handle area}[0.739, 0.568, 0.782, 0.591] in the chart?" }, { "from": "gpt", "value": "The color represents 'Phase 1' of the 3D printing growth segment." } ], "id_raw": 37 }, { "id": "mXCSM9VAayhAsafv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_17.png", "conversations": [ { "from": "human", "value": "\nWhich axis does the text at {coordinates}[0.366, 0.556, 0.396, 0.579] belong to?" }, { "from": "gpt", "value": "Y-axis in the Immunotherapy Growth segment." } ], "id_raw": 37 }, { "id": "vAKgBs7IVyv8n6ff", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the point at {coordinates}[0.956, 0.832] represent in the chart?" }, { "from": "gpt", "value": "It marks the second phase of investment growth in 3D printing, with a value of 95." } ], "id_raw": 37 }, { "id": "vdSHfNAtSRMSBJh9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_17.png", "conversations": [ { "from": "human", "value": "\nIs {the y-tick label '85'}[0.704, 0.268, 0.723, 0.292] higher or lower than {the y-tick label '90'}[0.704, 0.202, 0.723, 0.225]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 37 }, { "id": "1Dx0fk0KuwwjoOgn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_17.png", "conversations": [ { "from": "human", "value": "\nDoes {the endpoint of line 'Phase 2'}[0.301, 0.565] have a higher or lower investment value compared to {the second phase}'s endpoint[0.628, 0.565] in the Immunotherapy Growth?" }, { "from": "gpt", "value": "The same." } ], "id_raw": 37 }, { "id": "drGWkT5fGrSUDJgf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_17.png", "conversations": [ { "from": "human", "value": "\nWhich is greater at {the label 'Phase 1'[0.458, 0.570, 0.513, 0.592], the value at the initial point[0.417, 0.832] or at the final point[0.628, 0.565]?" }, { "from": "gpt", "value": "Initial point." } ], "id_raw": 37 }, { "id": "knwimoA8dPKPG4GB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the region [0.390, 0.862, 0.655, 0.942]." }, { "from": "gpt", "value": "The region contains {the x-axis tick label 'Phase 1'}[0.390, 0.862, 0.444, 0.942] and {the tick label 'Phase 2'}[0.602, 0.862, 0.655, 0.942] in the Immunotherapy Growth subplot." } ], "id_raw": 37 }, { "id": "Kp42YUjNdmsFWCMF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_17.png", "conversations": [ { "from": "human", "value": "\nExplain what is within the bounding rectangle at [0.039, 0.556, 0.068, 0.686]." }, { "from": "gpt", "value": "It incorporates several y-axis labels, indicating the investment values ranging from 140 to 150 in the Medical Imaging Growth subplot." } ], "id_raw": 37 }, { "id": "IxDG83RGJYjIqLu0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_17.png", "conversations": [ { "from": "human", "value": "\nWhat comprises the area within [0.718, 0.551, 0.983, 0.845]?" }, { "from": "gpt", "value": "This area involves the entire plotting region for 3D Printing Growth, including y-axis labels for values 94 to 101, x-axis labels 'Phase 1' and 'Phase 2', and the corresponding data points and line plot." } ], "id_raw": 37 }, { "id": "kvpdrPxxtkAMnzx8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the color of the line representing 'Run 1'}[0.080, 0.077] in the subplot labeled 'Web Design'?" }, { "from": "gpt", "value": "#1f77b4." } ], "id_raw": 39 }, { "id": "s9QChryFikXI3p6c", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_19.png", "conversations": [ { "from": "human", "value": "\nCan you identify what {the label at point 5 on the y-axis}[0.038, 0.068, 0.058, 0.091] represents in the subplot for 'Video Production'?" }, { "from": "gpt", "value": "25." } ], "id_raw": 39 }, { "id": "3q0zMGbTcVRldwQU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_2_19.png", "conversations": [ { "from": "human", "value": "\nWhat does {the marker at point 5}[0.628, 0.344] correspond to on the line chart in the subplot for 'Social Media Marketing'?" }, { "from": "gpt", "value": "Run 1." } ], "id_raw": 39 }, { "id": "WkFMdINXApH10wAb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_19.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher at {phase 5}[0.628, 0.358], the line labeled 'Run 1' or 'Run 2' in the 'SEO' subplot?" }, { "from": "gpt", "value": "Run 1." } ], "id_raw": 39 }, { "id": "xhzcMI828qSOkqcQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_19.png", "conversations": [ { "from": "human", "value": "\nIs {the performance at phase 1}[0.408, 0.358] on 'Run 1' above or below {the performance at phase 1}[0.408, 0.311] on 'Run 2' in the 'Social Media Marketing' subplot?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 39 }, { "id": "DyPSzHAS8vuwQoVs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_2_19.png", "conversations": [ { "from": "human", "value": "\nIs {the first y-axis label}[0.069, 0.399] in 'Web Design' lower or higher than {the second y-axis label}[0.069, 0.349]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 39 }, { "id": "kPuYb2D8dtNJhnwr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_19.png", "conversations": [ { "from": "human", "value": "\nWhat is included in region [0.047, 0.065, 0.054, 0.420] on the subplot for 'Content Writing'?" }, { "from": "gpt", "value": "The y-axis tick marks and labels ranging from 35 to 60." } ], "id_raw": 39 }, { "id": "TK3yVsB3fRXMgH20", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the segment [0.668, 0.063, 0.691, 0.360] in the subplot titled 'SEO'?" }, { "from": "gpt", "value": "This area contains vertical lines of the grid, labels for phases 1 through 5 on the x-axis, and part of the y-axis label 'Performance Level'." } ], "id_raw": 39 }, { "id": "wRdZGHAGKPBLJQzs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_2_19.png", "conversations": [ { "from": "human", "value": "\nWhat features exist within the boundaries [0.076, 0.277, 0.166, 0.347] in the 'Web Design' subplot?" }, { "from": "gpt", "value": "The sublegend for 'Web Design' with items labeled 'Run 1' and 'Run 2', each paired with a color-coded marker." } ], "id_raw": 39 }, { "id": "5XQcHfRT9XuoMkga", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_1.png", "conversations": [ { "from": "human", "value": "\nWhat does {the handle with color '#1f77b4'}[0.162, 0.074, 0.199, 0.088] represent in the legend?" }, { "from": "gpt", "value": "Social Stability Index." } ], "id_raw": 41 }, { "id": "LEvwduxhcf3hso3a", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the x-axis tick at}[0.498, 0.911]?" }, { "from": "gpt", "value": "2010.0." } ], "id_raw": 41 }, { "id": "y1mnThXkdK4BthGJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_1.png", "conversations": [ { "from": "human", "value": "\nWhat does {the marker shape 'o'}[0.101, 0.182] indicate on the line graph?" }, { "from": "gpt", "value": "A data point." } ], "id_raw": 41 }, { "id": "gcAuzBl1vvsMmvCH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_1.png", "conversations": [ { "from": "human", "value": "\nWhich line is represented by the color {the line with marker 'x'}[0.101, 0.263] on the chart?" }, { "from": "gpt", "value": "Government Approval Rating." } ], "id_raw": 41 }, { "id": "WF27rAaCtoYhsgf3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_1.png", "conversations": [ { "from": "human", "value": "\nIs {the line color '#2ca02c'}[0.101, 0.870] found above or below {the line color '#1f77b4'}[0.894, 0.368] at the far right of the plot?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 41 }, { "id": "YLAyh7KQzwCI7BHY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the y-axis label 'Maintains Value'}[0.015, 0.385, 0.029, 0.627] apply to {the 0.6 y-axis tick label}[0.061, 0.425] or {the 0.8 y-axis tick label}[0.061, 0.263]?" }, { "from": "gpt", "value": "0.6." } ], "id_raw": 41 }, { "id": "sNZSfQQZodwWXWVJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the bottom area of the chart covering [0.061, 0.952, 0.934, 0.975]." }, { "from": "gpt", "value": "It contains {the x-axis label 'Year'}[0.483, 0.952, 0.512, 0.975] centered in the area, styled in black color." } ], "id_raw": 41 }, { "id": "i0wppOqMTwIxnQEk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_1.png", "conversations": [ { "from": "human", "value": "\nWhat attributes are depicted in the region spanned by [0.161, 0.062, 0.839, 0.103]?" }, { "from": "gpt", "value": "The legend for the chart, including items for {Social Stability Index}[0.206, 0.071, 0.351, 0.094], {Government Approval Rating}[0.417, 0.071, 0.620, 0.094], and {Radicalization Trends}[0.686, 0.071, 0.833, 0.094] along with their corresponding colored handles." } ], "id_raw": 41 }, { "id": "vnlFYlmHNCumV8kp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_1.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained within the bounds [0.933, 0.101, 0.982, 0.911] on the right of the chart?" }, { "from": "gpt", "value": "This region holds {the y-axis label 'Government Approval Rating'}[0.971, 0.337, 0.985, 0.674] which tracks the approval ratings over years." } ], "id_raw": 41 }, { "id": "NxBlxFFcoTfvthWw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color {of the line representing 'Carbon Footprints'}[0.160, 0.155, 0.865, 0.855]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 43 }, { "id": "3CTfV1wZXiWkJ2VR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_3.png", "conversations": [ { "from": "human", "value": "\nHow is {the line correspondent to 'Energy Efficiency'}[0.160, 0.186, 0.865, 0.540] styled?" }, { "from": "gpt", "value": "Solid line with circles." } ], "id_raw": 43 }, { "id": "cRM9EC3D6gw2tVIs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the marker type for {the line indicating 'Renewable Sources'}[0.160, 0.155, 0.865, 0.855]?" }, { "from": "gpt", "value": "Triangles." } ], "id_raw": 43 }, { "id": "CCbsLdNOJhoUXlY0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_3.png", "conversations": [ { "from": "human", "value": "\nIs the level at point {x=10} for {Renewable Sources}[0.436, 0.442] higher than {Energy Efficiency}[0.436, 0.391]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 43 }, { "id": "12nZV7dHqf1ihUdB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_3.png", "conversations": [ { "from": "human", "value": "\nDoes the line for {Energy Efficiency}[0.160, 0.186, 0.865, 0.540] at point {x=13} have a lower value compared to {Carbon Footprints}[0.528, 0.658]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 43 }, { "id": "10nNAHnupTWQ1WyG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_3.png", "conversations": [ { "from": "human", "value": "\nBetween points x=5 and x=10, which line increases more, {Renewable Sources}[0.283, 0.557][0.436, 0.442] or {Energy Efficiency}[0.283, 0.477][0.436, 0.391]?" }, { "from": "gpt", "value": "Renewable Sources." } ], "id_raw": 43 }, { "id": "ms6QmQNksSv9tlV2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.160, 0.140, 0.865, 0.885]." }, { "from": "gpt", "value": "This region contains three lines representing 'Energy Efficiency', 'Carbon Footprints', and 'Renewable Sources' with varied colors and marker styles. Each line displays individual points from weeks 1 to 24 plotted to show respective growth trends." } ], "id_raw": 43 }, { "id": "gGQMrz3ZCpW2QwkN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.930, 0.031, 0.972, 0.972] represent?" }, { "from": "gpt", "value": "This area displays the y-axis ticks ranging from 0.6 to 2.2 on the subplot showing 'Renewable Sources', along with corresponding y-axis labels." } ], "id_raw": 43 }, { "id": "Chu3prgDdXIBALgm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_3.png", "conversations": [ { "from": "human", "value": "\nDescribe what is displayed within [0.125, 0.906, 1.057, 0.930]?" }, { "from": "gpt", "value": "This area includes the x-axis tick labels from '-5' through '30', aligning with the weeks plotted on both subplots for various data lines." } ], "id_raw": 43 }, { "id": "Wpr0ugULRNhbEspy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_7.png", "conversations": [ { "from": "human", "value": "\nCan you indicate the type of the first marker found in the legend?" }, { "from": "gpt", "value": "The marker is a circle {marker}[0.132, 0.138, 0.162, 0.149]." } ], "id_raw": 47 }, { "id": "06JKhSzp4yh8qTfB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the y-axis?" }, { "from": "gpt", "value": "The title is 'Market %' {ylabel}[0.082, 0.464, 0.094, 0.546]." } ], "id_raw": 47 }, { "id": "l7AFHFUccxLwCRbT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the data point {line_1, point 4}[0.271, 0.742] on 'Market Growth' line?" }, { "from": "gpt", "value": "The value of the data point is 0.22." } ], "id_raw": 47 }, { "id": "vIm6wcvrDQJgVnv3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_7.png", "conversations": [ { "from": "human", "value": "\nIs {the data point labeled '10'}[0.494,0.522] for 'Market Share' line higher or lower than the {data point labeled '10'}[0.494, 0.262] for 'Market Saturation' line?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 47 }, { "id": "FLJabxvBPgEYUw5m", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_7.png", "conversations": [ { "from": "human", "value": "\nIs the value of the {point 5}[0.309, 0.455] on 'Market Share' line higher or lower than the value of {point 5}[0.309, 0.702] on 'Market Growth' line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 47 }, { "id": "wGZgTK4uc3lpNE4N", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_7.png", "conversations": [ { "from": "human", "value": "\nCan you tell if the trend for 'Market Growth'{line_1}[0.160, 0.155, 0.865, 0.855] or 'Market Saturation' {line_2}[0.160, 0.242, 0.865, 0.315] is generally higher?" }, { "from": "gpt", "value": "Market Growth." } ], "id_raw": 47 }, { "id": "kLs6D0aVRBqiqYQ2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects found in the area [0.125, 0.120, 0.900, 0.890]?" }, { "from": "gpt", "value": "This area contains the main plot of the chart, where three different line graphs are presented representing 'Market Share', 'Market Growth', and 'Market Saturation'. These lines are represented with different markers and line types." } ], "id_raw": 47 }, { "id": "z1hic4y0SMLWdCWe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the region [0.131, 0.129, 0.277, 0.212]?" }, { "from": "gpt", "value": "This region includes a legend which has three items: {Market Share}[0.168, 0.136, 0.245, 0.153] represented by a blue circle, {Market Growth}[0.168, 0.162, 0.253, 0.179] represented by a red X, and {Market Saturation}[0.168, 0.188, 0.272, 0.205] represented by a green triangle." } ], "id_raw": 47 }, { "id": "lhriDtE6nBdWDWyg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_7.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the region [0.089, 1.015, 0.117, 1.032]?" }, { "from": "gpt", "value": "An out of bounds Y-axis label is found here, indicating a value of '\u22120.2'." } ], "id_raw": 47 }, { "id": "HP8rbiYGZEWGl8Fn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_9.png", "conversations": [ { "from": "human", "value": "\nIn this chart, what does the line with {coordinates}[0.160, 0.155, 0.865, 0.282] represent?" }, { "from": "gpt", "value": "Social Service Spending." } ], "id_raw": 49 }, { "id": "S3twmBRXTIhLlwoC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line representing {Unemployment Rate}[0.160, 0.782, 0.865, 0.855]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 49 }, { "id": "4Rx99XT1ZwzCHt3Q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the {y-axis label}[0.074, 0.398, 0.088, 0.612]?" }, { "from": "gpt", "value": "Normalized Values." } ], "id_raw": 49 }, { "id": "c1BKOE5rSC2KM1wE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_9.png", "conversations": [ { "from": "human", "value": "\nAt month 4, was the {Infrastructure Spending}[0.352, 0.228] higher or lower than the {Social Service Spending}[0.352, 0.210]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 49 }, { "id": "4BOz2gnlIMPaRCv9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_9.png", "conversations": [ { "from": "human", "value": "\nAt month 11, is the {Infrastructure Spending}[0.801, 0.391] above or below the {Unemployment Rate}[0.801, 0.791]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 49 }, { "id": "6XRxc3ya6QOzMfaN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_9.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at month 2, {Infrastructure Spending}[0.224, 0.237] or {Unemployment Rate}[0.224, 0.846]?" }, { "from": "gpt", "value": "Unemployment Rate." } ], "id_raw": 49 }, { "id": "plFyZQbUpi8yk1os", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in area [0.184, 0.940, 0.841, 0.982]." }, { "from": "gpt", "value": "The area contains the legend items: {Infrastructure Spending}[0.228, 0.949, 0.393, 0.973] with a blue marker, {Social Service Spending}[0.460, 0.949, 0.626, 0.973] with a red marker, and {Unemployment Rate}[0.693, 0.949, 0.836, 0.973] with a green marker." } ], "id_raw": 49 }, { "id": "0dhu9IXNN6Jrt7uG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_9.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.093, 0.928, 0.115, 0.951]?" }, { "from": "gpt", "value": "The y-axis tick label '0.0' is located here." } ], "id_raw": 49 }, { "id": "kQgtQRNHLfmKlSOJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in area [0.125, 0.120, 0.900, 0.890]?" }, { "from": "gpt", "value": "This area contains the main plot of the chart, which includes 3 line graphs that represent Infrastructure Spending, Social Service Spending, and Unemployment Rate over a 12 month period." } ], "id_raw": 49 }, { "id": "SscLxgXQPiUxI6lK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_10.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.012, 0.422, 0.024, 0.593] represent in the chart?" }, { "from": "gpt", "value": "Knowledge Score." } ], "id_raw": 50 }, { "id": "Q7AmUKxhxpMMPEYx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_10.png", "conversations": [ { "from": "human", "value": "\nWhat color is used for {the line representing 'Skills Score'}[0.079, 0.286, 0.893, 0.626]?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 50 }, { "id": "SpbaAcyJhdAttgpI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_10.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is used on {the 'Innovation Factor' line}[0.079, 0.383, 0.893, 0.785]?" }, { "from": "gpt", "value": "Triangle." } ], "id_raw": 50 }, { "id": "PlmXFI3KYYRStwgi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_10.png", "conversations": [ { "from": "human", "value": "\nIs {the point at week 15}[0.472, 0.473] for 'Skills Score' higher or lower than {the point at week 15}[0.584, 0.522] for 'Innovation Factor'?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 50 }, { "id": "w6zr5f9lwwvIpbaw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the last point on 'Knowledge Score' line}[0.893, 0.646] finish above or below {the last point on 'Innovation Factor'}[0.893, 0.383]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 50 }, { "id": "JEqexHAAp9f96Z8F", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_10.png", "conversations": [ { "from": "human", "value": "\nWhich line starts at a higher point on the chart, {the 'Skills Score' line}[0.079, 0.286] or {the 'Knowledge Score' line}[0.079, 0.307]?" }, { "from": "gpt", "value": "Skills Score." } ], "id_raw": 50 }, { "id": "HhRA4nIAaBezRhFp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects located within region [0.107, 0.276, 0.279, 0.436]?" }, { "from": "gpt", "value": "This region contains data points for weeks 2, 3, and 4, respectively located at {coordinates}[0.107, 0.314], {coordinates}[0.135, 0.328], {coordinates}[0.163, 0.341] for the 'Knowledge Score' line. Each point is marked with a blue circle." } ], "id_raw": 50 }, { "id": "UokKIF8dUQIKbTZu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_10.png", "conversations": [ { "from": "human", "value": "\nDescribe what is found in the region [0.668, 0.563, 0.809, 0.633]." }, { "from": "gpt", "value": "This area contains data points for weeks 22, 23, and 24 on the 'Innovation Factor' line represented by green triangles at their respective positions {coordinates}[0.668, 0.494], {coordinates}[0.696, 0.480], {coordinates}[0.724, 0.466]." } ], "id_raw": 50 }, { "id": "yexYdeFuzeVpd1Gt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents of the region [0.332, 0.600, 0.584, 0.640]?" }, { "from": "gpt", "value": "The region contains points from weeks 10, 11, 12 on the lines for 'Skills Score' and 'Innovation Factor'. The points are marked with red crosses and green triangles located at {coordinates}[0.556, 0.508], {coordinates}[0.528, 0.494] respectively." } ], "id_raw": 50 }, { "id": "gKw9P9qlvH2BFSCS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the color of the marker} at [0.280, 0.502, 0.280, 0.502]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 51 }, { "id": "0rhkP6XxyI3aT7Ua", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label of the x-axis} in the dataset located at [0.480, 0.952, 0.524, 0.975]?" }, { "from": "gpt", "value": "Period." } ], "id_raw": 51 }, { "id": "WnbfMQbDxe5Nq9FA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the description of {the line series representing Supply Capacity} identified by [0.212, 0.489, 0.349, 0.397]?" }, { "from": "gpt", "value": "It is a dashed red line marked with 'x'." } ], "id_raw": 51 }, { "id": "2bdYXJFrpUrlHlPw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_11.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '18'}[0.690, 0.471] above or below {the point labeled '12'}[0.485, 0.508] on the 'Trade Volume' line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 51 }, { "id": "mslqHj3DHdxWMhbj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Growth Rate'}[[0.110, 0.757], [0.894, 0.137]] or {the line for 'Supply Capacity'}[[0.110, 0.409], [0.144, 0.378]] yield higher value at period 7?" }, { "from": "gpt", "value": "Supply Capacity." } ], "id_raw": 51 }, { "id": "ExrOH2lXUGYmcDNQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_11.png", "conversations": [ { "from": "human", "value": "\nWhich endpoint is closer to the bottom of the chart, {the ending point of 'Trade Volume'}[0.894, 0.594] or {the ending point of 'Supply Capacity'}[0.894, 0.533]?" }, { "from": "gpt", "value": "Trade Volume." } ], "id_raw": 51 }, { "id": "1to3ayRhf2biMf1Z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.943, 0.748, 0.965, 0.917]." }, { "from": "gpt", "value": "This area includes y-axis labels for 'Growth Rate' displaying values from 0.2 to 1.0, with each label aligned closely to corresponding ticks." } ], "id_raw": 51 }, { "id": "4DySyRyXRuseJB0Q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_11.png", "conversations": [ { "from": "human", "value": "\nWhat data is presented in [0.110, 0.777, 0.417, 0.556]?" }, { "from": "gpt", "value": "This region covers parts of the 'Growth Rate' line graph, specifically the points from periods 1 to 11, showing a gradual increase in growth rate." } ], "id_raw": 51 }, { "id": "p7AUHt6huBYLYlNi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.110, 0.529, 0.758, 0.286]?" }, { "from": "gpt", "value": "This area contains the line graph for 'Supply Capacity', showing a generally decreasing trend from 1 to 19 period with markers indicated by 'x'." } ], "id_raw": 51 }, { "id": "IOoU5L1ZXOwp7e8s", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_12.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {the legend item for 'Performance'}[0.350, 0.071, 0.438, 0.094]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 52 }, { "id": "ildv0ZNbpKQLm2gE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of {the x-axis tick labeled '5.0'}[0.261, 0.919, 0.283, 0.942]?" }, { "from": "gpt", "value": "[0.272, 0.903]." } ], "id_raw": 52 }, { "id": "rZ8BMV7ISlSlVoqx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the text written at {coordinates}[0.500, 0.087, 0.500, 0.087] in the chart?" }, { "from": "gpt", "value": "Geographic Spatial Data Over Weeks." } ], "id_raw": 52 }, { "id": "WeR6JSg7p8m5StJl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_12.png", "conversations": [ { "from": "human", "value": "\nIs {the Efficiency line's point at 'Week 10'}[0.479, 0.470] above or below {the Performance line's point at 'Week 10'}[0.479, 0.446]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 52 }, { "id": "movqVYpieq69ATm3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the point for 'Week 1' in Performance}[0.106, 0.502] have a higher or lower value than {the point for 'Week 1' in Efficiency}[0.106, 0.422]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 52 }, { "id": "aTvYn6P1zHxOTGF5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_12.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher ending value, {the line for 'Performance'}[0.894, 0.438] or {the line for 'Efficiency'}[0.894, 0.406]?" }, { "from": "gpt", "value": "Efficiency." } ], "id_raw": 52 }, { "id": "OjAuTMH7ScZWTwMG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the visual elements in the region [0.642, 0.616, 0.706, 0.677]." }, { "from": "gpt", "value": "The area contains high-value points for both 'Performance' and 'Efficiency' and is colored blue and red respectively, showing significant values in the later weeks." } ], "id_raw": 52 }, { "id": "mMQr9AR1r6pcRtMa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.500, 0.069, 0.860, 0.133] contain?" }, { "from": "gpt", "value": "It includes part of the legend for 'Efficiency' in red and 'Growth' in green, with associated text labels and color markers." } ], "id_raw": 52 }, { "id": "MkNWReh1uJlODzA7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_12.png", "conversations": [ { "from": "human", "value": "\nProvide details about the sector [0.355, 0.438, 0.396, 0.486] on the chart." }, { "from": "gpt", "value": "This sector captures a point where the 'Efficiency' line shows a decrease, marked by a square red marker, indicating a dip in efficiency levels around Week 8." } ], "id_raw": 52 }, { "id": "58NuZtgs2FjoPtYR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_13.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted by the object at [0.101, 0.337, 0.899, 0.708] in the chart?" }, { "from": "gpt", "value": "Performance line graph." } ], "id_raw": 53 }, { "id": "5mL7N4cqMrwfZXaJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the coordinate [0.477, 0.952, 0.523, 0.975] represent?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 53 }, { "id": "PQGlXpPBwc10TRfX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_13.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the coordinates [0.943, 0.919, 0.974, 0.942]?" }, { "from": "gpt", "value": "X-axis tick label '22.5'." } ], "id_raw": 53 }, { "id": "PEBAh7eSDSn1QZwF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_13.png", "conversations": [ { "from": "human", "value": "\nAre {the point}[0.500, 0.621] and {the point}[0.540, 0.641] on the same line?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 53 }, { "id": "Ty6Y4bNTR9ltGcxW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the line with color #1f77b4}[0.101, 0.337, 0.899, 0.708] represent a higher value at week 1 than {the line with color #d62728}[0.101, 0.411, 0.899, 0.742]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 53 }, { "id": "xVkyiaogDhmSeqOs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_13.png", "conversations": [ { "from": "human", "value": "\nIs {the y-axis label '0.0'}[0.035, 0.902, 0.057, 0.925] below {the y-axis label '1.2'}[0.943, 0.092, 0.965, 0.115]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 53 }, { "id": "5Gigt5p2WTof6GYf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content between the coordinates [0.101, 0.337, 0.899, 0.708]." }, { "from": "gpt", "value": "This area includes a blue line graph representing performance data across weeks, with points marked from week 1 to week 21, decreasing in value from initial to final." } ], "id_raw": 53 }, { "id": "s2WeImCQQhF7uiE8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_13.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.061, 0.101, 0.939, 0.911]?" }, { "from": "gpt", "value": "This region consists of green-colored labels indicating weeks from 0 to 22.5 along the x-axis, and nine blue and red line graphs representing various statistical data points across the weeks." } ], "id_raw": 53 }, { "id": "CLMmzSMuXIEXQxDj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_13.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the region with coordinates [0.943, 0.902, 0.965, 0.115]?" }, { "from": "gpt", "value": "Y-axis labels for efficiency, ranging from 0.0 to 1.2, located along the y-axis on the right side, with corresponding tick marks." } ], "id_raw": 53 }, { "id": "Nx5UZwpmCsDYHS08", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the {title of the chart} at [0.365, 0.023, 0.635, 0.053]?" }, { "from": "gpt", "value": "Product Sales Trend Observation." } ], "id_raw": 56 }, { "id": "YuUg33OzaVzrFIt2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the marker for 'Revenue' in the legend}[0.440, 0.074, 0.476, 0.088]?" }, { "from": "gpt", "value": "#d62728." } ], "id_raw": 56 }, { "id": "zbi2hP2rSv3q5BuY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_16.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the line with color #d62728}[0.113, 0.138, 0.887, 0.874]?" }, { "from": "gpt", "value": "Revenue." } ], "id_raw": 56 }, { "id": "gkuJ3MaZGkRibqXo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Sales' in Q4}[0.887, 0.276] higher or lower on the chart than {the point for 'Revenue' in Q4}[0.887, 0.138]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 56 }, { "id": "uQznC7sSZwneSrjI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the relationship between {the 'Sales' point in Q1}[0.113, 0.736] and {the 'Revenue' point in Q1}[0.113, 0.874] in terms of their positions on the y-axis?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 56 }, { "id": "QsqYMpD3GZl1Vi4U", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_16.png", "conversations": [ { "from": "human", "value": "\nIs the {Sales line}[0.113, 0.138, 0.887, 0.736] generally above or below the {Revenue line}[0.113, 0.138, 0.887, 0.874]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 56 }, { "id": "PTNA5xIX80v6O205", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_16.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents of the area within [0.335, 0.062, 0.665, 0.103]." }, { "from": "gpt", "value": "This area contains the chart's legend. The legend items: {Sales}[0.379, 0.071, 0.416, 0.094], {Revenue}[0.483, 0.071, 0.543, 0.094], and {Growth}[0.609, 0.071, 0.660, 0.094], each have different colored markers." } ], "id_raw": 56 }, { "id": "Nban15qiv27xOTr5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_16.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area at [0.075, 0.101, 0.925, 0.911]?" }, { "from": "gpt", "value": "The space within these coordinates primarily consists of the line plots representing 'Sales', 'Revenue' and 'Growth' each differentiated by a unique color and linestyle." } ], "id_raw": 56 }, { "id": "1d6nlJKaY0QTJD40", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_16.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the region [0.075, 0.101, 0.113, 0.736]?" }, { "from": "gpt", "value": "The y-axis tick labels (0, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000) and a part of the line plot representing 'Sales' are located within this region. The color of the y-axis tick labels is blue and the color of 'Sales' line is #1f77b4." } ], "id_raw": 56 }, { "id": "rEUbbBn0MdJvBEvx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_17.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the handle for the legend item labeled 'Market_Entropy'}[0.237, 0.068, 0.273, 0.079]?" }, { "from": "gpt", "value": "#1f77b4." } ], "id_raw": 57 }, { "id": "C9Mdh5pVfXBtXTL9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_17.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.015, 0.438, 0.029, 0.574] say?" }, { "from": "gpt", "value": "Market_Entropy." } ], "id_raw": 57 }, { "id": "0U0faJo5hXlItfyj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_17.png", "conversations": [ { "from": "human", "value": "\nWhat is {the text at the very top}[0.195, 0.015, 0.805, 0.038]?" }, { "from": "gpt", "value": "Detailed Emerging Markets and Consumer Behavior Analysis Over Weeks." } ], "id_raw": 57 }, { "id": "1LgBY99TdK7qdiMi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_17.png", "conversations": [ { "from": "human", "value": "\nIs {point 5 on the Market_Growth line}[0.223, 0.646] higher than {point 5 on the Market_Knowledge line}[0.223, 0.506]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 57 }, { "id": "Xz0yEZnY8fZEziiQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_17.png", "conversations": [ { "from": "human", "value": "\nDoes the value of {Market_Entropy at point 20}[0.661, 0.436] exceed that of {Market_Growth at point 20}[0.661, 0.380]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 57 }, { "id": "7PGmBfkMN2kv6zkp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_17.png", "conversations": [ { "from": "human", "value": "\nIs the growth of {Market_Knowledge from point 24 to point 28}[0.777, 0.155], [0.894, 0.085] steeper than that of {Market_Growth from point 24 to point 28}[0.777, 0.436], [0.894, 0.415]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 57 }, { "id": "2yZBku9shGtVhYzc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_17.png", "conversations": [ { "from": "human", "value": "\nWhat information is present in the region [0.254, 0.055, 0.654, 0.096]?" }, { "from": "gpt", "value": "This region contains the legend for the chart with labels and colored markers for 'Market_Entropy', 'Market_Knowledge', and 'Market_Growth'." } ], "id_raw": 57 }, { "id": "vVvyLQxvCb6lKj4O", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the details in the axis region [0.015, 0.438, 0.029, 0.574]." }, { "from": "gpt", "value": "This region contains the y-axis labeled 'Market_Entropy' with tick labels ranging from 0.0 to 1.2, styled in blue." } ], "id_raw": 57 }, { "id": "2ixuPaf7iQJSs6Fz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_17.png", "conversations": [ { "from": "human", "value": "\nWhat elements are located in the area [0.285, 0.227, 0.531, 0.261]?" }, { "from": "gpt", "value": "The area includes points and annotations for Market_Growth and Market_Knowledge for weeks around 14 to 20, displaying a slight increase and decrease in both cases." } ], "id_raw": 57 }, { "id": "63CnxIMaz2tV5qCe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_18.png", "conversations": [ { "from": "human", "value": "\nWhat function does {the object}[0.454, 0.062, 0.546, 0.103] serve in this chart?" }, { "from": "gpt", "value": "It is the chart's legend." } ], "id_raw": 58 }, { "id": "Kd8HRFlhC6epo6ht", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_18.png", "conversations": [ { "from": "human", "value": "\nWhat represents {the Urban line}[0.097, 0.154, 0.916, 0.432] in the chart?" }, { "from": "gpt", "value": "Urbanization Indicator." } ], "id_raw": 58 }, { "id": "aYKmOsPBuyfFfTh1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_3_18.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.343, 0.023, 0.700, 0.053] narrate?" }, { "from": "gpt", "value": "It is the subtitle of the chart, labeled as 'Rural and Urban Development Comparison'." } ], "id_raw": 58 }, { "id": "5vOAFrMBCRbO414j", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_18.png", "conversations": [ { "from": "human", "value": "\nReferring to the {points on the Urban line}[0.097, 0.154, 0.916, 0.432], does the value increase or decrease between point 1 and point 2?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 58 }, { "id": "RDKr7XHEGFoUHkId", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_18.png", "conversations": [ { "from": "human", "value": "\nBetween {point 3}[0.157, 0.379] and {point 4}[0.188, 0.389] in the Urban line, was there an increase or decrease in urbanization indicator?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 58 }, { "id": "kewRCiPbOACCZEdT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_3_18.png", "conversations": [ { "from": "human", "value": "\nComparing {point 18}[0.612, 0.170] and {point 19}[0.643, 0.422] on the Urbanization Indicator line, which one has the higher value?" }, { "from": "gpt", "value": "Point 18." } ], "id_raw": 58 }, { "id": "3KPJBhRo7J4M2bda", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents found in the area [0.066, 0.101, 0.976, 0.903]." }, { "from": "gpt", "value": "The area includes the line chart comparing rural and urban development, with x-axis representing weeks and y-axis representing the Urbanization Indicator. The values range from point 1 to point 28 along x-axis and from 0.0 to 1.6 along y-axis." } ], "id_raw": 58 }, { "id": "HYCiGO2LZZccJudo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_18.png", "conversations": [ { "from": "human", "value": "\nPlease explain the objects within the region [0.498, 0.071, 0.541, 0.094]." }, { "from": "gpt", "value": "This region contains the label 'Urban' in the legend, paired with a marker in the color '#1f77b4'." } ], "id_raw": 58 }, { "id": "HoP9WDXWF9y9KZQo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_3_18.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the area [0.015, 0.373, 0.029, 0.631]?" }, { "from": "gpt", "value": "The y-axis label indicating 'Urbanization Indicator' is located in this area, and it's styled in the color 'tab:blue' with a rotation of 90.0 degrees." } ], "id_raw": 58 }, { "id": "QBYpAJ0Zyh5quSVv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_0.png", "conversations": [ { "from": "human", "value": "\nWhat does {the marker at}[0.474, 0.271] signify in the corporate earnings subplot?" }, { "from": "gpt", "value": "0.2." } ], "id_raw": 60 }, { "id": "lIhTdoh7wNi9Pzgs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_0.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at {coordinates}[0.581, 0.179] in the second subplot?" }, { "from": "gpt", "value": "1.0." } ], "id_raw": 60 }, { "id": "UvjarL6wIsGOkWns", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_0.png", "conversations": [ { "from": "human", "value": "\nWhat data is represented by {the square marker}[0.688, 0.715]?" }, { "from": "gpt", "value": "2.5." } ], "id_raw": 60 }, { "id": "HLrN7pjZqOtMaOVI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point}[0.528, 0.492] above or below {the 2.0 mark}[0.528, 0.782] on the GDP subplot?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 60 }, { "id": "vZ7drN8sO7aMPoka", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_0.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at {x=4}[0.367, 0.179], corporate earnings or GDP?" }, { "from": "gpt", "value": "Corporate earnings." } ], "id_raw": 60 }, { "id": "nwUhRdNz29HzexRJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the marker}[0.528, 0.774] match or exceed {the 2.0 inflation target}[0.528, 0.788]?" }, { "from": "gpt", "value": "Exceed." } ], "id_raw": 60 }, { "id": "4QMAZ63db2Hitekt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found within [0.140, 0.150, 0.290, 0.320]." }, { "from": "gpt", "value": "This region includes the corporate earnings subplot's x-axis label displaying 'Time' and y-axis label displaying 'Value' with {coordinates for x-label}[0.420, 0.327, 0.420, 0.327] and {coordinates for y-label}[0.040, 0.158, 0.063, 0.200]." } ], "id_raw": 60 }, { "id": "LLxWjmAcOYxvO4KK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_0.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the region [0.400, 0.175, 0.700, 0.200]?" }, { "from": "gpt", "value": "The region contains the line representing corporate earnings along with markers and annotations for values such as 1.0 at {the coordinates}[0.367, 0.179] and 1.1 at {the coordinates}[0.528, 0.168]." } ], "id_raw": 60 }, { "id": "60ieIL2TvDw6Dfcq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content of [0.600, 0.700, 0.900, 0.840]?" }, { "from": "gpt", "value": "This area includes the sublegend for the 'Inflation Over Time'subplot, containing the label 'Inflation' colored blue at {coordinates for label}[0.806, 0.812, 0.901, 0.828] and the legend handle at {coordinates for handle}[0.735, 0.815, 0.795, 0.824]." } ], "id_raw": 60 }, { "id": "M2pbUjx98hqBbNdo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of the object with these coordinates [0.096, 0.305, 0.154, 0.321]?" }, { "from": "gpt", "value": "That's the X-axis label indicating the year 2010." } ], "id_raw": 61 }, { "id": "sSyPfdJTZT6IwQH4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_1.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.621, 0.113, 0.790, 0.128]?" }, { "from": "gpt", "value": "It is a sublegend item label that reads 'Export Volume'." } ], "id_raw": 61 }, { "id": "C8nFPiDI0AvFhK8e", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_1.png", "conversations": [ { "from": "human", "value": "\nWhat kind of object is found within the boundary box [0.424, 0.946, 0.483, 0.962]?" }, { "from": "gpt", "value": "It is a X-axis label representing the year 2014." } ], "id_raw": 61 }, { "id": "gcTOm4smFxOG7ioR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_1.png", "conversations": [ { "from": "human", "value": "\nIs the {data point}[0.618, 0.168] from the Export Volume line above or below the corresponding {data point}[0.618, 0.477] on the Import Volume line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 61 }, { "id": "cSx6pbmTqhK98QdI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_1.png", "conversations": [ { "from": "human", "value": "\nConsidering the Export Volume line, is the data point in 2015 {at}[0.535, 0.156] higher or lower than that of 2020 {at}[0.946, 0.139]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 61 }, { "id": "cQdZDVCiJUNN77c7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_1.png", "conversations": [ { "from": "human", "value": "\nOn the Import Volume line, is the point for 2019 {at}[0.864, 0.423] above or below the point for 2020 {at}[0.946, 0.477]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 61 }, { "id": "CYX3ZDvOGCTVWbE8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.547, 0.106, 0.800, 0.134]?" }, { "from": "gpt", "value": "This area contains the sublegend of the first subplot indicating 'Export Volume'. The text of the sublegend is accompanied by a green square icon." } ], "id_raw": 61 }, { "id": "N7TNz1tlhIgEhuTz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_1.png", "conversations": [ { "from": "human", "value": "\nWhat is in the space [0.096, 0.305, 0.975, 0.321]?" }, { "from": "gpt", "value": "This area spans across several X-axis labels stating various years: {2010}[0.096, 0.305, 0.154, 0.321], {2012}[0.260, 0.305, 0.318, 0.321], {2014}[0.424, 0.305, 0.483, 0.321], {2016}[0.588, 0.305, 0.647, 0.321], {2018}[0.752, 0.305, 0.811, 0.321] and {2020}[0.917, 0.305, 0.975, 0.321]." } ], "id_raw": 61 }, { "id": "ivNOf7VdB8Ib0q6B", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within coordinates [0.094, 0.757, 0.109, 0.772]." }, { "from": "gpt", "value": "This box contains a value from the Y-axis tick label, specifically, it is the label for the value '1.25'." } ], "id_raw": 61 }, { "id": "de5Stqco65Q0ZD0k", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-label represent with coordinates [0.494, 0.640, 0.592, 0.656]?" }, { "from": "gpt", "value": "Platform." } ], "id_raw": 62 }, { "id": "CcUojHzTjY1FJHEM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_2.png", "conversations": [ { "from": "human", "value": "\nWhat coordinate does the title \"Emojis Sent Over 60 Days\" have?" }, { "from": "gpt", "value": "[0.363, 0.680, 0.723, 0.700]" } ], "id_raw": 62 }, { "id": "b4WYD8Dj7bQojA0C", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the handle of the legend item 'Emojis Sent'} [0.435, 0.713, 0.495, 0.722]?" }, { "from": "gpt", "value": "red." } ], "id_raw": 62 }, { "id": "qqhFm8kdaJ1UR3WO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_2.png", "conversations": [ { "from": "human", "value": "\nIs the {point for 'LinkedIn'}[0.936, 0.817] above or below the {point for 'Snapchat'}[0.621, 0.727] on the 'Emojis Sent' line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 62 }, { "id": "cnP1Tig2PSOH0HmO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_2.png", "conversations": [ { "from": "human", "value": "\nDoes {Instagram}[0.452, 0.555, 0.476, 0.634] have more or less messages sent than {Twitter}[0.767, 0.555, 0.790, 0.607] based on the line for 'Messages Sent'?" }, { "from": "gpt", "value": "More." } ], "id_raw": 62 }, { "id": "Z51nFkor823PHm8H", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_2.png", "conversations": [ { "from": "human", "value": "\nWhich platform has a higher emojis sent value at {point 5}[0.936, 0.817], Facebook or LinkedIn?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 62 }, { "id": "u4YOXy0jhEwuhdLc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.110, 0.064, 0.975, 0.217]." }, { "from": "gpt", "value": "The area contains a chart showing the active days on different platforms over 60 days, with the x-axis representing the platforms and the y-axis representing active days. {The line with the green color}[0.150, 0.071, 0.936, 0.210] represents the trend of 'Days Active'." } ], "id_raw": 62 }, { "id": "eCKsWSDD4WCdAiJM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_2.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region [0.415, 0.377, 0.671, 0.405]?" }, { "from": "gpt", "value": "It contains {the legend}[0.489, 0.383, 0.661, 0.398] for the chart that represents 'Messages Sent' with a blue colored marker." } ], "id_raw": 62 }, { "id": "zPZUnjpWJuHBN6oa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.110, 0.720, 0.975, 0.872]?" }, { "from": "gpt", "value": "This region contains a chart showing the number of emojis sent on different platforms over 60's days. It contains {the x-axis tick labels}[0.138, 0.883, 0.161, 0.956], [0.295, 0.883, 0.318, 0.961], [0.452, 0.883, 0.476, 0.962], [0.610, 0.883, 0.633, 0.956], [0.767, 0.883, 0.790, 0.935], [0.924, 0.883, 0.947, 0.948], {the line for 'Emojis Sent'}[0.150, 0.727, 0.936, 0.865], and an x-label and y-label indicating 'Platform' and 'Emojis' respectively." } ], "id_raw": 62 }, { "id": "DvBJKJlLfsqJ2pxa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line representing {Drug Development}[0.084, 0.054, 0.981, 0.284]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 63 }, { "id": "Ej9NgkXZCenPnqeB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_3.png", "conversations": [ { "from": "human", "value": "\nCan you identify the label displayed at {coordinates}[0.891, 0.312, 0.913, 0.324]?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 63 }, { "id": "hbZ2jVELwyBR1o3L", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the orientation of the y-axis labels seen in the {rectangle}[0.044, 0.049, 0.072, 0.311]?" }, { "from": "gpt", "value": "Vertical." } ], "id_raw": 63 }, { "id": "LAVDRD5DZkOAhfTc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_3.png", "conversations": [ { "from": "human", "value": "\nDoes the {point}[0.712, 0.104] for 2018 in Drug Development rest above or below the label '3.0' at {coordinates}[0.044, 0.099, 0.072, 0.111]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 63 }, { "id": "bnqThC6Fi4YXu8bd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_3.png", "conversations": [ { "from": "human", "value": "\nIs the {latest y-axis label}[0.033, 0.697, 0.072, 0.708] higher in value compared to {the first y-axis label}[0.019, 0.947, 0.072, 0.959] in Biotech Investment subplot?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 63 }, { "id": "Gt3BEnP3bAsEgsZS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_3.png", "conversations": [ { "from": "human", "value": "\nAre the intervals between the years {2014}[0.353, 0.312, 0.375, 0.324] and {2016}[0.510, 0.312, 0.555, 0.324] smaller than between {2018}[0.690, 0.312, 0.734, 0.324] and {2020}[0.870, 0.312, 0.913, 0.324] on the x-axis?" }, { "from": "gpt", "value": "No." } ], "id_raw": 63 }, { "id": "2jHRTGz9GTJuMFNP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the visual items between the coordinates [0.084, 0.054, 0.981, 0.304]." }, { "from": "gpt", "value": "This region contains a line graph for Drug Development labeled as 'Trends in Drug Development' with points representing data from 2011 to 2021. The colors include a green line through {various points}[0.084, 0.054, 0.981, 0.284] and x-axis labels spaced at intervals corresponding to years." } ], "id_raw": 63 }, { "id": "NOfegOmEj3SUkadI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe what occupies the region from [0.044, 0.049, 0.981, 0.311]?" }, { "from": "gpt", "value": "This area mostly comprises y-axis labels and corresponding ticks. The labels, oriented vertically, represent values incrementally from 1.0 to 3.5 across three subplots." } ], "id_raw": 63 }, { "id": "S7t7COHMwVKAXub3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_3.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the boundaries of [0.044, 0.049, 1.093, 0.971]?" }, { "from": "gpt", "value": "This large area includes all components of the three subplots; Drug Development, Vaccine Research, and Biotech Investment. It includes x-axis labels for years, y-axis labels for values, and lines representing various data trends in each sector. There are also subplot titles placed near the top of each subplot region." } ], "id_raw": 63 }, { "id": "BsJWMP1AlqeRG2A2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.485, 0.078, 0.665, 0.094] in the chart?" }, { "from": "gpt", "value": "Sublegend label." } ], "id_raw": 64 }, { "id": "q339gYLwsye7CQWv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_4.png", "conversations": [ { "from": "human", "value": "\nWhich mark can be found at [0.459, 0.156, 0.459, 0.156]?" }, { "from": "gpt", "value": "Point of the line." } ], "id_raw": 64 }, { "id": "G2xvxqoyaJE9aKRi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_4.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.543, 0.327, 0.543, 0.327]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 64 }, { "id": "Q9CDoFetGaW7hmOX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point}[0.208, 0.202] above or below {the point}[0.208, 0.426]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 64 }, { "id": "mijvOTsUDSfGGbZZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_4.png", "conversations": [ { "from": "human", "value": "\nDoes {the point at [0.375, 0.171]} lie higher or lower than {the point at [0.375, 0.468]}?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 64 }, { "id": "wyN9B6l1O572AI8E", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_4.png", "conversations": [ { "from": "human", "value": "\nWhich is lower: {the y-coordinate of line point}[0.793, 0.102] or {the y-coordinate of line point}[0.793, 0.447]?" }, { "from": "gpt", "value": "0.793, 0.102." } ], "id_raw": 64 }, { "id": "UpVm4Phl7S0MP6qa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area ranging from [0.072, 0.058, 0.109, 0.304]." }, { "from": "gpt", "value": "This area contains all y-axis labels for the first subplot, visually represented as textual labels arranged vertically." } ], "id_raw": 64 }, { "id": "LX1gAVG1QMWMRQF8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_4.png", "conversations": [ { "from": "human", "value": "\nWhat significant elements are located within the coordinates [0.118, 0.288, 0.975, 0.641]?" }, { "from": "gpt", "value": "This area includes x-axis labels for all subplots, encompassing different years labeled from '0' to '10', distributed horizontally across the bottom of each subplot." } ], "id_raw": 64 }, { "id": "YoHs4Vof3qogL2QV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content found in the area from [0.118, 0.016, 0.975, 0.036]." }, { "from": "gpt", "value": "This region features the subtitle \"Slow Expansion Over Time,\" styled in black text centered along the x-axis of the first subplot." } ], "id_raw": 64 }, { "id": "llVxHfRrfF3bbXrd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing Social Development}[0.130, 0.406, 0.946, 0.602]?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 65 }, { "id": "rCfWK4c64t4nmu5Z", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the shape of markers used in {the line for Environmental Protection}[0.130, 0.744, 0.946, 0.920]?" }, { "from": "gpt", "value": "Square." } ], "id_raw": 65 }, { "id": "nzvdUj75zf4NMKp4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_5.png", "conversations": [ { "from": "human", "value": "\nWhich year's data point is located at {coordinates}[0.864, 0.431] in the Social Development subplot?" }, { "from": "gpt", "value": "2019." } ], "id_raw": 65 }, { "id": "SkP9RthjDXoTygd0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_5.png", "conversations": [ { "from": "human", "value": "\nIs {the y-tick label '3'}[0.099, 0.196, 0.114, 0.212] higher or lower than {the y-tick label '5'}[0.099, 0.104, 0.114, 0.120] on the Y-axis of the Infrastructure subplot?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 65 }, { "id": "vr9toF3CdEJ1542Y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_5.png", "conversations": [ { "from": "human", "value": "\nDoes {the x-tick for 2012}[0.293, 0.615] align above {the x-tick for 2014}[0.456, 0.615] on the Social Development subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 65 }, { "id": "JCuNDBAlRgFgmCCz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_5.png", "conversations": [ { "from": "human", "value": "\nIs the marker at {2015}[0.538, 0.569] on the Social Development line above or below the marker at {2016}[0.619, 0.514]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 65 }, { "id": "80qO7DEq3iBbqd3m", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements between the coordinates [0.101, 0.305, 0.198, 0.321]." }, { "from": "gpt", "value": "This region contains x-axis label '2010' {at}[0.101, 0.305, 0.159, 0.321]." } ], "id_raw": 65 }, { "id": "tjrVX3OfCtXJMotp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_5.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the box [0.530, 0.948, 0.947, 0.990]?" }, { "from": "gpt", "value": "The x-axis label 'Year' for the Environmental Protection subplot is located in this region." } ], "id_raw": 65 }, { "id": "Dnc7nw5nl5NpEc5j", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_5.png", "conversations": [ { "from": "human", "value": "\nAnalyze the area defined by [0.130, 0.294, 0.270, 0.330]." }, { "from": "gpt", "value": "The start of the x-axis, including the first y-tick {label}[0.099, 0.288, 0.114, 0.304] and the first x-tick {label '2010'}[0.101, 0.305, 0.159, 0.321], is situated in this area." } ], "id_raw": 65 }, { "id": "cyICnjJnqtvP4H4y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the primary Y-axis label for the first subplot?" }, { "from": "gpt", "value": "Population (%)." } ], "id_raw": 66 }, { "id": "4Rv4VVVSggzr40ut", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_6.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the handle of the old population's legend item}[0.550, 0.687, 0.610, 0.696]?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 66 }, { "id": "JqJ6LWgMJLZWRLHV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_6.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at {these coordinates}[0.511, 0.968, 0.560, 0.983]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 66 }, { "id": "nwANAIZxcjjzI4uz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_6.png", "conversations": [ { "from": "human", "value": "\nIs {the marker at point '2015' for Young Population}[0.968, 0.289] above or below {the marker at point '2015' for Middle-Aged Population}[0.968, 0.407]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 66 }, { "id": "JVBJtS7GjZYzcgyD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the label color of the x-axis ticks in the last subplot}[0.012, 0.946, 1.059, 0.962] have the same color as {the label of the Middle-Age subplot's ylabel}[0.025, 0.443, 0.048, 0.556]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 66 }, { "id": "LuroDeDkA8y3TXxH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_6.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher at year '2009', {Middle-Aged Population}[0.597, 0.422] or {Old Population}[0.597, 0.870]?" }, { "from": "gpt", "value": "Middle-Aged Population." } ], "id_raw": 66 }, { "id": "AtYS0N7oi1Uz00ay", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within {this region}[0.621, 0.364, 0.894, 0.380]." }, { "from": "gpt", "value": "It contains the text for the 'Middle-Aged Population' label in blue color." } ], "id_raw": 66 }, { "id": "CqKoZtaFhEBXhkCB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area covered by [0.550, 0.046, 0.610, 0.055] and [0.550, 0.687, 0.610, 0.696]?" }, { "from": "gpt", "value": "These regions contain the handles for the legend items of Young Population and Old Population, colored green and red respectively." } ], "id_raw": 66 }, { "id": "7CB9wV4MbL6k6b3R", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_6.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements within {the rectangle}[0.058, 0.609, 0.087, 0.624] and [0.058, 0.091, 0.087, 0.106]?" }, { "from": "gpt", "value": "They are the y-axis tick labels '10' and '60' of the Middle-Aged and Old Population charts, displayed in black." } ], "id_raw": 66 }, { "id": "XY06K0wBOVDOKqNi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_7.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the color at coordinates [0.315, 0.425, 0.351, 0.433] in the chart?" }, { "from": "gpt", "value": "Steady Recovery." } ], "id_raw": 67 }, { "id": "PpwLBAQjPOqv2p5t", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_7.png", "conversations": [ { "from": "human", "value": "\nHow is the data for Month 6 labeled in the subplot for Substantial Growth?" }, { "from": "gpt", "value": "300." } ], "id_raw": 67 }, { "id": "WndFpk0AkzvCh73e", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be found at {the coordinates}[0.988, 0.932, 0.488, 0.583]?" }, { "from": "gpt", "value": "Rapid Expansion handle." } ], "id_raw": 67 }, { "id": "bQAq8ms4cTtoekqb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_7.png", "conversations": [ { "from": "human", "value": "\nIs {the tick mark for Month 2}[0.112, 0.459] aligned horizontally with {the tick mark for Month 12}[0.488, 0.459]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 67 }, { "id": "KegxfH3eE33vp0Gb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_7.png", "conversations": [ { "from": "human", "value": "\nWhich subplot has a higher sales number at Month 12, {Steady Recovery}[0.488, 0.063] or {Substantial Growth}[0.976, 0.064]?" }, { "from": "gpt", "value": "Substantial Growth." } ], "id_raw": 67 }, { "id": "8tfs4my18PCZ6YnM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the month label '4'}[0.186, 0.459] appear in the same position in the two different subplots?" }, { "from": "gpt", "value": "No." } ], "id_raw": 67 }, { "id": "l6UhWxAqMc9xG69q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the annotations found between [0.438, 0.449, 0.570, 0.473]." }, { "from": "gpt", "value": "This area includes the x-axis labels '8' and '10' in the first subplot, marking the respective months in the sales trend graph." } ], "id_raw": 67 }, { "id": "opK6h3mzbEDjCMSQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.765, 0.449, 0.976, 0.524]?" }, { "from": "gpt", "value": "The region contains the subtitles \"Rapid Expansion: Sales Trend\" and \"Unpredictable Decline: Sales Trend\" for the corresponding subplots, with text colored in black." } ], "id_raw": 67 }, { "id": "0btWnjj2XYFLz0gF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_7.png", "conversations": [ { "from": "human", "value": "\nWhat elements are positioned within the coordinates [0.640, 0.417, 0.969, 0.935]?" }, { "from": "gpt", "value": "The legend item for Substantial Growth including its color handle and label, as well as the subtitle and y-label for the respective subplot." } ], "id_raw": 67 }, { "id": "Pr6jJMb6zTUgSOVb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel} for the subplot with given coordinates [0.044, 0.479, 0.067, 0.841]?" }, { "from": "gpt", "value": "Value" } ], "id_raw": 68 }, { "id": "DR2Qpv4sogNTyxJN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_8.png", "conversations": [ { "from": "human", "value": "\nWhat color is used to plot {the line for 'Periodic Fluctuations'} within [0.130, 0.423, 0.960, 0.538]?" }, { "from": "gpt", "value": "Blue (#0000ff)" } ], "id_raw": 68 }, { "id": "ahZ01nPjYJs7bIPY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the xlabel} for the subplot with these coordinates [0.517, 0.968, 0.573, 0.983]?" }, { "from": "gpt", "value": "Time" } ], "id_raw": 68 }, { "id": "MtzFaf2UGVPEGXDv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_8.png", "conversations": [ { "from": "human", "value": "\nAre the y-values for {the 'Periodic Fluctuations'}[0.130, 0.423, 0.960, 0.538] typically higher or lower than for {the 'Sudden Decrease'}[0.130, 0.122, 0.960, 0.260]?" }, { "from": "gpt", "value": "Higher" } ], "id_raw": 68 }, { "id": "BY9YPEb0K7SDVIWE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the subplot labeled 'Volatile movement over time'}[0.344, 0.657, 0.746, 0.677] appear above or below {the subplot labeled 'Periodic fluctuations over time'}[0.333, 0.337, 0.757, 0.357]?" }, { "from": "gpt", "value": "Above" } ], "id_raw": 68 }, { "id": "BRakBKiIqCCPiIuq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_8.png", "conversations": [ { "from": "human", "value": "\nComparing the plots, does {the 'Volatile Movement'}[0.130, 0.763, 0.960, 0.859] start at a higher or lower y-value than {the 'Sudden Decrease'}[0.130, 0.122, 0.960, 0.260]?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 68 }, { "id": "o6r0UOleIi0FLYHF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_8.png", "conversations": [ { "from": "human", "value": "\nCould you describe the region that ranges from [0.344, 0.657, 0.746, 0.677]?" }, { "from": "gpt", "value": "This region contains the title of the subplot, which reads \"Volatile movement over time\". It's set in black font." } ], "id_raw": 68 }, { "id": "KnDIVThCyxfwMGCV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_8.png", "conversations": [ { "from": "human", "value": "\nCan you explain what's located within this area: [0.077, 0.609, 0.113, 0.624]?" }, { "from": "gpt", "value": "This area includes the y-axis label for the 'Periodic Fluctuations' subplot, which is marked as \"0.7\"." } ], "id_raw": 68 }, { "id": "KeMAs9zhFTNvW7kV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the region bounded by [0.946, 0.305, 0.975, 0.321]?" }, { "from": "gpt", "value": "Within this area there is an x-axis tick label '10' in the 'Sudden Decrease' subplot, styled in black and a normal font weight." } ], "id_raw": 68 }, { "id": "s6N8nCEdTLva6fyX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_9.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is the object at [0.282, 0.569, 0.569, 0.569] on the chart?" }, { "from": "gpt", "value": "It's a point on the 'Venture Capital' line." } ], "id_raw": 69 }, { "id": "I3RFe7l4oAtVpTom", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the content at the coordinates [0.274, 0.337, 0.805, 0.357]?" }, { "from": "gpt", "value": "It is a subtitle saying 'Venture Capital Investment Over Time'." } ], "id_raw": 69 }, { "id": "WEnQBS9dxbfv6cml", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_9.png", "conversations": [ { "from": "human", "value": "\nCould you please inform me what the object at coordinates [0.905, 0.015, 0.917, 0.027] is in this chart?" }, { "from": "gpt", "value": "It is the x-axis label '2018'." } ], "id_raw": 69 }, { "id": "3pvnfWXQe70cOmW8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_9.png", "conversations": [ { "from": "human", "value": "\nAt the time point of 2014, which trend has a higher investment amount, {the line for 'Venture Capital'}[0.711, 0.487] or {the line for 'Private Equity'}[0.711, 0.124]?" }, { "from": "gpt", "value": "Private Equity." } ], "id_raw": 69 }, { "id": "UXCUGpatG4zxgcE0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_9.png", "conversations": [ { "from": "human", "value": "\nIn year 2009, was {the 'Venture Capital' investment point}[0.282, 0.569] higher or lower than {the 'Private Equity' investment point}[0.282, 0.233]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 69 }, { "id": "vHMUgfHSf1wfvNra", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_9.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Angel Investment' point}[0.882, 0.763] in 2016 above or below {the 'Private Equity' point}[0.882, 0.117]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 69 }, { "id": "DmylNJIVEjf5gY0A", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_9.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is inside the area bounded by [0.111, 0.385, 0.968, 0.615]?" }, { "from": "gpt", "value": "Inside this area, there is a plot line representing 'Venture Capital' investment over time. There is also a subtitle 'Venture Capital Investment Over Time', an x-axis with year labels running from 2006 to 2018. The y-axis represents 'Investment (in Trillions)' with labels ranging from 0.4 to 1.9." } ], "id_raw": 69 }, { "id": "JsLU1YYi5sv5wjyp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_9.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects inside the area [0.111, 0.705, 0.968, 0.935]?" }, { "from": "gpt", "value": "This area contains a plot line showing the trend of 'Angel Investment' over time, year labels on the x-axis from 2006 to 2018, and labels on the y-axis representing 'Investment (in Trillions)', ranging from 0.5 to 1.5. There is also a subtitle stating 'Angel Investment Over Time'." } ], "id_raw": 69 }, { "id": "UL4sr62RMa0i3Wga", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_9.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content within the region from [0.111, 0.064, 0.968, 0.294]?" }, { "from": "gpt", "value": "Inside this region, there is a line graph tracking 'Private Equity' investments over a period labeled on the x-axis from year 2006 to year 2018. The y-axis label is 'Investment (in Trillions)', ranging from 1.5 to 3.5. The subplot's title 'Private Equity Investment Over Time' is also located here." } ], "id_raw": 69 }, { "id": "Nd69OCaPifz2GvVN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_11.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is used at the point with coordinates [0.401, 0.205]?" }, { "from": "gpt", "value": "Circle." } ], "id_raw": 71 }, { "id": "xVW7OMf0s9qBAEmF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line at {point coordinates}[0.554, 0.479]?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 71 }, { "id": "kOfqqKuzOcCcFDnl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_11.png", "conversations": [ { "from": "human", "value": "\nWhat label is associated with {tick mark}[0.937, 0.935]?" }, { "from": "gpt", "value": "12." } ], "id_raw": 71 }, { "id": "VCHb1Vv2iEfi8NVJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_11.png", "conversations": [ { "from": "human", "value": "\nWhich portfolio at {week 10}[0.937, 0.716] has the highest value?" }, { "from": "gpt", "value": "Portfolio 3." } ], "id_raw": 71 }, { "id": "uEpcLdkruba0ykTP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_11.png", "conversations": [ { "from": "human", "value": "\nAre the values of {Portfolio 1 at week 6}[0.630, 0.101] higher or lower than {Portfolio 3 at week 6}[0.630, 0.828]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 71 }, { "id": "ti8eEgqpqyxNvVTi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_11.png", "conversations": [ { "from": "human", "value": "\nWhich color represents {Portfolio 2's line}[0.171, 0.395] on the chart?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 71 }, { "id": "7r03OxShpiCf3wOL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.133, 0.064, 0.975, 0.294]." }, { "from": "gpt", "value": "This region includes the graph for 'Slow Recovery (Portfolio 1)', displaying markers at each week with corresponding values, characterized by a greenish line progressing through the points." } ], "id_raw": 71 }, { "id": "KxpCxmGvwvYQFyX9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects in [0.451, 0.037, 0.656, 0.065]?" }, { "from": "gpt", "value": "The region includes the legend entry for 'Portfolio_1' with a green color indicator beside the label text." } ], "id_raw": 71 }, { "id": "JBMbZCy46ZNhYDHK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_11.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the region [0.025, 0.479, 0.049, 0.521]?" }, { "from": "gpt", "value": "This region contains the y-axis label 'Value' for the subplot 'Continuous Decrease (Portfolio 2)', oriented vertically." } ], "id_raw": 71 }, { "id": "xOLS8QKPazt3b40f", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the year at {the x-axis tick} at [0.935, 0.294, 0.935, 0.294]?" }, { "from": "gpt", "value": "2018." } ], "id_raw": 73 }, { "id": "33TIUWz5eA9Jzyjc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_13.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is represented by coordinates [0.092, 0.064, 0.975, 0.294]?" }, { "from": "gpt", "value": "Line chart." } ], "id_raw": 73 }, { "id": "dwfwK8TYWOZBJjkF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_13.png", "conversations": [ { "from": "human", "value": "\nWhich color indicates the GDP Growth trend in the legend?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 73 }, { "id": "llhztr5gMK5Aymv8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_13.png", "conversations": [ { "from": "human", "value": "\nAre the {Unemployment data points}[0.453, 0.454] higher in the graph than the {GDP Growth data points}[0.453, 0.747] in 2012?" }, { "from": "gpt", "value": "No." } ], "id_raw": 73 }, { "id": "F51zapTYDveXKIwV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_13.png", "conversations": [ { "from": "human", "value": "\nIs the {year label}[0.214, 0.946] closer to the {label for GDP Growth}[0.178, 0.719] or {label for Inflation}[0.178, 0.078]?" }, { "from": "gpt", "value": "GDP Growth." } ], "id_raw": 73 }, { "id": "eeeLAIGUzxa9iIsh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_13.png", "conversations": [ { "from": "human", "value": "\nWhich has a larger value in 2013, {Unemployment}[0.534, 0.471] or {GDP Growth}[0.534, 0.764]?" }, { "from": "gpt", "value": "GDP Growth." } ], "id_raw": 73 }, { "id": "XCDVNP7jvnlJMfNU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.040, 0.212, 0.076, 0.228]." }, { "from": "gpt", "value": "The area includes the y-axis label '0.1' for inflation rate, described in black color and 0-degree rotation." } ], "id_raw": 73 }, { "id": "LZQLF1njUhSXrcri", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_13.png", "conversations": [ { "from": "human", "value": "\nWhat details are prevalent in the region [0.534, 0.327, 0.614, 0.415]?" }, { "from": "gpt", "value": "This region contains x-axis label of GDP growth subplot, placed centrally, and the y-axis label of unemployment rate subplot, styled vertically." } ], "id_raw": 73 }, { "id": "qqZ6Nc089lZNQCqv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within [0.534, 0.946, 0.614, 0.962]." }, { "from": "gpt", "value": "This region encompasses the year label '2014' on the x-axis of the GDP Growth chart, written in black with no rotation." } ], "id_raw": 73 }, { "id": "yRb6dTE0GGUKZob0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_14.png", "conversations": [ { "from": "human", "value": "\nWhat's the meaning of the label at the position [0.345, 0.655, 0.689, 0.667]?" }, { "from": "gpt", "value": "The subtitle is \"New Business Ownership Rate Over Time\"." } ], "id_raw": 74 }, { "id": "TeVZmhljAaKndxw8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the position [0.806, 0.316, 0.884, 0.325]?" }, { "from": "gpt", "value": "It's a legend item labeled \"Insignificant Increase\"." } ], "id_raw": 74 }, { "id": "m98u3p1nWqw99hun", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_14.png", "conversations": [ { "from": "human", "value": "\nWhat lies at coordinates [0.015, 0.471, 0.029, 0.504] in the figure?" }, { "from": "gpt", "value": "Y-axis label of subplot_1 which states \"Rate %\"." } ], "id_raw": 74 }, { "id": "izReSMx28IYBq5ck", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_14.png", "conversations": [ { "from": "human", "value": "\nIs {the 2011 point on 'Established Business Ownership Rate'}[0.156, 0.482] higher or lower than {the 2011 point on 'New Business Ownership Rate'}[0.156, 0.875] on the chart?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 74 }, { "id": "vmoIPc8XOYq361jA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_14.png", "conversations": [ { "from": "human", "value": "\nIs {the 'New Business Ownership Rate' line}'s last point[0.968, 0.818] closer to the top of the figure or to the bottom?" }, { "from": "gpt", "value": "Bottom." } ], "id_raw": 74 }, { "id": "tMQKUzSKhTQ16g1Q", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_14.png", "conversations": [ { "from": "human", "value": "\nCompare the values at {2020 point on 'Established Business Ownership Rate'}[0.968, 0.402] and {2020 point on 'New Business Ownership Rate'}[0.968, 0.818], which is higher?" }, { "from": "gpt", "value": "'Established Business Ownership Rate'" } ], "id_raw": 74 }, { "id": "vxwvbgbocvmaATmb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_14.png", "conversations": [ { "from": "human", "value": "\nCould you describe the contents of this region: [0.118, 0.684, 0.328, 0.693]?" }, { "from": "gpt", "value": "The region includes the legend item, {New Business Ownership Rate}[0.118, 0.684, 0.328, 0.693], in subplot_2." } ], "id_raw": 74 }, { "id": "bIJyEHw3OkyoEvg4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_14.png", "conversations": [ { "from": "human", "value": "\nWhat kind of information is contained within the region [0.229, 0.968, 0.264, 0.977]?" }, { "from": "gpt", "value": "The region contains the X-axis tick label {Year 2012}[0.229, 0.968, 0.264, 0.977] in subplot_2." } ], "id_raw": 74 }, { "id": "tNl7V5lhSVF1vGwc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_14.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the area from [0.118, 0.353, 0.377, 0.362]?" }, { "from": "gpt", "value": "It contains the legend item for {Established Business Ownership Rate}[0.118, 0.353, 0.377, 0.362] in subplot_1." } ], "id_raw": 74 }, { "id": "pU0bjKtcyNHlumjo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_15.png", "conversations": [ { "from": "human", "value": "\nWhat represents {the line for 'Farm Profit'}[0.147, 0.077, 0.968, 0.261] in terms of color?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 75 }, { "id": "0ET241mbjExI9A1Q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the x-axis tick}[0.229, 0.294] corresponding to the year 2006?" }, { "from": "gpt", "value": "2006." } ], "id_raw": 75 }, { "id": "I94qyWwWFiri9CWu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of {the highest point on the line for 'Agricultural Imports'}[0.968, 0.720]?" }, { "from": "gpt", "value": "[0.968, 0.720]." } ], "id_raw": 75 }, { "id": "C6CdyUNyd5qwRFP3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_15.png", "conversations": [ { "from": "human", "value": "\nIs {the line point for 2010}[0.558, 0.163] above or below {the line point for 2008}[0.393, 0.150] in 'Farm Profit' chart?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 75 }, { "id": "NIiObGI5Ob2M8UrD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_15.png", "conversations": [ { "from": "human", "value": "\nDoes {the initial point}[0.147, 0.615] for 'Food Sales' start higher or lower than {the final point}[0.968, 0.406] in 2015?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 75 }, { "id": "Pt7FJQRZ5y6BBVZy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_15.png", "conversations": [ { "from": "human", "value": "\nIs {the mark for 2009}[0.476, 0.843] on 'Agricultural Imports' horizontal line higher or lower than {the mark for 2015}[0.968, 0.720]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 75 }, { "id": "Ca49zBMK6G8GHvVa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.036, 0.305, 0.080, 0.321]." }, { "from": "gpt", "value": "The x-axis tick label '2004' is located here, displayed in black color." } ], "id_raw": 75 }, { "id": "FhnnlJQZzZvzMS7l", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.025, 0.135, 0.048, 0.223]." }, { "from": "gpt", "value": "This area contains the y-axis label 'Amount ($)' for the 'Farm Profit Over Years' chart, styled in black and rotated 90 degrees." } ], "id_raw": 75 }, { "id": "ZYyY5zZKB5luiwL0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_15.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.147, 0.385, 0.968, 0.615]?" }, { "from": "gpt", "value": "This region contains the line for 'Food Sales' with points marking sales from 2005 to 2015, as well as corresponding y-axis labels ranging from 20000 to 30000 dollars." } ], "id_raw": 75 }, { "id": "bBShGIF4oEQ3krmB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_16.png", "conversations": [ { "from": "human", "value": "\nWhat are the dominant colors of {the markers for the line representing Scientific Research}[0.079, 0.736, 0.128, 0.745] in the chart?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 76 }, { "id": "XbCVcRmiHWsq9vTL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_16.png", "conversations": [ { "from": "human", "value": "\nWhich x-axis tick label corresponds to {the point at year 5 for Scientific Research}[0.526, 0.147]?" }, { "from": "gpt", "value": "5." } ], "id_raw": 76 }, { "id": "xhpBq7oHTAk8k9nh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-coordinate for {the highest value point on the Technological Innovation line}[0.884, 0.409]?" }, { "from": "gpt", "value": "9." } ], "id_raw": 76 }, { "id": "EJFLPmv4R3d0431z", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_16.png", "conversations": [ { "from": "human", "value": "\nIs {the highest point on the Scientific Research line}[0.884, 0.121] higher than {the lowest point on the Technological Innovation line}[0.884, 0.409]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 76 }, { "id": "LPDPU4oftMSpusla", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_16.png", "conversations": [ { "from": "human", "value": "\nWhich point is higher, {the point at year 4 for Technological Innovation}[0.437, 0.601] or {the point at year 6 for Scientific Research}[0.616, 0.135]?" }, { "from": "gpt", "value": "Scientific Research." } ], "id_raw": 76 }, { "id": "PIcr6EaWw6QFr1tn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_16.png", "conversations": [ { "from": "human", "value": "\nIs {the first point on the Scientific Research line}[0.079, 0.299] higher than {the first point on the Technological Innovation line}[0.079, 0.612]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 76 }, { "id": "T9rjFl5e7nsgb6iH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the area with coordinates [0.039, 0.313, 0.068, 0.362]." }, { "from": "gpt", "value": "This region contains several y-axis tick labels for Technological Innovation, specifically the labels for \u2018300\u2019 and \u2018400\u2019." } ], "id_raw": 76 }, { "id": "73nsMdvUFdeRHdzv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_16.png", "conversations": [ { "from": "human", "value": "\nWhat information is present in [0.530, 0.312, 0.620, 0.328]?" }, { "from": "gpt", "value": "This area contains x-axis tick labels '6' and '8' for the subplots presenting timelines for Scientific Research and Technological Innovation." } ], "id_raw": 76 }, { "id": "yuOE8TpVylx1LZqI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_16.png", "conversations": [ { "from": "human", "value": "\nWhat is described in the region [0.136, 0.734, 0.282, 0.772]?" }, { "from": "gpt", "value": "This area contains the text labels for the legends specifying 'Scientific Research' and 'Technological Innovation' with associated markers in green and blue." } ], "id_raw": 76 }, { "id": "zMLj1i9uZULwDp66", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the shape that is coloured and placed at {coordinates}[0.401, 0.691, 0.447, 0.691] in the legend?" }, { "from": "gpt", "value": "It's a horizontal line." } ], "id_raw": 77 }, { "id": "C3ySCjoAedPO8zTI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {y-axis tick}[0.114, 0.945]?" }, { "from": "gpt", "value": "-1.5." } ], "id_raw": 77 }, { "id": "HhWXk4RHpXEOK8f4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_17.png", "conversations": [ { "from": "human", "value": "\nWhat axis is associated with {the label}[0.517, 0.968, 0.573, 0.983]?" }, { "from": "gpt", "value": "The X-axis." } ], "id_raw": 77 }, { "id": "NvbAYOx50bEhLHS2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_17.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Fossil fuel Emission'}[0.130, 0.742, 0.960, 0.908] start at a higher or lower value than {the line for 'Population Change'}[0.130, 0.408, 0.960, 0.592]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 77 }, { "id": "bZFVCS1Hu1ZwxZFV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_17.png", "conversations": [ { "from": "human", "value": "\nIs {coordinate for '8th point'}[0.794, 0.779] on the Fossil fuel Emission trend move along the positive or negative Y direction compared to {coordinate for '7th point'}[0.711, 0.797]?" }, { "from": "gpt", "value": "Negative." } ], "id_raw": 77 }, { "id": "lU9LpIpGUoLiKX2L", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_17.png", "conversations": [ { "from": "human", "value": "\nBetween the trend for 'Yield' and 'Population Change', which shows a positive slope between the 1st and 2nd points?" }, { "from": "gpt", "value": "Population Change." } ], "id_raw": 77 }, { "id": "kLTfcGxB1N4J5EeK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents of the region bounded by the coordinates [0.394, 0.016, 0.696, 0.036]." }, { "from": "gpt", "value": "This region contains the subtitle \"Yield Trend Over Time\" in black." } ], "id_raw": 77 }, { "id": "6IgGJfNPVMA0Zvd4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.130, 0.408, 0.960, 0.592] represent?" }, { "from": "gpt", "value": "This region represents the chart data for 'Population Change Over Time' with points plotted in red." } ], "id_raw": 77 }, { "id": "y450XR4it7wdA7eV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content of the subplot area [0.130, 0.064, 0.960, 0.294]." }, { "from": "gpt", "value": "This area illustrates 'Yield' trend according to time with the line drawn in green color connected vertically from the top left to the bottom right of this region." } ], "id_raw": 77 }, { "id": "iAcHl3t0E40HNWkv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_18.png", "conversations": [ { "from": "human", "value": "\nWhat object is represented at coordinates [0.082, 0.630, 0.091, 0.642] in the chart?" }, { "from": "gpt", "value": "X-axis tick label \"0\" for subplot 1." } ], "id_raw": 78 }, { "id": "bWYmhD41I7h8ocsG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_18.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.046, 0.421, 0.077, 0.433] indicate in the third plot?" }, { "from": "gpt", "value": "Y-axis tick label \"1.00\"." } ], "id_raw": 78 }, { "id": "QVM9WVtfRn50kaRO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_18.png", "conversations": [ { "from": "human", "value": "\nWhich element is located at [0.035, 0.459, 0.029, 0.523]?" }, { "from": "gpt", "value": "Y-axis label for the second subplot." } ], "id_raw": 78 }, { "id": "NN26uqTzdanbY9we", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_18.png", "conversations": [ { "from": "human", "value": "\nIs {the label '0'}[0.082, 0.301, 0.091, 0.313] higher or lower than {the label '2'}[0.260, 0.301, 0.269, 0.313] in terms of coordinates on the y-axis?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 78 }, { "id": "VAHzFTDnxiIXem9v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_18.png", "conversations": [ { "from": "human", "value": "\nIs {the tick at [0.087, 0.293]} within the bounds of {the line for 'Security Risk'}[[0.087, 0.063, 0.976, 0.284]]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 78 }, { "id": "x3IRk9sEB06H84oS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_18.png", "conversations": [ { "from": "human", "value": "\nIn the first subplot, does {the Y-axis label '1.4'}[0.055, 0.166, 0.077, 0.178] appear visually above {the Y-axis label '1.6'}[0.055, 0.131, 0.077, 0.143]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 78 }, { "id": "UgkFdZYtDvZT2abu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_18.png", "conversations": [ { "from": "human", "value": "\nDescribe what is found between the coordinates [0.376, 0.641, 0.589, 0.717]." }, { "from": "gpt", "value": "This region encompasses parts of the upper body of the chart's second subplot including the higher financial risk values and a subsection of the plotted financial risk line." } ], "id_raw": 78 }, { "id": "4sTujBXRYUltQLP6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in [0.087, 0.031, 0.250, 0.293]." }, { "from": "gpt", "value": "The lower left corner of the first subplot; includes the x-axis from labels '0' to '2', the starting section of the security risk line, and corresponding grid lines." } ], "id_raw": 78 }, { "id": "1L86I99NAYK1Caws", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_18.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.441, 0.636, 0.620, 0.658]?" }, { "from": "gpt", "value": "This area contains the midsection of the x-axis and y-axis of the second subplot, showing part of the x-axis tick labels ('4', '6'), part of the financial risk line, and a fraction of the y-axis with its grid lines." } ], "id_raw": 78 }, { "id": "Rp2c9yOOvigeKK2r", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_19.png", "conversations": [ { "from": "human", "value": "\nWhat is labeled '2022' located at {coordinates}[0.416, 0.626, 0.474, 0.641]?" }, { "from": "gpt", "value": "X-axis tick label." } ], "id_raw": 79 }, { "id": "S1nls0NM60qPc6C7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_19.png", "conversations": [ { "from": "human", "value": "\nWhat object has the coordinates [0.058, 0.196, 0.094, 0.212]?" }, { "from": "gpt", "value": "The '1.2' Y-axis tick label of the first subplot." } ], "id_raw": 79 }, { "id": "jAUA8VQTaC6vpw3M", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_4_19.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the main object at {coordinates}[0.025, 0.787, 0.049, 0.853]?" }, { "from": "gpt", "value": "The 'Progress' Y-axis label of the last subplot." } ], "id_raw": 79 }, { "id": "h4qajQBeRpAip9GC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_19.png", "conversations": [ { "from": "human", "value": "\nIs the {value in 2024 for 'Cloud Computing'}[0.779, 0.820] larger than the {value in 2020 of 'AI'}[0.111, 0.615]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 79 }, { "id": "9O3QMO3hHBb9PYbF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_19.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at the {point in 2023}, the 'AI' line [0.612, 0.534] or the 'Blockchain' line [0.612, 0.248]?" }, { "from": "gpt", "value": "AI." } ], "id_raw": 79 }, { "id": "4zC3NYMoEVeZ0dBL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_4_19.png", "conversations": [ { "from": "human", "value": "\nIs the value of 'Blockchain' in 2025 [0.946, 0.156] lower than its value in 2021 [0.278, 0.202]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 79 }, { "id": "dFtaNk4ftxHbytjH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_19.png", "conversations": [ { "from": "human", "value": "\nDescribe what is contained in the region [0.540, 0.358, 0.646, 0.386]." }, { "from": "gpt", "value": "This region contains the legend for the second subplot, which indicates 'AI' with a red 'x' marker." } ], "id_raw": 79 }, { "id": "bruxIk1rvtrxj6LI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_19.png", "conversations": [ { "from": "human", "value": "\nWhat content can be found in the area defined by [0.220, 0.657, 0.836, 0.677]?" }, { "from": "gpt", "value": "The area includes the title for the third subplot, which is 'Cloud Computing Growth from 2020 to 2025'." } ], "id_raw": 79 }, { "id": "iEJ0iMK6r9hBWqnB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_4_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.540, 0.678, 0.824, 0.706] contain?" }, { "from": "gpt", "value": "The region includes the legend for the third subplot: 'Cloud Computing' with a blue square marker." } ], "id_raw": 79 }, { "id": "B9ICv6azdXIY2GVv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.495, 0.855, 0.554, 0.925]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 80 }, { "id": "GV4sw0KB5clHBoTh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.114, 0.169, 0.935, 0.728]?" }, { "from": "gpt", "value": "A line graph." } ], "id_raw": 80 }, { "id": "kVgMGQ0OSIFYE2ds", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.069, 0.276, 0.104, 0.346]?" }, { "from": "gpt", "value": "50." } ], "id_raw": 80 }, { "id": "TLXW9xGGHimebjNy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point for year '2018'}[0.771, 0.318] above or below {the point for year '2019'}[0.853, 0.249] in the line graph?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 80 }, { "id": "Z6ulVtD1ifxkP4u2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point for year '2013'}[0.360, 0.664] higher or lower than {the point for year '2014'}[0.442, 0.631] in the line graph?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 80 }, { "id": "wCodCVHOQ6pheAJH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_0.png", "conversations": [ { "from": "human", "value": "\nWhich year has a higher CO2 reduction, {the point for 2016}[0.607, 0.459] or {the point for 2020}[0.935, 0.169]?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 80 }, { "id": "C2eKgTdkaAMLPKAz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.069, 0.114, 0.104, 0.734]." }, { "from": "gpt", "value": "The area contains the y-axis tick labels marked with values ranging from 10 to 65." } ], "id_raw": 80 }, { "id": "NHUpViI1Kbl2YOt0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the area [0.114, 0.169, 0.935, 0.728]?" }, { "from": "gpt", "value": "The area comprises a line graph representing the CO2 Reduction percentage from the year 2010 to 2020." } ], "id_raw": 80 }, { "id": "UEjylZrVZ0AJXkhQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.069, 0.757, 0.970, 0.827]?" }, { "from": "gpt", "value": "There are 11 objects. These are the x-axis tick labels representing the years 2010 to 2020. For instance, the labels '2010' and '2020' are located at {coordinates}[0.079, 0.757, 0.149, 0.827] and {coordinates}[0.900, 0.757, 0.970, 0.827], respectively." } ], "id_raw": 80 }, { "id": "Z0G8DTEO2zgFu4Aw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis in the chart?" }, { "from": "gpt", "value": "Vehicle Sales." } ], "id_raw": 81 }, { "id": "FSrOI8EVPWeWkwmm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the point labeled '2005'} [0.535, 0.239] ?" }, { "from": "gpt", "value": "74,000." } ], "id_raw": 81 }, { "id": "XG4RwRi73fiZyZVH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_1.png", "conversations": [ { "from": "human", "value": "\nWhat does {the point} [0.280, 0.656]represent on the x-axis?" }, { "from": "gpt", "value": "The year 2002." } ], "id_raw": 81 }, { "id": "NZfpAUdBWZRtlucq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point for '2009'}[0.874, 0.352] selling more or less than {the point for '2002'}[0.280, 0.656]?" }, { "from": "gpt", "value": "More." } ], "id_raw": 81 }, { "id": "WsWL7YmRWdHXyVmV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_1.png", "conversations": [ { "from": "human", "value": "\nIn terms of sales, does {the point for '2001'}[0.195, 0.732]perform better or worse than {the point for '2007'}[0.705, 0.371]?" }, { "from": "gpt", "value": "Worse." } ], "id_raw": 81 }, { "id": "xbYytaXO7YfzWuqR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_1.png", "conversations": [ { "from": "human", "value": "\nBased on the points, did the sales increase or decrease from {the point for '2008'}[0.789, 0.694] to {the point for '2010'}[0.959, 0.087]?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 81 }, { "id": "SR0MXZntGCmhhUYN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements in the region [0.513, 0.903, 0.557, 0.931]." }, { "from": "gpt", "value": "The region contains the x-axis label '2005'." } ], "id_raw": 81 }, { "id": "iCUFgf2gP3HWZXFe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.019, 0.366, 0.036, 0.547]?" }, { "from": "gpt", "value": "This region contains the y-axis label 'Vehicle Sales' written in black and rotated by 90 degrees." } ], "id_raw": 81 }, { "id": "DvRF3TsSlsGR8r8q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the region [0.600, 0.422, 0.639, 0.442]?" }, { "from": "gpt", "value": "The region contains the numerical label '62000' associated with the year 2006." } ], "id_raw": 81 }, { "id": "qmwBV4jrZH421AGV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object of {xlabel}[0.500, 0.855, 0.602, 0.925]?" }, { "from": "gpt", "value": "Gender." } ], "id_raw": 82 }, { "id": "alDfo7hVuLBDUbc3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis tick}[0.132, 0.733]?" }, { "from": "gpt", "value": "16." } ], "id_raw": 82 }, { "id": "RF9VQmcIQhqYTVIr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the {line_0}[0.342, 0.184, 0.761, 0.655] represent?" }, { "from": "gpt", "value": "It represents the Average Income and Population Indicators for each Gender." } ], "id_raw": 82 }, { "id": "ObanhKsMw3XiMZIn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_2.png", "conversations": [ { "from": "human", "value": "\nFor the gender point at {coordinates}[0.761, 0.184], does it map to a higher or lower value on the y-axis than the point at {coordinates}[0.342, 0.655]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 82 }, { "id": "0hG1Dri4Tmk2kwwL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_2.png", "conversations": [ { "from": "human", "value": "\nBetween {Females}[0.284, 0.757, 0.399, 0.827] and {Males}[0.720, 0.757, 0.801, 0.827], which has a higher indicator value?" }, { "from": "gpt", "value": "Females." } ], "id_raw": 82 }, { "id": "K5GaiQhGfE15RUFl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_2.png", "conversations": [ { "from": "human", "value": "\nIs the y-value of {Females}[0.342, 0.655] on the line greater than the y-value of {y-tick '30'}[0.087, 0.637, 0.122, 0.707]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 82 }, { "id": "q7XCItMNeWywc1aT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_2.png", "conversations": [ { "from": "human", "value": "\nWhat specific features are included in the region from {coordinates}[0.284, 0.757, 0.399, 0.827] to {coordinates}[0.720, 0.757, 0.801, 0.827] along the x-axis?" }, { "from": "gpt", "value": "This region contains two points on the line. The point \"Females\" is located at the coordinates {Females}[0.284, 0.757, 0.399, 0.827] on the left and the point \"Males\" at the coordinates {Males}[0.720, 0.757, 0.801, 0.827] on the right side of the x-axis." } ], "id_raw": 82 }, { "id": "phvahey074ooXvRU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is in the {coordinate}[0.342, 0.184, 0.761, 0.655] area." }, { "from": "gpt", "value": "The area contains a line chart, representing the Average Income and Population Indicators for each Gender. The line connects two points, one representing 'Females' and the other representing 'Males'." } ], "id_raw": 82 }, { "id": "bvtUYh5ZUlZMZ7Qx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is present in the vertical area from {coordinates}[0.087, 0.539, 0.122, 0.609] to {coordinates}[0.087, 0.647, 0.122, 0.717] of the y-axis tick labels?" }, { "from": "gpt", "value": "This region includes several y-axis tick labels. Specifically, it includes tick labels from '50' to '28'." } ], "id_raw": 82 }, { "id": "kfW9UAIhXczHJ5xf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the label represented by the coordinates [0.054, 0.282, 0.078, 0.728] refer to?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 83 }, { "id": "Gb55iLYLnD2lw8BT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_3.png", "conversations": [ { "from": "human", "value": "\nWhich x-axis tick is located at coordinates [0.627, 0.906, 0.686, 0.953]?" }, { "from": "gpt", "value": "2048." } ], "id_raw": 83 }, { "id": "1RGjhDZzRivgRvkT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_3.png", "conversations": [ { "from": "human", "value": "\nCan you inform me about what {the data point}[0.763, 0.213] represents on the line chart?" }, { "from": "gpt", "value": "The data point represents 4096 friends count and an engagement index of 15.3." } ], "id_raw": 83 }, { "id": "VFn6yPZjkmcs0QRv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_3.png", "conversations": [ { "from": "human", "value": "\nIs the value of engagement index at {the data point}[0.900, 0.164] higher or lower than at {the data point}[0.869, 0.180]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 83 }, { "id": "tsu6eagwo2eor5Wo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_3.png", "conversations": [ { "from": "human", "value": "\nIs the engagement index increasing or decreasing between {the data point}[0.550, 0.362] and {the data point}[0.657, 0.389]?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 83 }, { "id": "41Wu8ou2TWQ946GK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in engagement index from {the point at 128 friends count}[0.231, 0.775] to {the point at 512 friends count}[0.444, 0.532]?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 83 }, { "id": "y13ijsQGX2QR8OM7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the information contained within the range [0.125, 0.120, 0.900, 0.890]?" }, { "from": "gpt", "value": "This region contains the plotted line chart which bellows data points of friends count against engagement index, starting from 64 to 10000 friends." } ], "id_raw": 83 }, { "id": "MPY97NhxYdhNqZ7i", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_3.png", "conversations": [ { "from": "human", "value": "\nWhat content is found in the range [0.087, 0.322, 0.117, 0.368]?" }, { "from": "gpt", "value": "This region contains a Y-axis tick label indicating {13}[0.088, 0.322, 0.117, 0.368]." } ], "id_raw": 83 }, { "id": "vh0rur74qYSyYcln", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_3.png", "conversations": [ { "from": "human", "value": "\nWhat do we find in the region [0.209, 0.906, 0.253, 0.953]?" }, { "from": "gpt", "value": "There is an X-axis label for the friends count {128}[0.209, 0.906, 0.253, 0.953]." } ], "id_raw": 83 }, { "id": "UjIX67wnI8DcvIcZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the chart?" }, { "from": "gpt", "value": "CO2 Emissions (Gt)." } ], "id_raw": 84 }, { "id": "NuAvRFNho8LLiaJQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_4.png", "conversations": [ { "from": "human", "value": "\nWhere is the x-axis label located on the chart?" }, { "from": "gpt", "value": "[0.496, 0.927, 0.545, 0.963]." } ], "id_raw": 84 }, { "id": "f3wnObzahfVAP9OV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_4.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the point with coordinates [0.903, 0.170] on the line graph?" }, { "from": "gpt", "value": "The value for CO2 Emissions (Gt) in the year 2019." } ], "id_raw": 84 }, { "id": "kiMFWtOMQLwzZ6zF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_4.png", "conversations": [ { "from": "human", "value": "\nIs the value for CO2 emissions {in 2003}[0.223, 0.568] higher or lower than {in 2005}[0.308, 0.460]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 84 }, { "id": "6aW5RiGLigGxzvl0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_4.png", "conversations": [ { "from": "human", "value": "\nIs there a decline or increase in CO2 emissions from {2016}[0.776, 0.195] to {2018}[0.861, 0.158]?" }, { "from": "gpt", "value": "Decline." } ], "id_raw": 84 }, { "id": "Qyk339mizDFl5s7f", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_4.png", "conversations": [ { "from": "human", "value": "\nWhich point has a higher value: {the data point in 2008}[0.435, 0.282] or {the data point in 2015}[0.733, 0.191]?" }, { "from": "gpt", "value": "The data point in 2008." } ], "id_raw": 84 }, { "id": "WM5my2G7hNwr969p", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.491, 0.879, 0.550, 0.914]." }, { "from": "gpt", "value": "The region contains the year '2010' from the x-axis labels." } ], "id_raw": 84 }, { "id": "j89MvWzxNBEqjSmp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_4.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region [0.058, 0.438, 0.087, 0.473]?" }, { "from": "gpt", "value": "The region contains the y-axis label '30', representing a CO2 emission level of 30 Gt." } ], "id_raw": 84 }, { "id": "s3QJC6CpHw4O27t5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.095, 0.158, 0.903, 0.672] encompass?" }, { "from": "gpt", "value": "This region includes the entire line graph, showing the annual CO2 emissions from 2000 to 2019." } ], "id_raw": 84 }, { "id": "HUYtTL0MdXhtAwqy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the label represent at coordinates [0.015, 0.280, 0.029, 0.602] in the chart?" }, { "from": "gpt", "value": "Y-axis label, which is 'Intrusion Detection (%)'." } ], "id_raw": 85 }, { "id": "BIPYWQaIkCFxmoz6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the data point} at [0.511, 0.085]?" }, { "from": "gpt", "value": "98." } ], "id_raw": 85 }, { "id": "2C3Cgi5ChztEn57S", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.494, 0.942, 0.531, 0.970]?" }, { "from": "gpt", "value": "'Years'." } ], "id_raw": 85 }, { "id": "AQY9AYWMbRrpapaP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_5.png", "conversations": [ { "from": "human", "value": "\nIn reference to {the data point}[0.059, 0.687] and {the data point}[0.169, 0.276], which one has a higher value?" }, { "from": "gpt", "value": "The data point at [0.169, 0.276]." } ], "id_raw": 85 }, { "id": "aNa78tiBrJCO2s8N", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_5.png", "conversations": [ { "from": "human", "value": "\nIs {the data point}[0.059, 0.687] above or below {the data point}[0.112, 0.249] on the line chart?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 85 }, { "id": "NYvHXbDAep4Xa3me", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_5.png", "conversations": [ { "from": "human", "value": "\nWhich year had a higher intrusion detection percentage, the year at {point}[0.062, 0.660] or the year at {point}[0.070, 0.496]?" }, { "from": "gpt", "value": "The year at point [0.062, 0.660]." } ], "id_raw": 85 }, { "id": "qLcbQp4yUISh03ne", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within [0.041, 0.861, 0.985, 0.931]?" }, { "from": "gpt", "value": "The region contains x-axis labels representing the years from 2012 to 2020." } ], "id_raw": 85 }, { "id": "eeWcjI2jwP3MWiVE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_5.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.035, 0.156, 0.052, 0.869]?" }, { "from": "gpt", "value": "The area contains y-axis tick labels representing intrusion detection percentages from 70 to 95." } ], "id_raw": 85 }, { "id": "tfjWZ7PVd2AXPrXf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_5.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the area [0.059, 0.085, 0.968, 0.852]?" }, { "from": "gpt", "value": "This region represents the plot of the line chart, showing the trend of intrusion detection percentages over the years." } ], "id_raw": 85 }, { "id": "sL7iC7s9r7Yj12Hr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_6.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.021, 0.298, 0.041, 0.513] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 86 }, { "id": "aINIPKnS6hPIJw5z", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_6.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the coordinates of the label for the \"Education\" category?" }, { "from": "gpt", "value": "[0.142, 0.796, 0.226, 0.881]." } ], "id_raw": 86 }, { "id": "zGUqJ8EbdMtuRUHO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_6.png", "conversations": [ { "from": "human", "value": "\nWhat does {the text}[0.859, 0.491, 0.884, 0.511] related to \"Waste Management\" in the graph indicate?" }, { "from": "gpt", "value": "It indicates the satisfaction level, which is 77." } ], "id_raw": 86 }, { "id": "091zZxu5NThzkhem", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point for the \"Security\" category}[0.286, 0.482] higher or lower than {the point for the \"Roads\" category}[0.594, 0.559]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 86 }, { "id": "ZztCMZbIUCxLZEBH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_6.png", "conversations": [ { "from": "human", "value": "\nComparing {the point for \"Public Transport\"}[0.696, 0.405] and {the point for \"Roads\"}[0.594, 0.559], which category has a higher satisfaction level?" }, { "from": "gpt", "value": "Roads." } ], "id_raw": 86 }, { "id": "XznMzyu4JXfk7zo5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_6.png", "conversations": [ { "from": "human", "value": "\nIs {the satisfaction level of the \"Education\" category}[0.184, 0.405] more or less than {the \"Housing\" category}[0.389, 0.329]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 86 }, { "id": "kqg3tGPNyqHl67eh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_6.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content within the coordinates [0.081, 0.021, 0.901, 0.790]?" }, { "from": "gpt", "value": "It is the main plot area of the line graph, which includes a line graph, tick marks on the x and y-axes, labels for both axes, as well as labels for each data point in the plot." } ], "id_raw": 86 }, { "id": "kJaHvH1FD9fZUHfD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is contained within the region [0.142, 0.796, 0.226, 0.881]?" }, { "from": "gpt", "value": "This region contains the x-axis label for the \"Education\" category." } ], "id_raw": 86 }, { "id": "crQE0i9f847hPdAs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_6.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region defined by [0.081, 0.137, 0.901, 0.559]?" }, { "from": "gpt", "value": "This region represents the line graph's path, from the point of \"Healthcare\" to the point of \"Waste Management,\" demonstrating the variations in satisfaction levels across various categories." } ], "id_raw": 86 }, { "id": "JWILc641rE9D6UQC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_7.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.457, 0.927, 0.580, 0.963]?" }, { "from": "gpt", "value": "The X-axis label \"Age Group\"." } ], "id_raw": 87 }, { "id": "Wv4BckoABMeH0lqv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.025, 0.364, 0.048, 0.573]?" }, { "from": "gpt", "value": "The Y-axis label \"Stress Level\"." } ], "id_raw": 87 }, { "id": "3Oz755s9AmQXDxbU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_7.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.095, 0.093, 0.942, 0.777]?" }, { "from": "gpt", "value": "The overall line graph with data points." } ], "id_raw": 87 }, { "id": "0R74nYrqn7KIkDqB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_7.png", "conversations": [ { "from": "human", "value": "\nIs the {stress level for the age group '30-40'}[0.236, 0.173] higher or lower than the {stress level for the age group '20-30'}[0.095, 0.468]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 87 }, { "id": "edn8DQT8ezv5BIZg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_7.png", "conversations": [ { "from": "human", "value": "\nHow does the {stress level of the age group '70-80'}[0.800, 0.494] compare to the {stress level of the age group '60-70'}[0.659, 0.313]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 87 }, { "id": "qkLiFn5d4j1WKkCa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_7.png", "conversations": [ { "from": "human", "value": "\nIs the {stress level at the age group '40-50'}[0.377, 0.141] above or below the {stress level at the age group '50-60'}[0.518, 0.093]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 87 }, { "id": "aW2sEo7EBVkOKHqX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area enclosed by [0.062, 0.879, 0.975, 0.914]?" }, { "from": "gpt", "value": "This is the region containing the X-axis labels denoting different age groups: {20-30}[0.062, 0.879, 0.128, 0.914], {30-40}[0.203, 0.879, 0.269, 0.914], {40-50}[0.344, 0.879, 0.410, 0.914], {50-60}[0.485, 0.879, 0.552, 0.914], {60-70}[0.626, 0.879, 0.693, 0.914], {70-80}[0.767, 0.879, 0.834, 0.914] and {80-90}[0.908, 0.879, 0.975, 0.914]." } ], "id_raw": 87 }, { "id": "f5tJg3tqL1KrAHEF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the space between coordinates [0.058, 0.201, 0.087, 0.381]." }, { "from": "gpt", "value": "The region is comprised of the Y-axis tick labels {30}[0.058, 0.780, 0.087, 0.815], {40}[0.058, 0.635, 0.087, 0.670], {50}[0.058, 0.491, 0.087, 0.526], {60}[0.058, 0.346, 0.087, 0.381], {70}[0.058, 0.201, 0.087, 0.236] signifying different stress levels." } ], "id_raw": 87 }, { "id": "VJuXheFRTgK8oUv5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_7.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained in the region [0.077, 0.038, 0.959, 0.747]?" }, { "from": "gpt", "value": "This area includes coordinate points and corresponding stress levels for the age groups: {52.5 for '20-30'}[0.077, 0.413, 0.113, 0.438], {72.9 for '30-40'}[0.218, 0.118, 0.254, 0.143], {75.1 for '40-50'}[0.359, 0.086, 0.395, 0.111], {78.4 for '50-60'}[0.500, 0.038, 0.536, 0.063], {63.2 for '60-70'}[0.641, 0.258, 0.677, 0.283], {50.7 for '70-80'}[0.782, 0.439, 0.819, 0.464], and {31.2 for '80-90'}[0.924, 0.722, 0.959, 0.747]. These elements are connected by the line graph outlining the stress level by age group." } ], "id_raw": 87 }, { "id": "skTZAtSuC6h6X2qC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the labeled object that corresponds to [0.060, 0.095, 0.968, 0.867] in the chart?" }, { "from": "gpt", "value": "GDP Growth Rate line graph." } ], "id_raw": 88 }, { "id": "INcBthFt6S1ggFvq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the value at {the coordinate} [0.806, 0.400] on the graph?" }, { "from": "gpt", "value": "2.5." } ], "id_raw": 88 }, { "id": "FLh3b8NvnHxnn2iv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label at [0.015, 0.344, 0.029, 0.592] represent?" }, { "from": "gpt", "value": "GDP Growth Rate (%)." } ], "id_raw": 88 }, { "id": "Lmwz1ttsrHN4nKLo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_8.png", "conversations": [ { "from": "human", "value": "\nIs the GDP growth rate for the {year 2014} [0.695, 0.354] lower or higher than the GDP growth rate for {year 2016} [0.786, 0.436]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 88 }, { "id": "hB3euPfst9TuC8RS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_8.png", "conversations": [ { "from": "human", "value": "\nDoes the line graph start at {the point} [0.060, 0.310] or {the point} [0.968, 0.867]?" }, { "from": "gpt", "value": "It starts at [0.060, 0.310]." } ], "id_raw": 88 }, { "id": "HKL1fsRMj70p54EM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_8.png", "conversations": [ { "from": "human", "value": "\nIs the GDP growth rate for {year 2009} [0.470, 0.735] positive or negative?" }, { "from": "gpt", "value": "Negative." } ], "id_raw": 88 }, { "id": "MK97AwICHoL3gXOW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in [0.035, 0.206, 0.055, 0.229]?" }, { "from": "gpt", "value": "The y-axis tick label '6' is located here." } ], "id_raw": 88 }, { "id": "6Napp2q6jbzn5I6V", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the details of the area [0.042, 0.331, 0.350, 0.356]?" }, { "from": "gpt", "value": "This area includes the x-axis labels for the years 2000, 2002, 2004, and 2006." } ], "id_raw": 88 }, { "id": "WBcuAM3uPXYFhgFd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the space [0.349, 0.735, 0.968, 0.847]?" }, { "from": "gpt", "value": "Within this region are the points from year 2008 to year 2020 on the GDP growth rate line graph. Each point is labeled with the corresponding GDP growth rate for the year." } ], "id_raw": 88 }, { "id": "HPT4HouOQix7mVRd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_9.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the position [0.678, 0.164, 0.694, 0.184] in the chart?" }, { "from": "gpt", "value": "Text label '80'." } ], "id_raw": 89 }, { "id": "1Se9trUZqjcSrkgf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_9.png", "conversations": [ { "from": "human", "value": "\nCan you identify what is at [0.112, 0.878] in the chart?" }, { "from": "gpt", "value": "x-axis tick." } ], "id_raw": 89 }, { "id": "7NmgNGQh6bIqJe9R", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_9.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the coordinates [0.858, 0.888, 0.976, 0.931]?" }, { "from": "gpt", "value": "x-axis tick label for 'Water Conservation Programs'." } ], "id_raw": 89 }, { "id": "CuKlDAqm12Y0Pvqg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_9.png", "conversations": [ { "from": "human", "value": "\nIs the value of {the point}[0.571, 0.256] higher than {the point}[0.456, 0.539]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 89 }, { "id": "M9NeHWu0DWVEEbO5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_9.png", "conversations": [ { "from": "human", "value": "\nAre the percentages at {coordinates for text label '85'}[0.793, 0.108, 0.809, 0.128] higher than those at {coordinates for text label '40'}[0.908, 0.616, 0.924, 0.636]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 89 }, { "id": "YuSTnRt8JQ7PqiUO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_9.png", "conversations": [ { "from": "human", "value": "\nIs {the point with a label of '75'}[0.563, 0.221, 0.579, 0.241] higher on the chart than {the point with a label of '50'}[0.449, 0.503, 0.464, 0.523]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 89 }, { "id": "fPQ2PLeUGBSLGPwK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found within the area [0.043, 0.502, 0.065, 0.575]." }, { "from": "gpt", "value": "This area includes the y-axis tick labels '50'[0.043, 0.528, 0.065, 0.556] and '60'[0.043, 0.415, 0.065, 0.443], both marked in black against a pale background." } ], "id_raw": 89 }, { "id": "VZ2dP8x7TzkTYtBj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_9.png", "conversations": [ { "from": "human", "value": "\nWhat elements are included in the region [0.514, 0.888, 0.629, 0.931]?" }, { "from": "gpt", "value": "This section contains the x-axis label {Community Health Programs}[0.514, 0.888, 0.629, 0.931], paired with a bottom tick at coordinates [0.571, 0.878]." } ], "id_raw": 89 }, { "id": "o7VR9JTDacuAFqPW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the section from [0.856, 0.878, 0.976, 0.931]." }, { "from": "gpt", "value": "It contains {the x-axis tick label for 'Water Conservation Programs'}[0.856, 0.888, 0.976, 0.931] and its corresponding x-axis tick at {coordinates}[0.916, 0.878]." } ], "id_raw": 89 }, { "id": "m2MEm9DRwgdlzjAj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.496, 0.940, 0.611, 0.969]?" }, { "from": "gpt", "value": "Age Group." } ], "id_raw": 90 }, { "id": "UlkPQhNS3iqhTrpI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_10.png", "conversations": [ { "from": "human", "value": "\nWhat does {the data point} at [0.856, 0.788] represent in the graph?" }, { "from": "gpt", "value": "10000 User Count in the 65+ Age Group." } ], "id_raw": 90 }, { "id": "iZk5jwHUCYI4BlJd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_10.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by {the y-axis tick label}[0.054, 0.776, 0.123, 0.806]?" }, { "from": "gpt", "value": "10000." } ], "id_raw": 90 }, { "id": "TjKCGf00upXxrhoh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_10.png", "conversations": [ { "from": "human", "value": "\nIs the user count for {25-34 [0.372, 0.183]} age group higher or lower than {18-24 [0.251, 0.283]} age group?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 90 }, { "id": "VkJssYN2ZR182Zg1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_10.png", "conversations": [ { "from": "human", "value": "\nIs the age group {35-44 [0.493, 0.384]} having more or fewer users than the age group {45-54 [0.614, 0.536]}?" }, { "from": "gpt", "value": "More." } ], "id_raw": 90 }, { "id": "73H3gwSPhDGcFL7l", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_10.png", "conversations": [ { "from": "human", "value": "\nComparing age groups, does {55-64 [0.735, 0.637]} have the higher user quantity than{65+ [0.856, 0.788]}?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 90 }, { "id": "xSV77Pqgbg6KhFpv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is present in the range [0.251, 0.183, 0.372, 0.283]?" }, { "from": "gpt", "value": "Within this range, there's the data point {25-34}[0.372, 0.183] which is labeled with 22000 users and is the highest point in the chart." } ], "id_raw": 90 }, { "id": "rzCxx53jK4xE5LUR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_10.png", "conversations": [ { "from": "human", "value": "\nHow about the content in the region [0.833, 0.899, 0.879, 0.928]?" }, { "from": "gpt", "value": "The area contains the x-axis label {65+}[0.833, 0.899, 0.879, 0.928]." } ], "id_raw": 90 }, { "id": "Y6Ut03oeHEqlSQwT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents of the range [0.735, 0.637, 0.856, 0.788]." }, { "from": "gpt", "value": "This area holds the last two data points of the line chart. The first one is {55-64}[0.735, 0.637] with 13000 users, and the second one is {65+}[0.856, 0.788] with 10000 users." } ], "id_raw": 90 }, { "id": "1TNAfEZrnFIM5atQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at {coordinates} [0.711, 0.849, 0.747, 0.903]?" }, { "from": "gpt", "value": "It's the x-axis label 'Cisco'." } ], "id_raw": 91 }, { "id": "H2HVsSyz9PEZBB7A", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_11.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object located at {coordinates} [0.362, 0.541]?" }, { "from": "gpt", "value": "It represents the point corresponding to 'Facebook' on the line graph." } ], "id_raw": 91 }, { "id": "oRu9F8bRvX7dXgYv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label at coordinates [0.015, 0.254, 0.029, 0.613] represent?" }, { "from": "gpt", "value": "It represents the 'Net Profit (in billions of dollars)'." } ], "id_raw": 91 }, { "id": "1e6vQUN33qxAZFmV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_11.png", "conversations": [ { "from": "human", "value": "\nIs the {data point for Apple}[0.112, 0.573] above or below the {data point for Google}[0.195, 0.128] on the line graph?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 91 }, { "id": "Lpj3WLtw9shdBSmX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_11.png", "conversations": [ { "from": "human", "value": "\nDoes {Google}[0.195, 0.128] or {Oracle}[0.611, 0.175] have a higher net profit?" }, { "from": "gpt", "value": "Google." } ], "id_raw": 91 }, { "id": "lNqRAnDMv7087GbY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_11.png", "conversations": [ { "from": "human", "value": "\nIs the {data point representing IBM}[0.528, 0.557] to the left or right of the {data point representing HP}[0.777, 0.446]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 91 }, { "id": "3cV4uxbgpIXy9Jsd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_11.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what's in the area with coordinates [0.845, 0.825, 0.975, 0.935]?" }, { "from": "gpt", "value": "This area contains the x-axis labels 'Dell' and 'Intel' at {coordinates} [0.846, 0.849, 0.875, 0.897] and {coordinates} [0.928, 0.849, 0.959, 0.901] respectively." } ], "id_raw": 91 }, { "id": "3PXn6stoQ2pKNERb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_11.png", "conversations": [ { "from": "human", "value": "\nWhat's within the region [0.693, 0.100, 0.783, 0.618]?" }, { "from": "gpt", "value": "The region contains the data point {for Cisco on the line graph}[0.694, 0.609], the x-axis label {Cisco}[0.676, 0.849, 0.712, 0.909], and the y-axis tick label {70}[0.043, 0.424, 0.061, 0.448]." } ], "id_raw": 91 }, { "id": "cfYhQclUvBDe4q2h", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.240, 0.500, 0.400, 0.700]?" }, { "from": "gpt", "value": "This area contains the data point for {Microsoft}[0.278, 0.549] and the x-axis label {Microsoft}[0.251, 0.849, 0.306, 0.940]." } ], "id_raw": 91 }, { "id": "gplJ0004OlR7KupU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the label in the chart at coordinates [0.314, 0.025, 0.737, 0.058]?" }, { "from": "gpt", "value": "The title of the chart, which is \"Market Share of Smartphone Companies\"." } ], "id_raw": 92 }, { "id": "aNtXQYUaoM2Iurjc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the label}[0.899, 0.843, 0.943, 0.918] on the x-axis?" }, { "from": "gpt", "value": "The label is \"Google\"." } ], "id_raw": 92 }, { "id": "xBBNyz0PwvLXOfjR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label}[0.039, 0.342, 0.056, 0.365] on the y-axis?" }, { "from": "gpt", "value": "The label is \"10\"." } ], "id_raw": 92 }, { "id": "DbQrJpJrg24IgYQt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Samsung'}[0.184, 0.099] higher or lower than {the point for 'Huawei'}[0.260, 0.132]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 92 }, { "id": "hPE5hkWZubzMpvsS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_12.png", "conversations": [ { "from": "human", "value": "\nOn the line graph, is {the point for 'LG'}[0.715, 0.722] placed at a higher or lower position than {the point for 'Motorola'}[0.791, 0.727]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 92 }, { "id": "kCshjpkChOmtQrAf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Apple'}[0.108, 0.622] above or below {the point for 'OnePlus'}[0.867, 0.770]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 92 }, { "id": "7o5HvhgULger3v19", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the coordinates [0.066, 0.066, 0.985, 0.827]?" }, { "from": "gpt", "value": "This region contains the entire plot of smartphone companies' market share. The line graph within this area depicts the market share percentages of the companies Apple, Samsung, Huawei, Xiaomi, Vivo, Oppo, Realme, Lenovo, LG, Motorola, OnePlus and Google." } ], "id_raw": 92 }, { "id": "HYoIM6XcpbRA9C23", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_12.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area [0.070, 0.843, 0.943, 0.907]?" }, { "from": "gpt", "value": "This area contains x-axis labels, specifically the names of the smartphone companies being compared in the chart. They are {Apple}[0.070, 0.843, 0.108, 0.907], {Samsung}[0.128, 0.843, 0.184, 0.936], {Huawei}[0.213, 0.843, 0.260, 0.921], {Xiaomi}[0.292, 0.843, 0.336, 0.916], {Vivo}[0.380, 0.843, 0.412, 0.895], {Oppo}[0.451, 0.843, 0.487, 0.904], {Realme}[0.517, 0.843, 0.563, 0.921], {Lenovo}[0.594, 0.843, 0.639, 0.919], {LG}[0.692, 0.843, 0.715, 0.882], {Motorola}[0.738, 0.843, 0.791, 0.932], {OnePlus}[0.818, 0.843, 0.867, 0.926] and {Google}[0.899, 0.843, 0.943, 0.918]." } ], "id_raw": 92 }, { "id": "uxvvGITCpD3Oh9Ww", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_12.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.039, 0.152, 0.056, 0.175] contain?" }, { "from": "gpt", "value": "This area contains the y-axis label {14}[0.039, 0.152, 0.056, 0.175]." } ], "id_raw": 92 }, { "id": "JBpaEcsBJX2uawvY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the element labeled at coordinates [0.021, 0.341, 0.041, 0.582]?" }, { "from": "gpt", "value": "Y-axis (Market Share (%))." } ], "id_raw": 93 }, { "id": "8ClCz2mg4WwLyhJ8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line with coordinates spanning from [0.081, 0.190] to [0.954, 0.815]?" }, { "from": "gpt", "value": "Blue (#4c72b0)." } ], "id_raw": 93 }, { "id": "noFkVTcmsxwS9iWf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label of the first tick on the x-axis}[0.056, 0.903, 0.107, 0.931]?" }, { "from": "gpt", "value": "2015." } ], "id_raw": 93 }, { "id": "1CsiFXaWDzuyPCap", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the trend of the line with coordinates starting from [0.081, 0.807] and ending at [0.954, 0.815]?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 93 }, { "id": "w5wYphZUpFcHkWIs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_13.png", "conversations": [ { "from": "human", "value": "\nWhat year has a higher Market Share, {2018} at [0.331, 0.431] or {2019} at [0.455, 0.237]?" }, { "from": "gpt", "value": "2018." } ], "id_raw": 93 }, { "id": "55o7SGjd30KezyVQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_13.png", "conversations": [ { "from": "human", "value": "\nWhich point on the line plot has a higher y-coordinate, the point at [0.331, 0.431] or the point at [0.061, 0.763]?" }, { "from": "gpt", "value": "The point at [0.061, 0.763]." } ], "id_raw": 93 }, { "id": "lGStZ1DYhhkCGAQr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the area located at [0.050, 0.364, 0.075, 0.392]" }, { "from": "gpt", "value": "This area contains the y-axis tick label '42', horizontally oriented and presented in black color." } ], "id_raw": 93 }, { "id": "CXYXXWjMD8fq6Nm4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the section of the line plotted between coordinates [0.704, 0.250] and [0.954, 0.815]." }, { "from": "gpt", "value": "This segment of the line shows a negative trend from the year 2020 to 2022, with the market share decreasing from 44.9% to 31.8%." } ], "id_raw": 93 }, { "id": "SH7Fun89IQwCnvx3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.081, 0.030, 0.954, 0.893]?" }, { "from": "gpt", "value": "This region primarily contains the main plot area of the graph. It includes a blue line plot representing market share from 2015 to 2022, with respective data points labeled with their values. The grid is visible with white lines." } ], "id_raw": 93 }, { "id": "fW076Mi3sgC5kaBg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_14.png", "conversations": [ { "from": "human", "value": "\nThere is an object in the chart at these coordinates [0.387, 0.855, 0.593, 0.925]. Can you tell me what it is?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 94 }, { "id": "VnmK6ZqXweG8ogFJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_14.png", "conversations": [ { "from": "human", "value": "\nWhat object is present at the coordinates [0.157, 0.582]?" }, { "from": "gpt", "value": "The data point for 'Chatbot'." } ], "id_raw": 94 }, { "id": "9CrnDAOWLodLNMhw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.069, 0.238, 0.104, 0.308]?" }, { "from": "gpt", "value": "85." } ], "id_raw": 94 }, { "id": "Oj6Bz11KtBUnJGqf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_14.png", "conversations": [ { "from": "human", "value": "\nHow does the value of {the 'Speech Recognition' point}[0.324, 0.328] compare to the value of {the 'Chatbot' point}[0.157, 0.582]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 94 }, { "id": "YZ9KzLbIwjDulyXW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_14.png", "conversations": [ { "from": "human", "value": "\nIs the {value of 'Autonomous Vehicles'}[0.824, 0.202] greater or lesser than {the value of 'Image Recognition'}[0.657, 0.519]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 94 }, { "id": "j5aiFq50PitUxKon", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_14.png", "conversations": [ { "from": "human", "value": "\nWhich application has a lower success rate, {Autonomous Vehicles}[0.824, 0.202] or {Natural Language Processing}[0.490, 0.430]?" }, { "from": "gpt", "value": "Autonomous Vehicles." } ], "id_raw": 94 }, { "id": "OoOXhZ2EBUHQkTYi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_14.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the region [0.187, 0.757, 0.461, 0.827]?" }, { "from": "gpt", "value": "The x-axis label for 'Speech Recognition' is found here." } ], "id_raw": 94 }, { "id": "fC9gskrGmeKl3dN3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area with coordinates [0.069, -0.149, 0.058, 0.932]?" }, { "from": "gpt", "value": "The y-axis label, 'Successful Implementation (%)', is found in this area." } ], "id_raw": 94 }, { "id": "Hg3QEJAcCxm87X8U", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the region [0.145, 0.472, 0.336, 0.269]." }, { "from": "gpt", "value": "This area includes the data points for 'Chatbot' and 'Speech Recognition', with their respective values '60' and '80' labeled." } ], "id_raw": 94 }, { "id": "aa1TjxlHu8eVQCGi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_15.png", "conversations": [ { "from": "human", "value": "\nWhat type of label is attached to {this object}[0.015, 0.229, 0.029, 0.684] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 95 }, { "id": "XBveXsY5j4D0rGJD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_15.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis label located at {this coordinate}[0.509, 0.942, 0.538, 0.970] represent?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 95 }, { "id": "CerO87mMpK1dg3lR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the value indicated by {this label}[0.103, 0.724, 0.128, 0.744]?" }, { "from": "gpt", "value": "81.2." } ], "id_raw": 95 }, { "id": "hVsgjQFeDKcqz4hK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_15.png", "conversations": [ { "from": "human", "value": "\nIs the {point for 2009}[0.326, 0.693] higher or lower than the {point for 2010}[0.392, 0.736]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 95 }, { "id": "CH9y4S67oq0JF1ts", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_15.png", "conversations": [ { "from": "human", "value": "\nDoes the {Innovation Confidence Score for 2014}[0.655, 0.375] exceed the {Innovation Confidence Score for 2012}[0.523, 0.515]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 95 }, { "id": "nkDL3oeKwBQXN38a", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_15.png", "conversations": [ { "from": "human", "value": "\nAre the respective Innovation Confidence Scores in {2006}[0.128, 0.759] and {2007}[0.194, 0.639] increasing or decreasing?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 95 }, { "id": "FgYnPrlTULSqrqJZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_15.png", "conversations": [ { "from": "human", "value": "\nWhat can you observe in the region between {coordinates}[0.062, 0.174, 0.985, 0.856]?" }, { "from": "gpt", "value": "There is a line chart that plots the 'Innovation Confidence Score (%)' from the year 2005 up to 2019. The data points are plotted in blue and are connected by a blue line. The innovation confidence score seems to be generally increasing over these years." } ], "id_raw": 95 }, { "id": "NBcDuBUgK4dZScbS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_15.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the area bounded by these {coordinates}[0.035, 0.213, 0.052, 0.241]?" }, { "from": "gpt", "value": "The y-tick label '95' is represented in this area." } ], "id_raw": 95 }, { "id": "xchXSDfcD8HHYalc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the information contained within the {coordinates}[0.235, 0.480, 0.260, 0.500]?" }, { "from": "gpt", "value": "The value '87.5' corresponding to the year 2008 of 'Innovation Confidence Score (%)' is located." } ], "id_raw": 95 }, { "id": "ZyJw2tDdOmvW34VY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of this chart according to the JSON information and its coordinates?" }, { "from": "gpt", "value": "The title of the chart is \"Size of Training Data (No. of Data Points)\" and its coordinates are {X-axis label}[0.306, 0.927, 0.779, 0.963]." } ], "id_raw": 96 }, { "id": "dXJhH4UwVCJ9A7sC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_16.png", "conversations": [ { "from": "human", "value": "\nWhat object is at the coordinates [0.530, 0.383] in the JSON structure?" }, { "from": "gpt", "value": "That coordinate is a point on the data line, specifically the point corresponding to the x value of '32' and y value '87.6' in the given data." } ], "id_raw": 96 }, { "id": "z5pkZYlSUK5v5s13", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_16.png", "conversations": [ { "from": "human", "value": "\nAccording to the JSON data, what is the coordinate of the y-axis label in the chart?" }, { "from": "gpt", "value": "The coordinate of the y-axis label is {y-axis label}[0.025, 0.258, 0.048, 0.648]." } ], "id_raw": 96 }, { "id": "8PeuXL3l1TnX7MPi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_16.png", "conversations": [ { "from": "human", "value": "\nLooking at the JSON structure, is the data point for x='8' higher or lower than the data point for x='4' on the chart?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 96 }, { "id": "tzb2ie93PszPJFZq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_16.png", "conversations": [ { "from": "human", "value": "\nFrom the coordinates in the JSON structure, is the data point at x='16' closer to the x='8' point or the x='32' point?" }, { "from": "gpt", "value": "The x='32' point." } ], "id_raw": 96 }, { "id": "RQDi2HuyCBtX0hRk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_16.png", "conversations": [ { "from": "human", "value": "\nIn the JSON structure, which value for x has a higher corresponding y value, x='256' or x='128'?" }, { "from": "gpt", "value": "x='256'." } ], "id_raw": 96 }, { "id": "LSUIgdlHda3HQWqy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the content present in the region mentioned by the coordinates [0.171, 0.129, 0.951, 0.849] in the JSON data." }, { "from": "gpt", "value": "This area contains a line graph which represents the AI model accuracy based on the size of the training data set, with points plotted at specific data points with labelled y-axis values." } ], "id_raw": 96 }, { "id": "B4dZBXK0XBoF0O7T", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the region [0.058, 0.037, 0.102, 0.073] as per the JSON structure?" }, { "from": "gpt", "value": "This region contains the y-axis tick label '100'." } ], "id_raw": 96 }, { "id": "lxs8hOXNp0ur5Rbz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_16.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the region [0.215, 0.855, 0.228, 0.914] based on the JSON structure?" }, { "from": "gpt", "value": "This area contains the x-axis tick label '$2^{2}$'." } ], "id_raw": 96 }, { "id": "PWFBAfUzmxpcNQ1O", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.029, 0.722, 0.136, 0.899]?" }, { "from": "gpt", "value": "It is the x-axis label of household_incomes." } ], "id_raw": 97 }, { "id": "2zwf7wH4kVZd5ptb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_17.png", "conversations": [ { "from": "human", "value": "\nWhat item of information does the coordinate [0.650, 0.454] represent?" }, { "from": "gpt", "value": "It represents a data point for the interest_rate." } ], "id_raw": 97 }, { "id": "9SaGcd1Msv1eaRv4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of the label located at [0.015, 0.299, 0.029, 0.442]?" }, { "from": "gpt", "value": "It is the y-axis label, indicating Metric Value in the plot." } ], "id_raw": 97 }, { "id": "zv9tTi0gSMSSXatx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_17.png", "conversations": [ { "from": "human", "value": "\nIs the data point representing GDP_growth at [0.356, 0.454] higher or lower than the data point of consumer_price_index at [0.577, 0.453]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 97 }, { "id": "400e5zi9r6J5GYtN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_17.png", "conversations": [ { "from": "human", "value": "\nIs the data point representing household_incomes at [0.136, 0.058] to the left or to the right of the data point for salary_increase at [0.209, 0.420]?" }, { "from": "gpt", "value": "To the left." } ], "id_raw": 97 }, { "id": "31RD1nH3HkMJ1m7B", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_17.png", "conversations": [ { "from": "human", "value": "\nCompared to business_profit at [0.871, 0.398], is the data point for trade_balance at [0.945, 0.683] above or below?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 97 }, { "id": "D2uvObMkZHe8xDo5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_17.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the text located at [0.470, 0.952, 0.610, 0.975]?" }, { "from": "gpt", "value": "This is the xlabel or title for the X-axis, which is labeled as 'Economic Indicators'." } ], "id_raw": 97 }, { "id": "SwCWPwcficI1J8vv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.678, 0.722, 0.797, 0.921]." }, { "from": "gpt", "value": "This area contains a label on the x-axis: personal_savings_rate." } ], "id_raw": 97 }, { "id": "n02dX6cuWo2xo1Y8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.400, 0.722, 0.503, 0.894]?" }, { "from": "gpt", "value": "In this area, there is an x-axis tick label representing the economic indicator 'real_estate_pricing'." } ], "id_raw": 97 }, { "id": "RLpcDoJ89Ei77siJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at coordinates [0.030, 0.112, 0.058, 0.671] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 98 }, { "id": "AeUhlpWPPXerNrvj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the last tick label on the x-axis?" }, { "from": "gpt", "value": "[0.900, 0.757, 0.970, 0.827]" } ], "id_raw": 98 }, { "id": "7RfW3Lir1Zd1A1d4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the tick label on the y-axis}[0.069, 0.364, 0.105, 0.434]?" }, { "from": "gpt", "value": "90." } ], "id_raw": 98 }, { "id": "CWa678wQgKfe2ClL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_18.png", "conversations": [ { "from": "human", "value": "\nIn {the year 2014}[0.292, 0.757, 0.362, 0.827], was food production higher or lower compared to {the year 2012}[0.089, 0.757, 0.159, 0.827]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 98 }, { "id": "L9kkJrZpU0YxE8bB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_18.png", "conversations": [ { "from": "human", "value": "\nIs the food production in {the year 2019}[0.798, 0.757, 0.869, 0.827] higher or lower than in {the year 2018}[0.697, 0.757, 0.768, 0.827]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 98 }, { "id": "RjGyMUJpZyjItUMa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_18.png", "conversations": [ { "from": "human", "value": "\nComparing the food production in {the year 2015}[0.393, 0.757, 0.463, 0.827] and {the year 2017}[0.596, 0.757, 0.666, 0.827], which year had a lower food production?" }, { "from": "gpt", "value": "2015." } ], "id_raw": 98 }, { "id": "PNFeNRmegzJMzjKz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the coordinate area [0.500, 0.855, 0.559, 0.925]." }, { "from": "gpt", "value": "This area contains the x-axis label \"Year\", styled in a black color." } ], "id_raw": 98 }, { "id": "TOd2wUv8FmXt8aj5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.069, 0.111, 0.105, 0.181]." }, { "from": "gpt", "value": "This area contains the y-axis tick label '94', colored in black." } ], "id_raw": 98 }, { "id": "CCOoYnc6bBNvKp7e", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_18.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained within the coordinates [0.732, 0.238, 0.760, 0.178]?" }, { "from": "gpt", "value": "There is 1 object. The food production point for the year 2018 is located here, labeled with '92.43'." } ], "id_raw": 98 }, { "id": "JKTLYBPkqxucGmQy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_19.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object at coordinates [0.043, 0.152, 0.065, 0.180]?" }, { "from": "gpt", "value": "Y-axis tick label \"90\"." } ], "id_raw": 99 }, { "id": "SUfPTFf6j121XyPu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_19.png", "conversations": [ { "from": "human", "value": "\nWhat is positioned at these coordinates [0.113, 0.766]?" }, { "from": "gpt", "value": "Data point for \"Continuity of Culture (CC)." } ], "id_raw": 99 }, { "id": "IUoEZwfELJS40ydE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_5_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the xlabel} at [0.415, 0.942, 0.638, 0.970]?" }, { "from": "gpt", "value": "Social and Cultural Issues." } ], "id_raw": 99 }, { "id": "97u24yuPhbLRukBK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_19.png", "conversations": [ { "from": "human", "value": "\nIs {point for 'CC'}[0.113, 0.766] above or below {the point for 'TD'}[0.231, 0.505]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 99 }, { "id": "05qO6muRjOuGANiX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_19.png", "conversations": [ { "from": "human", "value": "\nBetween {point for 'HR'}[0.704, 0.150] and {point for 'N'}[0.822, 0.445], which one has higher value?" }, { "from": "gpt", "value": "Point for 'N'." } ], "id_raw": 99 }, { "id": "XfMmKQ7UKqO94qoh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_5_19.png", "conversations": [ { "from": "human", "value": "\nWhich has higher value at the 3rd point, {CC}[0.113, 0.766] or {TD}[0.231, 0.505]?" }, { "from": "gpt", "value": "CC." } ], "id_raw": 99 }, { "id": "MYSKROG4adPicSfy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the region [0.043, 0.152, 0.065, 0.180]?" }, { "from": "gpt", "value": "This region contains the Y-axis tick label '90'." } ], "id_raw": 99 }, { "id": "vC9UGsN3aiu5J8cn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_19.png", "conversations": [ { "from": "human", "value": "\nPlease give me details about the area [0.100, 0.903, 0.125, 0.931]." }, { "from": "gpt", "value": "This area contains the x-axis tick label for 'CC'." } ], "id_raw": 99 }, { "id": "t5PHS6TYLfyObyUL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_5_19.png", "conversations": [ { "from": "human", "value": "\nWhat objects are found within the coordinates [0.567, 0.903, 0.603, 0.931]?" }, { "from": "gpt", "value": "The region houses the x-axis tick label 'CC2'." } ], "id_raw": 99 }, { "id": "3BL6VbqRBMKbAiJS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at these coordinates [0.092, 0.635, 0.120, 0.668] on the chart?" }, { "from": "gpt", "value": "It is the y-axis label \"60\"." } ], "id_raw": 100 }, { "id": "ALhEhfByePUxglqn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.577, 0.809, 0.657, 0.842] in the chart?" }, { "from": "gpt", "value": "It is the x-axis label \"Rebirth\"." } ], "id_raw": 100 }, { "id": "fJnQFCRD4McLicDX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the title of the subplot}[0.460, 0.852, 0.577, 0.875]?" }, { "from": "gpt", "value": "Startup Lifecycle." } ], "id_raw": 100 }, { "id": "nvd9EGDT3KDZX5Zv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the relationship between {point 'Stagnation'}[0.420, 0.511] and {point 'Decline'}[0.519, 0.725] in terms of the 'Startup Success' line?" }, { "from": "gpt", "value": "The 'Stagnation' point is higher than the 'Decline' point on the 'Startup Success' line." } ], "id_raw": 100 }, { "id": "S5ZAsz2boR9q0j0c", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_0.png", "conversations": [ { "from": "human", "value": "\nCompared to {point 'Rebirth'}[0.617, 0.343], where is {point 'Transformation'}[0.715, 0.374] located on the 'Startup Success' line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 100 }, { "id": "29n6MdmkLshgFVfE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_0.png", "conversations": [ { "from": "human", "value": "\nWhich point on the 'Startup Success' line is lower, {point 'Rebirth'}[0.617, 0.343] or {point 'Reinvention'}[0.814, 0.160]?" }, { "from": "gpt", "value": "'Reinvention'." } ], "id_raw": 100 }, { "id": "3Vyum1YoahLnn0Fa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_0.png", "conversations": [ { "from": "human", "value": "\nDescribe what occupies the region [0.149, 0.809, 0.298, 0.842]." }, { "from": "gpt", "value": "In this region, there is an x-axis label of 'Mature Stage'." } ], "id_raw": 100 }, { "id": "lDgke0MMTGOMfvhw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_0.png", "conversations": [ { "from": "human", "value": "\nWhat is present within the region [0.389, 0.910, 0.648, 0.973]?" }, { "from": "gpt", "value": "The region includes the sub-legend titled 'Startup Success' along with its associated color marker." } ], "id_raw": 100 }, { "id": "C776akcP5YkqX8U9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.092, 0.635, 0.120, 0.668] contain?" }, { "from": "gpt", "value": "This region contains the y-axis label '60'." } ], "id_raw": 100 }, { "id": "TLP0iVSsHNQFV6Ri", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_1.png", "conversations": [ { "from": "human", "value": "\nWhat attribute does the y-axis label at [0.017, 0.143, 0.048, 0.765] represent?" }, { "from": "gpt", "value": "Performance of Integration." } ], "id_raw": 101 }, { "id": "pCeg8zFL8gjfSmT7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the marker of 'API Integration'}[0.688, 0.759, 0.728, 0.773] in the legend?" }, { "from": "gpt", "value": "#2ca02c." } ], "id_raw": 101 }, { "id": "ysvBXJuj4EQI6yaJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.407, 0.928, 0.673, 0.975] representing?" }, { "from": "gpt", "value": "Integration Steps." } ], "id_raw": 101 }, { "id": "BOayx8BXUs7NT4UZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_1.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Database Integration'}[0.112, 0.080, 0.968, 0.662] above or below {the line for 'Data Lake Integration'}[0.112, 0.205, 0.968, 0.495]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 101 }, { "id": "AjD0ebwPzH5pb5h0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_1.png", "conversations": [ { "from": "human", "value": "\nWhat line is higher at point '5', {the line for 'Data Warehouse Integration'}[0.492, 0.516] or {the line for 'API Integration'}[0.492, 0.350]?" }, { "from": "gpt", "value": "Data Warehouse Integration." } ], "id_raw": 101 }, { "id": "n7AISxookL5z3e2y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_1.png", "conversations": [ { "from": "human", "value": "\nWhich Integration has the highest performance at step '10', {Database Integration}[0.968, 0.080] or {Data Lake Integration}[0.968, 0.205]?" }, { "from": "gpt", "value": "Database Integration." } ], "id_raw": 101 }, { "id": "4sUPkV0NkQ2WbVQN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects found within rectangle [0.736, 0.721, 0.954, 0.853]." }, { "from": "gpt", "value": "It contains the legend description for 'Data Warehouse Integration' and 'Data Lake Integration' along with their respective colored markers." } ], "id_raw": 101 }, { "id": "CVieCYeN5jQrhFhK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in rectangle [0.054, 0.025, 0.112, 0.890]." }, { "from": "gpt", "value": "This area contains the y-axis tick labels ranging from 70 to 110, indicating the performance of integration." } ], "id_raw": 101 }, { "id": "0J6LPDRpy5jdeBQi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_1.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the area [0.687, 0.711, 0.960, 0.858]?" }, { "from": "gpt", "value": "This area contains the legend which includes names of the integration types 'Database Integration', 'API Integration', 'Data Warehouse Integration', and 'Data Lake Integration', each paired with a different colored marker." } ], "id_raw": 101 }, { "id": "mSEas14Y2lx8f2MJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the indication of the color {#008000}[0.101, 0.085, 0.137, 0.097] in the chart's legend?" }, { "from": "gpt", "value": "Healthy Lifestyle." } ], "id_raw": 102 }, { "id": "ILSOmvB7cNzFKaky", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the Y-axis located at [0.015, 0.137, 0.035, 0.634]?" }, { "from": "gpt", "value": "Percentage of Engagement (%)." } ], "id_raw": 102 }, { "id": "u3wediJmPAeDppWh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the marking at coordinate [0.813, 0.257] represent?" }, { "from": "gpt", "value": "Reading Hours for Healthy Lifestyle." } ], "id_raw": 102 }, { "id": "Vsn9x3xI481OJtUD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_2.png", "conversations": [ { "from": "human", "value": "\nIn {Outdoor Activities}[0.196, 0.270], which lifestyle is more popular, {Healthy Lifestyle}[0.196, 0.270] or {Sedentary Lifestyle}[0.196, 0.450]?" }, { "from": "gpt", "value": "Healthy Lifestyle." } ], "id_raw": 102 }, { "id": "mVkR1ZcCFKK8bTBv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_2.png", "conversations": [ { "from": "human", "value": "\nIs the point representing {Meditation[0.299, 0.514] in Sedentary Lifestyle} above or below the point representing {Meditation[0.299, 0.199] in Healthy Lifestyle}?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 102 }, { "id": "FHBJF6FS85EyQLvV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_2.png", "conversations": [ { "from": "human", "value": "\nWhich lifestyle has a higher percentage in {Reading}[0.813, 0.385], Healthy Lifestyle or Sedentary Lifestyle?" }, { "from": "gpt", "value": "Sedentary Lifestyle." } ], "id_raw": 102 }, { "id": "3agXQToxS3fY95Tj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in [0.250, 0.720, 0.348, 0.860]?" }, { "from": "gpt", "value": "This region contains the 'Meditation' label of the x-axis, which is displayed in black color and is horizontally aligned." } ], "id_raw": 102 }, { "id": "T9JmQUj4fNqrVzmd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.093, 0.302, 0.916, 0.610]." }, { "from": "gpt", "value": "This area contains the data points for the Healthy Lifestyle and Sedentary Lifestyle, plotted as lines with distinct markers for each datapoint. Healthy Lifestyle is plotted as a green line with circular markers, while Sedentary Lifestyle is plotted as a red dashed line with square markers." } ], "id_raw": 102 }, { "id": "2uNtQD94uhVytuy0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region [0.196, 0.270, 0.196, 0.450]?" }, { "from": "gpt", "value": "This region contains the comparison of a Healthy Lifestyle and a Sedentary Lifestyle for the Outdoor Activities. The point for the Healthy Lifestyle is above the point for the Sedentary Lifestyle." } ], "id_raw": 102 }, { "id": "bP0Whj3CvvK5PdFC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the main label in coordinates [0.017, 0.373, 0.039, 0.494] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 103 }, { "id": "o5Ft3GlmQDXpzaMa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third tick label on the X-axis?" }, { "from": "gpt", "value": "[0.308, 0.809, 0.324, 0.842]" } ], "id_raw": 103 }, { "id": "0YzzLojTQ7eiAeMQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_3.png", "conversations": [ { "from": "human", "value": "\nWhat represents the {coordinates}[0.223, 0.556] in the chart?" }, { "from": "gpt", "value": "Point of 'Population' line corresponding to 'Year 2'." } ], "id_raw": 103 }, { "id": "K435osIuRwyreR4o", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_3.png", "conversations": [ { "from": "human", "value": "\nIs the point {5 on the 'Population'}[0.502, 0.409] higher or lower than the point {5 on the 'GDP'}[0.502, 0.670]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 103 }, { "id": "TWBURldADpJRu5RE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_3.png", "conversations": [ { "from": "human", "value": "\nWhich line, either {Population}[0.129, 0.147, 0.968, 0.638] or {GDP}[0.129, 0.311, 0.968, 0.785], generally has higher values?" }, { "from": "gpt", "value": "Population." } ], "id_raw": 103 }, { "id": "jkqR077zPPlRreSd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_3.png", "conversations": [ { "from": "human", "value": "\nAt the {8th point on the x-axis}[0.781, 0.301], does the 'Population' line or the 'GDP' line have a higher value?" }, { "from": "gpt", "value": "'Population'." } ], "id_raw": 103 }, { "id": "21NT4FVsfH3hjAb5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the coordinates [0.353, 0.903, 0.744, 0.966]." }, { "from": "gpt", "value": "This area contains the legend for the chart. The legend includes {Population}[0.432, 0.918, 0.562, 0.952] and {GDP}[0.681, 0.918, 0.734, 0.952] each paired with a color marker." } ], "id_raw": 103 }, { "id": "GwcZmNJNnCaK8J3f", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_3.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the area defined by the coordinates [0.045, 0.297, 0.124, 0.331]?" }, { "from": "gpt", "value": "Y-axis tick label '30000' is located in this region." } ], "id_raw": 103 }, { "id": "iQjj8PcekhP8dAOe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_3.png", "conversations": [ { "from": "human", "value": "\nWhat components are located within [0.401, 0.809, 0.510, 0.842]?" }, { "from": "gpt", "value": "The '4' tick label on the x-axis and corresponding tick marker are located within this region." } ], "id_raw": 103 }, { "id": "uPYcJaac4gXGRv7G", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_4.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at coordinates [0.417, 0.918, 0.619, 0.946]?" }, { "from": "gpt", "value": "x-axis label." } ], "id_raw": 104 }, { "id": "O9AIkBDLbq74zIuo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_4.png", "conversations": [ { "from": "human", "value": "\nWhat does {the text}[0.574, 0.961, 0.695, 0.981] in the legend represent?" }, { "from": "gpt", "value": "Energy Efficiency." } ], "id_raw": 104 }, { "id": "m7TpN5CLDsiPvvhi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.035, 0.652, 0.083, 0.680]?" }, { "from": "gpt", "value": "70." } ], "id_raw": 104 }, { "id": "EJaZXXgc6yRzXxTr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_4.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at the efficiency level \"Very High\", {Material Efficiency line's point}[0.804, 0.344] or {Energy Efficiency line's point}[0.804, 0.327]?" }, { "from": "gpt", "value": "Material Efficiency." } ], "id_raw": 104 }, { "id": "ymv0tm7ov8AV3CjQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_4.png", "conversations": [ { "from": "human", "value": "\nAt the efficiency level \"Medium\", does the {Material Efficiency line's point}[0.374, 0.663] have a greater value than the {Energy Efficiency line's point}[0.374, 0.600]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 104 }, { "id": "N94phPFCgidbS70N", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_4.png", "conversations": [ { "from": "human", "value": "\nIs the {Energy Efficiency point}[0.947, 0.108] at the \"Outlier\" level below the {Material Efficiency point}[0.947, 0.033]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 104 }, { "id": "Yhl7Etsm7fadPv2X", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within coordinates [0.335, 0.953, 0.700, 0.989]." }, { "from": "gpt", "value": "This is the legend of the chart, with two items. The first item is {Material Efficiency}[0.379, 0.961, 0.508, 0.981] with its signifying marker, and the second one is {Energy Efficiency}[0.574, 0.961, 0.695, 0.981] with its identifying marker." } ], "id_raw": 104 }, { "id": "MPVwTPep9XLG6Lyc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the details at [0.055, 0.021, 0.083, 0.050]." }, { "from": "gpt", "value": "It contains a y-axis tick label of '100'." } ], "id_raw": 104 }, { "id": "Rccm8bTNR5iXJbxg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region within [0.088, 0.820, 0.947, 0.033]?" }, { "from": "gpt", "value": "This region includes the plot of Material Efficiency with a line and its points, leading from 'Low' to 'Outlier' levels of efficiency." } ], "id_raw": 104 }, { "id": "CNU8oaKqA40N5mJq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the content at the coordinates [0.050, 0.907, 0.127, 0.935]?" }, { "from": "gpt", "value": "The label '1970'." } ], "id_raw": 105 }, { "id": "4MXRFdwqMWbMl2tX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_5.png", "conversations": [ { "from": "human", "value": "\nWhat object is at {coordinates} [0.057, 0.946, 0.098, 0.974]?" }, { "from": "gpt", "value": "Y-axis tick label '400'." } ], "id_raw": 105 }, { "id": "9sentEAdSQoVgFSn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_5.png", "conversations": [ { "from": "human", "value": "\nWhat does {the line} at [0.107, 0.475, 0.957, 0.732] represent?" }, { "from": "gpt", "value": "Rural Population." } ], "id_raw": 105 }, { "id": "8DpNaggWu3GO85Kl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_5.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 1980 on the 'Rural Population' line}[0.277, 0.581] above or below {the point for 1980 on the 'Urban Population' line}[0.277, 0.719]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 105 }, { "id": "Cr8k1DdZbZfyio8f", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_5.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 2020 on the 'Rural Population' line}[0.957, 0.732] below or above {the point for 2020 on the 'Urban Population' line}[0.957, 0.138]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 105 }, { "id": "r39KlKiFOzz2taE5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_5.png", "conversations": [ { "from": "human", "value": "\nWhich trend shows a higher population in 2000, the Rural or Urban population?" }, { "from": "gpt", "value": "Urban Population." } ], "id_raw": 105 }, { "id": "qXmhOtMfDfpJODtB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_5.png", "conversations": [ { "from": "human", "value": "\nWhat contents are found at the position [0.118, 0.076, 0.390, 0.175]?" }, { "from": "gpt", "value": "The chart's legend entries: {Rural Population}[0.189, 0.089, 0.371, 0.118] and {Urban Population}[0.189, 0.133, 0.381, 0.162]. Each accompanied by a color-coded marker." } ], "id_raw": 105 }, { "id": "wSPcrNbBDIfEi5cm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content and elements present in the area [0.107, 0.060, 0.957, 0.900]?" }, { "from": "gpt", "value": "This is the main area of the chart where the 'Rural Population' and 'Urban Population' trends are plotted." } ], "id_raw": 105 }, { "id": "jGMEW28ZBLeHzlB4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region with coordinates [0.050, 0.900, 0.120, 0.945]?" }, { "from": "gpt", "value": "The region contains the X-axis tick label '1970' at {coordinates}[0.079, 0.913, 0.135, 0.942] and the Y-axis tick label '400' at {coordinates}[0.055, 0.888, 0.097, 0.917]." } ], "id_raw": 105 }, { "id": "s2Aukfu5Hid4u5TO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart object with coordinates [0.017, 0.305, 0.039, 0.521]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 106 }, { "id": "qWk6T0ZX6neQ35kU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_6.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the fifth x-axis tick label?" }, { "from": "gpt", "value": "[0.386, 0.809, 0.437, 0.842]" } ], "id_raw": 106 }, { "id": "vuEbgPUHT59TW5Qt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the x-axis tick} at [0.231, 0.809, 0.278, 0.842]?" }, { "from": "gpt", "value": "Mar." } ], "id_raw": 106 }, { "id": "UnyaPK2jAmwHitZc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled 'Jun'}[0.489, 0.579] above or below {the point labeled 'Apr'}[0.333, 0.801] on the 'Bstock' line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 106 }, { "id": "5ZVJrCNQByAexAfh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_6.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Astock'}[[0.098, 0.247, 0.959, 0.801]] generally above or below {the line for 'Bstock'}[[0.098, 0.136, 0.959, 0.801]]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 106 }, { "id": "ba4snWiLeFtMuDki", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_6.png", "conversations": [ { "from": "human", "value": "\nWhich stock has a higher value at the {point labeled 'Dec'}[0.959, 0.247]?" }, { "from": "gpt", "value": "Astock." } ], "id_raw": 106 }, { "id": "ggfsWGQWGGCkN9bB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.342, 0.914, 0.715, 0.977]." }, { "from": "gpt", "value": "The area hosts the chart's legend with two entries: {Astock}[0.421, 0.928, 0.504, 0.962] and {Bstock}[0.622, 0.928, 0.705, 0.962], each associated with a colored marker." } ], "id_raw": 106 }, { "id": "730Xk4SW8q2dRkJq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.098, 0.801, 0.959, 0.842]." }, { "from": "gpt", "value": "The area contains the x-axis tick labels representing each month from 'Jan' to 'Dec' spread across the entire width of the chart." } ], "id_raw": 106 }, { "id": "Zj6YbYKLP9nOzEER", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.079, 0.809, 0.198, 0.842]?" }, { "from": "gpt", "value": "There are 2 objects. The x-axis tick labels 'Jan' and 'Feb' are located at {coordinates}[0.079, 0.809, 0.117, 0.842] and {coordinates}[0.154, 0.809, 0.198, 0.842], respectively." } ], "id_raw": 106 }, { "id": "ZvySgWmRjj2r6Ppk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object represented by these coordinates - [0.080, 0.809, 0.115, 0.842]?" }, { "from": "gpt", "value": "The x-axis label 'Q1'." } ], "id_raw": 107 }, { "id": "bMaHgmzKaHNiILrE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line for 'Green Spaces Development'}[0.098, 0.314, 0.966, 0.558]?" }, { "from": "gpt", "value": "The color is #1f77b4." } ], "id_raw": 107 }, { "id": "mFNZifArUDTwEkse", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the item located at {the legend}[0.210, 0.865, 0.555, 0.898]?" }, { "from": "gpt", "value": "Green Spaces Development." } ], "id_raw": 107 }, { "id": "g8Sdq4Y5iLKa5a98", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_7.png", "conversations": [ { "from": "human", "value": "\nWhat line is higher at point 'Q4', {the line for 'Green Spaces Development'}[0.966, 0.314] or {the line for 'Public Health Infrastructure'}[0.966, 0.168]?" }, { "from": "gpt", "value": "The line for 'Green Spaces Development'." } ], "id_raw": 107 }, { "id": "exVhA3kJiFSt5IAL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Public Health Infrastructure'}[0.387, 0.265] higher or lower than {the point for 'Green Spaces Development'}[0.387, 0.509] in Q2?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 107 }, { "id": "nyh5Wjwi7KR3zcoN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_7.png", "conversations": [ { "from": "human", "value": "\nWhich project line had the highest completion rate in Q1, {the line for 'Green Spaces Development'}[0.098, 0.558], {the line for 'Public Health Infrastructure'}[0.098, 0.314], or {the line for 'Education Programs'}[0.098, 0.679]?" }, { "from": "gpt", "value": "The line for 'Education Programs'." } ], "id_raw": 107 }, { "id": "YPbeeFmfUb8xkPei", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents bounded by the coordinates [0.210, 0.865, 0.555, 0.898]." }, { "from": "gpt", "value": "It contains the text for the legend item 'Green Spaces Development' with a color marker to its left." } ], "id_raw": 107 }, { "id": "oXuZScD0GgCzMhrx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_7.png", "conversations": [ { "from": "human", "value": "\nWhat am I seeing in the region [0.098, 0.679, 0.966, 0.801]?" }, { "from": "gpt", "value": "This region contains the line graph for 'Education Programs', shown as a dashed line with triangle markers at the points. The line starts at point 'Q1' {at 75%}[0.098, 0.801] and ends at point 'Q4' {at 72%}[0.966, 0.752]." } ], "id_raw": 107 }, { "id": "6VUXz5lvfnk944Nl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content within [0.061, 0.301, 0.092, 0.334]?" }, { "from": "gpt", "value": "It contains the y-axis label '90', denoting the 90% completion rate." } ], "id_raw": 107 }, { "id": "Csg2b4n4RHSDsNQL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.012, 0.440, 0.029, 0.527] indicate on the Y-axis?" }, { "from": "gpt", "value": "Value." } ], "id_raw": 108 }, { "id": "lTY8wYDZX3BCOpqH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the subplot's second point on the 'CO2 ppm' line at [0.106, 0.179]?" }, { "from": "gpt", "value": "357." } ], "id_raw": 108 }, { "id": "Bxt90ZlRfLRbOKsa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_8.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is located at coordinates [0.957, 0.907]?" }, { "from": "gpt", "value": "The X-tick for the year 2017." } ], "id_raw": 108 }, { "id": "dVJ46EpF8JwoorTQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_8.png", "conversations": [ { "from": "human", "value": "\nIs the value of {CO2 ppm}[0.763, 0.096] in the year 2011 higher or lower than the {Temperature Change (\u00b0C)}[0.763, 0.905] in the same year?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 108 }, { "id": "P3dBES32klVF1FmJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_8.png", "conversations": [ { "from": "human", "value": "\nComparing the last values on the lines, which is greater, {CO2 ppm}[0.988, 0.060] or {Temperature Change (\u00b0C)}[0.988, 0.905]?" }, { "from": "gpt", "value": "CO2 ppm." } ], "id_raw": 108 }, { "id": "vxLAWfGoZKP73b0g", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_8.png", "conversations": [ { "from": "human", "value": "\nIs the value at the {Temperature Change (\u00b0C) line}[0.563, 0.905] greater than the {CO2 ppm line}[0.563, 0.122] at the same point?" }, { "from": "gpt", "value": "No." } ], "id_raw": 108 }, { "id": "HTIrDhGfpvj8oPnO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the area determined by [0.081, 0.074, 0.347, 0.165]." }, { "from": "gpt", "value": "This area contains the graph's legend, listing two items: {CO2 ppm}[0.133, 0.085, 0.209, 0.113] and {Temperature Change (\u00b0C)}[0.133, 0.127, 0.341, 0.154]. Each label has a corresponding colored marker." } ], "id_raw": 108 }, { "id": "whmvguEJ8x2E2pUp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_8.png", "conversations": [ { "from": "human", "value": "\nWhat information can you provide about the area [0.034, 0.080, 0.069, 0.108]?" }, { "from": "gpt", "value": "The label '400' from the Y-tick labels is located in this area." } ], "id_raw": 108 }, { "id": "2yUSF7FLZtV5KckL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the items located in [0.046, 0.793, 0.069, 0.822]?" }, { "from": "gpt", "value": "There's a Y-tick labels at this region, labeled '50'." } ], "id_raw": 108 }, { "id": "NWA3JQfLkvVxS7g8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of mark at point [0.236, 0.439] in the graph?" }, { "from": "gpt", "value": "Circle marker for line chart." } ], "id_raw": 110 }, { "id": "2zc0mxiB9moJ8TbH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_10.png", "conversations": [ { "from": "human", "value": "\nWhat online tool does the x-axis label refer to at [0.187, 0.753, 0.236, 0.828]?" }, { "from": "gpt", "value": "Zoom." } ], "id_raw": 110 }, { "id": "JHgsFrCrkFASMauR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_10.png", "conversations": [ { "from": "human", "value": "\nWhat percentage does the {y-axis tick label} indicate at coordinates [0.116, 0.224, 0.139, 0.249]?" }, { "from": "gpt", "value": "85." } ], "id_raw": 110 }, { "id": "7z6QUjdRu7d4ydI7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_10.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher at the 'Udemy' position, {Student Engagement}[0.422, 0.337] or {Learning Efficiency}[0.422, 0.234]?" }, { "from": "gpt", "value": "Student Engagement." } ], "id_raw": 110 }, { "id": "nA1MyRNmnzht3ibA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_10.png", "conversations": [ { "from": "human", "value": "\nIs the Learning Efficiency of {Lynda}[0.701, 0.337] less or more than the one of {BrainPOP}[0.794, 0.234]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 110 }, { "id": "Mf2qorPDpOn13sFY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the value relationship between the engagement level of {Google Classroom}[0.143, 0.439] and the efficiency of {Reading Eggs}[0.981, 0.357]?" }, { "from": "gpt", "value": "Google Classroom has higher Student Engagement." } ], "id_raw": 110 }, { "id": "Sr3iMAstYZQD8RKx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.143, 0.029, 0.981, 0.747]." }, { "from": "gpt", "value": "This area includes the primary chart content with the x-axes being different online educational tools (e.g. Google Classroom, Zoom, etc.) and y-axes being percentage values. The area also contains two lines indicating 'Student Engagement' and 'Learning Efficiency' across the different tools." } ], "id_raw": 110 }, { "id": "xdxoG3u5jDUzY7t6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.094, 0.281, 0.111, 0.495] represent?" }, { "from": "gpt", "value": "This region contains the label for the Y-axis, which is 'Percentage (%)'." } ], "id_raw": 110 }, { "id": "5kl8zc8V8MWginld", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_10.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.297, 0.902, 0.826, 0.949]?" }, { "from": "gpt", "value": "This region includes the legend, containing two items: 'Student Engagement' and 'Learning Efficiency', each paired with a respective colored symbol." } ], "id_raw": 110 }, { "id": "OvswewTtbbsdefZM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.015, 0.316, 0.033, 0.637] represent?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 111 }, { "id": "ExzZiMwyvffC4Lvb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_11.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of {the fifth x-axis tick label}?" }, { "from": "gpt", "value": "[0.495, 0.906, 0.538, 0.936]" } ], "id_raw": 111 }, { "id": "NxKVwyDm18Y1z6hm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-axis tick label} at [0.039, 0.534, 0.060, 0.564]?" }, { "from": "gpt", "value": "65." } ], "id_raw": 111 }, { "id": "Le70GNZmjrE5FuWy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_11.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Biotechnology Pharmacists' in year 2002}[0.159, 0.697] above or below {the point of 'Medical Equipment Manufacturers' in the same year}[0.159, 0.546]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 111 }, { "id": "qu7e7WwGUbLLviy3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_11.png", "conversations": [ { "from": "human", "value": "\nDoes the {trend of 'Biotechnology Pharmacists'}[0.069, 0.146, 0.964, 0.752] intersect with the {trend of 'Medical Equipment Manufacturers'}[0.069, 0.477, 0.964, 0.848]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 111 }, { "id": "T7joMnDtKqTgLwfy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_11.png", "conversations": [ { "from": "human", "value": "\nWhich industry has a higher value in 2017, {Biotechnology Pharmacists}[0.830, 0.229] or {Medical Equipment Manufacturers}[0.830, 0.779]?" }, { "from": "gpt", "value": "Biotechnology Pharmacists." } ], "id_raw": 111 }, { "id": "AE38WVg78n7ygPi0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area [0.306, 0.078, 0.956, 0.130]." }, { "from": "gpt", "value": "This is the legend area. It contains two items: {Biotechnology Pharmacists}[0.360, 0.089, 0.585, 0.119] with a color indication of '#1f77b4'{at coordinate}[0.308, 0.093, 0.352, 0.112], and {Medical Equipment Manufacturers}[0.665, 0.089, 0.949, 0.119] with a color indication of '#2ca02c'{at coordinate}[0.613, 0.093, 0.657, 0.112]." } ], "id_raw": 111 }, { "id": "7m9EtXRUOTy497p2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.069, 0.064, 0.964, 0.890]." }, { "from": "gpt", "value": "The main chart area is located here, showing the trends of 'Biotechnology Pharmacists' and 'Medical Equipment Manufacturers' over the years 2000 to 2020. The x-axis represents the years and the y-axis represents the percentage growth of each industry." } ], "id_raw": 111 }, { "id": "P4CRJ3FP1mpL8teb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.093, 0.093, 0.657, 0.112]?" }, { "from": "gpt", "value": "This region contains the color indicators for the two legend items: {Biotechnology Pharmacists}[0.308, 0.093, 0.352, 0.112] and {the Medical Equipment Manufacturers}[0.613, 0.093, 0.657, 0.112]." } ], "id_raw": 111 }, { "id": "aI02eZKyu3KwHy07", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label in the chart signify, located at [0.015, 0.326, 0.034, 0.606]?" }, { "from": "gpt", "value": "The y-axis represents 'Temperature (\u00b0C)'." } ], "id_raw": 112 }, { "id": "bclDyLm3y7r1aCAi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the first x-axis tick label at [0.057, 0.902, 0.089, 0.934] represent?" }, { "from": "gpt", "value": "It represents the 'Fall' temperature season." } ], "id_raw": 112 }, { "id": "RuuyEZisQ4HGwtIC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the description of the first item in the legend, found at [0.080, 0.059, 0.119, 0.078]?" }, { "from": "gpt", "value": "It is a line labeled 'Average Temp' and its color is blue." } ], "id_raw": 112 }, { "id": "B0G3EsnQF9QztSoe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_12.png", "conversations": [ { "from": "human", "value": "\nWhich temperature is higher in the 'Summer' season, {the Average Temp}[0.520, 0.086] or {the Minimal Temp}[0.520, 0.184]?" }, { "from": "gpt", "value": "Average Temp." } ], "id_raw": 112 }, { "id": "bGYoPmHWH0pKUzGx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_12.png", "conversations": [ { "from": "human", "value": "\nIn the 'Winter' season, is {the Average Temp}[0.222, 0.723] above or below {the Minimal Temp}[0.222, 0.870]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 112 }, { "id": "2QLjmAZXfGQw5CAg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_12.png", "conversations": [ { "from": "human", "value": "\nComparing the 'Fall' and 'Spring' seasons, is the {Average Temp at Fall}[0.073, 0.478] higher or lower than the {Average Temp at Spring}[0.371, 0.405]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 112 }, { "id": "Uggm8JFLEzAgcr6j", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the region [0.080, 0.049, 0.229, 0.126]?" }, { "from": "gpt", "value": "This region contains the chart legend with two items: {Average Temp}[0.125, 0.058, 0.223, 0.082] represented by a blue line, and {Minimal Temp}[0.125, 0.093, 0.221, 0.117], represented by a red line." } ], "id_raw": 112 }, { "id": "k0cyyFmw9HarpVrA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.420, 0.943, 0.620, 0.975] contain?" }, { "from": "gpt", "value": "This area contains the x-axis label 'Temperature Season'." } ], "id_raw": 112 }, { "id": "wvXrryh3CEXKpY69", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.073, 0.086, 0.966, 0.723]." }, { "from": "gpt", "value": "This region encompasses the line representing 'Average Temp', which has marked points corresponding to different seasons: Fall, Winter, Spring, Summer, Monsoon, Dry, and Wet. The line color is blue and it has a dashed style." } ], "id_raw": 112 }, { "id": "LCWNeSBNDYl6cVwN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the {ylabel} at [0.017, 0.398, 0.037, 0.476]?" }, { "from": "gpt", "value": "Score." } ], "id_raw": 113 }, { "id": "qaAoIILCivCK81Bf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.170, 0.936, 0.231, 0.964]?" }, { "from": "gpt", "value": "'Green Intelligence' color marker." } ], "id_raw": 113 }, { "id": "LFje412rzmMpo45G", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.542, 0.936, 0.842, 0.968]?" }, { "from": "gpt", "value": "'Environmental Philanthropy' label." } ], "id_raw": 113 }, { "id": "MjK7LhsdhfHKPvkP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_13.png", "conversations": [ { "from": "human", "value": "\nWhich line at the 'Retail' marker [0.190, 0.845, 0.216, 0.875] is higher, 'Green Intelligence' or 'Environmental Philanthropy'?" }, { "from": "gpt", "value": "'Green Intelligence'." } ], "id_raw": 113 }, { "id": "AqpKXNQY0xkKrYeg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_13.png", "conversations": [ { "from": "human", "value": "\nAt the 'Healthcare' point, is {the 'Green Intelligence' line}[0.510, 0.357] above or below {the 'Environmental Philanthropy' line}[0.510, 0.517]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 113 }, { "id": "CNMq2jn2JIbWtrA8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_13.png", "conversations": [ { "from": "human", "value": "\nAt point 'Automotive'[0.617, 0.581], is the 'Environmental Philanthropy' line above or below the 'Green Intelligence' line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 113 }, { "id": "5bEhSyy76s121Uid", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in area [0.170, 0.924, 0.851, 0.981]." }, { "from": "gpt", "value": "This region seems to be the {legend area}[0.170, 0.924, 0.851, 0.981]. It contains two entries. One for 'Green Intelligence' with a blue color symbol and other for 'Environmental Philanthropy' with a green color symbol." } ], "id_raw": 113 }, { "id": "VZjFC0JBQMZ5QI6a", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_13.png", "conversations": [ { "from": "human", "value": "\nProvide a description of the region between [0.055, 0.025, 0.078, 0.215]." }, { "from": "gpt", "value": "This region contains the y-axis tick labels '95' and '100'." } ], "id_raw": 113 }, { "id": "3eb0jBSPcX2G39Iv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_13.png", "conversations": [ { "from": "human", "value": "\nCould you please describe the region [0.460, 0.845, 0.561, 0.875]?" }, { "from": "gpt", "value": "This region contains the 'Healthcare' label on the x-axis." } ], "id_raw": 113 }, { "id": "UzcTTysE63xkrCvw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the x-axis?" }, { "from": "gpt", "value": "{Time Period}[0.462, 0.952, 0.553, 0.975]" } ], "id_raw": 114 }, { "id": "5VQznYdDmp1i21Z1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_14.png", "conversations": [ { "from": "human", "value": "\nWhat label is applied to {the point on the \"Recorded History Events (thousands)\" line}[0.424, 0.741]?" }, { "from": "gpt", "value": "Middle Ages." } ], "id_raw": 114 }, { "id": "roJdynkVhMMpnxb6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in this chart figure?" }, { "from": "gpt", "value": "{Count}[0.017, 0.370, 0.032, 0.439]" } ], "id_raw": 114 }, { "id": "E8boOG7kmTe7epjv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_14.png", "conversations": [ { "from": "human", "value": "\nBetween {the \"Human Population (millions) line\"}[0.088, 0.081, 0.927, 0.745] and {the \"Recorded History Events (thousands) line\"}[0.088, 0.660, 0.927, 0.745], which line has a higher point for the \"Industrial Age\"?" }, { "from": "gpt", "value": "Human Population (millions) line." } ], "id_raw": 114 }, { "id": "NzHqYGIU2StMu3gB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_14.png", "conversations": [ { "from": "human", "value": "\nWhat line falls below at the \"Post-Industrial Age\", is it {the \"Human Population (millions) line\"}[0.088, 0.081, 0.927, 0.745] or {the \"Recorded History Events (thousands) line\"}[0.088, 0.660, 0.927, 0.745]?" }, { "from": "gpt", "value": "Recorded History Events (thousands) line." } ], "id_raw": 114 }, { "id": "L17FHj2OpOQnjrnw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_14.png", "conversations": [ { "from": "human", "value": "\nIs the {first point of the \"Recorded History Events (thousands)\" line}[0.088, 0.745] lower than the {first point of the \"Human Population (millions)\" line}[0.088, 0.745]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 114 }, { "id": "CrejyfOsOOYllqWs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the rectangle [0.096, 0.075, 0.438, 0.152]." }, { "from": "gpt", "value": "This is where the chart legend is located, with two items: {Human Population (millions)}[0.145, 0.085, 0.363, 0.108] and {Recorded History Events (thousands)}[0.145, 0.120, 0.432, 0.143]. Each label is preceded by a colored marker." } ], "id_raw": 114 }, { "id": "OJqrvogL3EOkHMxx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen within these coordinates: [0.350, 0.650, 0.500, 0.800]?" }, { "from": "gpt", "value": "There are two line data points for {Middle Ages}[0.385, 0.762, 0.462, 0.877] on both lines, with a y-axis label {1000}[0.038, 0.651, 0.077, 0.674] and grid lines." } ], "id_raw": 114 }, { "id": "CWKuOcGtjW3bbgD8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_14.png", "conversations": [ { "from": "human", "value": "\nWhat elements occupy the region [0.850, 0.650, 1.00, 0.950]?" }, { "from": "gpt", "value": "This region contains the last x-axis tick label {Post-Industrial Age}[0.870, 0.762, 0.983, 0.932], the last points on both lines {Human Population (millions)}[0.927, 0.081] and {Recorded History Events (thousands)}[0.927, 0.660], and the right-side y-axis grid lines." } ], "id_raw": 114 }, { "id": "eNXS37pkeqcrsho4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the label '55' in y-axis tick labels?" }, { "from": "gpt", "value": "[0.061, 0.878, 0.092, 0.911]" } ], "id_raw": 115 }, { "id": "T8m4BGe3RxKxMDyK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label?" }, { "from": "gpt", "value": "Index." } ], "id_raw": 115 }, { "id": "lpoI9Yb6Ib2DVxa8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_15.png", "conversations": [ { "from": "human", "value": "\nWith respect to {the fifth index point}[0.536, 0.891], what is the percentage at the corresponding {point of the 'carbon emission'}[0.536, 0.516]?" }, { "from": "gpt", "value": "77." } ], "id_raw": 115 }, { "id": "Zc5hGmeHqIXjFneo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_15.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Carbon Emissions' line point at index '3'}[0.426, 0.550] above or below the {'Volunteers' line point at index '3'}[0.426, 0.362]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 115 }, { "id": "FsWHuIHtMcRq0luj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_15.png", "conversations": [ { "from": "human", "value": "\nComparing {the 'Carbon Emissions' line's starting point}[0.098, 0.124] and {its ending point}[0.974, 0.874], does it trend upward or downward?" }, { "from": "gpt", "value": "Downward." } ], "id_raw": 115 }, { "id": "o9CbdI9VW7pqZPNU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_15.png", "conversations": [ { "from": "human", "value": "\nComparing {index '4' point on the 'Volunteers' line}[0.536, 0.294] with {index '3' point on the same line}[0.426, 0.362], does it trend upward or downward?" }, { "from": "gpt", "value": "Upward." } ], "id_raw": 115 }, { "id": "52km8HuEnjJaPW2x", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.732, 0.772, 0.952, 0.858]." }, { "from": "gpt", "value": "The region contains the legend text for 'Carbon Emissions' and 'Volunteers'." } ], "id_raw": 115 }, { "id": "POzVXXKwl5IB3w6x", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_15.png", "conversations": [ { "from": "human", "value": "\nWhat information is held within the region [0.017, 0.322, 0.092, 0.607]?" }, { "from": "gpt", "value": "It is the Y-axis label, displaying 'Percentage (%)'." } ], "id_raw": 115 }, { "id": "lyg9ft8zu0ypldGw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_15.png", "conversations": [ { "from": "human", "value": "\nWhat elements are included in the region [0.309, 0.899, 0.544, 0.932]?" }, { "from": "gpt", "value": "This space includes x-axis tick labels for index points '2' and '4'." } ], "id_raw": 115 }, { "id": "eAgvoFRnJP4Pl5JL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_17.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the coordinates [0.088, 0.674] in the chart?" }, { "from": "gpt", "value": "The 2012 data point for the Online Training line." } ], "id_raw": 117 }, { "id": "JnXwUC7AangHlTSc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_17.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.209, 0.035, 0.630] in the chart?" }, { "from": "gpt", "value": "Index Score out of 100." } ], "id_raw": 117 }, { "id": "NYR6IrWMfp7JrOdy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_17.png", "conversations": [ { "from": "human", "value": "\nWhat data point does the coordinate [0.957, 0.801] refer to in the chart?" }, { "from": "gpt", "value": "The 2021 data point for the Classroom Training line." } ], "id_raw": 117 }, { "id": "pRY7xH9kgPRwAeps", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_17.png", "conversations": [ { "from": "human", "value": "\nIs {the point for '2017'}[0.571, 0.356] for Online Training line above or below {the point for '2017'}[0.571, 0.610] for Classroom Training line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 117 }, { "id": "5DFKEwN0mkHbqyFK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_17.png", "conversations": [ { "from": "human", "value": "\nIs {the point for '2018'}[0.667, 0.267] for Online Training line above or below {the point for '2018'}[0.667, 0.674] for Classroom Training line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 117 }, { "id": "fAa3GmgMKzz81A3a", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_17.png", "conversations": [ { "from": "human", "value": "\nComparing the Classroom Training and Online Training lines, which one has a higher index score in {2016}[0.474, 0.483] and [0.474, 0.420] respectively?" }, { "from": "gpt", "value": "Online Training." } ], "id_raw": 117 }, { "id": "t75OKRx98hf664OE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.240, 0.910, 0.805, 0.973]." }, { "from": "gpt", "value": "This region contains the subplot legend. It provides information about the two lines plotted on the chart: {Online Training}[0.311, 0.925, 0.478, 0.958] and {Classroom Training}[0.585, 0.925, 0.796, 0.958], each with a color marker preceding the label." } ], "id_raw": 117 }, { "id": "q6VOQAKvG25IuwpG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content located within the area [0.088, 0.102, 0.957, 0.801]." }, { "from": "gpt", "value": "This area contains all the plotted data points and lines for Online Training and Classroom training from 2012 to 2021. The coordinates of lines span the entire x-axis and y-axis within this region." } ], "id_raw": 117 }, { "id": "s0Nun341K1OQvaUQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_17.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.499, 0.852, 0.546, 0.885]?" }, { "from": "gpt", "value": "The x-axis label 'Year' is located within these coordinates." } ], "id_raw": 117 }, { "id": "nPS31hVXf2WjioCx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.176, 0.139, 0.290, 0.161] in the chart?" }, { "from": "gpt", "value": "Package Volume label." } ], "id_raw": 118 }, { "id": "HaPeWz70wSNalNef", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_18.png", "conversations": [ { "from": "human", "value": "\nWhat does {the marker}[0.133, 0.142, 0.170, 0.156] in the legend represent?" }, { "from": "gpt", "value": "Package Volume." } ], "id_raw": 118 }, { "id": "ekYWknEzfPxFJhCn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.080, 0.692, 0.117, 0.710]?" }, { "from": "gpt", "value": "26300." } ], "id_raw": 118 }, { "id": "SQ1eyePjdyAuBFpz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_18.png", "conversations": [ { "from": "human", "value": "\nIs {the first point on the Freight Revenue line}[0.125, 0.476] above or below {the first point on the Package Volume line}[0.125, 0.888]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 118 }, { "id": "1F1zwQIItMEGCVig", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_18.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Package Volume'}[0.125, 0.863, 0.900, 0.888] intersect with {the line for 'Freight Revenue'}[0.125, 0.129, 0.900, 0.476]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 118 }, { "id": "Nawc9NWKVLwMOn7h", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_18.png", "conversations": [ { "from": "human", "value": "\nWhich label had a higher end value in 2020, {Package Volume}[0.900, 0.863] or {Freight Revenue}[0.900, 0.129]?" }, { "from": "gpt", "value": "Freight Revenue." } ], "id_raw": 118 }, { "id": "YwbZqI8x9MId7Pzf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.176, 0.130, 0.300, 0.210]." }, { "from": "gpt", "value": "The legend subtitles and markers for {Package Volume}[0.176, 0.139, 0.290, 0.161] and {Freight Revenue}[0.176, 0.172, 0.289, 0.194] are located here, along with their corresponding color markers." } ], "id_raw": 118 }, { "id": "P5AT5HLxyzod77Tw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_18.png", "conversations": [ { "from": "human", "value": "\nWhat features are in the region [0.080, 0.400, 0.200, 0.920]?" }, { "from": "gpt", "value": "This region contains several {y-axis labels}[0.080, 0.466, 0.117, 0.484] and [0.080, 0.883, 0.117, 0.901], {y-axis label}[0.080, 0.710, 0.117, 0.727] and {ticks for y-axis scaling}[0.125, 0.716], [0.125, 0.456]." } ], "id_raw": 118 }, { "id": "9QxQjJ97EuYFoNNL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_18.png", "conversations": [ { "from": "human", "value": "\nWhat does the section between [0.450, 0.400, 0.900, 0.920] include?" }, { "from": "gpt", "value": "This area covers the right half of the plot where you can see plot points for both {Package Volume line}[0.474, 0.877, 0.512, 0.879] and {Freight Revenue line}[0.745, 0.259, 0.784, 0.233]." } ], "id_raw": 118 }, { "id": "ucPcW68AZpvu7Ga1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first tick label on the x-axis?" }, { "from": "gpt", "value": "[0.051, 0.809, 0.145, 0.842]" } ], "id_raw": 119 }, { "id": "Q4nhJTrlg0Ub0S7L", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the text label at {coordinates}[0.602, 0.925, 0.721, 0.958] in the sub legend?" }, { "from": "gpt", "value": "Product B." } ], "id_raw": 119 }, { "id": "41Hd2TdtZ0AUm7At", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_6_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object identified using {coordinates}[0.017, 0.370, 0.039, 0.469]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 119 }, { "id": "HiaMZWTkt3uKUgSX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_19.png", "conversations": [ { "from": "human", "value": "\nIs the point for May in {the Product A line}[0.397, 0.310] higher or lower than the point for May in {the Product B line}[0.397, 0.525]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 119 }, { "id": "275UwPAIoWwkMUby", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_19.png", "conversations": [ { "from": "human", "value": "\nAt which point does {the Product A line}[0.079, 0.601, 0.919, 0.420] overlap with {the Product B line}[0.098, 0.396, 0.919, 0.801]?" }, { "from": "gpt", "value": "January." } ], "id_raw": 119 }, { "id": "69sBCJotANIHoX0G", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_6_19.png", "conversations": [ { "from": "human", "value": "\nWho has a higher sales in the end of the year, {Product A}[0.919, 0.081] or {Product B}[0.919, 0.801]?" }, { "from": "gpt", "value": "Product B." } ], "id_raw": 119 }, { "id": "q9biSAel80SF4h2j", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in {coordinates}[0.286, 0.910, 0.731, 0.973]." }, { "from": "gpt", "value": "It is the chart's legend area with two items: {Product A}[0.365, 0.925, 0.484, 0.958] and {Product B}[0.602, 0.925, 0.721, 0.958], marked with square and circle respectively." } ], "id_raw": 119 }, { "id": "GCPcbkk50yn6Ntdw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the information given within {coordinates}[0.045, 0.406, 0.092, 0.440] and {coordinates}[0.045, 0.311, 0.092, 0.344]?" }, { "from": "gpt", "value": "These are y-axis labels \"100\" and \"120\" respectively." } ], "id_raw": 119 }, { "id": "ECduQrCU7VacjUXm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_6_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects in the bounding box [0.324, 0.491, 0.471, 0.563]?" }, { "from": "gpt", "value": "There are two items inside the bounding box, an April data point from the {Product A line}[0.322, 0.343] and a June data point from the {Product B line}[0.471, 0.563]." } ], "id_raw": 119 }, { "id": "BzwKbXLRxnhNBBAQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the color of the first line}[0.103, 0.187, 0.950, 0.280] in the chart?" }, { "from": "gpt", "value": "The line color is '#0c5da5'." } ], "id_raw": 120 }, { "id": "0c3IcK2xtC8aD17I", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the last vertical tick}[0.050, 0.051, 0.088, 0.083] in the y-axis?" }, { "from": "gpt", "value": "The y-axis label is '1.0'." } ], "id_raw": 120 }, { "id": "9LEsxYz5PvVzFTXc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the {object}[0.135, 0.025, 0.918, 0.057] just below the subplot box?" }, { "from": "gpt", "value": "The object is the chart subtitle 'Automotive and Transportation Market Preference (2015 - 2020)'." } ], "id_raw": 120 }, { "id": "vf5RFZzyY8q4CCjY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point corresponding to 2017 in the 'Public_Transportation' line}[0.442, 0.187] above or below {the point corresponding to 2017 in the 'Automobiles' line}[0.442, 0.203]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 120 }, { "id": "uZX506d2QKOeWiYo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Bicycles' line at 2020}[0.950, 0.334] have a lower or higher value than {the 'Public_Transportation' line at 2020}[0.950, 0.280]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 120 }, { "id": "jeFAXiYQ8q0UbUZm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_0.png", "conversations": [ { "from": "human", "value": "\nIn terms of preference, is {2015 for 'Automobiles'}[0.103, 0.141] higher or lower than {2015 for 'Bicycles'}[0.103, 0.681]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 120 }, { "id": "jrUzO41DmEQFqgMz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_0.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects within the region [0.050, 0.745, 0.088, 0.777]?" }, { "from": "gpt", "value": "The object within these coordinates is the y-axis tick label '0.1'." } ], "id_raw": 120 }, { "id": "AnxsU3Szxqnzohhc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_0.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the region [0.238, 0.934, 0.460, 0.964]?" }, { "from": "gpt", "value": "The region has the legend label of 'Public_Transportation'." } ], "id_raw": 120 }, { "id": "mUsI7jdO3dPCnn1n", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.171, 0.923, 0.882, 0.975]." }, { "from": "gpt", "value": "This region includes the legends for 'Public_Transportation', 'Automobiles', and 'Bicycles'. Their respective colors are '#0c5da5', '#ff9500', and '#00b945'." } ], "id_raw": 120 }, { "id": "U7fX27pIOrRSPd7y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the marker with handle at coordinates [0.650, 0.791, 0.703, 0.804] signify?" }, { "from": "gpt", "value": "Avg Temperature (\u00b0C)" } ], "id_raw": 121 }, { "id": "U15cmbi4ka2XmoLS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.034, 0.837, 0.050, 0.868]?" }, { "from": "gpt", "value": "The y-axis tick label '0'." } ], "id_raw": 121 }, { "id": "DswhXLKzy8aV0aMd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.714, 0.828, 0.901, 0.858] mention?" }, { "from": "gpt", "value": "Avg Snowfall (cm)." } ], "id_raw": 121 }, { "id": "VKUswDamKCV3T0Zd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_1.png", "conversations": [ { "from": "human", "value": "\nIs the {Avg Temperature (\u00b0C)}<[0.714, 0.784, 0.938, 0.814]> higher or lower in Bari compared to Aosta?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 121 }, { "id": "xyzm1Esp3pLxemDq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_1.png", "conversations": [ { "from": "human", "value": "\nIs {Avg Snowfall (cm)}<[0.714, 0.828, 0.901, 0.858]> more in Torino or Bologna?" }, { "from": "gpt", "value": "Torino." } ], "id_raw": 121 }, { "id": "fwvLTfPHqLP7nUGZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_1.png", "conversations": [ { "from": "human", "value": "\nDoes Aosta have a lower {Avg Temperature (\u00b0C)}<[0.714, 0.784, 0.938, 0.814]> or {Avg Snowfall (cm)}<[0.714, 0.828, 0.901, 0.858]>?" }, { "from": "gpt", "value": "Avg Temperature (\u00b0C)." } ], "id_raw": 121 }, { "id": "VGQe2sL2LaarWYx6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the area within coordinates [0.019, 0.051, 0.050, 0.083]." }, { "from": "gpt", "value": "The area contains the y-axis tick label '40'." } ], "id_raw": 121 }, { "id": "6NPQljvAHwm9gVmX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the area within [0.647, 0.773, 0.947, 0.869] contain?" }, { "from": "gpt", "value": "The area contains the legend with labels for Avg Temperature (\u00b0C) at {coordinates}[0.714, 0.784, 0.938, 0.814] and Avg Snowfall (cm) at {coordinates}[0.714, 0.828, 0.901, 0.858]." } ], "id_raw": 121 }, { "id": "qVXZ73iZgSc9Y9fR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content of the area [0.319, 0.025, 0.702, 0.057]?" }, { "from": "gpt", "value": "The area contains the title of the chart, \"Yearly Averages in Italian Cities\"." } ], "id_raw": 121 }, { "id": "gRtZ9Q7UALwjjnSZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.056, 0.697, 0.112, 0.790] refer to?" }, { "from": "gpt", "value": "Investment Strategy \"High Risk\"." } ], "id_raw": 122 }, { "id": "IYiC4H44OMrm3VOp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label {ylabel} at [0.015, 0.312, 0.033, 0.432] represent?" }, { "from": "gpt", "value": "Earnings." } ], "id_raw": 122 }, { "id": "VOrk0v4v69ibjrTG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the point shown at {coordinates}[0.263, 0.311] on the line chart?" }, { "from": "gpt", "value": "1.2." } ], "id_raw": 122 }, { "id": "gBClZbnGMuSVgGzy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_2.png", "conversations": [ { "from": "human", "value": "\nComparing the earnings at [0.112, 0.557] and [0.188, 0.434], which investment strategy shows higher earnings, {High Risk} or {Moderate Risk}?" }, { "from": "gpt", "value": "Moderate Risk." } ], "id_raw": 122 }, { "id": "4tGNgnKwF46c5gpF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_2.png", "conversations": [ { "from": "human", "value": "\nIs the earnings value at {Bonds}[0.641, 0.156] higher or lower than {Mutual Fund}[0.490, 0.218]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 122 }, { "id": "OvlVaSrji4knyrbD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_2.png", "conversations": [ { "from": "human", "value": "\nAre the earnings for the {Commodities}[0.943, 0.212] greater or less than {Options}[0.868, 0.419]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 122 }, { "id": "e1Smj2RNsHlHfyT0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is present within [0.351, 0.697, 0.490, 0.814]?" }, { "from": "gpt", "value": "Within these coordinates, we have two investment strategy labels: {Index Fund}[0.351, 0.697, 0.414, 0.803] and {Mutual Fund}[0.420, 0.697, 0.490, 0.814]." } ], "id_raw": 122 }, { "id": "pelPsAsJBYU5k6wv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the space within the coordinates [0.112, 0.135, 0.943, 0.557]." }, { "from": "gpt", "value": "Inside these boundaries, we can see a line graph representing the earnings of each Investment Strategy. The points for each strategy are represented along the line marked with 'o' markers." } ], "id_raw": 122 }, { "id": "Gc3kowsJ8QisPfCm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you see within the region bounded by [0.707, 0.697, 0.868, 0.777]?" }, { "from": "gpt", "value": "Within this area, we can see two x-axis labels referring to {Cryptocurrency}[0.707, 0.697, 0.792, 0.840] and {Options}[0.820, 0.697, 0.868, 0.777]." } ], "id_raw": 122 }, { "id": "zvmFcTUA7twopB4J", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_3.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the label of the y-axis is?" }, { "from": "gpt", "value": "Normalized Metrics." } ], "id_raw": 123 }, { "id": "03S4UUp66mqInKcM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the x-tick label located at [0.164, 0.851, 0.226, 0.883]?" }, { "from": "gpt", "value": "2011." } ], "id_raw": 123 }, { "id": "onpX9qae922S3FUG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line labeled 'Theater', with coordinates '[0.100, 0.334, 0.950, 0.642]'?" }, { "from": "gpt", "value": "#ff9500." } ], "id_raw": 123 }, { "id": "ysMrAl1twXpa83Xc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_3.png", "conversations": [ { "from": "human", "value": "\nAt the point of '2017', is the line representing 'BoxOffice' higher or lower than the line representing 'Awards'?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 123 }, { "id": "EbJKvYNdyRvbJCip", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_3.png", "conversations": [ { "from": "human", "value": "\nIs the 'BoxOffice' trend higher or lower than 'Theater' for the year '2010'?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 123 }, { "id": "i6G1DAEYZGukuovG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_3.png", "conversations": [ { "from": "human", "value": "\nComparatively, did the 'Theater' perform better or worse than the 'Awards' in '2012'?" }, { "from": "gpt", "value": "Better." } ], "id_raw": 123 }, { "id": "v0qOaz3FgTJ9M6cK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you say about the area of [0.260, 0.923, 0.791, 0.975]?" }, { "from": "gpt", "value": "This area presents the legend of the chart. Three items are displayed, 'BoxOffice' with color '#0c5da5', 'Theater' with color '#ff9500', and 'Awards' with color '#00b945'." } ], "id_raw": 123 }, { "id": "dMoBn5KwYelCTOnm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_3.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region bounded by [0.070, 0.851, 0.981, 0.883]?" }, { "from": "gpt", "value": "This region contains the x-axis tick labels for the data years from '2010' to '2019'." } ], "id_raw": 123 }, { "id": "YwLuUTV2ohOkWf6O", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_3.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the figure region [0.050, 0.206, 0.088, 0.237]?" }, { "from": "gpt", "value": "It is the y-axis tick label '1.0'." } ], "id_raw": 123 }, { "id": "KvI8JdgBtAgq3sPr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with the coordinates [0.367, 0.953, 0.398, 0.975]?" }, { "from": "gpt", "value": "Legend label for 'Coal'." } ], "id_raw": 124 }, { "id": "f91wAo7ZKEd46stO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the x-axis label?" }, { "from": "gpt", "value": "[0.510, 0.881, 0.551, 0.905]" } ], "id_raw": 124 }, { "id": "4OaryFyQ39lIMZHr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-tick label}[0.040, 0.523, 0.089, 0.547]?" }, { "from": "gpt", "value": "4500." } ], "id_raw": 124 }, { "id": "211EBT3TPnavhmu1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_4.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Coal' line}[0.101, 0.078, 0.960, 0.351] upward or downward trend in general?" }, { "from": "gpt", "value": "Upward." } ], "id_raw": 124 }, { "id": "SHwfOKQfqd5mA18J", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point in 'Oil'}[0.869, 0.775] higher or lower than {the point in 'Natural Gas'}[0.869, 0.503] ?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 124 }, { "id": "d0ai8v2KCghRsFUI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_4.png", "conversations": [ { "from": "human", "value": "\nWhich resource has a higher value at {close to mid-point of 2009}[0.840, 0.851, 0.889, 0.874], Coal or Natural Gas?" }, { "from": "gpt", "value": "Coal." } ], "id_raw": 124 }, { "id": "IukJU4IUHCZktSgT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.418, 0.960, 0.448, 0.978]." }, { "from": "gpt", "value": "It is the legend label for 'Coal' in the chart." } ], "id_raw": 124 }, { "id": "UnUrG59QDaGSkkym", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.557, 0.963, 0.593, 0.973]." }, { "from": "gpt", "value": "This area contains the colored handle for the 'Natural Gas' legend." } ], "id_raw": 124 }, { "id": "Q2A4PuSUmPXjbIjD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.363, 0.851, 0.412, 0.874]?" }, { "from": "gpt", "value": "It contains the x-axis tick label '2004'." } ], "id_raw": 124 }, { "id": "hTUGsFfHrK1HC6lL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object found at the coordinates [0.108, 0.064, 0.951, 0.835] on the chart?" }, { "from": "gpt", "value": "This is the main axes of the chart where the data is plotted." } ], "id_raw": 126 }, { "id": "TzFUzFwWFBUZ1eQI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the element described by the coordinates [0.188, 0.934, 0.381, 0.964]?" }, { "from": "gpt", "value": "It is part of the legend, labeling the line for 'High-tech startups'." } ], "id_raw": 126 }, { "id": "SGtqWSWFjKIzJ7uu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the item at [0.019, 0.363, 0.043, 0.536]?" }, { "from": "gpt", "value": "This is the label for the y-axis, indicating 'Rates in %'." } ], "id_raw": 126 }, { "id": "NwOxPhK8E7cptwbG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_6.png", "conversations": [ { "from": "human", "value": "\nIf we compare the point for 'Attr5' in {the line for 'High-tech startups'}[0.589, 0.699] and {the line for 'Government support'}[0.589, 0.174], which one is higher on the chart?" }, { "from": "gpt", "value": "The point for 'Attr5' in the line for 'High-tech startups' is higher." } ], "id_raw": 126 }, { "id": "N1IdHQ3akSVNBosM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the point for 'Attr1' in the 'Success rate' line}[0.108, 0.202] appear above or below {the point for 'Attr1' in the 'Government support' line}[0.108, 0.294]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 126 }, { "id": "CrkDBeST9pdEIIvo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the point for 'Attr8' in the 'High-tech startups' line}[0.951, 0.598] lie above or below {the point for 'Attr8' in the 'Success rate' line}[0.951, 0.101]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 126 }, { "id": "yWgejConkHpYHNDR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_6.png", "conversations": [ { "from": "human", "value": "\nWhat elements are found within the coordinates [0.121, 0.923, 0.937, 0.975]?" }, { "from": "gpt", "value": "This region includes the chart's legend, indicating the color key and category for 'High-tech startups', 'Success rate', and 'Government support'." } ], "id_raw": 126 }, { "id": "44yxgjsmsDXaoAGv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within the region defined by [0.065, 0.051, 0.096, 0.083]." }, { "from": "gpt", "value": "This region contains the y-axis tick label '100'." } ], "id_raw": 126 }, { "id": "ri1Ry46fDkZR97SI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the region [0.049, 0.051, 0.096, 0.083]?" }, { "from": "gpt", "value": "The region contains the y-axis tick label '100'." } ], "id_raw": 126 }, { "id": "deWkKAPjPEeQ3wpq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the text} located at [0.308, 0.954, 0.487, 0.977] in the sublegend?" }, { "from": "gpt", "value": "It is the label \"Global Trade Demand\"." } ], "id_raw": 127 }, { "id": "uuYqjbI3SPqxIge2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_7.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.015, 0.248, 0.034, 0.638] denote?" }, { "from": "gpt", "value": "Demand and Supply (in billions)." } ], "id_raw": 127 }, { "id": "I0XSAoRc6BFn70EV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_7.png", "conversations": [ { "from": "human", "value": "\nWhat year does {the x-axis label} at coordinates [0.856, 0.852, 0.898, 0.875] represent?" }, { "from": "gpt", "value": "2019" } ], "id_raw": 127 }, { "id": "XtrDyi76vzoe9Myu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 2010 in Global Trade Demand}[0.093, 0.706] above or below {the point for the same year in Global Supply Management}[0.093, 0.732]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 127 }, { "id": "cwyIK2IoX1iFY3nr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_7.png", "conversations": [ { "from": "human", "value": "\nAre the values at the 2017 mark for both {Global Trade Demand}[0.703, 0.278] and {Global Supply Management}[0.703, 0.311] equal or different?" }, { "from": "gpt", "value": "Equal." } ], "id_raw": 127 }, { "id": "x3y1pIpmN5kKB8gP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_7.png", "conversations": [ { "from": "human", "value": "\nComparing {the point for 2020 in Global Trade Demand}[0.964, 0.127] and {the point for the same year in Global Supply Management}[0.964, 0.114], which one registers a higher value?" }, { "from": "gpt", "value": "Global Supply Management." } ], "id_raw": 127 }, { "id": "sFDoj7KG5WcK0nYF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area defined by the coordinates [0.254, 0.946, 0.803, 0.985]?" }, { "from": "gpt", "value": "This area contains the legend for the line plot. It contains two entries: {Global Trade Demand} categorized with color #0c5da5 at coordinates [0.308, 0.954, 0.487, 0.977] and {Global Supply Management} categorized with color #ff9500 at coordinates [0.567, 0.954, 0.796, 0.977]." } ], "id_raw": 127 }, { "id": "8Yp7Gw9CzbMYHIsU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_7.png", "conversations": [ { "from": "human", "value": "\nWhat could be found in the region with coordinates [0.093, 0.048, 0.964, 0.838]?" }, { "from": "gpt", "value": "This region corresponds to the main content of the line plot, including the plotted lines representing the global trade demand and global supply management, as well as all labels, y-ticks, and x-ticks." } ], "id_raw": 127 }, { "id": "Nahow7rTeL3QtPLI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects placed at [0.508, 0.852, 0.550, 0.875]." }, { "from": "gpt", "value": "In this region, it contains one object which is the {x-axis label}[0.508, 0.852, 0.550, 0.875] representing the year \"2015\"." } ], "id_raw": 127 }, { "id": "OhS2Zy1Ym6rwBX4W", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.019, 0.348, 0.043, 0.551]?" }, { "from": "gpt", "value": "\"Growth Rate\"." } ], "id_raw": 129 }, { "id": "6z4pc06Pi2pAd063", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the bottom right coordinate of the third legend item, \"5G\"?" }, { "from": "gpt", "value": "[0.330, 0.964]." } ], "id_raw": 129 }, { "id": "vbq9EjqpyexNtMrg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the trend line for {Blockchain}[0.110, 0.481, 0.950, 0.794]?" }, { "from": "gpt", "value": "#800080 (purple)." } ], "id_raw": 129 }, { "id": "Fd9ydevpohq9mDib", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_9.png", "conversations": [ { "from": "human", "value": "\nWhich trend is higher at the end of the year 2023, {AI}[0.614, 0.489] or {Cloud computing}[0.614, 0.511]?" }, { "from": "gpt", "value": "Cloud computing." } ], "id_raw": 129 }, { "id": "YPXwaQEg3ENg255f", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_9.png", "conversations": [ { "from": "human", "value": "\nIs the trend line for {AR}[0.950, 0.720] above or below the trend line for {5G}[0.950, 0.138] in the final year 2025?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 129 }, { "id": "TKdUtGR2Obe33c9t", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_9.png", "conversations": [ { "from": "human", "value": "\nWhich trend line has a steeper growth, {Blockchain}[0.782, 0.571, 0.950, 0.481] or {VR}[0.782, 0.750, 0.950, 0.653]?" }, { "from": "gpt", "value": "Blockchain." } ], "id_raw": 129 }, { "id": "8HdjgySDB2uLVpD8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the detailed content in the area [0.049, 0.051, 0.096, 0.083]." }, { "from": "gpt", "value": "This is a y-axis tick label with the value '100'." } ], "id_raw": 129 }, { "id": "mA9gZn0b9W6aiRHn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the information within [0.577, 0.890, 0.689, 0.920]." }, { "from": "gpt", "value": "It's the legend text for 'Blockchain', colored with a unique color (purple) for its corresponding line plot." } ], "id_raw": 129 }, { "id": "9S3Wwi0TS0eAulv7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the data within the region [0.751, 0.855, 0.813, 0.886]?" }, { "from": "gpt", "value": "The region contains the x-axis tick label \"2024\"." } ], "id_raw": 129 }, { "id": "oMMDvZsSqxvokIEn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_10.png", "conversations": [ { "from": "human", "value": "\nWhat is represented at the coordinates [0.165, 0.934, 0.335, 0.964]?" }, { "from": "gpt", "value": "Label of the 'Heuristic Search' algorithm in the legend." } ], "id_raw": 130 }, { "id": "AlA0Q3DmiC48pzNv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label represent at the coordinates [0.019, 0.276, 0.043, 0.622]?" }, { "from": "gpt", "value": "The term 'Achievable Efficiency' is represented at these coordinates." } ], "id_raw": 130 }, { "id": "vUalM1bKIOFahkIs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the value represented by {the x-axis tick label} at [0.362, 0.851, 0.401, 0.883]?" }, { "from": "gpt", "value": "The value is 0.5." } ], "id_raw": 130 }, { "id": "HblCAvjl0oE9z3w7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_10.png", "conversations": [ { "from": "human", "value": "\nDoes the 'Heuristic Search' efficiency increase or decrease from the point at [0.663, 0.192] to the point at [0.944, 0.150]?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 130 }, { "id": "D4kEZkLvensPGwGO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_10.png", "conversations": [ { "from": "human", "value": "\nIs the efficiency of the {Genetic Algorithm}[0.804, 0.235, 0.944, 0.107] less or more than the efficiency of the {Simulated Annealing}[0.663, 0.235, 0.944, 0.150] at the highest level?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 130 }, { "id": "KzoQpfjS7LT7HjvD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_10.png", "conversations": [ { "from": "human", "value": "\nWhich algorithm represents the greater efficiency at {1.5}[0.944, 0.150, 0.964, 0.407], Genetic Algorithm or Simulated Annealing?" }, { "from": "gpt", "value": "Simulated Annealing." } ], "id_raw": 130 }, { "id": "M5ZkZb7Lk21Oz90W", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area within [0.098, 0.923, 0.947, 0.975]?" }, { "from": "gpt", "value": "This area contains the legend for the chart, including labels for 'Heuristic Search', 'Genetic Algorithm', and 'Simulated Annealing', each accompanied by a colored handle." } ], "id_raw": 130 }, { "id": "7mTdn9uCjnHnQpVL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.421, 0.892, 0.624, 0.924]." }, { "from": "gpt", "value": "This is the x-axis label with the term 'Efficiency Levels'. It's written in black text." } ], "id_raw": 130 }, { "id": "bREQAIbtF1R6in3Z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_10.png", "conversations": [ { "from": "human", "value": "\nWhat items can be found within the area from [0.050, 0.051, 0.088, 0.083]?" }, { "from": "gpt", "value": "At these coordinates, the y-axis tick label '1.0' is found." } ], "id_raw": 130 }, { "id": "B7O6YtgZAKXv0ztV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_11.png", "conversations": [ { "from": "human", "value": "\nWhat object is present at [0.564, 0.917, 0.586, 0.975]?" }, { "from": "gpt", "value": "x-axis tick label for the year 2011." } ], "id_raw": 131 }, { "id": "h8L50Zf9fUUIrqRm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing 'ICU Beds per 100k'}[0.106, 0.102, 0.940, 0.686]?" }, { "from": "gpt", "value": "#0c5da5." } ], "id_raw": 131 }, { "id": "0rFqI2pRpRnwyUxT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_11.png", "conversations": [ { "from": "human", "value": "\nWhat object does the coordinate [0.314, 0.901] represent in the chart?" }, { "from": "gpt", "value": "Tick mark for the year 2005." } ], "id_raw": 131 }, { "id": "iRUNQ8lvUqljPl7P", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_11.png", "conversations": [ { "from": "human", "value": "\nIs {the value at year 2005}[0.314, 0.663] greater than {the value at year 2004}[0.272, 0.670] for 'ICU Beds per 100k'?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 131 }, { "id": "rhlHv8jvRuR4asA1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_11.png", "conversations": [ { "from": "human", "value": "\nIs {line 'Telemedicine Service' value in 2015}[0.731, 0.852] higher than {line 'Rapid Tests in Mill.' value in 2015}[0.731, 0.860]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 131 }, { "id": "rY3o7pul1Vb7KFjp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the year marker 2020}[0.940, 0.901] have a higher y-point for 'ICU Beds per 100k' compared to 'Telemedicine Service'?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 131 }, { "id": "N0tceV2vodxqCTVP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the features in region [0.064, 0.840, 0.124, 0.960]." }, { "from": "gpt", "value": "It contains y-axis labels ranging from 500 to 0, formatted in black color and not rotated." } ], "id_raw": 131 }, { "id": "gpijy9Ly4AihI5qW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_11.png", "conversations": [ { "from": "human", "value": "\nWhat appears in the region [0.041, 0.075, 0.369, 0.218]?" }, { "from": "gpt", "value": "This region includes legend items, specifically {ICU Beds per 100k}[0.141, 0.089, 0.334, 0.119], along with corresponding color handles." } ], "id_raw": 131 }, { "id": "2r70ANac93mgdwS9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_11.png", "conversations": [ { "from": "human", "value": "\nCould you provide information about the elements in [0.522, 0.917, 0.544, 0.975]?" }, { "from": "gpt", "value": "The x-axis tick label '2010' is located here, printed in black color in a tilted manner." } ], "id_raw": 131 }, { "id": "QpKjQbUwaXrxdSYi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.015, 0.375, 0.034, 0.586]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 132 }, { "id": "oZdg2Y0KgiDovnDe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first x-axis tick label?" }, { "from": "gpt", "value": "[0.052, 0.927, 0.101, 0.951]" } ], "id_raw": 132 }, { "id": "fTIBrXl4LfOZdQZ1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.040, 0.903, 0.064, 0.926]?" }, { "from": "gpt", "value": "10." } ], "id_raw": 132 }, { "id": "IjnlwEALVZd4aKbr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point for year '2001'}[0.165, 0.718] above or below {the point for year '2000'}[0.076, 0.696] on the 'Company A' line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 132 }, { "id": "3EgyLIV3uduNDWTD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_12.png", "conversations": [ { "from": "human", "value": "\nAt year '2003', is {the line for 'Company A'}[0.341, 0.588] above or below {the line for 'Company B'}[0.341, 0.524]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 132 }, { "id": "7qZFUmygoSh0CF2m", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_12.png", "conversations": [ { "from": "human", "value": "\nWhich company has a higher market share at {the point for year '2010'}[0.960, 0.265], Company A or Company B?" }, { "from": "gpt", "value": "Company A." } ], "id_raw": 132 }, { "id": "7rQukqMhcXDInUjO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.138, 0.068, 0.233, 0.156]." }, { "from": "gpt", "value": "This region contains the legend items for the three lines: {Company A}[0.138, 0.134, 0.233, 0.156], {Company B}[0.138, 0.101, 0.233, 0.123], and {Company C}[0.138, 0.068, 0.233, 0.090], each paired with a colored marker." } ], "id_raw": 132 }, { "id": "dc09H3MFYtUVuxn1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.040, 0.687, 0.064, 0.711]." }, { "from": "gpt", "value": "This area contains the y-axis tick label: {20}[0.040, 0.687, 0.064, 0.711]." } ], "id_raw": 132 }, { "id": "Faa9XQ0AT9pGJGUC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.040, 0.903, 0.101, 0.951]?" }, { "from": "gpt", "value": "There are 2 objects. The y-axis tick label '10' is at these {coordinates}[0.040, 0.903, 0.064, 0.926] and the x-axis tick label '2000' is located at these {coordinates}[0.052, 0.927, 0.101, 0.951]." } ], "id_raw": 132 }, { "id": "2LcJluLqnf6NIoUj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at {the line_1}[0.074, 0.681, 0.960, 0.725] in the plot?" }, { "from": "gpt", "value": "It's the trend line representing Alcohol." } ], "id_raw": 133 }, { "id": "BsDzVk2E1aADMV6a", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the handle at {the legend}[0.604, 0.940, 0.646, 0.954]?" }, { "from": "gpt", "value": "#00b945." } ], "id_raw": 133 }, { "id": "hDIuoD2ICz7qSg7B", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis tick at {coordinates}[0.040, 0.822, 0.064, 0.854]?" }, { "from": "gpt", "value": "15." } ], "id_raw": 133 }, { "id": "HdW5Qifxz2luKOWB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_13.png", "conversations": [ { "from": "human", "value": "\nIs {the Smoking point in 2005}[0.296, 0.615] higher or lower than {the Obesity point in 2005}[0.296, 0.417]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 133 }, { "id": "6rCdML4NXa3hxn3n", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_13.png", "conversations": [ { "from": "human", "value": "\nIs {the Alcohol point in 2010}[0.517, 0.681] located at the same level as {the Smoking point in 2010}[0.517, 0.637]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 133 }, { "id": "Sy556fyazeRThrhC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_13.png", "conversations": [ { "from": "human", "value": "\nWhich trend increases faster in 2020, {Obesity}[0.960, 0.153]or {Smoking}[0.960, 0.703]?" }, { "from": "gpt", "value": "Obesity." } ], "id_raw": 133 }, { "id": "6b0kqj9NSwkVkabO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the area at [0.074, 0.066, 0.960, 0.835]." }, { "from": "gpt", "value": "The area contains the main plot of the chart. It marks the year (x-axis) against the percentage of population (y-axis) for three trend lines representing Smoking, Alcohol, and Obesity over time." } ], "id_raw": 133 }, { "id": "wQWL3teSCAzGLiO9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be found at [0.040, 0.822, 0.064, 0.854]?" }, { "from": "gpt", "value": "A single y-axis tick mark with the label '15' is located here." } ], "id_raw": 133 }, { "id": "6HfRneH9b4gqqfId", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_13.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the area at [0.074, 0.571, 0.960, 0.703]?" }, { "from": "gpt", "value": "This area includes the trend line for Smoking, which is plotted against the year, as well as five data points (2000, 2005, 2010, 2015, 2020)." } ], "id_raw": 133 }, { "id": "bDckuR9kdiyrdQGd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis of this chart?" }, { "from": "gpt", "value": "The label is {Metric Values}[0.019, 0.341, 0.043, 0.558]." } ], "id_raw": 134 }, { "id": "FfrCdzVyVIAHw0af", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the Population Growth line}[0.103, 0.144, 0.950, 0.546]?" }, { "from": "gpt", "value": "The color of the Population Growth line is #ff7f0e." } ], "id_raw": 134 }, { "id": "C7n2XUZLvqO0NXMu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the {text label} at [0.744, 0.934, 0.910, 0.964] in the legend represent?" }, { "from": "gpt", "value": "\"Climate Change\"." } ], "id_raw": 134 }, { "id": "VzX2ozC3mIHv9C2V", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Year 2015}[0.574, 0.739] from the 'Climate Change' line higher or lower than {the point for same Year}[0.574, 0.257] from the 'Population Growth' line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 134 }, { "id": "9lOqtbvqJVhy0tzu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Year 2016}[0.668, 0.747] from the 'Climate Change' line higher or lower than {the point for Year 2017}[0.762, 0.753] on the same line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 134 }, { "id": "Pr1A0ar5y1ueAsfp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_14.png", "conversations": [ { "from": "human", "value": "\nWhich metric sees a more drastic fall from {Year 2011}[0.197, 0.724] to {Year 2012}[0.291, 0.729], 'Desertification' or 'Climate Change'?" }, { "from": "gpt", "value": "Desertification." } ], "id_raw": 134 }, { "id": "wpYCm4C5NM0UQGgv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.135, 0.923, 0.918, 0.975]." }, { "from": "gpt", "value": "This area features the legend of the chart, listing three data series: {Desertification} represented by a line color of #1f77b4, {Population Growth} represented by a line color of #ff7f0e, and {Climate Change}[0.744, 0.934, 0.910, 0.964] represented by a line color of #2ca02c." } ], "id_raw": 134 }, { "id": "duZ8IXdaufHPkIyY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.543, 0.855, 0.605, 0.886]." }, { "from": "gpt", "value": "The area contains a x-axis tick for the {year 2015}[0.543, 0.855, 0.605, 0.886]." } ], "id_raw": 134 }, { "id": "IheHnLGuLC98hHmt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.390, 0.941, 0.442, 0.955]?" }, { "from": "gpt", "value": "It contains the color marker of the legend for 'Population Growth', with a representation color of #ff7f0e." } ], "id_raw": 134 }, { "id": "IQxYqer94YLHAAS3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of {the line chart object with its points coordinates from \"[0.103, 0.163]\" to \"[0.974, 0.800]\"}[0.103, 0.163, 0.974, 0.800]?" }, { "from": "gpt", "value": "ANN." } ], "id_raw": 135 }, { "id": "wtRh8ilHgthLNloo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third tick label on the x-axis?" }, { "from": "gpt", "value": "[0.385, 0.855, 0.401, 0.886]" } ], "id_raw": 135 }, { "id": "7lPvjL9SeyhMjgQu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the color of the line representing the SVM model}[0.435, 0.941, 0.487, 0.955]?" }, { "from": "gpt", "value": "#ff9500." } ], "id_raw": 135 }, { "id": "aO0HUSlKd8DtJzbd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_15.png", "conversations": [ { "from": "human", "value": "\nWhich model {line_1}[0.103, 0.099, 0.974, 0.375] or {line_0}[0.103, 0.163, 0.974, 0.800] performs better on the 7th dataset?" }, { "from": "gpt", "value": "SVM" } ], "id_raw": 135 }, { "id": "JZaCVZuAq8GLdlaL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_15.png", "conversations": [ { "from": "human", "value": "\nIs {the SVM model}[0.393, 0.163] performance better or worse than {the ANN model}[0.393, 0.375] on the third dataset?" }, { "from": "gpt", "value": "Better." } ], "id_raw": 135 }, { "id": "JEFcoTYKO0GTQYQs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_15.png", "conversations": [ { "from": "human", "value": "\nComparing {the Decision Tree model}[0.103, 0.481, 0.974, 0.694] and {the ANN model}[0.103, 0.163, 0.974, 0.800], which one has a higher accuracy on the first dataset?" }, { "from": "gpt", "value": "ANN." } ], "id_raw": 135 }, { "id": "bNh1INV6aKUMCbOa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.286, 0.923, 0.790, 0.975]." }, { "from": "gpt", "value": "This region contains the legend explaining the models, including {ANN}[0.353, 0.934, 0.398, 0.964] represented by a line in blue, {SVM}[0.498, 0.934, 0.543, 0.964] represented by a line in orange, and {Decision Tree}[0.643, 0.934, 0.782, 0.964] represented by a line in green." } ], "id_raw": 135 }, { "id": "RBzkAo881tTMdtZB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.049, 0.150, 0.088, 0.182]." }, { "from": "gpt", "value": "The area contains the label of the y-axis tick, which is \"0.9\"." } ], "id_raw": 135 }, { "id": "dqqn4sRLEpP9fS8L", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_15.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.530, 0.855, 0.546, 0.886]?" }, { "from": "gpt", "value": "The number \"4\" which refers to the 4th dataset on the x-axis is located at this coordinate." } ], "id_raw": 135 }, { "id": "W0UJF1KqSc4A7QSi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the object found at the coordinates [0.019, 0.404, 0.043, 0.495]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 136 }, { "id": "fkXKXePb47ayBrl5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the text representing the 'R&D Funding Rate'?" }, { "from": "gpt", "value": "[0.751, 0.934, 0.940, 0.964]" } ], "id_raw": 136 }, { "id": "Y94aaAMRZUmEah3X", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_16.png", "conversations": [ { "from": "human", "value": "\nWhat do the {coordinates}[0.872, 0.851, 0.933, 0.883] refer to in the chart?" }, { "from": "gpt", "value": "X-axis label for the year 2020." } ], "id_raw": 136 }, { "id": "88TaP68QQhYaxWRa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_16.png", "conversations": [ { "from": "human", "value": "\nIs {the Startup Rate line}[[0.139, 0.321, 0.902, 0.591]] generally above or below {the R&D Funding Rate line}[[0.139, 0.269, 0.902, 0.604]]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 136 }, { "id": "m25og96BzPwL4VUo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_16.png", "conversations": [ { "from": "human", "value": "\nFollowed the given coordinates, is {the point for the year '2018'}[0.711, 0.385] on the Startup Rate line above or below {the point for the year '2018'}[0.711, 0.205] on the Investment Funding Rate line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 136 }, { "id": "Myr1Pf3EwlE3d1Fu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_16.png", "conversations": [ { "from": "human", "value": "\nWhich rate had the higher value in the year 2018, Startup Rate or R&D Funding Rate?" }, { "from": "gpt", "value": "R&D Funding Rate." } ], "id_raw": 136 }, { "id": "l5rJ0MLbAt83Zb4R", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in the area [0.202, 0.025, 0.839, 0.057]?" }, { "from": "gpt", "value": "This is the area where the subtitle of the chart, 'Entrepreneurship and Innovation Ecosystem Metrics', is located." } ], "id_raw": 136 }, { "id": "hwE5V8x0lp9dUunh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the area [0.092, 0.923, 0.949, 0.975]." }, { "from": "gpt", "value": "This area contains the chart legend including the labels: {Startup Rate}[0.159, 0.934, 0.290, 0.964], {Investment Funding Rate}[0.390, 0.934, 0.651, 0.964], and {R&D Funding Rate}[0.751, 0.934, 0.940, 0.964]. Each label has a corresponding color marker." } ], "id_raw": 136 }, { "id": "sJxydWJWQPzG64sH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in [0.488, 0.892, 0.553, 0.924]?" }, { "from": "gpt", "value": "The label for the x-axis, specifically \"Years\", is located in this area." } ], "id_raw": 136 }, { "id": "ohlXzD9ZpmM93GNg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart type indicated in the JSON structured information?" }, { "from": "gpt", "value": "line" } ], "id_raw": 137 }, { "id": "hEW3B2ye154AbFs3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_17.png", "conversations": [ { "from": "human", "value": "\nWhat object is positioned at [0.054, 0.906, 0.089, 0.935]?" }, { "from": "gpt", "value": "y-axis tick label." } ], "id_raw": 137 }, { "id": "pQkAAfknxQoT6e5p", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label represent at coordinates [0.023, 0.286, 0.045, 0.624]?" }, { "from": "gpt", "value": "Gaming Users (Billions)" } ], "id_raw": 137 }, { "id": "6uGGOiSFiffbhj62", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_17.png", "conversations": [ { "from": "human", "value": "\nIs the {Gaming Revenue point in 2019}[0.735, 0.397] higher or lower than the {Gaming Revenue point in 2018}[0.616, 0.459]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 137 }, { "id": "DYXTmYsKiMrB3WIO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_17.png", "conversations": [ { "from": "human", "value": "\nDoes the {Gaming Users graph} [0.139, 0.070, 0.854, 0.840] show an increase or decrease in gaming users from 2014 to 2020?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 137 }, { "id": "GCKBpTJ6vWyCwZOE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_17.png", "conversations": [ { "from": "human", "value": "\nWhich is higher in the year 2016 - {Gaming Users}[0.378, 0.647] or {Gaming Revenue}[0.378, 0.631]?" }, { "from": "gpt", "value": "Gaming Users" } ], "id_raw": 137 }, { "id": "AgO4czQLt9ukFk4a", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the content in area [0.112, 0.899, 0.167, 0.928] represent?" }, { "from": "gpt", "value": "The x-axis tick label '2014' is situated here." } ], "id_raw": 137 }, { "id": "P47mTQUgzM1L98Ky", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements in the region [0.104, 0.31, 0.889, 0.879]?" }, { "from": "gpt", "value": "This region contains the chart's plot area with data points, lines representing Gaming Users and Gaming Revenues over the years 2014 to 2020 alongside the x-axis labels and grid lines." } ], "id_raw": 137 }, { "id": "ipoovLiN2S1YQHLl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.955, 0.294, 0.977, 0.616] include?" }, { "from": "gpt", "value": "The y-axis label 'Revenue (in billions $)' is located here." } ], "id_raw": 137 }, { "id": "bYA47l9VbP5O97lY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the {ylabel}[0.015, 0.261, 0.034, 0.637] in the chart?" }, { "from": "gpt", "value": "Number of participants." } ], "id_raw": 138 }, { "id": "pFDgtJn9tEAA1yx7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_18.png", "conversations": [ { "from": "human", "value": "\nWhich {year}[0.273, 0.849, 0.315, 0.879] is found on the x-axis label?" }, { "from": "gpt", "value": "2014." } ], "id_raw": 138 }, { "id": "cvS4Fw9M5fv2hqCg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_7_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the {color of the line}[0.593, 0.945, 0.629, 0.959] representing 'Sports' in the legend?" }, { "from": "gpt", "value": "#00b945." } ], "id_raw": 138 }, { "id": "moF7vXGatZZlx5a4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_18.png", "conversations": [ { "from": "human", "value": "\nWhich line is above at year {2018}[0.741, 0.622, 0.741, 0.142]: 'Routine', 'Sports', or 'Workout'?" }, { "from": "gpt", "value": "Sports." } ], "id_raw": 138 }, { "id": "Otwz6vpKDhkuYjfO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_18.png", "conversations": [ { "from": "human", "value": "\nIn the year {2016}[0.517, 0.660, 0.517, 0.181], was the 'Workout' trend higher or lower than the 'Routine' trend?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 138 }, { "id": "nPF7chP6pBhsaAao", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_7_18.png", "conversations": [ { "from": "human", "value": "\nIs the {value of 'Sports'}[0.294, 0.219] at 2014 higher or lower than the {value of 'Workout'}[0.294, 0.698]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 138 }, { "id": "si9UqW7alZrMAGqv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects in the region [0.348, 0.933, 0.686, 0.974]?" }, { "from": "gpt", "value": "This region contains the legend of the chart. It lists 'Workout', 'Routine', and 'Sports' labeled respectively in colors #0c5da5, #ff9500, and #00b945." } ], "id_raw": 138 }, { "id": "oKHU93DEAMwYUsAU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_18.png", "conversations": [ { "from": "human", "value": "\nCan you detail the contents within [0.040, 0.533, 0.061, 0.563]?" }, { "from": "gpt", "value": "The area contains a y-axis tick label, specifically the '50' mark." } ], "id_raw": 138 }, { "id": "WWDvlF7R4wuUqKyJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_7_18.png", "conversations": [ { "from": "human", "value": "\nDescribe what is found in the region [0.161, 0.849, 0.203, 0.879]." }, { "from": "gpt", "value": "The region contains an x-axis tick, particularly marking the year '2013'." } ], "id_raw": 138 }, { "id": "5zKkIBfgewQelBcd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object found at coordinates [0.121, 0.863]?" }, { "from": "gpt", "value": "Point on the 'Population Growth' line." } ], "id_raw": 140 }, { "id": "9tBANSTNXrdAWiEY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object positioned at coordinates [0.514, 0.952, 0.550, 0.975]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 140 }, { "id": "Ay0tlKMkMeCEC9nk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.131, 0.116, 0.231, 0.139] represent?" }, { "from": "gpt", "value": "The label 'Sea Level Rise' in the legend." } ], "id_raw": 140 }, { "id": "x9Vf4oQUxtbOTTnh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_0.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {the point on the 'Population Growth' line}[0.807, 0.862] or {the point on the 'Sea Level Rise' line}[0.807, 0.766] for the year 2015?" }, { "from": "gpt", "value": "The 'Population Growth' point." } ], "id_raw": 140 }, { "id": "8vZyfeIqTw6NwzOi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_0.png", "conversations": [ { "from": "human", "value": "\nIn terms of line placement on the chart, is {the 'CO2 Emission' line}[0.121, 0.065, 0.944, 0.351] above or below {the 'Sea Level Rise' line}[0.121, 0.763, 0.944, 0.776]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 140 }, { "id": "z9QuwEQuFntObfh2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point on the 'CO2 Emission' line} at [0.944, 0.065] higher or lower than {the point on the 'Population Growth' line} at [0.944, 0.862] for the year 2020?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 140 }, { "id": "NMKisPt4Xa865ay4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content located within the coordinates [0.032, -0.030, 0.070, -0.007]." }, { "from": "gpt", "value": "This region contains a y-axis tick label '3500'." } ], "id_raw": 140 }, { "id": "4WdnTSxOBPericMk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_0.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the region [0.086, 0.037, 0.264, 0.148]?" }, { "from": "gpt", "value": "The sublegend of the chart, including 'Population Growth', 'CO2 Emission', and 'Sea Level Rise' is located in this region with respective colored markers." } ], "id_raw": 140 }, { "id": "9rvYxtdIv8ODAyZf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_0.png", "conversations": [ { "from": "human", "value": "\nCould you describe the region [0.789, 0.919, 0.824, 0.942]?" }, { "from": "gpt", "value": "This area contains the x-axis tick label '2015'." } ], "id_raw": 140 }, { "id": "nfIkzPzTYKhHR0lA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_1.png", "conversations": [ { "from": "human", "value": "\nWhat text is located at the {coordinates} [0.987, 0.884, 0.971, 0.914]?" }, { "from": "gpt", "value": "The label 'Period 5'." } ], "id_raw": 141 }, { "id": "ZOEM2tyOzYNmUpbf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the {coordinates} [0.101, 0.180]?" }, { "from": "gpt", "value": "A point from the Series 3." } ], "id_raw": 141 }, { "id": "nDt8RtbkNLLaSydI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_1.png", "conversations": [ { "from": "human", "value": "\nWhat object is at the coordinates [0.532, 0.300, 0.576, 0.360] in the chart?" }, { "from": "gpt", "value": "The y-axis label '1.0'." } ], "id_raw": 141 }, { "id": "OjYyNWAcPXpP4cx2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_1.png", "conversations": [ { "from": "human", "value": "\nIs the {Series 1 point} at [0.337, 0.404, 0.340, 0.468] higher or lower than the {Series 2 point} at [0.337, 0.468, 0.340, 0.532]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 141 }, { "id": "BvAEqrdKOwieYQI0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_1.png", "conversations": [ { "from": "human", "value": "\nWhich Series has a higher value at {Period3} [0.534, 0.092, 0.556, 0.465], Series 1 or Series 2?" }, { "from": "gpt", "value": "Series 1." } ], "id_raw": 141 }, { "id": "4e94Et92aeo0bdF7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_1.png", "conversations": [ { "from": "human", "value": "\nIn the chart, is the {Series 1 point} at [0.534, 0.084, 0.537, 0.116] above or below the {Series 3 point} at [0.534, 0.301, 0.537, 0.404]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 141 }, { "id": "3l3clNOJpmIrbZtL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the area [0.301, 0.879, 0.578, 0.914] in the chart?" }, { "from": "gpt", "value": "The area contains x-axis labels for Period 3 and 4 which are located at {Period 3}[0.490, 0.879, 0.577, 0.914] and {Period 4}[0.687, 0.879, 0.774, 0.914] respectively." } ], "id_raw": 141 }, { "id": "p8AoUCaCfQR7NHxw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the area [0.101, 0.116, 0.579, 0.737] on the chart." }, { "from": "gpt", "value": "This region contains one point each from Series 1, Series 2, and Series 3 for Period 3. These are {Series 1 Point}[0.534, 0.092, 0.537, 0.116], {Series 2 Point}[0.534, 0.214, 0.537, 0.237], {Series 3 Point}[0.534, 0.301, 0.537, 0.404]." } ], "id_raw": 141 }, { "id": "akEqkRZnumTCfXqb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_1.png", "conversations": [ { "from": "human", "value": "\nCould you shed light on the region in the chart that contains coordinates [0.700, 0.884, 0.971, 0.914]?" }, { "from": "gpt", "value": "The region contains x-axis labels for Period 4 and 5 which are coincidently at {Period 4}[0.687, 0.879, 0.774, 0.914] and {Period 5}[0.884, 0.879, 0.971, 0.914] respectively." } ], "id_raw": 141 }, { "id": "LNYoOiIPd4xFZTna", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_2.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is represented in the figure?" }, { "from": "gpt", "value": "Line chart." } ], "id_raw": 142 }, { "id": "5uhWZ2cFQYIchiB7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_2.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at the coordinates [0.485, 0.927, 0.569, 0.963]?" }, { "from": "gpt", "value": "The x-axis label which is 'Months'." } ], "id_raw": 142 }, { "id": "WBlfCgPL6GLxV1TJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-coordinate of the point}[0.268, 0.782] for the Moisture line?" }, { "from": "gpt", "value": "0.72." } ], "id_raw": 142 }, { "id": "SdZipc9VyEzkoI1U", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_2.png", "conversations": [ { "from": "human", "value": "\nIn which month does {the 'Temperature' point}[0.638, 0.078] have a higher value than {the 'Moisture' point}[0.638, 0.784]?" }, { "from": "gpt", "value": "August." } ], "id_raw": 142 }, { "id": "K6m3L58z7W9Paril", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_2.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Temperature' data point in the 6th month}[0.490, 0.093] higher or lower than {the 'Air Quality' data point in the 6th month}[0.490, 0.708]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 142 }, { "id": "6VyAQLm9zoevgPx8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_2.png", "conversations": [ { "from": "human", "value": "\nCompared to {the 'Moisture' point in the 8th month}[0.638, 0.784], is {the 'Temperature' point in the 8th month}[0.638, 0.078] higher or lower?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 142 }, { "id": "sXmk3MDWET0UOl9d", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.772, 0.879, 0.801, 0.914]." }, { "from": "gpt", "value": "The x-axis tick label '10' is located here." } ], "id_raw": 142 }, { "id": "xjMVMuEygROmOEpC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.120, 0.073, 0.934, 0.180]." }, { "from": "gpt", "value": "This is the line representing 'Temperature'. It shows 12 data points ranging from {the beginning point}[0.120, 0.107] to {the end point}[0.934, 0.180]." } ], "id_raw": 142 }, { "id": "qnex9vGb5wE3rXf6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are found within the region [0.268, 0.782, 0.342, 0.784]?" }, { "from": "gpt", "value": "The region contains two points from the 'Moisture' line; corresponding to {the point in the 3rd month}[0.268, 0.782] and {the point in the 4th month}[0.342, 0.782]." } ], "id_raw": 142 }, { "id": "gDo6oIdLjyIABeXA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the textual content at coordinates [0.117, 0.083, 0.156, 0.106]?" }, { "from": "gpt", "value": "Emo." } ], "id_raw": 143 }, { "id": "0v3P2mh88Lq8TLcF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_3.png", "conversations": [ { "from": "human", "value": "\nWhat color is the line that represents {the label 'Emo'}[0.095, 0.302, 0.939, 0.843]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 143 }, { "id": "AP9a42NIdS5EAMMG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the x-axis in the chart?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 143 }, { "id": "FDGAQulPw1HD7GGg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_3.png", "conversations": [ { "from": "human", "value": "\nWhich trend starts at a higher value in the year 2000, {Emo trend}[0.095, 0.302] or {Punk trend}[0.095, 0.422]?" }, { "from": "gpt", "value": "Emo." } ], "id_raw": 143 }, { "id": "5GVGFKkzCkNKBBjg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the Emo trend}[0.095, 0.302, 0.939, 0.843] end at a higher value in 2020 compared to {the Punk trend}[0.095, 0.182, 0.939, 0.543]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 143 }, { "id": "vFr5YuarzUlGQs4C", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_3.png", "conversations": [ { "from": "human", "value": "\nComparing Emo and Gothic subcultures, which one had more followers in the year 2005? Is it {Emo}[0.306, 0.483] or {Gothic}[0.306, 0.302]?" }, { "from": "gpt", "value": "Gothic." } ], "id_raw": 143 }, { "id": "OPeClwAeUmL5ixFZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the region [0.062, 0.074, 0.180, 0.185]?" }, { "from": "gpt", "value": "This area contains the chart legend. Each label corresponds to a different trend line: {Emo}[0.117, 0.083, 0.156, 0.106] is associated with a blue line, {Punk}[0.117, 0.118, 0.159, 0.141] with a red line, and {Gothic}[0.117, 0.153, 0.173, 0.176] with an orange line." } ], "id_raw": 143 }, { "id": "K2oqQyl2cEqVQGzY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_3.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the region [0.095, 0.302, 0.306, 0.483]?" }, { "from": "gpt", "value": "This region includes the beginning portion of the Emo trend line, with data points representing the trend ratings for the years 2000 and 2005." } ], "id_raw": 143 }, { "id": "KDpIWc8uVpQnuODd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region specified by the coordinates [0.917, 0.919, 0.939, 0.843]." }, { "from": "gpt", "value": "This region contains the final data points of the Emo, Punk, and Gothic trend lines for the year 2020." } ], "id_raw": 143 }, { "id": "Kby6Wis5NY0uW0Ch", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the line with coordinates [0.141, 0.069, 0.935, 0.562] represent in the chart?" }, { "from": "gpt", "value": "Baseline Improvement." } ], "id_raw": 144 }, { "id": "pkPnqWazwLceW0Pp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the value on the y-axis at [0.025, 0.591, 0.062, 0.626]?" }, { "from": "gpt", "value": "1.0." } ], "id_raw": 144 }, { "id": "a40ok2ztaEzlZv46", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_4.png", "conversations": [ { "from": "human", "value": "\nIdentify the label of the x-axis present at [0.429, 0.927, 0.647, 0.963]." }, { "from": "gpt", "value": "Innovation Process." } ], "id_raw": 144 }, { "id": "4527TvHgmyIPchaO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_4.png", "conversations": [ { "from": "human", "value": "\nIs the {y-axis value represented at [0.025, 0.270, 0.062, 0.305]} higher or lower than the {y-axis value represented at [0.025, 0.162, 0.062, 0.197]}?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 144 }, { "id": "Kehuv0HzbZTKFVKv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_4.png", "conversations": [ { "from": "human", "value": "\nAt which marker point {of the 'Baseline Improvement' line}[0.141, 0.069, 0.935, 0.562] is the value higher: {at 40}[0.175, 0.433] or {at 90}[0.348, 0.476]?" }, { "from": "gpt", "value": "40." } ], "id_raw": 144 }, { "id": "PzctvBe5YlUxw7RW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_4.png", "conversations": [ { "from": "human", "value": "\nDoes the value of 'Baseline Improvement' {at 220}[0.797, 0.155] exceed the {y-axis value represented at [0.025, 0.162, 0.062, 0.197]}?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 144 }, { "id": "REP2247585Oo5VkQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the y-axis section from [0.025, 0.591, 0.062, 0.197]." }, { "from": "gpt", "value": "This section displays four labels on the y-axis with values '1.0', '1.5', '2.0', '2.5' and '3.0' from top to bottom." } ], "id_raw": 144 }, { "id": "6deWxx1yydqZmmLN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.775, 0.879, 0.957, 0.914]." }, { "from": "gpt", "value": "This region includes two x-axis labels: '220' and '260'." } ], "id_raw": 144 }, { "id": "0bKmoDwPAIfjcUzW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_4.png", "conversations": [ { "from": "human", "value": "\nWhat details are contained within [0.161, 0.879, 0.190, 0.914]?" }, { "from": "gpt", "value": "This area displays the '40' label on the x-axis." } ], "id_raw": 144 }, { "id": "hheorsB6ce6SaAtc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the object located at [0.172, 0.107, 0.937, 0.547]?" }, { "from": "gpt", "value": "Urban." } ], "id_raw": 146 }, { "id": "OZ9TpYbQFTUsL1Qh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-coordinate for the tick label '1990'?" }, { "from": "gpt", "value": "[0.143, 0.879, 0.202, 0.914]." } ], "id_raw": 146 }, { "id": "1v9mHrAZu770AUjE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the line represented by {coordinates} [0.172, 0.387, 0.937, 0.707] signify?" }, { "from": "gpt", "value": "Rural." } ], "id_raw": 146 }, { "id": "CCekwrjeuNlqXJua", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_6.png", "conversations": [ { "from": "human", "value": "\nIs the {Urban development line}[0.172, 0.107, 0.937, 0.547] generally above or below the {Rural development line}[0.172, 0.387, 0.937, 0.707]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 146 }, { "id": "0do4JCUO35tJahgH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_6.png", "conversations": [ { "from": "human", "value": "\nFor the {x-tick} labelled '2020', what is higher - {Urban}[0.746, 0.267] or {Rural}[0.746, 0.707]?" }, { "from": "gpt", "value": "Urban." } ], "id_raw": 146 }, { "id": "tinNvBdCMw7fvgRz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_6.png", "conversations": [ { "from": "human", "value": "\nDoes the {Rural development line}[0.172, 0.387, 0.937, 0.707] start at a higher or lower point than {Urban development line}[0.172, 0.107, 0.937, 0.547]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 146 }, { "id": "jMwqtQz3unDCBmHK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_6.png", "conversations": [ { "from": "human", "value": "\nGive a description of the region [0.145, 0.055, 0.299, 0.170]." }, { "from": "gpt", "value": "It contains the legend for the graph, with labels for {Urban}[0.219, 0.069, 0.290, 0.104] and {Rural}[0.219, 0.121, 0.279, 0.156] along with their corresponding color markers." } ], "id_raw": 146 }, { "id": "wolxBTS5uNIqNYJb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_6.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.524, 0.927, 0.585, 0.963]?" }, { "from": "gpt", "value": "It represents the label for the x-axis, which is labeled as 'Years'." } ], "id_raw": 146 }, { "id": "KZb1cJwGApJ9gIQ3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is in the coordinates [0.025, 0.312, 0.048, 0.545]." }, { "from": "gpt", "value": "The y-axis label, which is 'Development', is located here in this region." } ], "id_raw": 146 }, { "id": "kaf7lHJWDuE20YgO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_7.png", "conversations": [ { "from": "human", "value": "\nWhich text is located at [0.507, 0.942, 0.544, 0.970] in the chart figure?" }, { "from": "gpt", "value": "Year" } ], "id_raw": 147 }, { "id": "oCBNL6S2aM4a4TZb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_7.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what's the object at [0.126, 0.154, 0.170]?" }, { "from": "gpt", "value": "It is the color marker for 'Supply Chain Costs' in the legend." } ], "id_raw": 147 }, { "id": "13QueNuPhhSa8jx6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the object located at [0.664, 0.563] on the chart?" }, { "from": "gpt", "value": "The point represents the 'Intermodal Volumes' for the year 2015." } ], "id_raw": 147 }, { "id": "liRVfOTuxpq0YdIK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_7.png", "conversations": [ { "from": "human", "value": "\nComparing the points at [0.526, 0.714] and [0.572, 0.687], which has a higher value for 'Truckload Tonnage'?" }, { "from": "gpt", "value": "2013" } ], "id_raw": 147 }, { "id": "Prb4P53hhQaQIoM9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_7.png", "conversations": [ { "from": "human", "value": "\nIs the 'Supply Chain Costs' at [0.848, 0.134] higher or lower than its value at [0.756, 0.180]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 147 }, { "id": "L2Fv8GbPr2VmGXeH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_7.png", "conversations": [ { "from": "human", "value": "\nWhich is higher in the year 2011, 'Truckload Tonnage' at [0.480, 0.726] or 'Intermodal Volumes' at [0.480, 0.621]?" }, { "from": "gpt", "value": "Truckload Tonnage" } ], "id_raw": 147 }, { "id": "YMbr6KdbpjirqCp3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements present in the area [0.079, 0.055, 0.315, 0.189]?" }, { "from": "gpt", "value": "This area consists of the chart's legend. There are 3 items: 'Truckload Tonnage'[0.135, 0.066, 0.296, 0.094], 'Intermodal Volumes'[0.135, 0.108, 0.308, 0.136], and 'Supply Chain Costs'[0.135, 0.150, 0.302, 0.178], each associated a colored marker." } ], "id_raw": 147 }, { "id": "j45nB1mJ4jokhqQK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the chart within the region [0.112, 0.598, 0.940, 0.819]?" }, { "from": "gpt", "value": "The line plot representing 'Truckload Tonnage' from the year 2003 to 2021 is located here, with specific points denoting the value for each year." } ], "id_raw": 147 }, { "id": "Kwwa0hK7o3sXmM8l", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the region [0.019, 0.494, 0.041, 0.522]?" }, { "from": "gpt", "value": "It contains the y-axis label '20'." } ], "id_raw": 147 }, { "id": "vmAoQEg833Aydt4s", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart refer to with the coordinates [0.025, 0.169, 0.048, 0.688]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 148 }, { "id": "T5hOB8fYpE2uw4vI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fourth text in the x-axis?" }, { "from": "gpt", "value": "[0.473, 0.879, 0.667, 0.914]." } ], "id_raw": 148 }, { "id": "1kqS3MAAIuVfPBmF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.631, 0.879, 0.781, 0.914]?" }, { "from": "gpt", "value": "Life Sciences." } ], "id_raw": 148 }, { "id": "SmGe8aE66w5erfym", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the point for 'Biotech'}[0.297, 0.455] on the line, fall above or below {the point for 'Life Sciences'}[0.706, 0.332]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 148 }, { "id": "GWyDYmJ1IwO7VJ0B", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_8.png", "conversations": [ { "from": "human", "value": "\nAre the values for {Industry trends from 'Medical Services'}[0.570, 0.414] higher or lower than for {HealthCare Equipment}[0.433, 0.616]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 148 }, { "id": "x8YsKx5gAXmKnKiz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_8.png", "conversations": [ { "from": "human", "value": "\nWhich Industry trend has a lower value at the point, Life Sciences or HealthCare Technology?" }, { "from": "gpt", "value": "Life Sciences." } ], "id_raw": 148 }, { "id": "g2CzTtQksUkHDNhd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in [0.413, 0.927, 0.590, 0.963]?" }, { "from": "gpt", "value": "The region contains the chart's x-label, which is \"Industry Trends\", which is in black color." } ], "id_raw": 148 }, { "id": "zJH9u4c3DwyzLEOG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the section [0.302, 0.879, 0.565, 0.914]." }, { "from": "gpt", "value": "The area consists of the x-axis tick label for the industry trend {HealthCare Equipment}[0.302, 0.879, 0.565, 0.914]." } ], "id_raw": 148 }, { "id": "iElFNmiSEYK1jtea", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found within [0.160, 0.095, 0.843, 0.616]." }, { "from": "gpt", "value": "This region contains the plotted data for the Industry Trends. The data includes the points for {Pharmaceuticals}[0.160, 0.580], {Biotech}[0.297, 0.455], {HealthCare Equipment}[0.433, 0.616], {Medical Services}[0.570, 0.414], {Life Sciences}[0.706, 0.332], and {HealthCare Technology}[0.843, 0.095] connected via a line." } ], "id_raw": 148 }, { "id": "E9eScE4MsHOWrqS0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_9.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted by the object with coordinates [0.322, 0.879, 0.351, 0.914]?" }, { "from": "gpt", "value": "The x-axis label, '50'." } ], "id_raw": 149 }, { "id": "W3StnkDzTDkkHBmm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_9.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinates [0.935, 0.485] of the chart?" }, { "from": "gpt", "value": "A point on Line 3." } ], "id_raw": 149 }, { "id": "qbOdTfTr3hmgOmPK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.025, 0.130, 0.062, 0.165]?" }, { "from": "gpt", "value": "1.8." } ], "id_raw": 149 }, { "id": "LMi9b7AIO3mCnhlO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_9.png", "conversations": [ { "from": "human", "value": "\nDoes {Line 1}[0.137, 0.295, 0.935, 0.561] or {Line 2}[0.137, 0.144, 0.935, 0.561] have a lower value at the 200th Gradient Step?" }, { "from": "gpt", "value": "Line 1." } ], "id_raw": 149 }, { "id": "10AnLrC5RIHGpmEx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_9.png", "conversations": [ { "from": "human", "value": "\nIs {the y-value of Line 3}[0.536, 0.182] higher or lower than {the y-value of Line 1}[0.536, 0.409] in the 100th Gradient Step?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 149 }, { "id": "Gm7oqQ2mPMnPuFVd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_9.png", "conversations": [ { "from": "human", "value": "\nAt {y=1}[0.097, 0.447], does Line 3 intersect the y-axis before or after {Line 1}[0.137, 0.371]?" }, { "from": "gpt", "value": "After." } ], "id_raw": 149 }, { "id": "VBL61SoRFGjQkZSK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.025, 0.130, 0.062, 0.165]." }, { "from": "gpt", "value": "This area contains the y-axis tick label '1.8', represented in black color." } ], "id_raw": 149 }, { "id": "ydkd0KL8QPGwAxFL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.025, 0.433, 0.062, 0.468]." }, { "from": "gpt", "value": "The region contains the y-axis tick label '1.0', which is expressed in black color." } ], "id_raw": 149 }, { "id": "2hmklOkvhLOEFQAL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.137, 0.144, 0.935, 0.561]?" }, { "from": "gpt", "value": "This region contains three objects, {Line 1}[0.137, 0.295, 0.935, 0.561], {Line 2}[0.137, 0.144, 0.935, 0.561], and {Line 3}[0.137, 0.182, 0.935, 0.485], which represent different trends in the chart." } ], "id_raw": 149 }, { "id": "l8gE9upYz6yJc2cY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the xlabel of the chart at coordinates [0.451, 0.952, 0.600, 0.975]?" }, { "from": "gpt", "value": "The label is \"Cultural Heritage\"." } ], "id_raw": 151 }, { "id": "81VcOdqIb3OrLLqQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-value of {the point in the 'Diversity Index' line} at [0.730, 0.337]?" }, { "from": "gpt", "value": "The y-value is 0.6." } ], "id_raw": 151 }, { "id": "rCELMGsBXu2xUof0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_11.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis tick label} at [0.075, 0.919, 0.159, 0.942] represent?" }, { "from": "gpt", "value": "It represents the cultural heritage 'Culture_A'." } ], "id_raw": 151 }, { "id": "P3441pIUJOieHtu8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_11.png", "conversations": [ { "from": "human", "value": "\nAt the point {Culture_A}, is the { 'Diversity Index'}[0.117, 0.560] higher or lower than the {'Integration Index'}[0.117, 0.293]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 151 }, { "id": "7D388Rm7mnEnv073", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_11.png", "conversations": [ { "from": "human", "value": "\nHow does {Culture_B's Integration Index score}[0.321, 0.382] compare to its {Diversity Index score}[0.321, 0.471]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 151 }, { "id": "kEv4QxKatTV1WBlx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_11.png", "conversations": [ { "from": "human", "value": "\nWhich index is higher for {Culture_D}, the {Diversity Index}[0.730, 0.337] or {Outliers}[0.730, 0.738]?" }, { "from": "gpt", "value": "Outliers." } ], "id_raw": 151 }, { "id": "t3jdIE2VwvWKMkzj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_11.png", "conversations": [ { "from": "human", "value": "\nCould you describe the region [0.075, 0.919, 0.976, 0.942]?" }, { "from": "gpt", "value": "This region contains the x-axis labels for the chart. The labels are the names of five different cultural heritages, namely, {Culture_A}[0.075, 0.919, 0.159, 0.942], {Culture_B}[0.279, 0.919, 0.363, 0.942], {Culture_C}[0.483, 0.919, 0.567, 0.942], {Culture_D}[0.687, 0.919, 0.772, 0.942], and {Culture_E}[0.893, 0.919, 0.976, 0.942]." } ], "id_raw": 151 }, { "id": "uGVN9ANoNlDgyK7T", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_11.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents within the region [0.117, 0.071, 0.934, 0.560]?" }, { "from": "gpt", "value": "The region consists of two trend lines, namely, {Diversity Index} line with coordinates [0.117, 0.249, 0.934, 0.560] and {Integration Index} line with coordinates [0.117, 0.071, 0.934, 0.382], each accompanied by a series of data points marking their respective values for various cultural heritages." } ], "id_raw": 151 }, { "id": "wiiSXrcDXXfybBiU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_11.png", "conversations": [ { "from": "human", "value": "\nTell me about the region between coordinates [0.019, 0.862, 0.046, 0.085]." }, { "from": "gpt", "value": "The region primarily contains y-axis labels, specifically, the decimal values from '0.0' to '0.9' at each 10% interval. These labels indicate percentage values across the vertical axis of the chart." } ], "id_raw": 151 }, { "id": "xi6ZylDS0ABOaCkb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis represent in the plot with the label at [0.002, 0.389, 0.025, 0.502]?" }, { "from": "gpt", "value": "The y-axis represents \"Values\"." } ], "id_raw": 152 }, { "id": "5M9Yf6IQFB9RLyn5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the point marker {at [0.935, 0.812]}[0.935, 0.812]?" }, { "from": "gpt", "value": "The color of the point marker is orange, denoting 'Hotel Occupancy Rate'." } ], "id_raw": 152 }, { "id": "AS4FMvoa0G4H9KEm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_12.png", "conversations": [ { "from": "human", "value": "\nWhat value does the last x-axis label indicate at [0.906, 0.913, 0.964, 0.948]?" }, { "from": "gpt", "value": "The value is \"2020\"." } ], "id_raw": 152 }, { "id": "6k4VanjddREq5xef", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_12.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Hotel Occupancy Rate'}[0.129, 0.741, 0.935, 0.812] above or below {the line for 'Restaurant Revenue'}[0.129, 0.083, 0.935, 0.540] in 2017?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 152 }, { "id": "dDKa4RCpIqQ4FfR1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_12.png", "conversations": [ { "from": "human", "value": "\nWhich line ends at the higher point, {the line for 'Hotel Occupancy Rate'}[0.129, 0.741, 0.935, 0.812] or {the line for 'Tourist Arrival Number'}[0.129, 0.688, 0.935, 0.800]?" }, { "from": "gpt", "value": "The line for 'Tourist Arrival Number'." } ], "id_raw": 152 }, { "id": "OG74dG34GLW497yM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_12.png", "conversations": [ { "from": "human", "value": "\nAccording to the 2016 data, is the {Hotel Occupancy Rate}[0.129, 0.741] higher or lower than {Restaurant Revenue}[0.129, 0.083]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 152 }, { "id": "mb7TlAVwUJrvXy9V", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.100, 0.362, 0.433, 0.416]." }, { "from": "gpt", "value": "The area contains the legend labels for 'Hotel Occupancy Rate' and 'Restaurant Revenue', each paired with a point marker colored orange and blue respectively." } ], "id_raw": 152 }, { "id": "nxM8ddhzkwoQVkA2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within the region [0.507, 0.962, 0.556, 0.997]?" }, { "from": "gpt", "value": "The 'Year' label for the x-axis is situated within this region." } ], "id_raw": 152 }, { "id": "iKBqOIC80UFWNqWI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.935, 0.540, 0.935, 0.800]?" }, { "from": "gpt", "value": "The region contains the end points of two lines, 'Restaurant Revenue' at {coordinates}[0.935, 0.540], and 'Tourist Arrival Number' at {coordinates}[0.935, 0.800]." } ], "id_raw": 152 }, { "id": "xlQMogZW54Yag0rS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_13.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.461, 0.952, 0.607, 0.975]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 153 }, { "id": "bDwva7rHlK9zhs6O", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first colored marker in the legend?" }, { "from": "gpt", "value": "[0.832, 0.769, 0.878, 0.783]" } ], "id_raw": 153 }, { "id": "gE3zXhfGcz8GK4ZN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the {y-axis tick label} at [0.019, 0.448, 0.057, 0.471]?" }, { "from": "gpt", "value": "0.85" } ], "id_raw": 153 }, { "id": "0y6rsvreT2UCRPtg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_13.png", "conversations": [ { "from": "human", "value": "\nFrom the behavior of 'Precision' at [0.127, 0.739] and 'Recall' at [0.127, 0.626] during the first processing step, which measure has a higher value?" }, { "from": "gpt", "value": "'Recall'." } ], "id_raw": 153 }, { "id": "3IgwQAEC6QNwBouT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_13.png", "conversations": [ { "from": "human", "value": "\nAt the third step of processing with coordinates [0.534, 0.485] for Precision and [0.534, 0.372] for Recall, which measure experienced a higher increase?" }, { "from": "gpt", "value": "Recall." } ], "id_raw": 153 }, { "id": "TZrlKfobbOv1weU4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_13.png", "conversations": [ { "from": "human", "value": "\nLooking at {the final point of 'Accuracy'}[0.941, 0.203] and {the final point of 'Recall'}[0.941, 0.147], which measure is higher?" }, { "from": "gpt", "value": "Accuracy." } ], "id_raw": 153 }, { "id": "F8Id99upPljFY6Yk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in area [0.831, 0.756, 0.973, 0.868]." }, { "from": "gpt", "value": "This area contains the legend of the chart. It includes 'Accuracy', 'Precision', and 'Recall'. Each label is paired with a different color marker." } ], "id_raw": 153 }, { "id": "8kXB9Q217EGwLVrz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.122, 0.919, 0.157, 0.942]." }, { "from": "gpt", "value": "This region contains the x-axis label '0'." } ], "id_raw": 153 }, { "id": "31YwTAGhYcQxHHx4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the area [0.737, 0.316, 0.941, 0.147]?" }, { "from": "gpt", "value": "Within these coordinates, there are two points on the lines: the final point of {the 'Accuracy' line}[0.941, 0.203] and the final point of {the 'Recall' line}[0.941, 0.147]. These represent the final readings for the two performance measures after 200 processing steps." } ], "id_raw": 153 }, { "id": "NdoOrWMDe6J1Y6ZW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.025, 0.508, 0.054, 0.543]?" }, { "from": "gpt", "value": "50." } ], "id_raw": 154 }, { "id": "B2ndRPXhL9VDVkQZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_14.png", "conversations": [ { "from": "human", "value": "\nCan you identify the color of {the Focus Level line} at [0.334, 0.567]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 154 }, { "id": "v6sTXU4n8kfAORvT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_14.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinate [0.319, 0.879, 0.349, 0.914]?" }, { "from": "gpt", "value": "X-axis label \"50\"." } ], "id_raw": 154 }, { "id": "o4nECKmqWwQu34An", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Anxiety Level at session 200}[0.935, 0.433] positioned above or below {the point for Focus Level at session 200}[0.935, 0.373]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 154 }, { "id": "nTbGaZlcQDf23JOd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_14.png", "conversations": [ { "from": "human", "value": "\nWhich line is to the left on the y-axis, {Anxiety Level or Focus Level}[0.094, 0.225] at session 0?" }, { "from": "gpt", "value": "Anxiety Level." } ], "id_raw": 154 }, { "id": "R2dFPze2seXGQ4Tl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_14.png", "conversations": [ { "from": "human", "value": "\nWhich dataset has a higher final session value, {Anxiety Level}[0.935, 0.433] or {Intrusive Thoughts}[0.935, 0.076]?" }, { "from": "gpt", "value": "Intrusive Thoughts." } ], "id_raw": 154 }, { "id": "CkNWBHGKAnog4yjm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the bounds [0.025, 0.806, 0.054, 0.841]." }, { "from": "gpt", "value": "It contains {the y-axis tick label '30'}[0.025, 0.806, 0.054, 0.841] represented in black." } ], "id_raw": 154 }, { "id": "N3e1Wa6cesr3ViHd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_14.png", "conversations": [ { "from": "human", "value": "\nWhat information does the region [0.312,0.820, 0.556, 0.914] contain?" }, { "from": "gpt", "value": "It includes {x-axis tick label '100'}[0.512, 0.879, 0.556, 0.914] and {tick line for the '100' label}[0.534, 0.820, 0.534, 0.879]." } ], "id_raw": 154 }, { "id": "AeQUZ2MKJtuqfKWi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the area bounded by coordinates [0.134, 0.373, 0.957, 0.914]." }, { "from": "gpt", "value": "This region includes {x-axis labels}[0.126, 0.879, 0.957, 0.914] ranging from '0' to '200' and corresponding {tick lines}[0.820, 0.820] marked across the x-axis." } ], "id_raw": 154 }, { "id": "puBbja5VKsq86j2B", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of the chart with coordinates [0.108, 0.037, 0.975, 0.820]?" }, { "from": "gpt", "value": "Line chart." } ], "id_raw": 155 }, { "id": "2JR1rAq3SJ6yawUS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second text(X-axis tick label) in our chart?" }, { "from": "gpt", "value": "[0.228, 0.879, 0.243, 0.914]" } ], "id_raw": 155 }, { "id": "smjxbwJvqucVLDHi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel} at [0.016, 0.429, 0.016, 0.429]?" }, { "from": "gpt", "value": "The ylabel is empty." } ], "id_raw": 155 }, { "id": "LiEfzMlUw5WFmraa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_15.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '2' on 'Sales 2017'}[0.235, 0.550] above or below {the point labeled '2' on 'Sales 2018'}[0.235, 0.323]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 155 }, { "id": "gqMYQfsCLTD7LC5n", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_15.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Sales 2017'}[[0.148, 0.536, 0.936, 0.706]] generally above or below {the line for 'Sales 2019'}[[0.148, 0.082, 0.936, 0.479]]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 155 }, { "id": "j4Eg69417V1Z9o3F", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_15.png", "conversations": [ { "from": "human", "value": "\nWhich sale has a higher value at {point 7}[0.673, 0.110], Sales 2018 or Sales 2019?" }, { "from": "gpt", "value": "Sales 2019." } ], "id_raw": 155 }, { "id": "BAiFRoUKrTRm7Udl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.477, 0.927, 0.606, 0.963]." }, { "from": "gpt", "value": "This area contains the xlabel of the chart, \"Time Steps,\" in black color." } ], "id_raw": 155 }, { "id": "8CWIbKXQokMBn8zF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.025, 0.096, 0.069, 0.131]." }, { "from": "gpt", "value": "The area contains the y-axis tick label '250' in black color." } ], "id_raw": 155 }, { "id": "Jam2QmUbOcjXtPo4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_15.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.148, 0.536, 0.936, 0.706]?" }, { "from": "gpt", "value": "This area includes all ten data points from the 2017 sales data line chart." } ], "id_raw": 155 }, { "id": "yrDTYDE4tak0IOow", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the main object at {coordinates}[0.395, 0.927, 0.681, 0.963] represent in the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 157 }, { "id": "jXDg7CON7Bi6IT3p", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_17.png", "conversations": [ { "from": "human", "value": "\nCould you tell me what is plotted at the datapoint {coordinates}[0.935, 0.492]?" }, { "from": "gpt", "value": "The point represents the value of Predictive Learning Algorithms at 200 Gradient steps." } ], "id_raw": 157 }, { "id": "Z7L3nXIz1ZikhdC3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at {coordinates}[0.025, 0.406, 0.062, 0.441] correspond to?" }, { "from": "gpt", "value": "It corresponds to the y-axis tick label '1.0'." } ], "id_raw": 157 }, { "id": "RS2rcorhgoSim8bG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_17.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at 50 gradient steps, {EdTech AI Adoption}[0.339, 0.600] or {Predictive Learning Algorithms}[0.339, 0.492]?" }, { "from": "gpt", "value": "EdTech AI Adoption." } ], "id_raw": 157 }, { "id": "pxqNCZRokJDVA1WV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_17.png", "conversations": [ { "from": "human", "value": "\nDoes the {EdTech AI Adoption}[0.141, 0.744] value increase, decrease, or stay the same at 150 Gradient Steps {coordinates}[0.737, 0.347]?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 157 }, { "id": "XS9Ku6SJqQZ63nL0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_17.png", "conversations": [ { "from": "human", "value": "\nIs the {Predictive Learning Algorithms} line[0.141, 0.239, 0.935, 0.492] above or below the {Distance Learning Platforms} line[0.141, 0.130, 0.935, 0.636] in the range from 0 to 200 Gradient Steps?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 157 }, { "id": "shprN0dGKXDu6Pyx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_17.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the area with coordinates [0.101, 0.037, 0.975, 0.820]?" }, { "from": "gpt", "value": "This region is primarily filled with the main chart area consisting of the gridlines, x and y-axis tick labels, and lines representing EdTech AI Adoption, Predictive Learning Algorithms, and Distance Learning Platforms." } ], "id_raw": 157 }, { "id": "nU2MJdrVQonrxVGA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_17.png", "conversations": [ { "from": "human", "value": "\nDescribe what is specified within the region with bounding box coordinates [0.025, 0.044, 0.062, 0.079]." }, { "from": "gpt", "value": "On the y-axis, the tick label '2.0' falls within these coordinates." } ], "id_raw": 157 }, { "id": "cX8iwihjdVgJ8Y8x", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_17.png", "conversations": [ { "from": "human", "value": "\nWhat details are contained in the region [0.133, 0.879, 0.148, 0.914]?" }, { "from": "gpt", "value": "It contains the x-axis tick label '0', marking the Gradient Steps' initial point." } ], "id_raw": 157 }, { "id": "gSQjixEjxlgRclHt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the main graph item whose y-coordinate starts at 0.488?" }, { "from": "gpt", "value": "Interpersonal Relationships." } ], "id_raw": 158 }, { "id": "Z9WsI6N0SSGi34Tu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis value of {the tick label}[0.654, 0.838, 0.698, 0.885]?" }, { "from": "gpt", "value": "15." } ], "id_raw": 158 }, { "id": "Bq24bUBlxzmnw98F", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_18.png", "conversations": [ { "from": "human", "value": "\nWhat does the item with coordinates [0.450, 0.903, 0.654, 0.950] refer to?" }, { "from": "gpt", "value": "X-axis Label." } ], "id_raw": 158 }, { "id": "guc7zmsiRxNZ8edb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_18.png", "conversations": [ { "from": "human", "value": "\nAt the x-axis value \"10\", does {the line 'Social Networks'}[0.178, 0.080, 0.427, 0.420, 0.676, 0.250, 0.925, 0.080] lie above or below {the line 'Interpersonal Relationships'}[0.178, 0.488, 0.427, 0.454, 0.676, 0.556, 0.925, 0.590]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 158 }, { "id": "g5lgi5y34p72BPTO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_18.png", "conversations": [ { "from": "human", "value": "\nIs {the tick label '4'}[0.060, 0.605, 0.082, 0.652] higher or lower than {the tick label '8'}[0.060, 0.469, 0.082, 0.516] on the y-axis?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 158 }, { "id": "Vrnn2GbMdQyDRHXd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_18.png", "conversations": [ { "from": "human", "value": "\nWhich line gets to lower value at x=20, Social Networks or Interpersonal Relationships?" }, { "from": "gpt", "value": "Social Networks." } ], "id_raw": 158 }, { "id": "cgdd7h8GTL8Wk7Xd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_18.png", "conversations": [ { "from": "human", "value": "\nDescribe what is present in the region [0.141, 0.050, 0.963, 0.759]." }, { "from": "gpt", "value": "This region contains the plotting area of the graph. It includes two line charts, one for 'Social Networks' and one for 'Interpersonal Relationships'. The X-axis with labels 5, 10, 15, and 20 and the Y-Axis with labels 0, 4, 8, 12, 16, and 20 are also included in this region. The plotting grid is visible with a light gray color." } ], "id_raw": 158 }, { "id": "CgK7I1oP9xhJYxBK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_18.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects at the region from [0.676, 0.556, 0.925, 0.590]?" }, { "from": "gpt", "value": "The line for the trend of 'Interpersonal Relationships' from the x-axis value of 15 to 20 is present here." } ], "id_raw": 158 }, { "id": "nJN9yxrANetGEcBh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_18.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the region [0.141, 0.352, 0.082, 0.380] understanding from the JSON data?" }, { "from": "gpt", "value": "There is a y-axis tick label '12' located within these coordinates." } ], "id_raw": 158 }, { "id": "JllIAJU1nzUBehTE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the text associated with the y-coordinate located at [0.012, 0.452, 0.012, 0.452]?" }, { "from": "gpt", "value": "None." } ], "id_raw": 159 }, { "id": "VydyU0RYug3hhN5u", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_19.png", "conversations": [ { "from": "human", "value": "\nWhich label does this set of coordinates [0.399, 0.880] correspond to?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 159 }, { "id": "RI5HwrfbHDXBff46", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_8_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the element at {coordinates} [0.625, 0.919, 0.706, 0.942]?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 159 }, { "id": "tYzo2ORQWrVlf6VC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_19.png", "conversations": [ { "from": "human", "value": "\nAre {Facebook's followers}[0.666, 0.193] greater or lesser than {Twitter's followers}[0.399, 0.452]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 159 }, { "id": "rVi4u49Db7N4bTZH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_19.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher engagement rate at x-coordinate 0.399, {Twitter}[0.399, 0.841] or {Website}[0.132, 0.840]?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 159 }, { "id": "lOwUcpAMsRtSWTlo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_8_19.png", "conversations": [ { "from": "human", "value": "\nWhich platform's bounce rate is higher at x={point 0.666}, {Facebook's bounce rate}[0.666, 0.825] or {Instagram's bounce rate}[0.932, 0.831]?" }, { "from": "gpt", "value": "Instagram." } ], "id_raw": 159 }, { "id": "ufq9cuCaMKIwS14f", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_19.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.092, 0.025, 0.972, 0.880]?" }, { "from": "gpt", "value": "This region contains the main plot of the chart. It incorporates graphs for Followers, Engagement rate and Bounce rate for Website, Twitter, Facebook and Instagram." } ], "id_raw": 159 }, { "id": "qC2aBL2EiVzAHyhO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_19.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area [0.888, 0.919, 0.976, 0.942]?" }, { "from": "gpt", "value": "This area has the X-axis category label 'Instagram'." } ], "id_raw": 159 }, { "id": "p8bjcp8lUeLvTc4N", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_8_19.png", "conversations": [ { "from": "human", "value": "\nDescribe what lies between [0.052, 0.832, 0.063, 0.855]." }, { "from": "gpt", "value": "There is the Y-axis label '0' in this area." } ], "id_raw": 159 }, { "id": "SnMB2wQe6ibJyGHd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the x-axis tick} at [0.032, 0.789] in the first subplot?" }, { "from": "gpt", "value": "0." } ], "id_raw": 161 }, { "id": "LNcBj8Z3xTPpqpBj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the {Ethereum subtitle}?" }, { "from": "gpt", "value": "[0.320, 0.037, 0.383, 0.085]." } ], "id_raw": 161 }, { "id": "h8UEoLtn5N1YxznT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_1.png", "conversations": [ { "from": "human", "value": "\nWhat crypto currency's subplot includes {the axes} with the coordinates [0.964, 0.096, 0.983, 0.789]?" }, { "from": "gpt", "value": "Stellar." } ], "id_raw": 161 }, { "id": "RaOK2EL9uFjlSj2D", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_1.png", "conversations": [ { "from": "human", "value": "\nOn the Bitcoin subplot, is {the point on 'pred_length=96'}[0.032, 0.351] above or below {the point on 'pred_length=192'} at [0.035, 0.449]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 161 }, { "id": "gRzz8HD15AVFJQNa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_1.png", "conversations": [ { "from": "human", "value": "\nIn the Ethereum subplot, are {the x-axis ticks}[0.339, 0.813, 0.345, 0.848] further right or left than {the y-axis ticks} at [0.321, 0.775, 0.336, 0.810]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 161 }, { "id": "Un9w5nvYvGeUwo7k", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_1.png", "conversations": [ { "from": "human", "value": "\nIs the {4th point on 'pred_length=336'}[0.049, 0.575] on the Ripple subplot higher or lower than {the 1st point on 'pred_length=720'} at [0.032, 0.758]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 161 }, { "id": "FdzHQjMAt3ZzCRDO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.949, 0.037, 0.994, 0.085]." }, { "from": "gpt", "value": "This area contains the subtitle \"Stellar\" in black font and a no rotation setting." } ], "id_raw": 161 }, { "id": "GDbyfaTWCy26jW80", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.342, 0.096, 0.361, 0.789] in the second subplot." }, { "from": "gpt", "value": "This area includes the axes for the Ethereum subplot in white facecolor." } ], "id_raw": 161 }, { "id": "8Qlt8prKlBqq78Xn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.010, -0.026, 0.025, 0.009]?" }, { "from": "gpt", "value": "There is only one object: the y-axis tick label '0.6' is located in these coordinates." } ], "id_raw": 161 }, { "id": "MZH4oPCzMycQl3ht", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.010, 0.149, 0.023, 0.779]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 164 }, { "id": "EjMFOLB54G6enzMQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third x-axis tick label?" }, { "from": "gpt", "value": "[0.510, 0.857, 0.533, 0.892]" } ], "id_raw": 164 }, { "id": "aEfC5gcj5mUahkwL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_4.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the legend item} at [0.926, 0.889, 0.978, 0.936]?" }, { "from": "gpt", "value": "Over 45 age group." } ], "id_raw": 164 }, { "id": "NIjBTHSeSgMbpTHk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_4.png", "conversations": [ { "from": "human", "value": "\nAccording to the line graph, does {the '25 - 34' age group} at [0.096, 0.760, 0.947, 0.515] have a higher or lower population in 2000 compared to {the 'Under 25' age group} at [0.096, 0.548, 0.947,0.327]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 164 }, { "id": "gCSH1JrteSnbfLYY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point for the year '2020'} on {the 'Under 25' line}[0.096, 0.548, 0.947,0.327] above or below {the point for the same year} on {the 'Over 45' line}[0.096, 0.129, 0.947, 0.539]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 164 }, { "id": "fTA3zhopOBDjfmJk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_4.png", "conversations": [ { "from": "human", "value": "\nWhich age group has a higher population in 2040, {35 - 44}[0.096, 0.242, 0.947, 0.621] or {25 - 34}[0.096, 0.760, 0.947, 0.515]?" }, { "from": "gpt", "value": "25 - 34." } ], "id_raw": 164 }, { "id": "kuaOSbhGcCkSkfIr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within these coordinates [0.584, 0.889, 0.921, 0.936]." }, { "from": "gpt", "value": "It is a part of the chart's legend. The labels 'Under 25' and '25 - 34' reside here, each of them is followed by a colored handle representing their line color on the chart." } ], "id_raw": 164 }, { "id": "H72AVlTuz0o1r81Y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_4.png", "conversations": [ { "from": "human", "value": "\nCould you please describe what is within the area [0.096, 0.242, 0.947, 0.621]?" }, { "from": "gpt", "value": "The area consists of the line graph representing the '35 - 44' age group. The line graph contains markers at fixed intervals representing data points across the years. Their coordinates represent the population for that year." } ], "id_raw": 164 }, { "id": "xmtfcYTxTGHGyXCN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_4.png", "conversations": [ { "from": "human", "value": "\nWhat information lies in the box with [0.084, 0.857, 0.959, 0.892]?" }, { "from": "gpt", "value": "The x-axis label information lies here, showing the different years that include '2000', '2010', '2020', '2030', '2040'." } ], "id_raw": 164 }, { "id": "W11plYQn9spXAfoy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the text that is at the coordinate [0.012, 0.030, 0.072, 0.066] in the chart figure?" }, { "from": "gpt", "value": "\"Transportation\"." } ], "id_raw": 165 }, { "id": "FYFwuEFDRCljIVqQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_5.png", "conversations": [ { "from": "human", "value": "\nCan you identify the element located at [0.007, 0.243, 0.017, 0.628] in the chart?" }, { "from": "gpt", "value": "That is the y-axis label or title which is {Sector Performance}[0.007, 0.243, 0.017, 0.628]." } ], "id_raw": 165 }, { "id": "uOmCo3H6k0Aa5ypr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the value of y-axis tick} at coordinate [0.020, 0.545, 0.035, 0.573]?" }, { "from": "gpt", "value": "The value is 0.14." } ], "id_raw": 165 }, { "id": "29whc922uan4Gm4A", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_5.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {point 1 about Transportation sector performance}[0.040, 0.676] or {point 2 about Transportation sector performance}[0.041, 0.616]?" }, { "from": "gpt", "value": "Point 1 is higher." } ], "id_raw": 165 }, { "id": "w6Tz30bap24YWOh0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_5.png", "conversations": [ { "from": "human", "value": "\nRelative to {the value at time index 512 for the Financials sector}[0.359, 0.075] is the {value at time index 720}[0.360, 0.135] higher or lower?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 165 }, { "id": "pIb7Po1fNEnYDdad", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the relationship between {the value for Information Technology at instance 96}[0.672, 0.435] and {the value for Information Technology at instance 336}[0.674, 0.496]?" }, { "from": "gpt", "value": "The value at instance 336 is lower." } ], "id_raw": 165 }, { "id": "awK0L8QWUfVBiEgJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is found at region coordinates [0.988, 0.255, 0.992, 0.556]." }, { "from": "gpt", "value": "This region encompasses the {line of data points representing the Industrials' sector performance over five time steps}[0.988, 0.255, 0.992, 0.556]." } ], "id_raw": 165 }, { "id": "FAMHRWL1SZ4RkF6J", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the region defined by [0.356, 0.075, 0.361, 0.796]?" }, { "from": "gpt", "value": "The region contains a subplot for the Financials sector, it includes the line graph {coordinates} [0.356, 0.075, 0.361, 0.315] and associated x and y-axis labels and values." } ], "id_raw": 165 }, { "id": "7aq7hIsTjpqtwdp6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained within the region [0.672, 0.075, 0.677, 0.796]?" }, { "from": "gpt", "value": "This region includes a chart or subplot for the 'Information Technology' sector, showing performance levels across five points in time {points for Information Technology}[0.672, 0.375, 0.676, 0.616]." } ], "id_raw": 165 }, { "id": "gpZfN853vfPvobwk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line for 'Career_Skill_Demand'}[0.134, 0.530, 0.673, 0.701]?" }, { "from": "gpt", "value": "#4c92c3." } ], "id_raw": 168 }, { "id": "l3RRckLHRrYPGA8N", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis tick at}[0.094, 0.487, 0.125, 0.510]?" }, { "from": "gpt", "value": "0.44." } ], "id_raw": 168 }, { "id": "tq6lTZMKq7XECGmN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_8.png", "conversations": [ { "from": "human", "value": "\nWhat shape is {the marker on the line for 'Job Market Trends'}[0.134, 0.530, 0.673, 0.787]?" }, { "from": "gpt", "value": "Star." } ], "id_raw": 168 }, { "id": "EgWEbZyiJE44jQSS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_8.png", "conversations": [ { "from": "human", "value": "\nIs {the line point for 2020 in 'Career Tech Advance'}[0.673, 0.223] higher than {the line point for 2020 in 'Workplace Automation'}[0.673, 0.325]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 168 }, { "id": "WI3ayBlNA7JMlXH7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_8.png", "conversations": [ { "from": "human", "value": "\nWhich year has a higher {y-axis placement} between 2009[0.179, 0.616, 0.179, 0.787] and 2013[0.359, 0.530, 0.359, 0.787] for the 'Career_Skill_Demand' study?" }, { "from": "gpt", "value": "2013." } ], "id_raw": 168 }, { "id": "RA8ct1yK298QpaS9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the line point from 2019 under 'Workplace Automation'}[0.629, 0.360] fall above or below {the line point from 2019 under 'Career Tech Advance'}[0.629, 0.257]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 168 }, { "id": "5wYupqfwSG7nIsi2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_8.png", "conversations": [ { "from": "human", "value": "\nHow many different studies are highlighted in {the legend area}[0.682, 0.203, 0.931, 0.431]?" }, { "from": "gpt", "value": "There are four studies listed; each paired with a unique color & marker style." } ], "id_raw": 168 }, { "id": "yRhqFA3lldL61Sgx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_8.png", "conversations": [ { "from": "human", "value": "\nWhat elements are located within [0.359, 0.530, 0.494, 0.601]?" }, { "from": "gpt", "value": "It contains points from 'Career Tech Advance' and 'Workplace Automation' studies from 2013 to 2016, included with respective markers on the chart." } ], "id_raw": 168 }, { "id": "nmqibrB1Ggo915gD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the indicators found in [0.004, 0.820, 0.691, 0.843]." }, { "from": "gpt", "value": "This region includes {x-axis labels for years}[0.117, 0.820, 0.691, 0.843] ranging from 2008 to 2020, marked clearly under the chart." } ], "id_raw": 168 }, { "id": "abmiZ5FSkjwAFVit", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the first y-axis tick label on the Beige_Book_A subplot}[0.026, 0.866, 0.052, 0.894]?" }, { "from": "gpt", "value": "Black." } ], "id_raw": 169 }, { "id": "J0w66UDyEpdbrVXU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the tick mark for the fifth Timestep on the Bond_Index_A subplot}[0.382, 0.800]?" }, { "from": "gpt", "value": "[0.379, 0.820, 0.385, 0.848]." } ], "id_raw": 169 }, { "id": "ilxBu3uKwHpQAvIm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the marker style of {the last data point in Economic_Growth_A}[0.712, 0.110]?" }, { "from": "gpt", "value": "Square." } ], "id_raw": 169 }, { "id": "4KKnApNp59KzATSW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_9.png", "conversations": [ { "from": "human", "value": "\nIs {the first point in Beige_Book_A}[0.065, 0.658] higher than {the first point in Bond_Index_A}[0.329, 0.110] in the chart?" }, { "from": "gpt", "value": "No." } ], "id_raw": 169 }, { "id": "Z7J3eZD3GHJ9YhSI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_9.png", "conversations": [ { "from": "human", "value": "\nDoes {the y-axis label of Bond_Index_A chart}[0.274, 0.384, 0.286, 0.493] appear to the left or right of {the labels of Beige_Book_A chart}[0.010, 0.384, 0.022, 0.493]?" }, { "from": "gpt", "value": "To the right." } ], "id_raw": 169 }, { "id": "yLYeYz7a2XVLGAe9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_9.png", "conversations": [ { "from": "human", "value": "\nWhich is taller, {point 7 label in Economic_Growth_A}[0.673, 0.417] or {point 7 in Bond Index_A}[0.408, 0.417]?" }, { "from": "gpt", "value": "Economic_Growth_A." } ], "id_raw": 169 }, { "id": "ilGavochiQNs1ppn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.590, 0.820, 0.640, 0.850]." }, { "from": "gpt", "value": "This region includes the x-axis tick labels '4' {coordinates}[0.630, 0.820, 0.636, 0.848] and '3' {coordinates}[0.617, 0.820, 0.623, 0.848] for the Economic_Growth_A chart." } ], "id_raw": 169 }, { "id": "dqQC7lFUKdHxbeov", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_9.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area [0.285, 0.362, 0.330, 0.395]?" }, { "from": "gpt", "value": "It contains the y-axis tick label '0.024' {coordinates}[0.290, 0.362, 0.316, 0.390] alongside its corresponding tick {coordinates}[0.323, 0.373] for the Bond_Index_A chart." } ], "id_raw": 169 }, { "id": "YZqQw4gOt9tE0sOW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the region [0.060, 0.700, 0.110, 0.860]?" }, { "from": "gpt", "value": "This region includes the y-axis tick labels '0.022' {coordinates}[0.026, 0.537, 0.052, 0.565] and '0.024' {coordinates}[0.026, 0.428, 0.052, 0.456] for the Beige_Book_A subplot." } ], "id_raw": 169 }, { "id": "4y1Ej8Kv3talmZC0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_11.png", "conversations": [ { "from": "human", "value": "\nWhat object is at coordinates [0.272, 0.083, 0.317, 0.113]?" }, { "from": "gpt", "value": "Subtitle for Sport." } ], "id_raw": 171 }, { "id": "6TV2uJKiwSxSy9b3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_11.png", "conversations": [ { "from": "human", "value": "\nIdentify the object located at [0.540, 0.979]." }, { "from": "gpt", "value": "Y-tick coordinate." } ], "id_raw": 171 }, { "id": "6oVKwHXUn5uKQ390", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_11.png", "conversations": [ { "from": "human", "value": "\nWhat is located at coordinates [0.046, 0.919, 0.055, 0.942]?" }, { "from": "gpt", "value": "X-axis tick label '0'." } ], "id_raw": 171 }, { "id": "sA2ALc4z2KcOrohz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_11.png", "conversations": [ { "from": "human", "value": "\nIs {point}[0.540, 0.252] on the chart for Leisure higher than {point}[0.540, 0.287] on the chart for Work?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 171 }, { "id": "ckG41iQjzyQaCNGC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_11.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {point}[0.959, 0.137], the line for Leisure or the line for Work?" }, { "from": "gpt", "value": "Leisure." } ], "id_raw": 171 }, { "id": "tz0SWoaGCLqsvVEz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_11.png", "conversations": [ { "from": "human", "value": "\nIs the value at {point}[0.272, 0.370] greater than the value at {point}[0.450, 0.137] in the Sport subplot?" }, { "from": "gpt", "value": "No." } ], "id_raw": 171 }, { "id": "vMPKBuGhjCe9RM8B", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found within the bounding box [0.540, 0.121, 0.979, 0.477]." }, { "from": "gpt", "value": "This region includes the axes for Leisure with lines plotted indicating the performance over varying samples marked by {points}[0.560, 0.137, 0.959, 0.460] and the subtitle 'Leisure' located at {coordinates}[0.730, 0.083, 0.789, 0.113]." } ], "id_raw": 171 }, { "id": "juXKEwc5MosqcgI5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.075, 0.547, 0.514, 0.903] contain?" }, { "from": "gpt", "value": "It contains the chart for Hobby with MSE values plotted, distinct y-axis with labels, and the main title 'Hobby' marked at the top." } ], "id_raw": 171 }, { "id": "kO8ZidRjpprkmyqn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_11.png", "conversations": [ { "from": "human", "value": "\nDetail the objects within the region [0.075, 0.121, 0.514, 0.477]." }, { "from": "gpt", "value": "The plot area for Sport, inclusive of a line graph {line graph}[0.095, 0.137, 0.494, 0.460], y-axis and y-ticks {labels and ticks}[0.043, 0.369, 0.065, 0.493], and the subtitle 'Sport' located {location}[0.272, 0.083, 0.317, 0.113]." } ], "id_raw": 171 }, { "id": "1tVKtjfgUZ3WdulU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_12.png", "conversations": [ { "from": "human", "value": "\nWhat does {the ylabel}[0.015, 0.025, 0.034, 0.179] contain?" }, { "from": "gpt", "value": "Trends Value." } ], "id_raw": 172 }, { "id": "0ihDmIM61ZviOD7U", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the first legend handle}[0.080, 0.037, 0.116, 0.047]?" }, { "from": "gpt", "value": "#4c92c3." } ], "id_raw": 172 }, { "id": "kL7KrQnLe24uNW8G", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the x-axis tick at}[0.201, 0.197, 0.210, 0.215]?" }, { "from": "gpt", "value": "2." } ], "id_raw": 172 }, { "id": "RHbEL16E6hspHUkf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_12.png", "conversations": [ { "from": "human", "value": "\nWhich {tick label}[0.570, 0.197, 0.579, 0.215] has a higher trend value, '5' or '4'?" }, { "from": "gpt", "value": "5." } ], "id_raw": 172 }, { "id": "X7BCyJoHL8QHC4ry", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Technology in Education'}[0.205, 0.174, 0.574, 0.172] ascend or descend between points '2' and '6'?" }, { "from": "gpt", "value": "Descend." } ], "id_raw": 172 }, { "id": "UMV2BFbcxL23jOXM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_12.png", "conversations": [ { "from": "human", "value": "\nWhich trend is higher at {the last point}[0.943, 0.171], 'Technology in Education' or 'Online Learning'?" }, { "from": "gpt", "value": "Online Learning." } ], "id_raw": 172 }, { "id": "6IYrcU7Ul4iS77i6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content found within [0.015, 0.025, 0.034, 0.179]." }, { "from": "gpt", "value": "This region contains the y-axis label \"Trends Value\" oriented vertically in black color." } ], "id_raw": 172 }, { "id": "6lZd0IdsPf8dWVWT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_12.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the region [0.508, 0.222, 0.549, 0.245]?" }, { "from": "gpt", "value": "It contains the x-axis label \"Year\" in black text, positioned centrally below the chart." } ], "id_raw": 172 }, { "id": "H03RXgodjhKt1C5s", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements present in the rectangular area [0.113, 0.185, 0.943, 0.215]." }, { "from": "gpt", "value": "This region encompasses all the x-axis tick marks and labels ranging from '1' to '10', aligned horizontally along the bottom of the chart." } ], "id_raw": 172 }, { "id": "djA1y22k4MwuAG3c", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.012, 0.288, 0.028, 0.672]?" }, { "from": "gpt", "value": "Degree of Globalization." } ], "id_raw": 173 }, { "id": "xFSmEEydDaDdRS6E", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the handle of the fourth item in the legend?" }, { "from": "gpt", "value": "[0.075, 0.240, 0.119, 0.264]." } ], "id_raw": 173 }, { "id": "A7Bkol4ZBed8UEeB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the Subplot line 'Moderate Increase'}[0.250, 0.637, 0.984, 0.866]?" }, { "from": "gpt", "value": "#1f77b4." } ], "id_raw": 173 }, { "id": "3fPX3e6qefiqK7kV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_13.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher at {point 4}, 'Moderate Increase' with point coordinates [0.800, 0.723] or 'Persistent Growth' with point coordinates [0.800, 0.351]?" }, { "from": "gpt", "value": "Moderate Increase." } ], "id_raw": 173 }, { "id": "5ia5eva4E4E9lmO6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_13.png", "conversations": [ { "from": "human", "value": "\nComparing the 'Persistent Growth' trend at {point 2}[0.434, 0.637] and 'Marginal Improvement' trend at [0.434, 0.752], which one is higher?" }, { "from": "gpt", "value": "Marginal Improvement." } ], "id_raw": 173 }, { "id": "2zUpRXRd597vulvO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Persistent Growth' trend}[0.250, 0.208, 0.984, 0.780] intersect with {the 'Insignificant Increase' trend}[0.250, 0.780, 0.984, 0.780]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 173 }, { "id": "p47XGUQJSEkn2nL9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.075, 0.082, 0.317, 0.283]." }, { "from": "gpt", "value": "The area contains the chart legend with 4 entries: 'Moderate Increase', 'Marginal Improvement', 'Insignificant Increase', and 'Persistent Growth'. Each entry has a corresponding handle with a unique color." } ], "id_raw": 173 }, { "id": "HBpdIW0pfj9jnJYC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.477, 0.943, 0.573, 0.975]." }, { "from": "gpt", "value": "The area contains the x-axis label 'Time Period' in black color." } ], "id_raw": 173 }, { "id": "XzlzExYvZS0hHFGT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.063, 0.911, 0.988, 0.934]?" }, { "from": "gpt", "value": "These are all the x-axis labels, starting from '0' until '5' at the positions [0.063, 0.911, 0.070, 0.934], [0.246, 0.911, 0.254, 0.934], [0.430, 0.911, 0.437, 0.934], [0.613, 0.911, 0.621, 0.934], [0.797, 0.911, 0.804, 0.934], and [0.980, 0.911, 0.988, 0.934] respectively." } ], "id_raw": 173 }, { "id": "YYjzLfoM1hCeSMKo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_14.png", "conversations": [ { "from": "human", "value": "\nWhat item is on the y-axis with coordinates [0.010, 0.448, 0.023, 0.551] in the chart?" }, { "from": "gpt", "value": "Mean Squared Error (MSE)." } ], "id_raw": 174 }, { "id": "DzPm17t2Rt83n2Hg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the x-axis label for time_point 288 in the second subplot?" }, { "from": "gpt", "value": "[0.389, 0.927, 0.406, 0.962]" } ], "id_raw": 174 }, { "id": "xJ3PU9lINAExSeO7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the line} at [0.557, 0.774, 0.733, 0.839] representing?" }, { "from": "gpt", "value": "Stock 3 performance." } ], "id_raw": 174 }, { "id": "jDYc3r7uCCSSNteh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_14.png", "conversations": [ { "from": "human", "value": "\nIs the {value for 'Stock 1'}[0.238, 0.667] higher or lower than the {value for 'Stock 2'}[0.485, 0.709] at time point 480?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 174 }, { "id": "eDZ4eZDT1mfqJgtA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_14.png", "conversations": [ { "from": "human", "value": "\nCompare the Mean Squared Error (MSE) of {Stock 3}[0.733, 0.839] and {Stock 4}[0.980, 0.257] at time_point 480?" }, { "from": "gpt", "value": "Stock 3 has lower MSE." } ], "id_raw": 174 }, { "id": "yajYSPQPFdEO3rMT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_14.png", "conversations": [ { "from": "human", "value": "\nWhich MSE value is higher at the same time point 96, {Stock 1}[0.062, 0.500] or {Stock 4}[0.805, 0.160]?" }, { "from": "gpt", "value": "Stock 4." } ], "id_raw": 174 }, { "id": "eWNu7EM5mLyqPiru", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_14.png", "conversations": [ { "from": "human", "value": "\nDescribe what lies in the region [0.796, 0.096, 0.989, 0.903]." }, { "from": "gpt", "value": "It is a subplot representing Stock 4's performance over time. The MSE values are on the y-axis and the corresponding time points are on the x-axis. A line graph is plotted to represent the MSE values at various time points." } ], "id_raw": 174 }, { "id": "SAw2zVCtz1MFYqVD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_14.png", "conversations": [ { "from": "human", "value": "\nCan you give a description of area [0.621, 0.037, 0.669, 0.085]?" }, { "from": "gpt", "value": "This area contains the label \"Stock 3\", which is the subtitle for the third subplot." } ], "id_raw": 174 }, { "id": "2K4tr1nk52p59sxk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_14.png", "conversations": [ { "from": "human", "value": "\nWhat elements can be seen within the region [0.916, 0.823, 0.984, 0.886]?" }, { "from": "gpt", "value": "It contains the legend for subplot 3, specifying that the color '#de5253' corresponds to Stock 4." } ], "id_raw": 174 }, { "id": "NwPXQ7qJ5i5ZvlId", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_16.png", "conversations": [ { "from": "human", "value": "\nWhat is located at this bounding box [0.145, 0.139, 0.224, 0.168]?" }, { "from": "gpt", "value": "Legend label of 'Land_Value'." } ], "id_raw": 176 }, { "id": "OLbxxydHWtVR2FuY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label positioned at [0.015, 0.398, 0.034, 0.505]?" }, { "from": "gpt", "value": "Value." } ], "id_raw": 176 }, { "id": "ILUYNflT2saGfVqH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the first line at the left end point at [0.134, 0.663]?" }, { "from": "gpt", "value": "'Residential_Building'." } ], "id_raw": 176 }, { "id": "k8VORZf2xH6oLM8Z", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_16.png", "conversations": [ { "from": "human", "value": "\nIs the price of {Residential Buildings}[0.134, 0.575, 0.944, 0.663] higher or lower in 2011 than in 2005 according to the line chart?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 176 }, { "id": "pYaPOn5Ge8KTIDoe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_16.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at the time marker '2011', {Commercial_Building}[0.134, 0.647, 0.944, 0.701] or {Land_Value} [0.134, 0.068, 0.944, 0.452]?" }, { "from": "gpt", "value": "Land_Value." } ], "id_raw": 176 }, { "id": "URLF1cTMHO8vJw1t", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_16.png", "conversations": [ { "from": "human", "value": "\nAre the {Mortgage_Rate} values [0.134, 0.835, 0.944, 0.835] decreasing or increasing over the period 2000-2004?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 176 }, { "id": "Uz6rjHDKwrhorsSV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.100, 0.044, 0.297, 0.221]." }, { "from": "gpt", "value": "This region contains subplot legend featuring labels for Residential_Building, Commercial_Building, Land_Value, and Mortgage_Rate, each accompanied by its unique colored line marker." } ], "id_raw": 176 }, { "id": "NDq4q0CUfTV0u2qV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_16.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area from [0.134, 0.068, 0.944, 0.452]?" }, { "from": "gpt", "value": "The green dashed line representing the Land_Value from 2000 to 2011 is plotted in this region, with square markers indicating the annual values." } ], "id_raw": 176 }, { "id": "1fr3zy0TJFcuplIc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_16.png", "conversations": [ { "from": "human", "value": "\nDescribe what the chart presents inside the box [0.134, 0.575, 0.944, 0.663]?" }, { "from": "gpt", "value": "Inside this box, we see a dashed line with dot markers, which maps the Residential_Building value across the years." } ], "id_raw": 176 }, { "id": "dm7qiPy0H4UcW3Gr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.021, 0.342, 0.031, 0.687] in the chart?" }, { "from": "gpt", "value": "This is the y-axis label." } ], "id_raw": 177 }, { "id": "gPJjJWNoe8Smnk0c", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second text in the legend?" }, { "from": "gpt", "value": "[0.086, 0.190, 0.118, 0.208]" } ], "id_raw": 177 }, { "id": "QmdRuMKDdhqEz7ez", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_17.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.296, 0.892, 0.326, 0.916]?" }, { "from": "gpt", "value": "Year 2." } ], "id_raw": 177 }, { "id": "RssAQ3eUYTWGvvtm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_17.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Factory 1'}[[0.102, 0.383], [0.311, 0.383], [0.520, 0.470], [0.729, 0.412], [0.937, 0.500]] generally above or below {the line for 'Factory 2'}[[0.102, 0.295], [0.311, 0.353], [0.520, 0.266], [0.729, 0.236], [0.937, 0.207]]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 177 }, { "id": "yR1fRFvrvUA9DQNH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_17.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Year 1' for Factory 1}[0.102, 0.441] above or below {the point for 'Year 1' for Factory 5}[0.102, 0.383]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 177 }, { "id": "AFA9xtxunFX714ex", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_17.png", "conversations": [ { "from": "human", "value": "\nWhich Factory has a higher efficiency at {Year 5}[0.937, 0.441], Factory 4 or Factory 5?" }, { "from": "gpt", "value": "Factory 4." } ], "id_raw": 177 }, { "id": "k9qAarGNF0FTtRbS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.102, 0.207, 0.937, 0.792]." }, { "from": "gpt", "value": "This area mainly corresponds to the lines representing the manufacturing efficiency for each factory over the years. Each factory is represented by a unique line style and marker." } ], "id_raw": 177 }, { "id": "hgcPU4l2cagLuvfe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.086, 0.164, 0.118, 0.182]." }, { "from": "gpt", "value": "This area contains the legend item: {Factory 1}[0.086, 0.164, 0.118, 0.182] with its corresponding colored marker." } ], "id_raw": 177 }, { "id": "kI7MItdcHip92O7Q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_17.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.714, 0.892, 0.744, 0.916]?" }, { "from": "gpt", "value": "There is one object. The x-axis tick label 'Year 4' is located at {coordinates}[0.714, 0.892, 0.744, 0.916]." } ], "id_raw": 177 }, { "id": "iAXiIHRry5YviNhD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the coordinates [0.338, 0.025, 0.709, 0.055]?" }, { "from": "gpt", "value": "It's the subtitle of the chart, \"Media Influence & Public Opinion Guidance over Time\"." } ], "id_raw": 178 }, { "id": "aF98LN6vdBoPtJoP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_18.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the line at coordinates '[0.102, 0.131, 0.945, 0.722]' with a '--' style and '#4c92c3' color?" }, { "from": "gpt", "value": "The line represents the \"Social Media Influence\" trend." } ], "id_raw": 178 }, { "id": "FjWNlztCxXAdQ8Kg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the legend item for 'Election Public Opinion'}[0.807, 0.849, 0.841, 0.872]?" }, { "from": "gpt", "value": "The color is '#ff993e'." } ], "id_raw": 178 }, { "id": "abY8pqN5YPAa8MsQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_18.png", "conversations": [ { "from": "human", "value": "\nWhich trend reaches a higher influence level faster according to the points at {coordinates for Social Media Influence at x=4}[0.296, 0.585] and {coordinates for Election Public Opinion at x=4}[0.296, 0.722]?" }, { "from": "gpt", "value": "The 'Social Media Influence' trend." } ], "id_raw": 178 }, { "id": "o7LDHqkK06QKfmkO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_18.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Social Media Influence' at x=14}[0.945, 0.131] higher or lower than {the point of 'Election Public Opinion' at x=14}[0.945, 0.267]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 178 }, { "id": "hpemm91hPdxz6XBJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_18.png", "conversations": [ { "from": "human", "value": "\nWhich trend has a higher influence level at x=2, is it {Social Media Influence}[0.166, 0.676] or {Election Public Opinion}[0.166, 0.813]?" }, { "from": "gpt", "value": "Election Public Opinion." } ], "id_raw": 178 }, { "id": "HU84nAU5JZKw1WJ9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_18.png", "conversations": [ { "from": "human", "value": "\nWhat content is within the region [0.808, 0.806, 0.982, 0.883]?" }, { "from": "gpt", "value": "This is the area where the legend for the chart is located. It includes entries for 'Social Media Influence' and 'Election Public Opinion', with associated color markers." } ], "id_raw": 178 }, { "id": "OokOzwaUFakeKWEv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents at the coordinates [0.504, 0.943, 0.543, 0.975]?" }, { "from": "gpt", "value": "This region contains the x-axis label of the graph. The text there is 'Time'." } ], "id_raw": 178 }, { "id": "VLdXb17dld9psiYL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_18.png", "conversations": [ { "from": "human", "value": "\nExplain what is located within the coordinates [0.013, 0.372, 0.028, 0.617]." }, { "from": "gpt", "value": "This area assigns the y-axis label for the graph. It reads 'Influence Level'." } ], "id_raw": 178 }, { "id": "X4SzIgziQbjweCfT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_19.png", "conversations": [ { "from": "human", "value": "\nWhat does {the ylabel}[0.252, 0.262, 0.266, 0.688] indicate in the subplot?" }, { "from": "gpt", "value": "Effectiveness Evaluation." } ], "id_raw": 179 }, { "id": "gkosZatYt2pR2Oug", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the last x-axis tick label}[0.973, 0.879, 0.989, 0.914] in the subplot for Climate Change?" }, { "from": "gpt", "value": "720." } ], "id_raw": 179 }, { "id": "WkggNCGgFe1e8lKz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_9_19.png", "conversations": [ { "from": "human", "value": "\nWhat is positioned at {the coordinates}[0.675, 0.544] in the Healthcare Reform subplot?" }, { "from": "gpt", "value": "A point on the line." } ], "id_raw": 179 }, { "id": "bHr82c4ACtLMyrxw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_19.png", "conversations": [ { "from": "human", "value": "\nIs {the point marked at '512'}[0.922, 0.303] in the Climate Change subplot higher than {the point marked at '512'}[0.675, 0.544] in the Healthcare Reform subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 179 }, { "id": "yJVH4MH4ORoPkRpn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_19.png", "conversations": [ { "from": "human", "value": "\nWhich y-axis tick mark is closer to the bottom, {the one at '0.185'}[0.010, 0.944] or {the one at '0.148'}[0.765, 0.921]?" }, { "from": "gpt", "value": "0.148." } ], "id_raw": 179 }, { "id": "IUupcio7hkOJxtyW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_9_19.png", "conversations": [ { "from": "human", "value": "\nDoes {the line labeled '720'}[0.804, 0.130, 0.981, 0.820] in Climate Change reach a higher point than {the line labeled '192'}[0.309, 0.130, 0.486, 0.820] in Gun Control?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 179 }, { "id": "qnntdHepLU9zKPLW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within [0.251, 0.879, 0.846, 0.914]." }, { "from": "gpt", "value": "X-axis tick labels with periods labeled '96', '192', '336', '512', '720' across four subplots with their respective coordinates clearly marked under each subplot." } ], "id_raw": 179 }, { "id": "RcSkFxlgQcrpKf9t", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_19.png", "conversations": [ { "from": "human", "value": "\nDescribe what is located in [0.022, 0.262, 0.046, 0.688]." }, { "from": "gpt", "value": "Y-axis tick labels including values '0.185', '0.190', '0.195', '0.200', '0.205', '0.210', '0.215', '0.220' corresponding to the subplot of Tax Reform." } ], "id_raw": 179 }, { "id": "j4fMV866sZlZonuR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_9_19.png", "conversations": [ { "from": "human", "value": "\nWhat is found within [0.116, 0.037, 0.941, 0.085]?" }, { "from": "gpt", "value": "Titles for each subplot including 'Tax Reform', 'Gun Control', 'Healthcare Reform', and 'Climate Change', each aligned with their respective subplot areas." } ], "id_raw": 179 }, { "id": "IDFJB9JRKOoNFVIX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the vertical label correspond to on this chart at the specified coordinates?" }, { "from": "gpt", "value": "{The vertical label}[0.015, 0.373, 0.029, 0.573] corresponds to the \"Index (Base Year 2010)\"." } ], "id_raw": 180 }, { "id": "bvagMsVP20MczWkN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_0.png", "conversations": [ { "from": "human", "value": "\nCould you tell me what does the point represent at the coordinates [0.968, 0.679]?" }, { "from": "gpt", "value": "This point represents the year 2030, and the index for 'Population' in the chart." } ], "id_raw": 180 }, { "id": "gxY7uNG2tQQ2hgRS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_0.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the third item in the chart legend?" }, { "from": "gpt", "value": "The third item in the legend is called 'Food Import', its color is green and its marker is 'x'. The coordinates are {this}[0.122, 0.087, 0.206, 0.104]." } ], "id_raw": 180 }, { "id": "tdhaiCxzn348fn8K", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_0.png", "conversations": [ { "from": "human", "value": "\nWhich line in the graph, 'Food Production' with coordinates [0.071, 0.067, 0.968, 0.545] or 'Population' with coordinates [0.071, 0.679, 0.968, 0.832], starts at a higher index value in the year 2010?" }, { "from": "gpt", "value": "'Food Production'." } ], "id_raw": 180 }, { "id": "8k2lEUMXzQ83O6Xb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_0.png", "conversations": [ { "from": "human", "value": "\nIn the year 2030, is the index of 'Food Import' higher or lower than 'Population'?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 180 }, { "id": "uJkzdEiZolZCMvAt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_0.png", "conversations": [ { "from": "human", "value": "\nBetween lines for 'Food Production' and 'Agricultural Export', which one has experienced more growth from 2010 to 2030?" }, { "from": "gpt", "value": "Food Production." } ], "id_raw": 180 }, { "id": "YxpaEbKis1uXIXbM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents within the area [0.035, 0.203, 0.061, 0.251]?" }, { "from": "gpt", "value": "This area contains two y-axis labels, '135' and '150', with their coordinates being {these}[0.035, 0.203, 0.061, 0.221] and {these}[0.035, 0.060, 0.061, 0.077] respectively." } ], "id_raw": 180 }, { "id": "QbAuRQeWMUfdPjkx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_0.png", "conversations": [ { "from": "human", "value": "\nWhat information can be found in the area [0.035, -0.084, 0.061, 0.938]?" }, { "from": "gpt", "value": "This area contains all the y-axis labels from '60' to '165'. The '165' label is slightly off the frame." } ], "id_raw": 180 }, { "id": "Ebwtuontgkq6P1Hc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_0.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the object located in the region [0.078, 0.027, 0.258, 0.137]?" }, { "from": "gpt", "value": "This region contains the legend of the chart. It has four items representing 'Food Production', 'Agricultural Export', 'Food Import', and 'Population'. Each legend item has a different color and marker." } ], "id_raw": 180 }, { "id": "smVWXG4GW7pndLKO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the object with the label 'Cuisines'}[0.497, 0.952, 0.546, 0.975]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 181 }, { "id": "zR5EB8uLDKBYGdLQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_1.png", "conversations": [ { "from": "human", "value": "\nWhere is the y-axis label located on the graph?" }, { "from": "gpt", "value": "[0.012, 0.299, 0.024, 0.628]." } ], "id_raw": 181 }, { "id": "5XKJ9oEl9Ale7OqJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label}[0.036, 0.747, 0.065, 0.771]?" }, { "from": "gpt", "value": "5000." } ], "id_raw": 181 }, { "id": "tncgQOcYGVNtxscW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Italian Cuisine'}[0.073, 0.610] higher or lower than {the point for 'French Cuisine'}[0.173, 0.376]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 181 }, { "id": "sbb213R3fe2kS4PH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Mexican Cuisine'}[0.273, 0.201] above or below {the point for 'Indian Cuisine'}[0.372, 0.259]," }, { "from": "gpt", "value": "Below." } ], "id_raw": 181 }, { "id": "orkvUQP89wdC9Bc8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_1.png", "conversations": [ { "from": "human", "value": "\nWhich cuisine has a higher trend, 'Thai'[0.571, 0.113] or 'Vegan'[0.970, 0.084]?" }, { "from": "gpt", "value": "Thai." } ], "id_raw": 181 }, { "id": "5b2vbbAz5RwkN6N0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the box [0.449, 0.919, 0.495, 0.942]?" }, { "from": "gpt", "value": "The 'Chinese' label of x-axis is located here." } ], "id_raw": 181 }, { "id": "X7pFE05gssbHuyuR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_1.png", "conversations": [ { "from": "human", "value": "\nDescribe what's in the area [0.073, 0.903, 0.065, 0.917]." }, { "from": "gpt", "value": "The area contains a y-axis tick label of value '0'." } ], "id_raw": 181 }, { "id": "aIS6r7nHlfEwUKoc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_1.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.828, 0.919, 0.912, 0.942]?" }, { "from": "gpt", "value": "The 'Mediterranean' cuisine label of x-axis is found here." } ], "id_raw": 181 }, { "id": "f4m3rjczWwSZtfXF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_2.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at the coordinates [0.175, 0.030, 0.317, 0.053] in the chart?" }, { "from": "gpt", "value": "Legend label for \"Waste Recycling\"." } ], "id_raw": 182 }, { "id": "adJyy7xI4uAfVruO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_2.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object at the coordinates [0.088, 0.154, 0.959, 0.390]?" }, { "from": "gpt", "value": "This is the line for \"Waste Recycling\"." } ], "id_raw": 182 }, { "id": "RfoT65NBK58SXd4B", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the {y-axis tick} located at [0.054, 0.736, 0.076, 0.759]?" }, { "from": "gpt", "value": "20." } ], "id_raw": 182 }, { "id": "1t74ZQumGYjTQNFf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_2.png", "conversations": [ { "from": "human", "value": "\nIs {the value for 'Solar Energy'}[0.524, 0.588] in 2017 higher or lower than {the value for 'Waste Recycling'}[0.524, 0.272]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 182 }, { "id": "Bq4C5flfr35sNIII", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_2.png", "conversations": [ { "from": "human", "value": "\nWhich label has a higher value in 2019, {Waste Recycling}[0.959, 0.154] or {Nuclear Energy}[0.959, 0.154]?" }, { "from": "gpt", "value": "They have the name value." } ], "id_raw": 182 }, { "id": "xljGbldIdIqGWIR4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_2.png", "conversations": [ { "from": "human", "value": "\nIs {the value for the 'Nuclear Energy'}[0.741, 0.272] line at 2018 above or below {the 'Wind Energy'}[0.741, 0.193] line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 182 }, { "id": "q8VvefdeIYdLhAOZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within [0.120, 0.020, 0.928, 0.062]." }, { "from": "gpt", "value": "This region contains the chart legend with four items: {Waste Recycling}[0.175, 0.030, 0.317, 0.053], {Solar Energy}[0.400, 0.030, 0.511, 0.053], {Wind Energy}[0.594, 0.030, 0.704, 0.053], and {Nuclear Energy}[0.788, 0.030, 0.921, 0.053]. Each label is next to a marker." } ], "id_raw": 182 }, { "id": "Ygm3mDLKj7SSIEyi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_2.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.088, 0.154, 0.959, 0.390] on the chart?" }, { "from": "gpt", "value": "This region covers the 'Waste Recycling' line chart from the year 2015 to 2019." } ], "id_raw": 182 }, { "id": "AJZj9XAGYpVALKVu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.719, 0.919, 0.764, 0.942]?" }, { "from": "gpt", "value": "The x-axis tick label '2018' is located within this area." } ], "id_raw": 182 }, { "id": "A4F6IdUrP6ndiilD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_3.png", "conversations": [ { "from": "human", "value": "\nWhat does {the object} at [0.019, 0.367, 0.036, 0.570] represent in the chart?" }, { "from": "gpt", "value": "Y-axis label named \"Funding (Millions)\"." } ], "id_raw": 183 }, { "id": "1EKuYtYMVx1BD6fo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first line 'Education' in the sublegend?" }, { "from": "gpt", "value": "[0.132, 0.058, 0.177, 0.072]." } ], "id_raw": 183 }, { "id": "SlpGCQ31WRiYUhvs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label and its coordinates?" }, { "from": "gpt", "value": "The x-axis label is \"Year\" and its coordinates are [0.522, 0.952, 0.559, 0.975]." } ], "id_raw": 183 }, { "id": "pVxDbAPtXDgob3vG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_3.png", "conversations": [ { "from": "human", "value": "\nWhich line has higher initial value, {the line for 'Education'}[[0.121, 0.773, 0.959, 0.512]] or {the line for 'Healthcare'}[[0.121, 0.642, 0.959, 0.121]]?" }, { "from": "gpt", "value": "Education." } ], "id_raw": 183 }, { "id": "2la1shbVonjR1995", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Public Transport' line}[0.121, 0.425, 0.959, 0.729] or {the line for 'Environmental Protection'}[0.121, 0.382, 0.959, 0.859] end with a higher value in 2030?" }, { "from": "gpt", "value": "Environmental Protection." } ], "id_raw": 183 }, { "id": "Lgg0PG7XIBwlhr58", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_3.png", "conversations": [ { "from": "human", "value": "\nAround the year 2020, was {funding for 'Healthcare'}[0.540, 0.382] higher or lower than {funding for 'Education'}[0.540, 0.599]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 183 }, { "id": "OVCMkUsNA0ePbtkV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_3.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area [0.130, 0.046, 0.412, 0.192]?" }, { "from": "gpt", "value": "This area contains the legend for the chart which includes markers and texts for 'Education', 'Healthcare', 'Public Transport', and 'Environmental Protection'." } ], "id_raw": 183 }, { "id": "efZDNfsJOpzNOY7z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within [0.121, 0.425, 0.959, 0.729]." }, { "from": "gpt", "value": "This region contains parts of three different lines, representing 'Education', 'Public Transport', and 'Environmental Protection', as well as their associated data points." } ], "id_raw": 183 }, { "id": "HteUsdWE1whalJS7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.121, 0.642, 0.959, 0.773]?" }, { "from": "gpt", "value": "The line representing 'Education' from the years 2010 to 2030 is within these coordinates. In addition, a portion of the 'Healthcare' line is present." } ], "id_raw": 183 }, { "id": "1wMh4u85H34of6pH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_4.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at [0.540, 0.020, 0.540, 0.020] in the chart?" }, { "from": "gpt", "value": "Subtitle." } ], "id_raw": 184 }, { "id": "N2m6pdwlrr2xiVeB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_4.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {the line for 'Bonds'} at coordinates [0.099, 0.515, 0.976, 0.630]?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 184 }, { "id": "DUtddWahAzQeFWuj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the nature of {the y-axis'} text at [0.019, 0.402, 0.036, 0.516]?" }, { "from": "gpt", "value": "Vertical." } ], "id_raw": 184 }, { "id": "aQx5oNpZ9eNK9p4x", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_4.png", "conversations": [ { "from": "human", "value": "\nIs {the trend for 'Real Estate'}[0.440, 0.459] higher or lower than {the trend for 'Bonds'}[0.440, 0.621] in the year indexed by [0.440, 0.883]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 184 }, { "id": "KyJqD1DmwHCDbHqY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_4.png", "conversations": [ { "from": "human", "value": "\nWhich has a lesser value at coordinates [0.586, 0.883], {the 'Stock Market'} or {the 'Gold Price'}?" }, { "from": "gpt", "value": "Gold Price." } ], "id_raw": 184 }, { "id": "XVpzIzZK9pSolemu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_4.png", "conversations": [ { "from": "human", "value": "\nIs {the data point for 2001 at the 'Real Estate'}[0.099, 0.317] above or below {the equivalent for 'Bonds'}[0.099, 0.628]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 184 }, { "id": "GqxQvf1NuD4FmfKi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the rectangle [0.205, 0.937, 0.870, 0.979]." }, { "from": "gpt", "value": "This area contains the legend referencing the financial datasets. It lists 'Gold Price', 'Stock Market', 'Real Estate', and 'Bonds' each with corresponding color symbols." } ], "id_raw": 184 }, { "id": "vKAXbKO8gyP5QPBN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the zone [0.077, 0.874, 0.087, 0.923]." }, { "from": "gpt", "value": "The area includes the y-axis label, where '0' is displayed as the first label positioned vertically." } ], "id_raw": 184 }, { "id": "EcmibZi0owrnOp0I", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_4.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.519, 0.932, 0.556, 0.955]?" }, { "from": "gpt", "value": "This section holds the x-axis label, which clearly states 'Year' in horizontal alignment." } ], "id_raw": 184 }, { "id": "GOhmw05dfN66IQEN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at coordinates [0.019, 0.332, 0.036, 0.646]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 185 }, { "id": "tz6vt1mkW6HH061A", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_5.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is located at the coordinates [0.283, 0.030, 0.355, 0.053] in the figure?" }, { "from": "gpt", "value": "The label for 'Exercise' in the legend." } ], "id_raw": 185 }, { "id": "X8kwYOQ1iTUMSZn6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the element at coordinates [0.959, 0.919, 0.981, 0.942]?" }, { "from": "gpt", "value": "This is the x-axis tick label for '50'." } ], "id_raw": 185 }, { "id": "kN7TPLu6KfyiENNk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_5.png", "conversations": [ { "from": "human", "value": "\nDoes the {Meditation line}[0.088, 0.282, 0.970, 0.365] end at a higher or lower point on the Y-axis than the {Exercise line}[0.088, 0.241, 0.970, 0.323]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 185 }, { "id": "TheEowcCFUG6BaRW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_5.png", "conversations": [ { "from": "human", "value": "\nComparing {Journaling line}[0.088, 0.572, 0.970, 0.613] and {Trips line}[0.088, 0.737, 0.970, 0.820], which one starts from a lower point at x=30?" }, { "from": "gpt", "value": "The 'Trips' line." } ], "id_raw": 185 }, { "id": "JlZKi5JnOhbdS9we", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_5.png", "conversations": [ { "from": "human", "value": "\nBetween the {Exercise line}[0.088, 0.241, 0.970, 0.323] and {Meditation line}[0.088, 0.282, 0.970, 0.365], which one shows a decrease in the Activity Engagement Score as the age increases from 30 to 50?" }, { "from": "gpt", "value": "The 'Exercise' line." } ], "id_raw": 185 }, { "id": "cKLh1shlkZywRImW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_5.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what elements are found in the area defined by [0.227, 0.020, 0.831, 0.062]?" }, { "from": "gpt", "value": "This region contains the legend for the chart with four items: {Exercise}[0.283, 0.030, 0.355, 0.053], {Meditation}[0.438, 0.030, 0.531, 0.053], {Journaling}[0.614, 0.030, 0.701, 0.053], {Trips}[0.784, 0.030, 0.824, 0.053]." } ], "id_raw": 185 }, { "id": "6UmadAfmQ0FoaNwO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the bounding box at coordinates [0.077, 0.919, 0.099, 0.942]?" }, { "from": "gpt", "value": "Within these coordinates exists a label on the X-axis that reads '30'." } ], "id_raw": 185 }, { "id": "a3Eyr8OQqmmBpZTM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the area within coordinates [0.088, 0.241, 0.970, 0.323] contain?" }, { "from": "gpt", "value": "It contains the line representing 'Exercise', starting at the point [0.088, 0.241] and ending at [0.970, 0.323], showing a decline in Activity Engagement Score as the age increases." } ], "id_raw": 185 }, { "id": "FHjYzwsQWyqH0alv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.015, 0.391, 0.029, 0.537] in the chart?" }, { "from": "gpt", "value": "This is the y-axis label which is \"Progress (%)\"." } ], "id_raw": 186 }, { "id": "lmkygzp7WaughrJv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_6.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is the object located at [0.950, 0.919, 0.985, 0.942]?" }, { "from": "gpt", "value": "It is the x-axis tick label for the year \"2030\"." } ], "id_raw": 186 }, { "id": "vX6WHBvhgqAbOOX3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line representing {Public Infrastructure}[0.071, 0.135, 0.968, 0.683]?" }, { "from": "gpt", "value": "The color of the line for Public Infrastructure is #1f77b4 (blue)." } ], "id_raw": 186 }, { "id": "jAuGRjOmXk4pdAwX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_6.png", "conversations": [ { "from": "human", "value": "\nIn the year 2030, is {Progress in Public Infrastructure}[0.968, 0.135] higher or lower than {Progress in Residential Areas}[0.968, 0.135]?" }, { "from": "gpt", "value": "Equal." } ], "id_raw": 186 }, { "id": "G7Qj8482hXOasP3t", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_6.png", "conversations": [ { "from": "human", "value": "\nBetween {Residential Areas}[0.071, 0.135, 0.968, 0.793] and {Commercial Areas}[0.071, 0.244, 0.968, 0.683], which one shows higher progress in the year 2025?" }, { "from": "gpt", "value": "Commercial Areas." } ], "id_raw": 186 }, { "id": "wXQLSbkLjvUDHw0W", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_6.png", "conversations": [ { "from": "human", "value": "\nComparing the progress of {Public Infrastructure}[0.071, 0.574] and {Commercial Areas}[0.071, 0.683] in 2010, which one had a higher value?" }, { "from": "gpt", "value": "Commercial Areas." } ], "id_raw": 186 }, { "id": "dIyOTaIdvKuUGuED", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.770, 0.745, 0.961, 0.891]?" }, { "from": "gpt", "value": "This region contains the legend for the line charts. It includes labels for Public Infrastructure, Residential Areas, Commercial Areas, and Green Spaces along with their respective line color markers." } ], "id_raw": 186 }, { "id": "ueo7vBcKaxebLL9P", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.043, 0.345, 0.061, 0.368] represent?" }, { "from": "gpt", "value": "The region represents the y-axis tick label for the value \"90\"." } ], "id_raw": 186 }, { "id": "M0JbK83smOlhLeCJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_6.png", "conversations": [ { "from": "human", "value": "\nCould you describe the region [0.950, 0.919, 0.985, 0.942]?" }, { "from": "gpt", "value": "This area contains an x-axis tick label. The label is \"2030\", indicating that this region of the chart represents the year 2030." } ], "id_raw": 186 }, { "id": "jhQVOqfKu1NYBcDF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_7.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the set of coordinates [0.148, 0.141, 0.245, 0.158]?" }, { "from": "gpt", "value": "The legend label for \"AI Investment\"." } ], "id_raw": 187 }, { "id": "AZSsMLvYuUVTIFqq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_7.png", "conversations": [ { "from": "human", "value": "\nWhat object is found at the coordinates [0.975, 0.939, 0.985, 0.957]?" }, { "from": "gpt", "value": "The last x-axis tick label, which is \"2011\"." } ], "id_raw": 187 }, { "id": "I3Qzcd8ewEFR6EUu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.809, 0.267] referring to in the chart?" }, { "from": "gpt", "value": "This is a point on the 'AI Research' line showing data for the year 2009." } ], "id_raw": 187 }, { "id": "Z3F8Voyr8sijepDa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_7.png", "conversations": [ { "from": "human", "value": "\nWhich trend, {AI Research}[0.967, 0.212] or {AI Investment}[0.967, 0.377], has a higher value for the year 2011?" }, { "from": "gpt", "value": "AI Investment." } ], "id_raw": 187 }, { "id": "WUUgcYsiNRiCC6yc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_7.png", "conversations": [ { "from": "human", "value": "\nIs the {AI Adoption} trend point for 2002 [0.255, 0.914] higher or lower than the {AI Salaries} trend point for the same year [0.255, 0.597]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 187 }, { "id": "yDSxriDEIqLow6IE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_7.png", "conversations": [ { "from": "human", "value": "\nWhich trend line was higher in 2006, {AI Research}[0.572, 0.377] or {AI Investment}[0.572, 0.762]?" }, { "from": "gpt", "value": "AI Research." } ], "id_raw": 187 }, { "id": "MvjC9jtOvFjxdTX9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area within these coordinates [0.097, 0.102, 0.967, 0.652] on the chart?" }, { "from": "gpt", "value": "This region captures all 4 trend lines for 'AI Research', 'AI Adoption', 'AI Salaries', and 'AI Investment', showing their values from the year 2000 to 2011." } ], "id_raw": 187 }, { "id": "f0hP6LBQI8zZLzAn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_7.png", "conversations": [ { "from": "human", "value": "\nWhat is characterized by [0.633, 0.939, 0.668, 0.957]?" }, { "from": "gpt", "value": "It shows the x-axis tick label for the year '2007'." } ], "id_raw": 187 }, { "id": "4jAfYI2crBm11THC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the region [0.104, 0.055, 0.250, 0.165]?" }, { "from": "gpt", "value": "This region includes the legend that details the four category labels: {AI Research}[0.148, 0.062, 0.230, 0.080], {AI Adoption}[0.148, 0.088, 0.228, 0.106], {AI Salaries}[0.148, 0.115, 0.221, 0.132], and {AI Investment}[0.148, 0.141, 0.245, 0.158], each paired with a unique color and marker." } ], "id_raw": 187 }, { "id": "ht8ivtyXr3SZOxzg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the coordinates [0.043, 0.066, 0.076, 0.089]?" }, { "from": "gpt", "value": "Y-axis label '100'." } ], "id_raw": 188 }, { "id": "xpIB0Bu9aPvvETRB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_8.png", "conversations": [ { "from": "human", "value": "\nWhat is at the location [0.280, 0.030, 0.387, 0.053] in the chart?" }, { "from": "gpt", "value": "The legend label for 'Online Sales'." } ], "id_raw": 188 }, { "id": "HcfS9lwoQUNmM7SI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.850, 0.919, 0.894, 0.942]?" }, { "from": "gpt", "value": "2014." } ], "id_raw": 188 }, { "id": "O8hvVbrpdFzBmNGO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_8.png", "conversations": [ { "from": "human", "value": "\nIs the {line for 'Online Sales'}[0.088, 0.158, 0.959, 0.489] trending higher or lower over the years?" }, { "from": "gpt", "value": "Higher" } ], "id_raw": 188 }, { "id": "zLW5oppm1UhdQamL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_8.png", "conversations": [ { "from": "human", "value": "\nBetween {Offline Sales} [0.088, 0.323, 0.959, 0.696] and {Ecommerce Sales} [0.088, 0.365, 0.959, 0.820], which one has a steeper incline over the years?" }, { "from": "gpt", "value": "Ecommerce Sales" } ], "id_raw": 188 }, { "id": "byoLYCrjdPrgV3o4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_8.png", "conversations": [ { "from": "human", "value": "\nIs the data point for 'Online Sales' in 2009 {point_coordinates}[0.437, 0.381] higher or lower than the data point for 'Offline Sales' in the same year {point_coordinates}[0.437, 0.448]?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 188 }, { "id": "kU8o9TnwLGheTER1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_8.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects within the region [0.224, 0.020, 0.824, 0.062]?" }, { "from": "gpt", "value": "This area contains the chart legend, with labels for 'Online Sales' {text_coordinates}[0.280, 0.030, 0.387, 0.053], 'Offline Sales' {text_coordinates}[0.470, 0.030, 0.579, 0.053], and 'Ecommerce Sales' {text_coordinates}[0.663, 0.030, 0.817, 0.053]. Each of these has a corresponding color code." } ], "id_raw": 188 }, { "id": "rRZapOWsaukpHOk2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_8.png", "conversations": [ { "from": "human", "value": "\nWhat elements are found within the area marked by [0.066, 0.919, 0.110, 0.942] on this chart?" }, { "from": "gpt", "value": "The x-axis tick label '2005' is located within this area." } ], "id_raw": 188 }, { "id": "wlsAPLxeIFZyWZQI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.019, 0.458, 0.036, 0.520]?" }, { "from": "gpt", "value": "This area contains the Y-axis label 'Sales'." } ], "id_raw": 188 }, { "id": "pJoW0Nmh1UQT7s1z", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-label} at [0.079, 0.455, 0.090, 0.555]?" }, { "from": "gpt", "value": "Trend Value." } ], "id_raw": 189 }, { "id": "frRTIulhJtd2DiLd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the first x-axis label in the chart?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 189 }, { "id": "cWMqOMqLBwzPZW2P", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the legend item for 'Industry 4.0'}[0.168, 0.188, 0.237, 0.205]?" }, { "from": "gpt", "value": "#2ca02c." } ], "id_raw": 189 }, { "id": "l1YJKD41kQFd4EJo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_9.png", "conversations": [ { "from": "human", "value": "\nIs {the line representing 'Artificial Intelligence'}[0.125, 0.158, 0.900, 0.852] escalating or declining over the years?" }, { "from": "gpt", "value": "Escalating." } ], "id_raw": 189 }, { "id": "K2dqpcgXmkH9eCNH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_9.png", "conversations": [ { "from": "human", "value": "\nDoes the value represented by {the trend of 'Automation Processes'}[0.125, 0.351, 0.900, 0.774] in 2010 has increased or decreased by 2020?" }, { "from": "gpt", "value": "Increased." } ], "id_raw": 189 }, { "id": "qHiVlrnPOc3G0hpZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_9.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Internet of Things'}[0.125, 0.235, 0.900, 0.813] have a higher value in 2020 as compared to {the 'Industry 4.0'}[0.125, 0.274, 0.900, 0.659]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 189 }, { "id": "y4BAvbErdR0QEV72", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.168, 0.136, 0.287, 0.232]." }, { "from": "gpt", "value": "This region contains the legend items of 'Artificial Intelligence', 'Automation Processes', 'Industry 4.0', and 'Internet of Things', along with their respective color markers." } ], "id_raw": 189 }, { "id": "SRYHdRXyGwpeFOie", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region between [0.125, 0.885, 0.915, 0.930]?" }, { "from": "gpt", "value": "In this region, there are x-axis tick labels, ranging from 2010 to 2020 with a differing amount of space between each label." } ], "id_raw": 189 }, { "id": "w4LyK5AZlsaoh6l8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.365, 0.092, 0.660, 0.115]." }, { "from": "gpt", "value": "The region contains the subtitle of the chart \"Trends in AI and Automation (2010 - 2020)\"." } ], "id_raw": 189 }, { "id": "hikO3sT6QCPDFCQo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object at coordinates [0.154, 0.204, 0.176, 0.227]?" }, { "from": "gpt", "value": "It is the Y-axis label \"25\"." } ], "id_raw": 190 }, { "id": "qYcpOGuaV9s5NXLU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_10.png", "conversations": [ { "from": "human", "value": "\nWhat is at coordinates [0.806, 0.919, 0.817, 0.942]?" }, { "from": "gpt", "value": "It is the X-axis label \"5\"." } ], "id_raw": 190 }, { "id": "cH5X4DAkJufXonD5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_10.png", "conversations": [ { "from": "human", "value": "\nWhat's the label at [0.165, 0.756, 0.176, 0.779] on the Y-axis?" }, { "from": "gpt", "value": "It is \"5\"." } ], "id_raw": 190 }, { "id": "z2UYk7P9xUtWaPnt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the line of 'Industrial Development'}[0.188, 0.213, 0.812, 0.627] have a higher value at point 5 or does {the line of 'Technological Advancement'}[0.188, 0.379, 0.812, 0.765]?" }, { "from": "gpt", "value": "The line of 'Industrial Development'." } ], "id_raw": 190 }, { "id": "RTasTXuG4jFf4Bje", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_10.png", "conversations": [ { "from": "human", "value": "\nIn the second year, which development indicates a higher value: {the line of 'Agricultural Development'}[0.188, 0.130, 0.812, 0.351] or {the line of 'Industrial Development'}[0.188, 0.213, 0.812, 0.627]?" }, { "from": "gpt", "value": "The line of 'Agricultural Development'." } ], "id_raw": 190 }, { "id": "hXuxD2BlK9QQGnE9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_10.png", "conversations": [ { "from": "human", "value": "\nAt the first point, is {the value of 'Industrial Development'}[0.188, 0.627] greater or smaller than {the value of 'War Incidences'}[0.188, 0.489]?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 190 }, { "id": "k7gDVL2xhENnkX7o", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the description of the area [0.154, 0.066, 0.176, 0.089]?" }, { "from": "gpt", "value": "It contains the label \"30\" for the Y-axis." } ], "id_raw": 190 }, { "id": "moBtogBaSV67CWac", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_10.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area [-0.055, 0.033, -0.010, 0.047]?" }, { "from": "gpt", "value": "It contains the handle color marker for the legend entry of 'Agricultural Development'. The color is '#1f77b4'." } ], "id_raw": 190 }, { "id": "L81jNk0NbK3ISNKc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_10.png", "conversations": [ { "from": "human", "value": "\nWhat description can you give for the area within [-0.057, 0.020, 1.057, 0.062]?" }, { "from": "gpt", "value": "It contains the chart legend including the items 'Agricultural Development', 'Industrial Development', 'Technological Advancement', and 'War Incidences', which all have different colors associated with them." } ], "id_raw": 190 }, { "id": "4W9ldeo94KnrJIDW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_11.png", "conversations": [ { "from": "human", "value": "\nWhat content do you see at the {given points}[0.959, 0.158] on the chart?" }, { "from": "gpt", "value": "The point represents the data for MOOCs Usage in the year 2020." } ], "id_raw": 191 }, { "id": "LHy170v8RFR7jWfu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label of the chart with coordinates {coordinates}[0.019, 0.286, 0.036, 0.692]?" }, { "from": "gpt", "value": "The label is 'Increase in Usage / Investment (%)'." } ], "id_raw": 191 }, { "id": "IKEivjtfhzqKSYzL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_11.png", "conversations": [ { "from": "human", "value": "\nWhat is specified within the chart at the {coordinates}[0.765, 0.451, 0.783, 0.468]?" }, { "from": "gpt", "value": "It is the data point for the EdTech Investment in the year 2019." } ], "id_raw": 191 }, { "id": "NYoIMfIKk3A4Ziv3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_11.png", "conversations": [ { "from": "human", "value": "\nIs the {line of 'MOOCs Usage'}[0.088, 0.158, 0.959, 0.406] above or below the {line for 'University Online Education'}[0.088, 0.241, 0.959, 0.489]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 191 }, { "id": "Gxs3aHe0hVaSf2AZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_11.png", "conversations": [ { "from": "human", "value": "\nWhich has higher value in 2020, {MOOCs Usage}[0.959, 0.158] or {EdTech Investment}[0.959, 0.282]?" }, { "from": "gpt", "value": "MOOCs Usage." } ], "id_raw": 191 }, { "id": "rt9WJlgfpXCLAZDG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_11.png", "conversations": [ { "from": "human", "value": "\nWhich has a significant rise from the year 2015 to 2020, {University Online Education}[0.088, 0.489, 0.959, 0.241] or {EdTech Investment}[0.088, 0.754, 0.959, 0.282]?" }, { "from": "gpt", "value": "EdTech Investment." } ], "id_raw": 191 }, { "id": "izUU37gTwkaRMzNv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the chart components in the {coordinates}[0.244, 0.020, 0.804, 0.097]?" }, { "from": "gpt", "value": "This area contains a two-column legend with four items. The items are 'MOOCs Usage', 'K-12 Online Courses', 'University Online Education' and 'EdTech Investment' each with a different color marker." } ], "id_raw": 191 }, { "id": "RvR0lpBbzLAoLhd9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_11.png", "conversations": [ { "from": "human", "value": "\nWhat details are included in the {coordinates}[0.788, 0.224, 0.808, 0.254]?" }, { "from": "gpt", "value": "This region contains a chart data point for the EdTech Investment line in the year 2020." } ], "id_raw": 191 }, { "id": "TSQ3Vvz17eJLlRG9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the plot features located in the {coordinates}[0.088, 0.158, 0.611, 0.406]?" }, { "from": "gpt", "value": "It contains part of two data series. Specifically, part of the MOOCs Usage distinguishable by circle markers and part of the University Online Education line marked by cross symbols, from the year 2015 to 2018." } ], "id_raw": 191 }, { "id": "PFAEgrDpuBU4z4T6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the element positioned at [0.019, 0.385, 0.036, 0.593]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 192 }, { "id": "pvDsrvecc4NX3HlU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the marker for 'Solar' in the legend?" }, { "from": "gpt", "value": "[0.674, 0.033, 0.719, 0.047]." } ], "id_raw": 192 }, { "id": "RjWCDzcCDD8aMCjF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the data represented by {the line for 'Coal'}[0.077, 0.199, 0.959, 0.385]?" }, { "from": "gpt", "value": "It's the percentage of energy usage from 'Coal' between the years 2014 and 2019." } ], "id_raw": 192 }, { "id": "qoSQTTkjVEHRrKpD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Natural Gas'}[0.077, 0.179, 0.959, 0.437] show an increase or decrease in energy usage over the period?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 192 }, { "id": "3jWzLWpg4Ej2CUFF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_12.png", "conversations": [ { "from": "human", "value": "\nHow does the final data point for 'Solar'[0.959, 0.748] on y-axis compare with the final data point for 'Oil'[0.959, 0.354]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 192 }, { "id": "rbUD0oqOi6brcMqX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_12.png", "conversations": [ { "from": "human", "value": "\nWhich had a higher energy usage percentage in 2014, Oil [0.077, 0.385] or Natural Gas [0.077, 0.437]?" }, { "from": "gpt", "value": "Oil." } ], "id_raw": 192 }, { "id": "ohwv0rycBZdVzLQP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_12.png", "conversations": [ { "from": "human", "value": "\nCould you describe the contents within the coordinates [0.258, 0.020, 0.779, 0.062]?" }, { "from": "gpt", "value": "This region includes the legend items: {Oil}[0.313, 0.030, 0.336, 0.053], {Natural Gas}[0.420, 0.030, 0.523, 0.053], {Coal}[0.606, 0.030, 0.644, 0.053], and {Solar}[0.728, 0.030, 0.772, 0.053]. Each item is paired with a corresponding colored marker." } ], "id_raw": 192 }, { "id": "QnRKWMjlRtI5S43V", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_12.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained within the region [0.055, 0.919, 0.981, 0.942]?" }, { "from": "gpt", "value": "The x-axis labels '2014' [0.055, 0.919, 0.099, 0.942], '2015' [0.232, 0.919, 0.276, 0.942], '2016' [0.408, 0.919, 0.452, 0.942], '2017' [0.585, 0.919, 0.628, 0.942], '2018' [0.761, 0.919, 0.805, 0.942] and '2019' [0.937, 0.919, 0.981, 0.942] are included in this region." } ], "id_raw": 192 }, { "id": "IIE3FL35Mzx9SR1m", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located in the region from [0.055, 0.066, 0.065, 0.710]?" }, { "from": "gpt", "value": "The y-axis labels '0' [0.054, 0.894, 0.065, 0.917], '20' [0.043, 0.687, 0.065, 0.710], '40' [0.043, 0.480, 0.065, 0.503], '60' [0.043, 0.273, 0.065, 0.296] are located in this region." } ], "id_raw": 192 }, { "id": "zl9EVhEOFAtLNix6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_13.png", "conversations": [ { "from": "human", "value": "\nWhat is referred by the coordinate [0.019, 0.368, 0.036, 0.610] in the plot?" }, { "from": "gpt", "value": "It is the y-axis label." } ], "id_raw": 193 }, { "id": "k3LBVVpS4C32JYvI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate information for the third tick label on the x-axis?" }, { "from": "gpt", "value": "The coordinate is [0.451, 0.919, 0.462, 0.942]." } ], "id_raw": 193 }, { "id": "00trV9KQ2EvFLlbm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the tick label on the y-axis at the coordinates [0.043, 0.539, 0.098, 0.562]?" }, { "from": "gpt", "value": "The tick label is '30000'." } ], "id_raw": 193 }, { "id": "wqApjyvZI57tQWmN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_13.png", "conversations": [ { "from": "human", "value": "\nIs the data point for 'Resources' at {point 2}[0.456, 0.217] higher or lower than at {point 3}[0.630, 0.785]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 193 }, { "id": "PmApWW4DaMiuU0sS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_13.png", "conversations": [ { "from": "human", "value": "\nWell, for 'Resources', is energy consumption at {point 1}[0.283, 0.383] greater or smaller than at {point 0}[0.110, 0.146]?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 193 }, { "id": "gxzECbkxC637o1Jt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_13.png", "conversations": [ { "from": "human", "value": "\nWhich data point for 'Resources' has lower energy consumption, {point 5}[0.976, 0.300] or {point 4}[0.803, 0.702]?" }, { "from": "gpt", "value": "Point 4." } ], "id_raw": 193 }, { "id": "3YDBueK8mHlWKVMr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_13.png", "conversations": [ { "from": "human", "value": "\nCould you describe what the area with coordinates [0.110, 0.075, 0.976, 0.903] contains?" }, { "from": "gpt", "value": "It is the main plot area containing the line chart, with points marking 'Resources' energy consumption over 5 points." } ], "id_raw": 193 }, { "id": "ZuLIasCMvDKVZ8Uz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_13.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what's inside the coordinate [0.468, 0.020, 0.618, 0.062]?" }, { "from": "gpt", "value": "This area contains the legend {Resources}[0.523, 0.030, 0.611, 0.053] with a colored marker." } ], "id_raw": 193 }, { "id": "BLdGMGzdMnWTFCxw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_13.png", "conversations": [ { "from": "human", "value": "\nWhat is within the coordinates [0.087, 0.894, 0.098, 0.917] and [0.043, 0.066, 0.098, 0.089]?" }, { "from": "gpt", "value": "These two areas contain the y-axis labels '0' at {coordinates}[0.087, 0.894, 0.098, 0.917] and '70000' at {coordinates}[0.043, 0.066, 0.098, 0.089], respectively." } ], "id_raw": 193 }, { "id": "nsIBTM5I61qg6U1c", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart type depicted in the figure?" }, { "from": "gpt", "value": "Line chart." } ], "id_raw": 194 }, { "id": "FgO7gPfoOQHCUaAB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel} at [0.015, 0.395, 0.029, 0.583]?" }, { "from": "gpt", "value": "Popularity Score." } ], "id_raw": 194 }, { "id": "9fnequShmDpLaD9h", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_14.png", "conversations": [ { "from": "human", "value": "\nWhat object is represented by {coordinates} at [0.273, 0.030, 0.316, 0.053]?" }, { "from": "gpt", "value": "Italian label text." } ], "id_raw": 194 }, { "id": "p7OgmvV2lL3cIW8k", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_14.png", "conversations": [ { "from": "human", "value": "\nIs {the sixth line point of the French line}[0.775, 0.241] above or below {the sixth line point of the Italian line}[0.391, 0.572]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 194 }, { "id": "tZR9CQJtdVgOIexT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the point for the year 2010 on the Japanese line}[0.327, 0.671] have a higher or lower score than {the same year on the Mexican line}[0.327, 0.696]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 194 }, { "id": "vPrYeAb2IiJa2sHr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_14.png", "conversations": [ { "from": "human", "value": "\nWhich line showcases a higher popularity score in 2016, {French}[0.711, 0.282] or {Chinese}[0.711, 0.406]?" }, { "from": "gpt", "value": "French." } ], "id_raw": 194 }, { "id": "lDz4CH6a9SgUBWAC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the bounding box [0.185, 0.909, 0.999, 0.944]." }, { "from": "gpt", "value": "This region contains multiple x-axis tick labels representing the years from 2008 to 2020." } ], "id_raw": 194 }, { "id": "JFEvIq39NdaIDWmB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_14.png", "conversations": [ { "from": "human", "value": "\nWhat contents can be found within the area [0.180, 0.713, 0.710, 0.750]?" }, { "from": "gpt", "value": "Part of the lines for the Italian, French, Chinese, Japanese, and Mexican cuisine trends from the years 2009 to 2015 are found in this region." } ], "id_raw": 194 }, { "id": "gB40DdZQfxLNAq1z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the items located in the region [0.200, 0.019, 0.800, 0.060]." }, { "from": "gpt", "value": "The legend of the chart is found in this area, listing each type of cuisine with their corresponding color coded handles." } ], "id_raw": 194 }, { "id": "da0CwKrcJcDCxHnE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the set of coordinates [0.015, 0.419, 0.029, 0.555] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 195 }, { "id": "3uPmsGrBFneTcder", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_15.png", "conversations": [ { "from": "human", "value": "\nWhere is {the x-axis tick label} for the year 2010 located?" }, { "from": "gpt", "value": "[0.497, 0.939, 0.532, 0.957]" } ], "id_raw": 195 }, { "id": "sozdAtQrPRVJJDwL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_15.png", "conversations": [ { "from": "human", "value": "\nWhat text is referred to at these coordinates [0.261, 0.127, 0.439, 0.147]?" }, { "from": "gpt", "value": "The title of the chart that reads \"Energy Use and Resource Consumption over Years.\"" } ], "id_raw": 195 }, { "id": "4MujfQEXIRQXJ8rU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_15.png", "conversations": [ { "from": "human", "value": "\nBy the end of the period reflected in the chart, are the values for {Hydrogen Fuel Cell Consumption}[0.968, 0.751] greater or less than the values for {Oil Consumption}[0.968, 0.432]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 195 }, { "id": "36oZbH3m0PZLuQkn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_15.png", "conversations": [ { "from": "human", "value": "\nComparing {Coal Consumption}[0.288, 0.179] in 2005 to {Nature Gas Consumption}[0.288, 0.762] in the same year, which one had a higher consumption percentage?" }, { "from": "gpt", "value": "Coal Consumption." } ], "id_raw": 195 }, { "id": "xuHpZNghWPV4OISH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_15.png", "conversations": [ { "from": "human", "value": "\nLooking at the first point for {Nature Gas Consumption}[0.062, 0.817] and {Oil Consumption}[0.062, 0.487], which had a higher consumption percentage in year 2000?" }, { "from": "gpt", "value": "Oil Consumption." } ], "id_raw": 195 }, { "id": "K7nPjcKRQwBDPLii", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the information within these coordinates, [0.069, 0.055, 0.346, 0.165]?" }, { "from": "gpt", "value": "This area serves as the chart legend. It presents the names of different energy types: {Coal Consumption}[0.113, 0.062, 0.240, 0.080], {Oil Consumption}[0.113, 0.088, 0.228, 0.106], {Nature Gas Consumption}[0.113, 0.115, 0.288, 0.132], and {Hydrogen Fuel Cell Consumption}[0.113, 0.141, 0.340, 0.158], each one paired with a different colored symbol." } ], "id_raw": 195 }, { "id": "LZbpwYTFLYTZUm7P", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area within these coordinates, [0.062, 0.047, 0.968, 0.927]." }, { "from": "gpt", "value": "This is the main area of the chart. It depicts four lines representing the energy consumption of different energy types over the years. The X-axis represents years, ranging from 2000 to 2020, while Y-axis represents the percentage of consumption." } ], "id_raw": 195 }, { "id": "MjNN1E57jhSKHssi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_15.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within these coordinates [0.062, 0.102, 0.968, 0.212]?" }, { "from": "gpt", "value": "This is the line for {Coal Consumption}[0.062, 0.102, 0.968, 0.212], represented with a blue line and circle markers, showing the percentage every 5 years from 2000 to 2020." } ], "id_raw": 195 }, { "id": "Bb2IJdYGM4pXbQCl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_16.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.066, 0.919, 0.110, 0.942]?" }, { "from": "gpt", "value": "X-axis label '2010'." } ], "id_raw": 196 }, { "id": "jef1Dx2zXQMLgsno", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_16.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {a line with color '#2ca02c'}[0.088, 0.241, 0.959, 0.365]?" }, { "from": "gpt", "value": "This line represents the trend of 'Nuclear' energy consumption." } ], "id_raw": 196 }, { "id": "QDJ8HbxAEhPnLjHn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the marker located at [0.515, 0.033, 0.560, 0.047] signify in the legend?" }, { "from": "gpt", "value": "This marker stands for 'Nuclear' in the legend." } ], "id_raw": 196 }, { "id": "xVB1iyrY29Jn1CzG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_16.png", "conversations": [ { "from": "human", "value": "\nIs the {power source mark}[0.326, 0.033, 0.371, 0.047] given more representation compared to the {power source mark}[0.515, 0.033, 0.560, 0.047] in 2020?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 196 }, { "id": "HFNV1OsmFDVwF5xu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_16.png", "conversations": [ { "from": "human", "value": "\nAre coordinates for year 2011 for {line 'Coal'} [0.175, 0.282] higher or lower than the {line 'Hydropower'}[0.175, 0.290]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 196 }, { "id": "ELewnomfM29Fih6S", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_16.png", "conversations": [ { "from": "human", "value": "\nWhich energy source dips lower in 2016, 'Nuclear' or 'Coal'?" }, { "from": "gpt", "value": "'Coal'." } ], "id_raw": 196 }, { "id": "7jSL9Ucwm0JFgjmk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_16.png", "conversations": [ { "from": "human", "value": "\nPlease describe what can be found between the coordinates [0.203, 0.020, 0.845, 0.062]." }, { "from": "gpt", "value": "The region contains a legend with markers and labels denoting different power sources: {Coal}[0.258, 0.030, 0.297, 0.053], {Hydropower}[0.380, 0.030, 0.486, 0.053], {Nuclear}[0.569, 0.030, 0.636, 0.053], and {Green Energy}[0.719, 0.030, 0.838, 0.053]." } ], "id_raw": 196 }, { "id": "C1E81oLnAk5jUN39", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is between the coordinates [0.066, 0.919, 0.981, 0.942]?" }, { "from": "gpt", "value": "This region includes the labels for x-axis representing different years from 2010 to 2020." } ], "id_raw": 196 }, { "id": "k6mbqPVFmIUIfNB2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_16.png", "conversations": [ { "from": "human", "value": "\nWhat key elements can be found in the region with coordinates [0.088, 0.241, 0.785, 0.472]?" }, { "from": "gpt", "value": "This area encompasses a portion of each line representing 'Coal', 'Hydropower', 'Nuclear', and 'Green Energy' trends from 2010 to 2018." } ], "id_raw": 196 }, { "id": "dcjGU8U9VnPOwqZk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis represent in the chart?" }, { "from": "gpt", "value": "The y-axis represents the {Adoption Rates (%)}[0.031, 0.377, 0.048, 0.601]." } ], "id_raw": 197 }, { "id": "1ix3kCXLAc6ElGfs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.795, 0.033, 0.840, 0.047] indicate?" }, { "from": "gpt", "value": "The object indicates the color marker for the {Telemedicine}[0.849, 0.030, 0.962, 0.053] legend." } ], "id_raw": 197 }, { "id": "RpLoNfZ13pLWF1vx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the technology being represented by the line with coordinates [0.233, 0.257, 0.767, 0.340]?" }, { "from": "gpt", "value": "The technology being represented is {Electronic Health Record}[0.324, 0.030, 0.538, 0.053]." } ], "id_raw": 197 }, { "id": "oFbilkh35ytQgJKN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_17.png", "conversations": [ { "from": "human", "value": "\nIs the adoption rate of the technology {Telemedicine}[0.849, 0.030, 0.962, 0.053] higher or lower at {20 Years}[0.622, 0.919, 0.644, 0.942] compared to its rate at {15 Years}[0.489, 0.919, 0.511, 0.942]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 197 }, { "id": "WaLruE6NaNdXx9Dl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_17.png", "conversations": [ { "from": "human", "value": "\nBetween {AI in Radiography}[0.087, 0.030, 0.241, 0.053] and {AI in Diagnostics}[0.621, 0.030, 0.765, 0.053], which one had a higher adoption rate at {10 Years}[0.356, 0.919, 0.378, 0.942]?" }, { "from": "gpt", "value": "AI in Radiography." } ], "id_raw": 197 }, { "id": "l8c0hWNefSSpnlem", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_17.png", "conversations": [ { "from": "human", "value": "\nIs the adoption rate of {AI in Diagnostics}[0.621, 0.030, 0.765, 0.053] increasing or decreasing from {5 Years}[0.228, 0.919, 0.239, 0.942] to {25 Years}[0.756, 0.919, 0.778, 0.942]?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 197 }, { "id": "5ym4gDw6fBGB3OCs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the graph in the region [0.233, 0.133, 0.767, 0.655]?" }, { "from": "gpt", "value": "This region contains four lines representing different technologies: {AI in Radiography}[0.233, 0.133, 0.767, 0.257], {Electronic Health Record}[0.233, 0.257, 0.767, 0.340], {AI in Diagnostics}[0.233, 0.191, 0.767, 0.530] and {Telemedicine}[0.233, 0.166, 0.767, 0.655]. Each of these lines includes data points for 5, 10, 15, 20, and 25 years." } ], "id_raw": 197 }, { "id": "56FclKW6aOnSJyb4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.031, 0.020, 0.969, 0.062] include?" }, { "from": "gpt", "value": "This region represents the chart's legend. Four different items are listed here: {AI in Radiography}[0.087, 0.030, 0.241, 0.053], {Electronic Health Record}[0.324, 0.030, 0.538, 0.053], {AI in Diagnostics}[0.621, 0.030, 0.765, 0.053], and {Telemedicine}[0.849, 0.030, 0.962, 0.053]." } ], "id_raw": 197 }, { "id": "yIOw08R7puaWOYD3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.066, 0.066, 0.088, 0.917]?" }, { "from": "gpt", "value": "The region denotes the y-axis labels and ticks. The labels are increments of 20, starting from {0}[0.077, 0.894, 0.088, 0.917] and going up to {100}[0.055, 0.066, 0.088, 0.089]." } ], "id_raw": 197 }, { "id": "tt6Bk0o7sC5oZuiY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the object situated at the coordinates [0.522, 0.952, 0.559, 0.975]?" }, { "from": "gpt", "value": "Chart's x-axis label." } ], "id_raw": 198 }, { "id": "3CEKg285D9BZJKJR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_18.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates for the 'Manufacture' section of the legend in the chart?" }, { "from": "gpt", "value": "[0.387, 0.030, 0.497, 0.053]" } ], "id_raw": 198 }, { "id": "1JDUwlNJjMd8xi6F", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label}[0.043, 0.130, 0.109, 0.153]?" }, { "from": "gpt", "value": "120000." } ], "id_raw": 198 }, { "id": "nbP49ykT9EwDyHPT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_18.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'E-commerce'}[0.697, 0.393] at 2015 above or below {the point for 'Software development'}[0.697, 0.393] at 2015?" }, { "from": "gpt", "value": "Equal." } ], "id_raw": 198 }, { "id": "Vl4vVrsKQEzY4D8e", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_18.png", "conversations": [ { "from": "human", "value": "\nAccording to the data points, is the {E-commerce}[0.121, 0.215, 0.959, 0.584] generally above or below {Manufacture}[0.121, 0.457, 0.959, 0.635]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 198 }, { "id": "liwvsaLCECRjyleG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_18.png", "conversations": [ { "from": "human", "value": "\nWhich sector, {E-commerce}[0.959, 0.215] or {Robotics}[0.959, 0.113], had higher value in 2020?" }, { "from": "gpt", "value": "Robotics." } ], "id_raw": 198 }, { "id": "mU7en8uXeElwcUX2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_18.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content in the region specified by [0.748, 0.004, 0.927, 0.046]?" }, { "from": "gpt", "value": "The region contains the last two entries of the legend: {Software development}[0.780, 0.030, 0.777, 0.053] and {Robotics}[0.860, 0.030, 0.934, 0.053]. Each entry is paired with its corresponding color marker." } ], "id_raw": 198 }, { "id": "TL679YgIiTemFKvG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects in the area defined by [0.806, 0.033, 0.852, 0.047]." }, { "from": "gpt", "value": "The space contains a colored marker that denotes the 'Robotics' line on the graph." } ], "id_raw": 198 }, { "id": "s2suiEvONZa0WWS8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_18.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within the region [0.803, 0.103, 0.965, 0.142]?" }, { "from": "gpt", "value": "There are two objects. The y-axis tick labels '116000' and '120000' are located at {coordinates}[0.803, 0.104, 0.828, 0.127] and {coordinates}[0.855, 0.119, 0.880, 0.142], respectively." } ], "id_raw": 198 }, { "id": "DWBEecEaVPwngdTm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the specific location [0.015, 0.354, 0.029, 0.592]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 199 }, { "id": "taaPCbdAWE4oBNBq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_19.png", "conversations": [ { "from": "human", "value": "\nCan you tell the x-coordinate of the tick label for '2015'?" }, { "from": "gpt", "value": "[0.726, 0.931, 0.761, 0.951]" } ], "id_raw": 199 }, { "id": "5hj8ZOIUUoQc4BM6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_10_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line for {Middle-Income Households}[0.071, 0.242, 0.968, 0.526]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 199 }, { "id": "ATWjhPjpUd0XxMyc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_19.png", "conversations": [ { "from": "human", "value": "\nDoes the {Low-Income Households}[0.071, 0.367, 0.968, 0.899] line have a higher or lower value at the year 2020 than the {High-Income Households}[0.071, 0.065, 0.968, 0.242] line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 199 }, { "id": "CIiWlhQtWbLhTryI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_19.png", "conversations": [ { "from": "human", "value": "\nCompare the year 2015 point on the {Middle-Income Households}[0.743, 0.296] line with the year 2015 point on the {Average Households}[0.743, 0.313] line. Which one is higher?" }, { "from": "gpt", "value": "Middle-Income Households." } ], "id_raw": 199 }, { "id": "NtYk1Wn9B8BufnXf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_10_19.png", "conversations": [ { "from": "human", "value": "\nIs the line for {High-Income Households}[0.071, 0.065, 0.968, 0.242] generally above or below the line for {Average Households}[0.071, 0.260, 0.968, 0.562]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 199 }, { "id": "qzYoOzEfaUYGUBHk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in the region [0.724, 0.039, 0.961, 0.165]?" }, { "from": "gpt", "value": "This region contains the legend items for the line chart, which include {Low-Income Households}[0.768, 0.047, 0.936, 0.067], {Middle-Income Households}[0.768, 0.077, 0.955, 0.097], {High-Income Households}[0.768, 0.107, 0.941, 0.127], and {Average Households}[0.768, 0.137, 0.910, 0.157], each paired with a different color." } ], "id_raw": 199 }, { "id": "8J7FY3zDMlpHkLtU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_19.png", "conversations": [ { "from": "human", "value": "\nCould you describe the region with [0.043, 0.199, 0.061, 0.219]?" }, { "from": "gpt", "value": "This region contains the y-axis tick label with the value '90'." } ], "id_raw": 199 }, { "id": "pyLHuy3jhabkW9NQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_10_19.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the area [0.502, 0.931, 0.537, 0.951]?" }, { "from": "gpt", "value": "The x-axis tick label '2010' is within this region." } ], "id_raw": 199 }, { "id": "ccxDnt5d94GKf847", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the x-axis tick} at [0.664, 0.894]?" }, { "from": "gpt", "value": "Pre-Final_Stage." } ], "id_raw": 200 }, { "id": "7X2pebNdhjmreOFk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {line label 'Brand B'}[0.900, 0.134, 0.966, 0.164] in the legend?" }, { "from": "gpt", "value": "#dc3545." } ], "id_raw": 200 }, { "id": "PrEsGfk4g8cUp713", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the ylabel in the bar chart with coordinates shown in [0.015, 0.377, 0.034, 0.583]?" }, { "from": "gpt", "value": "Performance." } ], "id_raw": 200 }, { "id": "Y3Y6X0oGE5YHkdPF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_0.png", "conversations": [ { "from": "human", "value": "\nIs the {Performance of BrandA at Middle_Stage}[0.388, 0.363] higher or lower than {Performance of BrandB at the same stage}[0.388, 0.610]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 200 }, { "id": "V3aTPkrHAiJHNGQX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the performance comparison between {Performance of Brand A at the Pre-Final_Stage}[0.664, 0.610] and {Performance of Brand B at the same Stage}[0.664, 0.671]?" }, { "from": "gpt", "value": "Brand A is higher." } ], "id_raw": 200 }, { "id": "4MIqC5N2TQiSbGe2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_0.png", "conversations": [ { "from": "human", "value": "\nIs the performance of {BrandA in the Final_Stage}[0.939, 0.733] more or less than the {Initial_Stage of the same brand}[0.113, 0.116]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 200 }, { "id": "eAqbVIuMfOA8vlUy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content presented in this coordinate space [0.846, 0.079, 0.972, 0.176]." }, { "from": "gpt", "value": "This space includes the legend of the chart. There are two items: {Brand A}[0.900, 0.091, 0.966, 0.121] and {Brand B}[0.900, 0.134, 0.966, 0.164]." } ], "id_raw": 200 }, { "id": "5e8PASAMc30oW8Qo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area covering the coordinates from [0.070, 0.911, 0.979, 0.934]." }, { "from": "gpt", "value": "This is the x-axis showing the stages. It includes the label for {Initial_Stage}[0.070, 0.911, 0.155, 0.934], {Middle_Stage}[0.342, 0.911, 0.435, 0.934], {Pre-Final_Stage}[0.610, 0.911, 0.717, 0.934] and {Final_Stage}[0.900, 0.911, 0.979, 0.934]." } ], "id_raw": 200 }, { "id": "48INO1fIrUy6gJul", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.040, 0.106, 0.062, 0.130]?" }, { "from": "gpt", "value": "A y-axis tick label is located here, labelled {0.8}[0.040, 0.106, 0.062, 0.130]." } ], "id_raw": 200 }, { "id": "mTGFdBMcfchLJVzJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.015, 0.327, 0.034, 0.633] in the chart?" }, { "from": "gpt", "value": "It is Y-axis label." } ], "id_raw": 202 }, { "id": "vHCFGQpSCFX73ScB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinates of the second label in the legend?" }, { "from": "gpt", "value": "[0.893, 0.134, 0.943, 0.164]." } ], "id_raw": 202 }, { "id": "xfPsSyJyPrcS5JBc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the {xlabel}[0.476, 0.943, 0.580, 0.975] named in the chart?" }, { "from": "gpt", "value": "Age Group." } ], "id_raw": 202 }, { "id": "KFPMl4Xt2dY2t58q", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_2.png", "conversations": [ { "from": "human", "value": "\nOn the 'Millenials' line, does {the point at}[0.683, 0.182] have a higher or lower value than {the point at}[0.246, 0.649]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 202 }, { "id": "c40cDFwNnxFQy9GC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_2.png", "conversations": [ { "from": "human", "value": "\nAre the {line for 'Millenials'}[0.159, 0.148, 0.946, 0.729] generally above or below the {line for 'Gen Z'}[0.159, 0.231, 0.946, 0.729]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 202 }, { "id": "W0eD5Z7Dhwwr9PRH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_2.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {point 6}[0.596, 0.463], 'Gen Z' or 'Millenials'?" }, { "from": "gpt", "value": "Millenials." } ], "id_raw": 202 }, { "id": "4Rikte2WR0ebjPgG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.840, 0.079, 0.977, 0.176]." }, { "from": "gpt", "value": "This area contains the legend for the lines in the chart. There are two entries: {Millenials}[0.893, 0.091, 0.970, 0.121] and {Gen Z}[0.893, 0.134, 0.943, 0.164], both paired with respective markers." } ], "id_raw": 202 }, { "id": "XG1CK6SsIJN5axGk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.071, 0.895, 0.946, 0.895]." }, { "from": "gpt", "value": "The area contains the x-axis ticks at regular intervals, representing incremental age groups from 0.0 to 1.0." } ], "id_raw": 202 }, { "id": "c6nQxsmR8BZ9DgLs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.159, 0.231, 0.946, 0.729]?" }, { "from": "gpt", "value": "The coordinates contain two objects, the 'Millenials' and 'Gen Z' lines. The 'Millenials' line is generally above the 'Gen Z'." } ], "id_raw": 202 }, { "id": "l6051D1iybRvyZiF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the main object at coordinates [0.015,0.377,0.034,0.583] in the chart?" }, { "from": "gpt", "value": "Performance." } ], "id_raw": 204 }, { "id": "2EXgsNiCEC5dj5wO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first tick label on the x-axis?" }, { "from": "gpt", "value": "[0.060,0.911,0.082,0.934]." } ], "id_raw": 204 }, { "id": "Bi4We7MWOj5D6rhx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-value represented by {the data point}[0.159,0.314] from the AAPL line plot?" }, { "from": "gpt", "value": "0.7." } ], "id_raw": 204 }, { "id": "6xpxMBQsZdNUwycv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_4.png", "conversations": [ { "from": "human", "value": "\nIn the chart, which stock {AAPL Stock}[0.159,0.314] or {MSFT Stock}[0.159,0.356] had better performance at the investment ratio of 0.1?" }, { "from": "gpt", "value": "MSFT Stock." } ], "id_raw": 204 }, { "id": "kfaxYQ5aurWgLGOD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_4.png", "conversations": [ { "from": "human", "value": "\nAt the point {line_0, point3}[0.596, 0.414], is AAPL's performance above or below {line_3, point3}[0.596, 0.206] of MSFT's performance?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 204 }, { "id": "yT7gPAEJ43ejM05r", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_4.png", "conversations": [ { "from": "human", "value": "\nWhich of the two stocks {AAPL Stock}[0.946,0.480] or {MSFT Stock}[0.946,0.148] had a higher performance at an investment ratio of 1.0?" }, { "from": "gpt", "value": "MSFT Stock." } ], "id_raw": 204 }, { "id": "c33fOSIDAzLhbQSr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the entities appearing in the region: [0.080, 0.079, 0.235, 0.176]." }, { "from": "gpt", "value": "This area contains the legend of the graph, including {AAPL Stock}[0.133, 0.091, 0.227, 0.121] with a blue marker and {MSFT Stock}[0.133, 0.134, 0.229, 0.164] with a red marker." } ], "id_raw": 204 }, { "id": "0SBcipMTkBR8dLVp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the region: [0.071, 0.895, 0.957, 0.934]." }, { "from": "gpt", "value": "This region contains the x-axis of the chart along with its labels representing Investment Ratios, specifically value labels ranging from '0.0' to '1.0' at regular intervals." } ], "id_raw": 204 }, { "id": "tqIlNaRyr7u7gjQo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are in the region covered by [0.159, 0.148, 0.946, 0.356]?" }, { "from": "gpt", "value": "This region comprises of line plots representing the performance of both AAPL and MSFT stocks at different investment ratios. Starting from the lower limit, we have data points for MSFT performance, followed by data points for AAPL performance." } ], "id_raw": 204 }, { "id": "ELMBARanwjOWNPrg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the line for 'Design Score'} located at [0.159, 0.231, 0.946, 0.853] in the chart?" }, { "from": "gpt", "value": "It is the representation of the Design Innovation Score against the Creativity index." } ], "id_raw": 205 }, { "id": "Geu75AK7xCluuCJY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.282, 0.034, 0.678]?" }, { "from": "gpt", "value": "Design Innovation Score." } ], "id_raw": 205 }, { "id": "faiwIbUaogVO8Vuq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the {y-axis tick label}[0.040, 0.554, 0.062, 0.577]?" }, { "from": "gpt", "value": "0.4." } ], "id_raw": 205 }, { "id": "oeU2Fl8YQTHLzkQj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_5.png", "conversations": [ { "from": "human", "value": "\nIs the {Design Score point at '0.5'}[0.508, 0.439] higher or lower than the {Design Score point at '0.6'}[0.596, 0.563]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 205 }, { "id": "CRB2pjnaSbpQQjxl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in location between {the point for '0.1' on the Design Score line}[0.159, 0.231] and {the point for '0.2' on the same line}[0.246, 0.314]?" }, { "from": "gpt", "value": "The point for '0.2' is above and to the right of the point for '0.1'." } ], "id_raw": 205 }, { "id": "PSsPm3GaKZBDEsDg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_5.png", "conversations": [ { "from": "human", "value": "\nIs the {Design Score line point for '1.0'}[0.946, 0.853] above or below the {Design Score line point for '0.9'}[0.858, 0.770]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 205 }, { "id": "2dtJ6FmSDTSmza1n", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area given by these coordinates [0.807, 0.079, 0.977, 0.132]." }, { "from": "gpt", "value": "The area contains the legend for the Design Score line with a label and a corresponding color marker." } ], "id_raw": 205 }, { "id": "QDkfp5N5KwXvyJtq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is contained within this region [0.071, 0.895, 0.957, 0.934]?" }, { "from": "gpt", "value": "Yes. This area contains the x-axis tick labels and tick lines for different Creativity Index values ranging from 0.0 to 1.0." } ], "id_raw": 205 }, { "id": "HT5nItsZTIjiqbUF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained within the region [0.034,0.222,0.071,0.577]?" }, { "from": "gpt", "value": "This region consists of 4 y-axis tick labels with corresponding values '0.2', '0.4', '0.6' and '0.8'." } ], "id_raw": 205 }, { "id": "SHyqHbRpGi6kTZ1x", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.015, 0.364, 0.034, 0.596] denote in the chart?" }, { "from": "gpt", "value": "It is the Y-axis label, representing Public Opinion." } ], "id_raw": 206 }, { "id": "dMsXDcCU5EZqmdSe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_6.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the line with coordinates [0.159, 0.306, 0.946, 0.480]?" }, { "from": "gpt", "value": "The line represents the trend in 'Scandal' data." } ], "id_raw": 206 }, { "id": "AZbA74ISzwy8plZq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the tick on the X-axis at [0.246, 0.895] represent?" }, { "from": "gpt", "value": "It represents a point on the X-axis labeled '0.2'." } ], "id_raw": 206 }, { "id": "HAEmQytUy0xwCOhu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_6.png", "conversations": [ { "from": "human", "value": "\nWhich trend is higher at the point of x=0.8, is it 'Popularity' {line}[0.159, 0.148, 0.946, 0.422] or 'Scandal' {line}[0.159, 0.306, 0.946, 0.480]?" }, { "from": "gpt", "value": "'Popularity'." } ], "id_raw": 206 }, { "id": "9IbsVSqiB2syswwk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Popularity' trend}[0.596, 0.281] higher or lower than the corresponding point on the 'Scandal' trend?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 206 }, { "id": "3rIZaOV0kmfsNmwK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_6.png", "conversations": [ { "from": "human", "value": "\nDoes the 'Scandal' {line}[0.159, 0.306, 0.946, 0.480] curve ascend or descend from x=0.1 to x=0.6?" }, { "from": "gpt", "value": "Ascend." } ], "id_raw": 206 }, { "id": "LdzIRzyOgpYxvgat", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the region [0.833, 0.079, 0.977, 0.176]?" }, { "from": "gpt", "value": "This region contains the legend items for the two data series: {Popularity}[0.887, 0.091, 0.970, 0.121], and {Scandal}[0.887, 0.134, 0.952, 0.164], each represented by a unique color." } ], "id_raw": 206 }, { "id": "1usCpxQcjtL5TdP7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within the region [0.071, 0.066, 0.985, 0.895]?" }, { "from": "gpt", "value": "It is the area of the plot chart, including the X and Y axes, the 'Popularity' and 'Scandal' lines, and their data points." } ], "id_raw": 206 }, { "id": "gQNtkax8YLZe3ADk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.276, 0.025, 0.780, 0.058]." }, { "from": "gpt", "value": "The area serves as the location of the subtitle of the plot chart: \"Media Influence and Public Opinion Guidance\"." } ], "id_raw": 206 }, { "id": "97sy2zaky0gqgaH2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_7.png", "conversations": [ { "from": "human", "value": "\nWhat object corresponds to {coordinates}[0.040, 0.979, 0.073, 1.002] in this line chart?" }, { "from": "gpt", "value": "This corresponds to the y-axis tick label '-0.3'." } ], "id_raw": 207 }, { "id": "42KPfXjo1ie3mE17", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the third city}[0.281, 0.911, 0.331, 0.934] labeled along the x-axis in the plot?" }, { "from": "gpt", "value": "The third city along the x-axis is 'Atlanta'." } ], "id_raw": 207 }, { "id": "5paYCwBk1T6w8ast", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the legend item 'Longitude Changes'}[0.098, 0.148, 0.131, 0.148] in this line chart?" }, { "from": "gpt", "value": "The color of the 'Longitude Changes' line in the legend is '#dc3545' (a shade of red)." } ], "id_raw": 207 }, { "id": "7jcRKzPp9eN9Ep2G", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Atlanta' on the 'Latitude Changes' line}[0.306, 0.415] above or below the corresponding point on the 'Longitude Changes' line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 207 }, { "id": "wHpV3WjuYjkJQO00", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_7.png", "conversations": [ { "from": "human", "value": "\nWhere is {the 'Longitude Changes' point for 'Denver'}[0.671, 0.791] located in relation to {the 'Latitude Changes' point for 'Denver'}[0.671, 0.267]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 207 }, { "id": "EJY9tR7iN55hyLSw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_7.png", "conversations": [ { "from": "human", "value": "\nWhich line, 'Longitude Changes' or 'Latitude Changes', has a higher point for {the city 'New York'}[0.124, 0.365] and [0.124, 0.578] respectively?" }, { "from": "gpt", "value": "Longitude Changes." } ], "id_raw": 207 }, { "id": "IEABcUxmj2XKQkYe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the section of the line chart within the coordinates [0.083, 0.066, 0.985, 0.895]." }, { "from": "gpt", "value": "This area contains the entirety of the plotted data. Two line plots, representing 'Latitude Changes' and 'Longitude Changes' across various locations, are visible. The x-axis is labeled with the names of different cities, and the y-axis is labeled with a range of values from -0.3 to 0.3." } ], "id_raw": 207 }, { "id": "h3RAhy0lFjcljWFh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_7.png", "conversations": [ { "from": "human", "value": "\nWhat lies in the region defined by the coordinates [0.091, 0.079, 0.311, 0.176] on this chart?" }, { "from": "gpt", "value": "This is the chart's legend. It contains two items: 'Latitude Changes' with a teal color marker and 'Longitude Changes' with a red color marker." } ], "id_raw": 207 }, { "id": "IzK4jmojZ8Ar5eD5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area defined by [0.324, 0.025, 0.744, 0.058]." }, { "from": "gpt", "value": "This is the area occupied by the chart's subtitle, \"Geographic Spatial Data Visualization\", styled in black color." } ], "id_raw": 207 }, { "id": "TjYMTsR3MmqF3vdk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_8.png", "conversations": [ { "from": "human", "value": "\nWhat element is situated at [0.498, 0.943, 0.539, 0.975] in the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 208 }, { "id": "wAo9xBno2f98p8dH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_8.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is used for {the specific point}[0.110, 0.267]?" }, { "from": "gpt", "value": "Circle." } ], "id_raw": 208 }, { "id": "GXThE9AcNEUcZ7mq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_8.png", "conversations": [ { "from": "human", "value": "\nWhich label occupies the space at [0.164, 0.520, 0.246, 0.555]?" }, { "from": "gpt", "value": "Economic Decline point label for 2001." } ], "id_raw": 208 }, { "id": "S0RHdecRU2fpkLQQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_8.png", "conversations": [ { "from": "human", "value": "\nIs {the data point}[0.646, 0.141] higher or lower than {the base horizontal line}[0.110, 0.895, 0.914, 0.895]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 208 }, { "id": "6mJxy9c1oUkFD9fe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the label '3'}[0.060, 0.056] correspond below or above {the point '-2'}[0.110, 0.658] on the vertical axis?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 208 }, { "id": "12Ty7yWjEuZxdQUs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_8.png", "conversations": [ { "from": "human", "value": "\nIs {the first point of Population Growth}[0.110, 0.267] positioned above or below {the first point of Economic Decline}[0.110, 0.492]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 208 }, { "id": "izrmv9Ed2ooZA7YV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_8.png", "conversations": [ { "from": "human", "value": "\nCharacterize the zone within [0.040, 0.056, 0.060, 0.909]." }, { "from": "gpt", "value": "This region contains y-axis labels ranging from '3' to '\u22124', positioned vertically with black font color." } ], "id_raw": 208 }, { "id": "yoY0GrEQ2SkPEh5H", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_8.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.131, 0.091, 0.291, 0.164]?" }, { "from": "gpt", "value": "This section comprises labels for {Population Growth}[0.131, 0.091, 0.284, 0.121] and {Economic Decline}[0.131, 0.134, 0.279, 0.164], each associated with a respective color marker, outlined in {the region's coordinates}." } ], "id_raw": 208 }, { "id": "NYswsC55KvlRswf5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the rectangle [0.217, 0.895, 0.753, 0.934]." }, { "from": "gpt", "value": "This area features the x-axis with labeled years for {2002}[0.200, 0.911, 0.235, 0.934], {2004}[0.307, 0.911, 0.342, 0.934], and {2006}[0.414, 0.911, 0.449, 0.934], among others, all marked in black font color distributed evenly across the axis." } ], "id_raw": 208 }, { "id": "eIKkg7sW8wSCooB4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_9.png", "conversations": [ { "from": "human", "value": "\nWhat object lies at the coordinates [0.816, 0.487, 0.970, 0.517]?" }, { "from": "gpt", "value": "The label of the data category 'Data Virtualization'." } ], "id_raw": 209 }, { "id": "rY64G0qPvVtsA9pP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-value of the point at {coordinates}[0.537, 0.706] in the line chart?" }, { "from": "gpt", "value": "0.79." } ], "id_raw": 209 }, { "id": "CP6VFQKWCQ3E9Rwh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_9.png", "conversations": [ { "from": "human", "value": "\nWhat type of data does the color code at {coordinates}[0.763, 0.535, 0.808, 0.554] represent in the legend?" }, { "from": "gpt", "value": "Data Governance." } ], "id_raw": 209 }, { "id": "WnXe3uNJqhMcTmKz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_9.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, does {Data Integration}[0.944, 0.744] perform better than {Data Virtualization}[0.944, 0.631] in large data size?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 209 }, { "id": "vAIkwO32aeI0wu3V", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_9.png", "conversations": [ { "from": "human", "value": "\nIn the medium data size category, is {Data Quality}[0.537, 0.179] higher or lower compared to {Data Integration}[0.537, 0.706]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 209 }, { "id": "FZzPrbw1yOQ9LDL5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_9.png", "conversations": [ { "from": "human", "value": "\nComparing the performance of {Data Governance}[0.537, 0.668] and {Data Virtualization}[0.537, 0.744] for medium data size, which one is higher?" }, { "from": "gpt", "value": "Data Virtualization." } ], "id_raw": 209 }, { "id": "sdqGKlszodJhZI5q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_9.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content in the coordinates [0.040, 0.094, 0.079, 0.117]?" }, { "from": "gpt", "value": "It's the y-axis label '0.950'." } ], "id_raw": 209 }, { "id": "BizLF33PwIsGkz1m", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the area at [0.762, 0.388, 0.977, 0.572]." }, { "from": "gpt", "value": "This area contains the legend items for the chart. These include the legend for {Data Integration}[0.816, 0.399, 0.952, 0.429], {Data Quality}[0.816, 0.443, 0.920, 0.473], {Data Virtualization}[0.816, 0.487, 0.970, 0.517], and {Data Governance}[0.816, 0.531, 0.961, 0.561]. Each item is associated with a unique color marker." } ], "id_raw": 209 }, { "id": "AtntgpweviabSvFt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.925, 0.911, 0.964, 0.934]?" }, { "from": "gpt", "value": "The object in this region is the x-axis label 'Large'." } ], "id_raw": 209 }, { "id": "5zpgKjEhgezmB7CC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_10.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the line with color '007bff'}[0.113, 0.339, 0.943, 0.853] in the line chart?" }, { "from": "gpt", "value": "Mobile Gaming Growth" } ], "id_raw": 210 }, { "id": "p9SRleJz0MVIsNbs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_10.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.015, 0.421, 0.034, 0.539] refer to in the chart?" }, { "from": "gpt", "value": "Growth" } ], "id_raw": 210 }, { "id": "chseg5VLmUKAju4m", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_10.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-tick label}[0.926, 0.911, 0.961, 0.934] mean in the chart?" }, { "from": "gpt", "value": "2019" } ], "id_raw": 210 }, { "id": "DBjdlKmOYv96nRXk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_10.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Mobile Gaming Growth'}[0.943, 0.339] above or below {the point for 'Console Gaming Growth'}[0.943, 0.621] in the year 2019?" }, { "from": "gpt", "value": "Above" } ], "id_raw": 210 }, { "id": "jX7AMk6yfJHSnhjf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_10.png", "conversations": [ { "from": "human", "value": "\nWhich growth is greater in the year 2010, {Mobile Gaming Growth}[0.113, 0.853] or {Console Gaming Growth}[0.113, 0.356]?" }, { "from": "gpt", "value": "Console Gaming Growth" } ], "id_raw": 210 }, { "id": "zJY3fDXJ3ebIwXMd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_10.png", "conversations": [ { "from": "human", "value": "\nComparing the points for the year 2011, is {Console Gaming Growth}[0.205, 0.385] greater or smaller than {Mobile Gaming Growth}[0.205, 0.796]?" }, { "from": "gpt", "value": "Smaller" } ], "id_raw": 210 }, { "id": "qxZkGBO3nVSzvN7v", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.715, 0.079, 0.977, 0.176]?" }, { "from": "gpt", "value": "This region contains the legend of the chart. More specifically, it includes two items: {Mobile Gaming Growth}[0.769, 0.091, 0.959, 0.121] and {Console Gaming Growth}[0.769, 0.134, 0.970, 0.164]." } ], "id_raw": 210 }, { "id": "epuqHGTKsHPNJZ6C", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the points located in the region [0.667, 0.477, 0.759, 0.623]?" }, { "from": "gpt", "value": "Within this region, there are two points representing the growth rates in Mobile Gaming and Console Gaming in the year 2017." } ], "id_raw": 210 }, { "id": "cHydmZMRjunm60B2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.508, 0.943, 0.549, 0.975]." }, { "from": "gpt", "value": "The region represents the label of the x-axis, and the chosen label is 'Year'." } ], "id_raw": 210 }, { "id": "inCq05lsQtSPoL7L", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.023, 0.376, 0.053, 0.634] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 211 }, { "id": "BYxO5dbODRjBsUba", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_11.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of 'Data Scientist' in the legend?" }, { "from": "gpt", "value": "[0.695, 0.206, 0.877, 0.244]." } ], "id_raw": 211 }, { "id": "sW1sVkgtFoFfh9KM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_11.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-tick label}[0.780, 0.910, 0.950, 0.939] represent?" }, { "from": "gpt", "value": "'Executive Level'." } ], "id_raw": 211 }, { "id": "i43XwvqXldBQiRq6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_11.png", "conversations": [ { "from": "human", "value": "\nIs the performance of {Data Analyst}[0.160, 0.225, 0.865, 0.750] higher or lower than {Data Scientist}[0.160, 0.155, 0.865, 0.680] at 'Entry Level' stage?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 211 }, { "id": "j64fMWUElUEkdyjs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_11.png", "conversations": [ { "from": "human", "value": "\nIs {the Data Analyst line}[0.160, 0.225, 0.865, 0.750] rising or declining compared to {the Data Scientist line}[0.160, 0.155, 0.865, 0.680] from 'Mid Level' to 'Senior Level' stage?" }, { "from": "gpt", "value": "Declining." } ], "id_raw": 211 }, { "id": "QU4o0b2o5xWMuUGB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_11.png", "conversations": [ { "from": "human", "value": "\nWhich data set has a higher performance at {the 'Executive Level' stage}[0.865, 0.890], Data Analyst or Data Scientist?" }, { "from": "gpt", "value": "Data Scientist." } ], "id_raw": 211 }, { "id": "LZ3Gi3mEo3CXGEpd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.135, 0.069, 0.890, 0.111]." }, { "from": "gpt", "value": "This area contains the chart subtitle \"Career and Workplace Development Trends,\" in a default black color and non-rotated." } ], "id_raw": 211 }, { "id": "1lZfalifPaGcLoTd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.612, 0.137, 0.887, 0.258]." }, { "from": "gpt", "value": "This area contains the chart legend. It includes two items: {Data Analyst}[0.695, 0.151, 0.862, 0.189] and {Data Scientist}[0.695, 0.206, 0.877, 0.244]." } ], "id_raw": 211 }, { "id": "YxskwurO681sv6RP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.446, 0.910, 0.792, 0.939]?" }, { "from": "gpt", "value": "Inside these coordinates lie two x-tick labels, {Senior Level}[0.446, 0.910, 0.579, 0.939] and {Management Level}[0.585, 0.910, 0.792, 0.939]." } ], "id_raw": 211 }, { "id": "B0GRC27WaC7XnEmN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_13.png", "conversations": [ { "from": "human", "value": "\nWhat type of markers does {the line representing Environmental Quality} use according to the data at [0.098, 0.372, 0.945, 0.502]?" }, { "from": "gpt", "value": "Circle." } ], "id_raw": 213 }, { "id": "LyYvfNOD0brKX9wH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {x-axis labels} starting at [0.084, 0.933, 0.113, 0.951]?" }, { "from": "gpt", "value": "Black." } ], "id_raw": 213 }, { "id": "NY4FULLKwgyi7dTm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_13.png", "conversations": [ { "from": "human", "value": "\nWhat does {the grid line} at [0.056, 0.049, 0.987, 0.921] look like in the plot?" }, { "from": "gpt", "value": "Dashed and grey." } ], "id_raw": 213 }, { "id": "KTZm6yESz70bAKOA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_13.png", "conversations": [ { "from": "human", "value": "\nWhich line, 'Environmental Quality' or 'Life Expectancy', starts higher in the year 2000 at {coordinates} [0.098, 0.372] and [0.098, 0.171] respectively?" }, { "from": "gpt", "value": "Life Expectancy." } ], "id_raw": 213 }, { "id": "SAAbYjMDOAw6y1mV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_13.png", "conversations": [ { "from": "human", "value": "\nIs {the last marker for Life Expectancy}[0.945, 0.171] above or below {the last marker for Environmental Quality}[0.945, 0.502]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 213 }, { "id": "LHdu5q4NCA4FLZXg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_13.png", "conversations": [ { "from": "human", "value": "\nBetween 'Environmental Quality' and 'Life Expectancy', which line has a smoother curve from {start}[0.098, 0.372] to {end}[0.945, 0.502]?" }, { "from": "gpt", "value": "Life Expectancy." } ], "id_raw": 213 }, { "id": "3bYZ3TBiwzhUFzba", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the visual features found in the area [0.040, 0.049, 0.048, 0.932]." }, { "from": "gpt", "value": "This area contains the y-axis tick labels for values from 0 to 10, placed vertically and styled in black color." } ], "id_raw": 213 }, { "id": "2brfDIdhLuzIWhwP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_13.png", "conversations": [ { "from": "human", "value": "\nWhat visual elements are present within the bounds [0.063, 0.838, 0.267, 0.910]?" }, { "from": "gpt", "value": "This region includes the legend for the chart with labels 'Environmental Quality' in blue and 'Life Expectancy' in red, signifying each with their respective line colors on the plot." } ], "id_raw": 213 }, { "id": "CAWtL9QA01VtQ9Kv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_13.png", "conversations": [ { "from": "human", "value": "\nCan you outline the elements within the plot space [0.056, 0.049, 0.987, 0.921]?" }, { "from": "gpt", "value": "This expansive region includes the main graph area with grey dashed grid lines, data lines for Environmental Quality and Life Expectancy, primary x and y axes with labels, and multiple data points across the 21-year timespan." } ], "id_raw": 213 }, { "id": "SSXHv6KInO8jbhhf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the {handle's color}[0.081, 0.844, 0.125, 0.862] in the sublegend?" }, { "from": "gpt", "value": "#17a2b8." } ], "id_raw": 214 }, { "id": "U0nLhwUPzDB1IYkH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label coordinate for the year '2015' on the x-ticks of the chart?" }, { "from": "gpt", "value": "[0.557, 0.911, 0.592, 0.934]." } ], "id_raw": 214 }, { "id": "uT8xxjpyGftBTN3p", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the {label for the line}[0.133, 0.664, 0.266, 0.694] in the sublegend?" }, { "from": "gpt", "value": "Minimal Decline." } ], "id_raw": 214 }, { "id": "OQ6I8e3IiR1Uhk9E", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_14.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at the 2012 point, the {Minimal Decline line}[0.113, 0.302, 0.943, 0.421] or the {Gradual Increase line}[0.113, 0.160, 0.943, 0.421]?" }, { "from": "gpt", "value": "Gradual Increase." } ], "id_raw": 214 }, { "id": "2nBjqX8OplBs94GG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_14.png", "conversations": [ { "from": "human", "value": "\nBetween the first point of {Gradual Increase}[0.113, 0.421] and the last point of {Minimal Decline}[0.943, 0.421], which one is higher on the y-scale?" }, { "from": "gpt", "value": "First point of Gradual Increase." } ], "id_raw": 214 }, { "id": "kQKCQ3B2KPBlMfH4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_14.png", "conversations": [ { "from": "human", "value": "\nAre the color of {the marker for Minimal Decline}[0.081, 0.668, 0.125, 0.687] and the color of {the marker for Downward Trajectory}[0.081, 0.800, 0.125, 0.819] the same?" }, { "from": "gpt", "value": "No." } ], "id_raw": 214 }, { "id": "pw60mB6tO89X1PT4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_14.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area with these coordinates [0.080, 0.653, 0.315, 0.881]?" }, { "from": "gpt", "value": "This area contains a legend that includes labels and corresponding color markers for five different trends: Minimal Decline, Gradual Increase, Dramatic Decrease, Downward Trajectory, and Minimal Change." } ], "id_raw": 214 }, { "id": "6laMNTub8CIRbHC5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of this {ylabel}[0.015, 0.370, 0.034, 0.590]." }, { "from": "gpt", "value": "This presents the y-axis label 'Global Trends' with the text color to be black and the label is in vertical alignment." } ], "id_raw": 214 }, { "id": "i8opSAJzPbBxp7Rb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the information placed within these coordinates [0.095, 0.911, 0.130, 0.934]?" }, { "from": "gpt", "value": "These are the coordinates for the x-tick label '2010'." } ], "id_raw": 214 }, { "id": "wPJlgghk9FREQLET", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_15.png", "conversations": [ { "from": "human", "value": "\nWhat does {the subtitle}[0.321, 0.025, 0.736, 0.058] say?" }, { "from": "gpt", "value": "Venture Capital Markets Performance." } ], "id_raw": 215 }, { "id": "wsiIzlZsH5LjqzRg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_15.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-label}[0.015, 0.377, 0.034, 0.583] refer to?" }, { "from": "gpt", "value": "Performance." } ], "id_raw": 215 }, { "id": "ElrGXkacauBji97d", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the second item in the legend}[0.133, 0.134, 0.272, 0.164]?" }, { "from": "gpt", "value": "Series A Funding." } ], "id_raw": 215 }, { "id": "wNx6g4VfAfjqXUIx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_15.png", "conversations": [ { "from": "human", "value": "\nWhich of {Seed Funding}[0.113, 0.141, 0.943, 0.668] or {Series A Funding}[0.113, 0.412, 0.943, 0.774] has a higher value in the Q3?" }, { "from": "gpt", "value": "Seed Funding." } ], "id_raw": 215 }, { "id": "tDhS7vcWy60zvquU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_15.png", "conversations": [ { "from": "human", "value": "\nIn the Q6, is the {Seed Funding}[0.574, 0.261] above or below the {Series A Funding}[0.574, 0.774]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 215 }, { "id": "6M9cy1fnpPQKfFQe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_15.png", "conversations": [ { "from": "human", "value": "\nHow do the performances of {Seed Funding}[0.113, 0.141, 0.943, 0.668] and {Series A Funding}[0.113, 0.412, 0.943, 0.774] compare in Q10?" }, { "from": "gpt", "value": "Seed Funding is higher." } ], "id_raw": 215 }, { "id": "dKPCCJznJRMqU4iB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_15.png", "conversations": [ { "from": "human", "value": "\nWhat does the highlighted region from [0.442, 0.943, 0.614, 0.975] show?" }, { "from": "gpt", "value": "This area holds the x-axis label, which is \"Investment Stage\"." } ], "id_raw": 215 }, { "id": "BGywUVCgdXlyRUlT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the coordinates [0.080, 0.079, 0.278, 0.176]." }, { "from": "gpt", "value": "This area contains the legend for the chart. It distinguishes Seed Funding, indicated by a color '#007bff', and Series A Funding, indicated by a color '#dc3545'." } ], "id_raw": 215 }, { "id": "KZfwObL7UQCIixVx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of this region [0.390, 0.373, 0.482, 0.311]." }, { "from": "gpt", "value": "This region covers a section of the Q4 and Q5 points in both Seed Funding and Series A Funding lines. Seed Funding shows a performance value of 0.83 and 0.9 while Series A Funding shows values of 0.68 and 0.62 for Q4 and Q5 respectively." } ], "id_raw": 215 }, { "id": "74sEg81nrJiIeCTN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label in the chart with coordinates [0.446, 0.943, 0.593, 0.975]?" }, { "from": "gpt", "value": "Product Stages." } ], "id_raw": 216 }, { "id": "xz5jMyqu6wz8F8RM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_16.png", "conversations": [ { "from": "human", "value": "\nCan you identify {the object}[0.015, 0.435, 0.034, 0.525] in this chart?" }, { "from": "gpt", "value": "Yes, it is the y-axis label, read as \"Value\"." } ], "id_raw": 216 }, { "id": "2w93a0WxObqrFGxS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_16.png", "conversations": [ { "from": "human", "value": "\nWhat is identified by the coordinates [0.699, 0.911, 0.739, 0.934]?" }, { "from": "gpt", "value": "It's an x-axis tick label labeled as 'Retail'." } ], "id_raw": 216 }, { "id": "Bgde7HzHc4ND4je3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_16.png", "conversations": [ { "from": "human", "value": "\nDoes {the cost curve}[0.120, 0.276, 0.919, 0.594] lie above or below {the value curve}[0.120, 0.184, 0.919, 0.822] at the 'Retail' point?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 216 }, { "id": "ccCr9LuqY1aWuGh7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_16.png", "conversations": [ { "from": "human", "value": "\nWhich marker on the distribution stage is higher {Cost}[0.519, 0.594] or {Value}[0.519, 0.184]?" }, { "from": "gpt", "value": "Value." } ], "id_raw": 216 }, { "id": "xDnd15gii5648d5g", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_16.png", "conversations": [ { "from": "human", "value": "\nAre the costs at {procurement}[0.120, 0.276] higher or lower than the value at {manufacturing}[0.320, 0.367]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 216 }, { "id": "PFxMA1vm2pPYvWZx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the content shown within the coordinates [0.305, 0.025, 0.733, 0.058] in the chart?" }, { "from": "gpt", "value": "The content within these coordinates is the subplot title called 'Value Chain Visualization and Analysis'." } ], "id_raw": 216 }, { "id": "HMHqkvkInsfVhZRJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_16.png", "conversations": [ { "from": "human", "value": "\nDescribe what is inside this region - [0.845, 0.079, 0.950, 0.176]." }, { "from": "gpt", "value": "This region contains the subplot legend items: a blue marker labeled 'Cost' at {coordinates}[0.898, 0.091, 0.935, 0.121] and a red marker labeled 'Value' at {coordinates}[0.898, 0.134, 0.944, 0.164]." } ], "id_raw": 216 }, { "id": "6aQ9q4RWjpf5GNDg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_16.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in this set of coordinates [0.040, 0.221, 0.070, 0.244]?" }, { "from": "gpt", "value": "In these coordinates, the y-axis has a tick label of '25.0'." } ], "id_raw": 216 }, { "id": "IkZP6W21AViviO9Q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_17.png", "conversations": [ { "from": "human", "value": "\nWhat kind of element can be found at this location in the graph [0.015, 0.371, 0.034, 0.589]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 217 }, { "id": "Xy7i8Ksje4kwyHWI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_17.png", "conversations": [ { "from": "human", "value": "\nWhere is the fifth tick label on the x-axis located?" }, { "from": "gpt", "value": "[0.667, 0.895]" } ], "id_raw": 217 }, { "id": "JWjKmFJRvN0Wc8TX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the data point at these coordinates {point}[0.851, 0.795]?" }, { "from": "gpt", "value": "0.56." } ], "id_raw": 217 }, { "id": "4rf9auCse3lSOnls", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_17.png", "conversations": [ { "from": "human", "value": "\nBy comparing {the point labeled '2012'}[0.297, 0.480] and {the point labeled '2015'}[0.574, 0.563] on the line representing 'Rural Development', which year has a higher development value?" }, { "from": "gpt", "value": "2012." } ], "id_raw": 217 }, { "id": "YkxWBMU9Gih9LiXd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_17.png", "conversations": [ { "from": "human", "value": "\nHow does {the value of 'Urban development' in 2010}[0.113, 0.729] compare with {the value of 'Urban development' in 2011}[0.205, 0.646]?" }, { "from": "gpt", "value": "It is lower." } ], "id_raw": 217 }, { "id": "KJAb5IzJt1AytXUB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_17.png", "conversations": [ { "from": "human", "value": "\nWhich development has a higher value in the year 2019, Rural or Urban development?" }, { "from": "gpt", "value": "Urban Development." } ], "id_raw": 217 }, { "id": "LdAS4PTTW9Jv2CXj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_17.png", "conversations": [ { "from": "human", "value": "\nCan you give a description of what's contained within these coordinates [0.749, 0.079, 0.977, 0.176]?" }, { "from": "gpt", "value": "This rectangular region contains the legend that corresponds to the 'Rural Development' and 'Urban Development' lines. The 'Rural Development' label and its correlating blue circular marker are located in the top part of the region, while the 'Urban Development' label and its correlating red X-shaped marker are located in the lower part." } ], "id_raw": 217 }, { "id": "Cb45LaZih2rk1tys", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is found in the region [0.508, 0.943, 0.549, 0.975]." }, { "from": "gpt", "value": "The x-axis label 'Year' is found within this region." } ], "id_raw": 217 }, { "id": "5ASqfilBRywOvMXY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_17.png", "conversations": [ { "from": "human", "value": "\nWhat can one see in the region [0.113, 0.397, 0.943, 0.828]?" }, { "from": "gpt", "value": "This large region contains two lines with data points resulting from 'Rural Development' and 'Urban Development'. The 'Rural Development' line shows a trend of decreasing development values over the years, and it is marked with blue circles. The 'Urban Development' line shows a general trend of increasing values from 2010 to 2019, and it is marked with red X shapes." } ], "id_raw": 217 }, { "id": "b1vDZxRtKyLKmlPs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_18.png", "conversations": [ { "from": "human", "value": "\nWhat object correspond to the coordinates [0.015, 0.377, 0.034, 0.583]?" }, { "from": "gpt", "value": "This is the y-axis label which reads \"Performance\"." } ], "id_raw": 218 }, { "id": "IlAddKgFUfcHVulf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within the coordinates [0.095, 0.911, 0.130, 0.934]?" }, { "from": "gpt", "value": "This is an x-axis tick label, which indicates the year \"2010\"." } ], "id_raw": 218 }, { "id": "LqSIbz3NegnHLi87", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_11_18.png", "conversations": [ { "from": "human", "value": "\nWhat element corresponds to these coordinates [0.133, 0.091, 0.303, 0.121]?" }, { "from": "gpt", "value": "This corresponds to a legend item labeled \"Artificial Intelligence\"." } ], "id_raw": 218 }, { "id": "iUd5KyaimV9SxKwN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_18.png", "conversations": [ { "from": "human", "value": "\nHow does the {Internet of things line}[0.113, 0.687, 0.943, 0.853] trend compared to the {Artificial Intelligence line}[0.113, 0.066, 0.943, 0.729] in 2019 (at x-tick '2019')?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 218 }, { "id": "vyo3t9KMxZmB0Gan", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_18.png", "conversations": [ { "from": "human", "value": "\nIs {the 4th point on Artificial Intelligence line}[0.390, 0.480] above or below {the 4th point on the Internet of Things line}[0.390, 0.785]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 218 }, { "id": "EehpSDqXdIikeyBp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_11_18.png", "conversations": [ { "from": "human", "value": "\nWhat has a higher performance in 2010, {Artificial Intelligence}[0.113, 0.729] or {Internet of Things}[0.113, 0.853]?" }, { "from": "gpt", "value": "Artificial Intelligence." } ], "id_raw": 218 }, { "id": "Zzu6NWnYnWvJ1Pnj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the coordinates [0.113, 0.687, 0.943, 0.853]?" }, { "from": "gpt", "value": "This region encloses the line chart for 'Internet of Things' from the year 2010 to 2019, represented by 'X' markers on the line." } ], "id_raw": 218 }, { "id": "JZBrk36iAL5e7uIK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_18.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area inside [0.133, 0.091, 0.303, 0.121]?" }, { "from": "gpt", "value": "This area contains the \"Artificial Intelligence\" label from the legend, corresponding to one of the line charts in a blue color." } ], "id_raw": 218 }, { "id": "aDJzcVtIVowdGE8G", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_11_18.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.285, 0.025, 0.771, 0.058] contain?" }, { "from": "gpt", "value": "This region contains a text which is the subtitle of the chart \"Technological Industry Development Trends\"." } ], "id_raw": 218 }, { "id": "DimKUbBwurt3kpYz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_0.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at coordinates [0.025, 0.392, 0.048, 0.560] on the chart?" }, { "from": "gpt", "value": "This is the Y-axis label which represents 'Efficiency'." } ], "id_raw": 220 }, { "id": "q2LdCm6hJ7Iz9YKF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_0.png", "conversations": [ { "from": "human", "value": "\nCan you specify the coordinates of the 'Week' label in the x-axis?" }, { "from": "gpt", "value": "The 'Week' label on the x-axis is located at the coordinates [0.518, 0.927, 0.582, 0.963]." } ], "id_raw": 220 }, { "id": "hSU7HkbudAv2sasc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis tick label at the coordinates [0.522, 0.879, 0.551, 0.914]?" }, { "from": "gpt", "value": "It represents the value '15'." } ], "id_raw": 220 }, { "id": "BBLMRK4ACBwjZkic", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_0.png", "conversations": [ { "from": "human", "value": "\nWhich line, {Material Efficiency}[0.164, 0.438, 0.936, 0.693] or {Machine Efficiency}[0.164, 0.260, 0.936, 0.438], is higher on {x=10}[0.403, 0.438]?" }, { "from": "gpt", "value": "Material Efficiency." } ], "id_raw": 220 }, { "id": "EIFLlXayB1UbY0FY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_0.png", "conversations": [ { "from": "human", "value": "\nIs the {Machine Efficiency} line[0.164, 0.260, 0.936, 0.438] above or below the {Material Efficiency} line[0.164, 0.438, 0.936, 0.693] at x=20?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 220 }, { "id": "y3Zbz5wuZAw1ORxY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_0.png", "conversations": [ { "from": "human", "value": "\nWhich efficiency level had a higher value on week 15, Material Efficiency or Machine Efficiency?" }, { "from": "gpt", "value": "Machine Efficiency." } ], "id_raw": 220 }, { "id": "rD6kUs9Srr9st2cl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content found within the area [0.664, 0.116, 0.963, 0.231]?" }, { "from": "gpt", "value": "The region includes the chart legend with two items: {Material Efficiency}[0.738, 0.130, 0.952, 0.165] colored in cyan, and {Machine Efficiency}[0.738, 0.182, 0.954, 0.217] colored in salmon." } ], "id_raw": 220 }, { "id": "YmEnfkFxNtmRZKQM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_0.png", "conversations": [ { "from": "human", "value": "\nPlease provide a description of the area bounded by [0.111, 0.037, 0.989, 0.088]." }, { "from": "gpt", "value": "This area contains the subtitle of the chart, 'Manufacturing Production Efficiency Evaluation'." } ], "id_raw": 220 }, { "id": "V2PfPFLY8RhFkCQ2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained within [0.789, 0.879, 0.818, 0.914]?" }, { "from": "gpt", "value": "There is an x-axis tick label '25' located within these coordinates." } ], "id_raw": 220 }, { "id": "OyB31p83XtplOC2K", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_1.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object on the chart with coordinates [0.516, 0.927, 0.591, 0.963]?" }, { "from": "gpt", "value": "The x-axis label representing 'Weeks'." } ], "id_raw": 221 }, { "id": "TR2AWZrQRFcLVovc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_1.png", "conversations": [ { "from": "human", "value": "\nWhat is stated by the {ylabel}[0.025, 0.425, 0.048, 0.528]?" }, { "from": "gpt", "value": "It states 'Count.'" } ], "id_raw": 221 }, { "id": "oPopr3HCtgnP17MZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the content inside this coordinate {sublegend item}[0.218, 0.182, 0.355, 0.217] refer to?" }, { "from": "gpt", "value": "It refers to 'Interactions'." } ], "id_raw": 221 }, { "id": "AkXGd5nG9NtOR9oS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_1.png", "conversations": [ { "from": "human", "value": "\nIs the {point for 'Users'}[0.454, 0.639] above or below the {point for 'Interactions'}[0.454, 0.612] at the same value in weeks?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 221 }, { "id": "qpi2CZZkRHlctL6I", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_1.png", "conversations": [ { "from": "human", "value": "\nComparing the final points, does the 'Users'[0.908, 0.196] line end above or below the 'Interactions'[0.937, 0.178] line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 221 }, { "id": "6JoDda6iSo00ezlk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_1.png", "conversations": [ { "from": "human", "value": "\nWhich line starts with a higher y-value: the line for 'Users'[0.171, 0.784] or the line for 'Interactions'[0.171, 0.802]?" }, { "from": "gpt", "value": "'Interactions'." } ], "id_raw": 221 }, { "id": "iol5ez1dQsZ4YL04", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is present in the region [0.000, 0.854, 1.008, 0.914]." }, { "from": "gpt", "value": "This region contains the x-axis tick labels, which range from '\u22125' to '30'." } ], "id_raw": 221 }, { "id": "OeC8qUmGXJYkZxMU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_1.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area contained within the coordinates [0.144, 0.116, 0.365, 0.231]?" }, { "from": "gpt", "value": "This is the area of the subplot legend, indicating 'Users' with color '#00bfff' and 'Interactions' with color '#fa8072'." } ], "id_raw": 221 }, { "id": "bABND5TikXENFs7F", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of this area [0.169, 0.037, 0.939, 0.088]?" }, { "from": "gpt", "value": "The area contains the subtitle of the chart, \"Media and Entertainment Industry Trends\"." } ], "id_raw": 221 }, { "id": "FGI4QPr9H7TM1vcy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing 'Staff Recruitment'}[0.150, 0.864, 0.942, 0.865] in the chart?" }, { "from": "gpt", "value": "The color is coral." } ], "id_raw": 222 }, { "id": "QEZwZGqoqfbkeKKH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the '2020' label on the x-axis?" }, { "from": "gpt", "value": "[0.920, 0.919, 0.964, 0.942]" } ], "id_raw": 222 }, { "id": "CJHYEWUPS7UihxGz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the count at '2018'}[0.744, 0.147] for 'Student Enrollment'?" }, { "from": "gpt", "value": "28500." } ], "id_raw": 222 }, { "id": "5xK98G0NegasVbWD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_2.png", "conversations": [ { "from": "human", "value": "\nIn 2017, is the value of {Student Enrollment}[0.645, 0.159] higher or lower than the value of {Staff Recruitment}[0.645, 0.864]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 222 }, { "id": "WwLLn3EjJkuSzgK0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_2.png", "conversations": [ { "from": "human", "value": "\nWhich line starts first in 2012, {Student Enrollment}[0.150, 0.236] or {Staff Recruitment}[0.150, 0.864] in the chart?" }, { "from": "gpt", "value": "Both start at the same time." } ], "id_raw": 222 }, { "id": "ukKPIgDeVUz6JxlP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_2.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at {2015}[0.447, 0.864], Student Enrollment or Staff Recruitment?" }, { "from": "gpt", "value": "Student Enrollment." } ], "id_raw": 222 }, { "id": "KGzKl0z7NTk7gJkH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects in the area [0.119, 0.077, 0.349, 0.154]?" }, { "from": "gpt", "value": "This area contains the legend of the chart. Two objects are present: {Student Enrollment}[0.174, 0.086, 0.343, 0.110] with a light blue marker and {Staff Recruitment}[0.174, 0.121, 0.328, 0.145] with a coral marker." } ], "id_raw": 222 }, { "id": "fUg1aRhXHlC9VbKv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_2.png", "conversations": [ { "from": "human", "value": "\nWhat content is located in the region [0.043, 0.100, 0.098, 0.123]?" }, { "from": "gpt", "value": "The y-axis label '30000' is located in this region." } ], "id_raw": 222 }, { "id": "rKkiBtWqPOCoCzIk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_2.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the bounding box at [0.275, 0.025, 0.817, 0.058]?" }, { "from": "gpt", "value": "The subtitle of the chart \"Education and Training Industry Trends\" is contained in this region." } ], "id_raw": 222 }, { "id": "pH17ZMGUlS2AoCaW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.019, 0.447, 0.036, 0.522] within the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 223 }, { "id": "FeFQE82MCx6dJ84H", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second point on the 'Commodity Price' line?" }, { "from": "gpt", "value": "[0.139, 0.829]" } ], "id_raw": 223 }, { "id": "7BsFmkjX6F8Bkl3b", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label} at [0.131, 0.121, 0.337, 0.145] within the legend?" }, { "from": "gpt", "value": "Luxury Goods Spending." } ], "id_raw": 223 }, { "id": "l5n72gHuNvixf0iK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_3.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Commodity Price} at [0.254, 0.689] greater or lesser than {Luxury Goods Spending} at [0.254, 0.351]?" }, { "from": "gpt", "value": "Lesser." } ], "id_raw": 223 }, { "id": "XoMII79BMLPdCnsc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_3.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '5'}[0.231, 0.723] on the 'Commodity Price' line above or below {the point labeled '5'}[0.231, 0.440] on the 'Luxury Goods Spending' line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 223 }, { "id": "nONZ9m2bXvfPTeqa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_3.png", "conversations": [ { "from": "human", "value": "\nAt the point [0.940, 0.422] on the chart, is the line for Commodity Pricing higher or lower than Luxury Goods Spending?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 223 }, { "id": "gt20IpjpkZxr3hN3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_3.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within [0.075, 0.077, 0.344, 0.154] in the chart?" }, { "from": "gpt", "value": "There are 2 objects. The legend items: {Commodity Price}[0.131, 0.086, 0.279, 0.110], and {Luxury Goods Spending}[0.131, 0.121, 0.337, 0.145], each with their identified color markers." } ], "id_raw": 223 }, { "id": "eoFu3Zj4kN43KCQE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.282, 0.025, 0.766, 0.058] area." }, { "from": "gpt", "value": "This area contains the subtitle of the chart, \"Consumer Behavior Trend Analysis\"." } ], "id_raw": 223 }, { "id": "1r2zh2GhE1dyr3RB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area bound by [0.696, 0.511, 0.740, 0.530]." }, { "from": "gpt", "value": "The x-axis point '21' is positioned here on the line for 'Commodity Price'." } ], "id_raw": 223 }, { "id": "3yaQ1dMFa1l95GFT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.025, 0.425, 0.048, 0.528] represent?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 224 }, { "id": "aZpNNTjBP3uFsJ8a", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the attribute of the element located at [0.175, 0.135, 0.236, 0.155]?" }, { "from": "gpt", "value": "It is a color marker for 'Active Users' in the legend." } ], "id_raw": 224 }, { "id": "ziIYA5NebC5AMEP3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at coordinates [0.163, 0.037, 0.973, 0.088]?" }, { "from": "gpt", "value": "It's the subplot title - \"Active Users and Learning Hours Over Time\"." } ], "id_raw": 224 }, { "id": "iik3IxZJe0pwmjAr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_4.png", "conversations": [ { "from": "human", "value": "\nIs {Active Users} coordinate [0.198, 0.591] greater or less than {Learning Hours} coordinate [0.198, 0.699] at month 1?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 224 }, { "id": "8qPxJ2zxHuJSWLDf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_4.png", "conversations": [ { "from": "human", "value": "\nIs the {Active Users} value at coordinate [0.938, 0.160] in Month 12 greater than {Learning Hours} value at coordinate [0.938, 0.402]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 224 }, { "id": "qHrgOewBheoGHqD1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_4.png", "conversations": [ { "from": "human", "value": "\nComparing the {Active Users} and {Learning Hours} lines at timestamp {3}[0.333, 0.645], which one is higher?" }, { "from": "gpt", "value": "Learning Hours." } ], "id_raw": 224 }, { "id": "sV4OMEXM8VgKY0KJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region from [0.173, 0.116, 0.432, 0.231]?" }, { "from": "gpt", "value": "This region contains the legend for the two lines {Active Users}[0.247, 0.130, 0.390, 0.165] and {Learning Hours}[0.247, 0.182, 0.423, 0.217], each paired with a color marker." } ], "id_raw": 224 }, { "id": "hT8zVO0FD1mM9F3u", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the region at [0.198, 0.160, 0.938, 0.699] describe?" }, { "from": "gpt", "value": "It describes the {Active Users} trend line from Month 1 to 12, with each point representing the count of active users for the corresponding month." } ], "id_raw": 224 }, { "id": "votZQC1yMOS01llw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_4.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.198, 0.402, 0.938, 0.793]?" }, { "from": "gpt", "value": "This region describes the trend line for {Learning Hours} over a span of 12 months, with each point on the line representing the count of learning hours in a specific month." } ], "id_raw": 224 }, { "id": "0km56jjFJcsoGmmw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_5.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.218, 0.736, 0.369, 0.771] refer to on the chart?" }, { "from": "gpt", "value": "It refers to the label \"Stock Market\" in the legend." } ], "id_raw": 225 }, { "id": "YePS5IhqyJCdAwrY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at {coordinates} [0.025, 0.429, 0.048, 0.524] mean?" }, { "from": "gpt", "value": "It is the Y-axis label \"Value\"." } ], "id_raw": 225 }, { "id": "N4mwLggqqVJvn5rB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis tick label at the position {location}[0.658, 0.879, 0.687, 0.914]?" }, { "from": "gpt", "value": "The label is \"20\"." } ], "id_raw": 225 }, { "id": "3L0scsk1hdH3j2zP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_5.png", "conversations": [ { "from": "human", "value": "\nWhich is higher on January 15, {Stock Market}[0.540, 0.683] or {Bonds}[0.540, 0.160]?" }, { "from": "gpt", "value": "Stock Market." } ], "id_raw": 225 }, { "id": "T726kpNisGaYeYcu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_5.png", "conversations": [ { "from": "human", "value": "\nDoes {Stock Market}[0.567, 0.692] have a higher or lower value compared to its value at {previous point}[0.540, 0.683]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 225 }, { "id": "zujknL9RHhi9faCo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_5.png", "conversations": [ { "from": "human", "value": "\nConsidering the trends, is the {Bonds}[0.884, 0.136] value increasing or decreasing compared to its previous point at {Bonds}[0.857, 0.138]?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 225 }, { "id": "dCHcD3OhbC5BsA5g", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in region [0.058, 0.824, 0.116, 0.859]?" }, { "from": "gpt", "value": "This region contains the Y-axis tick label \"1000\"." } ], "id_raw": 225 }, { "id": "Twh3xvrv7LL8kmRt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_5.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region starting from [0.171, 0.133] to [0.937, 0.290]?" }, { "from": "gpt", "value": "This is the path of the line representing the Bonds over the whole of January." } ], "id_raw": 225 }, { "id": "1VYRgVlxeP8UsKp9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.311, 0.037, 0.797, 0.088] contain?" }, { "from": "gpt", "value": "It contains the subtitle of the chart: \"Financial Data Exploration\"." } ], "id_raw": 225 }, { "id": "rvcuKlNKGflhwClR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the element present at coordinates [0.019, 0.436, 0.036, 0.526] on the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 226 }, { "id": "6NXHUrdjuAeXIKAo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the coordinates [0.174, 0.104, 0.350, 0.132]?" }, { "from": "gpt", "value": "The label \"Avg Education Level\"." } ], "id_raw": 226 }, { "id": "bwMUxtDPZJADYfB9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_6.png", "conversations": [ { "from": "human", "value": "\nWhat does {the tick label} at [0.128, 0.903, 0.172, 0.931] represent?" }, { "from": "gpt", "value": "The year 1990." } ], "id_raw": 226 }, { "id": "ByJIcd58kDe448ID", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_6.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {the point represented by the year 1995}[0.291, 0.847], the 'Avg Education Level' line or the 'Avg Income' line?" }, { "from": "gpt", "value": "'Avg Income'." } ], "id_raw": 226 }, { "id": "tH2kwMOJUNkoblxm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_6.png", "conversations": [ { "from": "human", "value": "\nAre the {coordinates at year 1996}[0.319, 0.847] on the 'Avg Education Level' line above or below the corresponding point on the 'Avg Income' line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 226 }, { "id": "fMKaSWgpwB9LV17F", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_6.png", "conversations": [ { "from": "human", "value": "\nWhat has a higher value at {the year 2001}[0.461, 0.847], 'Avg Education Level' or 'Avg Income'?" }, { "from": "gpt", "value": "'Avg Income'." } ], "id_raw": 226 }, { "id": "94poJmPHfzezl51s", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.119, 0.093, 0.357, 0.185]." }, { "from": "gpt", "value": "This region consists of the legend of the chart, which includes \"Avg Education Level\" at {coordinates} [0.174, 0.104, 0.350, 0.132], and \"Avg Income\" at {coordinates} [0.174, 0.146, 0.276, 0.174]." } ], "id_raw": 226 }, { "id": "uGtyU4XoOqaj8vWQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.043, 0.104, 0.098, 0.132]." }, { "from": "gpt", "value": "This area comprises a y-axis tick label, displaying the value '60000'." } ], "id_raw": 226 }, { "id": "9TPUmfRXUYCfubQR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_6.png", "conversations": [ { "from": "human", "value": "\nWhat elements are contained within [0.150, 0.542, 0.942, 0.847]?" }, { "from": "gpt", "value": "Within this region are points on the 'Avg Education Level' line and the 'Avg Income' line, represented by years from 1990 to 2018." } ], "id_raw": 226 }, { "id": "3BFTQt4THvl1bDw0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the object [0.043, 0.475, 0.071, 0.498] on the chart represent?" }, { "from": "gpt", "value": "The y-axis tick label '1.0'." } ], "id_raw": 228 }, { "id": "tHa3vHRgSSvo4Kt0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the first x-axis label in the chart?" }, { "from": "gpt", "value": "Area_1." } ], "id_raw": 228 }, { "id": "f49kbnYaiUppZT1w", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_8.png", "conversations": [ { "from": "human", "value": "\nWhat does {the line}[0.124, 0.104, 0.940, 0.865] on the chart represent?" }, { "from": "gpt", "value": "Demographic Influx." } ], "id_raw": 228 }, { "id": "aBN4tkKDcRaIhvYH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_8.png", "conversations": [ { "from": "human", "value": "\nAccording to this chart, does {Area_24}[0.819, 0.234] have a higher demographic influx than {Area_18}[0.638, 0.365]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 228 }, { "id": "8T5PvX1dgSU8y7k6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_8.png", "conversations": [ { "from": "human", "value": "\nLooking at the positions on the y-axis, is {Area_9}[0.366, 0.745] having a higher or lower demographic influx compared to {Area_6}[0.275, 0.539]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 228 }, { "id": "bcDia4MOQbfVvrXD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_8.png", "conversations": [ { "from": "human", "value": "\nBased on the chart lines, does {Area_23}[0.789, 0.288] see more or less demographic influx than {Area_20}[0.698, 0.321]?" }, { "from": "gpt", "value": "More." } ], "id_raw": 228 }, { "id": "odapnyRpzFje6wRt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is presented in the region [0.043, 0.149, 0.071, 0.172]?" }, { "from": "gpt", "value": "The y-axis label '1.3' is present in this region." } ], "id_raw": 228 }, { "id": "pEZjdJhMs4q90oNb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_8.png", "conversations": [ { "from": "human", "value": "\nDescribe what the region [0.094, 0.919, 0.154, 0.942] of the chart includes." }, { "from": "gpt", "value": "This region contains the x-axis label representing {Area_1}[0.124, 0.810]." } ], "id_raw": 228 }, { "id": "uHgSjRIYhhHuubMx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the object located at [0.396, 0.473] in the chart?" }, { "from": "gpt", "value": "This point corresponds to the demographic influx level of {Area_10}[0.396, 0.473] which is 1.01." } ], "id_raw": 228 }, { "id": "OSlsQpmKhhCRvbW2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at coordinates [0.025, 0.295, 0.048, 0.657]?" }, { "from": "gpt", "value": "This is the y-axis label which is \"Energy Consumption\"." } ], "id_raw": 229 }, { "id": "j7PbJ7VQTjxTBKyl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_9.png", "conversations": [ { "from": "human", "value": "\nCan you specify the coordinates of the first tick label on the x-axis?" }, { "from": "gpt", "value": "The coordinates are [0.086, 0.879, 0.101, 0.914]." } ], "id_raw": 229 }, { "id": "zFHc8GeV5O6qcc3X", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the {xlabel}[0.489, 0.927, 0.565, 0.963] representing?" }, { "from": "gpt", "value": "It represents the label \"Weeks\" on the x-axis." } ], "id_raw": 229 }, { "id": "gt2dcSk9uoUMfWLY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_9.png", "conversations": [ { "from": "human", "value": "\nWhich line travels higher at {the 15th point}[0.613, 0.391], the 'Green Energy' line or the 'Non-Renewable Energy' line?" }, { "from": "gpt", "value": "Green Energy." } ], "id_raw": 229 }, { "id": "MyGjHNIKPfsh8JpD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_9.png", "conversations": [ { "from": "human", "value": "\nIs {the Green Energy line's point at 12th week}[0.509, 0.477] above or below {the Non-Renewable Energy line's point at 12th week}[0.509, 0.445]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 229 }, { "id": "bq9Dq9cQ17AWkynp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_9.png", "conversations": [ { "from": "human", "value": "\nWhich line shows a lower value of consumption at {the 10th point}[0.440, 0.502], the 'Green Energy' or the 'Non-Renewable Energy'?" }, { "from": "gpt", "value": "Green Energy." } ], "id_raw": 229 }, { "id": "2E48wU6PzoCos29N", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content located in [0.675, 0.736, 0.833, 0.771]?" }, { "from": "gpt", "value": "It contains the legend item {Green Energy}[0.675, 0.736, 0.833, 0.771], paired with a blue marker." } ], "id_raw": 229 }, { "id": "L62n2cw4Ri51M6De", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_9.png", "conversations": [ { "from": "human", "value": "\nWhat content is found in the region [0.675, 0.788, 0.944, 0.823]?" }, { "from": "gpt", "value": "This region contains the legend text {Non-Renewable Energy}[0.675, 0.788, 0.944, 0.823] with an associated red marker." } ], "id_raw": 229 }, { "id": "x5ZxD7yjRu2axPQ6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the region [0.058, 0.819, 0.072, 0.854]?" }, { "from": "gpt", "value": "The region contains the y-axis tick label '2'." } ], "id_raw": 229 }, { "id": "DoNqscmt4Ugf8JuI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the subplot title} placed at [0.147, 0.037, 0.945, 0.088]?" }, { "from": "gpt", "value": "Medical and Biotechnology Industry Trends." } ], "id_raw": 230 }, { "id": "FNXW9MdNzxtaRylP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of the {Y-axis label} in the chart?" }, { "from": "gpt", "value": "[0.025, 0.425, 0.048, 0.528]." } ], "id_raw": 230 }, { "id": "6BcSZcnKlayhQ33Y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the X-axis tick label} at [0.835, 0.879, 0.864, 0.914]?" }, { "from": "gpt", "value": "25." } ], "id_raw": 230 }, { "id": "utK4N1NmlZ7xATIE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_10.png", "conversations": [ { "from": "human", "value": "\nIs {the 15th point of 'New Drugs Development'}[0.561, 0.695] above or below {the 15th point of 'Biotech Patents'}[0.561, 0.333]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 230 }, { "id": "Ef6oudHgGAM8zf8v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_10.png", "conversations": [ { "from": "human", "value": "\nAt {the 18th Time Step}[0.647, 0.727, 0.657, 0.741], which trend is higher: 'Biotech Patents' or 'New Drugs Development'?" }, { "from": "gpt", "value": "New Drugs Development." } ], "id_raw": 230 }, { "id": "sFMS5TVYXYDcg9zD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_10.png", "conversations": [ { "from": "human", "value": "\nWhich trend is higher at {the 7th Time Step}[0.330, 0.716, 0.340, 0.730]: Biotech Patents or New Drugs Development?" }, { "from": "gpt", "value": "Biotech Patents." } ], "id_raw": 230 }, { "id": "3rhcAGx8emFY2eZi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_10.png", "conversations": [ { "from": "human", "value": "\nWhat details can you provide about the region [0.129, 0.116, 0.502, 0.231]?" }, { "from": "gpt", "value": "This is the region containing the plot legend. It consists of two items, {Biotech Patents}[0.203, 0.130, 0.384, 0.165] with a color marker in '#00bfff', and {New Drugs Development}[0.203, 0.182, 0.493, 0.217] with a color marker in '#fa8072'." } ], "id_raw": 230 }, { "id": "Y4WgHF0cA3FpYQO6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region [0.118, 0.098, 0.975, 0.854]." }, { "from": "gpt", "value": "This area contains the main plot of the chart, including lines representing 'Biotech Patents' and 'New Drugs Development', along with all their points plotted against the Y-axis ('Count') and X-axis ('Time Steps')." } ], "id_raw": 230 }, { "id": "dt6Qwtzd9jzaZEB4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area represented by the coordinates [0.118, 0.098, 0.975, 0.854]?" }, { "from": "gpt", "value": "This is the primary chart area. It includes the plotting of two trends: {Biotech Patents}[0.157, 0.183, 0.936, 0.404] and {New Drugs Development}[0.157, 0.529, 0.936, 0.805]. Metaphorically, it illustrates the trends of patents and new drugs development in the medical and biotechnology industry over time." } ], "id_raw": 230 }, { "id": "U2s8RCxHuD71pw6f", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the elevated point of the line representing 'Free Textbook Distribution'?" }, { "from": "gpt", "value": "{the line point}[0.273, 0.829] represents week 6 with 510 distributions." } ], "id_raw": 231 }, { "id": "8kRBlzzKBGTQNwLu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the 'eLibrary Subscriptions' line represented in the legend?" }, { "from": "gpt", "value": "The color is {purple}[0.088, 0.125, 0.124, 0.138]." } ], "id_raw": 231 }, { "id": "ra0axuLHWQXMIxKC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label of {coordinates} [0.015, 0.444, 0.029, 0.525] ?" }, { "from": "gpt", "value": "The label is \"Counts\"." } ], "id_raw": 231 }, { "id": "QInDiHbHmhqPKfTq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_11.png", "conversations": [ { "from": "human", "value": "\nAt school week 15, which resource has more counts: 'Free Textbook Distribution' or 'eLibrary Subscriptions'?" }, { "from": "gpt", "value": "{The eLibrary Subscriptions}[0.547, 0.294] is higher than {the Free Textbook Distribution}[0.547, 0.852]." } ], "id_raw": 231 }, { "id": "r6he6djxDw0hUMjD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_11.png", "conversations": [ { "from": "human", "value": "\nIs {the line point of 'eLibrary Subscriptions'}[0.944, 0.109] above or below {the line point of 'Free Textbook Distribution'}[0.944, 0.862] at school week 28?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 231 }, { "id": "qsK5y4YS2oimPhHj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_11.png", "conversations": [ { "from": "human", "value": "\nAt {point 20}[0.700, 0.223] of 'eLibrary Subscriptions', does the resource count exceed the point at {week 20}[0.700, 0.855] of 'Free Textbook Distribution'?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 231 }, { "id": "Sr8FPJRz9eQr87nu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.121, 0.109, 0.944, 0.413]." }, { "from": "gpt", "value": "The area represents the plots of 'eLibrary Subscriptions', drawn with a dashed line and star markers. The data points range from week 1 to 28 and show counts increasing from 2000 to 3050." } ], "id_raw": 231 }, { "id": "Luq3QkECl0hqSnVe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within [0.086, 0.077, 0.318, 0.154]." }, { "from": "gpt", "value": "This region contains the legend of the chart. It involves two elements: {Free Textbook Distribution}[0.131, 0.086, 0.312, 0.110] marked with a green color and {eLibrary Subscriptions}[0.131, 0.121, 0.286, 0.145] marked with a purple color." } ], "id_raw": 231 }, { "id": "zIAvblqn6U3yeDHk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_11.png", "conversations": [ { "from": "human", "value": "\nCan you explain the structure within the region [0.270, 0.025, 0.795, 0.058]?" }, { "from": "gpt", "value": "This region contains the subtitle of the chart, which reads \"Education Resource Distribution and Utilization\"." } ], "id_raw": 231 }, { "id": "PFYkMmvb9YuSkDOK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_13.png", "conversations": [ { "from": "human", "value": "\nWhat object position corresponds with [0.025, 0.420, 0.048, 0.533] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 233 }, { "id": "gzSLAdopf660NIag", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_13.png", "conversations": [ { "from": "human", "value": "\nIn this chart, which x-axis tick label corresponds to [0.891, 0.879, 0.971, 0.914]?" }, { "from": "gpt", "value": "2020.0." } ], "id_raw": 233 }, { "id": "Ip1IcHJRWFtdvXW1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the {last point on the 'Innovation Index' line}[0.931, 0.148]?" }, { "from": "gpt", "value": "[0.931, 0.148]." } ], "id_raw": 233 }, { "id": "2Npy51vbBXmRksY0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_13.png", "conversations": [ { "from": "human", "value": "\nAt the year {2006}[0.360, 0.573], is the {Startup Foundation line}[0.360, 0.534] above or below the {Innovation Index line}[0.360, 0.534]?" }, { "from": "gpt", "value": "Same level." } ], "id_raw": 233 }, { "id": "nkMdkU2loFqJ1b4f", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_13.png", "conversations": [ { "from": "human", "value": "\nIs the {first point of 'Startup Foundation'}[0.156, 0.804] higher or lower than the {first point of 'Innovation Index'}[0.156, 0.766]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 233 }, { "id": "cpNyVNgAM1gUbs97", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_13.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value for the year {2003}[0.238, 0.727], 'Startup Foundation' or 'Innovation Index'?" }, { "from": "gpt", "value": "Startup Foundation." } ], "id_raw": 233 }, { "id": "TWSqhFv1f6OeFXcF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area of [0.129, 0.116, 0.436, 0.231]." }, { "from": "gpt", "value": "This region contains the legend for the chart. It includes two items: {Startup Foundation}[0.203, 0.130, 0.426, 0.165] with a blue line color marker, and {Innovation Index}[0.203, 0.182, 0.397, 0.217] with a salmon line color marker." } ], "id_raw": 233 }, { "id": "4wrji0C7dGY7yOa3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the area [0.049, 0.037, 1.038, 0.088]." }, { "from": "gpt", "value": "This area holds the subtitle of the chart, labeled as {Entrepreneurship and Innovation Ecosystem Analysis}[0.049, 0.037, 1.038, 0.088]." } ], "id_raw": 233 }, { "id": "yXv1VhPalAi9mcyX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within the region [0.483, 0.879, 0.564, 0.914]?" }, { "from": "gpt", "value": "Within this region, the x-axis tick label for the {year 2010}[0.483, 0.879, 0.564, 0.914] is located." } ], "id_raw": 233 }, { "id": "97nxN4gxMR0JEMpg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.019, 0.417, 0.036, 0.551]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 234 }, { "id": "P9TpVZFAup5B7vmc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate for the {legend entry for 'Health Index'}?" }, { "from": "gpt", "value": "[0.745, 0.086, 0.855, 0.110]" } ], "id_raw": 234 }, { "id": "Ab3RNaYNdZXg6sIj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the year of {the x-axis tick} located at [0.782, 0.919, 0.826, 0.942]?" }, { "from": "gpt", "value": "2015." } ], "id_raw": 234 }, { "id": "mAIZfARt0w7PZO4E", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_14.png", "conversations": [ { "from": "human", "value": "\nWhich line is highest at {the year 2000} point[0.396,0.903]?" }, { "from": "gpt", "value": "Health Index." } ], "id_raw": 234 }, { "id": "EiWPWhx7oFMa5bOg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_14.png", "conversations": [ { "from": "human", "value": "\nAt the year 1992, is {the Health Index}[0.178, 0.697] line above or below {the Living Environment Index}[0.178, 0.613] line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 234 }, { "id": "oVG9rG5wiw5Mgt3M", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_14.png", "conversations": [ { "from": "human", "value": "\nIs the {Health Index} line[0.124, 0.315, 0.940, 0.724] generally above or below the {Living Environment Index} line[0.124, 0.245, 0.940, 0.639]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 234 }, { "id": "U75NJr8ttNdW8GBc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_14.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the contents of [0.083, 0.066, 0.981, 0.903]?" }, { "from": "gpt", "value": "This region primarily contains a line graph plot, representing the trends of 'Health Index' and 'Living Environment Index' over the years from 1990 to 2020. The Health Index line is colored in blue, while the Living Environment Index line is in red." } ], "id_raw": 234 }, { "id": "Y4fG2ikEVw7goxux", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.689, 0.077, 0.973, 0.154] contain?" }, { "from": "gpt", "value": "This region contains the legend for the line graph. It includes two entries: {Health Index}[0.745, 0.086, 0.855, 0.110] with a blue line marker and {Living Environment Index}[0.745, 0.121, 0.966, 0.145] with a red 'x' marker." } ], "id_raw": 234 }, { "id": "j8rzjln9uR6AfLkq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements in [0.510, 0.919, 0.554, 0.942] area." }, { "from": "gpt", "value": "This area only contains one item {2005 label}[0.510, 0.919, 0.554, 0.942] from the x-axis tick label." } ], "id_raw": 234 }, { "id": "M421Jo7PaqhQOhoF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the chart's label}[0.025, 0.420, 0.048, 0.533] ?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 235 }, { "id": "i36GFM82RYvHMIMU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_15.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.836, 0.879, 0.865, 0.914] refer to on the x-axis?" }, { "from": "gpt", "value": "The label '25'." } ], "id_raw": 235 }, { "id": "zzgOF8Zj8q3AI2xO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the legend item} at [0.211, 0.182, 0.548, 0.217] in Subplot 0?" }, { "from": "gpt", "value": "Technology Entrepreneurship." } ], "id_raw": 235 }, { "id": "YATeW7P27sfaSOGa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_15.png", "conversations": [ { "from": "human", "value": "\nIs {the data point '20'}[0.707, 0.715] for the Innovation curve above or below {the data point '20'}[0.707, 0.384] for the Technology Entrepreneurship curve?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 235 }, { "id": "wKPDSfEMypiDoNaX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_15.png", "conversations": [ { "from": "human", "value": "\nIs {the 'week 1'}[0.164, 0.803] data point for Innovation higher or lower than {the 'week 1'}[0.164, 0.722] data point for Technology Entrepreneurship?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 235 }, { "id": "OEUghjOez9tpZPNm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_15.png", "conversations": [ { "from": "human", "value": "\nDoes {point 17}[0.622, 0.311] for Technology Entrepreneurship has more or less value than {point 17}[0.622, 0.740] for Innovation?" }, { "from": "gpt", "value": "More." } ], "id_raw": 235 }, { "id": "Oy5z86vPXz1F6SxP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area between [0.211, 0.130, 0.548, 0.217]." }, { "from": "gpt", "value": "There are labels for two lines in the chart, the \"Innovation\" and \"Technology Entrepreneurship\" lines, each paired with a corresponding marker." } ], "id_raw": 235 }, { "id": "oWtTuUdzdRmdEDqZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.164, 0.590, 0.936, 0.820]." }, { "from": "gpt", "value": "This region contains 28 data points and lines representing the \"Innovation\" trend over 28 weeks. Each data point represents the value of Innovation for a specific week." } ], "id_raw": 235 }, { "id": "w3hc8JSYVxQCEoZy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_15.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what\u2019s inside the region [0.164, 0.133, 0.936, 0.764]?" }, { "from": "gpt", "value": "The region contains 28 data points and lines, representing the trend of \"Technology Entrepreneurship\" across 28 weeks. Each data point signifies the value of Technology Entrepreneurship for a given week." } ], "id_raw": 235 }, { "id": "YmbsbaviHCzHPvbP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the element at coordinates [0.025, 0.429, 0.048, 0.524] on the plot?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 236 }, { "id": "X7vt29oUG3kcn6Qv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first x-axis tick label?" }, { "from": "gpt", "value": "[0.121, 0.879, 0.135, 0.914]" } ], "id_raw": 236 }, { "id": "pmI3yfFv11rvkgyf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_16.png", "conversations": [ { "from": "human", "value": "\nWhat is {the text} at [0.203, 0.736, 0.357, 0.771] in the legend?" }, { "from": "gpt", "value": "\"Overall Index\"." } ], "id_raw": 236 }, { "id": "ZUv9286ZPMaBAd9N", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_16.png", "conversations": [ { "from": "human", "value": "\nAt point 16, is the value of {the Overall Index line}[0.590, 0.629] higher or lower than {the Air Quality Index line}[0.590, 0.184]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 236 }, { "id": "LA5FZP7xknfTD4lI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_16.png", "conversations": [ { "from": "human", "value": "\nIs {the Overall Index line}[[0.157, 0.222], [0.936, 0.794]] generally above or below {the Air Quality Index line}[[0.157, 0.158], [0.936, 0.349]]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 236 }, { "id": "8GUVHeRJMb8uqWs7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_16.png", "conversations": [ { "from": "human", "value": "\nWhich index shows a constant value after the 20th week, {the Overall Index line}[0.705, 0.794] or {the Air Quality Index line}[0.705, 0.158]?" }, { "from": "gpt", "value": "Air Quality Index." } ], "id_raw": 236 }, { "id": "o2QsFvaMWxITtKGv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.058, 0.208, 0.102, 0.243]." }, { "from": "gpt", "value": "This region contains the label '100' for the Y-axis." } ], "id_raw": 236 }, { "id": "T1CD0Yg1XkZXjM1i", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_16.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.129, 0.722, 0.406, 0.837]." }, { "from": "gpt", "value": "This area contains the legend for the chart. The labels 'Overall Index' and 'Air Quality Index' are situated here. Each item in the legend is paired with a marker of its respective colour." } ], "id_raw": 236 }, { "id": "5jbW2RKCugQf8C4b", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_16.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.157, 0.222, 0.936, 0.794]?" }, { "from": "gpt", "value": "This region contains the plot points and lines for the 'Overall Index' trend on the graph. The trend starts from the first week and ends at the 28th week." } ], "id_raw": 236 }, { "id": "p3FSWNwLZRt8wp2X", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_17.png", "conversations": [ { "from": "human", "value": "\nWhat is {the xlabel}[0.505, 0.952, 0.542, 0.975] of the chart?" }, { "from": "gpt", "value": "Year" } ], "id_raw": 237 }, { "id": "cpsexHGDfbWnw0Z3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second tick on the x-axis?" }, { "from": "gpt", "value": "[0.184, 0.919, 0.228, 0.942]" } ], "id_raw": 237 }, { "id": "Z5CJztXNVnsGUczy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_17.png", "conversations": [ { "from": "human", "value": "\nWhat coordinate consists of the fifth y-axis tick?" }, { "from": "gpt", "value": "[0.043, 0.264, 0.054, 0.287]" } ], "id_raw": 237 }, { "id": "hwGsadNeB3ismzmV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_17.png", "conversations": [ { "from": "human", "value": "\nIs {the data point in 2012 for Artificial Intelligence}[0.259, 0.696] higher or lower than {the data point in 2012 for Robotics}[0.259, 0.780]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 237 }, { "id": "F1FooAJINijwsCeB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_17.png", "conversations": [ { "from": "human", "value": "\nIs {the data point in 2021 for Artificial Intelligence}[0.940, 0.104] located above or below {the data point in 2021 for Robotics}[0.940, 0.527]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 237 }, { "id": "kdmRlxfbKVOkUafo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_17.png", "conversations": [ { "from": "human", "value": "\nWhich technology had a higher development indicator in 2010, {Artificial Intelligence}[0.108, 0.865] or {Robotics}[0.108, 0.848]?" }, { "from": "gpt", "value": "Artificial Intelligence." } ], "id_raw": 237 }, { "id": "JuZxiUPeo24RZgDZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content located in [0.220, 0.025, 0.827, 0.058]." }, { "from": "gpt", "value": "This area contains the subtitle of the chart: \"Technological Industry Development Trends\"." } ], "id_raw": 237 }, { "id": "r7wzsRKkFvZqF9bY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_17.png", "conversations": [ { "from": "human", "value": "\nCan you explain the content found between [0.075, 0.077, 0.316, 0.154]?" }, { "from": "gpt", "value": "The area contains the chart's legend with two items. {Artificial Intelligence}[0.131, 0.086, 0.309, 0.110] paired with an bright blue [#00bfff] marker and {Robotics}[0.131, 0.121, 0.204, 0.145] paired with a salmon [#fa8072] marker." } ], "id_raw": 237 }, { "id": "9Sx0riGibKazcY9V", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_17.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region from [0.259, 0.696] to [0.259, 0.780]?" }, { "from": "gpt", "value": "The region includes two data points from the year 2012, one each for {Artificial Intelligence}[0.259, 0.696] and {Robotics}[0.259, 0.780]." } ], "id_raw": 237 }, { "id": "S5FctOrud5wb5Z9a", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object at the coordinates [0.134, 0.689, 0.941, 0.826]?" }, { "from": "gpt", "value": "The trend line for Asian Cuisine." } ], "id_raw": 238 }, { "id": "sQvzzYyoxBH3vvkY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the first x-axis tick text with coordinate [0.017, 0.895, 0.065, 0.918]?" }, { "from": "gpt", "value": "3.75." } ], "id_raw": 238 }, { "id": "wXkZPTN7xkPqFafy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the {color}[0.104, 0.090, 0.149, 0.104] of the Asian Cuisine Trend line in the legend?" }, { "from": "gpt", "value": "Sky blue(#00bfff)." } ], "id_raw": 238 }, { "id": "8XKHYQ5eefQXtPhm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_18.png", "conversations": [ { "from": "human", "value": "\nIs {the point '2000'}[0.134, 0.689] for the Asian Cuisine Trend above or below {the point '2000'}[0.134, 0.767] for the European Cuisine Trend?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 238 }, { "id": "vg6uVKwHiSxb1DzQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_18.png", "conversations": [ { "from": "human", "value": "\nIs the {Asian Cuisine Trend label} [0.158, 0.086, 0.327, 0.110] in the legend above or below the {European Cuisine Trend label}[0.158, 0.121, 0.363, 0.145]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 238 }, { "id": "wR8vGrdR4rhJSRI2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_18.png", "conversations": [ { "from": "human", "value": "\nWhich trend, Asian Cuisine or European Cuisine, is higher at {year 2020}[0.941, 0.533]?" }, { "from": "gpt", "value": "Asian Cuisine Trend." } ], "id_raw": 238 }, { "id": "yS4eS4t2TnWZzLSj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the coordinates [0.102, 0.077, 0.369, 0.154]." }, { "from": "gpt", "value": "This region contains the legend of the chart with two items: the {Asian Cuisine Trend}[0.158, 0.086, 0.327, 0.110] in sky blue color and the {European Cuisine Trend}[0.158, 0.121, 0.363, 0.145] in coral color." } ], "id_raw": 238 }, { "id": "rJcbzRfPzH9ind7y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area within [0.134, 0.533, 0.941, 0.767]." }, { "from": "gpt", "value": "This area contains the trend lines for both Asian and European Cuisines trends from the year 2000 to 2020. The Asian Cuisine trend is a solid line with circle markers while the European Cuisine trend is a dashed line with 'x' markers." } ], "id_raw": 238 }, { "id": "2hBui437ZtAb3dSB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_18.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.043, 0.895, 0.081, 0.918]?" }, { "from": "gpt", "value": "There is one object, the y-axis tick, which is labeled as '3.75'." } ], "id_raw": 238 }, { "id": "1BbufclPFAHkhMX6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.025, 0.273, 0.048, 0.680] represent in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 239 }, { "id": "cxK0ayHTh4R63xvq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the 'Facebook' label in the legend?" }, { "from": "gpt", "value": "[0.845, 0.130, 0.954, 0.165]." } ], "id_raw": 239 }, { "id": "wSxamnS2YGwWxV6f", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_12_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the data point represented by the coordinates [0.445, 0.482] on the line for Facebook?" }, { "from": "gpt", "value": "The data point represents the user count of Facebook on the 11th week." } ], "id_raw": 239 }, { "id": "JIfllJvDo3RwgKiv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_19.png", "conversations": [ { "from": "human", "value": "\nIs {the Facebook data point at week 9}[0.388, 0.577] above or below {the Twitter data point at week 9}[0.388, 0.749]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 239 }, { "id": "I42fyGhgW2csA0jA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_19.png", "conversations": [ { "from": "human", "value": "\nWhich line, {the line for 'Facebook'}[0.157, 0.133, 0.936, 0.814] or {the line for 'Twitter'} [0.157, 0.613, 0.936, 0.820], reaches the highest point in user count?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 239 }, { "id": "cPbTnneI2elr6PIS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_12_19.png", "conversations": [ { "from": "human", "value": "\nWhich platform shows a higher user count at week 16: {Twitter}[0.590, 0.645] or {Facebook}[0.590, 0.148]?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 239 }, { "id": "Wzfv1sECPFPjXfGh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.771, 0.116, 0.963, 0.231]." }, { "from": "gpt", "value": "This area involves the legend of the chart. It comprises two elements: {the 'Facebook' label}[0.845, 0.130, 0.954, 0.165] with a blue marker and {the 'Twitter' label}[0.845, 0.182, 0.923, 0.217] with an orange marker." } ], "id_raw": 239 }, { "id": "Zn61qov8R4K8LYLB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.087, 0.860, 0.102, 0.895]." }, { "from": "gpt", "value": "This area contains {the first y-axis tick label '0'}[0.087, 0.860, 0.102, 0.895]." } ], "id_raw": 239 }, { "id": "KpFsdHq3P520AJNy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_12_19.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.214, 0.784, 0.243, 0.802]?" }, { "from": "gpt", "value": "Two data points are located within this region. One represents the {3rd week Facebook user count}[0.214, 0.784] and the other indicates the {4th week Twitter user count}[0.243, 0.802]." } ], "id_raw": 239 }, { "id": "QSXOB3AdNDdMXsIZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with the coordinates [0.024, 0.908, 0.045, 0.925] in the chart referring to?" }, { "from": "gpt", "value": "The y-axis tick label '1M'." } ], "id_raw": 240 }, { "id": "BUFUFYQwD8GsDlGh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_0.png", "conversations": [ { "from": "human", "value": "\nWhat object is at [0.137, 0.044, 0.241, 0.069] in the chart?" }, { "from": "gpt", "value": "Part of the legend - specifically, the label 'Facebook'." } ], "id_raw": 240 }, { "id": "UP12rdLgW202qIii", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the object at the coordinates}[0.015, 0.072, 0.045, 0.089] representing on the chart?" }, { "from": "gpt", "value": "The y-axis tick label '15M'." } ], "id_raw": 240 }, { "id": "FBo5l0Ta4Qq6nsaS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_0.png", "conversations": [ { "from": "human", "value": "\nAt point {2017}[0.435, 0.497], is {the 'Facebook' line above or below the 'Twitter' line} at the coordinates [0.435, 0.825]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 240 }, { "id": "z7tjKmBnWHaPzySG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_0.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Facebook' data point for 2015}[0.097, 0.735] greater or less than {the 'Twitter' data point for the same year'[0.097, 0.795]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 240 }, { "id": "djKMtuHIhqRs7Qiz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_0.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform has a higher user count in {the year 2020}[0.943, 0.078] - Facebook or Twitter?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 240 }, { "id": "5aLa8jNVDUzRXCer", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_0.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the area [0.065, 0.033, 0.250, 0.119]?" }, { "from": "gpt", "value": "This region contains the chart's legend. It includes two items: {Facebook}[0.137, 0.044, 0.241, 0.069] marked with color '#4267b2' and {Twitter}[0.137, 0.083, 0.212, 0.108] marked with color '#00acee'." } ], "id_raw": 240 }, { "id": "2wNsR7TQctpA8Siu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.097, 0.753, 0.943, 0.867]." }, { "from": "gpt", "value": "This area represents the line and points of the 'Twitter' data series over the years from 2015 to 2020." } ], "id_raw": 240 }, { "id": "GbuSx1xkwEYiBTno", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_0.png", "conversations": [ { "from": "human", "value": "\nWhat items are within [0.097, 0.078, 0.943, 0.735]?" }, { "from": "gpt", "value": "This region contains the 'Facebook' data series, with points at years 2015 to 2020 as represented by their respective coordinates." } ], "id_raw": 240 }, { "id": "AdXrb9LDXFWfZhhN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel}[0.010, 0.462, 0.010, 0.462] positioned for in the chart?" }, { "from": "gpt", "value": "Energy consumption." } ], "id_raw": 241 }, { "id": "8bmLORvCsv1WYJN0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_1.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the marker for 'Wind Energy Consumption'}[0.082, 0.126, 0.141, 0.140]?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 241 }, { "id": "KZDUpeGaMXfY71xE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate for {the last point on the 'Solar Energy Consumption' line}[0.921, 0.337]?" }, { "from": "gpt", "value": "[0.921, 0.337]." } ], "id_raw": 241 }, { "id": "ehn84F0C6B0EdIes", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_1.png", "conversations": [ { "from": "human", "value": "\nIs {the year 1996 for Solar Energy Consumption}[0.476, 0.632] higher than {the year 1996 for Wind Energy Consumption}[0.476, 0.253]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 241 }, { "id": "fICu6xzqcmeZOf8Q", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_1.png", "conversations": [ { "from": "human", "value": "\nWhich marker is closest to {the year '2008' point on 'Wind Energy Consumption'}[0.709, 0.152], Solar or Wind marker?" }, { "from": "gpt", "value": "Wind." } ], "id_raw": 241 }, { "id": "2YBykvNmz7rCfJxN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the year 2014 on the 'Solar Energy Consumption' line}[0.824, 0.384] have a higher value than {the year 2014 on the 'Wind Energy Consumption' line}[0.824, 0.113]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 241 }, { "id": "X9s4PQ0uLC3hs9n6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_1.png", "conversations": [ { "from": "human", "value": "\nWhat data is shown within the range [0.650, 0.133, 0.920, 0.340]?" }, { "from": "gpt", "value": "It represents the points on the 'Solar Energy Consumption' line for the years 2005 to 2019, showing values rising moderately." } ], "id_raw": 241 }, { "id": "dMvGF0A7CRWiM7L9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_1.png", "conversations": [ { "from": "human", "value": "\nWhat is detailed within the plot area [0.105, 0.034, 0.925, 0.492]?" }, { "from": "gpt", "value": "This area includes both the 'Solar Energy Consumption' and 'Wind Energy Consumption' lines, exhibiting various data points for different years indicated by markers of blue for solar and orange for wind." } ], "id_raw": 241 }, { "id": "2n5C5a1IGnOuCHcy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_1.png", "conversations": [ { "from": "human", "value": "\nDescribe what's happening in the section [0.553, 0.050, 0.924, 0.350]." }, { "from": "gpt", "value": "This section shows the data points representing 'Wind Energy Consumption' from the year 2000 to 2019, marked by steady increases in values, especially notable from 2015 onwards." } ], "id_raw": 241 }, { "id": "D5GwdQ5X0m0NZJdQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object represented by the coordinates [0.062, 0.380, 0.084, 0.630] in the chart?" }, { "from": "gpt", "value": "It is the Y-axis label." } ], "id_raw": 244 }, { "id": "sa85rI5Fqd6TeTcK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_4.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the fourth x-axis tick label?" }, { "from": "gpt", "value": "[0.329, 0.906, 0.368, 0.930]" } ], "id_raw": 244 }, { "id": "7Ejm064bd47Et4xQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {year}[0.442, 0.890] on the x-axis?" }, { "from": "gpt", "value": "2006" } ], "id_raw": 244 }, { "id": "q4z1SqJ0TL3QmpxP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point for '2006'}[0.442, 0.598] of the Pharmaceutical line higher or lower than {the point for '2006'}[0.442, 0.758] of the Biotech line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 244 }, { "id": "DKPC3kHtreBcB6p5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_4.png", "conversations": [ { "from": "human", "value": "\nDoes {the Pharmaceutical line}[0.160, 0.155, 0.865, 0.718] increase or decrease from {year '2005'}[0.395, 0.557] to {year '2010'}[0.630, 0.155]?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 244 }, { "id": "Erfff25vTQXbxFuu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_4.png", "conversations": [ { "from": "human", "value": "\nWhich industry has a higher trend value in {2013}[0.771, 0.276, 0.771, 0.807], Biotech or Pharmaceutical?" }, { "from": "gpt", "value": "Pharmaceutical." } ], "id_raw": 244 }, { "id": "4Vui5K4XRCFnTbTW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within coordinates [0.134, 0.134, 0.344, 0.230]." }, { "from": "gpt", "value": "This region contains the legend of the chart. There are 2 items: {Pharmaceutical}[0.194, 0.145, 0.336, 0.175] with a blue marker, and {Biotech}[0.194, 0.189, 0.263, 0.219] with an orange marker." } ], "id_raw": 244 }, { "id": "Jrmv9XL1kt3cSRdz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_4.png", "conversations": [ { "from": "human", "value": "\nWhat is within the coordinates [0.160, 0.155, 0.865, 0.718]?" }, { "from": "gpt", "value": "This area contains the Pharmaceutical line with data points ranging from years 2000 to 2015, the line is blue in color." } ], "id_raw": 244 }, { "id": "HQcmeUJeVqzGzM6Q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained within the region defined by [0.490, 0.939, 0.535, 0.970]?" }, { "from": "gpt", "value": "The label 'Year' which indicates the x-axis of the chart." } ], "id_raw": 244 }, { "id": "o07PmiOQojluTvJh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at {coordinates}[0.017, 0.316, 0.038, 0.604] represent?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 245 }, { "id": "XLx7VYmytYRnMPwZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_5.png", "conversations": [ { "from": "human", "value": "\nCan you provide the coordinates of the {xtick} labelled '2010.0'?" }, { "from": "gpt", "value": "[0.301, 0.911, 0.355, 0.934]." } ], "id_raw": 245 }, { "id": "BqjLQbdVo7NOz7Jq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the {line}[0.124, 0.596, 0.942, 0.734] in the plot?" }, { "from": "gpt", "value": "Physical Store Sales." } ], "id_raw": 245 }, { "id": "MnseKNBvmNLzB25O", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_5.png", "conversations": [ { "from": "human", "value": "\nAt year {2020}[0.738, 0.895], which sales were higher, Online or Physical Store?" }, { "from": "gpt", "value": "Online." } ], "id_raw": 245 }, { "id": "WzztldKw5UQeD1RZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_5.png", "conversations": [ { "from": "human", "value": "\nIs the {point for 'Online Sales'}[0.942, 0.065] at year 2025 above or below the {point for 'Physical Store Sales'}[0.942, 0.734]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 245 }, { "id": "37KxYZfMbtGZH7XQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_5.png", "conversations": [ { "from": "human", "value": "\nConsidering the points at year {2015}, is {Online Sales}[0.533, 0.402] higher or lower than {Physical Store Sales}[0.533, 0.666]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 245 }, { "id": "EEXQjD3s1JEC4ZMv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the region with the coordinates [0.095, 0.044, 0.432, 0.158] contain?" }, { "from": "gpt", "value": "This region contains two items in the legend; 'Online Sales' with corresponding marker at [0.102, 0.067, 0.157, 0.077] and 'Physical Store Sales' with its marker at [0.100, 0.117, 0.159, 0.131]." } ], "id_raw": 245 }, { "id": "szJmDgNak0xolzKX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_5.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region [0.916, 0.911, 0.969, 0.934]?" }, { "from": "gpt", "value": "This region contains the x-axis label '2025.0'." } ], "id_raw": 245 }, { "id": "3SsR4WSIXCWUjIeT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects located in the region from [0.301, 0.911, 0.764, 0.934]." }, { "from": "gpt", "value": "The objects in this region are the x-axis labels '2010.0', '2012.5', '2015.0', '2017.5', '2020.0', '2022.5', and '2025.0'." } ], "id_raw": 245 }, { "id": "hfVlT7JFiGK8rxQ5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_7.png", "conversations": [ { "from": "human", "value": "\nWhat does {the point at}[0.944, 0.816] on the line represent?" }, { "from": "gpt", "value": "The sales of Condos in 2009." } ], "id_raw": 247 }, { "id": "LLL64Ds073jHk4Ti", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of {the color handle}[0.127, 0.123, 0.185, 0.137] in the legend?" }, { "from": "gpt", "value": "Single-Family Home Sales." } ], "id_raw": 247 }, { "id": "UE8Nq3eKv98JKYXs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the y-axis}[0.017, 0.413, 0.038, 0.499]?" }, { "from": "gpt", "value": "Sales." } ], "id_raw": 247 }, { "id": "kDG4GXH4gRhmbbSi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point representing 2000 for 'Condo Sales'}[0.149, 0.843] above or below {the point representing 2000 for 'Single-Family Home Sales'}[0.149, 0.382] in the sales trend line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 247 }, { "id": "QtsbSgRhMoFJqKjB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_7.png", "conversations": [ { "from": "human", "value": "\nWhat is plotted higher at {the year 2006}[0.679, 0.069]: Condo Sales or Single-Family Home Sales?" }, { "from": "gpt", "value": "Single-Family Home Sales." } ], "id_raw": 247 }, { "id": "4fOhiPQmBqI75qt5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_7.png", "conversations": [ { "from": "human", "value": "\nAre {the sales of Condos in 2008}[0.855, 0.640] more or less than {the sales of Single-Family homes in 2008}[0.855, 0.166]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 247 }, { "id": "XmsGgTNsZITzBhdZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within {region}[0.122, 0.049, 0.530, 0.164]?" }, { "from": "gpt", "value": "This region contains the chart's legend. This includes 'Condo Sales' with its associated color marker and 'Single-Family Home Sales' with its associated color marker." } ], "id_raw": 247 }, { "id": "fn37labk4u88erog", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content in {the lower left part of the graph}[0.109, 0.031, 0.5, 0.5]?" }, { "from": "gpt", "value": "The lower left part of the graph consists of the beginning of the 'Condo Sales' and 'Single-Family Home Sales' trend lines. It also contains the labels and tick marks for early years on the x-axis and the lower limit values on the y-axis." } ], "id_raw": 247 }, { "id": "cyA5nJluOJ87e0Sg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.2, 0.5, 0.8, 0.9]." }, { "from": "gpt", "value": "This region encompasses the middle part of the plot, including the bulk of the trend lines for 'Condo Sales' and 'Single-Family Home Sales'. The section shows the fluctuating sales data from around the year 2002 to 2007." } ], "id_raw": 247 }, { "id": "UmAUTAamSIKRxeef", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.017, 0.414, 0.038, 0.505] in the graph?" }, { "from": "gpt", "value": "It is the Y-axis label, which in this case is {Users}[0.017, 0.414, 0.038, 0.505]." } ], "id_raw": 248 }, { "id": "KctpvqFdyb8ZcV3n", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the item labeled 'Traditional Education' in the legend?" }, { "from": "gpt", "value": "The coordinates are [0.175, 0.058, 0.436, 0.092]." } ], "id_raw": 248 }, { "id": "NeFiUMd64TMffI6V", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_8.png", "conversations": [ { "from": "human", "value": "\nWhat text is displayed at {the x-axis label}[0.511, 0.943, 0.557, 0.975]?" }, { "from": "gpt", "value": "The text is 'Year'." } ], "id_raw": 248 }, { "id": "g0MPu6AT7Yh9zvg1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_8.png", "conversations": [ { "from": "human", "value": "\nIs {the year 2002 point on 'Traditional Education' line}[0.242, 0.463] higher or lower than {the year 2002 point on 'Virtual Training' line}[0.242, 0.782]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 248 }, { "id": "iovB1Y8H5TCBj1aM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Traditional Education' line}[0.125, 0.350, 0.942, 0.687] or {the 'Virtual Training' line}[0.125, 0.855, 0.942, 0.065] end at a higher value in 2014?" }, { "from": "gpt", "value": "Virtual Training." } ], "id_raw": 248 }, { "id": "fIAaMgtNIcDCEDNN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_8.png", "conversations": [ { "from": "human", "value": "\nIs the {the year 2012 point on 'Traditional Education' line}[0.826, 0.664] above or below the {the year 2012 point on 'Virtual Training' line}[0.826, 0.222]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 248 }, { "id": "oL74G06MNbkORgum", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.096, 0.044, 0.446, 0.158]." }, { "from": "gpt", "value": "This area hosts the legend for the line chart. It includes two items: {Traditional Education}[0.175, 0.058, 0.436, 0.092] and {Virtual Training}[0.175, 0.110, 0.362, 0.144] with their respective color markers." } ], "id_raw": 248 }, { "id": "M4VVHqOfWw3hgjvi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the plot in the region [0.125, 0.065, 0.942, 0.855]?" }, { "from": "gpt", "value": "This area encompasses the entire plotting area of the line graph. It includes two lines: {the 'Traditional Education' line}[0.125, 0.350, 0.942, 0.687] and {the 'Virtual Training' line}[0.125, 0.065, 0.942, 0.855] each representing a different data series with their points plotted for different years." } ], "id_raw": 248 }, { "id": "DbAYvTqoVMFBdiM8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.923, 0.911, 0.962, 0.934]?" }, { "from": "gpt", "value": "This specific area contains the right-most X-axis label, which represents the {year '2014'}[0.923, 0.911, 0.962, 0.934]." } ], "id_raw": 248 }, { "id": "ll8CR48peRkbYvEa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the primary component at [0.017, 0.405, 0.038, 0.496]?" }, { "from": "gpt", "value": "It's the y-axis label." } ], "id_raw": 249 }, { "id": "UCnpzN9VPBLXbJbS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_9.png", "conversations": [ { "from": "human", "value": "\nWhat is at coordinates [0.139, 0.068] in the line graph?" }, { "from": "gpt", "value": "It's the point on the 'Standard Policy' line with '1' as the x-axis value." } ], "id_raw": 249 }, { "id": "jYxXkHFU2lX9gTTN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_9.png", "conversations": [ { "from": "human", "value": "\nWhat is located at coordinates [0.942, -0.118] in the line chart?" }, { "from": "gpt", "value": "It's the point on the 'Traditional Education' line with the year '2000' as the x-axis value." } ], "id_raw": 249 }, { "id": "K3Vivz3gKkw9nRjF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_9.png", "conversations": [ { "from": "human", "value": "\nIs the point of {the 'Standard Policy' curve}[0.212, 0.131] above or below the point of {the 'Extraordinary Policy' curve}[0.212, 0.193]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 249 }, { "id": "bugP9YZDdoDd66u9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_9.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Standard Policy' point at coordinates '[0.309, 0.068]'} higher or lower than the {corresponding 'Extraordinary Policy' point at coordinates '[0.309, 0.317]'}?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 249 }, { "id": "wpIUBqhX0mcYq2zj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_9.png", "conversations": [ { "from": "human", "value": "\nComparing the {point on the 'Traditional Education' line at [0.943, -0.129]} and the {point on the 'Virtual Training' line at [0.943, -0.129]}, which line has a higher y-value?" }, { "from": "gpt", "value": "'Virtual Training'." } ], "id_raw": 249 }, { "id": "A4Ks25HSNRt1nPDF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_9.png", "conversations": [ { "from": "human", "value": "\nCould you illustrate the content within the range [0.111, 0.639, 0.460, 0.857]?" }, { "from": "gpt", "value": "It includes the legend elements. Each item has its identification text next to a marker of its corresponding color. The legend items are {Traditional Education}[0.190, 0.653, 0.451, 0.687], {Virtual Training}[0.190, 0.705, 0.377, 0.739], {Standard Policy}[0.190, 0.757, 0.382, 0.791], and {Extraordinary Policy}[0.190, 0.809, 0.438, 0.842]." } ], "id_raw": 249 }, { "id": "j0cuCbJ627AEKlqL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_9.png", "conversations": [ { "from": "human", "value": "\nCould you describe the features found in the region [0.139, 0.068, 0.328, 0.379]?" }, { "from": "gpt", "value": "This area encompasses points on the 'Standard Policy' and 'Extraordinary Policy' line graphs. The 'Standard Policy' points range from x=1 to x=6, with corresponding y-values. Similarly, the 'Extraordinary Policy' points also go from x=1 to x=6 but have different y-values." } ], "id_raw": 249 }, { "id": "VanXv28LFIPN9DXL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_9.png", "conversations": [ { "from": "human", "value": "\nWhat content is covered in the region [0.944, -0.130, 0.944, 0.007]?" }, { "from": "gpt", "value": "This area includes the line graph named '_child2' that seems to delineate a threshold or reference line in the plot, with the start point at [0.943, 0.006] and end point at [0.943, -0.119]." } ], "id_raw": 249 }, { "id": "eujrHvV0k0yTEyYs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the xlabel with the coordinates [0.495, 0.943, 0.552, 0.975]?" }, { "from": "gpt", "value": "'Years'." } ], "id_raw": 250 }, { "id": "oWRKIC13azpUnYbd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_10.png", "conversations": [ { "from": "human", "value": "\nWhat color does {the handle in the Online Courses legend entry}[0.698, 0.073, 0.754, 0.083] have?" }, { "from": "gpt", "value": "'#1f77b4'." } ], "id_raw": 250 }, { "id": "M8YIn18xO9RKIWGA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label}[0.017, 0.855, 0.054, 0.886]?" }, { "from": "gpt", "value": "'$10^{-2}$'." } ], "id_raw": 250 }, { "id": "nXFMFX1XiC80dBPt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_10.png", "conversations": [ { "from": "human", "value": "\nIs {the point '2' for 'Online Courses'}[0.429, 0.147] higher or lower than {the point '2' for 'Online Degrees'}[0.429, 0.364]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 250 }, { "id": "maWFgYTA3ryjM1tt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_10.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value at point '3', {the Online Courses line}[0.618, 0.256] or {the Online Degrees line}[0.618, 0.473]?" }, { "from": "gpt", "value": "Online Degrees." } ], "id_raw": 250 }, { "id": "94le9WgYzqxwzgMo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_10.png", "conversations": [ { "from": "human", "value": "\nAt point '5', is the value for {Online Courses}[0.857, 0.473] greater than the value for {Online Degrees}[0.857, 0.690]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 250 }, { "id": "GeoUQr39kkIlCs2L", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_10.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the content in the area [0.692, 0.049, 0.971, 0.164]?" }, { "from": "gpt", "value": "The region contains two legend entries: {Online Courses}[0.771, 0.064, 0.958, 0.098] with a colored (#1f77b4) handle at [0.698, 0.073, 0.754, 0.083] and {Online Degrees}[0.771, 0.116, 0.961, 0.149] with a colored (#ff7f0e) handle at [0.697, 0.123, 0.755, 0.137]." } ], "id_raw": 250 }, { "id": "Gi2D7iZAz2gFW3de", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the covered area from [0.106, 0.039, 0.942, 0.581]?" }, { "from": "gpt", "value": "This area includes all the points plotted in the line for {Online Courses}[0.106, 0.039, 0.942, 0.581]." } ], "id_raw": 250 }, { "id": "DaM19rJCbAFvrk95", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_10.png", "conversations": [ { "from": "human", "value": "\nWhat is happening in the region [0.106, 0.256, 0.942, 0.798]?" }, { "from": "gpt", "value": "This region covers all points of {the Online Degrees line}[0.106, 0.256, 0.942, 0.798]." } ], "id_raw": 250 }, { "id": "uWQUojGEMYZJJj0S", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the {ylabel}[0.017, 0.318, 0.038, 0.601] of the chart?" }, { "from": "gpt", "value": "Investment Value." } ], "id_raw": 251 }, { "id": "qxThwMhIbHUVrUrV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the content at coordinates [0.195, 0.058, 0.370, 0.092] in the chart?" }, { "from": "gpt", "value": "Startup invest." } ], "id_raw": 251 }, { "id": "QJxDx3qK0S3DHoyz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {x-tick label}[0.932, 0.911, 0.954, 0.934] in this chart?" }, { "from": "gpt", "value": "Q6." } ], "id_raw": 251 }, { "id": "CZOIwXHAfz4QoNk0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_11.png", "conversations": [ { "from": "human", "value": "\nIs the {point of 'Startup invest'}[0.303, 0.639] higher or lower than {point of 'Corporate invest'}[0.303, 0.783] for Q2?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 251 }, { "id": "XzmRMFR1KYh9L8QY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_11.png", "conversations": [ { "from": "human", "value": "\nIs the {Corporate invest marker}[0.121, 0.117, 0.180, 0.131] located to the left or right of the {Startup invest marker}[0.121, 0.065, 0.180, 0.079] in the legend?" }, { "from": "gpt", "value": "The markers are vertically aligned, neither to the left nor the right." } ], "id_raw": 251 }, { "id": "Au66ptDcE08Hnz75", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_11.png", "conversations": [ { "from": "human", "value": "\nAre the investment values for 'Startup invest'[0.143, 0.065, 0.943, 0.783] generally higher or lower than 'Corporate invest'[0.143, 0.496, 0.943, 0.855] over Q1-Q6?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 251 }, { "id": "02QiTBM8JKSx6FIU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_11.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects within the bounding box [0.083, 0.918, 0.093, 0.941]?" }, { "from": "gpt", "value": "There is one object, which is the y-axis tick label '0'." } ], "id_raw": 251 }, { "id": "n8iZiP3ljEAEA4CF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.116, 0.044, 0.411, 0.158]." }, { "from": "gpt", "value": "This area contains the chart legend, which includes 2 items: {Startup invest} with a blue marker at [0.195, 0.058, 0.370, 0.092] and {Corporate invest} with an orange marker at [0.195, 0.110, 0.402, 0.144]." } ], "id_raw": 251 }, { "id": "i0sI7XltsKXDK3Bv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the region [0.043, 0.630, 0.093, 0.654]?" }, { "from": "gpt", "value": "This area contains the y-axis label \"20000\"." } ], "id_raw": 251 }, { "id": "3AZUPfSRuZgx55ry", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the label represent for the line in the color \"#ff7f0e\"?" }, { "from": "gpt", "value": "Experienced Entrepreneurs" } ], "id_raw": 252 }, { "id": "p6bnmh0Cyly4zLvU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_12.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the coordinates [0.938, 0.702] on the 'Novice Entrepreneurs' line?" }, { "from": "gpt", "value": "The data point for the year 2010." } ], "id_raw": 252 }, { "id": "rHLGZscMwODcSTCf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-tick label at {coordinates}[0.017, 0.858, 0.054, 0.889] signify?" }, { "from": "gpt", "value": "$10^{-4}$." } ], "id_raw": 252 }, { "id": "nzDJbxVCgMZwMkXh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_12.png", "conversations": [ { "from": "human", "value": "\nWho had a higher value in the year 2006, {Novice Entrepreneurs}[0.273, 0.448] or {Experienced Entrepreneurs}[0.273, 0.867]?" }, { "from": "gpt", "value": "Novice Entrepreneurs." } ], "id_raw": 252 }, { "id": "ARce7SyLGFczKmCd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_12.png", "conversations": [ { "from": "human", "value": "\nHas the value for {Experienced Entrepreneurs} at [0.938, 0.870] increased or decreased compared to its value in 2005 at [0.106, 0.863]?" }, { "from": "gpt", "value": "Decreased." } ], "id_raw": 252 }, { "id": "YPg86JtvbqgxCwEz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_12.png", "conversations": [ { "from": "human", "value": "\nIs the data point for {Novice Entrepreneurs} at [0.605, 0.787] higher or lower than its value in the prior year at point [0.439, 0.702]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 252 }, { "id": "t05a2Cj1qXxoHKbM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the space of [0.044, 0.777, 0.054, 0.801]?" }, { "from": "gpt", "value": "This is the space where the y-axis tick label '1' is positioned." } ], "id_raw": 252 }, { "id": "RgROWz60FKg6Gnbf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_12.png", "conversations": [ { "from": "human", "value": "\nWhat is within the coordinates of [0.620, 0.110, 0.957, 0.144]?" }, { "from": "gpt", "value": "This area contains the legend text for the 'Experienced Entrepreneurs' marker." } ], "id_raw": 252 }, { "id": "SeqYQ0SA7cuCCG3D", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects between [0.106, 0.448, 0.938, 0.787] on the chart." }, { "from": "gpt", "value": "This part of the chart contains the plot line for 'Novice Entrepreneurs'. It consists of several points corresponding to the years from 2005 to 2010." } ], "id_raw": 252 }, { "id": "nGvSDM5wL3BXedpL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.017, 0.357, 0.054, 0.388]?" }, { "from": "gpt", "value": "This is the y-axis tick label - $10^{-1}$." } ], "id_raw": 253 }, { "id": "6xnF2rR5gAVvNPxk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the item with coordinates [0.156, 0.071, 0.356, 0.105] in the legend represent?" }, { "from": "gpt", "value": "It represents the label for Digital Currency." } ], "id_raw": 253 }, { "id": "5hC4sB4GH7TQgVpp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the mark at [0.756, 0.718]?" }, { "from": "gpt", "value": "This is a point on the line labeled 'Digital Currency'." } ], "id_raw": 253 }, { "id": "nOdDqe45S1Adaf3R", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_13.png", "conversations": [ { "from": "human", "value": "\nAt the x-coordinate around 0.358, is {the line for 'Digital Currency'}[0.358, 0.718] above or below {the line for 'FinTech Application'}[0.358, 0.407]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 253 }, { "id": "ukxLjRqT5wZ7BqRl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_13.png", "conversations": [ { "from": "human", "value": "\nWhat has a higher y-value at x=5, {Digital Currency at point}[0.690, 0.739] or {FinTech Application at point}[0.690, 0.241]?" }, { "from": "gpt", "value": "Digital Currency." } ], "id_raw": 253 }, { "id": "37So2alxJsqSDQXP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_13.png", "conversations": [ { "from": "human", "value": "\nIs {the point representing Digital Currency}[0.756, 0.718] to the right or left of {the point representing FinTech Application}[0.756, 0.230]?" }, { "from": "gpt", "value": "Neither, they are aligned vertically on the x-axis." } ], "id_raw": 253 }, { "id": "ILItmM9PH9dHP1Ft", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within [0.495, 0.943, 0.553, 0.975]?" }, { "from": "gpt", "value": "This region contains the label for the x-axis, which reads \"Years\"." } ], "id_raw": 253 }, { "id": "8qUGpwSttU1D5oK6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_13.png", "conversations": [ { "from": "human", "value": "\nCould you provide a description of the objects within the coordinates [0.065, 0.038, 0.983, 0.868]?" }, { "from": "gpt", "value": "This region takes up the majority of the chart and contains the actual plotting area of the line graph, including x and y grid lines, two lines representing 'Digital Currency' and 'FinTech Application' respectively, and corresponding ticks and labels on both axes." } ], "id_raw": 253 }, { "id": "yEdiPrFVSsgCQHJR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is at the boundaries [0.077, 0.057, 0.403, 0.171]?" }, { "from": "gpt", "value": "This region holds the legend of the chart. It contains two entries: \"Digital Currency\" and \"FinTech Application\", each with their respective color markers." } ], "id_raw": 253 }, { "id": "zost7913MbzaCTlM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the value label of {the first point}[0.166, 0.065] on the 'Media Influence' line?" }, { "from": "gpt", "value": "120" } ], "id_raw": 254 }, { "id": "LkGmf0vDEvLkRfNm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.765, 0.110, 0.942, 0.144] in the chart legend?" }, { "from": "gpt", "value": "Public Opinion" } ], "id_raw": 254 }, { "id": "2Uj8f1reireIsamB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-axis label}[0.017, 0.344, 0.038, 0.576]?" }, { "from": "gpt", "value": "Public Opinion" } ], "id_raw": 254 }, { "id": "9BZPO0sdxVNieCYW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_14.png", "conversations": [ { "from": "human", "value": "\nLooking at the line for 'Media Influence'[0.166, 0.065, 0.942, 0.443], and for 'Public Opinion'[0.125, 0.202, 0.223, 0.855], which line represents higher values at the start of their respective scales?" }, { "from": "gpt", "value": "Media Influence" } ], "id_raw": 254 }, { "id": "weJR8FW1iArZqC71", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_14.png", "conversations": [ { "from": "human", "value": "\nWhich line terminates at a higher value, {Media Influence}[0.942, 0.443], or {Public Opinion}[0.223, 0.855]?" }, { "from": "gpt", "value": "Public Opinion" } ], "id_raw": 254 }, { "id": "kOORReXHLMd6LN6n", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_14.png", "conversations": [ { "from": "human", "value": "\nWhich points are at a higher value: {the points on 'Media Influence' line}[0.370, 0.236], or {the points on 'Public Opinion' line}[0.182, 0.546]?" }, { "from": "gpt", "value": "Public Opinion" } ], "id_raw": 254 }, { "id": "vO0rmnGS1h1j6Xrq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.686, 0.044, 0.971, 0.158]." }, { "from": "gpt", "value": "This area contains the chart's legend, with two items: {Media Influence}[0.765, 0.058, 0.961, 0.092] and {Public Opinion}[0.765, 0.110, 0.942, 0.144]. Each item's label is colored to match its corresponding line on the chart." } ], "id_raw": 254 }, { "id": "G2GkOi0vRTbcu1Uf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_14.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.166, 0.065, 0.942, 0.443]?" }, { "from": "gpt", "value": "This area contains {the line representing 'Media Influence'}[0.166, 0.065, 0.942, 0.443]. The line is marked with circular points at various coordinates along the x-axis scale." } ], "id_raw": 254 }, { "id": "7Q2uJryC1Wz873yT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.125, 0.202, 0.223, 0.855]." }, { "from": "gpt", "value": "The area contains {the line representing 'Public Opinion'}[0.125, 0.202, 0.223, 0.855]. The line is marked with x-shaped points along the scale." } ], "id_raw": 254 }, { "id": "1Gj7SfIfyB5kHuSm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.017, 0.855, 0.054, 0.886]?" }, { "from": "gpt", "value": "This represents a y-axis tick label." } ], "id_raw": 255 }, { "id": "8u8AQYupV3uePLlD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_15.png", "conversations": [ { "from": "human", "value": "\nWhich type of handle is associated with {the label 'Sporadic Grower'}[0.448, 0.801, 0.654, 0.835]?" }, { "from": "gpt", "value": "It is a cross shaped handle." } ], "id_raw": 255 }, { "id": "7ihUb2gKcWO3ludc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the label {Persistent Grower}[0.448, 0.750, 0.669, 0.783] in the chart?" }, { "from": "gpt", "value": "The color of the label 'Persistent Grower' is #1f77b4 (a deep blue color)." } ], "id_raw": 255 }, { "id": "0yysJr5PLOotiEAZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_15.png", "conversations": [ { "from": "human", "value": "\nWhich line, {Persistent Grower}[0.106, 0.038, 0.942, 0.155] or {Sporadic Grower}[0.106, 0.038, 0.942, 0.764], reaches a higher Y-value at the point where X=50 on the chart?" }, { "from": "gpt", "value": "Sporadic Grower." } ], "id_raw": 255 }, { "id": "c5y01PatbDuJFFpB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_15.png", "conversations": [ { "from": "human", "value": "\nIs the {6th point}[0.942, 0.155] of the Persistent Grower line above or below the {6th point}[0.942, 0.764] of the Sporadic Grower line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 255 }, { "id": "C2TIRWBEXmmq1RQp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_15.png", "conversations": [ { "from": "human", "value": "\nAt the {3rd point}[0.618, 0.066] of Persistent Grower line, is it above or below the {3rd point}[0.618, 0.246] of Sporadic Grower line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 255 }, { "id": "hbXskaV4AoBwKO7C", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.369, 0.735, 0.678, 0.850]." }, { "from": "gpt", "value": "This area contains the legend items for the chart. There are two labels, {Persistent Grower}[0.448, 0.750, 0.669, 0.783] and {Sporadic Grower}[0.448, 0.801, 0.654, 0.835], each with their respective colored handles, blue for Persistent Grower and orange for Sporadic Grower." } ], "id_raw": 255 }, { "id": "In6y5tlJ4C3YMOHq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_15.png", "conversations": [ { "from": "human", "value": "\nEnumerate the items in the region [0.106, 0.038, 0.942, 0.764]." }, { "from": "gpt", "value": "There are two lines in this region, the {line of Persistent Grower}[0.106, 0.038, 0.942, 0.155] and the {line of Sporadic Grower}[0.106, 0.038, 0.942, 0.764], as well as a series of points associated with each line." } ], "id_raw": 255 }, { "id": "9ArKRj6218FBpvpl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the region [0.065, 0.038, 0.983, 0.868]?" }, { "from": "gpt", "value": "This region contains the main plot of the chart with X and Y axis labels, tick lines and labels, gridlines, as well as two plotted lines: {Persistent Grower}[0.106, 0.038, 0.942, 0.155] and {Sporadic Grower}[0.106, 0.038, 0.942, 0.764], including all the points on both lines." } ], "id_raw": 255 }, { "id": "eJ9rzwMfPh9mHvJb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the label associated with the bounding box [0.771, 0.103, 0.967, 0.133] in the legend represent?" }, { "from": "gpt", "value": "Sustainable Business." } ], "id_raw": 256 }, { "id": "Lx7iuoQxtNnsA86Z", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the {y-axis tick label}[0.017, 0.781, 0.041, 0.804]?" }, { "from": "gpt", "value": "0.2." } ], "id_raw": 256 }, { "id": "kJyg1MCGkdIY4RCO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_16.png", "conversations": [ { "from": "human", "value": "\nIn the chart, what is represented by the coordinates of the point [0.835, 0.648]?" }, { "from": "gpt", "value": "The value of both 'Social Responsibility' and 'Sustainable Business' in year 2019." } ], "id_raw": 256 }, { "id": "Hgg6fmN0J6U7r1nv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_16.png", "conversations": [ { "from": "human", "value": "\nIn the chart, is the {2018 point of 'Social Responsibility'}[0.729, 0.611] above or below the corresponding {2018 point of 'Sustainable Business'}[0.729, 0.790]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 256 }, { "id": "QKAIHsYBo4SHee3L", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_16.png", "conversations": [ { "from": "human", "value": "\nWhich category, {Social Responsibility}[0.094, 0.223, 0.941, 0.686] or {Sustainable Business}[0.094, 0.318, 0.941, 0.790], decrease more dramatically from 2017 to 2018?" }, { "from": "gpt", "value": "Sustainable Business." } ], "id_raw": 256 }, { "id": "llODY5gbOEwP45g2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_16.png", "conversations": [ { "from": "human", "value": "\nIs the value for {Social Responsibility}[0.835, 0.648] higher or lower in 2019 compared to 2020?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 256 }, { "id": "RhCWJHjR3AtsbXdu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_16.png", "conversations": [ { "from": "human", "value": "\nExplain the contents displayed in the chart between coordinates [0.627, 0.554, 0.941, 0.790]." }, { "from": "gpt", "value": "This region includes the plot points for 'Social Responsibility' and 'Sustainable Business' from the years 2017, 2018, 2019 and 2020. While 'Social Responsibility' shows a decreasing trend, 'Sustainable Business' decreases abruptly from 2017 to 2018 and rose again from 2019 to 2020." } ], "id_raw": 256 }, { "id": "fEdagi7EYPqB2dy7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_16.png", "conversations": [ { "from": "human", "value": "\nProvide details on the area [0.712, 0.048, 0.974, 0.144] of the chart." }, { "from": "gpt", "value": "The area covers the chart legend which includes two items: {Social Responsibility}[0.771, 0.059, 0.960, 0.089] with a blue color label, and {Sustainable Business}[0.771, 0.103, 0.967, 0.133] with a orange color label." } ], "id_raw": 256 }, { "id": "BptuMyxurqq1VqYf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_16.png", "conversations": [ { "from": "human", "value": "\nWhat is notable within the boundaries [0.094, 0.223, 0.412, 0.601]?" }, { "from": "gpt", "value": "In this region we can observe points from 2012 up until 2015 for 'Social Responsibility' and 'Sustainable Business'. Both have shown a decreasing trend over these years." } ], "id_raw": 256 }, { "id": "sCoayTe0Ltft0xIv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_17.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at coordinates [0.017, 0.025, 0.045, 0.056]?" }, { "from": "gpt", "value": "Y-axis tick label." } ], "id_raw": 257 }, { "id": "cYctQagtlJU8WfAy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the {text inside sublegend}[0.823, 0.071, 0.961, 0.106] in the chart?" }, { "from": "gpt", "value": "VC_Series1." } ], "id_raw": 257 }, { "id": "iafoWWwwUe4ttwDG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_17.png", "conversations": [ { "from": "human", "value": "\nWhat is located at coordinates [0.601, 0.315] in the figure?" }, { "from": "gpt", "value": "A point on the VC_Series2 line." } ], "id_raw": 257 }, { "id": "9rvF5dnt8ufPOthR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_17.png", "conversations": [ { "from": "human", "value": "\nWhich line is {the point at [0.260, 0.266]} part of, VC_Series1 or VC_Series2?" }, { "from": "gpt", "value": "VC_Series1." } ], "id_raw": 257 }, { "id": "grPK8Pz7xH02BGDv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_17.png", "conversations": [ { "from": "human", "value": "\nIs the {point at [0.601, 0.315]} higher or lower on the chart than the {point at [0.601, 0.251]}?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 257 }, { "id": "VkDqLP0DpRVyeEeR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_17.png", "conversations": [ { "from": "human", "value": "\nWhat has a larger value {at point [0.098, 0.303]}, VC_Series1 or VC_Series2?" }, { "from": "gpt", "value": "VC_Series2." } ], "id_raw": 257 }, { "id": "sXKR8iwAxi0Dw3uA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region represented by the coordinates [0.744, 0.057, 0.971, 0.175]." }, { "from": "gpt", "value": "This section contains the legend items of the chart. It has labels for VC_Series1 at {coordinates}[0.823, 0.071, 0.961, 0.106] and VC_Series2 at {coordinates} [0.823, 0.125, 0.961, 0.160]." } ], "id_raw": 257 }, { "id": "QfH4XZIC9lWhUbxZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the area from [0.056, 0.038, 0.983, 0.868] contain?" }, { "from": "gpt", "value": "This is the main plot of the chart showing the two lines: VC_Series1 and VC_Series2, with values plotted along the x and y axes, and it also contains tick labels for these axes." } ], "id_raw": 257 }, { "id": "hXD8EQiBUcyizdru", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region from [0.098, 0.231, 0.941, 0.398]?" }, { "from": "gpt", "value": "In this area, the line for VC_Series1 is plotted with individual data points represented as circles. A similar line for VC_Series2 is also intersecting this region but with markers as crosses." } ], "id_raw": 257 }, { "id": "3q26ljaZAm2aPz7j", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.721, 0.414, 0.961, 0.447] in the chart?" }, { "from": "gpt", "value": "It's a legend text labeled as \"Student Enrollment\"." } ], "id_raw": 258 }, { "id": "m5TfQ8bk1COZ2JcT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_18.png", "conversations": [ { "from": "human", "value": "\nCan you identify what is located at coordinates [0.604, 0.797] on the chart?" }, { "from": "gpt", "value": "There is a 'x' mark on line chart depicting \"Tutor Recruitment\" data for the year 2018." } ], "id_raw": 258 }, { "id": "b209HqPkna2WauoR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.017, 0.561, 0.046, 0.584] on the chart?" }, { "from": "gpt", "value": "It's 100." } ], "id_raw": 258 }, { "id": "DTsCQFV8wpqrCDEs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_18.png", "conversations": [ { "from": "human", "value": "\nIs the {point on the 'Student Enrollment' line at 2018}[0.604, 0.111] higher or lower than the {point on the 'Tutor Recruitment' line at 2018}[0.604, 0.797] on the chart?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 258 }, { "id": "itDKlWnSygPuA0cR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_18.png", "conversations": [ { "from": "human", "value": "\nFor the year 2017, did {Student Enrollment}[0.436, 0.093] have a larger or smaller number than {Tutor Recruitment}[0.436, 0.820]?" }, { "from": "gpt", "value": "Larger." } ], "id_raw": 258 }, { "id": "UOxv0PyDd7y1QxLP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_18.png", "conversations": [ { "from": "human", "value": "\nIn 2016, which line - {Student Enrollment}[0.267, 0.079] or {Tutor Recruitment}[0.267, 0.824] - is closer to the x-axis?" }, { "from": "gpt", "value": "Student Enrollment." } ], "id_raw": 258 }, { "id": "dABJABiJjRKV3rKe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_18.png", "conversations": [ { "from": "human", "value": "\nWhat is in this chart location [0.099, 0.752, 0.941, 0.842]?" }, { "from": "gpt", "value": "This region represents the line for \"Tutor Recruitment\" with data from 2015 to 2020 represented as 'x' marks." } ], "id_raw": 258 }, { "id": "IUpV7w0HjH2tq9dv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents located between coordinates [0.642, 0.399, 0.971, 0.514] in the given chart?" }, { "from": "gpt", "value": "This is the area of the chart that contains the legend. Two labels are located here: {Student Enrollment}[0.721, 0.414, 0.961, 0.447] plotted in blue color and {Tutor Recruitment}[0.721, 0.466, 0.943, 0.499] plotted in orange color." } ], "id_raw": 258 }, { "id": "jrSH8SaPBKjqv22T", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_18.png", "conversations": [ { "from": "human", "value": "\nWhat elements are contained within the coordinate area of [0.057, 0.025, 0.983, 0.888]?" }, { "from": "gpt", "value": "This large area comprises the entirety of the main chart area. It includes the x-axis from 2014 to 2021, the y-axis from 50 to 200, the 'Student Enrollment' line from 2015 to 2020 represented as circular points, the 'Tutor Recruitment' line from 2015 to 2020 represented as \u2018x\u2019 marks, and also a grey dotted line at 2018." } ], "id_raw": 258 }, { "id": "SkSJ5h420gIAymaX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the description of the object at [0.498, 0.943, 0.544, 0.975]?" }, { "from": "gpt", "value": "It is the X-axis label displaying \"Year.\"" } ], "id_raw": 259 }, { "id": "l176eDGhWXzu1xCQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_19.png", "conversations": [ { "from": "human", "value": "\nWhat represents {the blue color line}[0.106, 0.470, 0.936, 0.851]?" }, { "from": "gpt", "value": "\"Waiting Time\"." } ], "id_raw": 259 }, { "id": "6Yg1XauUCngN49aK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_13_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object {located at}[0.106, 0.067, 0.936, 0.284]?" }, { "from": "gpt", "value": "It is the line representing \"Processing Time\"" } ], "id_raw": 259 }, { "id": "ErYq7VwddLi4Hviw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_19.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at the point of 2008, {Waiting Time}[0.106, 0.470, 0.936, 0.851] or {Processing Time}[0.106, 0.067, 0.936, 0.284]?" }, { "from": "gpt", "value": "Waiting Time." } ], "id_raw": 259 }, { "id": "fxm68NqXYlIaIzJZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_19.png", "conversations": [ { "from": "human", "value": "\nBy comparing {the plot at 2000}[0.106, 0.470] with {the plot at 2010}[0.936, 0.851] for Waiting Time, is the trend increasing or decreasing?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 259 }, { "id": "20ujdvDOqHrpCrvz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_13_19.png", "conversations": [ { "from": "human", "value": "\nIs the {Processing Time}[0.106, 0.067, 0.936, 0.284] higher or lower than {Waiting Time}[0.106, 0.470, 0.936, 0.851] at the point of 2005?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 259 }, { "id": "iTdWwDZvD5Hyxxiq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_19.png", "conversations": [ { "from": "human", "value": "\nWhat does [0.077, 0.047, 0.366, 0.161] area correspond to?" }, { "from": "gpt", "value": "This area corresponds to the legend of the chart. It includes two items: {Waiting Time}[0.156, 0.061, 0.317, 0.095], represented with a blue color line, and {Processing Time}[0.156, 0.113, 0.357, 0.147], represented with an orange color line." } ], "id_raw": 259 }, { "id": "SCmypmDHlfzCQS3Y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the area denoted by [0.106, 0.470, 0.936, 0.851]." }, { "from": "gpt", "value": "This is the area of the chart displaying the points of the \"Waiting Time\" line plot over different years." } ], "id_raw": 259 }, { "id": "1KpuIDgqbOgbVqoi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_13_19.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the region [0.106, 0.067, 0.936, 0.284]?" }, { "from": "gpt", "value": "This area contains the line plot for \"Processing Time\", plotted with marked points for different years." } ], "id_raw": 259 }, { "id": "9GdTResRmlEBPAKZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the object described by the coordinates [0.322, 0.034, 0.441, 0.066] in the legend?" }, { "from": "gpt", "value": "Creativity." } ], "id_raw": 260 }, { "id": "JG8g8EvGQ7F34r0B", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of {the object} at [0.050, 0.539, 0.061, 0.562] on the Y-axis?" }, { "from": "gpt", "value": "The label is '3'." } ], "id_raw": 260 }, { "id": "OFqAqOZ18JVlM3SF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the location of the 'Design Innovation' line in the legend?" }, { "from": "gpt", "value": "[0.557, 0.034, 0.778, 0.066]." } ], "id_raw": 260 }, { "id": "AJSVVJkT2fClqlwz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point on the Creativity line at '81'}[0.579, 0.721] higher or lower in value than {the point on the Design Innovation line at '81'}[0.579, 0.663]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 260 }, { "id": "39DGCbxyl0jZbsmB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_0.png", "conversations": [ { "from": "human", "value": "\nComparing {the Creativity line}[0.073, 0.663, 0.959, 0.866] and {the Design Innovation line}[0.073, 0.201, 0.959, 0.871], which ends at a lesser value on scale '2187'?" }, { "from": "gpt", "value": "Creativity." } ], "id_raw": 260 }, { "id": "EUKmXUSnQOFaSCGW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_0.png", "conversations": [ { "from": "human", "value": "\nCompare {the point of the Creativity line at '3'}[0.199, 0.779] and {the point of the Design Innovation line at '3'}[0.199, 0.548]. Which one is higher?" }, { "from": "gpt", "value": "Design Innovation." } ], "id_raw": 260 }, { "id": "EttBVhSBXj5mgOHb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_0.png", "conversations": [ { "from": "human", "value": "\nPlease elaborate on the area specified by [0.245, 0.021, 0.787, 0.079]." }, { "from": "gpt", "value": "This area contains the legend of the chart, which includes the labels {Creativity}[0.322, 0.034, 0.441, 0.066] and {Design Innovation}[0.557, 0.034, 0.778, 0.066], along with their respective color markers." } ], "id_raw": 260 }, { "id": "uSSsF2MwD3FzunfF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the details in the region [0.073, 0.663, 0.959, 0.866]." }, { "from": "gpt", "value": "It contains the 'Creativity' line of the chart, reflective of variable Creativity values on different scales from '1' to '2187'." } ], "id_raw": 260 }, { "id": "hHZGBfjV9T8rt3ap", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_0.png", "conversations": [ { "from": "human", "value": "\nWhat information is found within [0.050, 0.423, 0.061, 0.447]?" }, { "from": "gpt", "value": "It contains a Y-axis label, '4'." } ], "id_raw": 260 }, { "id": "oz7QWZoLNRSRvCn7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the largest object in the chart at the coordinates [0.106, 0.349, 0.925, 0.793]?" }, { "from": "gpt", "value": "This is the line representing Investment." } ], "id_raw": 261 }, { "id": "uKmzHfO8Hq2SiJqe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the forth tick label on the x-axis?" }, { "from": "gpt", "value": "[0.451, 0.911, 0.462, 0.934]" } ], "id_raw": 261 }, { "id": "LLeds18EiKb5yEVt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_1.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.775, 0.048, 0.824, 0.048]?" }, { "from": "gpt", "value": "The color handle for the 'Profit' trend in the legend." } ], "id_raw": 261 }, { "id": "0594QHa9gYs742jZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_1.png", "conversations": [ { "from": "human", "value": "\nIs {the 7th point of the 'Investment'}[0.808, 0.389] located above or below {the 7th point of the 'Profit'}[0.808, 0.440]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 261 }, { "id": "lDavPjfsDxXZMSDc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_1.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Investment'}[0.106, 0.349, 0.925, 0.793] generally above or below {the line for 'Profit'}[0.106, 0.409, 0.925, 0.874]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 261 }, { "id": "utqItsgKv0N3PIrK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_1.png", "conversations": [ { "from": "human", "value": "\nWhich line is at higher y-value at {point 4} [0.457], Investment or Profit?" }, { "from": "gpt", "value": "Profit." } ], "id_raw": 261 }, { "id": "YOFlXujybwhOYXnR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region [0.082, 0.48, 0.925, 0.93]?" }, { "from": "gpt", "value": "This region contains the chart graph, which includes 3 lines representing Investment, Risk, and Profit and their respective data points plotted in various positions." } ], "id_raw": 261 }, { "id": "zodQcyyhKpHXP5u1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_1.png", "conversations": [ { "from": "human", "value": "\nWhat do we have in the area [0.072, 0.021, 0.959, 0.079]?" }, { "from": "gpt", "value": "Content in this area includes the legend indicating the labels and their corresponding colors for the 'Investment', 'Risk', and 'Profit' lines." } ], "id_raw": 261 }, { "id": "iIrcMExlBZBRTjRO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_1.png", "conversations": [ { "from": "human", "value": "\nWhat type of information can be observed in the region [0.451, 0.911, 0.462, 0.934]?" }, { "from": "gpt", "value": "The forth x-axis label is in this region, which is the year '4' in the data set." } ], "id_raw": 261 }, { "id": "GWI8r3vbfnlRdTJb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.019, 0.406, 0.043, 0.574] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 262 }, { "id": "PZlyzk0Sr5yltJvk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first tick mark on the x-axis in the legend?" }, { "from": "gpt", "value": "[0.051, 0.911, 0.095, 0.934]" } ], "id_raw": 262 }, { "id": "1cBoche7UyYlL3qf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.139, 0.911, 0.183, 0.934]?" }, { "from": "gpt", "value": "2001." } ], "id_raw": 262 }, { "id": "1yV4vEInk3WIYDYV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point for year '2002'}[0.250, 0.672] on the Running line above or below {the point for year '2002'}[0.250, 0.440] on the Yoga line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 262 }, { "id": "AybEYm4msPR8DywD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_2.png", "conversations": [ { "from": "human", "value": "\nIs {the Running line}[[0.073, 0.298, 0.959, 0.763]] generally above or below {the Yoga line}[[0.073, 0.136, 0.959, 0.541]]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 262 }, { "id": "MYK9NOHVE9uoc9Ol", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_2.png", "conversations": [ { "from": "human", "value": "\nWhich activity has a higher frequency in 2010, Running or Yoga?" }, { "from": "gpt", "value": "Yoga." } ], "id_raw": 262 }, { "id": "m7DcY4fYx01MgB8Z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.484, 0.943, 0.548, 0.975]." }, { "from": "gpt", "value": "The section contains the x-axis label \"Years\", rendered in black color." } ], "id_raw": 262 }, { "id": "Qs3T861eKlMeiYEp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.233, 0.021, 0.799, 0.079]." }, { "from": "gpt", "value": "The area contains the legend items: {Running}[0.311, 0.034, 0.410, 0.066], {Yoga}[0.527, 0.034, 0.583, 0.066], and {Cycling}[0.700, 0.034, 0.790, 0.066], each paired with a marker of corresponding color." } ], "id_raw": 262 }, { "id": "vqeNAsctsXuulaIk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.671, 0.279, 0.981, 0.302]?" }, { "from": "gpt", "value": "There are 2 y-axis tick labels '7' and '8' within this region. They are located at {coordinates}[0.671, 0.279, 0.682, 0.302] and {coordinates}[0.937, 0.279, 0.948, 0.302], respectively." } ], "id_raw": 262 }, { "id": "aAEGw2nOrnrIlm0t", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label}[0.115, 0.034, 0.385, 0.066] in the chart's legend?" }, { "from": "gpt", "value": "Hotel Accommodation." } ], "id_raw": 263 }, { "id": "PjxCALVhWFSOcSEt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the data point coordinate represented by 'Q4' in the 'Food Services' line?" }, { "from": "gpt", "value": "[0.906, 0.591]." } ], "id_raw": 263 }, { "id": "8Q2DuvPGeH3cIGI2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.023, 0.431, 0.047, 0.549]?" }, { "from": "gpt", "value": "Growth." } ], "id_raw": 263 }, { "id": "FjF1GagCFqvC1WLr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_3.png", "conversations": [ { "from": "human", "value": "\nBetween {the 'Hotel Accommodation'}[0.365, 0.591] and {the 'Food Services'}[0.365, 0.652] lines in 'Q2', which one shows higher growth?" }, { "from": "gpt", "value": "Food Services." } ], "id_raw": 263 }, { "id": "DXrZPZQvvIz4Ue6e", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_3.png", "conversations": [ { "from": "human", "value": "\nAre the 'Q3' results for {the 'Food Services'}[0.635, 0.713] and {the 'Travel Agents'}[0.635, 0.571] growing or declining compared to the 'Q2' results?" }, { "from": "gpt", "value": "Food Services is declining while Travel Agents is growing." } ], "id_raw": 263 }, { "id": "6WBJ8JfML0i1ULfG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_3.png", "conversations": [ { "from": "human", "value": "\nWhich industry shows a decline in 'Q4' compared to 'Q3' between {the 'Food Services'}[0.906, 0.591] and {the 'Travel Agents'}[0.906, 0.793]?" }, { "from": "gpt", "value": "Travel Agents." } ], "id_raw": 263 }, { "id": "KLzBB8ZX5PyvXknG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area in [0.037, 0.021, 0.963, 0.079]?" }, { "from": "gpt", "value": "This area encompasses the legend of the chart. It includes three items: {Hotel Accommodation}[0.115, 0.034, 0.385, 0.066], {Food Services}[0.502, 0.034, 0.671, 0.066], and {Travel Agents}[0.788, 0.034, 0.953, 0.066], each accompanied by a color indicator." } ], "id_raw": 263 }, { "id": "fBPlMWQwtPfv1KPN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.094, 0.086, 0.906, 0.895]." }, { "from": "gpt", "value": "This is the main plotting area of the chart. Within this, three line plots represent the growth of 'Hotel Accommodation', 'Food Services', and 'Travel Agents' over four quarters. Each line plot has distinct coordinates indicating various growth values for each quarter." } ], "id_raw": 263 }, { "id": "aDHu6885KEgGOsH2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in [0.453, 0.943, 0.547, 0.975]?" }, { "from": "gpt", "value": "It contains the x-axis label, which reads 'Quarter'." } ], "id_raw": 263 }, { "id": "9UvyR0niMfGdEYH5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the coordinate [0.811, 0.055, 0.934, 0.078] in the legend?" }, { "from": "gpt", "value": "Public Schools." } ], "id_raw": 264 }, { "id": "C2vY46LhXFomPdqS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_4.png", "conversations": [ { "from": "human", "value": "\nWhat is at coordinates [0.950, 0.808]?" }, { "from": "gpt", "value": "The last point on the 'Online Schools' line." } ], "id_raw": 264 }, { "id": "Dal2wsMGfb3eX2Bz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_4.png", "conversations": [ { "from": "human", "value": "\nWhat is at {the coordinates}[0.196, 0.911, 0.240, 0.934]?" }, { "from": "gpt", "value": "The x-axis tick label '2011'." } ], "id_raw": 264 }, { "id": "29NN4N75nQhQb3uJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_4.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Public Schools' line}[0.095, 0.120, 0.959, 0.464] above or below {the 'Private Schools' line}[0.095, 0.421, 0.959, 0.550]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 264 }, { "id": "kyGWZXuKTtCurSqW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_4.png", "conversations": [ { "from": "human", "value": "\nDo {the 'Online Schools'}[0.095, 0.486, 0.959, 0.808] have more or less students in 2010 compared to {the 'Private Schools'}[0.095, 0.421, 0.959, 0.550]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 264 }, { "id": "6cHVd5UKUQwmmnxe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_4.png", "conversations": [ { "from": "human", "value": "\nComparing {the 'Public Schools' line}[0.095, 0.120, 0.959, 0.464] and {the 'Online Schools' line}[0.095, 0.486, 0.959, 0.808], which one depicts an overall decrease in numbers from 2010 to 2017?" }, { "from": "gpt", "value": "Public Schools." } ], "id_raw": 264 }, { "id": "QtIWhZVhaE5gsbI0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region from [0.095, 0.421, 0.959, 0.550]." }, { "from": "gpt", "value": "This region contains the 'Private Schools' line graph, which represents the number of students in private schools from 2010 to 2017. The line starts from the year 2010 with the label indicating 80 and ends at 2017 marking around 95." } ], "id_raw": 264 }, { "id": "9jobVX2G6LGyeQcg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_4.png", "conversations": [ { "from": "human", "value": "\nWhat content is located in the area from [0.755, 0.046, 0.951, 0.157]?" }, { "from": "gpt", "value": "The legend of the graph is located within this region. It has three entries: {Public Schools}[0.811, 0.055, 0.934, 0.078], {Private Schools}[0.811, 0.090, 0.944, 0.113], and {Online Schools}[0.811, 0.125, 0.939, 0.148], each paired with a colored line representing its respective line graph on the chart." } ], "id_raw": 264 }, { "id": "mXbvDMs618KdCKn4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_4.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region from [0.095, 0.034, 0.959, 0.895]?" }, { "from": "gpt", "value": "This regional box encapsulates the entire graph plot, including 3 line charts marked as 'Public Schools', 'Private Schools' and 'Online Schools'. There are eight x-axis ticks representing the years from 2010 to 2017 and nine y-axis ticks indicating counts from 0 to 200." } ], "id_raw": 264 }, { "id": "MFIXQIzKN1s3mgP2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_5.png", "conversations": [ { "from": "human", "value": "\nWhat category in the chart does {the colored line with coordinates [0.073, 0.357, 0.959, 0.787]}[0.073, 0.357, 0.959, 0.787] represent?" }, { "from": "gpt", "value": "Tutoring." } ], "id_raw": 265 }, { "id": "3f5jjrwWif3ErQ7s", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_5.png", "conversations": [ { "from": "human", "value": "\nWhat does {the fifth x-axis tick label}[0.494, 0.911, 0.538, 0.934] indicate?" }, { "from": "gpt", "value": "The year 2010." } ], "id_raw": 265 }, { "id": "IQ8o6j5edNJRMDs5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the third line chart}[0.073, 0.357, 0.959, 0.787] in this data presentation?" }, { "from": "gpt", "value": "Tutoring." } ], "id_raw": 265 }, { "id": "gXrPXaTmz5kR0lxp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_5.png", "conversations": [ { "from": "human", "value": "\n{The Classroom Learning line chart}[0.073, 0.034, 0.959, 0.249] records a drop after 2012. In comparison, does {the Online Learning line chart}[0.073, 0.357, 0.959, 0.787] show a similar or opposite trend?" }, { "from": "gpt", "value": "Opposite." } ], "id_raw": 265 }, { "id": "Jcv6zAUgPTzUnw8J", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_5.png", "conversations": [ { "from": "human", "value": "\nIs {the point representing 2004 for the Tutoring category} [0.250, 0.411] [0.250, 0.411] higher or lower than {the point representing 2006 for the same category}[0.339, 0.437]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 265 }, { "id": "5A8sBq6utY25Cv4v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_5.png", "conversations": [ { "from": "human", "value": "\nIn 2010, did {the Quality Score in Classroom Learning}[0.516, 0.088] surpass the score in {Online Learning}[0.516, 0.626]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 265 }, { "id": "GormkkkMjkL7qJ0z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_5.png", "conversations": [ { "from": "human", "value": "\nWhat components are enclosed in [0.050, 0.885, 0.061, 0.909]?" }, { "from": "gpt", "value": "The y-axis tick label '0' is located within these coordinates." } ], "id_raw": 265 }, { "id": "5qNNFDycY0MHiluQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents of the region defined by [0.619, 0.050, 0.947, 0.204]." }, { "from": "gpt", "value": "It contains the chart legend, which includes the following items: {Online Learning}[0.697, 0.063, 0.889, 0.095], {Classroom Learning}[0.697, 0.111, 0.938, 0.143], and {Tutoring}[0.697, 0.159, 0.796, 0.191], each marked with a distinctive color." } ], "id_raw": 265 }, { "id": "oi1Sw64mXKpBRnHK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_5.png", "conversations": [ { "from": "human", "value": "\nPlease detail the contents within the bounds [0.490, 0.943, 0.542, 0.975]." }, { "from": "gpt", "value": "The x-axis label 'Year' is located within these coordinates." } ], "id_raw": 265 }, { "id": "x8IJpdLJnzranPJE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the {x-axis tick label} '4'?" }, { "from": "gpt", "value": "[0.844, 0.911, 0.855, 0.934]" } ], "id_raw": 266 }, { "id": "qXA59RfkbqxLTd4V", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_6.png", "conversations": [ { "from": "human", "value": "\nWhat color is the subtitle in the chart?" }, { "from": "gpt", "value": "The subtitle is black." } ], "id_raw": 266 }, { "id": "sDqXTPacNOATiTRt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_6.png", "conversations": [ { "from": "human", "value": "\nWhat object is positioned at this coordinate [0.089, 0.411, 0.976, 0.679]?" }, { "from": "gpt", "value": "It's the line representing Optimization." } ], "id_raw": 266 }, { "id": "lBZRxVX4cGARdpGV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_6.png", "conversations": [ { "from": "human", "value": "\nIs the line for {Optimization}[0.089, 0.411, 0.976, 0.679] above or below the line for {Innovation}[0.089, 0.438, 0.976, 0.868]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 266 }, { "id": "eWjJH23k4DjIynSc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_6.png", "conversations": [ { "from": "human", "value": "\nWhich strategy has a higher potential for Efficiency Improvement at {time point '4'}[0.844, 0.911, 0.855, 0.934], Optimization or Expansion?" }, { "from": "gpt", "value": "Expansion." } ], "id_raw": 266 }, { "id": "YWl01A6MvOa0WqdF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_6.png", "conversations": [ { "from": "human", "value": "\nWhich strategy reaches its highest potential first, {Innovation}[0.089, 0.438, 0.976, 0.868] or {Expansion}[0.089, 0.303, 0.976, 0.814]?" }, { "from": "gpt", "value": "Innovation." } ], "id_raw": 266 }, { "id": "qaiaObs4NNapmw36", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within this region [0.220, 0.021, 0.844, 0.074]." }, { "from": "gpt", "value": "This area contains the legend for the graph. It features labels for the strategies: {Optimization}[0.287, 0.032, 0.420, 0.062], {Innovation}[0.520, 0.032, 0.629, 0.062] and {Expansion}[0.729, 0.032, 0.836, 0.062]. Each label is paired with a corresponding line color." } ], "id_raw": 266 }, { "id": "4ckcSrfN7LgxF5g0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained in the region [0.455, 0.911, 0.981, 0.934]?" }, { "from": "gpt", "value": "The x-axis tick labels '0.5', '1', '2', '4', and '8' are within this region." } ], "id_raw": 266 }, { "id": "c0PCg7I8dsrgy44P", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_6.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within the coordinates [0.089, 0.088, 0.976, 0.895]?" }, { "from": "gpt", "value": "This area contains the main plot of the chart, specifically the lines representing the strategies Optimization, Innovation, and Expansion." } ], "id_raw": 266 }, { "id": "i7eQ46MStFYWgWr3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the element with coordinates [0.058, 0.084, 0.967, 0.921]?" }, { "from": "gpt", "value": "It is the main graph area." } ], "id_raw": 267 }, { "id": "lwzYCPBOgArloHtj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the x-axis label?" }, { "from": "gpt", "value": "[0.492, 0.957, 0.533, 0.981]" } ], "id_raw": 267 }, { "id": "4BctL0E7OeQzVzLa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.040, 0.914, 0.048, 0.932]?" }, { "from": "gpt", "value": "0." } ], "id_raw": 267 }, { "id": "61akWNql1NHKU4yv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_7.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Hotels'}[[0.058, 0.419, 0.967, 0.712]] above or below {the line for 'Restaurants'}[[0.058, 0.293, 0.967, 0.586]]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 267 }, { "id": "CDcAKTtjDg466GvR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_7.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at {the year 2000 point}[0.058, 0.921], Hotels or Restaurants?" }, { "from": "gpt", "value": "Restaurants." } ], "id_raw": 267 }, { "id": "7oNSLoiGrbj7cE3d", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_7.png", "conversations": [ { "from": "human", "value": "\nDoes the revenue of {Travel Agencies}[0.058, 0.136, 0.967, 0.481] decrease or increase from the year 2000 to 2010?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 267 }, { "id": "jNCpjIEdUmJUj0ZS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.219, 0.012, 0.807, 0.055]." }, { "from": "gpt", "value": "The area contains the legend items: {Hotels}[0.281, 0.022, 0.343, 0.045], {Restaurants}[0.436, 0.022, 0.553, 0.045], and {Travel Agencies}[0.646, 0.022, 0.799, 0.045], each paired with a colored line marker." } ], "id_raw": 267 }, { "id": "olZ8E1BImjWQ5bZI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_7.png", "conversations": [ { "from": "human", "value": "\nWhat is in the rectangle ranging from [0.404, 0.933, 0.439, 0.951]?" }, { "from": "gpt", "value": "The x-axis tick label '2004' is located within these coordinates." } ], "id_raw": 267 }, { "id": "xuEQ8oGGwHTZQQcj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.058, 0.136, 0.967, 0.712]?" }, { "from": "gpt", "value": "There are 3 lines representing Hotel, Restaurant, and Travel Agency revenues over the years 2000 to 2010." } ], "id_raw": 267 }, { "id": "EEGDoBLEBULezgb5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_8.png", "conversations": [ { "from": "human", "value": "\nWhat does {the ylabel}[0.055, 0.116, 0.079, 0.880] represent in the chart?" }, { "from": "gpt", "value": "Measure (in billions of tons or million hectares)." } ], "id_raw": 268 }, { "id": "eYYzXJenbXRZoFpC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the first legend item in the chart?" }, { "from": "gpt", "value": "Annual CO2 Emissions." } ], "id_raw": 268 }, { "id": "sWR27GBI2xUUoiKL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the year, indicated by {the xtick label}[0.663, 0.911, 0.707, 0.934], in the chart?" }, { "from": "gpt", "value": "2015." } ], "id_raw": 268 }, { "id": "FiEVjXGmHg4YREP2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_8.png", "conversations": [ { "from": "human", "value": "\nIs the {New Forests Area} line[0.131, 0.181, 0.869, 0.668] above or below the {Carbon Capture} line[0.131, 0.838, 0.869, 0.883] at point '2015'?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 268 }, { "id": "MtxelZ2KQEO7437Y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_8.png", "conversations": [ { "from": "human", "value": "\nWhich indicator has a higher value at the year '2020', {Annual CO2 Emissions}[0.869, 0.829] or {Carbon Capture}[0.869, 0.838]?" }, { "from": "gpt", "value": "Carbon Capture." } ], "id_raw": 268 }, { "id": "6MX0IdL6xX47aUnY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_8.png", "conversations": [ { "from": "human", "value": "\nWhich line trend is steeper, {New Forests Area}[0.131, 0.181, 0.869, 0.668] or {Annual CO2 Emissions}[0.131, 0.829, 0.869, 0.838]?" }, { "from": "gpt", "value": "New Forests Area." } ], "id_raw": 268 }, { "id": "IhFEDLgmxI19nhIk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_8.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is in the area[r>0.001, 0.022, 0.999, 0.080?" }, { "from": "gpt", "value": "It contains the legend of the chart, which has three items: {Annual CO2 Emissions}[0.078, 0.035, 0.351, 0.067], {Carbon Capture}[0.468, 0.035, 0.660, 0.067], and {New Forests Area}[0.777, 0.035, 0.990, 0.067], each represented by a different color." } ], "id_raw": 268 }, { "id": "U3VzFxY4lf7tJm2y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_8.png", "conversations": [ { "from": "human", "value": "\nTell me about the contents in the region [0.131, 0.181, 0.869, 0.668]." }, { "from": "gpt", "value": "This region contains the lines representing different trends over years: {Annual CO2 Emissions}[0.131, 0.829, 0.869, 0.838], {Carbon Capture}[0.131, 0.838, 0.869, 0.883], and {New Forests Area}[0.131, 0.181, 0.869, 0.668]." } ], "id_raw": 268 }, { "id": "U9MA7Xc3ngrTrpqw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe what lies within the coordinates [0.131, 0.101, 0.869, 0.895]?" }, { "from": "gpt", "value": "This region represents the main plotting area of the chart. It includes three lines for different trends: {Annual CO2 Emissions}[0.131, 0.829, 0.869, 0.838], {Carbon Capture}[0.131, 0.838, 0.869, 0.883] and {New Forests Area}[0.131, 0.181, 0.869, 0.668], as well as the x and y-axis labels and ticks." } ], "id_raw": 268 }, { "id": "hmDyZLUv82NSO5EN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the annotation at the coordinates [0.359, 0.034, 0.527, 0.066] represent in the chart's legend?" }, { "from": "gpt", "value": "It represents the term \"AI Application\"." } ], "id_raw": 269 }, { "id": "gSYOg6uQJsdqYPuI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label indicated by coordinate [0.471, 0.943, 0.604, 0.975]?" }, { "from": "gpt", "value": "The label is \"Time Scale\"." } ], "id_raw": 269 }, { "id": "UlDVVX9rmW1QKNtc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-axis tick label}[0.049, 0.279, 0.088, 0.302]?" }, { "from": "gpt", "value": "15.0." } ], "id_raw": 269 }, { "id": "6gko15lxgt6O49mp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_9.png", "conversations": [ { "from": "human", "value": "\nAccording to the line plot, is {the point '2'}[0.350, 0.733] in the 'AI Application' line above {the point '2'}[0.350, 0.773] in the 'Automation' line?" }, { "from": "gpt", "value": "No." } ], "id_raw": 269 }, { "id": "EcoUfJpAVdAMd4bp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_9.png", "conversations": [ { "from": "human", "value": "\nIn relation to {the point '0'}[0.100, 0.854] on the 'Automation' line, where is {the point '0'}[0.100, 0.895] on the 'AI Application' line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 269 }, { "id": "I4PnDXvWiUUfskoY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_9.png", "conversations": [ { "from": "human", "value": "\nBase on the line plot, is {the value of '4'}[0.851, 0.814] in 'AI Application' greater or smaller than {the value of '4'}[0.851, 0.814] in 'Automation'?" }, { "from": "gpt", "value": "The values are equal." } ], "id_raw": 269 }, { "id": "4Kfd8PYQbppU8MeP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the region [0.281, 0.021, 0.795, 0.079]." }, { "from": "gpt", "value": "This region contains the legend of the chart. There are two entries: one for {AI Application}[0.359, 0.034, 0.527, 0.066] with a marker in blue color, and another for {Automation}[0.643, 0.034, 0.785, 0.066] with a marker in orange color." } ], "id_raw": 269 }, { "id": "ae0OIkNPdkG6NjWP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.100, 0.248, 0.976, 0.895] describe?" }, { "from": "gpt", "value": "This region corresponds to the plot of the 'AI Application' line. It starts from point 0 to point 8 and shows an increase then a decrease in Performance Gain (%)." } ], "id_raw": 269 }, { "id": "IZ9OfedVeOE4b9oO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.100, 0.450, 0.976, 0.874]." }, { "from": "gpt", "value": "This area shows the graph line for 'Automation'. The line begins at 0 and increases to a peak at point 1, then it decreases and flattens out by point 8, representing changes in Performance Gain (%)." } ], "id_raw": 269 }, { "id": "YsRff45ZmIUwzq7a", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the item handle}[0.111, 0.066, 0.150, 0.066] related to in the legend?" }, { "from": "gpt", "value": "Income." } ], "id_raw": 272 }, { "id": "5Q4Ic2i3PXCfkenv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the xlabel of the chart with its coordinates [0.510, 0.951, 0.551, 0.979]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 272 }, { "id": "4r1QWwIO9E651asp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_12.png", "conversations": [ { "from": "human", "value": "\nWhat is shown on the y-axis at {coordinates}[0.040, 0.608, 0.083, 0.628]?" }, { "from": "gpt", "value": "20000." } ], "id_raw": 272 }, { "id": "s5WRi8Q2QxUwHcb7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_12.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Income}[0.181, 0.161] greater or less than the value of {Housing Expenditure}[0.181, 0.601] in year 2001?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 272 }, { "id": "oixylUqTdn31RVBK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_12.png", "conversations": [ { "from": "human", "value": "\nIs {Food Expenditure}[0.2676, 0.756] in year 2002 above or below {Housing Expenditure}[0.268, 0.572]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 272 }, { "id": "UQcOD73EDQ26urW6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_12.png", "conversations": [ { "from": "human", "value": "\nWhich is larger in year 2009, {Income}[0.880, 0.073] or {Food Expenditure}[0.880, 0.704]?" }, { "from": "gpt", "value": "Food Expenditure." } ], "id_raw": 272 }, { "id": "MgWPJykYK79gA4x1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within {the legend coordinates}[0.103, 0.043, 0.376, 0.175]?" }, { "from": "gpt", "value": "The legend contains three items: Income with a color indicator of #020122, Food Expenditure with a color indicator of #f05a28, and Housing Expenditure with a color indicator of #76b041." } ], "id_raw": 272 }, { "id": "gUx7ymfv40IORFlL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_12.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region marked by [0.510, 0.951, 0.551, 0.979]?" }, { "from": "gpt", "value": "The xlabel 'Year' is located in this region." } ], "id_raw": 272 }, { "id": "hZMB9mKgJRQp6V2A", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_12.png", "conversations": [ { "from": "human", "value": "\nWhat elements are contained within the region [0.093, 0.059, 0.967, 0.176]?" }, { "from": "gpt", "value": "This region contains the 'Income' line chart points ranging from the year 2000 to 2010." } ], "id_raw": 272 }, { "id": "E2xGds79Y6Zx9WTx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label in the chart at these coordinates [0.023, 0.311, 0.046, 0.669]?" }, { "from": "gpt", "value": "Performance Gain (%)." } ], "id_raw": 273 }, { "id": "QBP3lhpHtaEVsx7b", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the {xlabel} at [0.442, 0.943, 0.559, 0.975]?" }, { "from": "gpt", "value": "Prospects." } ], "id_raw": 273 }, { "id": "iunR725J1JxQ0QTK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the legend item}[0.257, 0.034, 0.280, 0.066]?" }, { "from": "gpt", "value": "AI." } ], "id_raw": 273 }, { "id": "C9RugTx5wkZTEVnb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_13.png", "conversations": [ { "from": "human", "value": "\nIs the line of {AI}[0.087, 0.248, 0.913, 0.490] generally above or below the line of {IoIT}[0.087, 0.167, 0.913, 0.652]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 273 }, { "id": "FtYBhRd52KSpO67v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_13.png", "conversations": [ { "from": "human", "value": "\nIs {Quantum Computing}[0.087, 0.328, 0.913, 0.814] higher than {AI}[0.087, 0.248, 0.913, 0.490] at the 'Minimal Change' category?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 273 }, { "id": "SbQLsru2kHJn2PBP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_13.png", "conversations": [ { "from": "human", "value": "\nDoes the trend of {IoIT}[0.087, 0.167, 0.913, 0.652] move upwards or downwards across the categories from 'Minimal change' to 'Minimal decline'?" }, { "from": "gpt", "value": "Downwards." } ], "id_raw": 273 }, { "id": "TcNsRUcnTSo9Ltkp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.064, 0.238, 0.075, 0.262]." }, { "from": "gpt", "value": "It's a y-axis tick label '8'." } ], "id_raw": 273 }, { "id": "IeHMwBgxq1J3vNcX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents of the region [0.179, 0.021, 0.822, 0.079]?" }, { "from": "gpt", "value": "It includes legend items: {AI}[0.257, 0.034, 0.280, 0.066], {IoIT}[0.397, 0.034, 0.441, 0.066], and {Quantum Computing}[0.558, 0.034, 0.812, 0.066], each paired with a colored marker." } ], "id_raw": 273 }, { "id": "oTUMrmviMyHBOpsd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents of the region [0.278, 0.911, 0.448, 0.934]?" }, { "from": "gpt", "value": "It is the x-axis label 'Pronounced decline'." } ], "id_raw": 273 }, { "id": "iAeGgGqoWQpGXnlZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the graph title}[0.501, 0.943, 0.553, 0.975]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 274 }, { "id": "jncfPbOYLH60BVoZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing 'Social Welfare'}[0.095, 0.234, 0.959, 0.625]?" }, { "from": "gpt", "value": "#20a983." } ], "id_raw": 274 }, { "id": "K91PwLOYu4Op99h0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_14.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of {the 'Healthcare' label}[0.469, 0.034, 0.601, 0.066]?" }, { "from": "gpt", "value": "[0.469, 0.034, 0.601, 0.066]." } ], "id_raw": 274 }, { "id": "iUqDT1yugjwwXmI6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_14.png", "conversations": [ { "from": "human", "value": "\nWhich label has a higher point at '2010', {the 'Healthcare' line}[0.095, 0.187, 0.959, 0.591] or {the 'Education' line}[0.095, 0.153, 0.959, 0.558]?" }, { "from": "gpt", "value": "Healthcare." } ], "id_raw": 274 }, { "id": "ulQCPfw3AGJf9Qwg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_14.png", "conversations": [ { "from": "human", "value": "\nComparing their positions at '2005', is {the 'Education' line}[0.527, 0.342] above or below {the 'Social Welfare' line}[0.527, 0.423]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 274 }, { "id": "8jHVE2cnqGWKlRFq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_14.png", "conversations": [ { "from": "human", "value": "\nAt the '2001' marker, which line is higher, {the 'Healthcare' line}[0.181, 0.490] or {the 'Social Welfare' line}[0.181, 0.578]?" }, { "from": "gpt", "value": "Social Welfare." } ], "id_raw": 274 }, { "id": "Zh8rvxb267wi0qsL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_14.png", "conversations": [ { "from": "human", "value": "\nCan you please describe what is in the region [0.153, 0.021, 0.901, 0.079]?" }, { "from": "gpt", "value": "This region contains the sublegend of the chart with three entries: {Education}[0.231, 0.034, 0.352, 0.066], {Healthcare}[0.469, 0.034, 0.601, 0.066] and {Social Welfare}[0.718, 0.034, 0.891, 0.066], each accompanied by a color marker." } ], "id_raw": 274 }, { "id": "s8ox31N1zisUxHnk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the area [0.095, 0.086, 0.959, 0.895]?" }, { "from": "gpt", "value": "This area contains the plot area of the chart, including the lines representing 'Education', 'Healthcare', and 'Social Welfare', as well as the x and y grid lines of the chart." } ], "id_raw": 274 }, { "id": "02laMg1PLwOe0m1h", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the components in the region [0.095, 0.772, 0.082, 0.774]." }, { "from": "gpt", "value": "Inside this region, there is a y-axis tick with a label '20'." } ], "id_raw": 274 }, { "id": "erkuzfqML97Z0p9G", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of text located at the coordinates [0.472, 0.957, 0.587, 0.981]?" }, { "from": "gpt", "value": "The label of the x-axis, which is \"Time Period\"." } ], "id_raw": 275 }, { "id": "4WnPyFiXiGMvpp5s", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_15.png", "conversations": [ { "from": "human", "value": "\nWhat color is the line representing \"Tutoring Platform Expansion\" in the chart?" }, { "from": "gpt", "value": "Green, specifically color code is #008000." } ], "id_raw": 275 }, { "id": "1utswRGOyHymqYNu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.099, 0.088, 0.133, 0.088] in the chart legend?" }, { "from": "gpt", "value": "It is a handle represented by a line in the color Red, specifically color code is #ff0000." } ], "id_raw": 275 }, { "id": "YGHVAUIMI5yf1ViF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_15.png", "conversations": [ { "from": "human", "value": "\nIs the line for \"Tutoring Platform Expansion\" at {point 0.2}[0.084, 0.423] higher or lower than the line for \"Online Course Enrollment\" at point 0.2?[0.084, 0.305]" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 275 }, { "id": "ouIBMDoKGtA3QNS8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_15.png", "conversations": [ { "from": "human", "value": "\nAt point x=3, which line is higher, \"Tutoring Platform Expansion\"({coordinates}[0.847, 0.581]) or \"Investment in EdTech\"({coordinates}[0.847, 0.920])?" }, { "from": "gpt", "value": "Investment in EdTech." } ], "id_raw": 275 }, { "id": "v4OJZJEaPjE6WgSt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_15.png", "conversations": [ { "from": "human", "value": "\nAt point x=2, is the \"Online Course Enrollment\" trend({coordinates}[0.593, 0.227]) higher or lower compared to \"Tutoring Platform Expansion\" trend({coordinates}[0.593, 0.509])?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 275 }, { "id": "rggQS7MsGri64GMH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.093, 0.036, 0.386, 0.141] in the chart figure." }, { "from": "gpt", "value": "This region contains the chart legends, including labels for \"Tutoring Platform Expansion\", \"Online Course Enrollment\" and \"Investment in EdTech\" along with their respective colored handle lines." } ], "id_raw": 275 }, { "id": "rIblcS4wRD5ffEbe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_15.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area ranging from [0.040, 0.466, 0.075, 0.484]?" }, { "from": "gpt", "value": "The label '2000' from the y-axis tick labels is located in this area." } ], "id_raw": 275 }, { "id": "tduYpE2bTGljgmwc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_15.png", "conversations": [ { "from": "human", "value": "\nDescribe what is found within the region [0.040, 0.019, 0.075, 0.036]." }, { "from": "gpt", "value": "The y-axis tick label '4000' is in this region." } ], "id_raw": 275 }, { "id": "JWSWaJqgldzVnrgj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_16.png", "conversations": [ { "from": "human", "value": "\nWhat is at the position [0.045, 0.396, 0.069, 0.584]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 276 }, { "id": "wjn9dNRbX9PuePXD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_16.png", "conversations": [ { "from": "human", "value": "\nWhat is in the location [0.408, 0.034, 0.649, 0.066]?" }, { "from": "gpt", "value": "Legend text for Water Quality Index." } ], "id_raw": 276 }, { "id": "wN7pLpPS1mdCwV4Q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_16.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the box with coordinates [0.500, 0.409]?" }, { "from": "gpt", "value": "Data point for 2000 on the Soil Quality Index line." } ], "id_raw": 276 }, { "id": "w3KLQItiL0ZPURjB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_16.png", "conversations": [ { "from": "human", "value": "\nWhich index is higher in 1990, {Air Quality Index}[0.310, 0.393] or {Water Quality Index}[0.310, 0.417]?" }, { "from": "gpt", "value": "Air Quality Index." } ], "id_raw": 276 }, { "id": "ucLH3eVbwJFl86Gj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_16.png", "conversations": [ { "from": "human", "value": "\nIs the {Data point for 1985}[0.216, 0.328] on the Soil Quality Index line higher or lower than the {Data point for 1990}[0.310, 0.361] on the same line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 276 }, { "id": "97xS8eUTTTwJTSxG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_16.png", "conversations": [ { "from": "human", "value": "\nDoes the {Soil Quality Index}[0.879, 0.571] in 2020 have a higher value than the {Air Quality Index}[0.879, 0.611] in the same year?" }, { "from": "gpt", "value": "No." } ], "id_raw": 276 }, { "id": "TJ0m5Iawxkki7pNf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_16.png", "conversations": [ { "from": "human", "value": "\nCan you explain what is within the area defined by [0.011, 0.021, 0.989, 0.079]?" }, { "from": "gpt", "value": "This area contains the legend of the chart, with labels for Air Quality Index, Water Quality Index, and Soil Quality Index. Each label comes with a distinct color marker." } ], "id_raw": 276 }, { "id": "2XpyLe9HsEblrezr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.474, 0.943, 0.526, 0.975] contain?" }, { "from": "gpt", "value": "This is the location of the x-axis label, which is \"Year\"." } ], "id_raw": 276 }, { "id": "FPwoIDcS97927kgT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_16.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the region [0.121, 0.369, 0.879, 0.506]?" }, { "from": "gpt", "value": "This is the Water Quality Index trend line, appeared from 1980 to 2020." } ], "id_raw": 276 }, { "id": "y0t3Zq5bfkSBupUn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the object in these coordinates: [0.485, 0.943, 0.579, 0.975]?" }, { "from": "gpt", "value": "The x-axis label, which is \"Quarter\"." } ], "id_raw": 277 }, { "id": "0z3w1daKxe7rjk8i", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the object in the position {coordinates}[0.291, 0.034, 0.384, 0.066] in the legend?" }, { "from": "gpt", "value": "The label \"Exports\"." } ], "id_raw": 277 }, { "id": "Bt0pFQoSw5uCQeIN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_17.png", "conversations": [ { "from": "human", "value": "\nWhat is shown at the coordinates {tick tick coordinates}[0.969, 0.895] in the chart?" }, { "from": "gpt", "value": "The x-axis tick labeled 'Q4'." } ], "id_raw": 277 }, { "id": "7d93EcEK2JQND0TF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_17.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {Q1}[0.095, 0.895], {the Exports line}[0.095, 0.201, 0.969, 0.617] or {the Production line}[0.095, 0.201, 0.969, 0.571]?" }, { "from": "gpt", "value": "The Exports line." } ], "id_raw": 277 }, { "id": "rbwsd2tRosnhDKbF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_17.png", "conversations": [ { "from": "human", "value": "\nComparing {the Imports line}[0.095, 0.201, 0.969, 0.594] and {the Production line}[0.095, 0.201, 0.969, 0.571], which of them is lower at {Q2}[0.386, 0.895]?" }, { "from": "gpt", "value": "The Production line." } ], "id_raw": 277 }, { "id": "0CdHfVI4mCCOUUl6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_17.png", "conversations": [ { "from": "human", "value": "\nBetween {the Production line}[0.095, 0.201, 0.969, 0.571] and {the Exports line}[0.095, 0.201, 0.969, 0.617], which one peaks first?" }, { "from": "gpt", "value": "The Exports line." } ], "id_raw": 277 }, { "id": "k5MtD7UvxjrLLcg1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.214, 0.021, 0.850, 0.079]." }, { "from": "gpt", "value": "It is the legend of the chart, listing 3 items: {Exports}[0.291, 0.034, 0.384, 0.066] with a blue marker, {Imports}[0.500, 0.034, 0.594, 0.066] with an orange marker, and {Production}[0.711, 0.034, 0.840, 0.066] with a green marker." } ], "id_raw": 277 }, { "id": "96jXolmyGHPby7JI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_17.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.095, 0.201, 0.969, 0.571]?" }, { "from": "gpt", "value": "This region contains the 'Production' line graph tracing from Q1 to Q4 with multiple points, visualizing the trend in Volume changes." } ], "id_raw": 277 }, { "id": "9ktdO8THDfsIfdVA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.071, 0.885, 0.082, 0.909]." }, { "from": "gpt", "value": "It contains the y-axis tick labeled '0'." } ], "id_raw": 277 }, { "id": "dxUGuWbhUNcBXjqt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the component located at [0.019, 0.311, 0.043, 0.669]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 278 }, { "id": "hP6FlAqeknoDp0my", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second x-axis tick label?" }, { "from": "gpt", "value": "[0.177, 0.911, 0.226, 0.934]" } ], "id_raw": 278 }, { "id": "u1sVJHKzRSTeuuJG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the {y-axis tick label} at [0.050, 0.238, 0.061, 0.262]?" }, { "from": "gpt", "value": "4." } ], "id_raw": 278 }, { "id": "ARYhb7blBz1MHEWM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_18.png", "conversations": [ { "from": "human", "value": "\nIs {the point '2' on the 'Thoughtful design' line}[0.202, 0.571] above or below {the point '2' on the 'Rich form' line}[0.202, 0.700]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 278 }, { "id": "wBdEcDT7gOUqBFZj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_18.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Rich form' line}[0.073, 0.248, 0.976, 0.814] generally above or below {the 'Thoughtful design' line}[0.073, 0.409, 0.976, 0.733]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 278 }, { "id": "O02sgREfiYWEcjkK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_18.png", "conversations": [ { "from": "human", "value": "\nWhich line segment has a higher value at {point '4'}[0.847, 0.700], Thoughtful design or Rich form?" }, { "from": "gpt", "value": "Rich form." } ], "id_raw": 278 }, { "id": "FpRVLnsKVe3bLTAR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in [0.253, 0.021, 0.795, 0.079]." }, { "from": "gpt", "value": "This is the legend area. It contains labels of two line trends: {Thoughtful design}[0.331, 0.034, 0.551, 0.066] with a blue marker {at [0.263, 0.048, 0.312, 0.048]} and {Rich form}[0.668, 0.034, 0.785, 0.066] with an orange marker {at [0.600, 0.048, 0.649, 0.048]}." } ], "id_raw": 278 }, { "id": "MLsXfivdn7Wk6LWZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_18.png", "conversations": [ { "from": "human", "value": "\nWhat details does the area [0.446, 0.911, 0.473, 0.934] contain?" }, { "from": "gpt", "value": "It includes the x-axis tick label '0.5'." } ], "id_raw": 278 }, { "id": "5ZPbHXmtTmtfSMwi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects falling within [0.073, 0.409, 0.976, 0.733]." }, { "from": "gpt", "value": "The region includes the points from 'Thoughtful design' trend line ranging from x = 0 to x = 8 on the 'Detail' axis." } ], "id_raw": 278 }, { "id": "LWRsVF3zyXjzIl5d", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_19.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the y-axis label on the chart?" }, { "from": "gpt", "value": "[0.019, 0.201, 0.043, 0.728]." } ], "id_raw": 279 }, { "id": "qMwIuyLRWZNwTSqo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at coordinates [0.804, 0.159, 0.959, 0.191]?" }, { "from": "gpt", "value": "The object is the 'Solar Energy' legend." } ], "id_raw": 279 }, { "id": "QJW4LN4JxzWGDiDJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_14_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinate [0.113, 0.911, 0.157, 0.934] on the x-axis?" }, { "from": "gpt", "value": "The label is '2015'." } ], "id_raw": 279 }, { "id": "6xE5I9No8PIub7GJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_19.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Wind Energy}[0.135, 0.593, 0.941, 0.808] at point '2015'[0.135, 0.808] higher or lower than that of {Solar Energy}[0.135, 0.748, 0.941, 0.808] at the same point?" }, { "from": "gpt", "value": "They are equal." } ], "id_raw": 279 }, { "id": "bEMGLvYqRIQCSoSn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_19.png", "conversations": [ { "from": "human", "value": "\nComparing {Fossil Fuels}[0.135, 0.206, 0.941, 0.473] and {Solar Energy}[0.135, 0.748, 0.941, 0.808], which energy source had a higher usage percentage in '2022'[0.919, 0.911, 0.963, 0.934]?" }, { "from": "gpt", "value": "Fossil Fuels." } ], "id_raw": 279 }, { "id": "1JR48bXhrAQkjAjR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_14_19.png", "conversations": [ { "from": "human", "value": "\nAt the year labeled '2019'[0.573, 0.911, 0.618, 0.934] on the x-axis, is the data point for {Fossil Fuels}[0.135, 0.206, 0.941, 0.473] higher or lower than {Wind Energy}[0.135, 0.593, 0.941, 0.808]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 279 }, { "id": "wRl9HfB8Za6LBIgo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_19.png", "conversations": [ { "from": "human", "value": "\nProvide detail about the contents within the coordinates [0.726, 0.050, 0.969, 0.204]." }, { "from": "gpt", "value": "That region contains the legend details. It contains three items: {Fossil Fuels}[0.804, 0.063, 0.942, 0.095], {Wind Energy}[0.804, 0.111, 0.958, 0.143], and {Solar Energy}[0.804, 0.159, 0.959, 0.191]. Each item is represented with a colored marker." } ], "id_raw": 279 }, { "id": "zkaTU1oVTC2RKm26", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements lying within the region bounded by [0.095, 0.034, 0.981, 0.895]." }, { "from": "gpt", "value": "Many elements are located within these coordinates. On the x-axis, we have sequential years from '2015' to '2022'. On the y-axis, we observe percentage increments from '0' to '100'. This region also encapsulates three energy sources' data, tracked over these years as a line chart. The three lines represent the percentages of {Fossil Fuels}, {Wind Energy}, and {Solar Energy}." } ], "id_raw": 279 }, { "id": "nWxNrn4l5Vhtvq66", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_14_19.png", "conversations": [ { "from": "human", "value": "\nDiscuss the contents of the area [0.113, 0.911, 0.963, 0.934]." }, { "from": "gpt", "value": "This area contains eight ticks on the x-axis, each corresponding to a year from '2015' to '2022'." } ], "id_raw": 279 }, { "id": "6HX8lVKk3yS5IOgN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_0.png", "conversations": [ { "from": "human", "value": "\nWhat is represented within the coordinates [0.051, 0.228, 0.103, 0.258] in the chart?" }, { "from": "gpt", "value": "The y-axis label '4000'." } ], "id_raw": 280 }, { "id": "b061s2XkZA2hr4dq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_0.png", "conversations": [ { "from": "human", "value": "\nWhat is presented at [0.545, 0.875]?" }, { "from": "gpt", "value": "A data point on the 'Communication Companies Revenue (Billions)' line for the year 2010." } ], "id_raw": 280 }, { "id": "dlIS12MIyOGY1Gjg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {ytick label}[0.051, 0.064, 0.103, 0.094]?" }, { "from": "gpt", "value": "'5000'." } ], "id_raw": 280 }, { "id": "xMTTOuZcKyvUMHaU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_0.png", "conversations": [ { "from": "human", "value": "\nAt {the coordinate}[0.400, 0.721], is 'Internet Users (Millions)' more or less than the 'Communication Companies Revenue (Billions)'?" }, { "from": "gpt", "value": "More." } ], "id_raw": 280 }, { "id": "33z8aJnkE6h5eaWy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_0.png", "conversations": [ { "from": "human", "value": "\nConsidering {the point}[0.255, 0.827] and {the point}[0.836, 0.129], do 'Internet Users (Millions)' trend upwards or downwards over the years?" }, { "from": "gpt", "value": "Downwards." } ], "id_raw": 280 }, { "id": "ufUE8hdoXUunUW8R", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_0.png", "conversations": [ { "from": "human", "value": "\nLooking at {the y-axis label '3500'}[0.051, 0.310, 0.103, 0.340] and the point at {coordinates}[0.691, 0.380], is the 'Internet Users (Millions)' data for the year 2015 above or below 3500?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 280 }, { "id": "jWsgSLB6NcLJf3Xy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the key elements within [0.120, 0.089, 0.675, 0.229]." }, { "from": "gpt", "value": "This region contains the legend for the chart, specifically for the two lines represented. The legend title 'Indicators' is located, with the two indicators being 'Internet Users (Millions)' which is depicted in green and 'Communication Companies Revenue (Billions)' which is in an orange color." } ], "id_raw": 280 }, { "id": "IjMAxDzZaCSvCOQp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.255, 0.865, 0.836, 0.884] of the chart?" }, { "from": "gpt", "value": "In this area, you can find the trend line for 'Communication Companies Revenue (Billions)' going from the year 2000 to 2020, with markers for each year's data point represented with coordinates and shaped as circles." } ], "id_raw": 280 }, { "id": "t885AJ3DwmcouiOL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_0.png", "conversations": [ { "from": "human", "value": "\nPlease tell me what's within [0.255, 0.129, 0.836, 0.827]?" }, { "from": "gpt", "value": "This space houses the trend line for 'Internet Users (Millions)'. You can see the progression from 2000 to 2020 with circular markers indicating the number of users for each of those years." } ], "id_raw": 280 }, { "id": "nmrg4wKMw5fI3VOK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.698, 0.744, 0.748, 0.758] in the chart?" }, { "from": "gpt", "value": "Legend marker for Artificial Intelligence." } ], "id_raw": 281 }, { "id": "6H9ztMIOjVOMnvlE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the coordinates [0.758, 0.820, 0.864, 0.847]?" }, { "from": "gpt", "value": "Blockchain." } ], "id_raw": 281 }, { "id": "uFVhOdLpP6oiRVUl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.023, 0.341, 0.052, 0.609]?" }, { "from": "gpt", "value": "Projected Value." } ], "id_raw": 281 }, { "id": "9HNwDzWoZrkg8Vcw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 2023 of Artificial Intelligence}[0.237, 0.812] above or below {the point for 2023 of Big Data}[0.237, 0.669]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 281 }, { "id": "U34X5hTKhH0V2XEw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_1.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Big Data'}[[0.149, 0.834], [0.237, 0.669], [0.324, 0.724], [0.412, 0.702], [0.499, 0.657], [0.587, 0.691], [0.675, 0.713], [0.762, 0.746], [0.850, 0.779], [0.937, 0.801]] generally above or below {the line for 'Blockchain'}[[0.149, 0.724], [0.237, 0.702], [0.324, 0.613], [0.412, 0.558], [0.499, 0.447], [0.587, 0.337], [0.675, 0.282], [0.762, 0.226], [0.850, 0.171], [0.937, 0.116]]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 281 }, { "id": "NVJ53yfOWxE1UB4u", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_1.png", "conversations": [ { "from": "human", "value": "\nWhich technology shows a higher Projected Value at the year 2029, Artificial Intelligence or Blockchain?" }, { "from": "gpt", "value": "Blockchain." } ], "id_raw": 281 }, { "id": "q5Je6SJm7bnwHJGC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.060, 0.104, 0.095, 0.133]." }, { "from": "gpt", "value": "The y-axis tick label '1.0' is located here, in black color." } ], "id_raw": 281 }, { "id": "HTxvA0zw12CWDxkF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.758, 0.780, 0.842, 0.807]." }, { "from": "gpt", "value": "The area contains the legend text: {Big Data}[0.758, 0.780, 0.842, 0.807]." } ], "id_raw": 281 }, { "id": "I3IhfX2uAmw4w20m", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.758, 0.740, 0.959, 0.847]?" }, { "from": "gpt", "value": "There are 2 objects. The legend texts for 'Artificial Intelligence' and 'Blockchain' are located at {coordinates}[0.758, 0.740, 0.959, 0.767] and {coordinates}[0.758, 0.820, 0.864, 0.847], respectively." } ], "id_raw": 281 }, { "id": "PxoNT1H9jLxiJN65", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the function of {element}[0.019, 0.262, 0.044, 0.714]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 282 }, { "id": "1yNNwdgc2Kuay1ZS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_2.png", "conversations": [ { "from": "human", "value": "\nWhat integer do the coordinates {element}[0.292, 0.640] represent in the line plot?" }, { "from": "gpt", "value": "72." } ], "id_raw": 282 }, { "id": "4mfgUZn6CKjga7rr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object in the image at position {element}[0.779, 0.832]?" }, { "from": "gpt", "value": "2019 point on the Community Development Budget line." } ], "id_raw": 282 }, { "id": "J8omxXQOJOKVRcsn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, is {point 2020 on the Community Development Budget line}[0.941, 0.863] higher or lower than {point 2020 on the Local Government Support Budget line}[0.941, 0.113]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 282 }, { "id": "sdrwuyXmi1TnT0fL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_2.png", "conversations": [ { "from": "human", "value": "\nDoes the {Community Development Budget line}[[0.130, 0.592], [0.292, 0.640], [0.454, 0.704], [0.617, 0.752], [0.779, 0.832], [0.941, 0.863]] generally lie above or below the {Local Government Support Budget line}[[0.130, 0.512], [0.292, 0.480], [0.454, 0.369], [0.617, 0.321], [0.779, 0.225], [0.941, 0.113]]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 282 }, { "id": "ay3sCsZwX8UKEQ6z", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_2.png", "conversations": [ { "from": "human", "value": "\nWhere is the {Local Government Support Budget in 2017}[0.454, 0.369] in relation to the {Community Development Budget in 2018}[0.617, 0.752]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 282 }, { "id": "yTZzjXBU5y9y2H5e", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_2.png", "conversations": [ { "from": "human", "value": "\nWhat content do we have in region {region}[0.100, 0.089, 0.535, 0.229]?" }, { "from": "gpt", "value": "This region is the legend of the chart. It includes two items: {Community Development Budget}[0.167, 0.144, 0.515, 0.174] and {Local Government Support Budget}[0.167, 0.188, 0.526, 0.218], each associated with a specific color." } ], "id_raw": 282 }, { "id": "c6r0TI1yXWTBC4oQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is found in the area {region}[0.292, 0.640, 0.454, 0.704]?" }, { "from": "gpt", "value": "This area includes 2 points on the Community Development Budget line, corresponding to the years 2016 and 2017." } ], "id_raw": 282 }, { "id": "KwSM5QM5bnmtBN6p", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_2.png", "conversations": [ { "from": "human", "value": "\nPlease tell me more about the area enclosed by these coordinates {region}[0.617, 0.321, 0.779, 0.225]." }, { "from": "gpt", "value": "This region contains two points from the Local Government Support Budget line, representing data for the years 2018 and 2019. The trend shows a decline in budget." } ], "id_raw": 282 }, { "id": "FUjLfk8JBHdoRHOt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third y-tick in the second subplot?" }, { "from": "gpt", "value": "[0.083, 0.664]" } ], "id_raw": 283 }, { "id": "DW96GP1Syvbz8sak", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label in the first subplot?" }, { "from": "gpt", "value": "Years." } ], "id_raw": 283 }, { "id": "Oz9S7XHT67LLfVoz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_3.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.223, 0.089]?" }, { "from": "gpt", "value": "A data point from the line graph in both subplots." } ], "id_raw": 283 }, { "id": "onVMU9b0qMpGB7m5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_3.png", "conversations": [ { "from": "human", "value": "\nIn the second subplot, is the data point from 2010 located above or below the data point from 2005?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 283 }, { "id": "vkGGqfOHZ9JSV9sS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_3.png", "conversations": [ { "from": "human", "value": "\nIn the first subplot, is {the y-axis tick label '7.0'}[0.044, 0.716, 0.071, 0.739] above or below {the y-axis tick label '7.2'}[0.044, 0.558, 0.071, 0.581]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 283 }, { "id": "hegzka6q2dl3fbpk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_3.png", "conversations": [ { "from": "human", "value": "\nBetween 2000 and 2020, did the annual grain production increase or decrease according to {the line graph}[0.223, 0.089, 0.783, 0.864] in subplot one?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 283 }, { "id": "HWSSvwqNfTPyFN8N", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.201, 0.919, 0.805, 0.942]?" }, { "from": "gpt", "value": "This area contains all the x-axis labels representing years from 2000 to 2020. The label for the year 2000 is at {coordinates}[0.201, 0.919, 0.245, 0.942], the label for the year 2005 is at {coordinates}[0.341, 0.919, 0.385, 0.942], and so on until the year 2020 which is at {coordinates}[0.761, 0.919, 0.805, 0.942]." } ], "id_raw": 283 }, { "id": "NrvZTpqPV0MSQolJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.044, 0.716, 0.071, 0.739]." }, { "from": "gpt", "value": "This area contains the y-axis tick label '7.0' from the first subplot." } ], "id_raw": 283 }, { "id": "rMG5Aa6OMMfjuqZI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.083, 0.050, 0.923, 0.903]." }, { "from": "gpt", "value": "This area contains the plot for both subplots including the axes, tick labels, line graphs and subtitles. The first subplot displays a blue line graph representing grain production and the second subplot displays a red line graph representing per capita meat consumption over the years." } ], "id_raw": 283 }, { "id": "zhi4sNhKOrrzOdtv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_4.png", "conversations": [ { "from": "human", "value": "\nCan you identify the label of the x-axis in our chart?" }, { "from": "gpt", "value": "The label for the x-axis in the chart is \"Month\"." } ], "id_raw": 284 }, { "id": "SBt8LSQgaT0y5jle", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_4.png", "conversations": [ { "from": "human", "value": "\nWhat information do the coordinates [0.055, 0.214, 0.110, 0.243] on the y-axis represent?" }, { "from": "gpt", "value": "The coordinates point to the y-axis label \"1000\"." } ], "id_raw": 284 }, { "id": "HkYVg6UAbcCzcZp2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_4.png", "conversations": [ { "from": "human", "value": "\nWhat does {the third item in the legend}[0.746, 0.113, 0.980, 0.142] refer to?" }, { "from": "gpt", "value": "It refers to the label 'Competitor Efficiency'." } ], "id_raw": 284 }, { "id": "supY5CbPmyj3pVpC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_4.png", "conversations": [ { "from": "human", "value": "\nComparing {the Platform Efficiency line}[0.160, 0.155, 0.865, 0.772] and {the Competitor Efficiency line}[0.160, 0.438, 0.865, 0.855], which has a greater value in March?" }, { "from": "gpt", "value": "Platform Efficiency." } ], "id_raw": 284 }, { "id": "rTHqdiLdIVZZU3yV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_4.png", "conversations": [ { "from": "human", "value": "\nIn the sequence of objects represented on the x-axis, does {the label 'Aug'}[0.587, 0.910, 0.630, 0.939] come before or after {the label 'Jun'}[0.464, 0.910, 0.497, 0.939]?" }, { "from": "gpt", "value": "After." } ], "id_raw": 284 }, { "id": "AIJI7wb23dM12EHf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_4.png", "conversations": [ { "from": "human", "value": "\nLooking at the {Online Enrollment line}[0.160, 0.155, 0.865, 0.855], does the value in November (Nov) increase or decrease compared to the previous month (Oct)?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 284 }, { "id": "9e5Rzjbva3WriXyr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region marked by coordinates [0.125, 0.120, 0.900, 0.890]?" }, { "from": "gpt", "value": "This region contains the main plot area of the chart with three lines representing 'Online Enrollment', 'Platform Efficiency', and 'Competitor Efficiency'. The x-axis at the bottom lists months from January to December, and there are two y-axes on the left and right corresponding to different quantities." } ], "id_raw": 284 }, { "id": "vz6X7Zl7G67p3TYa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_4.png", "conversations": [ { "from": "human", "value": "\nWhat do you see in the region demarcated by [0.746, 0.026, 0.941, 0.055]?" }, { "from": "gpt", "value": "This region contains the first item of the legend marking 'Online Enrollment' with a blue color line sign." } ], "id_raw": 284 }, { "id": "YDl66zfnRUlSSHsp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_4.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area defined by the coordinates [0.915, 0.760, 0.950, 0.789]?" }, { "from": "gpt", "value": "This area holds the y-axis label '0.6'." } ], "id_raw": 284 }, { "id": "tYQb4OCvXfmH0kAv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.015, 0.346, 0.035, 0.632]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 285 }, { "id": "k2wVq1OjtseCijhM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_5.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the x-axis label?" }, { "from": "gpt", "value": "[0.489, 0.956, 0.547, 0.981]" } ], "id_raw": 285 }, { "id": "dOWbs0fXcmsSeh72", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis tick at [0.041, 0.278, 0.067, 0.301]?" }, { "from": "gpt", "value": "1.1." } ], "id_raw": 285 }, { "id": "hwaehENMIdyyW9Qp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_5.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Civilization Progress'}[[0.072, 0.287, 0.964, 0.863]] above or below {the line for 'Technological advancements'}[[0.072, 0.114, 0.964, 0.892]]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 285 }, { "id": "OHz2jTA1HYRcj5jM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_5.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Civilization Progress} at [0.607, 0.402] higher or lower than {Technological advancements} at the same point?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 285 }, { "id": "BAY0H4hElJwGlwXy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_5.png", "conversations": [ { "from": "human", "value": "\nAt what point do {the 'Civilization Progress' line}[0.786, 0.345] and {the 'Technological advancements' line}[0.786, 0.345] intersect?" }, { "from": "gpt", "value": "At ['4000', '1.0']." } ], "id_raw": 285 }, { "id": "0DtbVWxHkrzpMqY3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_5.png", "conversations": [ { "from": "human", "value": "\nWhat can be located within coordinates [0.080, 0.067, 0.380, 0.139]?" }, { "from": "gpt", "value": "This area contains the chart's legend with two items: {Civilization Progress}[0.134, 0.075, 0.301, 0.098] with an orange color marker and {Technological advancements}[0.134, 0.108, 0.373, 0.131] with a black color marker." } ], "id_raw": 285 }, { "id": "cAkHAvM99UAzQ7QU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_5.png", "conversations": [ { "from": "human", "value": "\nDescribe what can be observed in the coordinates [0.072, 0.287, 0.964, 0.863]." }, { "from": "gpt", "value": "These coordinates plot the {Civilization Progress line}, an orange line going upward as the years increase, with its points plotted using marker 'o'." } ], "id_raw": 285 }, { "id": "wDoHpmSMYFgHyFdG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_5.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.072, 0.114, 0.964, 0.892]?" }, { "from": "gpt", "value": "The region contains the {'Technological advancements' line}, a dotted line that increases as the years increase. No markers are used to plot its points." } ], "id_raw": 285 }, { "id": "nFiLX7niuPMHP4uQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_6.png", "conversations": [ { "from": "human", "value": "\nWhere is the label for the year 2020 on the x-axis?" }, { "from": "gpt", "value": "{The label for the year 2020}[0.790, 0.902, 0.842, 0.932]" } ], "id_raw": 286 }, { "id": "qdt7YjGtwPwhSsIw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the data markers representing 'Grocery Sales'?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 286 }, { "id": "AuiDgu7DE5zB8GPQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label in data?" }, { "from": "gpt", "value": "Sales." } ], "id_raw": 286 }, { "id": "2dg0OOHsJ8UIlre4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_6.png", "conversations": [ { "from": "human", "value": "\nAccording to the graph, did the Grocery Sales increase or decrease from 2016 to 2018?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 286 }, { "id": "5LZSEdPmY0cOWk74", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_6.png", "conversations": [ { "from": "human", "value": "\nAre the 'E-commerce Sales' at 2018 above or below the 'Grocery Sales'?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 286 }, { "id": "JSrlzERTTYVv5TZr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_6.png", "conversations": [ { "from": "human", "value": "\nWhich sales line, Grocery or E-commerce, is higher in 2021?" }, { "from": "gpt", "value": "E-commerce Sales." } ], "id_raw": 286 }, { "id": "F0jSzRhXabe4Kxl9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_6.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the given region [0.677, 0.740, 0.944, 0.880]?" }, { "from": "gpt", "value": "This region contains the legend of the graph indicating 'Grocery Sales' and 'E-commerce Sales' with their respective line colors." } ], "id_raw": 286 }, { "id": "99t1qwSjuUNkwTnM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_6.png", "conversations": [ { "from": "human", "value": "\nCan you please describe the content within bounding box [0.096, 0.902, 0.981, 0.932]?" }, { "from": "gpt", "value": "These coordinates encompass the x-axis labels, specifically the years from 2015 to 2021." } ], "id_raw": 286 }, { "id": "JM3fdZrBasoqwotB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_6.png", "conversations": [ { "from": "human", "value": "\nDetail the contents of the region [0.064, 0.883, 0.117, 0.094]." }, { "from": "gpt", "value": "This area captures the y-axis labels representing the sales range from 3000 to 14000 in units." } ], "id_raw": 286 }, { "id": "zVZTqWgTV05MZxAW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_7.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the blue line}[0.160, 0.374, 0.865, 0.549] in this chart?" }, { "from": "gpt", "value": "The trend of productivity under Innovative Leadership over years." } ], "id_raw": 287 }, { "id": "Ns6OV8R95EY8y8Jr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_7.png", "conversations": [ { "from": "human", "value": "\nIn the dataset, what does the coordinate [0.395, 0.374] refer to?" }, { "from": "gpt", "value": "It refers to the productivity level of Innovative Leadership in the year 2016." } ], "id_raw": 287 }, { "id": "htw0UUZciANyWzPL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_7.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at {the location}[0.601, 0.835, 0.831, 0.865] in the legend?" }, { "from": "gpt", "value": "The label 'Innovative Leadership'." } ], "id_raw": 287 }, { "id": "43xEZDkpLf7mWElA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_7.png", "conversations": [ { "from": "human", "value": "\nFor the year 2016, is the productivity of {Conventional Management}[0.395, 0.374] higher or lower than that of {Innovative Leadership}[0.395, 0.483]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 287 }, { "id": "BXwx0qIJwviupn0p", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_7.png", "conversations": [ { "from": "human", "value": "\nIn the 2014 data point, which management type shows more productivity, {Conventional Management}[0.160, 0.155] or {Innovative Leadership}[0.160, 0.374]?" }, { "from": "gpt", "value": "Conventional Management." } ], "id_raw": 287 }, { "id": "GZmYYfTChqtwdcGS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_7.png", "conversations": [ { "from": "human", "value": "\nComparing two points in 2020 on the chart, is {Conventional Management}'s productivity point[0.865, 0.855] positioned higher or lower than {Innovative Leadership}'s productivity point[0.865, 0.374]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 287 }, { "id": "bKvAuomv8XNJrGZ2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements found within the region [0.535, 0.736, 0.890, 0.876]?" }, { "from": "gpt", "value": "This region contains the chart's legend, detailing the color codes for two management types: {Conventional Management}[0.601, 0.791, 0.881, 0.821] in orange and {Innovative Leadership}[0.601, 0.835, 0.831, 0.865] in blue." } ], "id_raw": 287 }, { "id": "LJL4uFEaiYae1ciD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_7.png", "conversations": [ { "from": "human", "value": "\nCan you provide a description of the region [0.125, 0.120, 0.900, 0.890]?" }, { "from": "gpt", "value": "This region occupies the majority of the chart, showing the main plot area. It displays two varying productivity trends over the years 2014 to 2020, one for each type of management." } ], "id_raw": 287 }, { "id": "uQbecT2dRMDduNxu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_7.png", "conversations": [ { "from": "human", "value": "\nDescribe what is encompassed at coordinates [0.202, 0.069, 0.823, 0.116]." }, { "from": "gpt", "value": "This region contains the chart's subtitle \"Productivity Comparison Over Years,\" which provides context to the data being plotted." } ], "id_raw": 287 }, { "id": "VAHnWSavSZ6FVggi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at coordinates [0.019, 0.200, 0.044, 0.770]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 289 }, { "id": "jrK8dR1COwThHWCX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_9.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is represented by the coordinates [0.163, 0.621]?" }, { "from": "gpt", "value": "It is a point on the 'Online Education Impact' line." } ], "id_raw": 289 }, { "id": "IJK2ASdKNWF4KNfk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_9.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis tick label}[0.433, 0.902, 0.459, 0.932] represent?" }, { "from": "gpt", "value": "It represents the number 40." } ], "id_raw": 289 }, { "id": "yNReNlywhQ5Czmz1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_9.png", "conversations": [ { "from": "human", "value": "\nIs {the point on line 'Online Education Impact'}[0.163, 0.621] of higher value than {the point on line 'Online Education Impact'}[0.111, 0.348]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 289 }, { "id": "xcZ5jdsrgdndc62e", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_9.png", "conversations": [ { "from": "human", "value": "\nCompared to {the tick '20' on the x-axis}[0.261, 0.902, 0.288, 0.932], is {the tick '60' on the x-axis}[0.605, 0.902, 0.631, 0.932] positioned towards the left or right?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 289 }, { "id": "kcB00eduI2d4JzSU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_9.png", "conversations": [ { "from": "human", "value": "\nDoes {the point on the line 'Online Education Impact'}[0.704, 0.239] represent a higher or lower Online Education Impact Index value than {the next point on the same line}[0.833, 0.348]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 289 }, { "id": "avlttKwN7EY7jzZB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_9.png", "conversations": [ { "from": "human", "value": "\nCould you give some details on the area defined as [0.333, 0.942, 0.731, 0.975]?" }, { "from": "gpt", "value": "The area contains the X-axis label \"Technology Innovation Index\", it is colored in black." } ], "id_raw": 289 }, { "id": "RIIF1DUUa29cqKPw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area defined by the coordinates [0.621, 0.784, 0.951, 0.880]?" }, { "from": "gpt", "value": "This area incorporates the legend for the subplot, with the title being \"Impact Index\". It also includes an item labeled as \"Online Education Impact\" with its corresponding handle in green color at the coordinates [0.625, 0.846, 0.677, 0.860]." } ], "id_raw": 289 }, { "id": "JkTUgRItQIFAqMRd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_9.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.163, 0.621, 0.833, 0.758]?" }, { "from": "gpt", "value": "The region primarily contains the line for 'Online Education Impact' represented in green color with 8 data points starting from coordinates [0.111, 0.239] to [0.833, 0.348]." } ], "id_raw": 289 }, { "id": "wO98t1dgeiHoQrE1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at coordinates [0.167, 0.838, 0.418, 0.868]?" }, { "from": "gpt", "value": "Average Household Size." } ], "id_raw": 290 }, { "id": "ABOzXAiQGYYi196C", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_10.png", "conversations": [ { "from": "human", "value": "\nWhat is located at coordinates [0.964, 0.360, 0.981, 0.609] in the chart?" }, { "from": "gpt", "value": "Y-label for subplot_1." } ], "id_raw": 290 }, { "id": "lWnrIc1mSk9nwIGl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_10.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object at [0.296, -143027.565, 0.706, -49036.361]?" }, { "from": "gpt", "value": "Line showing the change in Median Housing Price over time." } ], "id_raw": 290 }, { "id": "EFxsFFwBDf0TAQ5Z", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_10.png", "conversations": [ { "from": "human", "value": "\nIs {the year 2020 value for Average Household Size}[0.706, 0.856] higher or lower than {the year 2010 value for Average Household Size}[0.296, 0.113]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 290 }, { "id": "R0VotbF9q3sd1IiA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_10.png", "conversations": [ { "from": "human", "value": "\nDoes the {Median Housing Price}[0.296, -143027.565, 0.706, -49036.361] correspondingly increase or decrease with decrease in {Average Household Size}[0.296, 0.113, 0.706, 0.856]?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 290 }, { "id": "Zc78DkWVzq00F4wp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_10.png", "conversations": [ { "from": "human", "value": "\nAt the point {2015}[0.501, 0.525], which is higher, the Average Household Size or the Median Housing Price?" }, { "from": "gpt", "value": "The Median Housing Price." } ], "id_raw": 290 }, { "id": "pFQnGrOS2gQqFfFj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_10.png", "conversations": [ { "from": "human", "value": "\nCould you explain what is plotted in the area [0.296, -143027.565, 0.706, -49036.361]?" }, { "from": "gpt", "value": "This area includes the details of the 'Median Housing Price': a line graph is plotted representing the fluctuations in the median housing price of families from 2010 to 2020. This line graph is displayed with a color of #202020 and a linestyle of '--', with no marker at the points." } ], "id_raw": 290 }, { "id": "ySczRRjdNFX67QNs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content at location [0.101, 0.783, 0.427, 0.879]." }, { "from": "gpt", "value": "This area contains the legend for the subplot_0, with the title being \"Average Household Size\". The handle for the legend is colored #ffa500 and placed at coordinates [0.104, 0.845, 0.156, 0.858]. The text \"Average Household Size\" is located at [0.167, 0.838, 0.418, 0.868]." } ], "id_raw": 290 }, { "id": "feJMYdr14FxxYopq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's present in the area [0.090, 0.076, 0.912, 0.893]?" }, { "from": "gpt", "value": "This area contains two subplots: subplot_0 and subplot_1. Subplot_0 displays the change in Average Household Size over the years, and subplot_1 represents the change in Median Housing Price over time. The title \"Family and Household Living Conditions over Time\" can also be found in this region." } ], "id_raw": 290 }, { "id": "6ZHXuIVBvMMD2W4m", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.417, 0.035, 0.552] as seen in the chart figure?" }, { "from": "gpt", "value": "Growth." } ], "id_raw": 291 }, { "id": "c5IhJhqWU5GJXd7t", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_11.png", "conversations": [ { "from": "human", "value": "\nWhat's the label text of the third object in the legend?" }, { "from": "gpt", "value": "Bioengineering." } ], "id_raw": 291 }, { "id": "6zxm9pJ79ipTDttd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the value provided for {the x-axis tick label}[0.259, 0.909, 0.294, 0.932]?" }, { "from": "gpt", "value": "2022." } ], "id_raw": 291 }, { "id": "illcZMqjMITtSWZc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_11.png", "conversations": [ { "from": "human", "value": "\nFrom the line data, does the 'Artificial Intelligence' label represented by the line coordinates {line_0}[0.110, 0.113, 0.943, 0.678] show an overall increase or decrease over time?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 291 }, { "id": "ChyO7JAXRNUdz3ZE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_11.png", "conversations": [ { "from": "human", "value": "\nComparing the lines' end coordinates, did Quantum Computing finish [0.943, 0.323] higher or lower than Bioengineering [0.943, 0.533] in the end year represented on the chart?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 291 }, { "id": "7oDOpqUV87YlPOSJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_11.png", "conversations": [ { "from": "human", "value": "\nOn the line for 'Artificial Intelligence', is the point represented by coordinate [0.860, 0.210] higher or lower than the point at [0.776, 0.242]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 291 }, { "id": "ETQsdkyzKXxl8rQM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_11.png", "conversations": [ { "from": "human", "value": "\nCould you describe what's in the region [0.076, 0.089, 0.310, 0.229]?" }, { "from": "gpt", "value": "This area contains the legend with 3 items: the labels 'Artificial Intelligence', 'Quantum Computing', and 'Bioengineering', each paired with a colored marker." } ], "id_raw": 291 }, { "id": "oROiZhOAtWWGJexR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_11.png", "conversations": [ { "from": "human", "value": "\nWhat's within the region marked by the bounding box [0.068, 0.076, 0.985, 0.893]?" }, { "from": "gpt", "value": "This region represents the main plot area of the chart, including 3 trends: 'Artificial Intelligence', 'Quantum Computing', 'Bioengineering', and the x and y axis with their labels and ticks." } ], "id_raw": 291 }, { "id": "Shfbgebkq7z08SIG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_11.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the region [0.110, 0.807, 0.943, 0.856]?" }, { "from": "gpt", "value": "This is the trend line for 'Bioengineering' as it develops over the years represented in the chart." } ], "id_raw": 291 }, { "id": "JuUo6IY3p1XghgSI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_12.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is presented in the figure?" }, { "from": "gpt", "value": "Line chart." } ], "id_raw": 292 }, { "id": "HAPgEcnkiBB8sYsr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_12.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the color of the line labeled \"Bitcoin Price\" in the legend?" }, { "from": "gpt", "value": "The color of the line labeled \"Bitcoin Price\" is #ffa500." } ], "id_raw": 292 }, { "id": "sXMEgR3taGZlZBNv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.019, 0.377, 0.044, 0.599]?" }, { "from": "gpt", "value": "Price of BTC." } ], "id_raw": 292 }, { "id": "ud5c0Go8wCZDauX1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_12.png", "conversations": [ { "from": "human", "value": "\nIs the {Bitcoin Price line}[0.238, 0.113, 0.745, 0.863] above or below the {Number of Transactions line}[0.238, -3.290, 0.745, -1.512]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 292 }, { "id": "ySMTG0U0JVbee2Y3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_12.png", "conversations": [ { "from": "human", "value": "\nFor the year 2019, is Bitcoin Price or Number of Transactions higher?" }, { "from": "gpt", "value": "Number of Transactions." } ], "id_raw": 292 }, { "id": "JTGWR3xjpXwIEUPX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_12.png", "conversations": [ { "from": "human", "value": "\nWhich line in the chart has a steeper slope, {Bitcoin Price}[0.238, 0.113, 0.745, 0.863] or {Number of Transactions}[0.238, -3.290, 0.745, -1.512]?" }, { "from": "gpt", "value": "Number of Transactions." } ], "id_raw": 292 }, { "id": "sWGWl4tAyqD003In", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area between [0.112, 0.076, 0.871, 0.901]." }, { "from": "gpt", "value": "The main chart area is located here, with two line charts showcasing Bitcoin Price and Number of Transactions. The area has a grey color ('#f5f5f5') and a white color ('#ffffff') background for the two line charts." } ], "id_raw": 292 }, { "id": "1PoWrr1jAoRLhUZT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.668, 0.846, 0.990, 0.986]?" }, { "from": "gpt", "value": "This area contains the chart legend. The legend items are: {Bitcoin Price with color #ffa500}[0.735, 0.901, 0.863, 0.931] and {Number of Transactions with color #202020}[0.735, 0.945, 0.981, 0.975]." } ], "id_raw": 292 }, { "id": "J1xEpXwPouT07skC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_12.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.112, 0.076, 0.871, 0.901]?" }, { "from": "gpt", "value": "This area contains two line charts. One for depicting Bitcoin Price from 2017 to 2021, with its line colored as '#ffa500'. The other for showing the Number of Transactions from 2017 to 2021, with its line colored as '#202020'." } ], "id_raw": 292 }, { "id": "N7QKn1KSwVFpEJ7n", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of values represented by the y-axis based on the tick labels?" }, { "from": "gpt", "value": "The range goes from 0.30 to 0.95." } ], "id_raw": 294 }, { "id": "SM7RWR5yc4rIu5lJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_14.png", "conversations": [ { "from": "human", "value": "\nWhat does {the line with color '#ffa500'}[0.176, 0.193, 0.908, 0.485] plot?" }, { "from": "gpt", "value": "Efficiency of Power Generation." } ], "id_raw": 294 }, { "id": "9QEJILYbbvORkjCw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_14.png", "conversations": [ { "from": "human", "value": "\nWhat is referred to by the label at the coordinates [0.625, 0.795, 0.944, 0.825]?" }, { "from": "gpt", "value": "Efficiency of Power Generation." } ], "id_raw": 294 }, { "id": "k0HygmcRPppf1Y70", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_14.png", "conversations": [ { "from": "human", "value": "\nAre the values of {Efficiency of Power Generation}[0.176, 0.193, 0.908, 0.485] mostly above or below the {Efficiency of Power Transmission}[0.176, 0.204, 0.908, 0.286]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 294 }, { "id": "ow9x2XpPXZVVtLUU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the Efficiency of Power Transmission line}[0.176, 0.204, 0.908, 0.286] show an increase or decrease over time?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 294 }, { "id": "3rrPqB6iwAzojzTJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {Efficiency of Power Generation}[0.176, 0.193, 0.908, 0.485] at the year 2020?" }, { "from": "gpt", "value": "0.9." } ], "id_raw": 294 }, { "id": "tvJ1JDRHqUsDyAXY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_14.png", "conversations": [ { "from": "human", "value": "\nWhat is positioned in the region [0.558, 0.740, 0.971, 0.880]?" }, { "from": "gpt", "value": "The legend of the chart \"Energy and Resource Allocation\" is located here, with two items {Efficiency of Power Generation}[0.625, 0.795, 0.944, 0.825] and {Efficiency of Power Transmission}[0.625, 0.839, 0.963, 0.869], each with a color code." } ], "id_raw": 294 }, { "id": "i1ntjytFhf1sOhVG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_14.png", "conversations": [ { "from": "human", "value": "\nWhat information is found within the coordinate space [0.051, 0.298, 0.097, 0.328]?" }, { "from": "gpt", "value": "The y-axis tick label '0.80' is located here." } ], "id_raw": 294 }, { "id": "ZT4NoixBpH4O2VM0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the region [0.081, 0.025, 1.003, 0.072]?" }, { "from": "gpt", "value": "This is the location of the subplot title; it reads \"Trend of Efficiency in Energy and Resource Allocation\" in black font color." } ], "id_raw": 294 }, { "id": "ScY8oPfeS1j6OUYn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel}[0.019, 0.376, 0.044, 0.592] in the chart?" }, { "from": "gpt", "value": "Index Value." } ], "id_raw": 295 }, { "id": "DHPNwHaKZXGARDNV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_15.png", "conversations": [ { "from": "human", "value": "\nWhat is positioned at the coordinates [0.112, 0.089, 0.416, 0.186]?" }, { "from": "gpt", "value": "Subplot Legend." } ], "id_raw": 295 }, { "id": "4gaCraQWwzButkMN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the third xtick labeled as on the x-axis with coordinates [0.279, 0.909, 0.323, 0.932]?" }, { "from": "gpt", "value": "2008." } ], "id_raw": 295 }, { "id": "31H37LJuAEy6guGl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_15.png", "conversations": [ { "from": "human", "value": "\nFor the year 2006, is the {Consumer Confidence}[0.195, 0.391] higher or lower than {Market Sentiment}[0.195, 0.150]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 295 }, { "id": "wcXZgNcvh6nFPTaD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_15.png", "conversations": [ { "from": "human", "value": "\nCompared to the {Market Sentiment}[0.941, 0.484], is the {Consumer Confidence}[0.941, 0.484] the same or different in the year 2020?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 295 }, { "id": "D8A8LoHOHDfxauXz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_15.png", "conversations": [ { "from": "human", "value": "\nFor the year 2019, which line {Consumer Confidence}[0.888, 0.150] or {Market Sentiment}[0.888, 0.206] is higher on the chart?" }, { "from": "gpt", "value": "Consumer Confidence." } ], "id_raw": 295 }, { "id": "CJACHQFYHjsFyB6U", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content of the region [0.112, 0.089, 0.416, 0.186]." }, { "from": "gpt", "value": "The area contains subplot legend with the items: {Consumer Confidence}[0.178, 0.101, 0.407, 0.131], and {Market Sentiment}[0.178, 0.144, 0.364, 0.174], each with a unique color marker." } ], "id_raw": 295 }, { "id": "GVOxoGtlhXLcjCPt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_15.png", "conversations": [ { "from": "human", "value": "\nWhat is present within the region having coordinates [0.919, 0.909, 0.963, 0.932]?" }, { "from": "gpt", "value": "The x-axis tick label '2020' is located within these coordinates." } ], "id_raw": 295 }, { "id": "Z4oUUXJQKJ7txMGI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_15.png", "conversations": [ { "from": "human", "value": "\nDescribe what is enclosed in the region with coordinates [0.051, 0.939, 0.089, 0.963]." }, { "from": "gpt", "value": "The y-axis label '0.35' is located within these coordinates." } ], "id_raw": 295 }, { "id": "ONTEqyI5bBLmCcxJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_16.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the objeto on the coordinates [0.019, 0.406, 0.044, 0.564]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 296 }, { "id": "laR30fpJih5CFN7i", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_16.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the coordinates of the xposicion of the marker for the second legend item?" }, { "from": "gpt", "value": "[0.727, 0.853, 0.768, 0.853]" } ], "id_raw": 296 }, { "id": "qSvWhKa3qxW6aZiW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the value associated with the x-axis tick label at position [0.298, 0.902, 0.311, 0.932]?" }, { "from": "gpt", "value": "20." } ], "id_raw": 296 }, { "id": "eyUMhABFOFwMynsp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_16.png", "conversations": [ { "from": "human", "value": "\nIs the point for the {line 'Rights Protection'}[0.378, 0.280] above or below the corresponding point for the {line 'Social Inequality'}[0.378, 0.690]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 296 }, { "id": "vugMczHDeKJwPzRG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_16.png", "conversations": [ { "from": "human", "value": "\nWhich line, {Social Inequality}[0.137, 0.212, 0.941, 0.894] or {Rights Protection}[0.137, 0.089, 0.941, 0.430], ends at a higher position for the last x-tick?" }, { "from": "gpt", "value": "Social Inequality." } ], "id_raw": 296 }, { "id": "ERy7XzCcRthAIFc3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_16.png", "conversations": [ { "from": "human", "value": "\nWhich line shows a higher value at period 60, Social Inequality or Rights Protection?" }, { "from": "gpt", "value": "Rights Protection." } ], "id_raw": 296 }, { "id": "E8SxQIps5IDNcguj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the region of coordinates [0.122, 0.025, 0.955, 0.072]?" }, { "from": "gpt", "value": "This region contains the subtitle of the chart: \"Social Inequality vs Rights Protection Over Time\", which is displayed in black font." } ], "id_raw": 296 }, { "id": "PXDOKnlOdCykkJbg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_16.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.718, 0.740, 0.971, 0.880]?" }, { "from": "gpt", "value": "This area contains the legend for the chart, which includes descriptions of the lines for \"Social Inequality\" and \"Rights Protection\", each associated with a uniquely colored marker." } ], "id_raw": 296 }, { "id": "StC05sHN7WKawqPt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_16.png", "conversations": [ { "from": "human", "value": "\nWhat is found within the region with corners at [0.096, 0.076, 0.096, 0.894] and [0.941, 0.076, 0.941, 0.894]?" }, { "from": "gpt", "value": "This region contains the plot area of the chart, which includes the drawn lines for \"Social Inequality\" and \"Rights Protection\" and their respective data points." } ], "id_raw": 296 }, { "id": "ybk9SVqj4eiblrnK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.019, 0.443, 0.044, 0.527] represent in the graph?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 297 }, { "id": "4V3CGZd65ZYJ4VSJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the '2015' x-axis tick label?" }, { "from": "gpt", "value": "[0.640, 0.902, 0.693, 0.932]" } ], "id_raw": 297 }, { "id": "sLcMd7y8WyIBiiHb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_17.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the object}[0.745, 0.902, 0.798, 0.932]?" }, { "from": "gpt", "value": "Year 2017." } ], "id_raw": 297 }, { "id": "wNS0nCx7jsv5Ir7s", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_17.png", "conversations": [ { "from": "human", "value": "\nWhich line, {Student Engagement line}[0.142, 0.198, 0.929, 0.731] or {Innovation in Teaching line}[0.142, 0.403, 0.929, 0.812], is above the other at point '2010'?" }, { "from": "gpt", "value": "Student Engagement line." } ], "id_raw": 297 }, { "id": "n3YJY1BLNr7Gk6qi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_17.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Student Engagement' trend}[0.142, 0.198, 0.929, 0.731] overall increasing or decreasing?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 297 }, { "id": "0B4zvWTpScwwkxT8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_17.png", "conversations": [ { "from": "human", "value": "\nIn 2020, is the {Student Engagement}[0.929, 0.198] lower or higher than {Innovation in Teaching}[0.929, 0.403]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 297 }, { "id": "sFjnwQCFbos0qqG2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.663, 0.740, 0.971, 0.880]." }, { "from": "gpt", "value": "This region contains the legend for the chart. It includes items for {Student Engagement}[0.730, 0.795, 0.950, 0.825] with an orange marker and {Innovation in Teaching}[0.730, 0.839, 0.962, 0.869] with a black marker." } ], "id_raw": 297 }, { "id": "4QRpIN2XSbXMSvwg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.536, 0.902, 0.588, 0.932]." }, { "from": "gpt", "value": "This is a segment of the x-axis where the label {2013}[0.536, 0.902, 0.588, 0.932] is located." } ], "id_raw": 297 }, { "id": "5vfp7M17VdHP92xM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_17.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.640, 0.902, 0.798, 0.932]?" }, { "from": "gpt", "value": "There are 2 objects. The x-axis tick labels '2015' and '2017' are located at {coordinates}[0.640, 0.902, 0.693, 0.932] and {coordinates}[0.745, 0.902, 0.798, 0.932], respectively." } ], "id_raw": 297 }, { "id": "GYUZStqnHNOWuJAR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_18.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinate {the x-axis label}[0.519, 0.956, 0.567, 0.981]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 298 }, { "id": "VNkF0jTLsxeycHVz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the Y-axis label}[0.015, 0.315, 0.035, 0.656] in the chart?" }, { "from": "gpt", "value": "Change in Measurement." } ], "id_raw": 298 }, { "id": "TC8uHMZHeXklSHuu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_15_18.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinate of the fifth tick on the x-axis?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 298 }, { "id": "tIKyAvk7cti57kzg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_18.png", "conversations": [ { "from": "human", "value": "\nIs {the Global Temperature Change point at 2000}[0.212, 0.424] higher or lower than {the Sea Level Rise point at 2020}[0.654, 0.630]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 298 }, { "id": "FkUlC4lOhYH15NCy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_18.png", "conversations": [ { "from": "human", "value": "\nWhat color does {the line indicating Global Temperature Change}[0.212, 0.174, 0.875, 0.424] have?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 298 }, { "id": "2Zhn4uHVqc4gpo0a", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_15_18.png", "conversations": [ { "from": "human", "value": "\nIs {the Air Pollution Index at 2030}[0.875, -16.368] higher or lower than {the Sea Level Rise at 2005}[.322, 0.817]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 298 }, { "id": "7nkegIMA5pW88A6z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_18.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is in the rectangle [0.109, 0.067, 0.401, 0.205]?" }, { "from": "gpt", "value": "This is the legend area for the chart. It labels the \"Global Temperature Change\" with a red color indicator, \"Sea Level Rise\" with a green color indicator, and the \"Air Pollution Index\" with a blue color indicator." } ], "id_raw": 298 }, { "id": "jwjNmmTeJ1UstAMi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_18.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.265, 0.019, 0.821, 0.054] contain?" }, { "from": "gpt", "value": "The subtitle \"Global Trends and Changes Observation\" is found in this region." } ], "id_raw": 298 }, { "id": "eXXMzIpFaz2f4rYD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_15_18.png", "conversations": [ { "from": "human", "value": "\nWhat's inside the region [0.212, 0.498, 0.875, 0.875]?" }, { "from": "gpt", "value": "The \"--\" green line associated with the Sea Level Rise is present in this region. The line has square markers at points from year 2000 to 2030." } ], "id_raw": 298 }, { "id": "KofxHGHt1JJ7KKIS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_0.png", "conversations": [ { "from": "human", "value": "\nWhat kind of object is {the element with coordinates}[0.019, 0.430, 0.044, 0.540]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 300 }, { "id": "3Mwa38utFJmbklBD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_0.png", "conversations": [ { "from": "human", "value": "\nLocate the coordinates of {the 5th tick label on the x-axis}." }, { "from": "gpt", "value": "[0.422, 0.902, 0.435, 0.932]" } ], "id_raw": 300 }, { "id": "Pf0OuBZ2VNrGohks", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.422, 0.902, 0.435, 0.932]?" }, { "from": "gpt", "value": "5." } ], "id_raw": 300 }, { "id": "QeqnIpQqCY1ZRPuH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point at [0.221, 0.298]} on the 'Orders' line higher or lower than {the point at [0.221, 0.356]} on the 'Shipments' line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 300 }, { "id": "JlmBXtprM7uOHiT9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_0.png", "conversations": [ { "from": "human", "value": "\nWhich trend line has a higher value at the 11th point, {the 'Orders' line}[0.843, 0.193] or {the 'Shipments' line}[0.843, 0.286]?" }, { "from": "gpt", "value": "Shipments." } ], "id_raw": 300 }, { "id": "BU7ISkXHFoDmjpyD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_0.png", "conversations": [ { "from": "human", "value": "\nLooking at {\"Inventory\"}[0.152, 0.684, 0.912, 0.801], is it generally above or below {the 'Orders' line}[0.152, 0.193, 0.912, 0.333] and {the 'Shipments' line}[0.152, 0.286, 0.912, 0.380]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 300 }, { "id": "889l1Brhmt2hF0m7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the area [0.146, 0.025, 0.918, 0.072]?" }, { "from": "gpt", "value": "This area accommodates the chart subtitle, \"Global Trade and Supply Chain Management\", in a black font and no rotation." } ], "id_raw": 300 }, { "id": "VMwIuGM1vW4iQpcD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_0.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.051, 0.181, 0.077, 0.211]?" }, { "from": "gpt", "value": "The Y-axis tick label '60' is located within this region." } ], "id_raw": 300 }, { "id": "mBtTjlL1kggXk1MI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe what's in the area [0.152, 0.193, 0.912, 0.801]." }, { "from": "gpt", "value": "This area contains three lines: {Orders line}[0.152, 0.193, 0.912, 0.333], {Shipments line}[0.152, 0.286, 0.912, 0.380], and {Inventory line}[0.152, 0.684, 0.912, 0.801] each marked with different colors and point markers. Each line also includes multiple points that represent monthly data." } ], "id_raw": 300 }, { "id": "ztC7zTVuuAOffRKy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the tick label '2023' in the subplot labeled 'Hydropower'?" }, { "from": "gpt", "value": "[0.542, 0.952, 0.586, 0.975]" } ], "id_raw": 301 }, { "id": "PWrl0FLMXYHkg7Ol", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_1.png", "conversations": [ { "from": "human", "value": "\nWhat element is located at coordinates [0.446, 0.464, 0.490, 0.487] in the subplot labeled 'Solar Power'?" }, { "from": "gpt", "value": "X-axis tick label '2022'" } ], "id_raw": 301 }, { "id": "HEt9G7NutETxou3g", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-tick}[0.554, 0.030, 0.578, 0.044] in the subplot labeled 'Geothermal'?" }, { "from": "gpt", "value": "700" } ], "id_raw": 301 }, { "id": "Ixv4paf4f7MV9UbY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_1.png", "conversations": [ { "from": "human", "value": "\nWhich year's data point in the 'Hydropower' subplot is at a lower position, {2020}[0.276, 0.743] or {2022}[0.468, 0.918]?" }, { "from": "gpt", "value": "2022" } ], "id_raw": 301 }, { "id": "nkBdFSULMzPkh5iy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_1.png", "conversations": [ { "from": "human", "value": "\nIs the {y-tick labeled '800'}[0.554, 0.480, 0.578, 0.494] in 'Geothermal' subplot higher or lower than the {y-tick labeled '800'}[0.064, 0.567, 0.088, 0.581] in 'Hydropower'?" }, { "from": "gpt", "value": "Higher" } ], "id_raw": 301 }, { "id": "xn7FfHRI5S1dFrOM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_1.png", "conversations": [ { "from": "human", "value": "\nDoes the {first data point}[0.083, 0.567] in 'Hydropower' have a higher or lower y-coordinate than the {first data point}[0.574, 0.918] in 'Geothermal'?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 301 }, { "id": "Qdc30uhvwCK3BGJe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the area defined by the coordinates [0.064, 0.497, 0.052, 0.520] in the 'Solar Power' subplot." }, { "from": "gpt", "value": "It contains the y-tick label '500'." } ], "id_raw": 301 }, { "id": "4ntZCYQPbwqUaeN5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region contained within the coordinates [0.456, 0.952, 0.499, 0.975] in the subplot labeled 'Geothermal'." }, { "from": "gpt", "value": "This region contains the x-axis tick label '2017'." } ], "id_raw": 301 }, { "id": "fJIapnjBp7lmBjjd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the area bounded by [0.157, 0.464, 0.201, 0.487] in 'Wind Power'?" }, { "from": "gpt", "value": "The x-axis tick label for the year '2019' is present." } ], "id_raw": 301 }, { "id": "tCQ68A059hHIxhs3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_2.png", "conversations": [ { "from": "human", "value": "\nWhat represents the Y-axis label in the subplot featuring the Interest Rate over the years?" }, { "from": "gpt", "value": "Y-axis label[0.503, 0.319, 0.513, 0.651]." } ], "id_raw": 302 }, { "id": "KP0kEP211R5dzW8k", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the Y-axis in the GDP subplot?" }, { "from": "gpt", "value": "[0.077, 0.343, 0.087, 0.627]." } ], "id_raw": 302 }, { "id": "JTSgIHPGYUZmd4N7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_2.png", "conversations": [ { "from": "human", "value": "\nCould you identify the location of the gridline in the inflation subplot at 4.0%?" }, { "from": "gpt", "value": "[0.353, 0.363]." } ], "id_raw": 302 }, { "id": "wQGoI3yPa4Gf6MYx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_2.png", "conversations": [ { "from": "human", "value": "\nIs the GDP value in the year 2004 above or below that of 2005 on the chart?" }, { "from": "gpt", "value": "Below[0.162, 0.765][0.170, 0.728]." } ], "id_raw": 302 }, { "id": "i1dOUJgm1gWrPdYc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_2.png", "conversations": [ { "from": "human", "value": "\nWhich year's point is higher for inflation, 2016 or 2017?" }, { "from": "gpt", "value": "2016[0.459, 0.655][0.466, 0.680]." } ], "id_raw": 302 }, { "id": "cky0gUGGjEcIceNz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_2.png", "conversations": [ { "from": "human", "value": "\nAre the bond yields for the years 2014 and 2015 in increasing order?" }, { "from": "gpt", "value": "No[0.855, 0.412][0.863, 0.485]." } ], "id_raw": 302 }, { "id": "dAZQIAfAaS5eR06R", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_2.png", "conversations": [ { "from": "human", "value": "\nExplain what is captured within the boundaries [0.142, 0.456, 0.234, 0.567]?" }, { "from": "gpt", "value": "This region displays a part of the line for GDP growth over several years. The specific points and their corresponding years are partially visible." } ], "id_raw": 302 }, { "id": "hrO90NicGgu600uT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the plot area enclosed in [0.653, 0.215, 0.783, 0.365]?" }, { "from": "gpt", "value": "It includes a segment of the bond yield chart, showcasing a few yearly data points." } ], "id_raw": 302 }, { "id": "U7nonltgffnQ6v8a", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_2.png", "conversations": [ { "from": "human", "value": "\nWhat entails the defined space [0.291, 0.619, 0.410, 0.729]?" }, { "from": "gpt", "value": "This area shows a portion of the Inflation subplot including a line graph that indicates annual inflation rates for several years." } ], "id_raw": 302 }, { "id": "Ddf4WqA7DG8IPKwx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinate [0.515, 0.943, 0.555, 0.975] in the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 303 }, { "id": "QNOgtsqlIVijfCUR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_3.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what {the object} is at [0.765, 0.090, 0.801, 0.104]?" }, { "from": "gpt", "value": "Its icon representative for 'Cybersecurity spending' in the legend." } ], "id_raw": 303 }, { "id": "ENfJeWQzddm486PQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.040, 0.459, 0.075, 0.483]?" }, { "from": "gpt", "value": "2500." } ], "id_raw": 303 }, { "id": "wKJFz76zzZrUzPbC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_3.png", "conversations": [ { "from": "human", "value": "\nIs {line for 'Cybersecurity spending'}[[0.125, 0.863, 0.944, 0.865]] generally above or below {line for 'Data breaches'}[[0.125, 0.364, 0.944, 0.420]]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 303 }, { "id": "GY8fnIjKZ4gZNUiG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_3.png", "conversations": [ { "from": "human", "value": "\nIs the first point of {line for 'Cybersecurity spending'} [0.125, 0.865] located to the left or right of the first point of {line for 'Data breaches'} [0.125, 0.364] on the X-axis?" }, { "from": "gpt", "value": "Neither, they align vertically on the X-axis." } ], "id_raw": 303 }, { "id": "TWnjpKCDgG7jsBYP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_3.png", "conversations": [ { "from": "human", "value": "\nWhich line ends at a higher point, {the line for 'Cybersecurity spending'}[0.944, 0.863] or {the line for 'Data breaches'}[0.944, 0.420]?" }, { "from": "gpt", "value": "Cybersecurity spending." } ], "id_raw": 303 }, { "id": "7Ys6PzMweWxWul0D", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the coordinate [0.764, 0.077, 0.978, 0.154]?" }, { "from": "gpt", "value": "The area contains the chart legend, which includes two items. {First item} labeled 'Cybersecurity spending'[0.808, 0.086, 0.973, 0.110] with its corresponding blue marker at [0.765, 0.090, 0.801, 0.104]. The {second item} labeled 'Data breaches'[0.808, 0.121, 0.909, 0.145] is paired with its red marker at [0.765, 0.125, 0.801, 0.138]." } ], "id_raw": 303 }, { "id": "AuyvihOGBGYjXoAh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.084, 0.066, 0.985, 0.895]." }, { "from": "gpt", "value": "This area corresponds to the main plot area of the chart, where we can see two data series represented as lines with markers: {line for 'Cybersecurity spending'}<[0.125, 0.863, 0.944, 0.865]> and {line for 'Data breaches'}<[0.125, 0.364, 0.944, 0.420]>." } ], "id_raw": 303 }, { "id": "f5YCLfVizGqITias", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are included within [0.231, 0.025, 0.839, 0.058]?" }, { "from": "gpt", "value": "The area contains one object which is the {subplot title} \"Cybersecurity Spending and Data Breaches Over Time\"." } ], "id_raw": 303 }, { "id": "VZG1ew0x1GoaCuGz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.013, 0.390, 0.033, 0.414] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 304 }, { "id": "bc03mmVhcjYUDgui", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the sixth y-tick label in the chart for Social Sciences?" }, { "from": "gpt", "value": "[0.547, 0.563, 0.568, 0.587]" } ], "id_raw": 304 }, { "id": "CkL20heSB9mHYCJt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.364, 0.894, 0.405, 0.918]?" }, { "from": "gpt", "value": "2024." } ], "id_raw": 304 }, { "id": "ccgfwqTjHr7YmEH8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '2020'}[0.645, 0.152] above or below {the point labeled '2021'}[0.713, 0.214] for the Artificial Intelligence research output?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 304 }, { "id": "UXQEXERxSElPv3tv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_4.png", "conversations": [ { "from": "human", "value": "\nIs {the y-tick label '80'}[0.547, 0.142, 0.568, 0.166] higher or lower than {the y-tick label '85'}[0.547, 0.081, 0.568, 0.104] on the y-axis?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 304 }, { "id": "td7IAQu14JJWSfw1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_4.png", "conversations": [ { "from": "human", "value": "\nWhich year had a higher value at {point marked for 2023}[0.850, 0.226], Social Science or Artificial Intelligence?" }, { "from": "gpt", "value": "Social Science." } ], "id_raw": 304 }, { "id": "fvD8B5Qh759u1tY1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.041, 0.028, 0.453, 0.399]." }, { "from": "gpt", "value": "This area includes the x-axis with labels from 2020 to 2024 {at 2020}[0.110, 0.411, 0.130, 0.435], {at 2024}[0.364, 0.411, 0.405, 0.435]; the y-axis labeled from 60 to 90 {at 60}[0.013, 0.390, 0.033, 0.414], {at 90}[0.013, 0.019, 0.033, 0.042]; and a line graph reflecting Natural Science Research Output with five data points spanned across the years." } ], "id_raw": 304 }, { "id": "mZZhtUM1nrTHnOlx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.576, 0.511, 0.988, 0.882]." }, { "from": "gpt", "value": "This region contains the Social Science research output data plot including the x-axis with year labels from 2020 to 2024 {for 2020}[0.624, 0.894, 0.665, 0.918], {for 2024}[0.898, 0.894, 0.940, 0.918] and y-axis tick labels from 60 to 90 {at 60}[0.547, 0.872, 0.568, 0.896], {at 90}[0.547, 0.501, 0.568, 0.525]. The plot has a purple line showing the trend over the years with distinct data points." } ], "id_raw": 304 }, { "id": "qx0r0LWxh5Acgu55", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.041, 0.511, 0.453, 0.882]?" }, { "from": "gpt", "value": "The x-axis labels for years 2020 to 2024 {at 2020}[0.089, 0.894, 0.130, 0.918], {at 2024}[0.364, 0.894, 0.405, 0.918] and y-axis tick labels from 60 to 90 {at 60}[0.013, 0.872, 0.033, 0.896], {at 90}[0.013, 0.501, 0.033, 0.525] can be found. Additionally, multiple line graphs representing Humanities research with each having distinct data points and colorations." } ], "id_raw": 304 }, { "id": "DqKkcD08Wlu4cYsa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.526, 0.914, 0.560, 0.949] in the chart?" }, { "from": "gpt", "value": "X-axis tick label." } ], "id_raw": 306 }, { "id": "GlPyHQg0x3k78z9m", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.091, 0.307, 0.117, 0.342][0.091, 0.307, 0.117, 0.342]?" }, { "from": "gpt", "value": "2.75." } ], "id_raw": 306 }, { "id": "NJTYI67cp8PrzXOL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.369, 0.062, 0.451, 0.109] represent in the diagram?" }, { "from": "gpt", "value": "Subtitle." } ], "id_raw": 306 }, { "id": "WRTvytx4Dn0T00CQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Type4'}[0.857, 0.155] higher or lower than {the point for 'Type5'}[0.893, 0.455] on the 'Snapchat' plot?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 306 }, { "id": "C6Gi49hBhC8amojf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_6.png", "conversations": [ { "from": "human", "value": "\nAre the values at {point for 'Type2'}[0.785, 0.622] above or below {point for 'Type3'}[0.821, 0.688] in the 'Snapchat' plot?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 306 }, { "id": "BevlO7Ty9gPy4nt8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_6.png", "conversations": [ { "from": "human", "value": "\nWhich type has a higher value, {point for 'Type1'}[0.338, 0.855] or {point for 'Type5'}[0.482, 0.487] in the 'Instagram' subplot?" }, { "from": "gpt", "value": "Type1." } ], "id_raw": 306 }, { "id": "w3UQdEu3MIbtvAJq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements contained in the range [0.732, 0.914, 0.910, 0.949]." }, { "from": "gpt", "value": "This region encompasses the x-axis labels for Types 1 through 5 for Snapchat: {Type1}[0.732, 0.914, 0.766, 0.949], {Type2}[0.768, 0.914, 0.802, 0.949], {Type3}[0.804, 0.914, 0.838, 0.949], {Type4}[0.840, 0.914, 0.874, 0.949], {Type5}[0.876, 0.914, 0.910, 0.949]." } ], "id_raw": 306 }, { "id": "bxLkkonaVBSis5WG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_6.png", "conversations": [ { "from": "human", "value": "\nProvide details about the section [0.297, 0.049, 0.499, 0.176] in the chart." }, { "from": "gpt", "value": "This section contains y-axis tick labels indicating values from 3.25 to 0.50 within the 'Instagram' subplot: {3.25}[0.297, 0.049, 0.323, 0.084], {3.00}[0.297, 0.141, 0.323, 0.176]." } ], "id_raw": 306 }, { "id": "1WcBICbUAZ3Da4W3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_6.png", "conversations": [ { "from": "human", "value": "\nWhat features are in the space [0.526, 0.914, 0.704, 0.949]?" }, { "from": "gpt", "value": "The x-axis labels for Types 1 to 5 are in this zone for the 'Twitter' subplot with corresponding values: {Type1}[0.526, 0.914, 0.560, 0.949], {Type2}[0.562, 0.914, 0.596, 0.949], {Type3}[0.598, 0.914, 0.632, 0.949], {Type4}[0.634, 0.914, 0.668, 0.949], {Type5}[0.670, 0.914, 0.704, 0.949]." } ], "id_raw": 306 }, { "id": "rVbpaMIPdjtBueAV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at {coordinates}[0.215, 0.942, 0.224, 0.970] signify in the context of the sales trend?" }, { "from": "gpt", "value": "Month 11." } ], "id_raw": 307 }, { "id": "L7yeVB7sfq7n1PL5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_7.png", "conversations": [ { "from": "human", "value": "\nIdentify the label shown at the x-axis position {coordinates}[0.052, 0.942, 0.056, 0.970]." }, { "from": "gpt", "value": "2." } ], "id_raw": 307 }, { "id": "QvM8YMmlKClcQQ51", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_7.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the label at {coordinates}[0.508, 0.873, 0.517, 0.901]?" }, { "from": "gpt", "value": "80." } ], "id_raw": 307 }, { "id": "TEcmeYjzQnLVm99X", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_7.png", "conversations": [ { "from": "human", "value": "\nIs the first point of Product A sales{point}[0.036, 0.884] higher than the first point of Product B sales{point}[0.284, 0.884]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 307 }, { "id": "aDEjiGzQdtx5lOxe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_7.png", "conversations": [ { "from": "human", "value": "\nWhich month {coordinates}[0.734, 0.884] for Product C shows the lowest sales compared to {point}[0.982, 0.884] for Product D?" }, { "from": "gpt", "value": "12." } ], "id_raw": 307 }, { "id": "Pq86EQPKDq8cUVuB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_7.png", "conversations": [ { "from": "human", "value": "\nIs the sales point at {coordinates}[0.854, 0.923] on Product D's plot above or below the line from the sales point {coordinates}[0.872, 0.884]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 307 }, { "id": "tNxJKDhgQP9v8FWl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_7.png", "conversations": [ { "from": "human", "value": "\nExplain the plotted area between {the x-tick labels '2'[r>[0.054, 0.923], [0.302, 0.923]]} and '3'{r>[0.073, 0.923], [0.321, 0.923]]}." }, { "from": "gpt", "value": "The area shows a positive sales trend for both Product A and Product B with upward slopes in their respective line plots." } ], "id_raw": 307 }, { "id": "II2F58I2wXjfUaDL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the section between the y-axis labels {100 and 120}[0.026, 0.647] and [0.026, 0.884]?" }, { "from": "gpt", "value": "In this section, vertical lines indicating grid visibility can be seen overlaying a portion of the line plots demonstrating varying sales volumes." } ], "id_raw": 307 }, { "id": "kJN9RsV6J7cUN2rE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the visual data presented between {Product A's first and second x-tick label}[0.036, 0.923] to [0.054, 0.923]." }, { "from": "gpt", "value": "This portion shows a steady increase from the first to the second sales data point on Product A's plot, indicating a rise in sales." } ], "id_raw": 307 }, { "id": "eoZGchtWan0r7cei", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_8.png", "conversations": [ { "from": "human", "value": "\nWhat type of object does {the label}[0.091, 0.280, 0.117, 0.297] represent in the chart?" }, { "from": "gpt", "value": "Y-axis tick label." } ], "id_raw": 308 }, { "id": "CUC4i1lwQUclkq1D", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_8.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at {coordinates}[0.564, 0.281] on the subplot?" }, { "from": "gpt", "value": "A point." } ], "id_raw": 308 }, { "id": "tUY0VHe6s9rexsEt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_8.png", "conversations": [ { "from": "human", "value": "\nIdentify the feature at {the exact location}[0.345, 0.203]." }, { "from": "gpt", "value": "Point." } ], "id_raw": 308 }, { "id": "lToaRmxfJvDmdxU4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_8.png", "conversations": [ { "from": "human", "value": "\nIs {the point at}[0.345, 0.203] higher in value than {the point at}[0.374, 0.236]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 308 }, { "id": "AFHhtzAqX1ThCIth", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the y-value at}[0.564, 0.640] on the third subplot exceed that at {location}[0.884, 0.690] on the fourth subplot?" }, { "from": "gpt", "value": "No." } ], "id_raw": 308 }, { "id": "JzvDMspR9dtXkqPL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_8.png", "conversations": [ { "from": "human", "value": "\nCompare {the scoring point}[0.248, 0.668] and {the point just before it}[0.248, 0.612], which one is higher?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 308 }, { "id": "k3G44hmMkMNuFAJK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the zone [0.564, 0.583, 0.884, 0.757]." }, { "from": "gpt", "value": "It contains several data points for the scores or growth rate from various semesters, plotted as blue circle markers, falling between the y-values of approximately 63.0 to 77.0." } ], "id_raw": 308 }, { "id": "DrLKQyVuMPhLkpee", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the area within [0.141, 0.562, 0.461, 0.873]?" }, { "from": "gpt", "value": "This region in the third subplot includes a series of points representing semester growth rates, plotted using markers without lines, with y-values ranging from about 64.0 to 81.0." } ], "id_raw": 308 }, { "id": "6IJUchz9vSJIRZlQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_8.png", "conversations": [ { "from": "human", "value": "\nCould you provide insights into the region marked by [0.079, 0.621, 0.461, 0.920]?" }, { "from": "gpt", "value": "This region features tick labels for various semesters on the x-axis, each label undertexted with a semester year ranging from 'Fall-2010' to 'Spring-2016'." } ], "id_raw": 308 }, { "id": "ymTQ0kqMhD2u5djO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.108, 0.917, 0.444, 0.967] in the chart?" }, { "from": "gpt", "value": "Subtitle for the first subplot \"(a) Application Analysis.\"." } ], "id_raw": 309 }, { "id": "JDzhjpN1DgkIVRLt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_9.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.019, 0.152, 0.050, 0.199]?" }, { "from": "gpt", "value": "Y-axis label '70' in the first subplot." } ], "id_raw": 309 }, { "id": "w6JjWcfW84Ox3vw6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_9.png", "conversations": [ { "from": "human", "value": "\nWhat does {the blue line with circular markers}[0.118, 0.170, 0.457, 0.503] represent in the chart?" }, { "from": "gpt", "value": "Series1 data points in the Application Analysis subplot." } ], "id_raw": 309 }, { "id": "FKjvuHZUlPIPmGTM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_9.png", "conversations": [ { "from": "human", "value": "\nHow does {Series1 represented by the blue line with circular markers}[0.118, 0.170, 0.457, 0.503] compare to {the grey line with underscore markers}[0.118, -0.095, 0.457, 0.303] in the first subplot?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 309 }, { "id": "2boe51xw4VazVD2I", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_9.png", "conversations": [ { "from": "human", "value": "\nIn the second subplot, is {Series1 represented by the blue line with circular markers}[0.609, 0.237, 0.947, 0.503] trending higher or lower than {the grey line with underscore markers}[0.609, 0.104, 0.947, 0.370]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 309 }, { "id": "amJMNgpNxSXuBN6B", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_9.png", "conversations": [ { "from": "human", "value": "\nIn the first subplot, which series is higher at {x coordinate 0.3}[0.344, 0.170], {Series1 represented by the blue line with circular markers}[0.118, 0.170, 0.457, 0.503] or {the grey line with underscore markers}[0.118, -0.095, 0.457, 0.303]?" }, { "from": "gpt", "value": "Series1." } ], "id_raw": 309 }, { "id": "7zziMx8HVfjYtSXP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found in the region from [0.552, 0.037, 0.981, 0.835]." }, { "from": "gpt", "value": "This region contains the second subplot called \"Research Innovation\", showing the comparison of multiple series represented by different markers and lines along with X and Y axis labels and ticks." } ], "id_raw": 309 }, { "id": "W3d9kGj4Q2mCMN5F", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.597, 0.917, 0.937, 0.967]." }, { "from": "gpt", "value": "This area contains the subtitle for the second subplot, \"(b) Research Innovation.\"." } ], "id_raw": 309 }, { "id": "O0lEyZeu90KMA2Ey", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_9.png", "conversations": [ { "from": "human", "value": "\nWhat is located between [0.609, 0.237, 0.947, 0.503]?" }, { "from": "gpt", "value": "This contains the blue line with circular markers representing Series1 data points in the Research Innovation subplot." } ], "id_raw": 309 }, { "id": "3vKM6yhd1tWZPGQc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the vertical axis in the first subplot?" }, { "from": "gpt", "value": "{the vertical axis label} at [0.018, 0.445, 0.018, 0.445]." } ], "id_raw": 310 }, { "id": "xTgep5AwOXsbUD1c", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_10.png", "conversations": [ { "from": "human", "value": "\nWhat object is positioned at [0.106, 0.445] in the first subplot?" }, { "from": "gpt", "value": "Y-axis tick label 65." } ], "id_raw": 310 }, { "id": "xSpkXjVBjazsPIqy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_10.png", "conversations": [ { "from": "human", "value": "\nWhat is shown at coordinates [0.860, 0.920, 0.860, 0.920] on the chart?" }, { "from": "gpt", "value": "It's the x-axis label in the subplot for 'Dramatic decrease.'" } ], "id_raw": 310 }, { "id": "pRktP0uYhiDKnsdb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_10.png", "conversations": [ { "from": "human", "value": "\nIs the value at {point 2}[0.353, 0.445] from the 'Fluctuating decline' subplot lower than {the y-axis 50 mark}[0.299, 0.835]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 310 }, { "id": "xOLueNtDrf525RnB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_10.png", "conversations": [ { "from": "human", "value": "\nWere there more {line entries for the '3Q'} in ['Periodic fluctuations', 'Dramatic decrease'] subplots above {y-axis mark 75} than below it?" }, { "from": "gpt", "value": "No." } ], "id_raw": 310 }, { "id": "k4545Ez0CLTkrqiD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_10.png", "conversations": [ { "from": "human", "value": "\nIs the {the lowest point}[0.940, 0.601] on the line for 'Dramatic decrease' in a different position compared to {the highest point}[0.800, -0.125] on the same line?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 310 }, { "id": "5DtsSM1J6kf0Yaen", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the chart area from [0.753, 0.859, 0.988, 0.907]." }, { "from": "gpt", "value": "This region contains the x-axis labels for '1st Quarter' to '4th Quarter' in the subplot for 'Dramatic decrease', shown in clear, black font." } ], "id_raw": 310 }, { "id": "9r3sOjk4RTNmmmck", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_10.png", "conversations": [ { "from": "human", "value": "\nWhat elements are included within the section [0.752, 0.056, 0.947, 0.116] in the chart?" }, { "from": "gpt", "value": "This region captures the annotations alongside the y-axis in the 'Dramatic decrease' subplot, displaying labels from '50' to '80' paired with ticks, all oriented vertically." } ], "id_raw": 310 }, { "id": "dND2qxku8n7axNbh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the components found in the area from the x-axis to the first y-axis tick label for the subplot marked as 'Unstable behavior'?" }, { "from": "gpt", "value": "The section includes the x-axis, labeled with '1st Quarter' to '4th Quarter', each paired with its respective tick mark along the axis, and {the first y-axis tick label}[0.052, 0.835] is labeled as '50'." } ], "id_raw": 310 }, { "id": "jiMtWSl21YbuguI8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_13.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the line representing 'Public Trust in Government'}[0.781, 0.486, 0.979, 0.700]?" }, { "from": "gpt", "value": "Purple." } ], "id_raw": 313 }, { "id": "KdppaUJrryCtB4Ks", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the last tick on the x-axis for the subplot 'Social Unrest Index'?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 313 }, { "id": "y4eBI2Oh3pLoU6x7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_13.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis tick at}[0.509, 0.192, 0.518, 0.217] represent on the 'Social Unrest Index' chart?" }, { "from": "gpt", "value": "90." } ], "id_raw": 313 }, { "id": "kJrgC78iu9nfKL09", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_13.png", "conversations": [ { "from": "human", "value": "\nIs the point at [0.427, 0.344] from the subplot 'Freedom of Speech Index' plotted higher than the one at [0.675, 0.439] in the 'Social Unrest Index'?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 313 }, { "id": "g32aUVS0tAX8uBKc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_13.png", "conversations": [ { "from": "human", "value": "\nDoes the chart value at [0.810, 0.676] in 'Public Trust in Government' represent a greater value than the one represented by [0.456, 0.320] in 'Freedom of Speech Index'?" }, { "from": "gpt", "value": "No." } ], "id_raw": 313 }, { "id": "XeSCOFYaHSQgTrO9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_13.png", "conversations": [ { "from": "human", "value": "\nWhich value is lower, the one at [0.095, 0.652] in 'Political Stability Index' or at [0.618, 0.356] in 'Social Unrest Index'?" }, { "from": "gpt", "value": "The one at [0.095, 0.652]." } ], "id_raw": 313 }, { "id": "02lqtb5ZLsD161n7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the features found within the coordinates [0.260, 0.088, 0.495, 0.130]." }, { "from": "gpt", "value": "This area primarily includes x-axis labels for the subplot 'Freedom of Speech Index', ranging from 1985 to 2025 at approximate intervals of 5 years, set in black font." } ], "id_raw": 313 }, { "id": "GkpJqRrOI8Kkc1jj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the main elements in the region [0.752, 0.083, 0.989, 0.913]?" }, { "from": "gpt", "value": "It contains the y-axis tick labels for 'Public Trust in Government', with values from 30 to 100, and a line graph representing the index from 1985 to 2025." } ], "id_raw": 313 }, { "id": "Rh8vGWcaGcPfCmXs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_13.png", "conversations": [ { "from": "human", "value": "\nWhat contents are visible in the region [0.006, 0.083, 0.246, 0.913]?" }, { "from": "gpt", "value": "The subplot contains the 'Political Stability Index' chart, which includes a line graph indicating the index from 1985 to 2020, supplemented by corresponding x-axis and y-axis labels." } ], "id_raw": 313 }, { "id": "YoweX5lGzedYhZjF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.015, 0.537, 0.029, 0.730] in the chart's subplot_0?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 315 }, { "id": "UIwbbGqSr8X87jg3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the subtitle}[0.177, 0.015, 0.177, 0.015] of subplot_0?" }, { "from": "gpt", "value": "It's an empty string, indicating no subtitle." } ], "id_raw": 315 }, { "id": "LahEdVVjpKkveMF5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_15.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the tick on the x-axis}[0.823, 0.461] in subplot_2?" }, { "from": "gpt", "value": "The label '0.8'." } ], "id_raw": 315 }, { "id": "w5S2TriHJAMGdbiJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_15.png", "conversations": [ { "from": "human", "value": "\nIs {the tick at '0.2'}[0.742, 0.461] in subplot_5 positioned to the left or right of {the tick at '0.4'}[0.808, 0.461]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 315 }, { "id": "kEWZVvvNPDc0skkj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_15.png", "conversations": [ { "from": "human", "value": "\nDoes {the y-tick labeled '0.8'}[0.361, 0.106, 0.376, 0.123] in subplot_4 have a higher or lower numerical label than {the y-tick labeled '0.6'}[0.361, 0.193, 0.376, 0.210]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 315 }, { "id": "WnaSr8aBDC4mDcaJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_15.png", "conversations": [ { "from": "human", "value": "\nWhich value is greater at {subplot_3's x-axis tick '0.4'}[0.148, 0.952] in comparison to {subplot_4's x-axis tick '0.2'}[0.419, 0.952]?" }, { "from": "gpt", "value": "0.4." } ], "id_raw": 315 }, { "id": "UAFpkIw1bwdeTe0S", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects located within [0.361, 0.473, 0.653, 0.491] in subplot_1." }, { "from": "gpt", "value": "This area contains x-axis tick labels '0.0' to '1.0', positioned sequentially from left to right." } ], "id_raw": 315 }, { "id": "IlarHzZkuNuVTaK8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_15.png", "conversations": [ { "from": "human", "value": "\nWhat can be found between the area [0.507, 0.473, 0.837, 0.491] on the x-axes of all subplots combined?" }, { "from": "gpt", "value": "All the middle x-axis tick labels ranging from '0.4' to '0.6' are found here spread across subplots 1 to 5." } ], "id_raw": 315 }, { "id": "1p8MQpj97GLPAKMi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the section of the chart within [0.010, 0.019, 0.025, 0.962]." }, { "from": "gpt", "value": "This area contains the y-axis tick labels from '0.0' to '1.0' in the last subplot, arranged vertically from bottom to top." } ], "id_raw": 315 }, { "id": "tm4fMKhlY0fXDCuf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at these coordinates [0.368, 0.062, 0.452, 0.109] represent?" }, { "from": "gpt", "value": "Subtitle." } ], "id_raw": 316 }, { "id": "wu147U4OODRC55A4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_16.png", "conversations": [ { "from": "human", "value": "\nIdentify the label present at [0.802, 0.913, 0.841, 0.962]." }, { "from": "gpt", "value": "X-axis tick label." } ], "id_raw": 316 }, { "id": "5XDgcOLA1PlQXLlB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_16.png", "conversations": [ { "from": "human", "value": "\nWhat element is described by the coordinates [0.282, 0.351, 0.297, 0.659]?" }, { "from": "gpt", "value": "Y-axis." } ], "id_raw": 316 }, { "id": "hGrKtghUraEFUtBh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_16.png", "conversations": [ { "from": "human", "value": "\nWhat year is represented by {the point}[0.338, 0.245] on the Nano Tech graph?" }, { "from": "gpt", "value": "2000." } ], "id_raw": 316 }, { "id": "3tZ4ZyllXZueiJkW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_16.png", "conversations": [ { "from": "human", "value": "\nWhich label has a higher numerical value, the one at {the point}[0.687, 0.155] or {the point}[0.749, 0.317]?" }, { "from": "gpt", "value": "Quantum Computing." } ], "id_raw": 316 }, { "id": "uJfGCi4R9R9Vbviw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_16.png", "conversations": [ { "from": "human", "value": "\nCompare the tech progress in 2005 for Blockchain and Nano Tech by evaluating {these coordinates}[0.579, 0.622] and {these coordinates}[0.374, 0.173]." }, { "from": "gpt", "value": "Nano Tech." } ], "id_raw": 316 }, { "id": "1UFWq0IWtudeIZ1Y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_16.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area mentioned by [0.125, 0.120, 0.283, 0.890]." }, { "from": "gpt", "value": "This region includes the AI Tech graph axis along with a grid background, x and y-axis labels, and title with a white background." } ], "id_raw": 316 }, { "id": "VP1In9xkfOBN39NU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents found within the bounds [0.536, 0.120, 0.694, 0.890]." }, { "from": "gpt", "value": "This area hosts the Blockchain Tech graph including the axes, grid lines, and various data points plotted along the timeline from year 1990 to 2030, with markers indicating values." } ], "id_raw": 316 }, { "id": "OGFDsy55mUBkJI91", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the section [0.125, 0.914, 0.296, 0.962]." }, { "from": "gpt", "value": "This region contains part of the x-axis with tick labels for the years 1990, 2000, and 2010 identifiable in black font." } ], "id_raw": 316 }, { "id": "0L0865DIHYecMPhD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_17.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at {coordinates}[0.236, 0.253, 0.236, 0.253] in the chart?" }, { "from": "gpt", "value": "Point." } ], "id_raw": 317 }, { "id": "fcJArj4qCCSDVNdp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_17.png", "conversations": [ { "from": "human", "value": "\nWhat data marker can be found at {the exact point}[0.735, 0.243, 0.735, 0.243]?" }, { "from": "gpt", "value": "Point." } ], "id_raw": 317 }, { "id": "lTuNlMdCcbVpbEg3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_17.png", "conversations": [ { "from": "human", "value": "\nWhat does {the element}[0.283, 0.204, 0.283, 0.204] represent in the chart?" }, { "from": "gpt", "value": "Point." } ], "id_raw": 317 }, { "id": "cigZkVDbfEPMRbD3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_17.png", "conversations": [ { "from": "human", "value": "\nIs {the point}[0.236, 0.253] higher or lower than {the point}[0.423, 0.081]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 317 }, { "id": "JdlKxXZ6R5jPP1T5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_17.png", "conversations": [ { "from": "human", "value": "\nWhich is to the left, {the y-axis tick label '72'}[0.022, 0.311] or {the y-axis tick label '76'}[0.022, 0.297]?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 317 }, { "id": "So1QoypPmMAoHpKf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_17.png", "conversations": [ { "from": "human", "value": "\nWhat is at a higher value on the y-axis, {the point}[0.969, 0.057] or {the point}[0.922, 0.098]?" }, { "from": "gpt", "value": "First." } ], "id_raw": 317 }, { "id": "vOdZIs9jkkQc9c8E", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects located in the region [0.549, 0.940, 0.969, 0.960]." }, { "from": "gpt", "value": "The tenth point for Rakuten is plotted in this area, appearing at coordinates {0.969, 0.549}." } ], "id_raw": 317 }, { "id": "8exWDGc3sGHBQzC2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the area covered by [0.049, 0.040, 0.470, 0.460]." }, { "from": "gpt", "value": "Walmart plot's trend line connecting all the points from values at thresholds 0.1 through 1.0." } ], "id_raw": 317 }, { "id": "K8SOXi47eJzaMZNn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_17.png", "conversations": [ { "from": "human", "value": "\nWhat contains the segment [0.549, 0.530, 0.969, 0.961]?" }, { "from": "gpt", "value": "The main plotting area for Rakuten including a line graph representing different points and y-tick labels corresponding to values like '62', '64', and '66' and others." } ], "id_raw": 317 }, { "id": "drtHFmC61aQOd6fD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_18.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at {the ylabel}[0.064, 0.272, 0.076, 0.318] in the first subplot?" }, { "from": "gpt", "value": "Sales." } ], "id_raw": 318 }, { "id": "YuvyQixHhcG9VzFw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_18.png", "conversations": [ { "from": "human", "value": "\nCan you identify the marker color of {the tenth point on the line chart in the third subplot}[0.429, 0.627, 0.429, 0.627]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 318 }, { "id": "MPNbnNkORhp51uQW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_18.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.686, 0.512, 0.761, 0.535] in the fourth subplot represent?" }, { "from": "gpt", "value": "Land Sales." } ], "id_raw": 318 }, { "id": "G16SAZh3wJsvoMRy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_18.png", "conversations": [ { "from": "human", "value": "\nIs {the point at 2008 in subplot 3}[0.397, 0.721] higher or lower than {the first point in the same subplot}[0.141, 0.874]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 318 }, { "id": "S37WmAfvC9lYo6ng", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_18.png", "conversations": [ { "from": "human", "value": "\nIs {the label '50000'}[0.080, 0.256, 0.117, 0.274] for a higher or lower value compared to {the label '35000'}[0.080, 0.495, 0.117, 0.512] on the Y-axis in subplot 1?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 318 }, { "id": "NdqQwICejCidGEiJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_18.png", "conversations": [ { "from": "human", "value": "\nDoes {the fourth tick on the x-axis in subplot 1}[0.269, 0.470] have a greater x-coordinate value than {the first x-axis tick in the same subplot}[0.077, 0.470]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 318 }, { "id": "FxWySLjQjRU6mPYl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the features seen in the region [0.064, 0.092, 0.450, 0.318]." }, { "from": "gpt", "value": "This region contains a Y-axis label 'Sales' styled in black and rotated 90 degrees, along with multiple Y-axis labels spanning values from 35000 to 55000." } ], "id_raw": 318 }, { "id": "TunTQ6678jXn961k", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_18.png", "conversations": [ { "from": "human", "value": "\nWhat contents are observed in the boundaries [0.548, 0.092, 0.900, 0.470]?" }, { "from": "gpt", "value": "The second subplot, which includes the subtitle 'Commercial Sales', Y-axis labels without specified values, and data points for annual commercial sales represented in blue line chart form." } ], "id_raw": 318 }, { "id": "IDWvuMYV20FOcnUd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_18.png", "conversations": [ { "from": "human", "value": "\nHow could one describe the segment located within [0.125, 0.540, 0.477, 0.890]?" }, { "from": "gpt", "value": "This segment shows the third subplot graphing industrial sales over time, containing a line chart with colored points ranging in descending Y-axis positions as the years progress, also including sales figures pointed on Y-axis from 38000 upwards." } ], "id_raw": 318 }, { "id": "GoNsqPAnQsukN5ln", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the data point for the year 2015 in the \"Poverty Alleviation\" plot}[0.454, 0.330] on the y-axis?" }, { "from": "gpt", "value": "0.77" } ], "id_raw": 319 }, { "id": "1ttXLmzdDnnFR0Mz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the first x-tick label in the \"Health Improvement\" subplot?" }, { "from": "gpt", "value": "[0.691, 0.909, 0.711, 0.945]" } ], "id_raw": 319 }, { "id": "BgNifdesI0jSAny5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_16_19.png", "conversations": [ { "from": "human", "value": "\nWhat does {the fourth marker on \"Food Security\" line}[0.184, 0.909, 0.204, 0.945] represent in terms of value?" }, { "from": "gpt", "value": "0.84" } ], "id_raw": 319 }, { "id": "Yr6Pv3mwqkUjL9Rv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_19.png", "conversations": [ { "from": "human", "value": "\nIs {the first point on the \"Food Security\" plot}[0.133, 0.855] higher than {the first point on the \"Poverty Alleviation\" plot}[0.335, 0.855]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 319 }, { "id": "zCP2LwRAiE2TeP5Y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_19.png", "conversations": [ { "from": "human", "value": "\nWhich y-tick label is closest to {the 2020 data point in \"Education Support\"}[0.690, 0.155]?" }, { "from": "gpt", "value": "[0.680, 0.909, 0.700, 0.945]" } ], "id_raw": 319 }, { "id": "MP4JLhMJHYD78xi4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_16_19.png", "conversations": [ { "from": "human", "value": "\nDoes {the 2015 endpoint of the \"Health Improvement\" line}[0.854, 0.330] exceed the corresponding y-axis tick value of '0.65'?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 319 }, { "id": "Yh595kVSzrXKQmqd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content of [0.071, 0.075, 0.287, 0.111]." }, { "from": "gpt", "value": "This is the subtitle area for \"Food Security\" displayed in black text, oriented horizontally." } ], "id_raw": 319 }, { "id": "BQumoExKiPaxU8Dm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_19.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.291, 0.909, 0.515, 0.945]?" }, { "from": "gpt", "value": "This region encompasses the first three x-tick labels of the \"Poverty Alleviation\" subplot, displaying values '0.68', '0.70', and '0.72'." } ], "id_raw": 319 }, { "id": "TMZUecMtMuGQPlVH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_16_19.png", "conversations": [ { "from": "human", "value": "\nWhat features are found in the area [0.489, 0.909, 0.739, 0.945]?" }, { "from": "gpt", "value": "It contains the x-tick labels ranging from '0.55' to '0.85' for the \"Education Support\" subplot." } ], "id_raw": 319 }, { "id": "Zz9ZeqfQrJHEvrHj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the {subplot_0's x-label}[0.506, 0.942, 0.565, 0.975] refers to?" }, { "from": "gpt", "value": "The X-label refers to 'Year'." } ], "id_raw": 320 }, { "id": "36h3pXfxxab41X9m", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line labelled as {Microsoft}[0.153, 0.092, 0.233, 0.116]?" }, { "from": "gpt", "value": "The color of the line labelled as 'Microsoft' is '#ff0000' (red)." } ], "id_raw": 320 }, { "id": "VSYfiXIN1Q9uPAf7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the value on y-axis for {line_3 labelled as 'Facebook'} at point '2007'[0.247, 0.884]?" }, { "from": "gpt", "value": "The value on y-axis for 'Facebook' at point '2007' is 0.2." } ], "id_raw": 320 }, { "id": "u5quVqFaIiIHCfr2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_0.png", "conversations": [ { "from": "human", "value": "\nBased on the graph, which company shows the highest growth from 2005 to 2020 [0.142, 0.079, 0.929, 0.861] [0.142, 0.696, 0.929, 0.849] [0.142, 0.611, 0.929, 0.872] [0.142, 0.420, 0.929, 0.886]?" }, { "from": "gpt", "value": "Google." } ], "id_raw": 320 }, { "id": "eiSQq9mlVtIw9vO2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_0.png", "conversations": [ { "from": "human", "value": "\nComparing the year 2005, which company between {Apple}[0.142, 0.872] and {Facebook}[0.142, 0.886] had the lower innovation output?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 320 }, { "id": "VskVIc0x42MhAVPv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_0.png", "conversations": [ { "from": "human", "value": "\nIn 2020, did {Facebook}[0.929, 0.420] surpass {Microsoft}[0.929, 0.696] in innovation output?" }, { "from": "gpt", "value": "No." } ], "id_raw": 320 }, { "id": "FMyghoGocY2XszGN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in [0.098, 0.048, 0.242, 0.195]." }, { "from": "gpt", "value": "This region contains the legend items for the chart, including {Google}[0.153, 0.057, 0.215, 0.081], {Microsoft}[0.153, 0.092, 0.233, 0.116], {Apple}[0.153, 0.127, 0.203, 0.151] and {Facebook}[0.153, 0.162, 0.235, 0.186]. Each label is paired with a colored marker." } ], "id_raw": 320 }, { "id": "Jm4TK7BbKVO2XlQ8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe [0.142, 0.079, 0.929, 0.861]." }, { "from": "gpt", "value": "This area traces the timeline of \"Google's\" innovation output from the year 2005 to 2020. The line representing Google is blue in color." } ], "id_raw": 320 }, { "id": "2f8InS9R6C48Icw1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_0.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the region [0.019, 0.293, 0.044, 0.630]?" }, { "from": "gpt", "value": "The Y-axis label \"Innovation Output\" located in this region which indicates the degree of innovation of the included companies per year." } ], "id_raw": 320 }, { "id": "n2jcV1gWRgYYcsug", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at coordinates [0.015, 0.242, 0.034, 0.672] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 321 }, { "id": "YGftrLwB1gNWtUsM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be found at the set of coordinates [0.594, 0.893, 0.629, 0.921]?" }, { "from": "gpt", "value": "It is the x-axis label for the year 2012." } ], "id_raw": 321 }, { "id": "oas6zVQRB8p9zBiq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the x-axis tick at coordinates [0.344, 0.893, 0.380, 0.921]?" }, { "from": "gpt", "value": "The label is the year 2006." } ], "id_raw": 321 }, { "id": "Kjf1tPEdHHEoOlMG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_1.png", "conversations": [ { "from": "human", "value": "\nComparing {point for the year 2015}[0.736, 0.207] with the {point for the year 2010}[0.528, 0.416], which year has a higher 'Sea Level Change'?" }, { "from": "gpt", "value": "The year 2010." } ], "id_raw": 321 }, { "id": "we96kIZdoamcc89l", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_1.png", "conversations": [ { "from": "human", "value": "\nLooking at the point for the year {2005}[0.320, 0.499] and the point for the year {2018}[0.860, 0.138], which one has a higher sea level?" }, { "from": "gpt", "value": "The year 2005." } ], "id_raw": 321 }, { "id": "k24B640X78hDaCjo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_1.png", "conversations": [ { "from": "human", "value": "\nBetween the {point of the year 2003}[0.237, 0.457] and the {point of the year 2017}[0.819, 0.152], which one recorded a lower sea level change?" }, { "from": "gpt", "value": "The year 2017." } ], "id_raw": 321 }, { "id": "yH1EOo8HYZ8w7xEE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_1.png", "conversations": [ { "from": "human", "value": "\nCould you describe the contents within the area defined by [0.040, 0.862, 0.062, 0.890]?" }, { "from": "gpt", "value": "The area contains the y-axis tick label \"0.0\" for the 'Sea Level Change'." } ], "id_raw": 321 }, { "id": "CFwgGAqvm9RCloGL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within the region [0.077, 0.873, 0.899, 0.921]." }, { "from": "gpt", "value": "The region contains the x-axis tick labels for the years from 1998 to 2022, specifically {the year 2000}[0.095, 0.893, 0.130, 0.921], {the year 2002}[0.178, 0.893, 0.213, 0.921], {the year 2004}[0.261, 0.893, 0.296, 0.921], {the year 2006}[0.344, 0.893, 0.380, 0.921], {the year 2008}[0.428, 0.893, 0.463, 0.921], {the year 2010}[0.511, 0.893, 0.546, 0.921], {the year 2012}[0.594, 0.893, 0.629, 0.921], {the year 2014}[0.677, 0.893, 0.712, 0.921], {the year 2016}[0.760, 0.893, 0.795, 0.921], {the year 2018}[0.843, 0.893, 0.878, 0.921], and {the year 2020}[0.926, 0.893, 0.961, 0.921]." } ], "id_raw": 321 }, { "id": "JArsAez1C2ltylV8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_1.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.528, 0.924, 0.549, 0.962]?" }, { "from": "gpt", "value": "This region contains the x-axis label \"Year\"." } ], "id_raw": 321 }, { "id": "JvcQpFk1Ewk38XEP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_2.png", "conversations": [ { "from": "human", "value": "\nWhat object can be found on the coordinates [0.853, 0.733, 0.977, 0.898]?" }, { "from": "gpt", "value": "It is the x-axis label for \"Virtual Integration\"." } ], "id_raw": 322 }, { "id": "8EDWyV0aMV5kGQ5E", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_2.png", "conversations": [ { "from": "human", "value": "\nWhat kind of data is represented by the object at [0.162, 0.617, 0.282, 0.647]?" }, { "from": "gpt", "value": "It is the legend item labeled \"Small Scale\"." } ], "id_raw": 322 }, { "id": "nW2DkxbCoUFj3hoo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-axis tick} at the coordinates [0.040, -0.053, 0.073, -0.030]?" }, { "from": "gpt", "value": "100." } ], "id_raw": 322 }, { "id": "Jmkehn5fWylqmLog", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_2.png", "conversations": [ { "from": "human", "value": "\nWhich has higher effectiveness in Small Scale, {Direct Method}[0.124, 0.094] or {Data Federation}[0.282, 0.371]?" }, { "from": "gpt", "value": "Direct Method." } ], "id_raw": 322 }, { "id": "2RjlsuZlEmjA6SK8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Direct Method'}[0.124, 0.094] for 'Big Scale' located higher or lower than its corresponding point in 'Small Scale'?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 322 }, { "id": "iZLjZQFjjfVynEeB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_2.png", "conversations": [ { "from": "human", "value": "\nIn the Big Scale, is the effectiveness of {Machine Learning}[0.441, 0.232] higher or lower than {Data Federation}[0.282, 0.306]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 322 }, { "id": "2ORBOGZfNAWsn8Ip", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within [0.095, 0.606, 0.290, 0.702]?" }, { "from": "gpt", "value": "It contains the chart legend with two items representing the \"Small Scale\" and \"Big Scale\" lines, marked in red and blue respectively." } ], "id_raw": 322 }, { "id": "AHyDbGJUxK0c4VRm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_2.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region demarcated by the coordinates [0.524, 0.733, 0.674, 0.932]?" }, { "from": "gpt", "value": "This area contains the x-axis label for \"Multidimensional View\"." } ], "id_raw": 322 }, { "id": "9IdjTkdOrIOVNGrY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects can be found within the coordinates [0.124, 0.094, 0.915, 0.578]?" }, { "from": "gpt", "value": "Within those coordinates, one can find the plotted line for 'Small Scale', representing the effectiveness of various methods on a small scale." } ], "id_raw": 322 }, { "id": "s8UIGL3u4Mem1BiP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the vertical axis of the chart?" }, { "from": "gpt", "value": "Number of Startups." } ], "id_raw": 323 }, { "id": "GFMxcBC1KxrmYHiK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the x-axis label?" }, { "from": "gpt", "value": "[0.519, 0.942, 0.578, 0.975]." } ], "id_raw": 323 }, { "id": "NTEjPGfrDTuZT6eh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the tick located at [0.909, 0.886] on the x-axis?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 323 }, { "id": "ojPX0EcRSbLrVhKV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_3.png", "conversations": [ { "from": "human", "value": "\nIs the data point for 2011 above or below the data point for 2012 in the line chart?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 323 }, { "id": "1TrgQYHlfKiGs5wO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_3.png", "conversations": [ { "from": "human", "value": "\nIs the data point for 2013 above or below the data point for 2014?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 323 }, { "id": "bXmcS7Mwog48kCqp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_3.png", "conversations": [ { "from": "human", "value": "\nDoes the data point in 2020 have a higher or lower value than the data point in 2010?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 323 }, { "id": "Om3Wf8VQ1qvlB9Q8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.188, 0.093, 0.909, 0.745]." }, { "from": "gpt", "value": "This region contains the line chart depicting the trends of the Number of Startups from 2010 to 2020. The line starts at the point corresponding to 5000 startups in 2010, peaks in 2019, and ends in 2020 with a downturn." } ], "id_raw": 323 }, { "id": "OBRm8Br6rZQcbFYy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.115, 0.037, 0.981, 0.886]." }, { "from": "gpt", "value": "This is the main chart area. It houses the plotted line chart, which represents the Number of Startups between 2010 and 2020. X-axis labels indicate the years from 2010 to 2020, and the y-axis labels indicate the Number of Startups from 4000 to 7000." } ], "id_raw": 323 }, { "id": "Fk0Pf9wZ0QdKzdSZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects can be found within [0.404, 0.745, 0.548, 0.603]?" }, { "from": "gpt", "value": "This area includes two data points from the line chart. One is corresponding to 4500 startups in 2013, and the other one represents 5000 startups in 2015." } ], "id_raw": 323 }, { "id": "qI71oDP0dHncsLb1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.502, 0.942, 0.636, 0.975] in the chart?" }, { "from": "gpt", "value": "Risk Factors." } ], "id_raw": 325 }, { "id": "qflyshHR7PI1Jv1o", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_5.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted by {the line with coordinates '[0.153, 0.138, 0.985, 0.580]'}[0.153, 0.138, 0.985, 0.580]?" }, { "from": "gpt", "value": "It shows the percentage of high-risk for various factors." } ], "id_raw": 325 }, { "id": "jABN2sEDqvFD4A1i", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-axis label}[0.097, 0.107, 0.117, 0.579] in the chart?" }, { "from": "gpt", "value": "High-Risk Percentage (%)." } ], "id_raw": 325 }, { "id": "XuSNyRYnq1XJNFjZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_5.png", "conversations": [ { "from": "human", "value": "\nIs {the risk factor 'Financial Impact'}[0.211, 0.665, 0.319, 0.845] considered higher or lower risk than {the risk factor 'Reputational Damage'}[0.262, 0.665, 0.403, 0.899]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 325 }, { "id": "vKqFAeJUA0dq9dXd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_5.png", "conversations": [ { "from": "human", "value": "\nIs the {risk associated with 'Natural Disasters'}[0.704, 0.665, 0.819, 0.856] lower or higher than {risk associated with 'Human Error'}[0.648, 0.665, 0.735, 0.811]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 325 }, { "id": "lDvyt8ay7gM6E2LB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_5.png", "conversations": [ { "from": "human", "value": "\nDoes {the risk factor 'Operational Impact'}[0.360, 0.665, 0.486, 0.875] hold a higher or lower percentage than {the risk factor 'Legal Consequences'}[0.435, 0.665, 0.569, 0.887]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 325 }, { "id": "8rpJG0G747ZbgwQK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.015, 0.665, 0.153, 0.895]." }, { "from": "gpt", "value": "This region contains the x-axis tick label for 'Critical Infrastructure'." } ], "id_raw": 325 }, { "id": "Gq9mYgh9zmEiGSKj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.704, 0.665, 0.819, 0.856] consist of?" }, { "from": "gpt", "value": "It contains the x-axis tick label for 'Natural Disasters'." } ], "id_raw": 325 }, { "id": "PXr89fwJ115mMH7e", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects in the area [0.319, 0.263, 0.403, 0.303]." }, { "from": "gpt", "value": "It includes two points on the line graph corresponding to 'Financial Impact' and 'Reputational Damage'." } ], "id_raw": 325 }, { "id": "DFZED00Tykk9viIp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_6.png", "conversations": [ { "from": "human", "value": "\nWhat is placed at the top center of the chart according to the coordinates [0.423, 0.942, 0.648, 0.975]?" }, { "from": "gpt", "value": "It's the xlabel, \"Investment Year\"." } ], "id_raw": 326 }, { "id": "niaRh3trhvneYVGO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_6.png", "conversations": [ { "from": "human", "value": "\nWhat kind of value does the y-axis show according to the coordinates [0.019, 0.241, 0.044, 0.682]?" }, { "from": "gpt", "value": "The ylabel shows the \"Amount (Billion Dollars)\"." } ], "id_raw": 326 }, { "id": "YMBIq8YRcRzsMxIO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_6.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.794, 0.057, 0.966, 0.081] within the legend?" }, { "from": "gpt", "value": "It is the label \"Investment Amount\"." } ], "id_raw": 326 }, { "id": "DpU5fyNiiTiR3Hzd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_6.png", "conversations": [ { "from": "human", "value": "\nBased on the coordinates, is the 'Investment Amount' in 2006 [0.238, 0.792] more or less than the 'MV Amount' in the same year [0.238, 0.745]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 326 }, { "id": "BWXteB7kQdDgCh12", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_6.png", "conversations": [ { "from": "human", "value": "\nLooking at the last year, 2020, is the 'Investment Amount'[0.932, 0.414] higher or lower than the 'MV Amount'[0.932, 0.131]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 326 }, { "id": "DYB9C9h4LCZ9Hw6Y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_6.png", "conversations": [ { "from": "human", "value": "\nIn 2008, considering the coordinates, did the 'Investment Amount' [0.337, 0.792] increase or decrease compared to the 'MV Amount'[0.337, 0.768]?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 326 }, { "id": "wC77qM3J5oaGGIx6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content of the region [0.738, 0.048, 0.973, 0.125]." }, { "from": "gpt", "value": "This region contains the legend of the chart with two entries: {Investment Amount}[0.794, 0.057, 0.966, 0.081] and {MV Amount}[0.794, 0.092, 0.895, 0.116], each entry is associated with a color marker." } ], "id_raw": 326 }, { "id": "3toKRbJz2swUrgw8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects situated in the region [0.089, 0.131, 0.932, 0.839]?" }, { "from": "gpt", "value": "This region includes two lines representing the 'Investment Amount' and the 'MV Amount' from year 2004 to 2020. Each line is marked with points at intervals indicating the amount for each year." } ], "id_raw": 326 }, { "id": "83H3QyA7bbYMs8ep", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.089, 0.037, 0.981, 0.886] represent?" }, { "from": "gpt", "value": "This region represents the main chart area where the datasets 'Investment Amount' and 'MV Amount' are plotted as lines against the x-axis ('Investment Year') and y-axis ('Amount (Billion Dollars)')." } ], "id_raw": 326 }, { "id": "bcNm70qbjSH5bjKa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_9.png", "conversations": [ { "from": "human", "value": "\nWhat object is found in the coordinates [0.041, 0.745, 0.051, 0.768]?" }, { "from": "gpt", "value": "Second y-axis tick label." } ], "id_raw": 329 }, { "id": "V36MwX262QH0bKaY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_9.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents of the location [0.041, 0.226, 0.051, 0.249]?" }, { "from": "gpt", "value": "The y-axis tick label '5'." } ], "id_raw": 329 }, { "id": "eTRYwqcDD8WbisAB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_9.png", "conversations": [ { "from": "human", "value": "\nWhat object is represented at coordinates [0.115, 0.062, 0.931, 0.841]?" }, { "from": "gpt", "value": "It is the line graph representing the Estimated Global Food Production from 2005 to 2020." } ], "id_raw": 329 }, { "id": "JQ7PCM455o7lERQ6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_9.png", "conversations": [ { "from": "human", "value": "\nWas the Estimated Global Food Production higher in {the year 2011}[0.441, 0.598] or in {the year 2013}[0.550, 0.564]?" }, { "from": "gpt", "value": "2011." } ], "id_raw": 329 }, { "id": "24qulrxkDfhriN3l", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_9.png", "conversations": [ { "from": "human", "value": "\nWas there a larger Estimated Global Food Production in the year labeled at {point}[0.550, 0.564] or in the year at {point}[0.768, 0.235]?" }, { "from": "gpt", "value": "The latter point, which represents the year 2017." } ], "id_raw": 329 }, { "id": "iS52ruUghFvOE5at", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_9.png", "conversations": [ { "from": "human", "value": "\nIs the Estimated Global Food Production {point}[0.768, 0.235] greater or less than the one at {point}[0.659, 0.322]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 329 }, { "id": "4H0nHlVWQbNOM9x0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the location within the coordinates [0.278, 0.884, 0.300, 0.949]." }, { "from": "gpt", "value": "The year 2008 is located in this area on the x-axis." } ], "id_raw": 329 }, { "id": "HXfgwrMLHFTfCwvo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_9.png", "conversations": [ { "from": "human", "value": "\nCould you provide a description of the objects in the region [0.692, 0.896, 0.735, 0.949]?" }, { "from": "gpt", "value": "This region contains the x-axis label for the year 2016." } ], "id_raw": 329 }, { "id": "6z6QUSVIOS4Go9ea", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found between [0.387, 0.624] and [0.441, 0.598]." }, { "from": "gpt", "value": "These coordinates contain two points on the line graph representing the Estimated Global Food Production in years 2010 and 2011 respectively." } ], "id_raw": 329 }, { "id": "sod1TEIMxAPbcZaa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the {object with the longest coordinates in length} in the chart?" }, { "from": "gpt", "value": "X-axis data points." } ], "id_raw": 330 }, { "id": "PyxJuImTlDQH8pj6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the {chart legend item} at [0.881, 0.779, 0.920, 0.802] represent?" }, { "from": "gpt", "value": "Music." } ], "id_raw": 330 }, { "id": "JRSM3VAZTsVRhlv7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_10.png", "conversations": [ { "from": "human", "value": "\nWhat is signified by {the line} [0.122, 0.374, 0.944, 0.855] on the graph?" }, { "from": "gpt", "value": "Revenue from Video games." } ], "id_raw": 330 }, { "id": "B2CKonKsY0qXvWRj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Box Office'}[0.122, 0.775, 0.944, 0.840] exhibit a higher or lower revenue trend compared to {the line for 'Music'}[0.122, 0.532, 0.944, 0.677]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 330 }, { "id": "GY1tBKpk1LKlL9j4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_10.png", "conversations": [ { "from": "human", "value": "\nWhich source saw a greater increase in revenue between 2007 and 2019, {Box Office}[0.122, 0.840, 0.944, 0.775] or {Video Games}[0.122, 0.855, 0.944, 0.374]?" }, { "from": "gpt", "value": "Video Games." } ], "id_raw": 330 }, { "id": "sTUAfjWR5vhBJE9o", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_10.png", "conversations": [ { "from": "human", "value": "\nWhich industry\u2019s revenue is higher in 2018, {Books} [0.875, 0.214] or {Music} [0.875, 0.557]?" }, { "from": "gpt", "value": "Books." } ], "id_raw": 330 }, { "id": "fVX2EpWnCOMzwJkv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the region having coordinates [0.836, 0.735, 0.978, 0.881]." }, { "from": "gpt", "value": "The region contains the legend indicating the color and associated revenue source: {Box Office}[0.881, 0.744, 0.952, 0.767], {Music}[0.881, 0.779, 0.920, 0.802], {Books}[0.881, 0.814, 0.922, 0.837], {Video Games}[0.881, 0.849, 0.973, 0.872]." } ], "id_raw": 330 }, { "id": "82GIsvTmXP8AVXad", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within coordinates [0.192, 0.025, 0.875, 0.065]." }, { "from": "gpt", "value": "This area contains the subtitle of the chart, \"Media & Entertainment Industry Revenue (2007-2019)\"." } ], "id_raw": 330 }, { "id": "9kCsvaBScriKl8LK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_10.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region with coordinates [0.081, 0.893, 0.995, 0.932]?" }, { "from": "gpt", "value": "It contains the x-axis labels showing the years 2006 through to 2020 at regular intervals." } ], "id_raw": 330 }, { "id": "ZInMz9Euxq6kDs9P", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with the coordinates [0.025, 0.373, 0.058, 0.521]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 331 }, { "id": "gHEP2KSJa5pBv4Qk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_11.png", "conversations": [ { "from": "human", "value": "\nWhat coordinates are used for the fifth x-axis tick label?" }, { "from": "gpt", "value": "[0.583, 0.883, 0.654, 0.919]" } ], "id_raw": 331 }, { "id": "cwjIJp1qVrPv1qYl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_11.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the point with coordinates}[0.618, 0.852] on the line chart?" }, { "from": "gpt", "value": "The Innovation score value in 2016." } ], "id_raw": 331 }, { "id": "BSpfb94VQ1I5fYuO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_11.png", "conversations": [ { "from": "human", "value": "\nIn 2012, is the {Innovation Score}[0.333, 0.853] higher or lower than the {Community Health Score}[0.333, 0.059]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 331 }, { "id": "glHSEe6zNWH5hp2x", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_11.png", "conversations": [ { "from": "human", "value": "\nIn the year 2014, is the {Innovation Score}[0.476, 0.853] higher or lower than the {Community Health Score}[0.476, 0.055]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 331 }, { "id": "M0IH98RX6hXomFJj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_11.png", "conversations": [ { "from": "human", "value": "\nDoes the {Community Health Score}[0.904, 0.044] rise or fall from 2010 [0.190, 0.061] to 2020?" }, { "from": "gpt", "value": "Rises." } ], "id_raw": 331 }, { "id": "vCARHxVRIWBfPvHK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found within the coordinates [0.131, 0.757, 0.503, 0.850]." }, { "from": "gpt", "value": "It's a legend area containing labels for {Innovation Score}[0.205, 0.769, 0.399, 0.797] and {Community Health Score}[0.205, 0.810, 0.494, 0.838], both color-coded." } ], "id_raw": 331 }, { "id": "ECNJnHIoU1QKvwg4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the verse [0.190, 0.044, 0.904, 0.061]?" }, { "from": "gpt", "value": "This area represents the {Community Health Score} line chart from 2010 to 2020, indicating an increasing trend over time." } ], "id_raw": 331 }, { "id": "xBMiPZcsHf7CMB1t", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_11.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the space delineated by [0.155, 0.883, 0.939, 0.919]?" }, { "from": "gpt", "value": "This area is occupied by the x-axis labels spanning the years 2010 to 2020." } ], "id_raw": 331 }, { "id": "8SI7mqJ5C4373WgW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the text located at these coordinates: [0.091, 0.576, 0.108, 0.612]?" }, { "from": "gpt", "value": "It is the y-axis label for '1'." } ], "id_raw": 332 }, { "id": "uAtlXV0YaH0A3dja", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_12.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object situated at [0.125, 0.889, 0.195, 0.920] on the chart?" }, { "from": "gpt", "value": "It is the x-axis tick label '2010'." } ], "id_raw": 332 }, { "id": "wYnCo55oRhWLhBRA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_12.png", "conversations": [ { "from": "human", "value": "\nWhat object is present at the position [0.550, 0.883, 0.620, 0.919]?" }, { "from": "gpt", "value": "The x-axis tick label '2016' is present." } ], "id_raw": 332 }, { "id": "c28kpywUPId3Zw0n", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 2015}[0.514, 0.235] higher on the chart than {the point for 2013}[0.373, 0.408]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 332 }, { "id": "m2cuWMQsoopQWZ7n", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point representing 2018}[0.727, 0.108] closer to the x-axis than {the 2012 point}[0.302, 0.481]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 332 }, { "id": "DfdRK9ErS5aYweHw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_12.png", "conversations": [ { "from": "human", "value": "\nWhich is higher on the y-axis, {point for 2019}[0.798, 0.135] or {point for 2016}[0.585, 0.226]?" }, { "from": "gpt", "value": "The point for 2016 is higher." } ], "id_raw": 332 }, { "id": "gSVMVX0c4ycRSiG1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_12.png", "conversations": [ { "from": "human", "value": "\nCould you describe the elements encapsulated within the area [0.160, 0.108, 0.869, 0.818]?" }, { "from": "gpt", "value": "This is the area occupied by the line chart. It includes 11 points, each representing average growth rate data for the years 2010-2020. The line follows a mostly increasing trend, with values ranging from -1.5 to 6.3." } ], "id_raw": 332 }, { "id": "UAtzOkTG3mwsOU22", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents present in the region defined by [0.125, 0.844, 0.904, 0.919]?" }, { "from": "gpt", "value": "This area covers the x-axis of the chart with tick labels for the years 2010, 2012, 2014, 2016, 2018, and 2020." } ], "id_raw": 332 }, { "id": "W4yhwbQDNrMUqY95", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_12.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.091, 0.030, 0.108, 0.885]?" }, { "from": "gpt", "value": "This area includes the y-axis of the chart with tick labels ranging from -2 to 7, representing the average growth rate." } ], "id_raw": 332 }, { "id": "HhlsZuLIxdpevLZx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis represent in this chart?" }, { "from": "gpt", "value": "Quality of Relationship." } ], "id_raw": 333 }, { "id": "sqthGSnTNQOt8b55", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_13.png", "conversations": [ { "from": "human", "value": "\nCan you identify the x-axis label coordinates?" }, { "from": "gpt", "value": "[0.394, 0.930, 0.688, 0.970]." } ], "id_raw": 333 }, { "id": "VD6bNoJyMVwypIKj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the third tick label on the x-axis located at [0.252, 0.883, 0.287, 0.919]?" }, { "from": "gpt", "value": "50." } ], "id_raw": 333 }, { "id": "x6EgmCwnOGqv8GgI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_13.png", "conversations": [ { "from": "human", "value": "\nIs {the point at '50' friends}[0.270, 0.601] above or below {the point at '60' friends}[0.304, 0.568] in the line chart?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 333 }, { "id": "DUy8terHVaPzlr4N", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_13.png", "conversations": [ { "from": "human", "value": "\nComparing {the point at '90' friends}[0.406, 0.372] and {the point at '110' friends}[0.473, 0.765], which one shows higher quality of relationship?" }, { "from": "gpt", "value": "90 friends." } ], "id_raw": 333 }, { "id": "fJineTAjkyh3xDmS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_13.png", "conversations": [ { "from": "human", "value": "\nDoes the point for {80 friends}[0.372, 0.765] indicate a higher or lower quality of relationship compared to the point for {190 friends}[0.745, 0.863]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 333 }, { "id": "NeBtwO3EggIDYtIn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area within the coordinates [0.068, 0.030, 0.111, 0.885]?" }, { "from": "gpt", "value": "This area contains the labels for the y-axis ticks ranging from '6.0' to '8.5'. The labels are styled in black color." } ], "id_raw": 333 }, { "id": "yTaI1ELz9jcZkUP5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the region [0.117, 0.883, 0.282, 0.919]." }, { "from": "gpt", "value": "This region contains three labels on the x-axis. They are the labels for the numbers of friends: {10}[0.117, 0.883, 0.152, 0.919], {30}[0.185, 0.883, 0.220, 0.919], and {50}[0.252, 0.883, 0.287, 0.919]." } ], "id_raw": 333 }, { "id": "WXJM9rzFCB9V4K3V", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the area defined by the coordinates [0.134, 0.208, 0.948, 0.863]." }, { "from": "gpt", "value": "This area encapsulates the line plot, which represents the trend of 'Quality of Relationship' with increasing 'Friends Number' from 10 to 250." } ], "id_raw": 333 }, { "id": "tET6pkrvQu0sUbjh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.440, 0.956, 0.638, 0.981] represent in the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 334 }, { "id": "b75zgRrnCvVSqvww", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first x-axis tick label in the chart?" }, { "from": "gpt", "value": "[0.124, 0.764, 0.142, 0.932]" } ], "id_raw": 334 }, { "id": "8u7VF0qoczjvhlpk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the {the plot line}[0.133, 0.052, 0.944, 0.719] represent in the chart?" }, { "from": "gpt", "value": "This represents the growth of population in various civilization periods." } ], "id_raw": 334 }, { "id": "QFQAJPdq8zcLgrEv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_14.png", "conversations": [ { "from": "human", "value": "\nIs the population in {Paleolithic Late}[0.241, 0.718] more or less than in {Paleolithic Middle}[0.187, 0.718]" }, { "from": "gpt", "value": "More." } ], "id_raw": 334 }, { "id": "iLId0jmaEAwTzGTG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_14.png", "conversations": [ { "from": "human", "value": "\nIs the point at {Bronze Age Early}[0.512, 0.717] higher than the point at {Neolithic Late}[0.458, 0.718]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 334 }, { "id": "SdAz9Q9lHyAbMLIG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_14.png", "conversations": [ { "from": "human", "value": "\nWhich civilization period had a lower population, {Medieval Early}[0.728, 0.711] or {Medieval Late}[0.782, 0.707]?" }, { "from": "gpt", "value": "Medieval Early." } ], "id_raw": 334 }, { "id": "mi240Xck29IU5zU6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.111, 0.019, 0.879, 0.752]" }, { "from": "gpt", "value": "This region contains the main plot of the line chart. It starts from the point representing 'Paleolithic_Early' and ends at the point representing 'Modern_Age', showing the growth of the population from nearly 1 million to 7800 million across different civilization periods." } ], "id_raw": 334 }, { "id": "WU6YJPTlmzOwPqXY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.072, 0.710, 0.083, 0.732]" }, { "from": "gpt", "value": "This area contains the '0' label on the Y-axis, representing zero population." } ], "id_raw": 334 }, { "id": "pYbBb2SMzfxw9oDO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents within [0.827, 0.764, 0.845, 0.894]?" }, { "from": "gpt", "value": "Within this area, there is an x-axis tick label corresponding to the 'Renaissance' period." } ], "id_raw": 334 }, { "id": "7ZCaVRgTtk6XSyJM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_15.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.019, 0.242, 0.044, 0.669] represent?" }, { "from": "gpt", "value": "The y-axis label, which indicates 'Attendance & Revenue'." } ], "id_raw": 335 }, { "id": "2JIuTA6Ev1TFMQpq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.193, 0.094, 0.283, 0.124] in the legend?" }, { "from": "gpt", "value": "'Revenue'." } ], "id_raw": 335 }, { "id": "z2zD9W1wBRsq2CN3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the text in the label located at coordinates [0.522, 0.902, 0.575, 0.932]?" }, { "from": "gpt", "value": "The year '2015'." } ], "id_raw": 335 }, { "id": "ANtXJqmhjbmROrfk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_15.png", "conversations": [ { "from": "human", "value": "\nWhich line reaches a higher value in 2018, {the 'Attendance'}[0.785, 0.851] or {the 'Revenue'}[0.785, 0.076]?" }, { "from": "gpt", "value": "'Revenue'." } ], "id_raw": 335 }, { "id": "oRnhARdItjbmB2CM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_15.png", "conversations": [ { "from": "human", "value": "\nWhich shows a higher value in 2020, {the 'Attendance'}[0.942, 0.866] or {the 'Revenue'}[0.942, 0.430]?" }, { "from": "gpt", "value": "'Attendance'." } ], "id_raw": 335 }, { "id": "5f1lKRD5WJGcSW14", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_15.png", "conversations": [ { "from": "human", "value": "\nIn 2011, is {the 'Attendance'}[0.234, 0.859] value higher or lower than {the 'Revenue'}[0.234, 0.340]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 335 }, { "id": "aI59BR2W6bWksI9E", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects in the range [0.126, 0.039, 0.320, 0.135]?" }, { "from": "gpt", "value": "This area includes the chart's legend. It contains two labels, {the 'Attendance'}[0.193, 0.050, 0.312, 0.080] with its associated color marker at coordinates [0.134, 0.064, 0.176, 0.064], and {the 'Revenue'}[0.193, 0.094, 0.283, 0.124], with its respective color marker at coordinates [0.134, 0.108, 0.176, 0.108]." } ], "id_raw": 335 }, { "id": "ivOzJ3JtMQMIJAFi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_15.png", "conversations": [ { "from": "human", "value": "\nWhat content is found within the box ranging from [0.515, 0.942, 0.585, 0.975]?" }, { "from": "gpt", "value": "The x-axis label 'Years' is located in this region." } ], "id_raw": 335 }, { "id": "iccSD19u4UpAg1mP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content included in the area from [0.207, 0.902, 0.260, 0.932]." }, { "from": "gpt", "value": "A single object, the x-axis tick label for the year '2011', is located in this area." } ], "id_raw": 335 }, { "id": "l8dXZH6tn9ZdpZIT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the text labeled at coordinate [0.755, 0.846, 0.859, 0.869] in the legend?" }, { "from": "gpt", "value": "Periodic Spikes." } ], "id_raw": 336 }, { "id": "Qom0vXgLlhzjTPBy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate for 'Fluctuating Growth' in the legend?" }, { "from": "gpt", "value": "[0.755, 0.776, 0.887, 0.799]." } ], "id_raw": 336 }, { "id": "LTFx2Tb99E3TgNRQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_16.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the entity at coordinates [0.160, 0.235, 0.865, 0.298]?" }, { "from": "gpt", "value": "It represents the line for 'Rapid Recovery' in the line chart." } ], "id_raw": 336 }, { "id": "xVurWs76bgcEGjY2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_16.png", "conversations": [ { "from": "human", "value": "\nWhich of the {line for 'Rapid Recovery'}[0.160, 0.235, 0.865, 0.298] and the {line for 'Sharp Rise'}[0.160, 0.537, 0.865, 0.775] is higher on the chart?" }, { "from": "gpt", "value": "The line for 'Sharp Rise'." } ], "id_raw": 336 }, { "id": "xA8YyWH9fCeCSWKY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_16.png", "conversations": [ { "from": "human", "value": "\nIs the {10th x-axis label}[0.856, 0.906, 0.874, 0.930] to the right of the {5th x-axis label}[0.156, 0.906, 0.165, 0.930]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 336 }, { "id": "bMz4IGvAcOrkUVqq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_16.png", "conversations": [ { "from": "human", "value": "\nDoes the {line for 'Fluctuating Growth'}[0.160, 0.497, 0.865, 0.537] have a higher value at the rightmost point, compared to the {line for 'Periodic Spikes'}[0.160, 0.378, 0.865, 0.457]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 336 }, { "id": "hP5e6jMlELTf1nXr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the region at [0.755, 0.741, 0.863, 0.764] in the chart." }, { "from": "gpt", "value": "The region contains the legend text 'Rapid Recovery' labeled in the chart with a red color line marker to its left." } ], "id_raw": 336 }, { "id": "QRg0fVup5vV353fD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's inside the region [0.160, 0.235, 0.865, 0.298]?" }, { "from": "gpt", "value": "It contains the representation of the 'Rapid Recovery' feature, depicted as a red line in the chart, starting from 'x'=5 and ending at 'x'=30." } ], "id_raw": 336 }, { "id": "KxPlVj54nkrOteP4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the area located in coordinates [0.715, 0.846, 0.859, 0.869]." }, { "from": "gpt", "value": "The region encompasses the legend item 'Periodic Spikes' along with its corresponding line color marker." } ], "id_raw": 336 }, { "id": "VxkxPCoqeVG8S4dp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_17.png", "conversations": [ { "from": "human", "value": "\nWhat do {the yticks} located on the coordinates [0.068, 0.923, 0.120, 0.959], [0.068, 0.800, 0.120, 0.836], [0.068, 0.678, 0.120, 0.714] etc represent?" }, { "from": "gpt", "value": "They represent interval values on the Y-axis which corresponds to the 'Engagement Trend' in this context." } ], "id_raw": 337 }, { "id": "wlYsOd9MGubOATr8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the {line_0} at coordinate [0.169, 0.030, 0.943, 0.863] represent in this line chart?" }, { "from": "gpt", "value": "It represents the engagement trend over time." } ], "id_raw": 337 }, { "id": "wVtomfdaTmI6YONI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_17.png", "conversations": [ { "from": "human", "value": "\nWhat is {the object}[0.510, 0.930, 0.601, 0.970] at the lower part of the graph?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 337 }, { "id": "sjxPj1CzdUgHxxbe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_17.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '810'}[0.523, 0.055] on the 'Engagement Trend' line higher or lower than {the point labeled '800'}[0.491, 0.079]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 337 }, { "id": "o2YmyFd1aHZkMoin", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_17.png", "conversations": [ { "from": "human", "value": "\nIn terms of 'Engagement Trend', is value at {the point 6}[0.169, 0.814] greater or less than value at {the point 30}[0.943, 0.863]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 337 }, { "id": "PsIoa6Sx3OE9jAV8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_17.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '730'} on this line chart closer to {the left boundary}[0.136, 0.030] or {the right boundary}[0.975, 0.863]?" }, { "from": "gpt", "value": "Right boundary." } ], "id_raw": 337 }, { "id": "7ZjWLo2RYH3XpwvI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the function of the region starting from [0.169, 0.030] and ending at [0.943, 0.863]." }, { "from": "gpt", "value": "It is the main plotting area of the line chart that displays the 'Engagement Trend' over 'Time'. The line within this area indicates the trend of engagement." } ], "id_raw": 337 }, { "id": "7L9KtTdBGz5pyQc6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements of the chart within the coordinate range [0.510, 0.930, 0.601, 0.970]?" }, { "from": "gpt", "value": "In this region, there's the label for the x-axis, which is 'Time'." } ], "id_raw": 337 }, { "id": "TEN9f9FMgTG5kpu2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_17.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the area at [0.025, 0.237, 0.058, 0.657]?" }, { "from": "gpt", "value": "The area is occupied by the label for the Y-axis, which is 'Engagement Trend'." } ], "id_raw": 337 }, { "id": "EnhESHxP9pnfXle2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_18.png", "conversations": [ { "from": "human", "value": "\nWhat component of the chart lies within the coordinates [0.079, 0.038, 0.223, 0.110]?" }, { "from": "gpt", "value": "This is the area for the plot's legend." } ], "id_raw": 338 }, { "id": "XxPAW8aph86f4wsZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line for {Industry 1}[0.085, 0.056, 0.119, 0.056] in the legend?" }, { "from": "gpt", "value": "The color of the line is blue." } ], "id_raw": 338 }, { "id": "x3SC6VNBRzUUddUH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the text contained within the element with coordinates [0.507, 0.957, 0.548, 0.981]?" }, { "from": "gpt", "value": "The text is 'Year'." } ], "id_raw": 338 }, { "id": "5oKPJiRUcTm7HSuM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_18.png", "conversations": [ { "from": "human", "value": "\nIn the year 2009, did {Industry 1}[0.506, 0.751] have higher or lower market share compared to {Industry 2}[0.506, 0.776]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 338 }, { "id": "qf4FDGVaIIMbLvDA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_18.png", "conversations": [ { "from": "human", "value": "\nAre {the 2000 data point for Industry 1}[0.114, 0.757] and {the 2000 data point for Industry 2}[0.114, 0.624] above or below the 78% mark on the y-axis?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 338 }, { "id": "Pkkm5w1dPd8BKF8y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_18.png", "conversations": [ { "from": "human", "value": "\nWhich line, between {Industry 1}[0.114, 0.078, 0.941, 0.916] and {Industry 2}[0.114, 0.243, 0.941, 0.776], ends at a higher position in 2019?" }, { "from": "gpt", "value": "Industry 1." } ], "id_raw": 338 }, { "id": "MIfY5yzjeykAZygx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_18.png", "conversations": [ { "from": "human", "value": "\nDescribe what objects are located within the coordinates [0.615, 0.928, 0.658, 0.951]." }, { "from": "gpt", "value": "Within these coordinates, there is the x-axis tick label '2012'." } ], "id_raw": 338 }, { "id": "1Qzu93iR7xEOvmJz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region with coordinates [0.790, 0.928, 0.832, 0.951]?" }, { "from": "gpt", "value": "This region contains the '2016' x-axis tick label." } ], "id_raw": 338 }, { "id": "vM4lL2Uy1R0mu0hZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_18.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the area with coordinates [0.040, 0.146, 0.061, 0.168]?" }, { "from": "gpt", "value": "This area contains the '90' y-axis tick label." } ], "id_raw": 338 }, { "id": "H5uJhJyCWYPMcvxo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_19.png", "conversations": [ { "from": "human", "value": "\nWhat is at coordinates [0.820, 0.043, 0.935, 0.069] in the chart?" }, { "from": "gpt", "value": "It's the text label {Slow Increase} in the legend." } ], "id_raw": 339 }, { "id": "fZO1oaXGBpIaoIYi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label at coordinates [0.015, 0.361, 0.035, 0.563] display?" }, { "from": "gpt", "value": "It displays the label {Performance}." } ], "id_raw": 339 }, { "id": "K1rjJ9KgFrPZQAWn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_17_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object present at coordinates [0.774, 0.092, 0.807, 0.092]?" }, { "from": "gpt", "value": "It's the color handle for the {Stable Growth} trend in the legend." } ], "id_raw": 339 }, { "id": "664h7jndEwB4ACkG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_19.png", "conversations": [ { "from": "human", "value": "\nIs the {Slow Increase} line [0.107, 0.311, 0.950, 0.538] above or below the {Abrupt Drop} line [0.107, 0.221, 0.950, 0.860]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 339 }, { "id": "NkGfB9zk5vZeJLx5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_19.png", "conversations": [ { "from": "human", "value": "\nAre the points of the {Irregular Variation} line [0.107, 0.062, 0.950, 0.294] generally increasing or decreasing?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 339 }, { "id": "pspTgURe9yBrKnsS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_17_19.png", "conversations": [ { "from": "human", "value": "\nIs the y-coordinate of the point at {1}[0.107, 0.402] for {Stable Growth} line higher or lower than the corresponding point of the {Irregular Variation} line [0.107, 0.086]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 339 }, { "id": "vUt9QyA0oRYCVTBb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within coordinates [0.767, 0.033, 0.977, 0.191]." }, { "from": "gpt", "value": "The area contains the legend items: {Slow Increase}[0.820, 0.043, 0.935, 0.069], {Stable Growth}[0.820, 0.080, 0.938, 0.106], {Abrupt Drop}[0.820, 0.118, 0.922, 0.144], and {Irregular Variation}[0.820, 0.156, 0.970, 0.181], each paired with a colored handle." } ], "id_raw": 339 }, { "id": "yNi16lEPIFgy173W", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_19.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region specified by [0.041, -0.056, 0.062, -0.031]?" }, { "from": "gpt", "value": "The y-axis label \"70\" is present in this region." } ], "id_raw": 339 }, { "id": "tzlVBoyVNkuNDpY0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_17_19.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region defined by [0.101, 0.916, 0.112, 0.942]?" }, { "from": "gpt", "value": "In this region, the first x-axis tick label '1' is present." } ], "id_raw": 339 }, { "id": "UWlvjg46D3HZORDg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.013, 0.346, 0.028, 0.614] in the first subplot?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 340 }, { "id": "reuQPuCV0RSRGv8O", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_0.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the fourth x-axis tick label in the second subplot?" }, { "from": "gpt", "value": "[0.656, 0.912, 0.686, 0.936]." } ], "id_raw": 340 }, { "id": "lOsP12Bmuq0nhqNY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the {line}[0.572, 0.132, 0.968, 0.828] in the second subplot represent?" }, { "from": "gpt", "value": "Art Development." } ], "id_raw": 340 }, { "id": "5T1ZutShyStZivGe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_0.png", "conversations": [ { "from": "human", "value": "\nDoes the {Science Development line}[0.078, 0.141, 0.474, 0.819] in the first subplot ascend or descend from 500 to 1500?" }, { "from": "gpt", "value": "Ascend." } ], "id_raw": 340 }, { "id": "MdG0q0qPDRyIcZ8m", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_0.png", "conversations": [ { "from": "human", "value": "\nDid {Art Development}[0.572, 0.132, 0.968, 0.828] in the second subplot begin at a lower point than {Science Development}[0.078, 0.141, 0.474, 0.819] in the first subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 340 }, { "id": "8R69f514vaCnicYq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_0.png", "conversations": [ { "from": "human", "value": "\nAt the period of 2500, is the {Art Development}[0.819, 0.223] level in the second subplot higher or lower than {Science Development}[0.326, 0.173] level in the first subplot?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 340 }, { "id": "1JtKQjd7FS8jN3gg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the area between [0.552, 0.064, 0.988, 0.896]." }, { "from": "gpt", "value": "This region contains the subplot illustrating the Art Development Over Time with the x-axis labeled as 'Period' and y-axis labeled as 'Development Level'. The art development trend is represented by a line chart with corresponding points marked at various periods." } ], "id_raw": 340 }, { "id": "ItDpBIdA1pHA4yIm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_0.png", "conversations": [ { "from": "human", "value": "\nWhat content can be found in the region [0.656, 0.025, 0.884, 0.057]?" }, { "from": "gpt", "value": "This region contains the subtitle for the second subplot, which reads 'Art Development Over Time'." } ], "id_raw": 340 }, { "id": "cRpD3uOMrVrGRQbA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_0.png", "conversations": [ { "from": "human", "value": "\nWhat elements are present within the coordinates [0.841, 0.760, 0.982, 0.801]?" }, { "from": "gpt", "value": "This area includes the legend for the second subplot named, specifically the {Art Development line with the color indicator}[0.843, 0.773, 0.872, 0.785], and corresponding label situated at [0.878, 0.769, 0.977, 0.792]." } ], "id_raw": 340 }, { "id": "Awy5qbJkzIpkIQg3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel}[0.017, 0.231, 0.037, 0.709] in the chart?" }, { "from": "gpt", "value": "Design Elements Value." } ], "id_raw": 341 }, { "id": "qV9t2e15BX06AQbG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_1.png", "conversations": [ { "from": "human", "value": "\nWhat type of line does {the line labeled 'Ideas'}[0.093, 0.175, 0.384, 0.810] use in the plot?" }, { "from": "gpt", "value": "Solid line with circle markers." } ], "id_raw": 341 }, { "id": "tYQSCwfpneiGtAEA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_1.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {the dataset 'Refinement (FT)'} in the second subplot's legend[0.806, 0.238, 0.845, 0.256]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 341 }, { "id": "UO6sZzVRbxFqRayo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point at x=4}[0.202, 0.485] from the 'Originality' dataset higher than the corresponding point in {the 'Refinement'} dataset[0.202, 0.470]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 341 }, { "id": "1oWh0uLWNat2THoM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_1.png", "conversations": [ { "from": "human", "value": "\nWhich dataset has a higher final point, {the 'Ideas'} at [0.384, 0.175] or {the 'Execution (FT)'} at [0.782, 0.130]?" }, { "from": "gpt", "value": "Execution (FT)." } ], "id_raw": 341 }, { "id": "b3xOtZabP9V6mAXz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_1.png", "conversations": [ { "from": "human", "value": "\nAre value labels for x-tick '4' in both subplots aligned at the same vertical level, comparing {first subplot's x-tick label 4}[0.202, 0.869, 0.207, 0.904] and {second subplot's x-tick label 4}[0.600, 0.869, 0.605, 0.904]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 341 }, { "id": "RHF0Vud704yboc5G", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects visible in the chart area defined by [0.491, 0.582, 0.782, 0.743]." }, { "from": "gpt", "value": "This region in the second subplot encompasses points on the lines for 'Ideas (FT)', 'Originality (FT)', and 'Refinement (FT)', where they are showing line segments interconnecting the points." } ], "id_raw": 341 }, { "id": "CLc5lxhlvftanW0e", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_1.png", "conversations": [ { "from": "human", "value": "\nProvide a description of the graphical components located in the region [0.078, 0.154, 0.398, 0.319]." }, { "from": "gpt", "value": "The region covers the lower part of the y-axis including y-tick labels for '0.8' and '0.6', and part of the 'Ideas', 'Originality', 'Refinement', and 'Execution' lines starting phase in the first subplot." } ], "id_raw": 341 }, { "id": "LuzpSkkRNSb5eawe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_1.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the bounds from [0.076, 0.844, 0.420, 0.904]?" }, { "from": "gpt", "value": "This region holds all the x-axis tick labels ('0' to '10') from the first subplot, displaying the iteration steps of the chart." } ], "id_raw": 341 }, { "id": "dow42wFUGbjVWjvJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object type at the location [0.067, 0.171, 0.475, 0.790]?" }, { "from": "gpt", "value": "It's a line for Old-Business in the first subplot." } ], "id_raw": 342 }, { "id": "T1ScuqJphN4VBXOm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_2.png", "conversations": [ { "from": "human", "value": "\nWhat is represented at {coordinates}[0.257, 0.945, 0.285, 0.975] on the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 342 }, { "id": "LbfQlslDlaJnhUo5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_2.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart element is located at {position}[0.627, 0.238] within the second subplot?" }, { "from": "gpt", "value": "Point." } ], "id_raw": 342 }, { "id": "tkLsekeefUpbD9iH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_2.png", "conversations": [ { "from": "human", "value": "\nDoes the dataset at {starting point}[0.442, 0.896] indicate a higher value in {Old-Business}[0.562, 0.170] or {Streaming Platform}[0.970, 0.783] in the second subplot?" }, { "from": "gpt", "value": "Old-Business." } ], "id_raw": 342 }, { "id": "k4oJFFWZdN0GA4zp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_2.png", "conversations": [ { "from": "human", "value": "\nIs the data point {label}[0.310, 0.310] on the Social Media line higher or lower than the data point {label}[0.320, 0.310] on the Old-Business line in the first subplot?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 342 }, { "id": "vwzOh94fCKpZWdXf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_2.png", "conversations": [ { "from": "human", "value": "\nWhich is rising faster in the year 2015 between {Old-Business}[0.884, 0.742] and {Streaming-Platform}[0.884, 0.173]?" }, { "from": "gpt", "value": "Streaming-Platform." } ], "id_raw": 342 }, { "id": "aFfgqlE3Ff7RkuEZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the setup in the area [0.500, 0.800, 0.850, 0.950]." }, { "from": "gpt", "value": "This region includes the x-axis label {Years}[0.753, 0.945, 0.780, 0.975] and some tick labels for the years in the second subplot." } ], "id_raw": 342 }, { "id": "kpSXjSUfTYGsna7T", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the region [0.443, 0.080, 0.545, 0.175]?" }, { "from": "gpt", "value": "It contains {x-tick labels}[0.494, 0.912, 0.524, 0.936] from the second subplot arranged in chronological increment from years 2017.5 to 2020.0." } ], "id_raw": 342 }, { "id": "ZUt7qzapaC2dcacG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_2.png", "conversations": [ { "from": "human", "value": "\nExplain the elements found in the region [0.250, 0.200, 0.350, 0.300]." }, { "from": "gpt", "value": "This area encompasses the {x-axis ticks}[0.282, 0.896] and the data points for {Old-Business}[0.282, 0.673] and {Social Media}[0.282, 0.294] around the year 2010 in the first subplot." } ], "id_raw": 342 }, { "id": "TrVKAbDDrP2q5M6u", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the coordinates [0.017, 0.349, 0.037, 0.591]?" }, { "from": "gpt", "value": "Y-axis label of the first sub-chart." } ], "id_raw": 343 }, { "id": "GYZQC77uoIZOHseT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the line}[0.322, 0.243, 0.458, 0.716] in the second sub-chart?" }, { "from": "gpt", "value": "Corporate Social Responsibility vs Crime Against Humanity." } ], "id_raw": 343 }, { "id": "Kf6sUEw06A0rZ0aq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the coordinates [0.259, 0.037, 0.521, 0.085]?" }, { "from": "gpt", "value": "The subtitle of the second sub-chart: 'SBP and GoB Evaluation'." } ], "id_raw": 343 }, { "id": "VbpmFRxRNvls35Qj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_3.png", "conversations": [ { "from": "human", "value": "\nIn the first subplot, does {the point at Year 1997}[0.190, 0.243] have a higher or lower value than {the point at Year 1991}[0.100, 0.405] on the 'Corporate Social Responsibility vs Crime Against Humanity' chart?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 343 }, { "id": "iHdmSqYf7yLHF2Ih", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the line of \u2018Corporate Social Responsibility vs Crime Against Humanity\u2019}[0.322, 0.243, 0.458, 0.716] start or end with a higher index value in the second subplot?" }, { "from": "gpt", "value": "End." } ], "id_raw": 343 }, { "id": "5VW4dkbyduWxaygk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_3.png", "conversations": [ { "from": "human", "value": "\nIn first subplot, is {the 1995 point}[0.160, 0.276] on 'Corporate Social Responsibility vs Crime Against Humanity' line higher or lower than {the 1995 point}[0.160, 0.438] on 'Sustainable Business Practices vs Greening of Business' line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 343 }, { "id": "HKkZq4diWDlnnRey", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the region [0.472, 0.787, 0.983, 0.902]?" }, { "from": "gpt", "value": "This region contains a legend for the second sub-chart. The legend has 2 items: {Corporate Social Responsibility vs Crime Against Humanity}[0.522, 0.801, 0.977, 0.836] with a handle color of '#0a6ae1' and {Sustainable Business Practices vs Greening of Business}[0.522, 0.853, 0.949, 0.888] with a handle color of '#d75faa'." } ], "id_raw": 343 }, { "id": "Pupce3LAdF3FkSFx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents of the region [0.315, 0.096, 0.465, 0.844]." }, { "from": "gpt", "value": "The area contains the second sub-chart titled 'SBP and GoB Evaluation' and its x and y axes with labels 'Year' and 'Index Value'. The x-axis has ticks labeled from '1985' to '2000' and the y-axis has ticks from '0.10' to '0.55'. There are two line plots with points ranging from '1990' to '1999'." } ], "id_raw": 343 }, { "id": "AKjGk526WuuCd1gJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within [0.078, 0.096, 0.227, 0.844]." }, { "from": "gpt", "value": "This region has the first sub-chart titled 'CSR and CAH Evaluation' and its x and y axes with labels 'Year' and 'Index Value'. Similarly, there are four ticks on the x-axis from '1985' to '2000' and six ticks on the y-axis from '0.5' to '1.0'. Two line charts are plotted in this area showcasing trends of 'Corporate Social Responsibility vs Crime Against Humanity' and 'Sustainable Business Practices vs Greening of Business' from '1990' to '1999'." } ], "id_raw": 343 }, { "id": "41smMJID788oDlhv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.439, 0.810] in the chart?" }, { "from": "gpt", "value": "It's a data point on the line plot representing final reading of Electricity Consumption." } ], "id_raw": 344 }, { "id": "sxXpw5TzsNNjCTfg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the y-axis label in the right subplot?" }, { "from": "gpt", "value": "[0.508, 0.333, 0.528, 0.607]" } ], "id_raw": 344 }, { "id": "NxEMeUNPF9drrdop", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the point at {coordinates}[0.601, 0.130] stand for?" }, { "from": "gpt", "value": "The initial reading of Gas Consumption." } ], "id_raw": 344 }, { "id": "1l3PAHMT5XNjWBxe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_4.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher at the final reading, the {gas consumption}[0.931, 0.300] or the {water consumption}[0.931, 0.810]?" }, { "from": "gpt", "value": "Gas consumption." } ], "id_raw": 344 }, { "id": "wEEHg463WdddjXsd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_4.png", "conversations": [ { "from": "human", "value": "\nIs the {initial reading of Gas Consumption}[0.601, 0.130] greater than the {initial reading of Electricity Consumption}[0.109, 0.130]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 344 }, { "id": "OvY8wJZGSAVmUgRY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_4.png", "conversations": [ { "from": "human", "value": "\nConsidering the final points, did the {Electricity Consumption}[0.439, 0.810] decrease more than the {Water Consumption}[0.931, 0.810]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 344 }, { "id": "7yBubx6u2rKAO3lR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in region [0.584, 0.096, 0.948, 0.844]." }, { "from": "gpt", "value": "The region contains an subplot illustrating the decrease in Gas and Water Consumption over time. It includes line plots for both consumptions, having data points marked with coordinates, axes with labels and ticks, and a legend indicating the color code for each type of consumption." } ], "id_raw": 344 }, { "id": "GwpmLhhumQxnWq5G", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_4.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the region [0.093, 0.096, 0.456, 0.844]?" }, { "from": "gpt", "value": "It is a subplot showcasing the decrease in Electricity Consumption. The plot includes a line tracing the consumption from Initial Reading to Final Reading, Axes with labels and ticks, and a subtitle giving a brief summary of the plot." } ], "id_raw": 344 }, { "id": "SNCK8eKGuMHjRPH5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within the region [0.839, 0.113, 0.940, 0.228]?" }, { "from": "gpt", "value": "This area contains a legend with labels and colored markers for Gas Consumption and Water Consumption. The coordinates for the labels are {'Gas'}[0.888, 0.127, 0.917, 0.162] and {'Water'}[0.888, 0.179, 0.934, 0.214]." } ], "id_raw": 344 }, { "id": "uLiLzvmw1UvFvo9s", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at these coordinates [0.513, 0.159, 0.533, 0.782]?" }, { "from": "gpt", "value": "It the y-axis label of the second subplot." } ], "id_raw": 345 }, { "id": "vKSBEGkcYXBNFeKw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the fifth y-axis tick label coordinate in the first subplot?" }, { "from": "gpt", "value": "[0.043, 0.539, 0.077, 0.574]." } ], "id_raw": 345 }, { "id": "L2Hd9f3NGBY7Opme", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the text}[0.670, 0.037, 0.888, 0.085] in the second subplot?" }, { "from": "gpt", "value": "Series A Investment." } ], "id_raw": 345 }, { "id": "0lj6cJlfgAbefhON", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_5.png", "conversations": [ { "from": "human", "value": "\nIn the first subplot, does the {point at time period 2}[0.181, 0.369] have a higher or lower y- value compared to the {point at time period 3}[0.218, 0.461]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 345 }, { "id": "sVvDaDOfmTByojMP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_5.png", "conversations": [ { "from": "human", "value": "\nComparing the first and second subplots, is the {y-axis label in the second subplot}[0.513, 0.159, 0.533, 0.782] to the left or right of the {y-axis label in the first subplot}[0.017, 0.159, 0.037, 0.782]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 345 }, { "id": "9zTo2ruaxd6ux0Se", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_5.png", "conversations": [ { "from": "human", "value": "\nDoes the {point for time period 0 in subplot 1}[0.106, 0.185] have a higher or lower value than the {point for time period 0 in subplot 2}[0.593, 0.175]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 345 }, { "id": "GHm7n56tPj277X1G", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_5.png", "conversations": [ { "from": "human", "value": "\nWhat is described within the region of [0.043, 0.355, 0.077, 0.390] in the chart?" }, { "from": "gpt", "value": "The 7th y-axis tick label which is '0.90' lies within this region for the first subplot." } ], "id_raw": 345 }, { "id": "ce5IKWHDd6NmNZlh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.0575, 0.096, 0.983, 0.844]." }, { "from": "gpt", "value": "This region encompasses the second subplot including the axes, title 'Series A Investment', x/y labels, as well as the plotted line for 'Series A'. Y-ticks labels from '0.7' to '1.3' and x-ticks labels from '-2' to '12' are displayed." } ], "id_raw": 345 }, { "id": "oxI2ZxWeDmAmOFNm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_5.png", "conversations": [ { "from": "human", "value": "\nWhat items can be found within the coordinates [0.106, 0.185, 0.478, 0.755]?" }, { "from": "gpt", "value": "The plot line for the 'Angel & Seed' data in subplot 1 is within theses coordinates. The line includes markers for time periods 0 through 10." } ], "id_raw": 345 }, { "id": "nPrxmMmbHc7357mL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the coordinates [0.091, 0.037, 0.475, 0.085] in the first subplot?" }, { "from": "gpt", "value": "GPT-6 Technological Advancements." } ], "id_raw": 346 }, { "id": "VzqOJeEL0AMre0PC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label on the y-axis with the coordinates [0.508, 0.272, 0.528, 0.668] on the right subplot?" }, { "from": "gpt", "value": "Advancement Rate." } ], "id_raw": 346 }, { "id": "iXCJXY24ItpYlnNZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label near {the point}[0.648, 0.808] on the 'Upgraded' line of the second subplot?" }, { "from": "gpt", "value": "The year '2035'." } ], "id_raw": 346 }, { "id": "d5mvPhy3SFczXIeK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point labelled '2040'}[0.221, 0.385] on the 'Original' line of the first subplot above or below {the point labelled '2040'}[0.713, 0.380] on the 'Original' line of the second subplot?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 346 }, { "id": "25B0dA8VLlVoRluy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_6.png", "conversations": [ { "from": "human", "value": "\nWhich line in the second plot, {Original}[0.588, 0.180, 0.961, 0.580] or {Upgraded}[0.588, 0.430, 0.961, 0.780] ends at a higher rate in 2060?" }, { "from": "gpt", "value": "Original." } ], "id_raw": 346 }, { "id": "EAB6eotMyb2PpF0h", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_6.png", "conversations": [ { "from": "human", "value": "\nAre the {values for '2050'}[0.837, 0.290] on the 'Original' line of the second subplot higher or lower than the {values for '2040'}[0.713, 0.380]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 346 }, { "id": "8e25gkHsCKMxKpTZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.840, 0.637, 0.972, 0.752]." }, { "from": "gpt", "value": "This region contains the subplot legend for the second plot. It includes two entries: {Original}[0.890, 0.651, 0.950, 0.686] and {Upgraded}[0.890, 0.703, 0.966, 0.738], displayed with their respective colors." } ], "id_raw": 346 }, { "id": "S3JyOG9AlqrR3Q6A", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.078, 0.096, 0.488, 0.844]." }, { "from": "gpt", "value": "This region represents the first subplot. It contains two lines, labeled as 'Original' and 'Upgraded', representing the advancement rate of 'GPT-6' and 'GPT-6 Upgraded' from the years 2030 to 2060." } ], "id_raw": 346 }, { "id": "Xx7FYZJjKyPceHvd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in [0.585, 0.037, 0.964, 0.085]?" }, { "from": "gpt", "value": "The region contains the subtitle \"AAI-1 Technological Advancements\" for the second subplot." } ], "id_raw": 346 }, { "id": "Zu89KL5tgIK45Mst", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the object at coordinates [0.197, 0.030, 0.367, 0.068]?" }, { "from": "gpt", "value": "Slow Decline System, a subtitle for the first subplot." } ], "id_raw": 347 }, { "id": "lFdO9JrRid5O2QsB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_7.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinates of [0.739, 0.934, 0.805, 0.970]?" }, { "from": "gpt", "value": "It's the x-label of the second subplot, namely \"Iterations\"." } ], "id_raw": 347 }, { "id": "TL2u4hRaUHJ8W0wf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of the object located at [0.576, 0.153, 0.968, 0.799]?" }, { "from": "gpt", "value": "A line representing 'Fast Decline' for the second subplot." } ], "id_raw": 347 }, { "id": "oal8G3nIEClce1IC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point at [0.282, 0.580]} on the 'Slow Decline' line above or below {the point at [0.331, 0.632]} on the same line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 347 }, { "id": "Mrl3yf470ZDlGPX1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_7.png", "conversations": [ { "from": "human", "value": "\nWhich value is larger at Iteration 500, the {data point at [0.184, 0.424]} in the 'Slow Decline' system or the {data point at [0.674, 0.315]} in the 'Fast Decline' system?" }, { "from": "gpt", "value": "The data point in the 'Slow Decline' system." } ], "id_raw": 347 }, { "id": "PMKZcG7Cs9FX4GN9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_7.png", "conversations": [ { "from": "human", "value": "\nThe {line for 'Fast Decline'}[0.576, 0.153, 0.968, 0.799] on the right has an upward or downward trend compared to {the line for 'Slow Decline'}[0.086, 0.165, 0.478, 0.787] on the left?" }, { "from": "gpt", "value": "Downward." } ], "id_raw": 347 }, { "id": "X2vYxwIM3OD11OhT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the bounding box [0.556, 0.077, 0.988, 0.875]." }, { "from": "gpt", "value": "This region contains the second subplot. It includes an x-label of \"Iterations\" at {coords}[0.739, 0.934, 0.805, 0.970], a y-label of \"Relative Performance\" at {coords}[0.510, 0.298, 0.525, 0.654], and a line chart representing the 'Fast Decline' at {coords}[0.576, 0.153, 0.968, 0.799]. The subplot has its own set of x-axis and y-axis tick labels." } ], "id_raw": 347 }, { "id": "eHMtsmXbqsACRkY3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_7.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the coordinates [0.066, 0.077, 0.497, 0.875]?" }, { "from": "gpt", "value": "The first subplot of the chart is in this region, described as 'Slow Decline System'. The chart includes x and y-axis labels, representing \"Iterations\" and \"Relative Performance\" respectively, along with x-axis and y-axis value labels, and a line chart showing the 'Slow Decline' trend." } ], "id_raw": 347 }, { "id": "P8m43qIbn3loV7OY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_7.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region from [0.870, 0.809, 0.982, 0.860]?" }, { "from": "gpt", "value": "This area includes a legend for the 'Fast Decline' line of the second subplot with a colored marker and label at {coords}[0.907, 0.821, 0.977, 0.849]." } ], "id_raw": 347 }, { "id": "iLwvXGMn7JRlgmO7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the first object located at [0.568, 0.446] in this chart?" }, { "from": "gpt", "value": "It's the data point {'0'}[0.568, 0.446] of the Total label in the \"Connections over Time\" subplot." } ], "id_raw": 348 }, { "id": "u7PZNg91ic5rt5Lw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_8.png", "conversations": [ { "from": "human", "value": "\nWhat label corresponds to the y-axis tick at [0.522, 0.819, 0.541, 0.842]?" }, { "from": "gpt", "value": "The label is '200'." } ], "id_raw": 348 }, { "id": "fjniiukZMumNk8nB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_8.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is located within these coordinates [0.548, 0.064, 0.989, 0.896]?" }, { "from": "gpt", "value": "It's a line chart." } ], "id_raw": 348 }, { "id": "lhO1Fv4wm6UzmOPW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_8.png", "conversations": [ { "from": "human", "value": "\nWhich line is the point located at [0.819, 0.675] on?" }, { "from": "gpt", "value": "It's on the \"Active\" line." } ], "id_raw": 348 }, { "id": "UvZjqd7O63Ssi5wA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_8.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Total' line point}[0.969, 0.140] above or below {the 'Active' line point}[0.969, 0.583]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 348 }, { "id": "flYxepXYXn2gJedb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_8.png", "conversations": [ { "from": "human", "value": "\nAt {point 3}[0.224, 0.331], is the 'Total' line's value higher or lower than the 'Active' line's value at {point 3}[0.224, 0.583]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 348 }, { "id": "3w6rxZVw3OCiPV69", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.053, 0.064, 0.495, 0.896]." }, { "from": "gpt", "value": "The area represents a subplot of the chart showing the 'Friends over Time' trend. It contains a line chart with two lines - 'Total' and 'Active'. Different quarters are plotted on the x-axis, and the count of friends is displayed on the y-axis." } ], "id_raw": 348 }, { "id": "ta5FLf3ES0yUveYb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.689, 0.025, 0.849, 0.057]." }, { "from": "gpt", "value": "This region contains the subtitle for the subplot - \"Connections over Time.\"" } ], "id_raw": 348 }, { "id": "p88bi10BDrrlRBfS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.548, 0.064, 0.989, 0.896]?" }, { "from": "gpt", "value": "This region contains the 'Connections over Time' subplot, which includes a line chart showing 'Total' and 'Active' connections over time, an x-axis labeled 'Quarters', a y-axis labeled 'Count', and a legend showing line colors for 'Total' and 'Active'." } ], "id_raw": 348 }, { "id": "WyPRMEUVFvwvbSle", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at coordinates [0.017, 0.415, 0.037, 0.525] refer to in the chart?" }, { "from": "gpt", "value": "Sales." } ], "id_raw": 350 }, { "id": "V8m6363bPuxP7SUl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the {xlabel}[0.750, 0.917, 0.808, 0.963] in the 2nd subplot?" }, { "from": "gpt", "value": "Month." } ], "id_raw": 350 }, { "id": "bx8ViHTtjmF8ZTgs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_10.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.881, 0.689, 0.976, 0.752] in the chart?" }, { "from": "gpt", "value": "It's the legend for the 2020 sales trend in the 2nd subplot." } ], "id_raw": 350 }, { "id": "d0FgmWxGV8c0s0kc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_10.png", "conversations": [ { "from": "human", "value": "\nWhich data point in 2020 sales trend line is higher, the point at {month 6}[0.762, 0.432] or the one at {month 3}[0.661, 0.735]?" }, { "from": "gpt", "value": "Month 6." } ], "id_raw": 350 }, { "id": "zOkq0NMXx7f2H6Kk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_10.png", "conversations": [ { "from": "human", "value": "\nIs the point at {month 2} in the 2020 sales trend [0.627, 0.583] higher or lower than the point at {month 5}[0.728, 0.644]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 350 }, { "id": "a9N6dGrBvygfIOQR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_10.png", "conversations": [ { "from": "human", "value": "\nIn the 2019 sales trend, does the sales increase or decrease from {month 3}[0.169, 0.727] to {month 4}[0.203, 0.644]?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 350 }, { "id": "4JhT8mg1nZ16l2ea", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_10.png", "conversations": [ { "from": "human", "value": "\nWhat's in the region with coordinates [0.575, 0.096, 0.983, 0.844]?" }, { "from": "gpt", "value": "This region contains the chart for the 2020 sales trend, represented by a line graph. It includes an x-label 'Month', a y-label 'Sales', several x and y ticks with their labels, and a legend." } ], "id_raw": 350 }, { "id": "1HoIPnaX0pgUO6sI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_10.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region marked by [0.083, 0.096, 0.492, 0.844]?" }, { "from": "gpt", "value": "This is the area where the 2019 sales trend is plotted with a line graph. It includes the x-label 'Month', the y-label 'Sales', and several x and y ticks with labels." } ], "id_raw": 350 }, { "id": "6AnOQ8CXUH58NmSx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.881, 0.689, 0.976, 0.752] contains?" }, { "from": "gpt", "value": "The region contains the legend for the 2020 sales trend line graph." } ], "id_raw": 350 }, { "id": "9B6unQ24ZI21slUz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_12.png", "conversations": [ { "from": "human", "value": "\nWhat object is positioned at [0.017, 0.315, 0.037, 0.625] in the chart?" }, { "from": "gpt", "value": "Y-axis label of the first subplot." } ], "id_raw": 352 }, { "id": "XE3N2q7CRCsnKtFa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_12.png", "conversations": [ { "from": "human", "value": "\nWhere is the {legend label 'YieldCurve'} located?" }, { "from": "gpt", "value": "[0.887, 0.703, 0.969, 0.738]" } ], "id_raw": 352 }, { "id": "XsT0OJDEK4bJhzOM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_12.png", "conversations": [ { "from": "human", "value": "\nHow is {the year label '2010'} on the x-axis at [0.134, 0.869, 0.173, 0.904] indicated?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 352 }, { "id": "QizqCf9TQfAxzL3E", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_12.png", "conversations": [ { "from": "human", "value": "\nIs the {YieldCurve point for the year 2016}[0.822, 0.318] above or below the {BlueChip point for the same year}[0.324, 0.345]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 352 }, { "id": "WYpOAvM6NadDFEGP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_12.png", "conversations": [ { "from": "human", "value": "\nWhich trend, BlueChip or YieldCurve, shows a higher value for {the year 2018} pointing at [0.879, 0.251] ?" }, { "from": "gpt", "value": "YieldCurve." } ], "id_raw": 352 }, { "id": "VFZri7Ec8sKkRu4I", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_12.png", "conversations": [ { "from": "human", "value": "\nIs the {BlueChip point for the year 2009}[0.125, 0.774] above or below the {BlueChip point for the year 2010}[0.154, 0.739]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 352 }, { "id": "pBikofLWfoT2RyUV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_12.png", "conversations": [ { "from": "human", "value": "\nCan you elaborate on what's found in the coordinates [0.576, 0.096, 0.983, 0.844]?" }, { "from": "gpt", "value": "This area contains the second subplot labeled \"Yield Curve Trend\". The YieldCurve line graph resides in this region, represented with the color #d75faa. Additionally, the y-axis label 'Indicator Value' and the x-axis label 'Year' are placed here, including the corresponding tick labels and marks." } ], "id_raw": 352 }, { "id": "I7HVpalVLKXnHTPq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_12.png", "conversations": [ { "from": "human", "value": "\nWhat components are included in the region defined by [0.078, 0.096, 0.485, 0.844]?" }, { "from": "gpt", "value": "This region encloses the first subplot labeled \"Blue Chip Stock Trend\", which includes the BlueChip line graph, the y-axis label 'Indicator Value', and x-axis label 'Year' along with related tick labels and marks." } ], "id_raw": 352 }, { "id": "9ZyM7qSUYXER0f2W", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region outlined by coordinates [0.838, 0.689, 0.976, 0.752]." }, { "from": "gpt", "value": "This region holds the legend of the second subplot, where the label 'YieldCurve' is found with the associated color marker, colored #d75faa." } ], "id_raw": 352 }, { "id": "ch6LZS5wTGSnRECc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label located at [0.017, 0.259, 0.037, 0.681] represent?" }, { "from": "gpt", "value": "Attack Success Rate." } ], "id_raw": 353 }, { "id": "DvTeuJC94rn1xQZG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third tick on the x-axis?" }, { "from": "gpt", "value": "[0.190, 0.844]." } ], "id_raw": 353 }, { "id": "pDovp78VCMlEDQ8y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_13.png", "conversations": [ { "from": "human", "value": "\nWhich line does the legend with coordinates [0.815, 0.637, 0.972, 0.752] refer to?" }, { "from": "gpt", "value": "Fine-tuned." } ], "id_raw": 353 }, { "id": "AlQWQG1oFHrKud7D", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_13.png", "conversations": [ { "from": "human", "value": "\nAt the point [0.682, 0.305], is the 'Conventional' line above or below the 'Fine-tuned' line at [0.682, 0.572]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 353 }, { "id": "x3s4ojqr6PPs2CVI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_13.png", "conversations": [ { "from": "human", "value": "\nIs {the fifth point on the 'Conventional' line}[0.775, 0.214] higher or lower than the fifth point on the 'Fine-tuned' line at [0.775, 0.523]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 353 }, { "id": "qBlS2wNBspGa1bdy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_13.png", "conversations": [ { "from": "human", "value": "\nWhich line, 'Conventional' at [0.470, 0.173] or 'Fine-tuned' at [0.470, 0.366], has a higher value at 2000 iterations?" }, { "from": "gpt", "value": "Fine-tuned." } ], "id_raw": 353 }, { "id": "IMV1nTtEFREUKG6c", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_13.png", "conversations": [ { "from": "human", "value": "\nProvide a description of the object at the coordinates [0.693, 0.037, 0.856, 0.085]." }, { "from": "gpt", "value": "This is the subtitle of the second sub-plot. It reads \"AES Evaluation\" and is displayed in black color." } ], "id_raw": 353 }, { "id": "pcfQynsd5HWZkppo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.815, 0.637, 0.972, 0.752]?" }, { "from": "gpt", "value": "This area contains the legend items for the second subplot. It includes {Conventional}[0.864, 0.651, 0.966, 0.686] and {Fine-tuned}[0.864, 0.703, 0.946, 0.738]. Each legend item is matched with a colored marker." } ], "id_raw": 353 }, { "id": "uKMauAzNpbJk2HA7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_13.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area [0.588, 0.165, 0.961, 0.761]?" }, { "from": "gpt", "value": "This region corresponds to the 'Conventional' line in the second subplot. It contains several data points representing different 'Iterations' and corresponding 'Attack Success Rate'." } ], "id_raw": 353 }, { "id": "1zhqut3kNUueEITp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.017, 0.332, 0.037, 0.608] on the y-axis represent?" }, { "from": "gpt", "value": "The label represents the 'Success Rate'." } ], "id_raw": 354 }, { "id": "kG65BsBkufa3y6zC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_14.png", "conversations": [ { "from": "human", "value": "\nWhat coordinate corresponds to the number '1500' on the x-axis?" }, { "from": "gpt", "value": "The coordinate is [0.243, 0.287, 0.287, 0.313]." } ], "id_raw": 354 }, { "id": "2nKvrun0AcqCM2A2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the content marked at {coordinates} [0.863, 0.703, 0.966, 0.738]?" }, { "from": "gpt", "value": "That is the legend for 'AR Education'." } ], "id_raw": 354 }, { "id": "ctOHkWiSz47GS3p6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_14.png", "conversations": [ { "from": "human", "value": "\nIs the success rate of {AR Education}[0.643, 0.439] higher or lower than that of {VR Education}[0.152, 0.313] for 500 students?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 354 }, { "id": "m9eXViwDDfX9AhzN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_14.png", "conversations": [ { "from": "human", "value": "\nIs the success rate of {VR Education} [0.470, 0.679] at 4000 students higher or lower than that of {AR Education} [0.962, 0.192]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 354 }, { "id": "dkRdd0O9yHct6FQq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_14.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher success rate at 3000 students, {AR Education}[0.871, 0.212] or {VR Education}[0.379, 0.549]?" }, { "from": "gpt", "value": "VR Education." } ], "id_raw": 354 }, { "id": "V443LFmrUHkxIpYP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.508, 0.332, 0.528, 0.608]." }, { "from": "gpt", "value": "The region covers the y-axis label of the subplot chart on the right, which represents the 'Success Rate' of 'AR Education'. The label is stylized with black color and a rotation of 90.0 degrees." } ], "id_raw": 354 }, { "id": "sjzQThuxiqJtEe3T", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_14.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the area specified by the coordinates [0.814, 0.689, 0.972, 0.752]?" }, { "from": "gpt", "value": "This area includes the chart legend. It contains the label for 'AR Education' [0.863, 0.703, 0.966, 0.738], which is paired with a colored marker [0.816, 0.710, 0.854, 0.727]." } ], "id_raw": 354 }, { "id": "WUrEVVJUpICaUr45", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the region [0.579, 0.096, 0.980, 0.844]." }, { "from": "gpt", "value": "This region includes the right subplot of the figure. It covers the area from the x and y-axis labels to the data line representing 'AR Education'. The x-axis labels range from '\u22121000' to '5000' representing the 'Number of Students', and the y-axis labels range from '0.55' to '0.95', representing the 'Success Rate'. The line for 'AR Education' has been plotted with color '#d75faa' using a marker of 'o'." } ], "id_raw": 354 }, { "id": "S2KjWxwbumOTSXaE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_15.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the right axis in the chart with coordinates [0.584, 0.096, 0.980, 0.844]?" }, { "from": "gpt", "value": "The right axis represents the Trend of Progressivism Index." } ], "id_raw": 355 }, { "id": "1pbl5YlxPp4E7mwr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the label for the year 2005 on the right half of the chart?" }, { "from": "gpt", "value": "[0.673, 0.869, 0.712, 0.904]" } ], "id_raw": 355 }, { "id": "A6Ei4tZWCzyXuU24", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the {ylabel} at [0.503, 0.349, 0.523, 0.591] on the right subplot?" }, { "from": "gpt", "value": "Index Value." } ], "id_raw": 355 }, { "id": "KtT7f00RxrGinckT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_15.png", "conversations": [ { "from": "human", "value": "\nIs the {index value '0.55'} at [0.196, 0.470] higher or lower than the {index value '0.6'} at [0.286, 0.130] for the Conservatism Index?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 355 }, { "id": "SJiYlAtIYtckuWVr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_15.png", "conversations": [ { "from": "human", "value": "\nIn 2020, did the Progressivism Index {[0.962, 0.130]} score higher or lower than the Conservatism Index {[0.465, 0.334]}?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 355 }, { "id": "14rdepElrZ9IqwH9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_15.png", "conversations": [ { "from": "human", "value": "\nIs the {point representing the year 2015}[0.872, 0.300] higher or lower than the {point representing the year 2010}[0.782, 0.470] in the Progressivism Index?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 355 }, { "id": "cWFAPsDdQdghe38Y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_15.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the region with coordinates [0.131, 0.037, 0.440, 0.085]?" }, { "from": "gpt", "value": "This region contains the subtitle of the left subplot, which is \"Trend of Conservatism Index\", styled in black color." } ], "id_raw": 355 }, { "id": "BziMQFJryA8JrifE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the area with the coordinates [0.584, 0.096, 0.980, 0.844]." }, { "from": "gpt", "value": "This area describes the right subplot of the figure, it represents the trend of \"Progressivism Index\" over years with a line chart." } ], "id_raw": 355 }, { "id": "XcEos9GNYq9uTbxQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_15.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the region [0.627, 0.037, 0.937, 0.085]?" }, { "from": "gpt", "value": "The title \"Trend of Progressivism Index\" is displayed in this region, it's styled in black color and does not have any rotation." } ], "id_raw": 355 }, { "id": "OyYrGOf8P25kkOK4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.017, 0.422, 0.037, 0.518] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 356 }, { "id": "7JlKtNaoai59jgTV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the xtick at coordinates [0.269, 0.869, 0.308, 0.904]?" }, { "from": "gpt", "value": "\"2014\"." } ], "id_raw": 356 }, { "id": "ODNVJW7p89xDP87D", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_16.png", "conversations": [ { "from": "human", "value": "\nWhat is {the line} at [0.598, 0.130, 0.962, 0.810]?" }, { "from": "gpt", "value": "Economic growth rate." } ], "id_raw": 356 }, { "id": "3agbdD0ufXP1Ko61", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_16.png", "conversations": [ { "from": "human", "value": "\nWhich data line is {the point}[0.598, 0.595] part of, Unemployment or Economic Growth?" }, { "from": "gpt", "value": "Economic Growth." } ], "id_raw": 356 }, { "id": "rHV25bpCKzUzNMNc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_16.png", "conversations": [ { "from": "human", "value": "\nIs the {point at coordinate [0.901, 0.768]} on the unemployment line higher or lower than the {point at coordinate [0.901, 0.229]} on the economic growth?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 356 }, { "id": "4rCfXKA0wKkRz84R", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_16.png", "conversations": [ { "from": "human", "value": "\nIn 2020, which rate is higher, {Unemployment rate at [0.470, 0.364]} or {Economic Growth rate at [0.962, 0.744]}?" }, { "from": "gpt", "value": "Economic Growth rate." } ], "id_raw": 356 }, { "id": "fFxWPsTBkc80FZr8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.026, 0.869, 0.065, 0.904]." }, { "from": "gpt", "value": "It is a tick label on the x-axis, reading \"2006\"." } ], "id_raw": 356 }, { "id": "NRsxKy6IMVi0n57l", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.798, 0.689, 0.972, 0.752]." }, { "from": "gpt", "value": "This region contains the legend identifying the Unemployment line with a distinct color at {coordinates}[0.800, 0.710, 0.839, 0.727]." } ], "id_raw": 356 }, { "id": "qPsfJWG98cRdWXHx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_16.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.658, 0.711, 0.689, 0.644]?" }, { "from": "gpt", "value": "There are 2 objects. The x-axis tick label \"2010\", and a point from the Economic Growth line quantifying the growth rate for that year." } ], "id_raw": 356 }, { "id": "bAmbjjGjjo4JqGkz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend item with coordinates [0.826, 0.703, 0.969, 0.738] refer to?" }, { "from": "gpt", "value": "Density (log scale)." } ], "id_raw": 357 }, { "id": "3mjzgzIXNZDBbZYc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_17.png", "conversations": [ { "from": "human", "value": "\nWhat are the top left coordinates of the distance value of 400 on the x-axis?" }, { "from": "gpt", "value": "[0.274, 0.869]." } ], "id_raw": 357 }, { "id": "fZHTKS3DKE4wPOOy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {y-axis tick} at [0.043, 0.116, 0.072, 0.151]?" }, { "from": "gpt", "value": "550." } ], "id_raw": 357 }, { "id": "DnBjfgsoEbr2brBJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_17.png", "conversations": [ { "from": "human", "value": "\nAlong the 'Density' line, is the point at distance 200 from the city center higher or lower than the point at distance 800 from the city center?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 357 }, { "id": "PLxdG9qCSwYDQ1T7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_17.png", "conversations": [ { "from": "human", "value": "\nConsidering the 'Density (log scale)' line, is the density value at distance 100 from the city center higher or lower than that at distance 700 from the city center?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 357 }, { "id": "smSaT5um5e1ROnYr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_17.png", "conversations": [ { "from": "human", "value": "\nOn the 'Density' line, does the point at distance 100 have a higher or lower population density than the point at distance 400?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 357 }, { "id": "MThSvOxT9DyJfckM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region with coordinates [0.776, 0.689, 0.976, 0.752]." }, { "from": "gpt", "value": "This region contains the legend for the 'Density (log scale)' line. The legend item is colored in '#d75faa' and is accompanied by a marker of the same color." } ], "id_raw": 357 }, { "id": "aM2Hy7pD7O17EyYU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the region [0.097, 0.869, 0.106, 0.904]." }, { "from": "gpt", "value": "Within this region is the label '0' from the x-axis representing the distance from the city center." } ], "id_raw": 357 }, { "id": "AEJZFL0oA1IMJuos", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_17.png", "conversations": [ { "from": "human", "value": "\nWhat is within [0.043, 0.116, 0.072, 0.151]?" }, { "from": "gpt", "value": "Within these coordinates, there is a y-axis tick label indicating a population density of '550'." } ], "id_raw": 357 }, { "id": "Ez2R3eK5ekY79PJ3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_18.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.053, 0.221, 0.072, 0.256] on the y-axis represent?" }, { "from": "gpt", "value": "90." } ], "id_raw": 358 }, { "id": "skNrIMT3UsxuC2WT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_18.png", "conversations": [ { "from": "human", "value": "\nWhat is located at {coordinates}[0.273, 0.844] in the graph?" }, { "from": "gpt", "value": "Tick mark of '6' for the Time (Month)." } ], "id_raw": 358 }, { "id": "vu3S9IW27oPBGSX5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line marker}[0.753, 0.710, 0.791, 0.727]?" }, { "from": "gpt", "value": "Pink." } ], "id_raw": 358 }, { "id": "a2MsVoOJMcNTpcHU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_18.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher at {point 5} for Rate of Social Interaction{[0.238, 0.360]} or Self-satisfaction Index{[0.725, 0.598]}?" }, { "from": "gpt", "value": "Self-satisfaction Index." } ], "id_raw": 358 }, { "id": "jtsOWFFiAFX7envB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_18.png", "conversations": [ { "from": "human", "value": "\nIs {the term '12'}[0.478, 0.844] on the x-axis of Rate of Social Interaction subplot to the left or right of {the term '8'}[0.828, 0.844] on the Self-satisfaction Index subplot?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 358 }, { "id": "K5MvNCx2lFdBYPa9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_18.png", "conversations": [ { "from": "human", "value": "\nAre the values at {point 3}[0.170, 0.214] for the Rate of Social Interaction higher or lower than at {point 3}[0.657, 0.683] for the Self-satisfaction Index?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 358 }, { "id": "nNDQebhfTgfALFKm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the region [0.500, 0.080, 0.800, 0.200]." }, { "from": "gpt", "value": "The region contains the subplot title for the chart \"Self-satisfaction Index Over Time\" styled in black text." } ], "id_raw": 358 }, { "id": "fliAZQHiS3Wi0cWc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the features within the area [0.040, 0.840, 0.090, 0.910]." }, { "from": "gpt", "value": "This area includes multiple y-axis tick labels such as '90', '80', '70' along the left side of the Rate of Social Interaction chart, all styled in black." } ], "id_raw": 358 }, { "id": "av214pDtREpIpenA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_18.png", "conversations": [ { "from": "human", "value": "\nWhat does the space between [0.200, 0.800, 0.900, 0.950] contain?" }, { "from": "gpt", "value": "This section hosts the x-axis labels of 'Time (Month)' for both subplots, colored in black and ranging from values '0' to '12' for the Rate of Social Interaction and '0' to '14' for the Self-satisfaction Index." } ], "id_raw": 358 }, { "id": "ik9h1KuM578G85XA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label at coordinates [0.015, 0.285, 0.033, 0.667] in the subplot_0 represent?" }, { "from": "gpt", "value": "Number of Participants." } ], "id_raw": 359 }, { "id": "htbXj7C2uC3ujEea", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the information at [0.640, 0.102, 0.685, 0.130] in the subplot_1 legend?" }, { "from": "gpt", "value": "Online." } ], "id_raw": 359 }, { "id": "WhyQiKCn8CsR7KzE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_18_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the line plot at these coordinates [0.119, 0.769, 0.479, 0.839] in subplot_0?" }, { "from": "gpt", "value": "In-person." } ], "id_raw": 359 }, { "id": "bC4ZKm0VrWO4VDsy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_19.png", "conversations": [ { "from": "human", "value": "\nIn subplot_0, is the {plot labeled 'Online'}[0.119, 0.113, 0.479, 0.839] above or below the {plot labeled 'In-person'}[0.119, 0.769, 0.479, 0.839] in 2014?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 359 }, { "id": "SoidcCrIcTWvMB4c", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_19.png", "conversations": [ { "from": "human", "value": "\nIn subplot_1, which line plot is higher at year 2015: {Online certificates}[0.727, 0.699] or {In-person certificates}[0.727, 0.425]?" }, { "from": "gpt", "value": "Online certificates." } ], "id_raw": 359 }, { "id": "LkMmeRXTOUEynF3D", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_18_19.png", "conversations": [ { "from": "human", "value": "\nIs the {subplot_0 legend item 'Online'}[0.640, 0.102, 0.685, 0.130] plotted with the same color as the {subplot_1 legend item 'Online'}[0.640, 0.102, 0.685, 0.130]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 359 }, { "id": "El9opfDqHAYZvIYX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the region with coordinates [0.596, 0.091, 0.711, 0.183]." }, { "from": "gpt", "value": "This region contains the legend for subplot_1. It includes two entries labeled \"Online\" and \"In-person\", each paired with a colored marker." } ], "id_raw": 359 }, { "id": "HyhzdTi3HaLHvSyG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_19.png", "conversations": [ { "from": "human", "value": "\nWhat kind of information can I find in box [0.589, 0.077, 0.985, 0.875]?" }, { "from": "gpt", "value": "This region contains the second subplot showing the comparison of \"Online vs In-Person Certificates Issued\" over various years. It includes two line plots labeled \"Online\" and \"In-person\", an x-axis labeled \"Year\", and a y-axis labeled \"Number of Certificates Issued\"." } ], "id_raw": 359 }, { "id": "wxYmmNPlr0DoMKTy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_18_19.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the coordinates [0.261, 0.895, 0.297, 0.923] in subplot_0?" }, { "from": "gpt", "value": "This area contains the x-axis tick label '2016'." } ], "id_raw": 359 }, { "id": "aARyMk9oawt3F2qm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object labelled at point [0.146, 0.087, 0.206, 0.108] according to the legend?" }, { "from": "gpt", "value": "Population." } ], "id_raw": 360 }, { "id": "UKjaqVks0X0G0L1a", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis represent given its label coordinates [0.513, 0.927, 0.574, 0.963]?" }, { "from": "gpt", "value": "Years." } ], "id_raw": 360 }, { "id": "ejbgPnoDRlQIoapR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the y-axis label with coordinates [0.025, 0.169, 0.048, 0.736], what does the y-axis represent in the graph?" }, { "from": "gpt", "value": "Population and Technology Level." } ], "id_raw": 360 }, { "id": "mn67UVKcCmeSysRQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_0.png", "conversations": [ { "from": "human", "value": "\nAccording to their positions, does the {Population line}[0.170, 0.112, 0.917, 0.853] start before or after the {Technology line}[0.170, 0.372, 0.917, 0.854] in terms of Years?" }, { "from": "gpt", "value": "Both lines start at the same time." } ], "id_raw": 360 }, { "id": "GdnVJrw8rzR2M2Ty", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_0.png", "conversations": [ { "from": "human", "value": "\nIs the {Population point}[0.668, 0.427] at 6000 Years above or below the {Technology point}[0.668, 0.597]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 360 }, { "id": "QldSYaKJposXN24e", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_0.png", "conversations": [ { "from": "human", "value": "\nBetween 0 and 2500 Years, does the {Population line}[0.170, 0.112, 0.917, 0.853] increase at a faster or slower rate than the {Technology line}[0.170, 0.372, 0.917, 0.854]?" }, { "from": "gpt", "value": "Slower." } ], "id_raw": 360 }, { "id": "rIiZ9muL6J39LpSi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_0.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the region represented by [0.144, 0.069, 0.357, 0.184]?" }, { "from": "gpt", "value": "This region contains the legend of the chart which consists of two items - {Population}[0.146, 0.087, 0.206, 0.108] and {Technology}[0.146, 0.140, 0.206, 0.161], each with corresponding color markers." } ], "id_raw": 360 }, { "id": "X9BnYgFmNlHXzfLx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is presented in the section with coordinates [0.132, 0.051, 0.954, 0.854]?" }, { "from": "gpt", "value": "This region includes the main area of the plot, which depicts two lines - {Population line}[0.170, 0.112, 0.917, 0.853] and {Technology line}[0.170, 0.372, 0.917, 0.854], representing population and technology level over the years, respectively." } ], "id_raw": 360 }, { "id": "TT0SrBDjKh6guH01", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_0.png", "conversations": [ { "from": "human", "value": "\nWhat's described in the area marked by [0.172, 0.879, 0.250, 0.914]?" }, { "from": "gpt", "value": "The area contains the x-axis label '{-5000}'[0.172, 0.879, 0.250, 0.914], representing the year -5000" } ], "id_raw": 360 }, { "id": "yIiTY0FI1Z5V6Aeg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the description of the object located at coordinates [0.669, 0.121, 0.824, 0.156]?" }, { "from": "gpt", "value": "It's the label for Dropout Rate in the legend." } ], "id_raw": 361 }, { "id": "oQfOuJHN5aqs8Iuf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the data at the point {coordinates}[0.671, 0.179]?" }, { "from": "gpt", "value": "It represents data for the year 2018 where the Student Enrollment Rate was 75%." } ], "id_raw": 361 }, { "id": "Ru05u8dwL3JaiMSp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label that exists at [0.509, 0.927, 0.570, 0.963]?" }, { "from": "gpt", "value": "The x-axis label 'Years'." } ], "id_raw": 361 }, { "id": "Jw0RC7wN4K4ZMJI5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_1.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {the point}[0.319, 0.106], {the Student Enrollment Rate line}[0.143, 0.075, 0.935, 0.232] or {the Dropout Rate line}[0.143, 0.650, 0.935, 0.817] ?" }, { "from": "gpt", "value": "The Student Enrollment Rate line." } ], "id_raw": 361 }, { "id": "MvZJeVQagIyk3HZf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_1.png", "conversations": [ { "from": "human", "value": "\nAre the values for Student Enrollment Rate and Dropout Rate at {point coordinates}[0.935, 0.232] and [0.935, 0.650] respectively, increasing or decreasing?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 361 }, { "id": "4DfCFfNqSkDT6Pgj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_1.png", "conversations": [ { "from": "human", "value": "\nAt the last data point, does the {the Student Enrollment Rate line}[0.935, 0.232] have a higher or lower value than the {Dropout Rate line}[0.935, 0.650]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 361 }, { "id": "CGixBDCKvBipIAZw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_1.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area bounded by [0.595, 0.055, 0.963, 0.170]?" }, { "from": "gpt", "value": "It contains the legend for the chart, which includes labels for 'Student Enrollment Rate' and 'Dropout Rate', each with their respective markers." } ], "id_raw": 361 }, { "id": "3rKoZm8uw9U6HPzX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the details within the coordinates [0.103, 0.038, 0.975, 0.854]." }, { "from": "gpt", "value": "It's the main plotting area of the chart. It contains two lines, one for the 'Student Enrollment Rate' and the other for the 'Dropout Rate'. Each line has ten points corresponding to the years from 2012 to 2021. The x-axis and y-axis labels, tick labels and gridlines are also within this area." } ], "id_raw": 361 }, { "id": "FY0BysiJYWC08HKY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the content between coordinates [0.058, 0.009, 0.087, 0.044]?" }, { "from": "gpt", "value": "the y-axis tick label '90'" } ], "id_raw": 361 }, { "id": "T9yTrJlxrmLiivhj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label}[0.842, 0.879, 0.857, 0.914]?" }, { "from": "gpt", "value": "8." } ], "id_raw": 362 }, { "id": "TEXwTjU1EqLglDVb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object found at the coordinates [0.665, 0.069, 0.963, 0.184]?" }, { "from": "gpt", "value": "The sublegend." } ], "id_raw": 362 }, { "id": "wmgdFd8RnptbuYOU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label}[0.072, 0.573, 0.102, 0.608]?" }, { "from": "gpt", "value": "40." } ], "id_raw": 362 }, { "id": "xMaMzhsRfbhMvgGt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_2.png", "conversations": [ { "from": "human", "value": "\nIs {the Intrusion Attempts point at 5 months}[0.590, 0.419] higher or lower than the {Data Breaches point at 5 months}[0.590, 0.687]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 362 }, { "id": "iiTMMmeysbY9uLi8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_2.png", "conversations": [ { "from": "human", "value": "\nFor point 2, does {Intrusion Attempts}[0.330, 0.319] have more instances than {Data Breaches}[0.330, 0.620]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 362 }, { "id": "cGjYP9sYOJdKCPEA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_2.png", "conversations": [ { "from": "human", "value": "\nAt point 9, which has more instances {Intrusion Attempts}[0.936, 0.587] or {Data Breaches}[0.936, 0.741]?" }, { "from": "gpt", "value": "Data Breaches." } ], "id_raw": 362 }, { "id": "XtlAXQ3BbhwI7iRf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the content found in the region [0.665, 0.069, 0.963, 0.184]?" }, { "from": "gpt", "value": "This region contains the sublegend with two items: {Intrusion Attempts}[0.739, 0.083, 0.954, 0.118] and {Data Breaches}[0.739, 0.135, 0.909, 0.170]. Each legend item is accompanied by a color marker." } ], "id_raw": 362 }, { "id": "zWuG35jUOoczvHfT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.025, 0.370, 0.048, 0.535] entail?" }, { "from": "gpt", "value": "It includes the Y-axis label, which says {Instances}[0.025, 0.370, 0.048, 0.535]." } ], "id_raw": 362 }, { "id": "uRwn2Esvb7PsQl1H", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within the region [0.149, 0.879, 0.164, 0.914]?" }, { "from": "gpt", "value": "There is 1 object in this area. It is the x-axis tick label indicating the '0' time point." } ], "id_raw": 362 }, { "id": "97l6itWN5wmAk8RO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label in the given chart?" }, { "from": "gpt", "value": "The Y-axis label is {Values}[0.025, 0.389, 0.048, 0.502]." } ], "id_raw": 365 }, { "id": "7OUHVJLPDywT44Ju", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_5.png", "conversations": [ { "from": "human", "value": "\nWhich label is present at the coordinates [0.591, 0.879, 0.649, 0.914]?" }, { "from": "gpt", "value": "The label present at these coordinates is {the year '2014'}[0.591, 0.879, 0.649, 0.914]." } ], "id_raw": 365 }, { "id": "jYsVvoKblZoZDCIK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_5.png", "conversations": [ { "from": "human", "value": "\nWhat element in the legend is located at coordinates [0.463, 0.683, 0.661, 0.718]?" }, { "from": "gpt", "value": "It's the {Real GDP Growth item}[0.463, 0.683, 0.661, 0.718]." } ], "id_raw": 365 }, { "id": "NLewhkhDw8VDyX4u", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_5.png", "conversations": [ { "from": "human", "value": "\nIn the year 2008, was {the Real GDP Growth}[0.305, 0.524] higher or lower than {the Earnings Per Share}[0.305, 0.265]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 365 }, { "id": "Y77gFHHNSQSd8Oqr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_5.png", "conversations": [ { "from": "human", "value": "\nComparing the end points, did {Dividend Per Share}[0.936, 0.170] increase or decrease more than {the Real GDP Growth}[0.936, 0.817] from 2005 to 2020?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 365 }, { "id": "8xmn4Yfir7lydoZJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_5.png", "conversations": [ { "from": "human", "value": "\nIn the year 2019, was {Earnings Per Share}[0.883, 0.109] higher or lower than {Dividend Per Share}[0.883, 0.204]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 365 }, { "id": "TzSguMPzb4QUy7Od", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within coordinates [0.108, 0.037, 0.975, 0.854]." }, { "from": "gpt", "value": "This area represents the main chart area which includes the plots for {Real GDP Growth}[0.147, 0.265, 0.936, 0.817], {Earnings Per Share}[0.147, 0.075, 0.936, 0.394], and {Dividend Per Share}[0.147, 0.170, 0.936, 0.411], along with x-axis and y-axis labels, and tick labels." } ], "id_raw": 365 }, { "id": "Tl9wSg5BARl6GLp3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content located in the area [0.389, 0.669, 0.694, 0.837]." }, { "from": "gpt", "value": "The specific region includes the chart's {legend}[0.389, 0.669, 0.694, 0.837] detailing the meaning of each plot line color, including {Real GDP Growth}[0.463, 0.683, 0.661, 0.718], {Earnings Per Share}[0.463, 0.736, 0.682, 0.771], and {Dividend Per Share}[0.463, 0.788, 0.684, 0.823]." } ], "id_raw": 365 }, { "id": "1GwfKfMdgv7CMe4y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_5.png", "conversations": [ { "from": "human", "value": "\nWhat content is found within the coordinates [0.381, 0.879, 0.439, 0.914]?" }, { "from": "gpt", "value": "The {year 2010}[0.381, 0.879, 0.439, 0.914] x-axis tick label is found within these coordinates." } ], "id_raw": 365 }, { "id": "GCKRM2vnFdXlAHzX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the Y-axis at these coordinates [0.019, 0.305, 0.041, 0.655]?" }, { "from": "gpt", "value": "Energy Production (GWh)." } ], "id_raw": 366 }, { "id": "Xx4JeriK7sMG5Qyk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at [0.794, 0.120, 0.966, 0.143] within the chart legend?" }, { "from": "gpt", "value": "Hydro Conventional." } ], "id_raw": 366 }, { "id": "chF5LCcYGBYznQ2D", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_6.png", "conversations": [ { "from": "human", "value": "\nWhat is being depicted by {this line}[0.144, 0.102, 0.941, 0.761]?" }, { "from": "gpt", "value": "Hydro Conventional power production trend over time." } ], "id_raw": 366 }, { "id": "LMkqLrvrsZKwlHQa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_6.png", "conversations": [ { "from": "human", "value": "\nAt the year 2035, is {hydro power energy production}[0.587, 0.723] higher or lower than the {hydro conventional energy production}[0.587, 0.490]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 366 }, { "id": "o1ahZIyzPHls4Gkc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_6.png", "conversations": [ { "from": "human", "value": "\nWhich has a larger energy production in the year 2030, {hydro power}[0.144, 0.179] or {hydro conventional}[0.144, 0.102]?" }, { "from": "gpt", "value": "Hydro Conventional." } ], "id_raw": 366 }, { "id": "c2A0msbdX6PD1yRn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_6.png", "conversations": [ { "from": "human", "value": "\nIs the {hydro power energy production}[0.233, 0.296] in 2031 lower or higher than that of 2030[0.144, 0.179]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 366 }, { "id": "lBFQdimMDWXmfEdO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.048, 0.073, 0.092, 0.096]" }, { "from": "gpt", "value": "This region contains the Y-axis tick label '8250'." } ], "id_raw": 366 }, { "id": "hXq2MBg0MjIqIEfw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements contained in the area [0.738, 0.075, 0.973, 0.152]" }, { "from": "gpt", "value": "This area contains a legend with two entries: {Hydro Power}[0.794, 0.085, 0.904, 0.108] marked in blue, and {Hydro Conventional}[0.794, 0.120, 0.966, 0.143] in orange." } ], "id_raw": 366 }, { "id": "m7I1opTDawSwHUHm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_6.png", "conversations": [ { "from": "human", "value": "\nWhat features are contained within the chart at this coordinate region [0.477, 0.912, 0.520, 0.936]?" }, { "from": "gpt", "value": "The X-axis tick label '2034' is located in this region." } ], "id_raw": 366 }, { "id": "9EKW2u52fpj3lbwO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object identified by the coordinates [0.019, 0.409, 0.036, 0.529]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 367 }, { "id": "a5SkWoTaYFMdTDEz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the marker associated with {Market Risk}[0.088, 0.163, 0.133, 0.177] in the legend?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 367 }, { "id": "e0IGwogFJ7acP8Ju", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_7.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the points {line_1}[0.118, 0.382, 0.940, 0.764]?" }, { "from": "gpt", "value": "Compliance Risk." } ], "id_raw": 367 }, { "id": "TCtemSQxW5Y6WPpk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Credit Risk' at Time 4}[0.484, 0.660] above or below {the point for 'Market Risk' at Time 4}[0.484, 0.173]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 367 }, { "id": "2qbIYXVfM2dAonoI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_7.png", "conversations": [ { "from": "human", "value": "\nIs {the line representing 'Compliance Risk'}[0.118, 0.382, 0.940, 0.764] generally above or below {the line for 'Operational Risk'}[0.118, 0.469, 0.940, 0.642]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 367 }, { "id": "81hQAal5KT5z2rwj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_7.png", "conversations": [ { "from": "human", "value": "\nWhich Risk has a higher score at Time 9, {Operational Risk}[0.940, 0.469] or {Compliance Risk}[0.940, 0.382]?" }, { "from": "gpt", "value": "Operational Risk." } ], "id_raw": 367 }, { "id": "iFsgdAYPGdOM5we5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the coordinates [0.086, 0.046, 0.292, 0.192]?" }, { "from": "gpt", "value": "This area contains the legend for the plots. It includes markers and corresponding labels for Operational Risk, Compliance Risk, Credit Risk, and Market Risk." } ], "id_raw": 367 }, { "id": "uT200ApWq8usZl7P", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area within the coordinates [0.019, 0.409, 0.036, 0.529]." }, { "from": "gpt", "value": "This is the vertical, y-axis label area, labeled as 'Risk Score'." } ], "id_raw": 367 }, { "id": "EYtYMHjAnQL8j2Lo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the range [0.301, 0.573, 0.392, 0.555]?" }, { "from": "gpt", "value": "This region includes the third point on the Operational Risk line and the third point on the Compliance Risk line." } ], "id_raw": 367 }, { "id": "TosTMQluqtnGYjtq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_8.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the first tick on the X-axis?" }, { "from": "gpt", "value": "[0.017, 0.919, 0.061, 0.942]" } ], "id_raw": 368 }, { "id": "FNRMv0DtJ2vrfUvE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the Y-axis label}[0.019, 0.406, 0.036, 0.522]?" }, { "from": "gpt", "value": "Popularity." } ], "id_raw": 368 }, { "id": "EZMjhwyemq2wQ1Fm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the point}[0.670, 0.247] on the 'Streetwear' line represents?" }, { "from": "gpt", "value": "The data for the year 2011 with a Popularity value of 835." } ], "id_raw": 368 }, { "id": "DXhY6JCi68sIcWQr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_8.png", "conversations": [ { "from": "human", "value": "\nAt {the point}[0.309, 0.304], is the 'Streetwear' popularity higher or lower than the 'Haute Couture' at {the point}[0.309, 0.490]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 368 }, { "id": "jtzj6AALLmxRqTiZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_8.png", "conversations": [ { "from": "human", "value": "\nIs the {point}[0.850, 0.226] on the 'Streetwear' trend line towards the end of the chart higher or lower than the {point}[0.850, 0.763] on the 'Haute Couture' trend line?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 368 }, { "id": "vycBEHHZxaSVwSIq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_8.png", "conversations": [ { "from": "human", "value": "\nWhich trend has a higher value at the beginning of the data, {Haute Couture at point}[0.129, 0.077] or {Streetwear at point}[0.129, 0.386]?" }, { "from": "gpt", "value": "Haute Couture." } ], "id_raw": 368 }, { "id": "HmLbU5PHirEdVJec", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the region [0.783, 0.037, 0.973, 0.113]." }, { "from": "gpt", "value": "This area contains the legend items: two line labels, {Haute Couture}[0.839, 0.046, 0.966, 0.069] in blue and {Streetwear}[0.839, 0.081, 0.935, 0.104] in orange. Each label is paired with a color indicator." } ], "id_raw": 368 }, { "id": "eIJJVDbm1wJLOlJk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area {here}[0.516, 0.952, 0.553, 0.975]." }, { "from": "gpt", "value": "This area contains the X-axis label, which is 'Year'." } ], "id_raw": 368 }, { "id": "vH00r0n70TOPMBta", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_8.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region with the coordinates [0.558, 0.919, 0.602, 0.942]?" }, { "from": "gpt", "value": "The X-axis tick label '2010' is found in this region." } ], "id_raw": 368 }, { "id": "Tvh2QoKEO8yO3LZO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_10.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.772, 0.085, 0.932, 0.108]?" }, { "from": "gpt", "value": "The label \"Employee Training\"." } ], "id_raw": 370 }, { "id": "wf9JsFQNlU78121d", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_10.png", "conversations": [ { "from": "human", "value": "\nWhat is present at the coordinates [0.019, 0.292, 0.036, 0.675]?" }, { "from": "gpt", "value": "The Y-axis label, \"Employee Training & Satisfaction\"." } ], "id_raw": 370 }, { "id": "Ayhtm9UvtOmq4dWK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object having coordinate [0.488, 0.952, 0.571, 0.975]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 370 }, { "id": "3wyNlxuJdITOL7ql", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_10.png", "conversations": [ { "from": "human", "value": "\nIs the point {[0.210, 0.623]} larger or smaller than the point {[0.665, 0.413]} on 'Employee Training' line?" }, { "from": "gpt", "value": "Smaller." } ], "id_raw": 370 }, { "id": "bpyjskH0GDw3mHuV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_10.png", "conversations": [ { "from": "human", "value": "\nIs the value marked at coordinate {[0.577, 0.344]} on 'Employee Training' line higher or lower than the point labeled at coordinate {[0.666, 0.441]} on 'Employee Satisfaction' line?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 370 }, { "id": "NNVjkynkElwDQ8C8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_10.png", "conversations": [ { "from": "human", "value": "\nAt coordinate {[0.940, 0.483]}, is 'Employee Training' or 'Employee Satisfaction' having higher value?" }, { "from": "gpt", "value": "Employee Satisfaction." } ], "id_raw": 370 }, { "id": "F6jxGyIWTQ19OIdP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the region [0.716, 0.075, 0.973, 0.152]?" }, { "from": "gpt", "value": "The region includes the legend detailing the labels and colors representing \"Employee Training\" at coordinate {handle}[0.718, 0.088, 0.763, 0.102] and \"Employee Satisfaction\" at coordinate {handle}[0.718, 0.123, 0.763, 0.137]." } ], "id_raw": 370 }, { "id": "68WSpeURKmGdBbnJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_10.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed within the area defined by the coordinates [0.278, 0.025, 0.781, 0.057]?" }, { "from": "gpt", "value": "The subplot title \"Career & Workplace Development Trends\" is located within these coordinates." } ], "id_raw": 370 }, { "id": "xv8C2rWSbwnWlDLn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_10.png", "conversations": [ { "from": "human", "value": "\nDescribe what is present in the region bounded by [0.118, 0.190, 0.940, 0.763]." }, { "from": "gpt", "value": "This region contains the line chart for \"Employee Training\". The plotted markers representing various iterations are present in this area along with their respective values." } ], "id_raw": 370 }, { "id": "TzCsGILr8hQ4tlQF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_12.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the label is for the object found at the coordinates, [0.840, 0.055, 0.878, 0.078] in the chart?" }, { "from": "gpt", "value": "Tech." } ], "id_raw": 372 }, { "id": "hqp7kKBOZqcropYf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_12.png", "conversations": [ { "from": "human", "value": "\nCan you identify the {ylabel} in the chart from the provided coordinates [0.019, 0.345, 0.041, 0.585]?" }, { "from": "gpt", "value": "Market Share (%)." } ], "id_raw": 372 }, { "id": "Gyl4h37Rz2DMKhLA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_12.png", "conversations": [ { "from": "human", "value": "\nAre you able to identify {the line labeled 'Tech'} from its starting coordinates [0.123, 0.322] to its ending coordinates [0.940, 0.753] on the chart?" }, { "from": "gpt", "value": "Yes, it is a line graph representing the trend of Tech from 2005 to 2014." } ], "id_raw": 372 }, { "id": "zOciSmifYEa4soFx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Tech' in 2007}[0.305, 0.753] higher or lower than {its point in 2008}[0.396, 0.523]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 372 }, { "id": "H00geqHFxl8mAsjH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_12.png", "conversations": [ { "from": "human", "value": "\nIn comparison with {the point for 'Manufacturing' in 2010}[0.577, 0.235], is {the point for 'Tech' in 2010}[0.577, 0.379] higher or lower?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 372 }, { "id": "ImVeFl7t8FxX1WP5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_12.png", "conversations": [ { "from": "human", "value": "\nWhich sector had a higher market share in 2014, {Tech}[0.940, 0.322] or {Manufacturing}[0.940, 0.322]?" }, { "from": "gpt", "value": "They were equal." } ], "id_raw": 372 }, { "id": "De8gUHtaG4ZSiVx7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_12.png", "conversations": [ { "from": "human", "value": "\nCould you describe the contents within the region [0.784, 0.046, 0.973, 0.123]?" }, { "from": "gpt", "value": "This area serves as the legend for the chart including two items: {Tech}[0.840, 0.055, 0.878, 0.078] indicated by the color '#1f77b4' and {Manufacturing}[0.840, 0.090, 0.966, 0.113] indicated by the color '#ff7f0e'." } ], "id_raw": 372 }, { "id": "vkSQ0SO7gNO5pT4m", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.048, 0.312, 0.070, 0.336] in the chart contain?" }, { "from": "gpt", "value": "The region contains the y-axis tick label '30'." } ], "id_raw": 372 }, { "id": "n9UKIGgGVrTpL0vd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects found within the region [0.396, 0.523, 0.486, 0.695]?" }, { "from": "gpt", "value": "This region captures two data points for the year 2008: a lower {Tech}[0.396, 0.523] value and a higher {Manufacturing}[0.396, 0.695] value." } ], "id_raw": 372 }, { "id": "4FaUqjiuvSa5A8tl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at coordinates [0.122, 0.046, 0.299, 0.069] refer to in the chart legend?" }, { "from": "gpt", "value": "Innovation Patent Growth." } ], "id_raw": 373 }, { "id": "dviKzU5v7O7DpEiB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_13.png", "conversations": [ { "from": "human", "value": "\nWhat is identified by the coordinates [0.510, 0.952, 0.546, 0.975] on the x-axis?" }, { "from": "gpt", "value": "The label \"Years\"." } ], "id_raw": 373 }, { "id": "1t0rV4U241HhMcoF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object {at these coordinates}[0.035, 0.179, 0.061, 0.202] on the y-axis?" }, { "from": "gpt", "value": "It is the y-axis tick label '225'." } ], "id_raw": 373 }, { "id": "OlFzU3WKX3ng7Lht", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_13.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher {at year 2005}[0.528, 0.372], the line representing 'Innovation Patent Growth' or 'Intellectual Property Cases'?" }, { "from": "gpt", "value": "Innovation Patent Growth." } ], "id_raw": 373 }, { "id": "Aq3ohF9iQGyFCUHA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_13.png", "conversations": [ { "from": "human", "value": "\nDoes the line representing {the 'Innovation Patent Growth'}[0.112, 0.045, 0.943, 0.719] cross above or below the line representing {the 'Intellectual Property Cases'}[0.112, 0.045, 0.943, 0.903]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 373 }, { "id": "sTtgslIlNLWzU5wk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_13.png", "conversations": [ { "from": "human", "value": "\nWhich line dips below the other at {the year 2000}[0.389, 0.544], 'Innovation Patent Growth' or 'Intellectual Property Cases'?" }, { "from": "gpt", "value": "Intellectual Property Cases." } ], "id_raw": 373 }, { "id": "tGfZU2vQ1c4GLBAQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region from [0.078, 0.037, 0.314, 0.113]?" }, { "from": "gpt", "value": "This region contains the legend for the chart, listing out the two lines present in the chart: {Innovation Patent Growth}[0.122, 0.046, 0.299, 0.069] with a color marker at {coordinates}[0.079, 0.049, 0.115, 0.063] and {Intellectual Property Cases}[0.122, 0.081, 0.308, 0.104] with a color marker at {coordinates}[0.079, 0.084, 0.115, 0.098]." } ], "id_raw": 373 }, { "id": "CwzHWDpzmdStEuD2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the region [0.372, 0.919, 0.407, 0.942]?" }, { "from": "gpt", "value": "The region contains the x-axis tick label for the {year 2000}[0.372, 0.919, 0.407, 0.942]." } ], "id_raw": 373 }, { "id": "7uvdohSVYoINp0Uf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_13.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the region from [0.043, 0.894, 0.061, 0.917]?" }, { "from": "gpt", "value": "It is the y-axis tick label for the {value 50}[0.043, 0.894, 0.061, 0.917]." } ], "id_raw": 373 }, { "id": "mdmu0NT9IlsrhtVr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the object in the chart at [0.109, 0.085, 0.182, 0.108]?" }, { "from": "gpt", "value": "The text labelled 'Population' in the chart's legend." } ], "id_raw": 376 }, { "id": "i15JKEFQdWYoXMbw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third x-axis tick label on the chart?" }, { "from": "gpt", "value": "[0.186, 0.912, 0.234, 0.936]" } ], "id_raw": 376 }, { "id": "zx1Kzd7GgnxhGlGE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_16.png", "conversations": [ { "from": "human", "value": "\nWhat does {the ylabel} at [0.015, 0.301, 0.033, 0.659] represent?" }, { "from": "gpt", "value": "The label for both Population and Inflation Rate." } ], "id_raw": 376 }, { "id": "dkExdrETnDwalz98", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_16.png", "conversations": [ { "from": "human", "value": "\nWhich series is represented by the line with coordinates [0.543, 0.709, 0.721, 0.709]?" }, { "from": "gpt", "value": "Inflation Rate." } ], "id_raw": 376 }, { "id": "VHM3vW1TBWZYVYmD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_16.png", "conversations": [ { "from": "human", "value": "\nDoes the {value of Population}[0.366, 0.223] in year 2006 exceed the {Inflation Rate}[0.366, 0.551]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 376 }, { "id": "zJvkjFWWgvSNnvRD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_16.png", "conversations": [ { "from": "human", "value": "\nIs the {Population series}[0.065, 0.088, 0.102, 0.102] represented by the same color as the {Inflation Rate series}[0.065, 0.123, 0.102, 0.137]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 376 }, { "id": "wg0OgdYZicoInB6P", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_16.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content located in [0.065, 0.075, 0.207, 0.152]?" }, { "from": "gpt", "value": "This area contains the legend for the chart. It includes entries for the Population and Inflation Rate trends, each paired with a distinct colored marker." } ], "id_raw": 376 }, { "id": "1kM8SP59L02t3dXB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_16.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area [0.282, 0.025, 0.760, 0.057]?" }, { "from": "gpt", "value": "The subtitle of the chart, \"Population and Inflation Rate Trends (2000-2019)\", can be found in this area." } ], "id_raw": 376 }, { "id": "9pLGVEGsBxrOV96O", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_16.png", "conversations": [ { "from": "human", "value": "\nWhat is seen in the region [0.076, 0.912, 0.123, 0.936]?" }, { "from": "gpt", "value": "In this region, there is an x-axis tick label of '2000.0'." } ], "id_raw": 376 }, { "id": "vGvccsMnbt3WB6rU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the label {coordinate} at [0.023, 0.373, 0.045, 0.583] represent?" }, { "from": "gpt", "value": "Stock Price ($)." } ], "id_raw": 377 }, { "id": "rUlE73jUU3rSQDEf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the status of the object located at {coordinates}[0.146, 0.114, 0.854, 0.842]?" }, { "from": "gpt", "value": "A line graph representing the Stock Price trend over Days." } ], "id_raw": 377 }, { "id": "2DyLBnG3G54gr8lB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_19_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the tick label at coordinate {location}[0.769, 0.899, 0.782, 0.928]?" }, { "from": "gpt", "value": "9." } ], "id_raw": 377 }, { "id": "ElN4EAYdKtp5mrae", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_17.png", "conversations": [ { "from": "human", "value": "\nAt {Day 6} [0.539, 0.899, 0.546, 0.928], is {the stock price} [0.539, 0.438] higher or lower than it was at {Day 1} [0.146, 0.899, 0.153, 0.928]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 377 }, { "id": "5BOZGMGBhOvDuRsd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_17.png", "conversations": [ { "from": "human", "value": "\nOn the last Day, {Day 10} [0.840, 0.899, 0.868, 0.928], is the {Stock Price} [0.854, 0.114] higher or lower compared to {Day 5} [0.454, 0.899, 0.468, 0.928]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 377 }, { "id": "3u6pCBtOBZGuj55f", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_19_17.png", "conversations": [ { "from": "human", "value": "\nIs the 'Stock Price' higher on {Day 5} [0.461, 0.519] or {Day 1} [0.139, 0.899, 0.153, 0.928]?" }, { "from": "gpt", "value": "Day 5." } ], "id_raw": 377 }, { "id": "KOZLgyHvR73AeN6k", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements present within the region [0.139, 0.899, 0.310, 0.928]." }, { "from": "gpt", "value": "There are four X-axis labels located within this region. They correspond to the days: '1', '2', '3', and '4' located at coordinates {location}[0.139, 0.899, 0.153, 0.928], {location}[0.218, 0.899, 0.232, 0.928], {location}[0.297, 0.899, 0.310, 0.928] and {location}[0.375, 0.899, 0.389, 0.928] respectively." } ], "id_raw": 377 }, { "id": "ejKoQ2sJ7VSJgZDB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content present in the region [0.111, 0.078, 0.890, 0.879]." }, { "from": "gpt", "value": "The main region of the graph is here, comprising the data points indicating Stock Price trends over the course of several days including lines and tick labels. This also includes X and Y axis labels - 'Day' and 'Stock Price ($)'." } ], "id_raw": 377 }, { "id": "ABk7SKJ2bpHHtSPk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_19_17.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area [0.139, 0.899, 0.782, 0.928]?" }, { "from": "gpt", "value": "This area contains all the tick labels from '1' to '9' for the x-axis corresponding to Days." } ], "id_raw": 377 }, { "id": "p5etOKe0maumQPW9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_0.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {this line}[0.056, 0.239, 0.976, 0.471] in the chart?" }, { "from": "gpt", "value": "This line represents the performance trend of 'Running'." } ], "id_raw": 380 }, { "id": "LNOKc0DyfQkyOgem", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_0.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the text at {the coordinates}[0.107, 0.108, 0.164, 0.136] says?" }, { "from": "gpt", "value": "The text is 'Running'." } ], "id_raw": 380 }, { "id": "DICaHP7nbO8r4qz8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.015, 0.393, 0.034, 0.640] represent?" }, { "from": "gpt", "value": "This is the label for the y-axis with the name 'Performance'." } ], "id_raw": 380 }, { "id": "PggGFtUdwSiSw1t1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_0.png", "conversations": [ { "from": "human", "value": "\nAre the performance trends of the activities {Running}[0.056, 0.239, 0.976, 0.471] and {Zumba}[0.056, 0.568, 0.976, 0.704] increasing or decreasing?" }, { "from": "gpt", "value": "Both are increasing over time." } ], "id_raw": 380 }, { "id": "k4bPvbQ5T2skKgkN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_0.png", "conversations": [ { "from": "human", "value": "\nAt month 10, is the performance of {Running}[0.809, 0.276] better or worse than {Zumba}[0.809, 0.596]?" }, { "from": "gpt", "value": "Running performance is better." } ], "id_raw": 380 }, { "id": "L3hjCjRvcPyeSXy4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_0.png", "conversations": [ { "from": "human", "value": "\nWhich athletic activity is performed better at {the 12th month}[0.976, 0.239] for Running and [0.976, 0.568] for Zumba?" }, { "from": "gpt", "value": "Running." } ], "id_raw": 380 }, { "id": "raDotS4cy6KqKBFV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within this region: [0.063, 0.097, 0.286, 0.148]." }, { "from": "gpt", "value": "This area is the legend, which contains the labels for the two lines on the chart, namely 'Running' with color code '#ff00ff' and 'Zumba' with color code '#ffd700'." } ], "id_raw": 380 }, { "id": "StZS4lSQKWDIG285", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is present within this coordinate sector: [0.052, 0.893, 0.985, 0.921]?" }, { "from": "gpt", "value": "This area contains the labels for the x-axis ticks from 1 to 12, representing the months of the year." } ], "id_raw": 380 }, { "id": "pRAj4RZ1TgWw0vDf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_0.png", "conversations": [ { "from": "human", "value": "\nWhat do you notice about the items inside the frame [0.056, 0.156, 0.976, 0.878]?" }, { "from": "gpt", "value": "In this region, you can see the plotted data points and trend lines of 'Running' and 'Zumba' along with the grid lines and y-axis labels. This is the main area of the chart." } ], "id_raw": 380 }, { "id": "cOTD2Tcf6DuCc4fW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the xlabel} at [0.480, 0.940, 0.533, 0.969] in the first subplot?" }, { "from": "gpt", "value": "Time." } ], "id_raw": 381 }, { "id": "4zLkYj3A5pE9hAtA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_1.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what {the ylabel} at [0.955, 0.306, 0.977, 0.604] is in the second subplot?" }, { "from": "gpt", "value": "Unemployment Rate." } ], "id_raw": 381 }, { "id": "nz9MxGCZxdBzYCEh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the {subplot title} at [0.225, -0.104, 0.788, -0.063]?" }, { "from": "gpt", "value": "Economic Indicator Presentation." } ], "id_raw": 381 }, { "id": "BDohaTJWiAminNy1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_1.png", "conversations": [ { "from": "human", "value": "\nIs the {Unemployment Rate at '2012-10'}[0.346, 0.441] higher or lower than the {Unemployment Rate at '2013-03'}[0.668, 0.840]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 381 }, { "id": "lBNwseZBfiBTergy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_1.png", "conversations": [ { "from": "human", "value": "\nDoes the {Consumer Confidence Index at '2012-11'}[0.410, 0.370] have a higher or lower value than the {Consumer Confidence Index at '2012-07'}[0.153, 0.840]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 381 }, { "id": "YSPrOPe0jV7GVMxp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_1.png", "conversations": [ { "from": "human", "value": "\nDoes the {Consumer Confidence Index at '2013-06'}[0.861, 0.070] have a higher or lower value compared to its value at {Consumer Confidence Index at '2013-01'}[0.539, 0.303]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 381 }, { "id": "1KPgeH6lMS9tSLjJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the area [0.117, 0.031, 0.896, 0.879] of subplot_0." }, { "from": "gpt", "value": "The first subplot contains the x-axis labels with time stamps from '2012-07' to '2013-06', and the line representing the Consumer Confidence Index which rises from 92.38 to 96.02 over this period." } ], "id_raw": 381 }, { "id": "kUg20s3Ve6LwSanI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_1.png", "conversations": [ { "from": "human", "value": "\nCan you give me a brief description of the region [0.117, 0.031, 0.896, 0.879] in subplot_1?" }, { "from": "gpt", "value": "This region in the second subplot contains x-axis labels showcasing the timeline from '2012-07' to '2013-06', along with a line representing the Unemployment Rate that fluctuates from 4.85 to 4.03 over this period." } ], "id_raw": 381 }, { "id": "ZIsVUHjeSMs0ucgK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.108, 0.899, 0.906, 0.928]." }, { "from": "gpt", "value": "This area contains the x-axis labels that mark the timeline from '2012-07' to '2013-06' in both subplots." } ], "id_raw": 381 }, { "id": "Ojlz51xqVLhfNzU9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the element at coordinates [0.013, 0.477, 0.028, 0.564]?" }, { "from": "gpt", "value": "It is the y-axis label." } ], "id_raw": 382 }, { "id": "uAZBG2LJ4My0I2V0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the element at coordinates [0.819, 0.092, 0.848, 0.104]?" }, { "from": "gpt", "value": "It's a color handle in the legend corresponding to the 'Vanilla Sales Trend'." } ], "id_raw": 382 }, { "id": "IPwS0FxolKCNDa31", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_2.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about {the x-axis tick}[0.153, 0.898]?" }, { "from": "gpt", "value": "It corresponds to the Year 2010." } ], "id_raw": 382 }, { "id": "Pkt5iIYyX8Jvvs1y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_2.png", "conversations": [ { "from": "human", "value": "\nAt {Year 2009} [0.061, 0.898], which had higher sales - {Vanilla}[0.061, 0.319] or {Mint}[0.061, 0.730]?" }, { "from": "gpt", "value": "Vanilla." } ], "id_raw": 382 }, { "id": "vee2WZLCLtw4bkjd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_2.png", "conversations": [ { "from": "human", "value": "\nFor {Year 2019} [0.973, 0.898], was the Mint sales trend [0.973, 0.327] higher or lower than the Vanilla sales trend [0.973, 0.831]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 382 }, { "id": "aJjzV0bvGpgJhMrU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_2.png", "conversations": [ { "from": "human", "value": "\nIs the {point labeled '52'} at [0.153, 0.713] above or below the {point labeled '97'} at [0.153, 0.336]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 382 }, { "id": "pmGUKjMvF5sAMhpt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents in area [0.817, 0.079, 0.967, 0.155]?" }, { "from": "gpt", "value": "This area contains the chart legend. It includes two items: {Vanilla Sales Trend} with representative color handle at [0.819, 0.092, 0.848, 0.104] and {Mint Sales Trend} with color handle at [0.819, 0.127, 0.848, 0.139]." } ], "id_raw": 382 }, { "id": "g0j19odrk32TBYIb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region spanning from [0.047, 0.911, 0.988, 0.934]." }, { "from": "gpt", "value": "This area contains all the x-axis tick labels for the years from 2009 to 2019." } ], "id_raw": 382 }, { "id": "N1nTGHxZLTD6Eaxb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within the region [0.033, 0.301, 0.055, 0.325]." }, { "from": "gpt", "value": "The y-tick label of '100' is located within this region." } ], "id_raw": 382 }, { "id": "bsHXvBzzEaTjWBCk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.446, 0.034, 0.587]?" }, { "from": "gpt", "value": "Growth." } ], "id_raw": 383 }, { "id": "7nfD3Zb6sDTmFNBK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.496, 0.932, 0.537, 0.970]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 383 }, { "id": "sKh3FWVK6cyTYM1x", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_3.png", "conversations": [ { "from": "human", "value": "\nWhat is on the {coordinates}[0.116, 0.223, 0.247, 0.251] in the legend?" }, { "from": "gpt", "value": "Label: 'Arts and Literature'." } ], "id_raw": 383 }, { "id": "gv8Y6E5Feg6ILn0X", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_3.png", "conversations": [ { "from": "human", "value": "\nIs {the point for the year 1900 from the 'Science and Technology' line}[0.366, 0.713] higher or lower than {the point for the year 1900 from the 'Arts and Literature' line}[0.366, 0.737]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 383 }, { "id": "BhOFyTcBY0GM5UMU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_3.png", "conversations": [ { "from": "human", "value": "\nWhich point is higher at {year 2050}, the {point from 'Science and Technology'}[0.817, 0.395] or {point from 'Arts and Literature'}[0.817, 0.690]?" }, { "from": "gpt", "value": "Science and Technology." } ], "id_raw": 383 }, { "id": "NYddXuuCRkSMzWie", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_3.png", "conversations": [ { "from": "human", "value": "\nAt {year 2100}, is the {Science and Technology line}[0.967, 0.182] above or below the {Arts and Literature line}[0.967, 0.682]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 383 }, { "id": "sC7Q08wqGSD3pHKj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_3.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects within this region[0.065, 0.182, 0.967, 0.826]?" }, { "from": "gpt", "value": "This area contains the 'Science and Technology' line of the chart, with different points indicating the growth rate in different years." } ], "id_raw": 383 }, { "id": "mdu02g3NIXbaOSzC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_3.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region[0.116, 0.181, 0.283, 0.251]?" }, { "from": "gpt", "value": "The label 'Science and Technology' from the legend is in this area." } ], "id_raw": 383 }, { "id": "jlAbFbSv1ZMUYn70", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_3.png", "conversations": [ { "from": "human", "value": "\nWhat's the content of this region[0.065, 0.682, 0.967, 0.748]?" }, { "from": "gpt", "value": "This region includes the 'Arts and Literature' line with different points that show growth in various years." } ], "id_raw": 383 }, { "id": "tKSwfpqC3R1ytk56", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis in the coordinates [0.029, 0.284, 0.048, 0.749]?" }, { "from": "gpt", "value": "Innovation Performance" } ], "id_raw": 384 }, { "id": "xSGVI6wqbt0ueKAF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the x-axis in the coordinates [0.460, 0.932, 0.565, 0.970]?" }, { "from": "gpt", "value": "Categories" } ], "id_raw": 384 }, { "id": "3bzfasnSFsbUZHsM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.066, 0.145, 0.083, 0.173]?" }, { "from": "gpt", "value": "40" } ], "id_raw": 384 }, { "id": "7g7PPlLU2mfZjrTC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Regression Model'}[0.091, 0.245] above or below {the point of 'AI Assistant'}[0.091, 0.403] at 'Consistent_expansion'?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 384 }, { "id": "L3HmaWpIHbFBU5oz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Regression Model'}[0.934, 0.817] above or below {the point of 'AI Assistant'}[0.934, 0.332] at 'Erratic_decline'?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 384 }, { "id": "KaWGyNPGizKKAmGG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_4.png", "conversations": [ { "from": "human", "value": "\nWhich data type has a higher value at {Consistent_expansion}[0.091], Regression Model or AI Assistant?" }, { "from": "gpt", "value": "AI Assistant." } ], "id_raw": 384 }, { "id": "6jxX8jCpv4uJlcxl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_4.png", "conversations": [ { "from": "human", "value": "\nWhat content is within [0.228, 0.035, 0.796, 0.075]?" }, { "from": "gpt", "value": "The subtitle of the chart, \"Education & Training Technology Innovation Trends,\" is located here." } ], "id_raw": 384 }, { "id": "fg6UXnh3bizwVhJ7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_4.png", "conversations": [ { "from": "human", "value": "\nKindly describe what's within these coordinates [0.607, 0.097, 0.927, 0.148]." }, { "from": "gpt", "value": "The area contains the legend items: {Regression Model}[0.652, 0.109, 0.774, 0.137] with a handle colored in '#1e90ff', and {AI Assistant}[0.840, 0.109, 0.921, 0.137] with a handle colored in '#800080'." } ], "id_raw": 384 }, { "id": "kyVVWM2YAPDs1tD0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.091, 0.245, 0.934, 0.817]." }, { "from": "gpt", "value": "This area contains 2 lines representing the Regression Model's and the AI Assistant's trend from 'Consistent Expansion' to 'Erratic Decline'. The Regression Model's coordinates are [0.091, 0.245], [0.934, 0.817], and the AI Assistant's coordinates are [0.091, 0.403], [0.934, 0.332]." } ], "id_raw": 384 }, { "id": "zJokTBApmuCaWEgJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the object}[0.015, 0.361, 0.034, 0.672] to the far left of the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 385 }, { "id": "NrciYO8H4ia2JZks", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of {the subtitle}?" }, { "from": "gpt", "value": "[0.295, 0.035, 0.749, 0.075]." } ], "id_raw": 385 }, { "id": "TqOvh98eUDiXHDYB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the {x-axis title} at position [0.492, 0.932, 0.552, 0.970] mention?" }, { "from": "gpt", "value": "Month." } ], "id_raw": 385 }, { "id": "hxNpYMT2mpx7wsBP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_5.png", "conversations": [ { "from": "human", "value": "\nIs {the third point of Stock A}[0.233, 0.582] higher or lower compared to {the third point of Stock B}[0.233, 0.799]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 385 }, { "id": "Gq1nnrWzk8nDniBU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_5.png", "conversations": [ { "from": "human", "value": "\nBetween {Stock A point at Month 9}[0.728, 0.293] and {Stock B point at Month 9}[0.728, 0.628], which has a higher value?" }, { "from": "gpt", "value": "Stock A." } ], "id_raw": 385 }, { "id": "hAegCLRcBQ02cuwG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_5.png", "conversations": [ { "from": "human", "value": "\nIs {the sixth point of the Stock A}[0.481, 0.418] positioned above or below {the sixth point of the Stock B}[0.481, 0.714]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 385 }, { "id": "4YR96EjBHb5oBMBg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the information in [0.075, 0.097, 0.296, 0.148]." }, { "from": "gpt", "value": "The area contains the legend items, Stock A with a handle color of '#ff00ff' between coordinates [0.077, 0.114, 0.111, 0.128] and Stock B with a handle color of '#ffd700' between coordinates [0.196, 0.114, 0.230, 0.128]." } ], "id_raw": 385 }, { "id": "zSqtqt20ymtqlOX2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_5.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area [0.068, 0.156, 0.976, 0.661]?" }, { "from": "gpt", "value": "It is a line graph representing the 'Return Rate (%)' of 'Stock A' over 12 months." } ], "id_raw": 385 }, { "id": "mif5ojIp2qgiiMfa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.068, 0.517, 0.976, 0.858]." }, { "from": "gpt", "value": "It is a line graph representing the 'Return Rate (%)' of 'Stock B' over 12 months." } ], "id_raw": 385 }, { "id": "6B4U4mrNUXlZIKLZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.083, 0.893, 0.131, 0.921] on the x-axis of the chart?" }, { "from": "gpt", "value": "It is the Year label \"2000.0\"" } ], "id_raw": 386 }, { "id": "o2BN1i7Ic2blbVER", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.040, 0.384, 0.075, 0.412] in the y-axis section of the chart?" }, { "from": "gpt", "value": "It is the your label \"4000\"." } ], "id_raw": 386 }, { "id": "GKs51GaEpBtvWsmI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the color according to the legend associated with {the data line}[0.107, 0.844, 0.594, 0.845]?" }, { "from": "gpt", "value": "The color is \"#ff00ff\" which corresponds to the indicator \"Life Expectancy\"." } ], "id_raw": 386 }, { "id": "urlKsIv6dnvZfdoR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_6.png", "conversations": [ { "from": "human", "value": "\nIs {the value of Life Expectancy in 2000}[0.107, 0.845] greater or lesser than {the value of Life Expectancy in 2002}[0.155, 0.845]?" }, { "from": "gpt", "value": "Lesser." } ], "id_raw": 386 }, { "id": "m0ya0M5lQQqLY7PQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_6.png", "conversations": [ { "from": "human", "value": "\nIs {the value of Area Gross Product in 2000}[0.107, 0.566] above or below {the value of Area Gross Product in 2003}[0.180, 0.515]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 386 }, { "id": "ifsNhywS0ceTw8qL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_6.png", "conversations": [ { "from": "human", "value": "\nWhich has seen a greater increase from 2001 to 2002, {Life Expectancy}[0.131, 0.845] to [0.155, 0.845] or {Area Gross Product}[0.131, 0.555] to [0.155, 0.532]?" }, { "from": "gpt", "value": "Life Expectancy." } ], "id_raw": 386 }, { "id": "uRCxhbea08mCMces", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects in the coordinates range [0.627, 0.114, 0.780, 0.136]." }, { "from": "gpt", "value": "In that region, the indicator {Life Expectancy}[0.670, 0.108, 0.780, 0.136] is present, which is paired with a color marker of \"#ff00ff\"." } ], "id_raw": 386 }, { "id": "x13uqoLJ6X75EKfz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_6.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the area [0.326, 0.893, 0.618, 0.921]?" }, { "from": "gpt", "value": "The x-axis labels for the Years 2010.0 to 2020.0 are located within that region." } ], "id_raw": 386 }, { "id": "iXutkqrY9Pzy8yHn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the bounding box [0.107, 0.188, 0.594, 0.566]." }, { "from": "gpt", "value": "The line representing the 'Area Gross Product' from the years 2000 to 2020 is located here. It is marked by a line and triangles in the color \"#ffd700\"." } ], "id_raw": 386 }, { "id": "Z7XoRXcNhRDJF37Q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the bottom-most point on the 'Personal Development' line?" }, { "from": "gpt", "value": "{Advanced Career}[0.666, 0.100]" } ], "id_raw": 387 }, { "id": "AP2VbH7Q40GBKqAO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the top-most y-axis label in the chart?" }, { "from": "gpt", "value": "27.5" } ], "id_raw": 387 }, { "id": "pQa9GQ6IFLFkdG47", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label for the third stage}[0.606, 0.919, 0.726, 0.942]?" }, { "from": "gpt", "value": "'Advanced Career'" } ], "id_raw": 387 }, { "id": "Jk9PztyzksgKVTMr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_7.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher point at {Retirement stage}[0.940, 0.865], 'Personal Development' or 'Workplace Development'?" }, { "from": "gpt", "value": "Workplace Development." } ], "id_raw": 387 }, { "id": "LEQtd1q39KmJ63Av", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point at 'Early Career'}[0.116, 0.483] on 'Personal Development' line higher or lower than the point on 'Workplace Development' line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 387 }, { "id": "hJkXBqE5IcLLS986", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_7.png", "conversations": [ { "from": "human", "value": "\nIs the {last tick label on the y-axis}[0.035, -0.004, 0.065, 0.019] below or above the first point on 'Workplace Development' line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 387 }, { "id": "vWEJw8HyQ7k9T2pA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area with coordinates [0.075, 0.062, 0.982, 0.903]." }, { "from": "gpt", "value": "This region contains the entire plot area of the chart which includes both 'Personal Development' and 'Workplace Development' lines with their respective points, x-axis and y-axis with their labels and ticks, and a colored bg." } ], "id_raw": 387 }, { "id": "4pkl7xfVOSQnT9cE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area with coordinates [0.709, 0.793, 0.973, 0.889]." }, { "from": "gpt", "value": "This area contains the legend of the plot. It consists of two items: {Personal Development}[0.763, 0.804, 0.951, 0.834] marked with a magenta-colored handle and {Workplace Development}[0.763, 0.848, 0.967, 0.878] marked with a gold-colored handle." } ], "id_raw": 387 }, { "id": "3Lsu92QBYgvwCVnD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.043, 0.855, 0.065, 0.879]?" }, { "from": "gpt", "value": "The y-axis tick label at {coordinate}[0.043, 0.855, 0.065, 0.879], indicating the value '5.0', resides within this area." } ], "id_raw": 387 }, { "id": "it3KmmtDMIQR6KZM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the content at these coordinates [0.015, 0.251, 0.034, 0.782]?" }, { "from": "gpt", "value": "Y-axis label which is \"Emissions (Millions of Tons)\"." } ], "id_raw": 388 }, { "id": "Lqh1u4qGdpZ2sOn0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_8.png", "conversations": [ { "from": "human", "value": "\nWhat is at the second coordinate in the x-axis ticks?" }, { "from": "gpt", "value": "The label '2011' at {coordinates}[0.135, 0.893, 0.170, 0.921]." } ], "id_raw": 388 }, { "id": "ZutqcRfC6VksmYVI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_8.png", "conversations": [ { "from": "human", "value": "\nWhich label is at these coordinates [0.848, 0.108, 0.973, 0.136] in the legend?" }, { "from": "gpt", "value": "The label 'Emission Increase' is found at those coordinates." } ], "id_raw": 388 }, { "id": "XDwzH3V4aA6NlxBl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_8.png", "conversations": [ { "from": "human", "value": "\nIs {the point '2' for 'Emission Increase'}[0.228, 0.654] higher or lower than {the point '2' for 'Emission Reduction'}[0.228, 0.585]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 388 }, { "id": "lLL5Gw4fBFZyzz4l", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_8.png", "conversations": [ { "from": "human", "value": "\nWhich line is above at point '2015': {the line for 'Emission Reduction'}[0.455, 0.473] or {the line for 'Emission Increase'}[0.455, 0.580]?" }, { "from": "gpt", "value": "The line for 'Emission Increase'." } ], "id_raw": 388 }, { "id": "VaQsPOGMHaXSrHq8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_8.png", "conversations": [ { "from": "human", "value": "\nWhich line is below at point '2021': {the line for 'Emission Reduction'}[0.909, 0.304] or {the line for 'Emission Increase'}[0.909, 0.493]?" }, { "from": "gpt", "value": "The line for 'Emission Reduction'." } ], "id_raw": 388 }, { "id": "KdlbAAykY5YNwifO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the information in this region [0.602, 0.097, 0.978, 0.148]?" }, { "from": "gpt", "value": "This region contains the legend of the chart which represents two labels: {Emission Reduction}[0.646, 0.108, 0.781, 0.136] and {Emission Increase}[0.848, 0.108, 0.973, 0.136], each paired with a unique color marker." } ], "id_raw": 388 }, { "id": "xmFyxwX6HEQhNh0S", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_8.png", "conversations": [ { "from": "human", "value": "\nWhat information is located within [0.218, 0.035, 0.843, 0.075]?" }, { "from": "gpt", "value": "This is the subtitle of the chart, which is \"Environmental Protection and Sustainable Development\"." } ], "id_raw": 388 }, { "id": "Ir2PpdynrCDAU06e", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_8.png", "conversations": [ { "from": "human", "value": "\nWhat elements can be identified in the region [0.076, 0.878, 0.929, 0.921]?" }, { "from": "gpt", "value": "This region contains all the X-axis labels for the years from 2010 until 2021." } ], "id_raw": 388 }, { "id": "MdtG70n9hcLJmPrA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the visual element with coordinates [0.115, 0.159, 0.907, 0.873] in the chart?" }, { "from": "gpt", "value": "Main plotting area." } ], "id_raw": 389 }, { "id": "TapN5mZNyYFUpNin", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_9.png", "conversations": [ { "from": "human", "value": "\nWhat object is at {coordinate}[0.862, 0.680, 0.883, 0.700] in this chart?" }, { "from": "gpt", "value": "The Marked Point For 'Fluctuating Decline' in 'Spark'." } ], "id_raw": 389 }, { "id": "6Ugh6PTnFjtAG0Lj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the text represented by {coordinates}[0.062, 0.082, 0.103, 0.111] on the y-axis?" }, { "from": "gpt", "value": "160" } ], "id_raw": 389 }, { "id": "wVeMalKAX2XUHP66", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_9.png", "conversations": [ { "from": "human", "value": "\nIn scenario 'Unpredictable Decline', which method has higher performance {Hadoop point}[0.151, 0.427] or {Spark point}[0.151, 0.840]?" }, { "from": "gpt", "value": "Hadoop." } ], "id_raw": 389 }, { "id": "AvFsGwotmfipeGvK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_9.png", "conversations": [ { "from": "human", "value": "\nDoes the Hadoop line {coordinates}[0.151, 0.191, 0.871, 0.427] generally have a higher or lower performance than Spark {coordinates}[0.151, 0.683, 0.871, 0.840]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 389 }, { "id": "qJkU1VUu90Z4oqzS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_9.png", "conversations": [ { "from": "human", "value": "\nIn which case is the Hadoop's performance lower, 'Sporadic Growth' {coordinate}[0.511, 0.309] or 'Fluctuating Decline' {coordinate}[0.871, 0.191]?" }, { "from": "gpt", "value": "Fluctuating Decline." } ], "id_raw": 389 }, { "id": "ggzYSbml0nYEP4gn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_9.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects found in this region [0.567, 0.102, 0.896, 0.154]?" }, { "from": "gpt", "value": "This region contains the legend of the chart. It has two items, Hadoop is represented by purple at {coordinates}[0.570, 0.119, 0.624, 0.134] with corresponding label at {coordinates}[0.636, 0.114, 0.720, 0.143]. Likewise, Spark is represented by gold at {coordinates}[0.758, 0.119, 0.813, 0.134] with the label at {coordinates}[0.825, 0.114, 0.887, 0.143]." } ], "id_raw": 389 }, { "id": "hOFisd53NcFAXDFX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the area defined by [0.041, 0.868, 0.109, 0.918]?" }, { "from": "gpt", "value": "This section includes the label '80' for the y-tick, situated at {coordinates}[0.076, 0.868, 0.103, 0.897]." } ], "id_raw": 389 }, { "id": "QeG0Pn5tfAk0TDnl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_9.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.031, 0.888, 0.977, 0.918]?" }, { "from": "gpt", "value": "This region contains five x-axis labels, specifically 'Unpredictable Decline', 'Unpredictable Fluctuations', 'Sporadic Growth', 'Downward Trend', and 'Fluctuating Decline' located respectively at {coordinates}[0.031, 0.888, 0.271, 0.918] , {coordinates}[0.185, 0.888, 0.477, 0.918] , {coordinates}[0.421, 0.888, 0.601, 0.918] , {coordinates}[0.600, 0.888, 0.782, 0.918] and {coordinates}[0.766, 0.888, 0.976, 0.918]." } ], "id_raw": 389 }, { "id": "JaTZvDYbrqvuogj8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_11.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.015, 0.353, 0.034, 0.600] represent in the chart?" }, { "from": "gpt", "value": "Performance." } ], "id_raw": 391 }, { "id": "JAUx0beY8fezYD9m", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of the fourth tick label on the x-axis at {coordinates}[0.591, 0.893, 0.627, 0.921]?" }, { "from": "gpt", "value": "The year 2036." } ], "id_raw": 391 }, { "id": "KtWxRnQWRhbOPmHq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_11.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the marker coordinate}[0.825, 0.551, 0.859, 0.565] in the legend?" }, { "from": "gpt", "value": "Wind Energy." } ], "id_raw": 391 }, { "id": "6arpHXU6FlixW4NR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_11.png", "conversations": [ { "from": "human", "value": "\nIs {the Hydropower line in 2040}[0.968, 0.688] higher or lower than {the Bioenergy line in 2040}[0.968, 0.609]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 391 }, { "id": "xPSIOgDOT9hC9vtj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_11.png", "conversations": [ { "from": "human", "value": "\nWhat has a higher performance value in 2030, {Geothermal}[0.071, 0.767] or {Hydropower}[0.071, 0.714]?" }, { "from": "gpt", "value": "Geothermal." } ], "id_raw": 391 }, { "id": "ilZT5bXnF54h0l3I", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_11.png", "conversations": [ { "from": "human", "value": "\nCompare the performance of {Solar Power in 2030}[0.071, 0.291] with {Wind Energy in 2030}[0.071, 0.397]. Which one is higher?" }, { "from": "gpt", "value": "Wind Energy." } ], "id_raw": 391 }, { "id": "TAAyBZuCgwqzG2AE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the bounding box [0.823, 0.367, 0.961, 0.585]?" }, { "from": "gpt", "value": "This area contains the legend for the various energy sources: {Hydropower}[0.867, 0.378, 0.951, 0.406], {Solar Power}[0.867, 0.420, 0.949, 0.448], {Geothermal}[0.867, 0.462, 0.949, 0.490], {Bioenergy}[0.867, 0.504, 0.937, 0.532], and {Wind Energy}[0.867, 0.546, 0.955, 0.574]. Each legend item is paired with a colored marker." } ], "id_raw": 391 }, { "id": "lw5omgMXy2cQVTiU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_11.png", "conversations": [ { "from": "human", "value": "\nPlease detail the content within the region [0.305, 0.030, 0.734, 0.070]?" }, { "from": "gpt", "value": "The chart's subtitle, \"Future Energy and Resource allocation\", is located within this area, displayed in black color." } ], "id_raw": 391 }, { "id": "axHGgsLBvOg3tKn3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_11.png", "conversations": [ { "from": "human", "value": "\nCould you describe the elements within the region [0.494, 0.932, 0.545, 0.970]?" }, { "from": "gpt", "value": "This region contains the xlabel of the chart which is titled \"Years\"." } ], "id_raw": 391 }, { "id": "j2ChrpHytk3OwbGQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the representation of {this specific data point on the line}[0.383, 0.582] in the chart?" }, { "from": "gpt", "value": "It is a data point for the 'Tech Stock' trend." } ], "id_raw": 392 }, { "id": "fJ81QvLaPXQk8zwA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_12.png", "conversations": [ { "from": "human", "value": "\nWhat does {this legend item}[0.279, 0.073, 0.313, 0.087] in the chart represent?" }, { "from": "gpt", "value": "It is the color marker for the 'Tech Stock' trend." } ], "id_raw": 392 }, { "id": "pGVHPp9eX2iyMagt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {this y-axis tick}[0.125, 0.780]?" }, { "from": "gpt", "value": "4." } ], "id_raw": 392 }, { "id": "Dt26jmyh5zAQ5dEL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_12.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Bank Stock' point at 0.4 Assets}[0.383, 0.604] higher or lower than {the 'Tech Stock' point at 0.4 Assets}[0.383, 0.582] on the graph?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 392 }, { "id": "MSREGHLUbMXpuCVz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_12.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Bank Stock' point at 0.7 Assets}[0.642, 0.505] above or below {the 'Tech Stock' point at 0.7 Assets}[0.642, 0.560]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 392 }, { "id": "EVSwTXI46YKL6TAU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_12.png", "conversations": [ { "from": "human", "value": "\nWhich stock, 'Bank Stock' or 'Tech Stock', had higher performance at 1.0 Assets?" }, { "from": "gpt", "value": "Bank Stock." } ], "id_raw": 392 }, { "id": "hYbyi5a4rFwbOKN9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.132, 0.057, 0.400, 0.107]." }, { "from": "gpt", "value": "The area includes two legend items for the chart: {Bank Stock}[0.176, 0.068, 0.254, 0.096] and {Tech Stock}[0.321, 0.068, 0.395, 0.096], each paired with a color marker in purple and gold, respectively." } ], "id_raw": 392 }, { "id": "aT7hT7OYe43cV8Fo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_12.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region between [0.125, 0.208, 0.900, 0.846]?" }, { "from": "gpt", "value": "This region contains the line that represents the 'Tech Stock' trend. The line includes multiple data points, with markers indicating each observation from the dataset." } ], "id_raw": 392 }, { "id": "h0hFB5yB7AGqT9GD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the visual content available in the region [0.125, 0.417, 0.900, 0.615]." }, { "from": "gpt", "value": "This area contains the line representing the 'Bank Stock' trend as per the dataset. The line includes several data points, with markers signifying each datapoint from the data." } ], "id_raw": 392 }, { "id": "R0fFXJPukFDS96Tv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the chart?" }, { "from": "gpt", "value": "Performance. {the y-axis label}[0.015, 0.393, 0.034, 0.640]" } ], "id_raw": 393 }, { "id": "AiLtQ5HwRisleyu3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the last tick on the x-axis?" }, { "from": "gpt", "value": "1.0. {the x-axis tick label}[0.422, 0.893, 0.444, 0.921]" } ], "id_raw": 393 }, { "id": "yTEEEzVj1tNOgntl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line representing 'AI Technology Performance'?" }, { "from": "gpt", "value": "Orange. {the line for 'AI Technology Performance'}[0.069, 0.607, 0.433, 0.715]" } ], "id_raw": 393 }, { "id": "l9ASwXH8cz41M6yq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Physician Technology Performance' line}[0.069, 0.246, 0.433, 0.643] generally trend upward or downward as the 'Healthcare Condition' increases?" }, { "from": "gpt", "value": "Upward." } ], "id_raw": 393 }, { "id": "0M3OiIzL03fw6O5P", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_13.png", "conversations": [ { "from": "human", "value": "\nWhich one reaches a higher performance value at healthcare condition value 0.91, {the 'Physician Technology Performance'}[0.069, 0.246, 0.433, 0.643] or {the 'AI Technology Performance'}[0.069, 0.607, 0.433, 0.715]?" }, { "from": "gpt", "value": "Physician Technology Performance." } ], "id_raw": 393 }, { "id": "fQgVM3tjpsgxO0xl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_13.png", "conversations": [ { "from": "human", "value": "\nAt the health condition value of 0.64, is {the 'Physician Technology Performance' line}[0.069, 0.246, 0.433, 0.643] above or below {the 'AI Technology Performance' line}[0.069, 0.607, 0.433, 0.715]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 393 }, { "id": "9G0FHlvLa3Le7fhD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_13.png", "conversations": [ { "from": "human", "value": "\nWhat content is within the region [0.069, 0.878, 0.433, 0.697]?" }, { "from": "gpt", "value": "Within this region are the 'Healthcare Condition' x-axis, tick labels ranging from 0.1 to 1.0, and the lines representing 'Physician Technology Performance' and 'AI Technology Performance'." } ], "id_raw": 393 }, { "id": "VoHECQotk4rkEibb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the details of the figure in area [-0.010, 0.035, 0.513, 0.075]?" }, { "from": "gpt", "value": "This area contains the subtitle of the chart: 'Healthcare & Medical Tech Performance Trends'. The text is black and it is not rotated." } ], "id_raw": 393 }, { "id": "qtyndNtTHCBUjhqJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained within the region [0.440, 0.097, 0.985, 0.148]?" }, { "from": "gpt", "value": "The region includes the legend of the chart, with two entries: 'Physician Technology Performance' colored purple and 'AI Technology Performance' colored orange, alongside their respective color markers." } ], "id_raw": 393 }, { "id": "F33J34h6686bNqv1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the chart with coordinates [0.015, 0.461, 0.034, 0.568]?" }, { "from": "gpt", "value": "The label is 'Index'." } ], "id_raw": 394 }, { "id": "wyW7ru4Rq9GFH2ZC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the text from the legend located at [0.127, 0.181, 0.198, 0.209]?" }, { "from": "gpt", "value": "It's 'CSR Index'." } ], "id_raw": 394 }, { "id": "mo3xWltAptmfAQmz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_14.png", "conversations": [ { "from": "human", "value": "\nWhat year does the x-axis tick label at [0.950, 0.893, 0.985, 0.921] represent?" }, { "from": "gpt", "value": "It represents the year '2019'." } ], "id_raw": 394 }, { "id": "6SUIpjsOXUG0Vmxa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_14.png", "conversations": [ { "from": "human", "value": "\nDoes the {CSR Index} line [0.076, 0.467, 0.967, 0.634] overall increase or decrease over the years?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 394 }, { "id": "9hqDd5kEBUHQrbE2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_14.png", "conversations": [ { "from": "human", "value": "\nIs the {CSR Index} point [0.373, 0.538] higher or lower than the {Sustainable Development Index} point [0.373, 0.491] in 2013?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 394 }, { "id": "Qjm7J0JwaNOOiRig", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_14.png", "conversations": [ { "from": "human", "value": "\nIn the last year depicted on the graph, does the {CSR Index}[0.967, 0.467] exceed the {Sustainable Development Index}[0.967, 0.395]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 394 }, { "id": "gHhxKXiwehbCIrGn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content at coordinates [0.076, 0.395, 0.967, 0.586]?" }, { "from": "gpt", "value": "This area contains two lines. The first one is the Sustainable Development Index line, and the second one is CSR Index line. The lines depict the trend of these indices from 2010 to 2019." } ], "id_raw": 394 }, { "id": "n3ivcdFpDLBSbmVe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the information contained within the area [0.057, 0.384, 0.066, 0.412]?" }, { "from": "gpt", "value": "There is a y-axis label '100' within this area." } ], "id_raw": 394 }, { "id": "TrfgNAXc0AyB3rfu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the information presented in the area [0.048, 0.671, 0.066, 0.699]?" }, { "from": "gpt", "value": "The area includes a y-axis label '40'." } ], "id_raw": 394 }, { "id": "DSKwzx4ysCCUXFvu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_15.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at {the y-axis label}[0.015, 0.409, 0.034, 0.624]?" }, { "from": "gpt", "value": "Price Trend." } ], "id_raw": 395 }, { "id": "D2puMdCSfHDz7e3E", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_15.png", "conversations": [ { "from": "human", "value": "\nWhere is the ninth mark located on the x-axis?" }, { "from": "gpt", "value": "[0.723, 0.893, 0.732, 0.921]." } ], "id_raw": 395 }, { "id": "evdlOCdKsVInmMle", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_15.png", "conversations": [ { "from": "human", "value": "\nWhat value does {the y-axis tick label}[0.040, 0.454, 0.057, 0.482] represent?" }, { "from": "gpt", "value": "45." } ], "id_raw": 395 }, { "id": "bfyDNTK1I3osGH3P", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_15.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Stock Price Trend' point labeled '2'}[0.148, 0.731] above or below {the 'Commodity Price Trend' point labeled '2'}[0.148, 0.406]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 395 }, { "id": "RumF9AaPgspbq7e7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_15.png", "conversations": [ { "from": "human", "value": "\nIs the last point of {the 'Stock Price Trend' line}[[0.976, 0.595]] higher or lower than the last point of {the 'Commodity Price Trend' line}[[0.976, 0.274]]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 395 }, { "id": "ruUpgUQZBtXwLu3R", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_15.png", "conversations": [ { "from": "human", "value": "\nWhich trend shows a greater value at the third point, Commodity Price Trend or Stock Price Trend?" }, { "from": "gpt", "value": "Commodity Price Trend." } ], "id_raw": 395 }, { "id": "1Nq0EtxgqLJDAVo2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements located in the region [0.803, 0.108, 0.964, 0.136]?" }, { "from": "gpt", "value": "This region includes the text for one legend entry: {Commodity Price Trend}[0.803, 0.108, 0.964, 0.136]." } ], "id_raw": 395 }, { "id": "CEbH0yROgSMdE4dk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_15.png", "conversations": [ { "from": "human", "value": "\nWhat details are there in the area [0.065, 0.595, 0.976, 0.766]?" }, { "from": "gpt", "value": "This is the line chart for the {Stock Price Trend}[0.065, 0.595, 0.976, 0.766] that includes various data points across time." } ], "id_raw": 395 }, { "id": "jwIFBTw5llB9u78w", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the plot objects found within the region [0.065, 0.274, 0.976, 0.459]?" }, { "from": "gpt", "value": "It includes the line chart for the {Commodity Price Trend}[0.065, 0.274, 0.976, 0.459] with data points representing values over a series of months." } ], "id_raw": 395 }, { "id": "PiXDDRCUYX6ANOcl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at [0.888, 0.125, 0.988, 0.153] in the chart?" }, { "from": "gpt", "value": "Training Hours." } ], "id_raw": 396 }, { "id": "eheD1rdlJFazR0re", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_16.png", "conversations": [ { "from": "human", "value": "\nWhat shape is oriented at coordinates [0.845, 0.130, 0.880, 0.144] in the legend?" }, { "from": "gpt", "value": "It is a marker line for the legend." } ], "id_raw": 396 }, { "id": "mrCENwVFld7mwJQB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_16.png", "conversations": [ { "from": "human", "value": "\nWhat value does {the ylabel at the left}[0.015, 0.374, 0.034, 0.655] represent in the chart?" }, { "from": "gpt", "value": "It represents the Training Hours." } ], "id_raw": 396 }, { "id": "xxzfeRG5d097ObYu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_16.png", "conversations": [ { "from": "human", "value": "\nDoes {the point in 2010 in Training Hours line}[0.341, 0.754] have a higher or lower value than {the point in 2009 in Training Hours line}[0.294, 0.730]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 396 }, { "id": "KbXpW9TSPfcL8j5e", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point in 2006 on the Salary line}[0.152, 0.544] higher or lower than {the point in 2005 on the Salary line}[0.105, 0.574]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 396 }, { "id": "34jR0wKmChkFGweF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_16.png", "conversations": [ { "from": "human", "value": "\nIs {the Training Hours point in 2021}[0.860, 0.215] higher than or lower than {the Salary point in the same year}[0.860, 0.215]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 396 }, { "id": "a08Rv9o0V8WVV9dD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's within the coordinates [0.134, 0.814, 0.860, 0.850]?" }, { "from": "gpt", "value": "The area includes the Training Hours line, with multiple data points, representing each year from 2006 to 2021." } ], "id_raw": 396 }, { "id": "mjBnp1bm9tWBpfci", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents at area [0.843, 0.114, 0.993, 0.206]." }, { "from": "gpt", "value": "This area contains the chart's legend section, which includes the labels for \"Training Hours\" and \"Salary\", accompanied by their respective colored line markers." } ], "id_raw": 396 }, { "id": "vbbmKY6sSF6v2s72", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_16.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.040, 0.264, 0.960, 0.292]?" }, { "from": "gpt", "value": "It includes a y-axis tick label reading '100000' on the right side of the chart." } ], "id_raw": 396 }, { "id": "DptPuAWdV3UgSV7T", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.123, 0.159, 0.923, 0.868] seen in the chart?" }, { "from": "gpt", "value": "This is the subplot area of the plot." } ], "id_raw": 399 }, { "id": "rgOxLrVFsycDd16I", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first legend's text in the chart?" }, { "from": "gpt", "value": "[0.281, 0.114, 0.535, 0.143]" } ], "id_raw": 399 }, { "id": "O8gp2ndZ91azRTCq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_20_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.062, 0.739, 0.108, 0.768]?" }, { "from": "gpt", "value": "-10" } ], "id_raw": 399 }, { "id": "3bDCDRJ1PKZuCcQq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_19.png", "conversations": [ { "from": "human", "value": "\nIs {the value of 'Pre Cultural Integration'} at 'Rituals'[0.283, 0.438] higher or lower than {the value of 'Post Cultural Integration'} at 'Rituals'[0.283, 0.223]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 399 }, { "id": "N6uG0JlRMM0oKWN0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_19.png", "conversations": [ { "from": "human", "value": "\nDoes the {the 'Post Cultural Integration' value for 'Art'}[0.123, 0.159] in the line chart have a higher or lower value than its {corresponding 'Pre Cultural Integration' value}[0.123, 0.750]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 399 }, { "id": "ClHhId4rOXN4VX2H", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_20_19.png", "conversations": [ { "from": "human", "value": "\nWhich Culture Aspect sees a greater increase from {Pre to Post Cultural Integration} at the fifth X-axis labeled 'Dance'[0.763, 0.868]?" }, { "from": "gpt", "value": "Post Cultural Integration." } ], "id_raw": 399 }, { "id": "cxth9H9eXCWTgIDj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.212, 0.102, 0.912, 0.155]." }, { "from": "gpt", "value": "This region contains the legend area of the chart, providing color markers for the two series {Pre Cultural Integration} with coordinates [0.215, 0.120, 0.269, 0.134] in purple and {Post Cultural Integration} with coordinates [0.573, 0.120, 0.627, 0.134] in gold." } ], "id_raw": 399 }, { "id": "W3BqMx6IVqVmGNs8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.123, 0.159, 0.923, 0.868]." }, { "from": "gpt", "value": "The chart's main plot area is situated within these coordinates, featuring two line charts representing the 'Pre Cultural Integration' and 'Post Cultural Integration' series, with associated data points." } ], "id_raw": 399 }, { "id": "1vjKD5xgEqM1QoEU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_20_19.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.123, 0.301, 0.923, 0.750]?" }, { "from": "gpt", "value": "This region hosts the line chart for the series 'Pre Cultural Integration'. It comprises of data points for 'Culture Aspects' such as 'Art', 'Rituals', 'Architecture', 'Music', 'Dance' and 'Literature'." } ], "id_raw": 399 }, { "id": "fc4RvV2YhSKI9hsC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_0.png", "conversations": [ { "from": "human", "value": "\nWhich chart event corresponds to the coordinate [0.682, 0.423]?" }, { "from": "gpt", "value": "Urban Farming." } ], "id_raw": 400 }, { "id": "Hs8MUINeB00yO4Uz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-axis label} at the coordinate [0.043, 0.155, 0.071, 0.178]?" }, { "from": "gpt", "value": "0.9." } ], "id_raw": 400 }, { "id": "r4Duwm81XJSC3RPO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_0.png", "conversations": [ { "from": "human", "value": "\nCan you reveal which color is associated with {the second legend}[0.147, 0.833, 0.351, 0.856]?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 400 }, { "id": "1E6GER5T7zd36Pkp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_0.png", "conversations": [ { "from": "human", "value": "\nFor the event {Park Maintenance}, is the fund allocation of 2020 at {the point}[0.233, 0.553] higher or lower than that of 2019 at {the point}[0.233, 0.293]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 400 }, { "id": "g9aWqGeo0ucJaaDy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_0.png", "conversations": [ { "from": "human", "value": "\nIn the context of {Community Outreach}, does {point}[0.532, 0.190] of Fund Allocation in 2020 show a higher or lower fund allocation as compared to Fund Allocation in 2019 at {point}[0.532, 0.242]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 400 }, { "id": "jRWrNuY9bK9wW1m9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_0.png", "conversations": [ { "from": "human", "value": "\nWhich color represents the {line}[0.233, 0.242, 0.832, 0.423] corresponding to Fund Allocation in 2019?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 400 }, { "id": "QyK2yOGqKuK4T812", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_0.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects within [0.460, 0.698, 0.908, 0.904]?" }, { "from": "gpt", "value": "It contains the last two event labels on the x-axis, namely, 'Urban Farming' at {coordinates}[0.630, 0.698, 0.733, 0.836] and 'Digital Implementation' at {coordinates}[0.755, 0.698, 0.908, 0.904]." } ], "id_raw": 400 }, { "id": "GPWLeffNjGUTHgBU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.092, 0.788, 0.358, 0.865] illustrate?" }, { "from": "gpt", "value": "This {region} represents the legend of the line chart. It comprises two items, 'Fund Allocation in 2019' found at {coordinates}[0.147, 0.798, 0.351, 0.821] and 'Fund Allocation in 2020' at {coordinates}[0.147, 0.833, 0.351, 0.856], each associated with a colored marker." } ], "id_raw": 400 }, { "id": "K6dDOZ4w1jAh0afU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_0.png", "conversations": [ { "from": "human", "value": "\nWhat information can be derived from [0.083, 0.034, 0.981, 0.682]?" }, { "from": "gpt", "value": "This {region} contains the main plot area of the line chart, displaying data for five events regarding their fund allocation in 2019 and 2020, as well as tick labels and a legend." } ], "id_raw": 400 }, { "id": "PFcSsRXYdPJcfgYZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.184, 0.101, 0.414, 0.130]?" }, { "from": "gpt", "value": "'Market Share Growth' legend item." } ], "id_raw": 401 }, { "id": "gWxGESfhv8zyXjPD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.777, 0.899, 0.832, 0.928]?" }, { "from": "gpt", "value": "2027." } ], "id_raw": 401 }, { "id": "gqaVO2Qs6Ce7o7Kr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.114, 0.146, 0.175, 0.169] in the chart?" }, { "from": "gpt", "value": "Color marker for 'Revenue Growth'." } ], "id_raw": 401 }, { "id": "UVMvFPFmObJPyBwz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_1.png", "conversations": [ { "from": "human", "value": "\nAre {the points of 'Revenue Growth'}[0.143, 0.070, 0.937, 0.459] generally above or below {the points of 'Sales Growth'}[0.143, 0.498, 0.937, 0.809]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 401 }, { "id": "RC5Y2BhRl00ITfz3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_1.png", "conversations": [ { "from": "human", "value": "\nWhich growth line has a higher value for the year 2025, 'Sales Growth' or 'Market Share Growth'?" }, { "from": "gpt", "value": "Sales Growth." } ], "id_raw": 401 }, { "id": "IImUG1CM02T8Z9A6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_1.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Revenue Growth' point at 2026}[0.672, 0.070] above or below {the 'Market Share Growth' point at 2026}[0.672, 0.817]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 401 }, { "id": "Vpgb3ho2zcrIzSOu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.114, 0.046, 0.423, 0.185]." }, { "from": "gpt", "value": "This area contains the chart legend. It includes 'Sales Growth' with a blue marker{marker coordinates}[0.114, 0.059, 0.175, 0.082], 'Market Share Growth' with a red marker{marker coordinates}[0.114, 0.102, 0.175, 0.125] and 'Revenue Growth' with a green marker{marker coordinates}[0.114, 0.146, 0.175, 0.169]." } ], "id_raw": 401 }, { "id": "tPMpcbPElIcRplQZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained within [0.248, 0.899, 0.435, 0.928]?" }, { "from": "gpt", "value": "There are two x-axis tick labels, '2023'{label coordinates}[0.248, 0.899, 0.303, 0.928] and '2024'{label coordinates}[0.380, 0.899, 0.435, 0.928]." } ], "id_raw": 401 }, { "id": "1E15HMuuaJO5J5ot", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_1.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the area [0.054, 0.525, 0.088, 0.554]?" }, { "from": "gpt", "value": "There is a y-axis tick label, '0.4'." } ], "id_raw": 401 }, { "id": "M0xbgAYFDjBJTpCM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the function of the line described by these coordinates [0.152, 0.285, 0.942, 0.345] in the chart?" }, { "from": "gpt", "value": "This line represents the Unemployment Rate trend over the years." } ], "id_raw": 402 }, { "id": "4Z1ry8m4GVpCiTri", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_2.png", "conversations": [ { "from": "human", "value": "\nWhat element is at the coordinates [0.415, 0.902, 0.446, 0.934] in the figure?" }, { "from": "gpt", "value": "It is an x-axis label marking the year '2004'." } ], "id_raw": 402 }, { "id": "o1kQZYxlbFIAlroC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_2.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {this marker}[0.118, 0.055, 0.174, 0.082] in the legend?" }, { "from": "gpt", "value": "The color of this marker corresponds to the 'Inflation Rate' line in the chart." } ], "id_raw": 402 }, { "id": "k5h9ZIRddPXbMYQu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_2.png", "conversations": [ { "from": "human", "value": "\nAt the point in 2009, is the {Unemployment Rate}[0.854, 0.288] higher or lower than the {Inflation Rate}[0.854, 0.406]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 402 }, { "id": "18KpMtjcoYcl67Do", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_2.png", "conversations": [ { "from": "human", "value": "\nHow do the {Stock Market Returns}[0.240, 0.669] in 2002 compare to the {Unemployment Rate}[0.240, 0.331]?" }, { "from": "gpt", "value": "The Stock Market Returns are significantly lower." } ], "id_raw": 402 }, { "id": "2lc2CGXImoXTyszE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_2.png", "conversations": [ { "from": "human", "value": "\nIn terms of number representation, is the {y-axis label}[0.019, 0.391, 0.043, 0.532] 'Rate (%)' greater at the top or bottom end?" }, { "from": "gpt", "value": "The top." } ], "id_raw": 402 }, { "id": "Qv671EjnB4psWsbt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.122, 0.049, 0.369, 0.161] in the chart?" }, { "from": "gpt", "value": "This region contains the legend of the chart, which includes 'Inflation Rate', 'Unemployment Rate', and 'Stock Market Returns'. Each category is paired with a distinct marker color." } ], "id_raw": 402 }, { "id": "6JdLob0u0A77hRog", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.823, 0.902, 0.885, 0.934] contain?" }, { "from": "gpt", "value": "It contains the x-axis label '2009'." } ], "id_raw": 402 }, { "id": "pz1S3iUuStgYQYxV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the significance of items in the area [0.152, 0.053, 0.942, 0.850]?" }, { "from": "gpt", "value": "This region corresponds to a line chart representing 'Stock Market Returns' across different years. It has points representing data for each year ranging from '2001' to '2010'." } ], "id_raw": 402 }, { "id": "GfGvHFJRa3oysvGd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.812, 0.435, 0.928, 0.452] in the chart?" }, { "from": "gpt", "value": "It is the text label for \"Innovation Index\" in the legend." } ], "id_raw": 403 }, { "id": "KqcZ3m9xNxnUfyTs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the label '5' in the x-axis labels?" }, { "from": "gpt", "value": "[0.588, 0.926, 0.601, 0.949]" } ], "id_raw": 403 }, { "id": "qwGr0cMtxbtcN4wx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the {y-axis tick label} at [0.041, 0.818, 0.072, 0.835]?" }, { "from": "gpt", "value": "'$0.55$'" } ], "id_raw": 403 }, { "id": "tCns9eHocBmG45Ae", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_3.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Innovation Index'}[0.594, 0.292] above or below {the point for 'Business Environment'}[0.594, 0.612] at Time Period 5?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 403 }, { "id": "0N0taOnEbKQ0LMWC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_3.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Business Environment'}[[0.210, 0.434], [0.338, 0.470], [0.466, 0.558], [0.594, 0.612]] generally above or below {the line for 'High-Tech Enterprises'}[[0.210, 0.736], [0.338, 0.647], [0.466, 0.612], [0.594, 0.505]] in the first half of the time period?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 403 }, { "id": "LNxyERTN69O0lio5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_3.png", "conversations": [ { "from": "human", "value": "\nWhich index has a higher value at {Time Period 8}[0.973, 0.926, 0.985, 0.949], Innovation Index or High-Tech Enterprises?" }, { "from": "gpt", "value": "Innovation Index." } ], "id_raw": 403 }, { "id": "cGvds5DwtZPNc9ZR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.812, 0.435, 0.972, 0.511]." }, { "from": "gpt", "value": "This area contains the legend descriptions and associated color markers for the line graph. The labels include \"Innovation Index\", \"Business Environment\", and \"High-Tech Enterprises\"." } ], "id_raw": 403 }, { "id": "GeRkL2OAnvwuExE1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.076, 0.926, 0.601, 0.949]." }, { "from": "gpt", "value": "This area contains the x-axis labels, representing Time Periods from '1' through '5'." } ], "id_raw": 403 }, { "id": "ru1EVV5e0bGfbiS5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.204, 0.926, 0.344, 0.949]?" }, { "from": "gpt", "value": "There are 2 objects - The x-axis tick labels '2' and '3' are located in these coordinates." } ], "id_raw": 403 }, { "id": "7UEeqVOmF2O6jRF8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at coordinates [0.019, 0.319, 0.043, 0.604] represent?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 404 }, { "id": "VEeqM3hUM0wCl48y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.496, 0.837, 0.551, 0.864]?" }, { "from": "gpt", "value": "The legend item marker for 'Nuclear Energy'." } ], "id_raw": 404 }, { "id": "usz0Pvr03aPxwMle", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the {x-axis label} at [0.937, 0.901, 0.981, 0.934]?" }, { "from": "gpt", "value": "1.0." } ], "id_raw": 404 }, { "id": "kCXKTlx6lP74b1KQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_4.png", "conversations": [ { "from": "human", "value": "\nIs the {point of 'Fossil Fuels'}[0.277, 0.187] greater or lesser than the value of {point of 'Nuclear Energy'}[0.277, 0.546] at the same time period?" }, { "from": "gpt", "value": "Lesser." } ], "id_raw": 404 }, { "id": "OsXSryjFt6JLIrN6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_4.png", "conversations": [ { "from": "human", "value": "\nIs the last point of {the 'Renewable Energy' line}[0.959, 0.250] located above or below the last point of {the 'Fossil Fuels' line}[0.959, 0.335]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 404 }, { "id": "BiiW2vUam79AvufS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_4.png", "conversations": [ { "from": "human", "value": "\nWhich energy source has a higher allocation at time period {0.4}[0.448, 0.610], Renewable Energy or Nuclear Energy?" }, { "from": "gpt", "value": "Nuclear Energy." } ], "id_raw": 404 }, { "id": "ICYO7OU4VvtVYsGP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.107, 0.039, 0.959, 0.885] contain?" }, { "from": "gpt", "value": "This area contains the plot of trends in energy allocation over time. It has line graphs for 'Renewable Energy', 'Fossil Fuels', and 'Nuclear Energy'." } ], "id_raw": 404 }, { "id": "S3wbEG0bWuMo2Nwm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_4.png", "conversations": [ { "from": "human", "value": "\nDescribe what is within [0.074, 0.831, 0.695, 0.873]." }, { "from": "gpt", "value": "The area represents the legends for the line plots. It includes {Renewable Energy}[0.129, 0.840, 0.291, 0.864], {Fossil Fuels}[0.374, 0.840, 0.471, 0.864], and {Nuclear Energy}[0.555, 0.840, 0.688, 0.864], each with a corresponding color marker." } ], "id_raw": 404 }, { "id": "L4NvmZOa58kQkDUz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_4.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region [0.107, 0.462, 0.789, 0.631]?" }, { "from": "gpt", "value": "This region includes portions of all three energy line graphs, specifically the mid-latter trends for 'Renewable Energy', 'Fossil Fuels', and 'Nuclear Energy'." } ], "id_raw": 404 }, { "id": "sc9YHlqP5yhsMK38", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label associated with {the Y-axis}[0.019, 0.313, 0.043, 0.558] in the chart?" }, { "from": "gpt", "value": "Reported Level." } ], "id_raw": 405 }, { "id": "IMxcP6e0dKQM7Zvk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing Anxiety}[0.107, 0.237, 0.959, 0.773]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 405 }, { "id": "eYSgAYkE3a06h48Y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-tick label for a tick} at [0.049, 0.422, 0.094, 0.456]?" }, { "from": "gpt", "value": "$3.0$." } ], "id_raw": 405 }, { "id": "sfsV9v7oCTNm55YJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_5.png", "conversations": [ { "from": "human", "value": "\nWhich line is located higher at {Symptom intensity: 0.7}[0.675, 0.654]: Anxiety or Depression?" }, { "from": "gpt", "value": "Depression." } ], "id_raw": 405 }, { "id": "Pp8pxTWrWFFgRScS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_5.png", "conversations": [ { "from": "human", "value": "\nWhere does {the line for Depression}[0.107, 0.039, 0.959, 0.515] cross {the y-axis at the level of 3.0} [0.107, 0.436]?" }, { "from": "gpt", "value": "At Symptom intensity of approximately 0.9." } ], "id_raw": 405 }, { "id": "rI651JoUn5vEBRWJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_5.png", "conversations": [ { "from": "human", "value": "\nDoes {the Anxiety line}[0.107, 0.237, 0.959, 0.773] end higher or lower \nthan {the Depression line}[0.107, 0.039, 0.959, 0.515]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 405 }, { "id": "dlBsIhNpfuMnGPi4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.179, 0.849, 0.223, 0.882]." }, { "from": "gpt", "value": "The area contains the x-axis tick label '0.2' in black color." } ], "id_raw": 405 }, { "id": "aovRJgUaKGjjWolF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_5.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.115, 0.903, 0.275, 0.980]?" }, { "from": "gpt", "value": "This region contains the legend for the lines: {Anxiety}[0.112, 0.909, 0.237, 0.936] which is represented by a blue line and {Depression}[0.112, 0.943, 0.268, 0.971] which is represented by a green line." } ], "id_raw": 405 }, { "id": "ZYPrWsyLB1EpvOK9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.484,0.849,0.508,0.882]?" }, { "from": "gpt", "value": "This region includes the x-axis label '0.5'." } ], "id_raw": 405 }, { "id": "TnCbHDeFS9jeWUnR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the text contained within this coordinates [0.181, 0.950, 0.249, 0.973]?" }, { "from": "gpt", "value": "Savings." } ], "id_raw": 406 }, { "id": "cT2oTLwcsCWOxBK5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_6.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the curve}[0.116, 0.098, 0.959, 0.465] in the chart?" }, { "from": "gpt", "value": "Savings." } ], "id_raw": 406 }, { "id": "ChGDAUzSmVsreKkV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_6.png", "conversations": [ { "from": "human", "value": "\nWhat text is found at {the legend item}[0.332, 0.950, 0.405, 0.973]?" }, { "from": "gpt", "value": "Expense." } ], "id_raw": 406 }, { "id": "syMkfgUtXiqfMEkx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_6.png", "conversations": [ { "from": "human", "value": "\nIs the point for {Expense}[0.622, 0.722] higher or lower than the point for {Revenue}[0.622, 0.245] at the 0.6 mark on the x-axis?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 406 }, { "id": "09h4fyJt89xSGMQm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_6.png", "conversations": [ { "from": "human", "value": "\nDoes the {Savings line}[0.959, 0.098] intersect with the {Revenue line}[0.959, 0.062]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 406 }, { "id": "19A0ozVQ9ob255b9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_6.png", "conversations": [ { "from": "human", "value": "\nAt x-axis point 0.8, is {Revenue}[0.791, 0.098] higher or lower than {Savings}[0.791, 0.172]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 406 }, { "id": "PTo8JDkVKnabKXUl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the region [0.125, 0.941, 0.571, 0.983]." }, { "from": "gpt", "value": "The area contains legend items - {Savings}[0.181, 0.950, 0.249, 0.973], {Expense}[0.332, 0.950, 0.405, 0.973], and {Revenue}[0.489, 0.950, 0.564, 0.973]. Each legend item is accompanied by its line color." } ], "id_raw": 406 }, { "id": "5sL2UBtonyNvn7NE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_6.png", "conversations": [ { "from": "human", "value": "\nWhat are the components of the area denoted by [0.263, 0.849, 0.307, 0.882]?" }, { "from": "gpt", "value": "The area contains {the x-axis tick label}[0.263, 0.849, 0.307, 0.882] indicating the value '0.2'." } ], "id_raw": 406 }, { "id": "10Oql8FGyPKVFrFB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_6.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the region [0.431, 0.849, 0.476, 0.882]?" }, { "from": "gpt", "value": "The region includes {the x-axis tick label}[0.431, 0.849, 0.476, 0.882] with a value of '0.4'." } ], "id_raw": 406 }, { "id": "4r0vbzB80kx09aAm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.015, 0.368, 0.033, 0.553] in the chart image?" }, { "from": "gpt", "value": "This is the y-axis label." } ], "id_raw": 408 }, { "id": "g4GxhNWe3CaTat2t", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_8.png", "conversations": [ { "from": "human", "value": "\nWhat exists at the coordinate [0.615, 0.117, 0.654, 0.135]?" }, { "from": "gpt", "value": "It is a color marker for 'Population Growth' legend item." } ], "id_raw": 408 }, { "id": "EFJEgLvE8KRXIZVM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_8.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the position [0.495, 0.912, 0.623, 0.936]?" }, { "from": "gpt", "value": "The x-axis tick label 'Contemporary Era'." } ], "id_raw": 408 }, { "id": "KnmPZ4dL7W6xwQAH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_8.png", "conversations": [ { "from": "human", "value": "\nBetween {the 'Civilization Development Index' line}[0.095, 0.221, 0.559, 0.533] and {the 'Science and Technology Development Index' line}[0.095, 0.199, 0.559, 0.824], which line has a higher value at the 'Renaissance Era' point?" }, { "from": "gpt", "value": "The 'Science and Technology Development Index' line." } ], "id_raw": 408 }, { "id": "QEzRDqoWmJFkV95v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_8.png", "conversations": [ { "from": "human", "value": "\nAt the coordinate {position}[0.559, 0.170], which line is represented?" }, { "from": "gpt", "value": "The 'Population Growth' line." } ], "id_raw": 408 }, { "id": "vlFEvvETL5dztmwZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_8.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Population Growth' data point at 'Contemporary Era'}[0.559, 0.170] higher or lower than {the 'Science and Technology Development Index' data point at 'Contemporary Era'}[0.559, 0.199]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 408 }, { "id": "OQR6TgesZkTxW2Kz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within [0.615, 0.037, 0.972, 0.148]." }, { "from": "gpt", "value": "The enclosed area is the legend of the chart. It contains three items: {Civilization Development Index}[0.659, 0.046, 0.876, 0.069], {Science and Technology Development Index}[0.659, 0.081, 0.966, 0.104], and {Population Growth}[0.659, 0.116, 0.787, 0.139] each coupled with a colored marker." } ], "id_raw": 408 }, { "id": "S5irS6s7Dlqwc9NU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_8.png", "conversations": [ { "from": "human", "value": "\nBriefly explain what is contained in the coordinates [0.095, 0.199, 0.559, 0.533]." }, { "from": "gpt", "value": "The coordinates encompass the 'Civilization Development Index' line plot with its corresponding data points." } ], "id_raw": 408 }, { "id": "3l4vwhRfRndwgFSx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_8.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the region box [0.055, 0.912, 0.623, 0.936]?" }, { "from": "gpt", "value": "This region includes the x-axis captions for the time periods: {Ancient Era} at [0.055, 0.912, 0.135, 0.936], {Medieval Era} at [0.166, 0.912, 0.256, 0.936], {Renaissance Era} at [0.270, 0.912, 0.384, 0.936], {Modern Era} at [0.403, 0.912, 0.483, 0.936], and {Contemporary Era} at [0.495, 0.912, 0.623, 0.936]." } ], "id_raw": 408 }, { "id": "cu2OYtyfQK0c6DVg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label for 'Bitcoin'}[0.181, 0.950, 0.240, 0.973] in the legend?" }, { "from": "gpt", "value": "Line of Bitcoin Price." } ], "id_raw": 409 }, { "id": "WLSFItnwV77XcBkr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.019, 0.389, 0.043, 0.468] on the chart?" }, { "from": "gpt", "value": "Price." } ], "id_raw": 409 }, { "id": "QcIwLBgyJiDGcjl9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-coordinate}[0.627, 0.833] for 2019?" }, { "from": "gpt", "value": "The X-coordinate represents the Year 2019." } ], "id_raw": 409 }, { "id": "7Yb8o44lSSmi1OQl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_9.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Ethereum'}[0.156, 0.756, 0.942, 0.795] higher or lower than the line for 'Bitcoin'[0.156, 0.062, 0.942, 0.779] in its general direction?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 409 }, { "id": "gum3TArsOvkqKKG8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_9.png", "conversations": [ { "from": "human", "value": "\nHow does the position of the {2018 data point}[0.470, 0.622] on the 'Bitcoin' line compare to that on the 'Ethereum' line[0.470, 0.788]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 409 }, { "id": "ULlrjaozhL4P72ZI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_9.png", "conversations": [ { "from": "human", "value": "\nWhich line is at a higher value at {2020}[0.785, 0.833], 'Bitcoin' or 'Ethereum'?" }, { "from": "gpt", "value": "Bitcoin." } ], "id_raw": 409 }, { "id": "2Sm98FU9jieZdXVN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_9.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects located inside the region [0.156, 0.062, 0.942, 0.779]?" }, { "from": "gpt", "value": "The region encompasses three line plots labeled Bitcoin, Ethereum, and Ripple, representing their respective price trends from 2016 to 2021." } ], "id_raw": 409 }, { "id": "CY5hbw9pEMOyRsJ9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_9.png", "conversations": [ { "from": "human", "value": "\nWhat contents are housed in the region [0.125, 0.941, 0.553, 0.983]?" }, { "from": "gpt", "value": "The region includes the legend of the chart with labels for Bitcoin, Ethereum, and Ripple, each paired with a colored marker." } ], "id_raw": 409 }, { "id": "C31D6zoSB3RLaqJ7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.116, 0.025, 0.981, 0.833] on the chart contain?" }, { "from": "gpt", "value": "This area contains the main chart axis with labeled tick marks for the Years on the x-axis and Prices on the y-axis. It also contains the actual data points and lines of Bitcoin, Ethereum, and Ripple." } ], "id_raw": 409 }, { "id": "vZqI9Absl1SsF4A5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the object with coordinates [0.019, 0.241, 0.043, 0.616] represent on the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 412 }, { "id": "i4o8GCJIO2e5pbvY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_12.png", "conversations": [ { "from": "human", "value": "\nWhat is at the seventh coordinate of the X-axis tick label?" }, { "from": "gpt", "value": "[0.759, 0.849, 0.803, 0.882]" } ], "id_raw": 412 }, { "id": "sbzsINYiVsT9RtY8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_12.png", "conversations": [ { "from": "human", "value": "\nWhat value does {the y-axis tick label} at [0.051, 0.636, 0.112, 0.669] represent?" }, { "from": "gpt", "value": "0.85." } ], "id_raw": 412 }, { "id": "P6iPHwb6kTP7r7wt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Continuous integration performance'}[0.781, 0.355] higher or lower than {the point of 'Innovation potential'}[0.781, 0.245] at x=1.0?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 412 }, { "id": "qlAjAZi9952OVCkf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_12.png", "conversations": [ { "from": "human", "value": "\nWhich line is at a higher position at x=0.4, {the 'Continuous integration performance'}[0.387, 0.209] line or {the 'Innovation potential'}[0.387, 0.098] line?" }, { "from": "gpt", "value": "'Continuous integration performance' line." } ], "id_raw": 412 }, { "id": "ydEPUN13EqAx9YYn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_12.png", "conversations": [ { "from": "human", "value": "\nAt the x-coordinate 0.2, which measure shows a higher performance, 'Continuous integration performance' or 'Project management quality'?" }, { "from": "gpt", "value": "'Continuous integration performance'." } ], "id_raw": 412 }, { "id": "2j2LYhstpqm5UsR2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_12.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained in the region [0.133, 0.941, 1.099, 0.983]?" }, { "from": "gpt", "value": "It is the location of the legend of the chart. It contains three items: {Continuous integration performance}[0.189, 0.950, 0.504, 0.973] with a blue marker, {Project management quality}[0.588, 0.950, 0.834, 0.973] with a green marker, and {Innovation potential}[0.918, 0.950, 1.093, 0.973] with a red marker." } ], "id_raw": 412 }, { "id": "EIsm4Z0q0WSEq4mf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_12.png", "conversations": [ { "from": "human", "value": "\nDescribe what is in the area [0.124, 0.172, 0.781, 0.355]." }, { "from": "gpt", "value": "This area contains the data points and line for the 'Continuous integration performance' measure. The line starts from {point}[0.124, 0.209] with a value of 0.0, and ends at {point}[0.781, 0.355] with a value of 1.0." } ], "id_raw": 412 }, { "id": "U3eHkUUr90d5C6tS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_12.png", "conversations": [ { "from": "human", "value": "\nWhat types of objects are observed in the region [0.518, 0.209, 0.781, 0.355]?" }, { "from": "gpt", "value": "There are 3 objects in this area. They are {[0.518, 0.245], [0.650, 0.319], [0.781, 0.355]} representing the 4th, 5th, and 6th markers on the 'Continuous integration performance' line." } ], "id_raw": 412 }, { "id": "TiMaicd2zMEdGUua", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_13.png", "conversations": [ { "from": "human", "value": "\nCan you tell the color of {the line representing 'Tech Industry'}[0.123, 0.164, 0.940, 0.534]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 413 }, { "id": "xJX7a2xhZxTiJEHs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of y-axis at the coordinates [0.019, 0.290, 0.041, 0.579]?" }, { "from": "gpt", "value": "Market Share (%)." } ], "id_raw": 413 }, { "id": "65Fv8VQVe0fjZ44G", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label}[0.265, 0.810, 0.309, 0.838]?" }, { "from": "gpt", "value": "2016." } ], "id_raw": 413 }, { "id": "N4f1QMue2NNb9OTu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_13.png", "conversations": [ { "from": "human", "value": "\nComparing two points in 2020 on the 'Food Industry' and 'Tech Industry' lines, which industry had the higher market share?" }, { "from": "gpt", "value": "Food Industry." } ], "id_raw": 413 }, { "id": "AA0tcTWiLvWBnoBZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_13.png", "conversations": [ { "from": "human", "value": "\nWhere does {the line for 'Tech Industry'}[0.123, 0.164, 0.940, 0.534] at the point in 2018 lie in relation to {the line for 'Food Industry'}[0.123, 0.278, 0.940, 0.648]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 413 }, { "id": "gIYKTwytK7GSlwbB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_13.png", "conversations": [ { "from": "human", "value": "\nHow does the position of {the line for 'Automotive Industry'}[0.123, 0.591, 0.940, 0.762] at the point in 2016 compare to the position of {the line for 'Food Industry'}[0.123, 0.278, 0.940, 0.648] at the same point in time?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 413 }, { "id": "1J7CX8ragn257GiY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the section with boundaries at [0.290, 0.030, 0.774, 0.070]?" }, { "from": "gpt", "value": "This section contains the subtitle of the chart, \"Industry Market Share Comparison\", written in black." } ], "id_raw": 413 }, { "id": "k6OimCqEgywaM9dU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_13.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the content within the region [0.091, 0.911, 0.731, 0.961]?" }, { "from": "gpt", "value": "The region contains the Legend of the chart with three industries: {Tech Industry}[0.147, 0.922, 0.262, 0.950], {Automotive Industry}[0.345, 0.922, 0.523, 0.950], and {Food Industry}[0.606, 0.922, 0.724, 0.950] marked by blue, red, and green markers, respectively." } ], "id_raw": 413 }, { "id": "TJkhjGfE0IbS9zoH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements located within the region [0.101, 0.810, 0.799, 0.838]?" }, { "from": "gpt", "value": "The region contains x-axis tick labels representing the years from 2015 to 2019." } ], "id_raw": 413 }, { "id": "oiSg0Y10V56SmBF1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_14.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the object in the coordinates of [0.015, 0.407, 0.034, 0.513] represents in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 414 }, { "id": "FdhPAcbjT5IFsRB2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_14.png", "conversations": [ { "from": "human", "value": "\nWhat is present at the coordinates [0.902, 0.081, 0.953, 0.104] in the chart?" }, { "from": "gpt", "value": "The label for Series2." } ], "id_raw": 414 }, { "id": "IHJuqEFM3xtlpefQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the point of Series1 in Trend type 'Gradual increase'} at the coordinates [0.383, 0.100]?" }, { "from": "gpt", "value": "The value is 2.03." } ], "id_raw": 414 }, { "id": "W9dI3SbHm1VymMfj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_14.png", "conversations": [ { "from": "human", "value": "\nWho has higher value at 'Gradual Increase' {Series1}[0.383, 0.100] or {Series2}[0.383, 0.661]?" }, { "from": "gpt", "value": "Series1." } ], "id_raw": 414 }, { "id": "HIdv1oXLyq2nM2ih", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point of Series3 for 'Unstable behavior'}[0.654, 0.345] located higher or lower than {the point of Series1 for 'Unstable behavior'}[0.654, 0.201]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 414 }, { "id": "zv955PEbWCQsnFJR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_14.png", "conversations": [ { "from": "human", "value": "\nAre the values in {Series1}[0.112, 0.100, 0.925, 0.826] generally higher or lower than the values in {Series3}[0.112, 0.065, 0.925, 0.812]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 414 }, { "id": "3QnvhYbVQqmuYkKJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is found in the area given by [0.322, 0.880, 0.920, 0.950]?" }, { "from": "gpt", "value": "It contains x-axis labels, including 'Persistent rise', 'Gradual increase', 'Unstable behavior', and 'Gradual decline'." } ], "id_raw": 414 }, { "id": "gf3ZqSUnigV2RzMy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_14.png", "conversations": [ { "from": "human", "value": "\nWhat lies in the space defined by [0.857, 0.037, 0.958, 0.148]?" }, { "from": "gpt", "value": "This area houses the legend entries: {Series1}[0.902, 0.046, 0.953, 0.069] with a red marker, {Series2}[0.902, 0.081, 0.953, 0.104] with a blue marker, and {Series3}[0.902, 0.116, 0.953, 0.139] with a green marker." } ], "id_raw": 414 }, { "id": "KxFuB5cD8VvhK5sy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_14.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the sector from [0.112, 0.065, 0.925, 0.826] encompasses?" }, { "from": "gpt", "value": "The space includes the entire data plot, including all trend lines and data points from Series1, Series2, and Series3." } ], "id_raw": 414 }, { "id": "KCS94MbTzluDL4Qj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the object labeled with 'Year' in the chart?" }, { "from": "gpt", "value": "It's {the x-axis label}[0.498, 0.836, 0.545, 0.865]." } ], "id_raw": 415 }, { "id": "TrE70Ih29IHicQFL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_15.png", "conversations": [ { "from": "human", "value": "\nWhat object is described by the coordinates [0.346, 0.918, 0.462, 0.938]?" }, { "from": "gpt", "value": "It's {the legend for 'DNA Sequencing'}[0.346, 0.918, 0.462, 0.938]." } ], "id_raw": 415 }, { "id": "n1iIQBZJbXrPaiBC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.041, 0.561, 0.072, 0.588]?" }, { "from": "gpt", "value": "'$0.6$'." } ], "id_raw": 415 }, { "id": "fmNBHLrSQIo7NvpP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_15.png", "conversations": [ { "from": "human", "value": "\nIn 2023, did {AI Research}[0.609, 0.248, 0.634, 0.275] have a higher development scale than {Quantum Computing}[0.609, 0.572, 0.634, 0.599]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 415 }, { "id": "PA1qBCa3N9y1sWQu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_15.png", "conversations": [ { "from": "human", "value": "\nDoes the {line representing DNA Sequencing}[0.082, 0.226, 0.960, 0.528] intersect with the {line representing Quantum Computing}[0.082, 0.356, 0.960, 0.733]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 415 }, { "id": "U8AEMcdyvtixokgv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_15.png", "conversations": [ { "from": "human", "value": "\nBetween 2021 and 2022, did {AI Research}[0.258, 0.464, 0.433, 0.323] see a downward or upward trend in development scale?" }, { "from": "gpt", "value": "Upward." } ], "id_raw": 415 }, { "id": "dhOowMgNXzSE6Sgx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.041, 0.777, 0.785, 0.828]." }, { "from": "gpt", "value": "This region contains the x-axis tick labels for each year from 2020 to 2025, which are evenly spaced out and positioned below the main chart area." } ], "id_raw": 415 }, { "id": "ESPUdBhpp15bU64R", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.089, 0.911, 0.468, 0.976]." }, { "from": "gpt", "value": "This region contains the legend for the chart with three items: {AI Research}[0.134, 0.918, 0.215, 0.938], {Quantum Computing}[0.134, 0.948, 0.279, 0.968], and {DNA Sequencing}[0.346, 0.918, 0.462, 0.938]." } ], "id_raw": 415 }, { "id": "0MAYctdbMutTBdWA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_15.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.409, 0.801, 0.634, 0.828]?" }, { "from": "gpt", "value": "Within these coordinates, there are two x-axis tick labels, {2022}[0.409, 0.801, 0.458, 0.828] and {2023}[0.584, 0.801, 0.634, 0.828]." } ], "id_raw": 415 }, { "id": "bJMWJEWmlVP41ck2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_16.png", "conversations": [ { "from": "human", "value": "\nWhat lies within the coordinates [0.015, 0.255, 0.035, 0.564] in the plot?" }, { "from": "gpt", "value": "It's the y-axis label, which is 'Development Scale'." } ], "id_raw": 416 }, { "id": "aIGPQ0LcFbqxhu86", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_16.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.233, 0.801, 0.282, 0.828] in this chart?" }, { "from": "gpt", "value": "It's an x-axis tick label, the label is '2021'." } ], "id_raw": 416 }, { "id": "qZuPHJBQA0zlyyoM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_16.png", "conversations": [ { "from": "human", "value": "\nWhat is {the color of the line}[0.082, 0.356, 0.960, 0.733] in the chart?" }, { "from": "gpt", "value": "The color is red, indicating the 'Quantum Computing' trend." } ], "id_raw": 416 }, { "id": "tnPMMFPzAXEUJ8zm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_16.png", "conversations": [ { "from": "human", "value": "\nWhat lies higher on the y-axis at 2022: {the 'AI Research' trend}[0.433, 0.323] or {the 'Quantum Computing' trend}[0.433, 0.626]?" }, { "from": "gpt", "value": "AI Research." } ], "id_raw": 416 }, { "id": "LCowUtiPHBldTGDV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_16.png", "conversations": [ { "from": "human", "value": "\nComparatively, is the {trend for 'DNA Sequencing'}[0.609, 0.226] higher or lower than the {trend for 'Quantum Computing'}[0.609, 0.572] in 2023?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 416 }, { "id": "NEaFxZ7K16V1XhsQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_16.png", "conversations": [ { "from": "human", "value": "\nAt 2025, which has a greater value: {the 'Quantum Computing' trend}[0.960, 0.356] or {the 'DNA Sequencing' trend}[0.960, 0.377]?" }, { "from": "gpt", "value": "DNA Sequencing." } ], "id_raw": 416 }, { "id": "sbIQlILFXAQDLRNp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_16.png", "conversations": [ { "from": "human", "value": "\nWhat content is found within the region [0.089, 0.911, 0.468, 0.976]?" }, { "from": "gpt", "value": "This region contains the legend for the chart with labels 'AI Research' and 'Quantum Computing' found at {coordinates}[0.134, 0.918, 0.215, 0.938] and {coordinates}[0.134, 0.948, 0.279, 0.968] respectively. Also, label 'DNA Sequencing' is found at {coordinates}[0.346, 0.918, 0.462, 0.938]. Each label is paired with a different color." } ], "id_raw": 416 }, { "id": "9nIYYhcF1F3NsiwX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.082, 0.787, 0.960, 0.828] enclose?" }, { "from": "gpt", "value": "It includes the x-axis tick labels, from '2020' to '2025', spaced evenly along the x-axis." } ], "id_raw": 416 }, { "id": "3cwbX1u7CqH6VCPD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.082, 0.140, 0.960, 0.787]." }, { "from": "gpt", "value": "This area contains three line graphs, representing 'AI Research', 'Quantum Computing', and 'DNA Sequencing'. They have different colors and marker styles, and their coordinates represent their values from 2020 to 2025." } ], "id_raw": 416 }, { "id": "j2lQIGNpAlWjXlTw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_17.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.098, -0.043, 0.115, -0.019] in the chart?" }, { "from": "gpt", "value": "Y-axis label '60'." } ], "id_raw": 417 }, { "id": "fx4F934uCexKgZBR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend item at [0.176, 0.211, 0.277, 0.234] represent?" }, { "from": "gpt", "value": "Music Industry." } ], "id_raw": 417 }, { "id": "JTMqPBX3HUmQ2pnB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_17.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.160, 0.769, 0.865, 0.855] in the chart?" }, { "from": "gpt", "value": "The line representing Movie Box Office revenue." } ], "id_raw": 417 }, { "id": "Ib37X76MzMrLe73R", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_17.png", "conversations": [ { "from": "human", "value": "\nIs the {year 2004 point of Video Game Revenue}[0.301, 0.583] higher or lower than the {year 2004 point of Music Industry}[0.301, 0.613]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 417 }, { "id": "NrtxsbhXF3TFTaQe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_17.png", "conversations": [ { "from": "human", "value": "\nWhat's the relation between the {year 2010 point of Video Game Revenue}[0.512, 0.330] and the {year 2010 point of Movie Box Office}[0.512, 0.729]?" }, { "from": "gpt", "value": "The Video Game Revenue is higher." } ], "id_raw": 417 }, { "id": "R5JcPOfzXGWpxC6U", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_17.png", "conversations": [ { "from": "human", "value": "\nWhich industry had higher revenue in 2000, {Video Game Revenue}[0.160, 0.615] or {Music Industry}[0.160, 0.530]?" }, { "from": "gpt", "value": "Music Industry." } ], "id_raw": 417 }, { "id": "8mDM6UkMKRsToWgF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.098, 0.112, 0.115, 0.135]." }, { "from": "gpt", "value": "The area contains the Y-axis label '50'." } ], "id_raw": 417 }, { "id": "RfUoafrXIIwReglp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_17.png", "conversations": [ { "from": "human", "value": "\nWhat exists in the space [0.265, 0.700, 0.275, 0.715]?" }, { "from": "gpt", "value": "The area contains a point on the Movie Box Office revenue line for a specific year." } ], "id_raw": 417 }, { "id": "Qd1xVPudoSX4wEps", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_17.png", "conversations": [ { "from": "human", "value": "\nDescribe all elements within [0.849, 0.865, 0.889, 0.930]." }, { "from": "gpt", "value": "There are two elements within this region. One is the X-axis label '2020.0' located at {coordinates}[0.841, 0.906, 0.889, 0.930] and the other is the last data point for all three lines indicating the revenue of each industry in the year 2020." } ], "id_raw": 417 }, { "id": "7LHU8fzjV7km6yvU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_18.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.015, 0.335, 0.034, 0.585] refer to in the chart?" }, { "from": "gpt", "value": "This is the location of the y-axis label, which is \"Number of Jobs\"." } ], "id_raw": 418 }, { "id": "Y6AioLlPDcVKXBl9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.153, 0.781, 0.321, 0.804] in the subplot legend?" }, { "from": "gpt", "value": "It is the label for the \"Technology-Related Jobs\"." } ], "id_raw": 418 }, { "id": "oywYCscl8AGbStzj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_18.png", "conversations": [ { "from": "human", "value": "\nWhat does {the point on the 'Public Sector Jobs' line}[0.945, 0.167] signify?" }, { "from": "gpt", "value": "It signifies the number of 'Public Sector Jobs' in the year 2020, which is 93000." } ], "id_raw": 418 }, { "id": "XQfTij2vu7ulw5zA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_18.png", "conversations": [ { "from": "human", "value": "\nIn the year 2020, is {the point for 'Education Worker Jobs'}[0.945, 0.548] above or below {the point for 'Public Sector Jobs'}[0.945, 0.167]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 418 }, { "id": "zD9vgEnwlEAvdbS8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_18.png", "conversations": [ { "from": "human", "value": "\nAt the year 2016, does {the point on the 'Technology-Related Jobs' line}[0.303, 0.833] have a higher or lower value than {the point on the 'Public Sector Jobs' line}[0.303, 0.079]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 418 }, { "id": "ZLsBR4Vq7NNMNs0V", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_18.png", "conversations": [ { "from": "human", "value": "\nBetween the years 2018 and 2019, does the value of {the 'Education Worker Jobs' line}[0.624, 0.526], [0.784, 0.533] increase or decrease?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 418 }, { "id": "DcGKw8DZkW0fu0EI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content located within the coordinates [0.109, 0.771, 0.327, 0.883]?" }, { "from": "gpt", "value": "This is the area of the chart's legend. It includes labels for 'Technology-Related Jobs', 'Education Worker Jobs', and 'Public Sector Jobs', each accompanied by a different color-coded marker." } ], "id_raw": 418 }, { "id": "LfDEVgXN7dFzptln", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_18.png", "conversations": [ { "from": "human", "value": "\nCan you provide details about the content in the area [0.142, 0.738, 0.945, 0.855]?" }, { "from": "gpt", "value": "This area maps the trend of 'Technology-Related Jobs' from 2015 to 2020. The points represent the job numbers for each respective year." } ], "id_raw": 418 }, { "id": "99GdibogKWefcWcM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_18.png", "conversations": [ { "from": "human", "value": "\nWhat information does the region [0.142, 0.065, 0.945, 0.167] present?" }, { "from": "gpt", "value": "This region represents the trend of 'Public Sector Jobs', with different points marking the number of jobs for each year from 2015 to 2020." } ], "id_raw": 418 }, { "id": "DicPmhcAHehw2cPh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_19.png", "conversations": [ { "from": "human", "value": "\nWhat the label of {the y-axis}[0.015, 0.249, 0.034, 0.535] in the chart?" }, { "from": "gpt", "value": "Subscription Rate." } ], "id_raw": 419 }, { "id": "a9Z3ueNakOqlF084", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the last quarter label in the x-axis?" }, { "from": "gpt", "value": "[0.681, 0.763, 0.783, 0.934]." } ], "id_raw": 419 }, { "id": "qMdz0vjD9mRaxnsX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_21_19.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the legend for 'Walmart Plus'}[0.824, 0.149, 0.934, 0.179] associated with in the chart?" }, { "from": "gpt", "value": "Green (#008000)." } ], "id_raw": 419 }, { "id": "xJqJaSw3prqLXHrS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_19.png", "conversations": [ { "from": "human", "value": "\nIs {the data point of 'eBay Prime' for 'Q2_next_year'}[0.322, 0.585] higher or lower compared to {the 'Amazon Subscribe'}[0.322, 0.463] in same quarter?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 419 }, { "id": "0AVrF9Y8UooyNtG1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_19.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Amazon Subscribe'}[0.117, 0.138, 0.732, 0.646] generally above or below {the line for 'Walmart Plus'}[0.117, 0.422, 0.732, 0.625]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 419 }, { "id": "qA6xWXUpqcedN9Je", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_21_19.png", "conversations": [ { "from": "human", "value": "\nWhich subscriptions show a higher rate in 'Q1_next_year': eBay Prime or Walmart Plus?" }, { "from": "gpt", "value": "eBay Prime." } ], "id_raw": 419 }, { "id": "o9gWDPSZ584QWOGY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.771, 0.051, 0.985, 0.191]." }, { "from": "gpt", "value": "This area contains the legend of the chart. It contains three labels with each paired with a colored marker. The labels are: {eBay Prime}[0.824, 0.062, 0.919, 0.092] (Blue marker), {Amazon Subscribe}[0.824, 0.106, 0.978, 0.136] (Red marker), and {Walmart Plus}[0.824, 0.149, 0.934, 0.179] (Green marker)." } ], "id_raw": 419 }, { "id": "eI9HrOR2Da3FFC5E", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.234, 0.763, 0.328, 0.919]." }, { "from": "gpt", "value": "This area contains the x-axis label for {Q1_next_year}[0.234, 0.763, 0.328, 0.919] which is roughly in the middle of the x-axis." } ], "id_raw": 419 }, { "id": "DisJs0i0f4cRuvOl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_21_19.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.516, 0.022, 0.588, 0.065]?" }, { "from": "gpt", "value": "This span appears to contain no objects, it is likely blank space on the chart." } ], "id_raw": 419 }, { "id": "hWf3WpGo1DhSagkW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the bottom right corner, indicated by coordinates [0.881, 0.919, 0.916, 0.942]?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 420 }, { "id": "pGxriH5SVkqOeRLl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the y-axis label}[0.015, 0.386, 0.029, 0.579]?" }, { "from": "gpt", "value": "tab:blue." } ], "id_raw": 420 }, { "id": "shnGyr0DqmQJoCVo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_0.png", "conversations": [ { "from": "human", "value": "\nIdentify the shape of {the marker associated with the 'Knowledge Economy Index'}[0.070, 0.121, 0.106, 0.135]." }, { "from": "gpt", "value": "Square." } ], "id_raw": 420 }, { "id": "27B6P0nLUzMo7Gok", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '76'}[0.580, 0.413] above or below {the point labeled '77'}[0.381, 0.578]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 420 }, { "id": "TCXzyuTx3G5nd1Cz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_0.png", "conversations": [ { "from": "human", "value": "\nAre the lines for {the 'Innovation Index'}[0.102, 0.100, 0.898, 0.865] and {the 'Knowledge Economy Index'}[0.102, 0.100, 0.898, 0.865] intersecting or separate?" }, { "from": "gpt", "value": "Separate." } ], "id_raw": 420 }, { "id": "jFdliRtdKjwuEd2r", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_0.png", "conversations": [ { "from": "human", "value": "\nWhich index had a higher value in the year 2017, {Innovation}[0.779, 0.268] or {Knowledge Economy}[0.779, 0.205]?" }, { "from": "gpt", "value": "Innovation." } ], "id_raw": 420 }, { "id": "KJuWulDYqX33Ujcb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object located at [0.096, 0.919, 0.131, 0.942] in the chart?" }, { "from": "gpt", "value": "Year label." } ], "id_raw": 422 }, { "id": "WbWACMjA84VAoYRc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at {coordinates}[0.307, 0.649]?" }, { "from": "gpt", "value": "23.3." } ], "id_raw": 422 }, { "id": "JXBZya1NFG2wwKod", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_2.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {the label 'CO2 Emissions'}[0.127, 0.118, 0.229, 0.141]?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 422 }, { "id": "i7MwN5ZjcX3rrUY2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point at '1990'}[0.114, 0.865] higher than {the point at '1991'}[0.152, 0.806] on the CO2 Emissions plot?" }, { "from": "gpt", "value": "No." } ], "id_raw": 422 }, { "id": "RkfW8vxPUlMgK7oU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the tick for '1996' on the CO2 emissions plot}[0.346, 0.903] occur before or after {the tick for '1995' on the Temperature plot}[0.307, 0.903]?" }, { "from": "gpt", "value": "After." } ], "id_raw": 422 }, { "id": "ozeFSeVq0FHNkKZG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_2.png", "conversations": [ { "from": "human", "value": "\nWhich label is closer to the bottom, {the tick labeled '2008'}[0.809, 0.903] or {the tick labeled '2010'}[0.886, 0.903]?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 422 }, { "id": "sX7VjWRzzcPUdyQv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_2.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region bounded by [0.075, 0.915, 0.200, 0.942]?" }, { "from": "gpt", "value": "The year labels '1990', '1991', '1992' are within this region, each represented with {black text}[0.096, 0.919, 0.131, 0.942], {black text}[0.135, 0.919, 0.170, 0.942], {black text}[0.173, 0.919, 0.209, 0.942] respectively." } ], "id_raw": 422 }, { "id": "Dglx1j8DNviZGkBc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_2.png", "conversations": [ { "from": "human", "value": "\nDescribe what is located in the area [0.025, 0.855, 0.065, 0.991]." }, { "from": "gpt", "value": "This region contains y-axis tick labels for Global Temperature: {14.4}[0.035, 0.631, 0.065, 0.654], {14.5}[0.035, 0.518, 0.065, 0.542], {14.6}[0.035, 0.406, 0.065, 0.429] in blue color." } ], "id_raw": 422 }, { "id": "z146xu4ImBLxD8bk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_2.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed within the area [0.089, 0.086, 0.325, 0.141]?" }, { "from": "gpt", "value": "This region includes legend items for {Average Global Temperature}[0.127, 0.083, 0.325, 0.106] and {CO2 Emissions}[0.127, 0.118, 0.229, 0.141], each associated with a color marker, blue and red respectively." } ], "id_raw": 422 }, { "id": "rvVU99961dJwVpuV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object found at coordinates [0.118, 0.865, 0.158, 0.829] in the chart?" }, { "from": "gpt", "value": "Point" } ], "id_raw": 423 }, { "id": "i1xvk0mJYgUWsQpA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_3.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is located at [0.118, 0.865] on the chart?" }, { "from": "gpt", "value": "Circle" } ], "id_raw": 423 }, { "id": "HA1qZ5EUwcitSxNr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_3.png", "conversations": [ { "from": "human", "value": "\nIdentify {the x-axis label} located in bounds[0.490, 0.952, 0.519, 0.975]." }, { "from": "gpt", "value": "Year" } ], "id_raw": 423 }, { "id": "1kf7TjFYnPfvrNka", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_3.png", "conversations": [ { "from": "human", "value": "\nIs {the starting point}[0.118, 0.865] lower or higher in value compared to {the ending point}[0.891, 0.100] in the chart tracking 'Number of Games'?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 423 }, { "id": "zQdayNeGW6TulJH3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_3.png", "conversations": [ { "from": "human", "value": "\nWhich line has more increasing trend, {line for 'Number of Games'}[0.118, 0.865, 0.891, 0.100] or {line for 'User Base'}[0.118, 0.865, 0.891, 0.100]?" }, { "from": "gpt", "value": "User Base" } ], "id_raw": 423 }, { "id": "qMa8Jap02ls30vfj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the data point at 2001}[0.157, 0.829] belong to {the 'Number of Games' or 'User Base'} line in the chart?" }, { "from": "gpt", "value": "Number of Games" } ], "id_raw": 423 }, { "id": "UHk94wk1LY0IUrjP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_3.png", "conversations": [ { "from": "human", "value": "\nPlease provide details about the contents found in the region[0.079, 0.062, 0.929, 0.903]." }, { "from": "gpt", "value": "This region primarily displays two line graphs one tracking the 'Number of Games' in blue with circle markers and the other tracking 'User Base' in red with square markers against years from 2000 to 2020." } ], "id_raw": 423 }, { "id": "lr4DrzoSUGHTXCg5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_3.png", "conversations": [ { "from": "human", "value": "\nWhat elements are embedded within the area[0.939, 0.528, 0.965, 0.551]?" }, { "from": "gpt", "value": "This region contains the 'User Base' y-axis label colored in red as well as accompanying tick labels denoting user base in millions." } ], "id_raw": 423 }, { "id": "uT7ASPPoBEG51ZIE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the section[0.035, 0.713, 0.070, 0.737] on the chart." }, { "from": "gpt", "value": "The area houses the y-axis label for 'Number of Games' in blue color along with numerical labels for 2000 games." } ], "id_raw": 423 }, { "id": "lWHkmOVtVzbmNjbo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_4.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.265, 0.025, 0.744, 0.055] indicate?" }, { "from": "gpt", "value": "The subtitle of the chart." } ], "id_raw": 424 }, { "id": "rzPj0PTxtDBAW1dv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_4.png", "conversations": [ { "from": "human", "value": "\nCan you specify what {the second label from the right in the x-axis}[0.840, 0.919, 0.875, 0.942] represents?" }, { "from": "gpt", "value": "2019." } ], "id_raw": 424 }, { "id": "bndJdrM1MuNZ9yLz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the data represented by {the first tick on the y-axis}[0.044, 1.021, 0.061, 1.044]?" }, { "from": "gpt", "value": "70." } ], "id_raw": 424 }, { "id": "MDpnQ96HfB4gbBvp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_4.png", "conversations": [ { "from": "human", "value": "\nWhich is higher on the chart, {the tick at 2008}[0.401, 0.903] or {the tick at 2018}[0.816, 0.903]?" }, { "from": "gpt", "value": "[0.401, 0.903]." } ], "id_raw": 424 }, { "id": "KllmqRm7JJfXNpXB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_4.png", "conversations": [ { "from": "human", "value": "\nDo {the values}[0.733, 0.903] for 2016 on each axis align vertically in the plot area?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 424 }, { "id": "mz7HKpWJldUEB5nf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_4.png", "conversations": [ { "from": "human", "value": "\nIs {the marker for the Stock Price Trend in 2014}[0.650, 0.431] above or below {the marker for Consumer Sentiment in 2014}[0.650, 0.415]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 424 }, { "id": "HPo3kLQYB8E2WQT0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the region between [0.100, 0.900, 0.220, 0.940]." }, { "from": "gpt", "value": "This region contains x-axis tick labels for the years 2001 to 2005, specifically {2001}[0.110, 0.919, 0.128, 0.942], {2002}[0.134, 0.919, 0.169, 0.942], {2003}[0.176, 0.919, 0.211, 0.942], {2004}[0.217, 0.919, 0.252, 0.942], and {2005}[0.259, 0.919, 0.294, 0.942]." } ], "id_raw": 424 }, { "id": "eBEET9nEcPjEfnql", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_4.png", "conversations": [ { "from": "human", "value": "\nCan you detail the objects in the top left quadrant of the plotting area?" }, { "from": "gpt", "value": "This quadrant features the y-axis labels and corresponding markers for values from 90 to 130, particularly emphasizing {90}[0.043, 0.650, 0.061, 0.673], {100}[0.035, 0.464, 0.061, 0.487], {110}[0.035, 0.279, 0.061, 0.302], and {120}[0.035, 0.093, 0.061, 0.116]." } ], "id_raw": 424 }, { "id": "S3cfaDB69zSVJ2GJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_4.png", "conversations": [ { "from": "human", "value": "\nWhat information is displayed near the bottom center of the chart?" }, { "from": "gpt", "value": "The x-axis tick labels for the years from 2010 to 2015 are found here; these include {2010}[0.466, 0.919, 0.501, 0.942], {2011}[0.508, 0.919, 0.543, 0.942], {2012}[0.549, 0.919, 0.584, 0.942], {2013}[0.591, 0.919, 0.626, 0.942], {2014}[0.632, 0.919, 0.667, 0.942], and {2015}[0.674, 0.919, 0.709, 0.942]." } ], "id_raw": 424 }, { "id": "gIxTHSWqFGjuz5NV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis}[0.015, 0.400, 0.029, 0.565] for the chart?" }, { "from": "gpt", "value": "Social Welfare." } ], "id_raw": 425 }, { "id": "DhhXXknDV39duDEU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line representing {Public Services Budget}[0.096, 0.121, 0.133, 0.135] in the chart?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 425 }, { "id": "BoK2vxRVlAMBv5Nm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the seventh data point in the social welfare line}[0.580, 0.373]?" }, { "from": "gpt", "value": "9005." } ], "id_raw": 425 }, { "id": "MVXseuHHqFdg1MRa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_5.png", "conversations": [ { "from": "human", "value": "\nIn 2013, which budget was higher, {Social Welfare Budget}[0.353, 0.526] or {Public Services Budget}[0.353, 0.766]?" }, { "from": "gpt", "value": "Public Services Budget." } ], "id_raw": 425 }, { "id": "BksjCqShyppaK8hO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_5.png", "conversations": [ { "from": "human", "value": "\nIs {the data point in 2010 for Public Services Budget}[0.126, 0.323] higher or lower than {the data point in 2010 for Social Welfare Budget}[0.126, 0.865]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 425 }, { "id": "dqEzJPPeDbt3V7O8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_5.png", "conversations": [ { "from": "human", "value": "\nDoes {Social Welfare Budget}[0.883, 0.100] or {Public Services Budget}[0.883, 0.221] have a higher budget in 2020?" }, { "from": "gpt", "value": "Public Services Budget." } ], "id_raw": 425 }, { "id": "tPiQKwUqL9nSyCQW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_5.png", "conversations": [ { "from": "human", "value": "\nCan you provide a description of the region [0.108, 0.919, 0.900, 0.942]." }, { "from": "gpt", "value": "This region includes x-axis labels, representing the years from 2010 to 2020. The year labels are in black color and have no rotation." } ], "id_raw": 425 }, { "id": "o2ySPzbKjyQCEL1L", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.095, 0.074, 0.303, 0.151]." }, { "from": "gpt", "value": "This area is a legend describing two lines: {Social Welfare Budget}[0.140, 0.083, 0.293, 0.106], which is colored in blue, and {Public Services Budget}[0.140, 0.118, 0.298, 0.141], which is colored in red." } ], "id_raw": 425 }, { "id": "5tjFm4Q9gIDeettN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the region [0.126, 0.100, 0.883, 0.865]." }, { "from": "gpt", "value": "This region includes two lines representing: {Social Welfare Budget}[0.126, 0.100, 0.883, 0.865], which is a solid line with circle markers, and {Public Services Budget}[0.126, 0.100, 0.883, 0.865], which is a dashed line with square markers. Points on these lines represent annual budget amounts over the years from 2010 to 2020." } ], "id_raw": 425 }, { "id": "HvPsxwXLOP89XIEX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the line chart given its coordinates [0.298, 0.025, 0.702, 0.055]?" }, { "from": "gpt", "value": "The title is \"Social Cultural and Value Change Over a Decade\"." } ], "id_raw": 427 }, { "id": "qZtHe6fbo9vdkdtV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_7.png", "conversations": [ { "from": "human", "value": "\nWhat type of object is located at coordinates [0.971, 0.403, 0.985, 0.562]?" }, { "from": "gpt", "value": "It is the y-axis label for the subplot_1 which is \"Value Change\"." } ], "id_raw": 427 }, { "id": "EX1tld0U8zlw5NbG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the tick label at coordinates [0.244, 0.919, 0.279, 0.942] signify?" }, { "from": "gpt", "value": "It represents the year 2002 on the x-axis." } ], "id_raw": 427 }, { "id": "NcAIOrvAF3YKQAlP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_7.png", "conversations": [ { "from": "human", "value": "\nComparing the value of {Cultural change over time line}[0.102, 0.100, 0.898, 0.865] and {Value change over time line}[0.102, 0.100, 0.898, 0.865], which value is higher in 2003?" }, { "from": "gpt", "value": "Cultural change." } ], "id_raw": 427 }, { "id": "iDYpHfw5BFyarcZs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_7.png", "conversations": [ { "from": "human", "value": "\nIs {the Cultural change over time line}[0.102, 0.100, 0.898, 0.865] above or below {the Value change over time line}[0.102, 0.100, 0.898, 0.865] at 2006?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 427 }, { "id": "4Kel3catzJ3Js4fO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_7.png", "conversations": [ { "from": "human", "value": "\nIs the Cultural change over time represented by {the line}[0.102, 0.100, 0.898, 0.865] experiencing an overall increase or decrease over the years?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 427 }, { "id": "vDIsimABAzqQZUz5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_7.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.084, 0.919, 0.186, 0.942]?" }, { "from": "gpt", "value": "There is a label '2000' for the x-axis tick mark." } ], "id_raw": 427 }, { "id": "6Ee92mmZ8lAc6HdR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in the region [0.069, 0.074, 0.300, 0.151]?" }, { "from": "gpt", "value": "This region contains the legend for the two lines in the chart, {Cultural change over time}[0.113, 0.083, 0.295, 0.106] with color #1f77b4 and {Value change over time}[0.113, 0.118, 0.278, 0.141] with color #d62728." } ], "id_raw": 427 }, { "id": "AfZAzhZVuPVnRS19", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_7.png", "conversations": [ { "from": "human", "value": "\nWhat information is in the region [0.971, 0.403, 0.985, 0.562]?" }, { "from": "gpt", "value": "The y-axis label for the subplot_1, 'Value Change', is located in this area." } ], "id_raw": 427 }, { "id": "GQV7mnRFZTdmhDkg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.971, 0.340, 0.985, 0.625] in the chart?" }, { "from": "gpt", "value": "Y-axis label in subplot_1." } ], "id_raw": 428 }, { "id": "rFgDAwhEAQJFnHaE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object found at [0.922, 0.856, 0.957, 0.879]?" }, { "from": "gpt", "value": "The y-axis tick label in subplot_1 which is '7000'." } ], "id_raw": 428 }, { "id": "I72FJWapxaTrizYy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the content present at the coordinates [0.131, 0.083, 0.280, 0.106]?" }, { "from": "gpt", "value": "Legend item stating 'Pharmaceutical Sales'." } ], "id_raw": 428 }, { "id": "vVgL1pgzdO7m4LRb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_8.png", "conversations": [ { "from": "human", "value": "\nIs the {Pharmaceutical Sales line point at '2'}[0.186, 0.359] higher or lower than the {Pharmaceutical Sales line point at '3'}[0.255, 0.466]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 428 }, { "id": "nHxfym44PBXlq5AA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_8.png", "conversations": [ { "from": "human", "value": "\nComparing {Biotech Investment line point at '1'}[0.117, 0.100] and {Biotech Investment line point at '7'}[0.530, 0.865], which point is higher?" }, { "from": "gpt", "value": "The point '7' is higher." } ], "id_raw": 428 }, { "id": "16vA1HugTMkE3zve", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_8.png", "conversations": [ { "from": "human", "value": "\nDoes the {Biotech Investment line point at '5'}[0.392, 0.829] have a higher or lower y-value than the {Pharmaceutical Sales line point at '5'}[0.392, 0.648]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 428 }, { "id": "uzYMJilW4nDntsHN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.113, 0.919, 0.122, 0.942]." }, { "from": "gpt", "value": "This is a label of the x-axis, indicative of the month '1'." } ], "id_raw": 428 }, { "id": "PVlgzG85uL2cgkWy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents within the coordinates [0.922, 0.974, 0.957, 0.997]?" }, { "from": "gpt", "value": "It contains the y-axis tick label '6500' for subplot_1." } ], "id_raw": 428 }, { "id": "HCxYTVqYTK5iowex", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements in the region [0.117, 0.100, 0.874, 0.865]." }, { "from": "gpt", "value": "This region contains the data points of the Biotech Investment and Pharmaceutical Sales lines for subplot_0 and subplot_1." } ], "id_raw": 428 }, { "id": "vVMjnlso8GOA0YSU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_9.png", "conversations": [ { "from": "human", "value": "\nWhat is shown by {the line with a series of points} at coordinates [[0.119, 0.865], [0.205, 0.804], [0.292, 0.712], [0.379, 0.559], [0.465, 0.559], [0.552, 0.498], [0.639, 0.406], [0.726, 0.284], [0.812, 0.253], [0.899, 0.100]]?" }, { "from": "gpt", "value": "The line represents the trend of 'Innovation' over the years." } ], "id_raw": 429 }, { "id": "097NAKi4rCHB9k6A", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_9.png", "conversations": [ { "from": "human", "value": "\nWhat element of the plot can be found {at the coordinates}[0.971, 0.421, 0.985, 0.544]?" }, { "from": "gpt", "value": "This is the right y-axis label." } ], "id_raw": 429 }, { "id": "0cHB7GzEghsHr28w", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_9.png", "conversations": [ { "from": "human", "value": "\nWhat does {the text}[0.242, 0.025, 0.776, 0.055] refer to?" }, { "from": "gpt", "value": "This refers to the subtitle of the chart, \"Technological Innovation and Intellectual Property over 10 years.\"." } ], "id_raw": 429 }, { "id": "Ci1vPPILhirh5F0N", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_9.png", "conversations": [ { "from": "human", "value": "\nAccording to the data, does the 'Number of Patents' in {2026}[0.552, 0.291] exceed the 'Number of Patents' in {2023}[0.292, 0.578]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 429 }, { "id": "4rizjeTr8e6768kK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_9.png", "conversations": [ { "from": "human", "value": "\nDoes the 'Innovation' score in {2029}[0.812, 0.253] exceed the 'Innovation' score in {2025}[0.465, 0.559]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 429 }, { "id": "kTrcoRlcPj93312w", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_9.png", "conversations": [ { "from": "human", "value": "\nBetween {the year '2025'}[0.465, 0.919, 0.483, 0.942] and {the year '2028'}[0.708, 0.919, 0.743, 0.942], did 'Innovation' show a rising trend, constant or a declining trend?" }, { "from": "gpt", "value": "Rising trend." } ], "id_raw": 429 }, { "id": "sHUoayCc0fePugf6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_9.png", "conversations": [ { "from": "human", "value": "\nWhat's contained in this region starting from top left {corner}[0.080, 0.062] to the bottom right {corner}[0.938, 0.903]?" }, { "from": "gpt", "value": "This area contains the body of the plot, including two lines marking the trends of 'Innovation' and 'Number of Patents' across the years, along with tick labels on both the x-axis and y-axis." } ], "id_raw": 429 }, { "id": "m1fSTA6gRlZFfpyR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the text item located at the coordinates {the coordinates}[0.242, 0.025, 0.776, 0.055]." }, { "from": "gpt", "value": "This area has the chart's title, \"Technological Innovation and Intellectual Property over 10 years,\" in black." } ], "id_raw": 429 }, { "id": "SIn6h4IXoufrxU6j", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region starting from {this point}[0.086, 0.074] to {this point}[0.266, 0.151]." }, { "from": "gpt", "value": "This area holds the sub-legend of the chart, describing the colors of 'Number of Patents' and 'Innovation' lines on the plot." } ], "id_raw": 429 }, { "id": "dgRhHfCw4weqmhAF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the object located at [0.971, 0.340, 0.985, 0.625]?" }, { "from": "gpt", "value": "Technological Innovation." } ], "id_raw": 430 }, { "id": "3kpfZGjjOOWD8jJS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {marker}[0.088, 0.086, 0.124, 0.100] in the legend?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 430 }, { "id": "AABzIgakqxYHZkn8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at the coordinates [0.250, 0.025, 0.750, 0.055] represent?" }, { "from": "gpt", "value": "The subtitle of the chart." } ], "id_raw": 430 }, { "id": "YoLGqw0KE0ztZz4v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_10.png", "conversations": [ { "from": "human", "value": "\nIs {the point}[0.797, 0.540] above or below {the line for 'Yearly Patents'}[0.118, 0.100, 0.882, 0.865]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 430 }, { "id": "fEdafd1qGF3sQMJr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_10.png", "conversations": [ { "from": "human", "value": "\nComparing {the point for 'Yearly Technological Innovations'}[0.797, 0.540] and {the point for 'Yearly Patents'}[0.797, 0.244] at year 9 in the trend lines, which one has a higher value?" }, { "from": "gpt", "value": "Yearly Patents." } ], "id_raw": 430 }, { "id": "kjEqFLAEkyR7X6AE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_10.png", "conversations": [ { "from": "human", "value": "\nIs the change in {Yearly Technological Innovations line}[0.118, 0.100, 0.882, 0.865] from year 7 to 8 rising or declining?" }, { "from": "gpt", "value": "Rising." } ], "id_raw": 430 }, { "id": "kBAe2ROzaLBOS7pp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the coordinates [0.086, 0.074, 0.361, 0.151]?" }, { "from": "gpt", "value": "This area contains the legend for the line chart, which includes two entries: {Yearly Patents}[0.131, 0.083, 0.228, 0.106], marked in blue color, and {Yearly Technological Innovations}[0.131, 0.118, 0.355, 0.141], marked in red color." } ], "id_raw": 430 }, { "id": "Yh8ndMLGc5Lqy2QG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area within [0.368, 0.919, 0.377, 0.942]." }, { "from": "gpt", "value": "It contains a single text element, which is the x-axis tick label for '4'." } ], "id_raw": 430 }, { "id": "GTcSSiRBZUdRc4y9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_10.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.079, 0.062, 0.921, 0.903]?" }, { "from": "gpt", "value": "This is the main plot area of the chart, which contains all the lines for 'Yearly Patents' and 'Yearly Technological Innovations', the tick labels, and markers." } ], "id_raw": 430 }, { "id": "UMTIVxvfLBYSqeJf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_11.png", "conversations": [ { "from": "human", "value": "\nWhat does {the line}[0.078, 0.087, 0.922, 0.887] represent in the chart?" }, { "from": "gpt", "value": "It represents the Innovation Index." } ], "id_raw": 431 }, { "id": "3ncfzkSROFVpkKZz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the y-axis label}[0.010, 0.417, 0.019, 0.557]?" }, { "from": "gpt", "value": "tab:blue." } ], "id_raw": 431 }, { "id": "MbyAuXX1WlkR0Blt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the xlabel}[0.446, 0.964, 0.554, 0.981] of the chart?" }, { "from": "gpt", "value": "Design Decor Elements." } ], "id_raw": 431 }, { "id": "iAFp5eGbEDf2aQKm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_11.png", "conversations": [ { "from": "human", "value": "\nIs the rating higher or lower for Layout than for Typography?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 431 }, { "id": "dF0Bpoob81lgcO60", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_11.png", "conversations": [ { "from": "human", "value": "\nAt which Decor Element is the Index Rating lower, {Color Choice}[0.078, 0.087] or {Style}[0.547, 0.525]?" }, { "from": "gpt", "value": "Style." } ], "id_raw": 431 }, { "id": "suyn9A7Ke2vxiGVx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_11.png", "conversations": [ { "from": "human", "value": "\nIs {the rating for Functionality}[0.641, 0.887] higher or lower than {the Innovation Index for Material Choice}[0.735, 0.585]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 431 }, { "id": "gSsf7inNUKbb1h7P", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.023, 1.001, 0.977, 1.019]." }, { "from": "gpt", "value": "This region contains the y-tick label '0'." } ], "id_raw": 431 }, { "id": "hOivPVioENP7WxFF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents in the area [0.040, 0.055, 0.122, 0.113]?" }, { "from": "gpt", "value": "This area holds the legend items for Rating and Innovation Index, each paired with a color marker." } ], "id_raw": 431 }, { "id": "u1s7aif1MX5Xyd8W", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.891, 0.939, 0.954, 0.957]?" }, { "from": "gpt", "value": "The label 'Sustainability' is located within these coordinates." } ], "id_raw": 431 }, { "id": "io9olR5rxJLDX0db", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_12.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {the line representing 'Commercial Buildings'}[0.125, 0.100, 0.866, 0.865]?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 432 }, { "id": "LnRCGPydE30VmNeW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the label associated with {the tick mark}[0.125, 0.903] on the x-axis?" }, { "from": "gpt", "value": "2000." } ], "id_raw": 432 }, { "id": "v5cc66nVmPUgVlb9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the rotation angle of {the label for the y-axis}[0.015, 0.362, 0.029, 0.603]?" }, { "from": "gpt", "value": "90.0." } ], "id_raw": 432 }, { "id": "WLiB7roHDM1JdSyQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_12.png", "conversations": [ { "from": "human", "value": "\nWhich has higher data points at the year 2019, {line for 'Residential Buildings'}[0.829, 0.172] or {line for 'Commercial Buildings'}[0.829, 0.127]?" }, { "from": "gpt", "value": "Commercial Buildings." } ], "id_raw": 432 }, { "id": "lxvngmCYjZ0GPtoa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_12.png", "conversations": [ { "from": "human", "value": "\nAre {the first tick}[0.125, 0.903] and {the last tick}[0.866, 0.903] on the x-axis for years 2000 and 2020 respectively located at the same vertical height?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 432 }, { "id": "NZ40G88JEjZrxuPb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_12.png", "conversations": [ { "from": "human", "value": "\nDid {the point on the 'Residential Buildings' line}[0.348, 0.650] from year 2006 reach a higher value than {the point from the previous year}[0.310, 0.674]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 432 }, { "id": "HLr3eFb1isaqqx3z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the region covering the subplot area of {Residential Buildings}[0.088, 0.062, 0.903, 0.903]." }, { "from": "gpt", "value": "The subplot includes x-axis tick labels for years 2000 to 2020, y-axis tick labels from 40000 to 80000, and a blue colored line representing Residential Buildings data points from 2000 to 2020. There are marker symbols at each data point along the line." } ], "id_raw": 432 }, { "id": "l3toM51u9YdxO9wt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is found within the coordinates of {the legend area}[0.095, 0.074, 0.296, 0.151]." }, { "from": "gpt", "value": "This area contains the legend which defines 'Residential Buildings' and 'Commercial Buildings', represented by a blue line with circle markers and a red line with square markers, respectively. Each legend entry includes a text label and a corresponding color marker." } ], "id_raw": 432 }, { "id": "zEdFQDEumBHXD0rd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_12.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region defined by {the y-axis ticks}[0.035, 0.020, 0.078, 0.998]?" }, { "from": "gpt", "value": "Tick marks and labels indicating the numeric values representing the count of Residential Buildings, ranging from 40000 to 80000 in staggered positions along the y-axis." } ], "id_raw": 432 }, { "id": "JyJpLjuM5PXk2kTn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_13.png", "conversations": [ { "from": "human", "value": "\nWhat does {the text}[0.116, 0.083, 0.201, 0.106] represent in the lower left corner of the plot?" }, { "from": "gpt", "value": "It represents the label 'GDP Growth'." } ], "id_raw": 433 }, { "id": "K0zX1KmjPnapdC0K", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of this graph given at {the coordinates}[0.328, 0.025, 0.675, 0.055]?" }, { "from": "gpt", "value": "The title is 'American Economic Indicators 2009-2020'." } ], "id_raw": 433 }, { "id": "spc0OpOkqqNaSyPl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_13.png", "conversations": [ { "from": "human", "value": "\nBased on the provided coordinates, what does the label at {the location} [0.971, 0.340, 0.985, 0.625] refer to?" }, { "from": "gpt", "value": "The label represents 'Unemployment Rate (%)'." } ], "id_raw": 433 }, { "id": "4i6vB5JNhO326Q6S", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_13.png", "conversations": [ { "from": "human", "value": "\nWhich is greater in the year 2020, the {GDP Growth} at [0.898, 0.865] or the {Unemployment Rate} at [0.898, 0.322]?" }, { "from": "gpt", "value": "Unemployment Rate." } ], "id_raw": 433 }, { "id": "QVKsyYDoD2bdod96", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_13.png", "conversations": [ { "from": "human", "value": "\nIs the {GDP Growth rate} at [0.177, 0.136] higher or lower than the {Unemployment Rate} at [0.177, 0.137] in 2010?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 433 }, { "id": "GV3GRiVkh30iWEle", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_13.png", "conversations": [ { "from": "human", "value": "\nComparing the first points of each line, was the {GDP Growth} at [0.104, 0.745] more or less than the {Unemployment Rate} at [0.104, 0.100] in 2009?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 433 }, { "id": "b8dCKgPbHiqLij99", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the area [0.072, 0.074, 0.265, 0.151]?" }, { "from": "gpt", "value": "It refers to the legend with two entries: {GDP Growth}[0.116, 0.083, 0.201, 0.106] and {Unemployment Rate}[0.116, 0.118, 0.259, 0.141]. Each label is paired with a colored marker." } ], "id_raw": 433 }, { "id": "BlCyKz1csHLH3wNF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_13.png", "conversations": [ { "from": "human", "value": "\nWhat details can be inferred from the area specified by the coordinates [0.065, 0.100, 0.898, 0.865]?" }, { "from": "gpt", "value": "This area contains the line plots for both GDP Growth and Unemployment Rate from 2009 to 2020, with the points connected by lines of different styles." } ], "id_raw": 433 }, { "id": "4IsbnNnucsZ7CtEx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_13.png", "conversations": [ { "from": "human", "value": "\nWhat can we find within the region [0.159, 0.919, 0.194, 0.942]?" }, { "from": "gpt", "value": "The x-axis tick label '2010' can be found in this area." } ], "id_raw": 433 }, { "id": "0ZHYkBvYAXZpbhHu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_14.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object with coordinates [0.943, 0.340, 0.985, 0.625]?" }, { "from": "gpt", "value": "The y-axis label of the secondary chart, which is 'Unemployment Rate (%)." } ], "id_raw": 434 }, { "id": "Cd4xMtMj92ynrcbg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fifth label in the x-axis?" }, { "from": "gpt", "value": "[0.399, 0.919, 0.408, 0.942]" } ], "id_raw": 434 }, { "id": "Cfr0k6weExAOBNCA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.943, 0.855, 0.965, 0.879]?" }, { "from": "gpt", "value": "3.8" } ], "id_raw": 434 }, { "id": "e4H2necQCZIbLEEn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_14.png", "conversations": [ { "from": "human", "value": "\nIs {the Unemployment rate at Month 1}[0.122, 0.100] higher or lower than {the GDP at Month 1}[0.122, 0.865]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 434 }, { "id": "10Olfi8pdui6YQMx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_14.png", "conversations": [ { "from": "human", "value": "\nIs {the GDP at Month 5}[0.403, 0.587] greater or less than {the GDP at Month 4}[0.333, 0.656]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 434 }, { "id": "Zofu3sem2z9Pczcj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_14.png", "conversations": [ { "from": "human", "value": "\nWhat has a higher value at month 12, GDP or Unemployment Rate?" }, { "from": "gpt", "value": "GDP." } ], "id_raw": 434 }, { "id": "bvNMzHS2fhwq3qFF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content present within the coordinates [0.091, 0.074, 0.284, 0.151]?" }, { "from": "gpt", "value": "The area contains the legend for the charts, including {GDP}[0.135, 0.083, 0.165, 0.106] which is shown in colour '#1f77b4' and {Unemployment Rate}[0.135, 0.118, 0.278, 0.141] shown in colour '#d62728'." } ], "id_raw": 434 }, { "id": "KwgXizgYZlbora8j", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within these coordinates [0.259, 0.919, 0.267, 0.942]." }, { "from": "gpt", "value": "Within these coordinates, there is the {x-axis tick label '3'}[0.259, 0.919, 0.267, 0.942]." } ], "id_raw": 434 }, { "id": "jdMacSsLX3Xc4wZ5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_14.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the box defined by [0.035, 0.396, 0.074, 0.420]?" }, { "from": "gpt", "value": "It contains the {y-axis tick label '22.75'}[0.035, 0.396, 0.074, 0.420]." } ], "id_raw": 434 }, { "id": "Ya8OEmASFhw8ggaT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_15.png", "conversations": [ { "from": "human", "value": "\nWhat does the {y-axis label}[0.015, 0.326, 0.029, 0.639] represent?" }, { "from": "gpt", "value": "Population Growth Rate(%)." } ], "id_raw": 435 }, { "id": "9oks334yIi2ELHFy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the {x-axis tick label} that corresponds to the month of August?" }, { "from": "gpt", "value": "[0.598, 0.919, 0.607, 0.942]." } ], "id_raw": 435 }, { "id": "3NneBZyo9XMCasOa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the figure text located at {coordinates}[0.393, 0.025, 0.603, 0.055]?" }, { "from": "gpt", "value": "Statistical Data Overview." } ], "id_raw": 435 }, { "id": "HPYeQ9PPIFHo8hkZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_15.png", "conversations": [ { "from": "human", "value": "\nDoes the {point in the Average Monthly Income line}[0.742, 0.355] (October) fall below or above the {point in the Population Growth Rate line}[0.742, 0.319]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 435 }, { "id": "T6ZaGQ61zvXUa7TA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_15.png", "conversations": [ { "from": "human", "value": "\nComparing the {Population Growth Rate value at point 7}[0.533, 0.428] with the {Average Monthly Income value at point 7}[0.533, 0.610], which is higher?" }, { "from": "gpt", "value": "Average Monthly Income." } ], "id_raw": 435 }, { "id": "zy4PPFmCFAebCzfB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_15.png", "conversations": [ { "from": "human", "value": "\nIs the {ending point for Population Growth line}[0.882, 0.100] below or above the {ending point for Average Monthly Income line}[0.882, 0.865]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 435 }, { "id": "PX4QDaQQFnxd6YgI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements inside the box bounded by this rectangle: [0.082, 0.074, 0.304, 0.151]?" }, { "from": "gpt", "value": "This region contains the legend items for the two lines in the plot: {Population Growth Rate}[0.126, 0.083, 0.291, 0.106] and {Average Monthly Income}[0.126, 0.118, 0.299, 0.141]." } ], "id_raw": 435 }, { "id": "HLq2SDfyN1qrtb6F", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the region [0.249, 0.919, 0.258, 0.942]?" }, { "from": "gpt", "value": "This area contains the x-axis tick label '3'." } ], "id_raw": 435 }, { "id": "xtXlMM5YZ6sd2Ibx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the region [0.971, 0.322, 0.985, 0.643]." }, { "from": "gpt", "value": "This area houses the y-axis label that represents 'Average Monthly Income($)'." } ], "id_raw": 435 }, { "id": "nYqEthw0r2WvRWY2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_16.png", "conversations": [ { "from": "human", "value": "\nWhat type of tick label corresponds to the coordinates [0.043, 0.932, 0.070, 0.955]?" }, { "from": "gpt", "value": "Y-axis tick label." } ], "id_raw": 436 }, { "id": "aP6Armhm4LpSuyHw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_16.png", "conversations": [ { "from": "human", "value": "\nWhat is {the plot title}'s coordinates located at the bottom-middle of the chart?" }, { "from": "gpt", "value": "[0.273, 0.025, 0.718, 0.055]." } ], "id_raw": 436 }, { "id": "b4oGY7GWYt2y8Xrm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the text for the first legend item of subplot_0?" }, { "from": "gpt", "value": "[0.131, 0.083, 0.245, 0.106]." } ], "id_raw": 436 }, { "id": "djGOYaP6PoYLGRw8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_16.png", "conversations": [ { "from": "human", "value": "\nAmongst the plotted lines, which one intersects with {the point}[0.307, 0.253]?" }, { "from": "gpt", "value": "Annual Concerts." } ], "id_raw": 436 }, { "id": "Osk2BR9NF5C55nQH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point indicating '2012'}[0.117, 0.330] part of {the line for 'Annual Concerts'}?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 436 }, { "id": "GEJq0KKbMsNKCY1s", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_16.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Annual Concerts'} have a higher or lower value at {point 2020}[0.874, 0.865] compared to {the line for 'Annual Sites Visits'}?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 436 }, { "id": "KGnCs52ThX0RgsuE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the region area in [0.080, 0.062, 0.912, 0.903]." }, { "from": "gpt", "value": "This region houses all the primary elements of the chart, including all the lines representing 'Annual Concerts' and 'Annual Sites Visits', all the x-tick and y-tick labels, and the chart subtitle \"Culture and Arts Industry Development (2012 - 2020)\"." } ], "id_raw": 436 }, { "id": "VxqL71MeprsFyvnV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_16.png", "conversations": [ { "from": "human", "value": "\nWhat is present within the region: [0.086, 0.074, 0.264, 0.151]?" }, { "from": "gpt", "value": "This region contains the legend for subplot_0 with two items: {Annual Concerts} located at [0.131, 0.083, 0.245, 0.106] and paired with a colored marker, and {Annual Sites Visits} located at [0.131, 0.118, 0.258, 0.141] and paired with a different colored marker." } ], "id_raw": 436 }, { "id": "oLyRY76cFuXlIMS1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_16.png", "conversations": [ { "from": "human", "value": "\nWhat objects are present within [0.035, 0.015, 0.070, 0.038]?" }, { "from": "gpt", "value": "Within these coordinates, you can find the y-axis tick label '1400'." } ], "id_raw": 436 }, { "id": "prs26FxX22Ti4yPo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_17.png", "conversations": [ { "from": "human", "value": "\nIdentify the object in the chart at coordinates [0.015, 0.330, 0.029, 0.635]." }, { "from": "gpt", "value": "Y-axis label for subplot_0." } ], "id_raw": 437 }, { "id": "DYro8GhaLXzJsZ1i", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the {y-axis tick label} at [0.939, 0.695, 0.957, 0.718] in subplot_1?" }, { "from": "gpt", "value": "90." } ], "id_raw": 437 }, { "id": "Pi8qRO24Rglp9sgK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the text for the second legend item?" }, { "from": "gpt", "value": "[0.131, 0.118, 0.361, 0.141]" } ], "id_raw": 437 }, { "id": "EV8Ea7v5SSx4xC7I", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_17.png", "conversations": [ { "from": "human", "value": "\nIs the {point at Month 5 for 'Online Student Enrollment'}[0.399, 0.511] higher or lower than the {point at Month 5 for 'Daily Average Video Call Minutes'}[0.399, 0.503]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 437 }, { "id": "qvBR8AkjYAuSgeRy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_17.png", "conversations": [ { "from": "human", "value": "\nDoes the 'Daily Average Video Call Minutes' trend line start at a higher or lower point than the 'Online Student Enrollment' trend line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 437 }, { "id": "R190lzAYRxcHp8gM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_17.png", "conversations": [ { "from": "human", "value": "\nAt Month 6, did the online student enrollment increase or decrease compared to Month 5?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 437 }, { "id": "Xu4traT3IKVGhg7b", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region with coordinates [0.079, 0.062, 0.929, 0.903] on the chart." }, { "from": "gpt", "value": "This region is the main body of the chart, where both the 'Online Student Enrollment' and 'Daily Average Video Call Minutes' line charts are plotted." } ], "id_raw": 437 }, { "id": "O6tbrmq04mDwwMiy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.131, 0.083, 0.314, 0.106]." }, { "from": "gpt", "value": "This region contains the {legend item} for 'Online Student Enrollment', with a blue color marker." } ], "id_raw": 437 }, { "id": "wuVe4jUlUqlWsLHx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe what can be seen in the region [0.939, 0.292, 0.965, 0.316]." }, { "from": "gpt", "value": "In this region, there is a red-colored y-axis tick label, showing the '110' marker for 'Daily Average Video Call Minutes'." } ], "id_raw": 437 }, { "id": "tYptkWu85ItdpagZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label}[0.317, 0.919, 0.354, 0.942]?" }, { "from": "gpt", "value": "1999." } ], "id_raw": 438 }, { "id": "MEXJVqwLtTM5IWeU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.294, 0.029, 0.671] for the first subplot?" }, { "from": "gpt", "value": "Residential Energy Consumption." } ], "id_raw": 438 }, { "id": "8scSxatkFU4fIBmD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_18.png", "conversations": [ { "from": "human", "value": "\nWhere can I find the legend label 'Commercial'?" }, { "from": "gpt", "value": "{coordinate}[0.113, 0.118, 0.197, 0.141]." } ], "id_raw": 438 }, { "id": "ps2MLmrg3AZYlmpm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_18.png", "conversations": [ { "from": "human", "value": "\nIs {the point for the year 2008}[0.572, 0.276] above or below {the point for the year 2009}[0.598, 0.367] for Residential Energy Consumption?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 438 }, { "id": "rB8FumIIvojH7PnW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_18.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at {year '2014 point'}[0.729, 0.326], Residental or Commercial Energy Consumption?" }, { "from": "gpt", "value": "Residential." } ], "id_raw": 438 }, { "id": "zq5AhAM81b0jB4JP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_18.png", "conversations": [ { "from": "human", "value": "\nWhich {label}[0.113, 0.118, 0.197, 0.141] corresponds to {the line}[0.101, 0.100, 0.886, 0.865] using a dashed line style and square markers for Commercial Energy Consumption?" }, { "from": "gpt", "value": "Commercial." } ], "id_raw": 438 }, { "id": "6kMyCcfG0QgqF5XI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region[0.035, 0.055, 0.052, 0.925]." }, { "from": "gpt", "value": "This area contains the y-axis labels for both subplots, specifically \"Residential Energy Consumption\" in a blue font from the top bounding box at 74 down to the bottom bounding box at 64, and \"Commercial Energy Consumption\" in red font from 70 down to 50.0." } ], "id_raw": 438 }, { "id": "nwDRqSrKCPDX4blj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_18.png", "conversations": [ { "from": "human", "value": "\nWhat information is present between[0.101, 0.903, 0.886, 0.919]?" }, { "from": "gpt", "value": "This area contains the x-axis tick labels ranging from '1990' to '2020', positioned below each tick on the x-axis." } ], "id_raw": 438 }, { "id": "8CUxZVUc8QFRVUj8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_18.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the chart area[0.069, 0.074, 0.202, 0.151]?" }, { "from": "gpt", "value": "This region displays the legend of the subplot with two entries: 'Residential' with a blue color marker and 'Commercial' with a red color marker." } ], "id_raw": 438 }, { "id": "12Ab6GTXtL7CKv2q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.119, 0.865]?" }, { "from": "gpt", "value": "It is a data point on the 'ML Deployment' line graph." } ], "id_raw": 439 }, { "id": "AZdh66CEgWXUZ7AX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-label for the coordinates [0.971, 0.266, 0.985, 0.699]?" }, { "from": "gpt", "value": "It is \"ML Deployment (per 1000 personnel)\"." } ], "id_raw": 439 }, { "id": "fTLIpZgatJtVqE6A", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_22_19.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the red square marker with coordinates [0.088, 0.121, 0.124, 0.135] in the legend?" }, { "from": "gpt", "value": "It represents the 'ML Deployment' trend line." } ], "id_raw": 439 }, { "id": "SJtvPqQZoR4BO6oI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_19.png", "conversations": [ { "from": "human", "value": "\nIs the point for the 'AI Research' on month 4, with coordinates [0.331, 0.865], above or below the AI Research value for month 5 with coordinates [0.402, 0.464]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 439 }, { "id": "6V0WRM4IIw9jX8Rm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_19.png", "conversations": [ { "from": "human", "value": "\nOn month 7, is the 'ML Deployment' marker {coordinates}[0.544, 0.319] higher or lower than the 'AI Research' marker {coordinates}[0.544, 0.555]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 439 }, { "id": "lTcR356lOfPqJLQr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_22_19.png", "conversations": [ { "from": "human", "value": "\nIn terms of position, does the 'ML Deployment' data point {coordinates}[0.190, 0.428] for the 2nd month sit above or below the 'AI Research' data point {coordinates}[0.190, 0.646] in the chart?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 439 }, { "id": "5OIF5KYJGDvra0lT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_19.png", "conversations": [ { "from": "human", "value": "\nCould you describe the elements found between the bounding box [0.080, 0.062, 0.938, 0.903]?" }, { "from": "gpt", "value": "This region contains the entire plotted graph, including both the 'AI Research' and 'ML Deployment' trend lines with their corresponding datapoints, the X-axis and Y-axis with their labels, all the tick marks and labels, and the legends with their respective labels and markers." } ], "id_raw": 439 }, { "id": "kHs1eH8tNReFUQDc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements enclosed in the coordinates [0.086, 0.074, 0.246, 0.151]." }, { "from": "gpt", "value": "This area contains the legend with two entries: The first entry is for 'AI Research' with the corresponding blue circle marker {[0.088, 0.086, 0.124, 0.100]}. The second entry is for 'ML Deployment' with the corresponding red square marker {[0.088, 0.121, 0.124, 0.135]}." } ], "id_raw": 439 }, { "id": "lpHAxbSbigNLLM32", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_22_19.png", "conversations": [ { "from": "human", "value": "\nCould you provide a description of what lies within the coordinates [0.080, 0.062, 0.938, 0.865]?" }, { "from": "gpt", "value": "This region covers most of the chart area and includes 'AI Research' and 'ML Deployment' trend lines along with their corresponding datapoints. The X-axis and Y-axis with their labels, ticks, and tick labels are also encompassed within this area along with the legends." } ], "id_raw": 439 }, { "id": "TbmMzkPOpYwz8My2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the content located at coordinates [0.250, 0.037, 0.817, 0.085]?" }, { "from": "gpt", "value": "Subtitle \"Risk Assessment and Management\"." } ], "id_raw": 441 }, { "id": "JwicbalOZaHy2PQj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_1.png", "conversations": [ { "from": "human", "value": "\nWhat object is at the coordinates [0.025, 0.295, 0.055, 0.511]?" }, { "from": "gpt", "value": "The label for the Y-axis." } ], "id_raw": 441 }, { "id": "hF6U4fa4OXBQ8ol5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the first label on the X-axis that is at [0.109, 0.734, 0.188, 0.854]?" }, { "from": "gpt", "value": "\"Political\"." } ], "id_raw": 441 }, { "id": "fxh1WhKeXMO0amvB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_1.png", "conversations": [ { "from": "human", "value": "\nRelative to {the coordinate labeled 'Industrial'}[0.405, 0.203], is {the coordinate labeled 'Operational'}[0.534, 0.283] higher or lower?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 441 }, { "id": "D6BGb8UIdDXqqtFX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '80'}[0.148, 0.124] on the left or right of {the point labeled '75'}[0.277, 0.164]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 441 }, { "id": "M7dbz7W4kFraPC8P", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_1.png", "conversations": [ { "from": "human", "value": "\nComparing {the 'Compliance' coordinate}[0.790, 0.443] with {the 'Reputation' coordinate}[0.919, 0.682], which one indicates a higher risk score?" }, { "from": "gpt", "value": "Compliance." } ], "id_raw": 441 }, { "id": "1SuUxQOCK67R74gu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the section of the chart with coordinates [0.110, 0.096, 0.957, 0.710]." }, { "from": "gpt", "value": "This is the main charting area which contains the line graph plot. The line graph plot represents different risk types along the X-axis and their corresponding risk scores along the y-axis." } ], "id_raw": 441 }, { "id": "RqCfPv0BxGwl79BU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.079, 0.748, 0.094, 0.783]?" }, { "from": "gpt", "value": "This region contains the y-axis label '0'." } ], "id_raw": 441 }, { "id": "h6QBIicJd1hJl1Z5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region from [0.477, 0.734, 0.590, 0.903]." }, { "from": "gpt", "value": "This region includes the X-axis label 'Operational'." } ], "id_raw": 441 }, { "id": "ExXX0scoNm6ORJfr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_2.png", "conversations": [ { "from": "human", "value": "\nWhat object is at the coordinates [0.021, 0.164, 0.047, 0.522]?" }, { "from": "gpt", "value": "The y-axis label." } ], "id_raw": 442 }, { "id": "zCsi6lXat0GvQsXO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_2.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the coordinates for the legend item labeled as \"Total Activities\"?" }, { "from": "gpt", "value": "[0.681, 0.150, 0.824, 0.178]." } ], "id_raw": 442 }, { "id": "OTBmxpQktRcuXuDg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_2.png", "conversations": [ { "from": "human", "value": "\nWhat is encapsulated in the region [0.165, 0.664, 0.298, 0.850] on the chart?" }, { "from": "gpt", "value": "The x-axis tick label \"Watching Movies\"." } ], "id_raw": 442 }, { "id": "x8zQm8tBJpRLc19A", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-value for {Weekend Activities} at the point 'Traveling'[0.362, 0.242]?" }, { "from": "gpt", "value": "6.0." } ], "id_raw": 442 }, { "id": "K4vfXUClb0HAisaY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_2.png", "conversations": [ { "from": "human", "value": "\nBetween {Routine Activities}[0.100, 0.111, 0.886, 0.635] and {Total Activities}[0.100, 0.041, 0.886, 0.323] which has higher engagement during 'Partying'[0.493, 0.484]?" }, { "from": "gpt", "value": "Total Activities." } ], "id_raw": 442 }, { "id": "c4rtLpl2KPTBu4Sj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_2.png", "conversations": [ { "from": "human", "value": "\nIn comparison to {Routine Activities}[0.100, 0.111, 0.886, 0.635] curve, does the {Weekend Activities}[0.100, 0.081, 0.886, 0.323] curve show more or less engagement during 'Going to Music Concerts'[0.886, 0.323]?" }, { "from": "gpt", "value": "More." } ], "id_raw": 442 }, { "id": "js3QlIUK3zkE1ZtT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the space encapsulated by [0.617, 0.055, 0.876, 0.189] contain?" }, { "from": "gpt", "value": "This region includes the sub-legend of the chart containing the labels and corresponding markers for 'Weekend Activities', 'Routine Activities' and 'Total Activities'." } ], "id_raw": 442 }, { "id": "UgbFvctnXOObxFtc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_2.png", "conversations": [ { "from": "human", "value": "\nPlease tell me about the area from [0.704, 0.664, 0.807, 0.809]." }, { "from": "gpt", "value": "This region of the chart encapsulates the x-axis tick label for 'Volunteering'." } ], "id_raw": 442 }, { "id": "QyuYDjZRM6TiRpog", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content visible around position [0.595, 0.664, 0.654, 0.747] in the chart." }, { "from": "gpt", "value": "At this position, you can find the x-axis tick label 'Sports'." } ], "id_raw": 442 }, { "id": "dplftMXmVt1VRFok", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_3.png", "conversations": [ { "from": "human", "value": "\nWhat kind of object do the coordinates [0.023, 0.251, 0.045, 0.659] represent in the chart?" }, { "from": "gpt", "value": "Y-axis label for subplot_0." } ], "id_raw": 443 }, { "id": "UQkxeKPtU7Xi8MoO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_3.png", "conversations": [ { "from": "human", "value": "\nAt what coordinates can we find the '2008' x-axis tick label?" }, { "from": "gpt", "value": "[0.325, 0.899, 0.380, 0.928]." } ], "id_raw": 443 }, { "id": "w5sJaYHDUeINs05V", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_3.png", "conversations": [ { "from": "human", "value": "\nWhere is {the xlabel}['Years'] located in the first subplot?" }, { "from": "gpt", "value": "At [0.485, 0.940, 0.542, 0.969]." } ], "id_raw": 443 }, { "id": "tKcXwojemNLo0Sq0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_3.png", "conversations": [ { "from": "human", "value": "\nBetween {Year 2005}[0.159, 0.840] and {Year 2006}[0.224, 0.781], which year had a higher Graduation Rate?" }, { "from": "gpt", "value": "Year 2005." } ], "id_raw": 443 }, { "id": "btEGkMGAirCYEmZU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_3.png", "conversations": [ { "from": "human", "value": "\nUsing the line plot, did the Per Student Spending rise or fall from {2007}[0.288, 0.778] to {2008}[0.353, 0.717]?" }, { "from": "gpt", "value": "It rose." } ], "id_raw": 443 }, { "id": "KDW6Q5MQYbmKZim9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_3.png", "conversations": [ { "from": "human", "value": "\nAt {Year 2013}[0.675, 0.193] and {Year 2014}[0.739, 0.101] which year reports a higher Per Student Spending?" }, { "from": "gpt", "value": "Year 2014." } ], "id_raw": 443 }, { "id": "GEQXVLudhb2vlnCk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements present in the region [0.068, 0.899, 0.895, 0.928]?" }, { "from": "gpt", "value": "This area contains all the x-axis tick labels ranging from '2004' to '2016' for both subplots." } ], "id_raw": 443 }, { "id": "suEqS85qOrkprG0j", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_3.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the region [0.918, 0.829, 0.946, 0.858]?" }, { "from": "gpt", "value": "It contains the y-axis tick label '78' for subplot_1." } ], "id_raw": 443 }, { "id": "cI7UijyqsosuVfLN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_3.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.159, 0.070, 0.868, 0.840]?" }, { "from": "gpt", "value": "This region contains the line plot for both Per Student Spending and Graduation Rate, covering the data points from Year 2005 to Year 2016." } ], "id_raw": 443 }, { "id": "5l34kbYIFbG86WnF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_4.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.019, 0.151, 0.044, 0.767] denote?" }, { "from": "gpt", "value": "Transaction Volume (Billion Dollars)." } ], "id_raw": 444 }, { "id": "ccNs7wZt15x3Gkx3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-coordinate of {the third tick on x-axis}[0.453, 0.909, 0.497, 0.932]?" }, { "from": "gpt", "value": "[0.475, 0.893]." } ], "id_raw": 444 }, { "id": "Ky1wt1OcBT32v7EX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_4.png", "conversations": [ { "from": "human", "value": "\nWhat line is represented by {the point at [0.728, 0.502]}[0.728, 0.502]?" }, { "from": "gpt", "value": "Ethereum." } ], "id_raw": 444 }, { "id": "nBUgMbyy56Ehjs50", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Ethereum in 2020}[0.728, 0.502] above or below {the point for Bitcoin in 2020}[0.728, 0.329] on the trend line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 444 }, { "id": "PKqx9TCufwbbfwEE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_4.png", "conversations": [ { "from": "human", "value": "\nWhich cryptocurrency had a higher transaction volume {in 2017}, Bitcoin at [0.349, 0.285] or Ethereum at [0.349, 0.589]?" }, { "from": "gpt", "value": "Bitcoin." } ], "id_raw": 444 }, { "id": "XEVaaUEgQZzlufqB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Bitcoin in 2018}[0.475, 0.589] located to the right or left of {the point for Ethereum in 2016}[0.222, 0.763]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 444 }, { "id": "dR9f7sbHhsqRTOiB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.794, 0.039, 0.971, 0.135]." }, { "from": "gpt", "value": "This area contains the legend for the line graph. It includes two entries: {Bitcoin} [0.861, 0.050, 0.932, 0.080] marked with the color blue, and {Ethereum} [0.861, 0.094, 0.962, 0.124] marked with the color red." } ], "id_raw": 444 }, { "id": "lpdwFLxVqNLBpB9x", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.511, 0.942, 0.566, 0.975] represent?" }, { "from": "gpt", "value": "The area represents the x-axis label which is 'Year'." } ], "id_raw": 444 }, { "id": "tkjjAxlL82SdRnVd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.222, 0.199, 0.855, 0.589]?" }, { "from": "gpt", "value": "This area represents the trend line for Bitcoin. It spans from the year 2016 to 2021, with varying transaction volumes marked at each year." } ], "id_raw": 444 }, { "id": "tpIp72w2OA6s4FfY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.456, 0.917, 0.526, 0.963] in the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 445 }, { "id": "wPYhFjYhvJsK3t3v", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first tick label in the X-axis?" }, { "from": "gpt", "value": "[0.206, 0.869, 0.276, 0.904]" } ], "id_raw": 445 }, { "id": "MK4PWxPuRFo4xIJp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the {the coordinates}[0.158, 0.037, 0.824, 0.844] refer to?" }, { "from": "gpt", "value": "The plotting area." } ], "id_raw": 445 }, { "id": "TtcuaWtTSdAj0AK3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_5.png", "conversations": [ { "from": "human", "value": "\nDoes {the point for year 2020}[0.574, 0.268] in 'Temperature Increase (\u00b0C)' line rise or fall compared to {the point for year 2015}[0.491, 0.383]?" }, { "from": "gpt", "value": "Rise." } ], "id_raw": 445 }, { "id": "pEpqtL0NImGUmw0Z", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_5.png", "conversations": [ { "from": "human", "value": "\nIs {the point for the year 2000 on Temperature Increase (\u00b0C) line}[0.241, 0.729] higher or lower than {the point for the year 2000 on CO2 Emission (billion ton) line}[0.241, 0.791]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 445 }, { "id": "KJKsfNrHQuCMDBSM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_5.png", "conversations": [ { "from": "human", "value": "\nIs there a lower temperature increase or higher CO2 emission at {the point for the year 2025}[0.658, 0.176, 0.658, 0.145]?" }, { "from": "gpt", "value": "Higher CO2 emission." } ], "id_raw": 445 }, { "id": "B25Qo3LoUarBQcxn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in the area with coordinates [0.344, 0.383, 0.810, 0.498]?" }, { "from": "gpt", "value": "This area contains the legend with labels for {Temperature Increase (\u00b0C)}[0.433, 0.397, 0.799, 0.432] and {CO2 Emission (billion ton)}[0.433, 0.450, 0.791, 0.485]." } ], "id_raw": 445 }, { "id": "c0wxzryaKYy9IPUk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_5.png", "conversations": [ { "from": "human", "value": "\nCould you please describe the objects within the region [0.206, 0.869, 0.276, 0.904]?" }, { "from": "gpt", "value": "The '2000' label on the X-axis is located in this region." } ], "id_raw": 445 }, { "id": "PZZL6X6uf2Nry5h5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_5.png", "conversations": [ { "from": "human", "value": "\nCould you please describe the area [0.158, 0.037, 0.824, 0.844] in detail?" }, { "from": "gpt", "value": "This region includes the main plot area of the chart, where the 'Temperature Increase (\u00b0C)' and 'CO2 Emission (billion ton)' data lines drawn." } ], "id_raw": 445 }, { "id": "7NpWPwMGKAPwhj8e", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object placed at coordinates [0.052, 0.298, 0.078, 0.712] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 446 }, { "id": "F3zmPwhN5uEGWnjs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the 'Series5' label in the x-tick section?" }, { "from": "gpt", "value": "[0.609, 0.909, 0.675, 1.002]" } ], "id_raw": 446 }, { "id": "4aeSuzEvf1wIWaEf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_6.png", "conversations": [ { "from": "human", "value": "\nWhat does {the text} at [0.570, 0.388, 0.713, 0.416] represent?" }, { "from": "gpt", "value": "The deviation at Series5." } ], "id_raw": 446 }, { "id": "R4fS74xJjYWznsEA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_6.png", "conversations": [ { "from": "human", "value": "\nWhich Series has higher Innovation Efficiency between {Series2}[0.254, 0.688] and {Series7}[0.900, 0.206]?" }, { "from": "gpt", "value": "Series7." } ], "id_raw": 446 }, { "id": "NEB0wNYtsxqsDsc4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_6.png", "conversations": [ { "from": "human", "value": "\nDoes {Series1}[0.125, 0.789] have higher or lower Innovation Efficiency than {Series4}[0.512, 0.464]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 446 }, { "id": "v7BVihVOynDd7NRJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_6.png", "conversations": [ { "from": "human", "value": "\nWhich Series has the lowest deviation text, {Series3}[0.312, 0.596, 0.455, 0.624] or {Series5}[0.570, 0.388, 0.713, 0.416]?" }, { "from": "gpt", "value": "Series5." } ], "id_raw": 446 }, { "id": "0DbMSBdPOSkakeSz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_6.png", "conversations": [ { "from": "human", "value": "\nKindly describe the region occupying the coordinates [0.125, 0.206, 0.900, 0.789]." }, { "from": "gpt", "value": "This region contains the line denoting efficiency with each point on the line corresponding to a series. Each point has a label indicating its deviation, colored markers, and a solid line connecting them." } ], "id_raw": 446 }, { "id": "y45ZOq6YrYjtohLP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area within [0.221, 0.909, 0.287, 1.002]." }, { "from": "gpt", "value": "In this area, the x-axis tick label for 'Series2' is visible. The rotation is 45 degrees and the text color is black." } ], "id_raw": 446 }, { "id": "ACOV2s9dp7jJgDYP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the region [0.086, 0.879, 0.111, 0.907]?" }, { "from": "gpt", "value": "The y-axis tick label '40' is found in this area. It is placed vertically (0 degrees rotation) and colored black." } ], "id_raw": 446 }, { "id": "F5LhZmjnjQ4Z9XxZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the element located at [0.521, 0.945, 0.565, 0.975] in the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 447 }, { "id": "vpYZxqAIsyluakPl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the {yth-tick}[0.918, 0.806, 0.919, 0.818] shown in the chart?" }, { "from": "gpt", "value": "2100." } ], "id_raw": 447 }, { "id": "N0ypUt7xyszTsXlk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {first line}[0.167, 0.806, 0.919, 0.896] in the chart?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 447 }, { "id": "PaVfylic7GzBp7Xu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_7.png", "conversations": [ { "from": "human", "value": "\nDoes the {blue line for 'FinTech Investment'}[0.167, 0.806, 0.919, 0.896] reach higher or lower value than the {red line for 'Digital Currency Market Cap'}[0.167, 0.075, 0.919, 0.896] in year 2015?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 447 }, { "id": "CCOh1QjMHqoGSCWw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_7.png", "conversations": [ { "from": "human", "value": "\nAt what year does the {line for 'Digital Currency Market Cap'}[0.668, 0.650] exceed the {line for 'FinTech Investment'}[0.668, 0.850]?" }, { "from": "gpt", "value": "2017." } ], "id_raw": 447 }, { "id": "cr73puoYca0Rxf59", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_7.png", "conversations": [ { "from": "human", "value": "\nWhich line starts at a higher point in 2009, the {blue line for 'FinTech Investment'}[0.167, 0.896] or the {red line for 'Digital Currency Market Cap'}[0.167, 0.896]?" }, { "from": "gpt", "value": "They start at the same point." } ], "id_raw": 447 }, { "id": "jwAasoYnh4fDlNP2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the area [0.625, 0.055, 0.769, 0.107]." }, { "from": "gpt", "value": "This area includes points and part of line segment from the {red line for 'Digital Currency Market Cap'}[0.668, 0.650]. The points represent the values of 'Digital Currency Market Cap' in years 2016, 2017, and 2018." } ], "id_raw": 447 }, { "id": "ySMoYwtPP9i42dFY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.113, 0.046, 0.421, 0.123] represent in the chart?" }, { "from": "gpt", "value": "This is the location of the legend where two items are listed: {FinTech Investment}[0.113, 0.058, 0.160, 0.072] represented with a blue marker and {Digital Currency Market Cap}[0.115, 0.093, 0.160, 0.107] represented with a red marker." } ], "id_raw": 447 }, { "id": "VGtMsE8kHHdjqxJo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_7.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the region [0.019, 0.392, 0.041, 0.539] of the chart?" }, { "from": "gpt", "value": "The Y-axis label 'Billions ($)' is located in this region." } ], "id_raw": 447 }, { "id": "l7JzoS2nA8sjadDb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the handle object with color #0000ff at [0.696, 0.423, 0.747, 0.440] represent in the chart?" }, { "from": "gpt", "value": "Cyber Security." } ], "id_raw": 448 }, { "id": "nr0OmpubjPOFSThW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the label for year '2016' on the x-axis?" }, { "from": "gpt", "value": "[0.592, 0.895, 0.642, 0.923]." } ], "id_raw": 448 }, { "id": "nDCUhYNN4QInIgQ9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.021, 0.288, 0.047, 0.662]?" }, { "from": "gpt", "value": "Popularity (1-10 scale)." } ], "id_raw": 448 }, { "id": "60OfAcqu6aCH3TFZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_8.png", "conversations": [ { "from": "human", "value": "\nIs {the point for year '2013'}[0.376, 0.719] on the 'Cyber Security' line above or below {the point for year '2013'}[0.376, 0.659] on the 'Artificial Intelligence' line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 448 }, { "id": "Hd60oFGEN642vD7V", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_8.png", "conversations": [ { "from": "human", "value": "\nWhich technology had a higher popularity in 2020, {Cyber Security}[0.938, 0.449] or {Artificial Intelligence}[0.938, 0.111]?" }, { "from": "gpt", "value": "Artificial Intelligence." } ], "id_raw": 448 }, { "id": "gWe5721ffry723Q0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_8.png", "conversations": [ { "from": "human", "value": "\nIn 2018, did {Cyber Security}[0.778, 0.524] have a higher or lower popularity compared to {Virtual Reality}[0.778, 0.689]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 448 }, { "id": "tIbOemx1QgnBEWay", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.694, 0.408, 0.969, 0.542]." }, { "from": "gpt", "value": "This area contains the chart legend. It represents three categories: {Cyber Security}[0.757, 0.419, 0.904, 0.447] depicted in color #0000ff, {Artificial Intelligence}[0.757, 0.461, 0.961, 0.489] depicted in color #ffa500, and {Virtual Reality}[0.757, 0.503, 0.897, 0.531] depicted in color #008000." } ], "id_raw": 448 }, { "id": "NRZZorEVegIMWBEc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.913, 0.895, 0.963, 0.923]." }, { "from": "gpt", "value": "This area includes the x-axis label {2020}[0.913, 0.895, 0.963, 0.923]." } ], "id_raw": 448 }, { "id": "uH6QfV0UC8v3FD3H", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.215, 0.802, 0.295, 0.764]?" }, { "from": "gpt", "value": "In this area, there are 2 points from the lines representing different technologies. The point {for Cyber Security in 2011}[0.215, 0.802] and the point {for Cyber Security in 2012}[0.295, 0.764]." } ], "id_raw": 448 }, { "id": "JvEVtDrphfkty0VT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.019, -0.065, 0.041, 0.986] represent in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 450 }, { "id": "DxQeeNe6vKS9I6dL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of the label '2040' on the x-axis in the chart?" }, { "from": "gpt", "value": "[0.735, 0.912, 0.778, 0.936]." } ], "id_raw": 450 }, { "id": "uKq9dqShCLN7qPzq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_10.png", "conversations": [ { "from": "human", "value": "\nWhat does {the point}[0.869, 0.087] denote on the 'CO2 Emissions' line?" }, { "from": "gpt", "value": "Year 2050 data." } ], "id_raw": 450 }, { "id": "b7Dz29ahTLCbUSdx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_10.png", "conversations": [ { "from": "human", "value": "\nIs {the 2040 point on the 'Temperature Increase' line}[0.757, 0.833] higher or lower on the chart compared to {[the 2040 point on the 'CO2 Emissions' line]}[0.757, 0.118]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 450 }, { "id": "F6WcvsNs3IyxqqAa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_10.png", "conversations": [ { "from": "human", "value": "\nWhich line is {the point with coordinates}[0.532, 0.846] on?" }, { "from": "gpt", "value": "Temperature Increase." } ], "id_raw": 450 }, { "id": "0nri87R2H42DvKqc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_10.png", "conversations": [ { "from": "human", "value": "\nDoes the line with points at {coordinates}[0.195, 0.825, 0.869, 0.874] or {coordinates}[0.195, 0.087, 0.869, 0.513] represent a steeper increase?" }, { "from": "gpt", "value": "CO2 Emissions." } ], "id_raw": 450 }, { "id": "cH6Vb1hDz4k29DOW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.091, 0.037, 0.343, 0.113] in detail?" }, { "from": "gpt", "value": "The area contains the chart's legend. It consists of two entries: {Temperature Increase}[0.147, 0.046, 0.336, 0.069] and {CO2 Emissions}[0.147, 0.081, 0.275, 0.104], each accompanied by a color marker." } ], "id_raw": 450 }, { "id": "Z19xNcQfw5F2uYK1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_10.png", "conversations": [ { "from": "human", "value": "\nPlease tell me more about the region [0.510, 0.945, 0.554, 0.975]." }, { "from": "gpt", "value": "The region contains the label for the x-axis of the chart, specifically the term 'Year'." } ], "id_raw": 450 }, { "id": "cjzXw1vagrgxbSLL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_10.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.195, 0.825, 0.869, 0.874]?" }, { "from": "gpt", "value": "This region contains the 'Temperature Increase' line spanning from 1990 to 2050 as evidenced by the seven points at various corresponding coordinates." } ], "id_raw": 450 }, { "id": "BCxgbyCEriy2y3KM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis?" }, { "from": "gpt", "value": "Time Spent Online (hours)." } ], "id_raw": 451 }, { "id": "oXAVSPiJHK8oFTAr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_11.png", "conversations": [ { "from": "human", "value": "\nAt what coordinate is the label '45-54' on the x-axis located?" }, { "from": "gpt", "value": "[0.456, 0.869, 0.522, 0.904]" } ], "id_raw": 451 }, { "id": "xKza815aLJ5RSt79", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {this second line}[0.089, 0.074, 0.890, 0.808] in subplot_1?" }, { "from": "gpt", "value": "Comment Frequency." } ], "id_raw": 451 }, { "id": "nHiEtYDaZSOIwm9g", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_11.png", "conversations": [ { "from": "human", "value": "\nIs the {Data point '45-54' for 'Time Spent Online'}[0.489, 0.578] higher or lower than {Data point '45-54' for 'Comment Frequency'}[0.489, 0.150]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 451 }, { "id": "hSXa3bI1JIW0NBRJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_11.png", "conversations": [ { "from": "human", "value": "\nIn the age group '65-74', which was higher, 'Time Spent Online' or 'Comment Frequency'?" }, { "from": "gpt", "value": "Time Spent Online." } ], "id_raw": 451 }, { "id": "jfSbx0hiah1EYP38", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_11.png", "conversations": [ { "from": "human", "value": "\nIs the {Comment Frequency for age '25-34'}[0.222, 0.327] higher or lower than {Time Spent Online for the same age group} [0.222, 0.315]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 451 }, { "id": "v4LtmAIJxovspo7C", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_11.png", "conversations": [ { "from": "human", "value": "\nWhat content is in the area of [0.355, 0.740, 0.755, 0.805]?" }, { "from": "gpt", "value": "These coordinates encompass the data points for the Age Group '45-54' and '55-64' for both 'Time Spent Online' and 'Comment Frequency' line graphs." } ], "id_raw": 451 }, { "id": "6ezTaYTNVoKE8ze0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.489, 0.017, 0.489, 0.017]?" }, { "from": "gpt", "value": "This is a single point that does not contain any visible content on the chart." } ], "id_raw": 451 }, { "id": "rE8HrvXy6J3Bv16B", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_11.png", "conversations": [ { "from": "human", "value": "\nWhat information is located within [0.489, 0.578, 0.623, 0.665]?" }, { "from": "gpt", "value": "This space contains 'Time Spent Online' data points for '45-54' age group and '55-64' age group." } ], "id_raw": 451 }, { "id": "IkQM5ZQVrAxOpeod", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.467, 0.945, 0.511, 0.975] in the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 452 }, { "id": "4dkyK1LniYmaAQef", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_12.png", "conversations": [ { "from": "human", "value": "\nWhere is {the sixth x-axis tick label} located in the diagram?" }, { "from": "gpt", "value": "[0.709, 0.912, 0.753, 0.936]" } ], "id_raw": 452 }, { "id": "UXEqvFhYulH2gBXF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis tick} at [0.919, 0.370, 0.941, 0.393]?" }, { "from": "gpt", "value": "60" } ], "id_raw": 452 }, { "id": "js4xiSheyVRjKeqH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_12.png", "conversations": [ { "from": "human", "value": "\nIs the {trade volume point for 2030}[0.386, 0.345] higher or lower than the {globalization index for 2030}[0.386, 0.293]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 452 }, { "id": "pmJ0oueDaSd1ykpz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_12.png", "conversations": [ { "from": "human", "value": "\nComparing the {globalization index at 2000}[0.178, 0.418] and the {trade volume at 2000}[0.178, 0.705], which one is higher?" }, { "from": "gpt", "value": "Trade Volume." } ], "id_raw": 452 }, { "id": "dwEl0lFKD6ti1o7w", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_12.png", "conversations": [ { "from": "human", "value": "\nAt {year 2100}, is the {trade volume}[0.869, 0.073] higher or lower than the {globalization index}[0.869, 0.120]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 452 }, { "id": "lhesrHDRaJLifliO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the section [0.080, 0.046, 0.309, 0.123] of the chart?" }, { "from": "gpt", "value": "This section contains the chart's legend, which includes two items: {Trade Volume}[0.136, 0.055, 0.253, 0.078] and {Globalization Index}[0.136, 0.090, 0.303, 0.113]. Each item has a different color marker." } ], "id_raw": 452 }, { "id": "KSxVK6TuMtpZQyRi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_12.png", "conversations": [ { "from": "human", "value": "\nWhat is within the coordinates [0.018, 0.912, 0.062, 0.936]?" }, { "from": "gpt", "value": "The x-axis label for the year '1980'." } ], "id_raw": 452 }, { "id": "vkpAy3ZWiTLV8EVy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within [0.919, 0.715, 0.941, 0.738]." }, { "from": "gpt", "value": "This region contains a y-axis tick label, specifically the number '20'." } ], "id_raw": 452 }, { "id": "xyqhz9HDlvSH3Jif", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_13.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.096, 0.919, 0.132, 0.942] in the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 453 }, { "id": "zIWfvQzhBX1OWMoF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_13.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object at coordinates [0.851, 0.215, 0.869, 0.239]?" }, { "from": "gpt", "value": "Y-axis tick label." } ], "id_raw": 453 }, { "id": "Y1XeRcWrJ9zCpyMa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object found at [0.253, 0.225] in the chart figure?" }, { "from": "gpt", "value": "Data point." } ], "id_raw": 453 }, { "id": "wRl36HQfHmYsHGZR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_13.png", "conversations": [ { "from": "human", "value": "\nIs {the label '2014'}[0.374, 0.919] to the left or right of {the label '2016'}[0.512, 0.919] on the X-axis?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 453 }, { "id": "mx4CfcUzrhhomGKG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_13.png", "conversations": [ { "from": "human", "value": "\nAre {the y-axis tick labeled '30'}[0.851, 0.854] higher or lower than {the y-axis tick labeled '40'}[0.851, 0.056] in the subplot for average work hours?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 453 }, { "id": "CdXBLUnFTJFMxkKc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_13.png", "conversations": [ { "from": "human", "value": "\nDoes the label {2016}[0.512, 0.919] appear before or after the label {2018}[0.651, 0.919]?" }, { "from": "gpt", "value": "Before." } ], "id_raw": 453 }, { "id": "JsT0H6Jb1Nbv4Z4a", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects located within the bounding box of [0.035, 0.011, 0.070, 0.921] on the chart?" }, { "from": "gpt", "value": "Y-axis tick labels ranging from '200' to '1200' in increments of 200, colored in red." } ], "id_raw": 453 }, { "id": "yh6PKZxAt6Ps4TCm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the features in the area with coordinates [0.096, 0.919, 0.963, 0.942]." }, { "from": "gpt", "value": "This area includes the x-axis labels for years from '2008' to '2022' at regular intervals." } ], "id_raw": 453 }, { "id": "2LgI4qV7jAfJAl5d", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_13.png", "conversations": [ { "from": "human", "value": "\nWhat elements are contained in the boundary [0.114, 0.065, 0.807, 0.863]?" }, { "from": "gpt", "value": "Multiple data points plotting the trends of number of employees trained, average work hours, and workplace expansion strategy from 2010 to 2020." } ], "id_raw": 453 }, { "id": "2qSQLnaRtOyHycEs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the line}[0.220, 0.100, 0.855, 0.689] referring to in the chart?" }, { "from": "gpt", "value": "It refers to the change in 'Technological Progress' over years from 2022 to 2030." } ], "id_raw": 455 }, { "id": "Ek4YvGRiXLWqq7kW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis}[0.030, 0.221, 0.066, 0.692]?" }, { "from": "gpt", "value": "Technological Progress." } ], "id_raw": 455 }, { "id": "ouOYnEVa6kXKWDXz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_15.png", "conversations": [ { "from": "human", "value": "\nWhat is in the location [0.417, 0.394, 0.658, 0.429]?" }, { "from": "gpt", "value": "It contains a text labeling the 'Gradual recovery' point on the line." } ], "id_raw": 455 }, { "id": "6djWKYwGHyFYBOXy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_15.png", "conversations": [ { "from": "human", "value": "\nIs the trend point for 2028 above or below the trend point for 2027?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 455 }, { "id": "XwFuT5ue9LYhnMrA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_15.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Slow recovery'}[0.220, 0.689] higher or lower than {the point for 'Marginal growth'}[0.299, 0.596]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 455 }, { "id": "CU0ud38f3paDxiLB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_15.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Slow growth'}[0.458, 0.410] more towards right or left compared to {the point for 'Upward trajectory'}[0.379, 0.503]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 455 }, { "id": "VpiWTfdiOVJm1tzr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_15.png", "conversations": [ { "from": "human", "value": "\nDescribe what is in the box [0.690, 0.131, 0.861, 0.166]?" }, { "from": "gpt", "value": "The box contains the label 'Slow growth' for the year 2029." } ], "id_raw": 455 }, { "id": "EWjMlSJVDacWWXyK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_15.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the area [0.742, 0.038, 0.968, 0.073]?" }, { "from": "gpt", "value": "The area contains the 'Marginal growth' label referring to the year 2030 on 'Technological Progress' line." } ], "id_raw": 455 }, { "id": "w1VP7k8NgcjibBk4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_15.png", "conversations": [ { "from": "human", "value": "\nCan you tell what's in the zone [0.186, 0.534, 0.412, 0.569]?" }, { "from": "gpt", "value": "It contains label 'Marginal growth' which specifies a point on the 'Technological Progress' line for the year 2023." } ], "id_raw": 455 }, { "id": "diDnvADsayqXYTa9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_16.png", "conversations": [ { "from": "human", "value": "\nWhat is found at the location [0.839, 0.896] in the chart?" }, { "from": "gpt", "value": "x-axis tick for the year 2016." } ], "id_raw": 456 }, { "id": "mryQ3L1uNdNHrVMu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_16.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.107, 0.184, 0.148, 0.201]?" }, { "from": "gpt", "value": "Label for 'Futures'." } ], "id_raw": 456 }, { "id": "Jj18G46myfsAIiSL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_16.png", "conversations": [ { "from": "human", "value": "\nIdentify the object at the coordinate [0.107, 0.106, 0.133, 0.123]." }, { "from": "gpt", "value": "ETFs label." } ], "id_raw": 456 }, { "id": "KPSeLC43NX1qv4na", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_16.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at {coordinate}[0.903, 0.684], 'Mutual Funds' or 'Forex'?" }, { "from": "gpt", "value": "Forex." } ], "id_raw": 456 }, { "id": "uyruTpOBdVFegqnL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_16.png", "conversations": [ { "from": "human", "value": "\nAre the results for 'Bonds' at {point}[0.259, 0.857] higher or lower than 'Futures' at {point}[0.259, 0.571]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 456 }, { "id": "95Kehf2IFRwHL1pH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_16.png", "conversations": [ { "from": "human", "value": "\nWhich investment returns more at {coordinate}[0.517, 0.656], 'Stocks' or 'Options'?" }, { "from": "gpt", "value": "Stocks." } ], "id_raw": 456 }, { "id": "xOyUx4vNI8EEZ99G", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region [0.130, 0.123, 0.710, 0.201]." }, { "from": "gpt", "value": "This region encompasses the year labels for 'Forex' with a color-coded marker ranging from years 2005 to 2014." } ], "id_raw": 456 }, { "id": "XMJgl9NuJKoAfW1b", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_16.png", "conversations": [ { "from": "human", "value": "\nWhat details are included in the space [0.130, 0.502, 0.710, 0.641]?" }, { "from": "gpt", "value": "It contains multiple points from {Options line}[0.452, 0.502] to {Futures line}[0.710, 0.571] representing the years 2010 to 2014, marked with several data labels and respective markers." } ], "id_raw": 456 }, { "id": "XNv7IuiChkh52BQd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_16.png", "conversations": [ { "from": "human", "value": "\nProvide a summary of what is displayed in the area [0.048, 0.065, 0.985, 0.171]." }, { "from": "gpt", "value": "This area covers the x-axis, including labels for years from 2004 to 2018, and the y-axis label with percentages incrementing in steps of 5%, from 10% to 55%." } ], "id_raw": 456 }, { "id": "6ODYJt0Bgzic5wuZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the element present at coordinates [0.122, 0.870, 0.954, 0.451] on the chart?" }, { "from": "gpt", "value": "This position corresponds to the line plot representing the {Market Index}[0.122, 0.068, 0.954, 0.451]." } ], "id_raw": 457 }, { "id": "11ss4q6tyUTr88xi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the chart?" }, { "from": "gpt", "value": "The y-axis label is {Global Trend (%)}[0.021, 0.278, 0.050, 0.624]." } ], "id_raw": 457 }, { "id": "rABBle2ZTWcs1Hu6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_17.png", "conversations": [ { "from": "human", "value": "\nWhere is the \"Global GDP\" line located in the chart?" }, { "from": "gpt", "value": "The \"Global GDP\" line is plotted at {coordinates}[0.122, 0.870, 0.954, 0.870]." } ], "id_raw": 457 }, { "id": "0snLYC9261TbfTOW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_17.png", "conversations": [ { "from": "human", "value": "\nWhat element is closer to the x-axis, {the point in 2014 on the 'Global GDP'}[0.455, 0.870], or {the point in 2014 on the 'Market Index'}[0.455, 0.298]?" }, { "from": "gpt", "value": "The point in 2014 on the \"Market Index\" is closer." } ], "id_raw": 457 }, { "id": "QMhva7M81qDDmtJA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_17.png", "conversations": [ { "from": "human", "value": "\nDoes the {point in 2018 on 'Market Index'}[0.787, 0.145] shows higher or lower value from the {point in 2018 on 'Global GDP'}[0.787, 0.870]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 457 }, { "id": "kR9bmSETnsJEKR28", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_17.png", "conversations": [ { "from": "human", "value": "\nWhat is closer to the origin, the {first point on the 'Global GDP' line}[0.122, 0.870], or the {first point on the 'Market Index' line}[0.122, 0.451]?" }, { "from": "gpt", "value": "The first point on the 'Market Index' line." } ], "id_raw": 457 }, { "id": "y0mlzViQyzCEhj7I", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements present between the coordinates [0.122, 0.451, 0.954, 0.870]." }, { "from": "gpt", "value": "This region houses two lines. The {Global GDP line}[0.122, 0.870, 0.954, 0.870] and the {Market Index line}[0.122, 0.068, 0.954, 0.451]. Both lines are represented with different color codes." } ], "id_raw": 457 }, { "id": "lvHOqDfrmXtValvk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the area between [0.122, 0.030, 0.954, 0.871]." }, { "from": "gpt", "value": "This region includes the main plot area of a line graph. It contains two lines representing {Global GDP}[0.122, 0.870, 0.954, 0.870] and {Market Index}[0.122, 0.068, 0.954, 0.451], with corresponding data points indicated." } ], "id_raw": 457 }, { "id": "c1vILlHNzbb1IkAw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the space located within [0.122, 0.068, 0.954, 0.451]?" }, { "from": "gpt", "value": "This is the area of the chart that plots the {Market Index line}[0.122, 0.068, 0.954, 0.451]. It maps each year's data with different positions along the line to show the performance of the market index over years." } ], "id_raw": 457 }, { "id": "SPS3za28RfTYm7bg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_18.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis in the chart signify based on the label with coordinates [0.045, 0.276, 0.074, 0.734]?" }, { "from": "gpt", "value": "Currency Performance (%)." } ], "id_raw": 458 }, { "id": "aXCCf0sxAFUE0Ftd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_18.png", "conversations": [ { "from": "human", "value": "\nPlease provide the coordinates for the label of year 2019 on the x-axis." }, { "from": "gpt", "value": "[0.602, 0.910, 0.657, 0.939]" } ], "id_raw": 458 }, { "id": "YTLtADKiXcnT6fBn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the tick label} at [0.082, 0.756, 0.110, 0.785] on the y-axis?" }, { "from": "gpt", "value": "55." } ], "id_raw": 458 }, { "id": "94zm6jkPpvXyOnR6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_18.png", "conversations": [ { "from": "human", "value": "\nAt the {year 2017}[0.395, 0.890], is {Bitcoin}[0.395, 0.505] performing above or below {Ethereum}[0.395, 0.453]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 458 }, { "id": "2QPej3evflvUlvk8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_18.png", "conversations": [ { "from": "human", "value": "\nComparing {Ethereum} and {Ripple} at the {year 2020}[0.747, 0.890], which one has a higher value?" }, { "from": "gpt", "value": "Ethereum." } ], "id_raw": 458 }, { "id": "XGDpO4czhwm4FaeA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_18.png", "conversations": [ { "from": "human", "value": "\nFor {Bitcoin}[0.865, 0.418], is the performance at {2020}[0.747, 0.663] higher or lower than in {2021}[0.865, 0.418]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 458 }, { "id": "imdXTwBy2vA3Tk1y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content found within the coordinates [0.136, 0.134, 0.320, 0.274]?" }, { "from": "gpt", "value": "This area contains the legend of the chart indicating three digital currencies: Bitcoin - {Blue label}[0.138, 0.150, 0.194, 0.168], Ethereum - {Red label}[0.138, 0.194, 0.194, 0.211], and Ripple - {Green label}[0.138, 0.237, 0.194, 0.255]." } ], "id_raw": 458 }, { "id": "167mErT79xdl7V6R", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects present within [0.160, 0.155, 0.865, 0.855]." }, { "from": "gpt", "value": "There are three line trends for Bitcoin, Ethereum, and Ripple plotted across various years from 2015-2021. Each trend is represented by a distinct color and marked with dots signifying yearly values." } ], "id_raw": 458 }, { "id": "LRSvpnbXVAHvoZ2d", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the items found within the region defined by [0.144, 0.069, 0.881, 0.111]." }, { "from": "gpt", "value": "This area contains the subtitle of the graph, labeled as \"Digital Currency Performance Over The Years\"." } ], "id_raw": 458 }, { "id": "O9ZLXP3BKpGbT6Re", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.015, 0.220, 0.033, 0.635] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 459 }, { "id": "1Vznkvta6AKXTS5s", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the 'France' text in the x-axis?" }, { "from": "gpt", "value": "[0.387, 0.795, 0.401, 0.888]." } ], "id_raw": 459 }, { "id": "kKF6MEptPYqXmkyX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_23_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the {y-axis tick label}[0.039, 0.264, 0.056, 0.292]?" }, { "from": "gpt", "value": "70." } ], "id_raw": 459 }, { "id": "XtkFSoYuqdSqIIHw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_19.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled 'USA'}[0.788, 0.275] higher or lower than {the point labeled 'India'}[0.722, 0.609] on the \"_child0\" line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 459 }, { "id": "Mecd3gy6nKralZTr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_19.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled 'Germany'}[0.525, 0.108] to the left or right of {the point labeled 'UK'}[0.460, 0.191] on the \"_child0\" line?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 459 }, { "id": "HXsA3VgCwwszR723", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_23_19.png", "conversations": [ { "from": "human", "value": "\nWhich country has a higher International Trade Index at their respective point, India or Japan?" }, { "from": "gpt", "value": "India." } ], "id_raw": 459 }, { "id": "NiqfXYMT5IQtBaE3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.296, 0.030, 0.755, 0.070]." }, { "from": "gpt", "value": "The area contains the subtitle of the chart, \"International Trade and Globalization Trends,\" styled in black color." } ], "id_raw": 459 }, { "id": "Ivmes2r6NErAkG8r", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.128, 795, 0.920, 0.875]." }, { "from": "gpt", "value": "The area contains the labels for the x-axis. The labels are the names of various countries such as {Japan}[0.125, 0.795, 0.139, 0.873], {Australia}[0.190, 0.795, 0.204, 0.918], and {China}[0.912, 0.795, 0.926, 0.875]." } ], "id_raw": 459 }, { "id": "A7wkbQnWzCtJHCaT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_23_19.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.066, 0.776, 0.132, 0.776]?" }, { "from": "gpt", "value": "There is one object. The y-axis tick label '40' is located at {coordinates}[0.039, 0.765, 0.056, 0.793]." } ], "id_raw": 459 }, { "id": "SQ4Ttci0zJzADbfb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label{ylabel}[0.015, 0.392, 0.035, 0.526] on Y-axis text?" }, { "from": "gpt", "value": "Metrics." } ], "id_raw": 460 }, { "id": "ZgZvTYBwFJOCfkEy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the {chart's title}[0.512, 0.942, 0.559, 0.975] say?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 460 }, { "id": "m8g9hliYbE591vUJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the eighth {x-axis tick}[0.777, 0.909, 0.812, 0.932]?" }, { "from": "gpt", "value": "2016." } ], "id_raw": 460 }, { "id": "jWfN0gAHZLZqH08a", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_0.png", "conversations": [ { "from": "human", "value": "\nIs the {GDP Growth Point in 2000}[0.103, 0.893] higher or lower than the {Freight Volume Point in 2000}[0.103, 0.384]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 460 }, { "id": "qnej45tYvDvL23rz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_0.png", "conversations": [ { "from": "human", "value": "\nCompare the {Transit time point of 2001}[0.146, 0.893] and the {Transit time point of 2002}[0.189, 0.893]. Which one is higher?" }, { "from": "gpt", "value": "Transit time point of 2001." } ], "id_raw": 460 }, { "id": "QYWFLqgctfV8FQJv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_0.png", "conversations": [ { "from": "human", "value": "\nIs the {Freight Volume Point in 2005}[0.319, 0.141] higher or lower than the {Freight Volume Point in 2006}[0.362, 0.170]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 460 }, { "id": "Xnb6D7c9Opa8Riok", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.103, 0.893, 0.924, 0.893]." }, { "from": "gpt", "value": "This area represents the line of GDP growth rate, with distinct data points plotted for each year between 2000 and 2019." } ], "id_raw": 460 }, { "id": "uzdNFUAM4TfutrRh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.110, 0.037, 0.275, 0.148]." }, { "from": "gpt", "value": "The area contains the legend, including {GDP growth rate}[0.154, 0.046, 0.270, 0.069], {Freight Volume}[0.154, 0.081, 0.258, 0.104] and {Transit Time}[0.154, 0.116, 0.239, 0.139], each labeled with a unique color." } ], "id_raw": 460 }, { "id": "Z3Ud1U5qX8TeOLRL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_0.png", "conversations": [ { "from": "human", "value": "\nWhat is located within [0.172, 0.893, 0.207, 0.893]?" }, { "from": "gpt", "value": "Within these coordinates is the GDP growth rate data point for the year 2002." } ], "id_raw": 460 }, { "id": "zBHN9iG38XxeBqr8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_1.png", "conversations": [ { "from": "human", "value": "\nWhat type of line corresponds to {this handle}[0.702, 0.128, 0.747, 0.142] in the legend?" }, { "from": "gpt", "value": "The line for \"Life Satisfaction\"." } ], "id_raw": 461 }, { "id": "xaKtBW1JuvScSFKV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_1.png", "conversations": [ { "from": "human", "value": "\nWhere is the y-axis label located in the figure?" }, { "from": "gpt", "value": "[0.019, 0.394, 0.044, 0.533]" } ], "id_raw": 461 }, { "id": "A1fY0PPWEFySpV1a", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the tick label} on y-axis at [0.062, 0.540, 0.084, 0.564]?" }, { "from": "gpt", "value": "40." } ], "id_raw": 461 }, { "id": "S9S9j1OSHcUT3wJr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_1.png", "conversations": [ { "from": "human", "value": "\nIs the {Environmental Quality line}[0.145, 0.163, 0.932, 0.721] at age 40 higher or lower than the {Life Satisfaction line}[0.145, 0.189, 0.932, 0.764]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 461 }, { "id": "NcvhnMtNRRJQDnxX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_1.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value at age 90, {Environmental Quality line}[0.145, 0.163, 0.932, 0.721] or {Life Satisfaction line}[0.145, 0.189, 0.932, 0.764]?" }, { "from": "gpt", "value": "Environmental Quality." } ], "id_raw": 461 }, { "id": "GTr3fNaOjHyrNZRz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_1.png", "conversations": [ { "from": "human", "value": "\nWhich line shows a decrease in value from age 80 to age 90, {Physical Health Average line}[0.145, 0.163, 0.932, 0.635] or {Life Satisfaction line}[0.145, 0.189, 0.932, 0.764]?" }, { "from": "gpt", "value": "Both." } ], "id_raw": 461 }, { "id": "qrWa4mldzqaKWq6H", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.096, 0.034, 0.981, 0.893]." }, { "from": "gpt", "value": "This is the main plot area of the chart which includes X and Y axis grids, labels for 'Age' and 'Ratings' on x and y axes respectively. It also contains the lines for 'Life Satisfaction', 'Environmental Quality' and 'Physical Health Average', each with their corresponding points at every decade from 20 to 100." } ], "id_raw": 461 }, { "id": "eKgqmZxiZGymTg0W", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is located in the area [0.700, 0.046, 0.973, 0.157]?" }, { "from": "gpt", "value": "It contains the three legend items: {Physical Health Average}[0.755, 0.055, 0.966, 0.078], {Environmental Quality}[0.755, 0.090, 0.950, 0.113], and {Life Satisfaction}[0.755, 0.125, 0.895, 0.148], each paired with a color-specific marker." } ], "id_raw": 461 }, { "id": "4MEI6tJFlFF63D3q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents in the region [0.916, 0.909, 0.949, 0.932]?" }, { "from": "gpt", "value": "The x-axis label '100' is located in this area." } ], "id_raw": 461 }, { "id": "fIC8L3pZ4Ee2WQdG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the object}[0.025, 0.375, 0.058, 0.502] on the chart?" }, { "from": "gpt", "value": "Rate." } ], "id_raw": 462 }, { "id": "9in30dQQRnaNhMGp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third line present in the chart?" }, { "from": "gpt", "value": "[0.163, 0.496, 0.932, 0.782]." } ], "id_raw": 462 }, { "id": "ya5R0zdNRxyYsswA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line represented by {the legend item 'Greenhouse Emissions'}[0.693, 0.121, 0.954, 0.156]?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 462 }, { "id": "qQ0nlKRuguiUzii8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_2.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Deforestation'}[0.163, 0.095, 0.932, 0.496] above or below {the line for 'Animal Extinction'}[0.163, 0.496, 0.932, 0.782]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 462 }, { "id": "zYdNjpIKV2fxMwYi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_2.png", "conversations": [ { "from": "human", "value": "\nWhich line starts first, {the line for 'Greenhouse Emissions'}[0.163, 0.553, 0.932, 0.627] or {the line for 'Deforestation'}[0.163, 0.095, 0.932, 0.496]?" }, { "from": "gpt", "value": "Both start at the same time." } ], "id_raw": 462 }, { "id": "H9I2UIZ2knPp2ObD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_2.png", "conversations": [ { "from": "human", "value": "\nWhich trend shows a higher rate in year 2003, {Deforestation}[0.334, 0.095] or {Greenhouse Emissions}[0.334, 0.622]?" }, { "from": "gpt", "value": "Deforestation." } ], "id_raw": 462 }, { "id": "DAMsGw9u4OheYrhE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the area [0.305, 0.864, 0.363, 0.899]." }, { "from": "gpt", "value": "This area contains the x-axis label '2003'." } ], "id_raw": 462 }, { "id": "jYrlqrokVKkd7A28", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.619, 0.055, 0.963, 0.222]." }, { "from": "gpt", "value": "This area contains the chart legend with labels for 'Deforestation' at [0.693, 0.069, 0.851, 0.104], 'Greenhouse Emissions' at [0.693, 0.121, 0.954, 0.156], and 'Animal Extinction' at [0.693, 0.173, 0.896, 0.208]." } ], "id_raw": 462 }, { "id": "ws2HkSzCeg4KLVIB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_2.png", "conversations": [ { "from": "human", "value": "\nWhat is present within the coordinates [0.618, 0755, 0.708, 0.790]?" }, { "from": "gpt", "value": "The area contains the y-axis label '0.7'." } ], "id_raw": 462 }, { "id": "m0tu4x6GbOex8MxT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the text value of the coordinate [0.310, 0.909, 0.345, 0.932] on the x-axis?" }, { "from": "gpt", "value": "2007." } ], "id_raw": 463 }, { "id": "UOx2bFWdzX1RTwhb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_3.png", "conversations": [ { "from": "human", "value": "\nWhat denote the object at coordinate [0.089, 0.058, 0.125, 0.072] referenced in the legend section?" }, { "from": "gpt", "value": "The marker for 'Internet Use'." } ], "id_raw": 463 }, { "id": "rJ9ijxx4twmh5QmB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.041, 0.259, 0.071, 0.283] on y-axis represent?" }, { "from": "gpt", "value": "The y-label '0.40'." } ], "id_raw": 463 }, { "id": "jebZ2guVkhRlojtK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_3.png", "conversations": [ { "from": "human", "value": "\nWhere does {line for 'Mobile Use'}[0.163, 0.268, 0.903, 0.815] stand in relation to {line for 'Social Media Use'}[0.163, 0.581, 0.903, 0.877] at the year 2010?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 463 }, { "id": "a3kP17qG7EB7TPdm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_3.png", "conversations": [ { "from": "human", "value": "\nWhich line, {line for 'Internet Use'}[0.163, 0.112, 0.903, 0.815] or {line for 'Social Media Use'}[0.163, 0.581, 0.903, 0.877], is higher on the chart for the year 2013?" }, { "from": "gpt", "value": "Internet Use." } ], "id_raw": 463 }, { "id": "GxmHOlCS0GtVvVcX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of {the line of 'Internet Use'}[0.163, 0.112, 0.903, 0.815] in relation to {the line of 'Mobile Use'}[0.163, 0.268, 0.903, 0.815] at point '[0.492, 0.503]'?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 463 }, { "id": "2tEWH8QVaaTGRvAW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the region enclosed by [0.081, 0.034, 0.985, 0.893]." }, { "from": "gpt", "value": "This region is the main plot area. It is white and contains the trends for 'Internet Use', 'Mobile Use', and 'Social Media Use' from 2005 to 2014. The x-axis represents years, and the y-axis represents the usage percentage." } ], "id_raw": 463 }, { "id": "GNgit0ydXM6ocURa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region inside [0.088, 0.046, 0.256, 0.157]?" }, { "from": "gpt", "value": "This region contains the legend of the chart showing the different lines for the 'Internet Use', 'Mobile Use', and 'Social Media Use' with their respective marker colors and labels." } ], "id_raw": 463 }, { "id": "TL7uqcjUAXU8iHmu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_3.png", "conversations": [ { "from": "human", "value": "\nWhat information is located in the area [0.015, 0.296, 0.035, 0.631]?" }, { "from": "gpt", "value": "This area holds the y-axis label 'Usage Percentage', rotated by 90 degree." } ], "id_raw": 463 }, { "id": "8OHLGE6rcLxd5tJW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the first x-axis tick label according to the available coordinates?" }, { "from": "gpt", "value": "2008." } ], "id_raw": 464 }, { "id": "zQ2U5e0TplkQVkuK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the role of the object within [0.025, 0.218, 0.058, 0.659]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 464 }, { "id": "QBGlMKNhxfJhdwLG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_4.png", "conversations": [ { "from": "human", "value": "\nWhat type of data is represented by the series located at [0.174, 0.118, 0.933, 0.294]?" }, { "from": "gpt", "value": "Media_Total." } ], "id_raw": 464 }, { "id": "8ihxcuSko8j1fsi9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_4.png", "conversations": [ { "from": "human", "value": "\nAccording to the data points, did Media_Total or SocialMedia have a higher value in the year 2012?" }, { "from": "gpt", "value": "Media_Total." } ], "id_raw": 464 }, { "id": "Z4xO201WGOdkzbcm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_4.png", "conversations": [ { "from": "human", "value": "\nWhich had a lower value in 2019, SocialMedia or TraditionalMedia?" }, { "from": "gpt", "value": "TraditionalMedia." } ], "id_raw": 464 }, { "id": "kj5HekEo4XGoojWs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_4.png", "conversations": [ { "from": "human", "value": "\nIn terms of trend direction, is the SocialMedia line generally ascending or descending between the given coordinates [0.174, 0.086, 0.933, 0.278]?" }, { "from": "gpt", "value": "Ascending." } ], "id_raw": 464 }, { "id": "3WawKXm9JJKs6YFQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_4.png", "conversations": [ { "from": "human", "value": "\nWhat information can you provide about the region [0.74, 0.055, 0.963, 0.223]?" }, { "from": "gpt", "value": "This region contains the chart legend, with markers and labels for Media_Total, SocialMedia, and TraditionalMedia." } ], "id_raw": 464 }, { "id": "rZSOSrEgYAaLauhj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found within the area spanned by [0.132, 0.037, 0.975, 0.839]." }, { "from": "gpt", "value": "This large region encompasses the primary graph area, including the x and y axes with their respective labels (\"Year\" and \"Media Influence\"), the axes' tick labels, and the data lines for Media_Total, SocialMedia, and TraditionalMedia." } ], "id_raw": 464 }, { "id": "ZauEyPW4ohp12UUq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the coordinates [0.174, 0.118, 0.933, 0.294]." }, { "from": "gpt", "value": "This area contains the \u2018Media_Total\u2019 trend line with variable data points from 2010 to 2019." } ], "id_raw": 464 }, { "id": "tohlgpQC9r5B6nBR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_5.png", "conversations": [ { "from": "human", "value": "\nWhat's the name of the {object}[0.836, 0.069, 0.945, 0.104] in the figure's legend?" }, { "from": "gpt", "value": "Car Sales." } ], "id_raw": 465 }, { "id": "7WIVbvIvejxtWQcR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {this y-axis tick label}[0.068, 0.291, 0.112, 0.326]?" }, { "from": "gpt", "value": "100." } ], "id_raw": 465 }, { "id": "XmTc8NVMoNsJOPN6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_5.png", "conversations": [ { "from": "human", "value": "\nWhat year does {this x-axis tick label}[0.819, 0.864, 0.877, 0.899] correspond to?" }, { "from": "gpt", "value": "2018." } ], "id_raw": 465 }, { "id": "7Zf2mnR0IWLuvPfN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_5.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Car Sales'}[0.848, 0.225] higher or lower than {the point of 'Bike Sales'}[0.848, 0.481] for the year 2018?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 465 }, { "id": "EvhiJ90fgDXwuKc6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_5.png", "conversations": [ { "from": "human", "value": "\nCompare {the 'Car Sales' value}[0.594, 0.144] in 2015 to {the 'EV Sales' value}[0.594, 0.625], which is greater?" }, { "from": "gpt", "value": "Car Sales." } ], "id_raw": 465 }, { "id": "76FRGozJ3qyru0Dx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_5.png", "conversations": [ { "from": "human", "value": "\nIn 2011, did {Car Sales}[0.255, 0.198] or {EV Sales}[0.255, 0.706] have higher sales?" }, { "from": "gpt", "value": "Car Sales." } ], "id_raw": 465 }, { "id": "i0yqiqzxzH102gTP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the region [0.762, 0.055, 0.963, 0.222]?" }, { "from": "gpt", "value": "This section contains the figure's legend with three items: {Car Sales}[0.836, 0.069, 0.945, 0.104], {Bike Sales}[0.836, 0.121, 0.954, 0.156], and {EV Sales}[0.836, 0.173, 0.935, 0.208]. Each item includes a colored marker symbol and a label naming the sales category it represents." } ], "id_raw": 465 }, { "id": "nzOrvrkgHT6D58ts", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_5.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region [0.128, 0.038, 0.975, 0.839]?" }, { "from": "gpt", "value": "This region primarily encompasses the plotting area of the line chart. It includes three distinct lines relating to 'Car Sales', 'Bike Sales', and 'EV Sales', and features corresponding markers showing yearly data for each category from 2010 to 2019." } ], "id_raw": 465 }, { "id": "F6ufSKkhLtPcchAU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_5.png", "conversations": [ { "from": "human", "value": "\nCould you describe the items in the region [0.903, 0.864, 0.962, 0.899]?" }, { "from": "gpt", "value": "This region contains the last tick label on the x-axis, correlating to the year '2019'." } ], "id_raw": 465 }, { "id": "Bu5qD8YLLBLzOd1m", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the axis name for the position set as [0.510, 0.945, 0.554, 0.975]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 467 }, { "id": "XpqPU8Otx7pHMJjT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_7.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the label {point}[0.757, 0.660] on the line chart represents?" }, { "from": "gpt", "value": "Augmented Reality in 2025." } ], "id_raw": 467 }, { "id": "32EVwWM4JWqBXtQl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the label in the coordinates [0.019, 0.257, 0.041, 0.673] represent?" }, { "from": "gpt", "value": "Number of Students (millions)." } ], "id_raw": 467 }, { "id": "l1bzRqvwndbJKbQx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_7.png", "conversations": [ { "from": "human", "value": "\nAt {point 2030}[0.869,0.173], does 'Online Learning' have a larger number of students than 'Augmented Reality'[0.869,0.590]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 467 }, { "id": "kuuRuUZGKPFj73em", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the On-campus Learning line}[0.195, 0.312, 0.869, 0.507] depict a decrease in the number of students from the point at {2000}[0.195, 0.854] to the point at {2020}[0.644, 0.423]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 467 }, { "id": "e23s6YUw5bR1IJyR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_7.png", "conversations": [ { "from": "human", "value": "\nIs the {On-campus Learning}[0.195, 0.451] point higher than the {Online Learning}[0.195, 0.854] point at year 2000?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 467 }, { "id": "Z5Gvyy2p8R5SzfBO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the main area bordered by [0.082, 0.034, 0.981, 0.896]." }, { "from": "gpt", "value": "This is the main plotting area of the chart, containing three line graphs representing 'On-campus Learning', 'Online Learning', and 'Augmented Reality'. The x-axis represents 'Year', ranging from 2000 to 2030. The y-axis represents 'Number of Students (millions)', ranging from 0 to 30." } ], "id_raw": 467 }, { "id": "BnlIjrn3BXnoSkCy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.091, 0.046, 0.333, 0.157] contain?" }, { "from": "gpt", "value": "It contains the legend of the chart, with three labels: {On-campus Learning}[0.147, 0.055, 0.326, 0.078], {Online Learning}[0.147, 0.090, 0.283, 0.113], and {Augmented Reality}[0.147, 0.125, 0.313, 0.148], each associated with a distinct color." } ], "id_raw": 467 }, { "id": "ytNolNCiP10wTHHh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the region [0.048, 0.164, 0.070, 0.187]?" }, { "from": "gpt", "value": "The area contains the '25' y-axis label, representing 25 million students." } ], "id_raw": 467 }, { "id": "Olb0DrMZbNiIMcK4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with the coordinates [0.068, 0.235, 0.104, 0.270] on the chart?" }, { "from": "gpt", "value": "y-axis tick label '0.9'." } ], "id_raw": 469 }, { "id": "Ch1NLeH2Szwtg0V7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second point in the 'Firewall Detection Accuracy' line?" }, { "from": "gpt", "value": "[0.230, 0.445]." } ], "id_raw": 469 }, { "id": "y7WXfOKtWen36Ri3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.222, 0.864, 0.237, 0.899]?" }, { "from": "gpt", "value": "2." } ], "id_raw": 469 }, { "id": "8iQATNNqtgamAgc3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_9.png", "conversations": [ { "from": "human", "value": "\nIs {the point for month '3'}[0.320, 0.524] of 'Two-Factor Authentication Accuracy' above or below {the point for month '3'}[0.320, 0.445] of 'Firewall Detection Accuracy'?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 469 }, { "id": "xtqLgljq2rahLCGI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_9.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Secure Login Accuracy'}[[0.139, 0.130, 0.957, 0.347]] generally above or below {the line for 'Firewall Detection Accuracy'}[[0.139, 0.248, 0.957, 0.544]]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 469 }, { "id": "ru8q1BFsCgqmeIhh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_9.png", "conversations": [ { "from": "human", "value": "\nWhich security measure has a higher accuracy at {month 10}[0.957, 0.248], Secure Login or Firewall Detection?" }, { "from": "gpt", "value": "Secure Login." } ], "id_raw": 469 }, { "id": "UvD23Ozbxb9Wc8b2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.540, 0.668, 0.803, 0.703]." }, { "from": "gpt", "value": "This contains the label for \"Secure Login Accuracy\", which is accompanied by a blue marker." } ], "id_raw": 469 }, { "id": "FjNYp3DtHPzqvHJO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.466, 0.654, 0.963, 0.822]." }, { "from": "gpt", "value": "This area contains the legend items: {Secure Login Accuracy}[0.540, 0.668, 0.803, 0.703], {Two-Factor Authentication Accuracy}[0.540, 0.721, 0.954, 0.756], and {Firewall Detection Accuracy}[0.540, 0.773, 0.861, 0.808], each paired with a colored marker." } ], "id_raw": 469 }, { "id": "gXoNFBM7hMEz8K7v", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.068, 0.432, 0.104, 0.467]?" }, { "from": "gpt", "value": "There is 1 object. The y-axis tick label '0.8' is located within these coordinates." } ], "id_raw": 469 }, { "id": "qMDBNhHqqGIZLFDw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_10.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart does this JSON structure represent?" }, { "from": "gpt", "value": "line chart." } ], "id_raw": 470 }, { "id": "CESLRP84ZMsTYQAQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the xlabel} located at [0.508, 0.912, 0.587, 0.963]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 470 }, { "id": "73YBg5de0cXSg8rb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.134, 0.178, 0.195, 0.199] in the legend?" }, { "from": "gpt", "value": "It is the colored marker corresponding to 'Market Expansion'." } ], "id_raw": 470 }, { "id": "UTzzST7zC8mzRMcD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_10.png", "conversations": [ { "from": "human", "value": "\nIs {the point for the year 2002 in Prototype Success}[0.334, 0.387] higher or lower than {the same year's point in Patent Filings}[0.334, 0.402]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 470 }, { "id": "sNUfF13LWFkmQ5tP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_10.png", "conversations": [ { "from": "human", "value": "\nComparing the year 2005, which line {Prototype Success}[0.548, 0.497] or {Market Expansion}[0.548, 0.271] has a higher value?" }, { "from": "gpt", "value": "Market Expansion." } ], "id_raw": 470 }, { "id": "UnDTkoE3ozpVOYpS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_10.png", "conversations": [ { "from": "human", "value": "\nWhich category, {Prototype Success}[0.904, 0.628] or {Patent Filings}[0.904, 0.169], had higher values for the year 2010?" }, { "from": "gpt", "value": "Prototype Success." } ], "id_raw": 470 }, { "id": "gHB2wv6t8MxOqrPs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_10.png", "conversations": [ { "from": "human", "value": "\nDescribe what is contained within the region [0.192, 0.169, 0.904, 0.511]." }, { "from": "gpt", "value": "This region contains the line plots for 'Patent Filings' and 'Prototype Success' from 2000-2010 and the points along each line representing yearly values." } ], "id_raw": 470 }, { "id": "vlQw4eiOdsT87OJW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the region [0.134, 0.055, 0.427, 0.222]." }, { "from": "gpt", "value": "It is the region where the chart's legend is located. It contains labels for 'Prototype Success', 'Patent Filings', 'Market Expansion' along with their respective color markers." } ], "id_raw": 470 }, { "id": "VfjMj09ID09yISjK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the region [0.163, 0.864, 0.933, 0.899]?" }, { "from": "gpt", "value": "It's the x-axis of the chart, with tick labels showing the years from 2000 to 2010." } ], "id_raw": 470 }, { "id": "VjhW3tIVtKNmQy2g", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the coordinates [0.859, 0.055, 0.966, 0.078]?" }, { "from": "gpt", "value": "Label of Electric cars in the legend." } ], "id_raw": 471 }, { "id": "Gk8wgjRBgG7MFpd8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_11.png", "conversations": [ { "from": "human", "value": "\nWhat is at the position [0.019, 0.340, 0.044, 0.587] on the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 471 }, { "id": "uQoPhdXZ4sllrzji", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_11.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the text at coordinates [0.811, 0.909, 0.855, 0.932]?" }, { "from": "gpt", "value": "X-axis tick label \"2019\"." } ], "id_raw": 471 }, { "id": "3F4bVWgSnACQHWeL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_11.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at the 2015 point, the {Electric cars line}[0.165, 0.206, 0.907, 0.876] or the {Hybrid cars line}[0.165, 0.412, 0.907, 0.738]?" }, { "from": "gpt", "value": "Hybrid cars." } ], "id_raw": 471 }, { "id": "zKwgYCVgvY7c16wo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_11.png", "conversations": [ { "from": "human", "value": "\nAt the point corresponding to the year 2012, is the {Diesel cars line}[0.165, 0.206, 0.907, 0.670] above or below {the Electric cars line}[0.165, 0.206, 0.907, 0.876]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 471 }, { "id": "nplcdzTVDvJBJGp8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_11.png", "conversations": [ { "from": "human", "value": "\nWhich one reaches higher in 2020, {the Electric cars line} [0.165, 0.206, 0.907, 0.876] or {the Hybrid cars line}[0.165, 0.412, 0.907, 0.738]?" }, { "from": "gpt", "value": "Electric cars." } ], "id_raw": 471 }, { "id": "LMYEdLiFCOyr4UX1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area of [0.804, 0.046, 0.973, 0.157]." }, { "from": "gpt", "value": "This area contains the legend of the chart, which includes makers and labels for \"Electric cars\", \"Hybrid cars\", and \"Diesel cars\", represented by different colors." } ], "id_raw": 471 }, { "id": "q2CTToDob1aOeW1x", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.288, 0.711, 0.331, 0.933]." }, { "from": "gpt", "value": "This region contains the x-axis labels for the years 2010 through 2020, placed appropriately at their respective positions." } ], "id_raw": 471 }, { "id": "JghPJxt5fXSuWHJ2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_11.png", "conversations": [ { "from": "human", "value": "\nWhat elements are within the coordinates [0.051, 0.025, 0.078, 0.907]?" }, { "from": "gpt", "value": "The region hosts the y-axis labels specifying different market shares, ranging from 0.0 to 0.5." } ], "id_raw": 471 }, { "id": "Ul33fkpjppA9ILwv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.065, 0.887, 0.074, 0.910]?" }, { "from": "gpt", "value": "It's the y-axis label for the value '0'." } ], "id_raw": 473 }, { "id": "byERWlxnurqfQwTc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object labeled {Mobile Phone Users} with handle color '#ff7f0e'?" }, { "from": "gpt", "value": "The color refers to the line in the chart representing the Mobile Phone Users." } ], "id_raw": 473 }, { "id": "ZBlWLa5FIb8wwZ5G", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the point}[0.277, 0.831] within the boundary of 'Email Users' line in the chart?" }, { "from": "gpt", "value": "The point represents the number of Email Users in the year 2002." } ], "id_raw": 473 }, { "id": "7cs9RcCOIA1cpCSh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_13.png", "conversations": [ { "from": "human", "value": "\nAre the coordinates of the {Internet Users}[0.148, 0.626, 0.921, 0.857] above or below {Mobile Phone Users}[0.148, 0.145, 0.921, 0.814]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 473 }, { "id": "N76hAnRroYRZ3CPI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_13.png", "conversations": [ { "from": "human", "value": "\nIs the {Email Users}[0.148, 0.509, 0.921, 0.844] line between {Internet Users}[0.148, 0.626, 0.921, 0.857] and {Mobile Phone Users}[0.148, 0.145, 0.921, 0.814]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 473 }, { "id": "bUMieDLT0tRJfmhP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_13.png", "conversations": [ { "from": "human", "value": "\nWhich set of users has a higher value at the year 2012, {Mobile Phone Users}[0.921, 0.145] or {Email Users}[0.921, 0.509]?" }, { "from": "gpt", "value": "Mobile Phone Users." } ], "id_raw": 473 }, { "id": "XtOUNI1hQXuDwMON", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content and color of the line indicated by these coordinates [0.148, 0.626, 0.921, 0.857]." }, { "from": "gpt", "value": "It is the line representing 'Internet Users', colored blue and marked with circular points." } ], "id_raw": 473 }, { "id": "fNZaDO0WcgDokSw0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the structure present within the coordinates [0.083, 0.034, 0.985, 0.896]?" }, { "from": "gpt", "value": "These coordinates represent a complete graph with y-grid and x-grid lines. It includes the plotting of 'Internet Users', 'Mobile Phone Users', and 'Email Users'. It also has the x-axis label 'Year' and y-axis label 'Number of Users (in Millions)'." } ], "id_raw": 473 }, { "id": "N34iaJpobp9SiToi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.090, 0.046, 0.276, 0.157]." }, { "from": "gpt", "value": "This area contains the legend of the chart, including the labels 'Internet Users', 'Mobile Phone Users', and 'Email Users', each with their corresponding colors." } ], "id_raw": 473 }, { "id": "tosZ0puoHZsJAiBs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.015, 0.408, 0.035, 0.510]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 474 }, { "id": "sHRDgtDR9x0LfwjT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the point for the year 2009 in the 'Global Trade' line?" }, { "from": "gpt", "value": "[0.466, 0.546]" } ], "id_raw": 474 }, { "id": "RLlYH9aXt4yRBFNN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_14.png", "conversations": [ { "from": "human", "value": "\nWhat year does the x-axis tick label at [0.924, 0.909, 0.959, 0.932] represent?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 474 }, { "id": "SSFv4OzNtPcrtU1O", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point for the year 2005 on the 'Global Trade' line}[0.293, 0.536] above or below {the point for the year 2005 on the 'Supply Chain Management' line}[0.293, 0.664]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 474 }, { "id": "veQDON3WRu60uw6t", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_14.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Global Trade'}[[0.120, 0.507, 0.942, 0.652]] overall above or below {the line for 'Volatile Market'}[[0.120, 0.616, 0.942, 0.866]]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 474 }, { "id": "0UFfosgRJv7fSgLi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_14.png", "conversations": [ { "from": "human", "value": "\nWhich metric has a higher value for the year 2008, Global Trade or Volatile Market?" }, { "from": "gpt", "value": "Global Trade." } ], "id_raw": 474 }, { "id": "dskAZvy42WGCtxQl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.741, 0.037, 0.978, 0.183]." }, { "from": "gpt", "value": "This region contains the chart legend titled \"Metrics\", which includes items: {Global Trade}[0.785, 0.081, 0.872, 0.104], {Supply Chain Management}[0.785, 0.116, 0.972, 0.139], and {Volatile Market}[0.785, 0.151, 0.888, 0.174], each paired with a color marker respectively." } ], "id_raw": 474 }, { "id": "rSJgGvHt8Zpyblsr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.041, 0.112, 0.067, 0.136]." }, { "from": "gpt", "value": "This region contains the Y-axis tick label for the value '200'." } ], "id_raw": 474 }, { "id": "4T9ihofnfYAx4HOK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.405, 0.909, 0.440, 0.932]?" }, { "from": "gpt", "value": "There is 1 object. The x-axis tick label '2008' is located at this coordinate." } ], "id_raw": 474 }, { "id": "OnXVLOGkSgo2DwLx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_15.png", "conversations": [ { "from": "human", "value": "\nWhat symbol represents the \"Urban Population\" in the chart?" }, { "from": "gpt", "value": "Blue circle." } ], "id_raw": 475 }, { "id": "AWKg9auO1Vz2ePMY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_15.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.159, 0.778] in the chart?" }, { "from": "gpt", "value": "The starting point of {the Urban Population line}[0.159, 0.456, 0.936, 0.778]." } ], "id_raw": 475 }, { "id": "N18fPU7mqd0ZT0Ex", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at coordinates [0.068, 0.487, 0.104, 0.522]?" }, { "from": "gpt", "value": "5.0." } ], "id_raw": 475 }, { "id": "2n6Yq9j2P0ttNSYh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_15.png", "conversations": [ { "from": "human", "value": "\nIs {the Urban Population marker}[0.159, 0.456] higher than {the Total Population marker}[0.159, 0.381] in the year 2000 on the chart?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 475 }, { "id": "5zM0HRq35xBh1z8l", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_15.png", "conversations": [ { "from": "human", "value": "\nWhich is earlier, {the x-tick labeled '2002'}[0.208, 0.864, 0.266, 0.899] or {the x-tick labeled '2010'}[0.518, 0.864, 0.577, 0.899]?" }, { "from": "gpt", "value": "2002." } ], "id_raw": 475 }, { "id": "ttzEJ0gT8PedeBON", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_15.png", "conversations": [ { "from": "human", "value": "\nCompare {the value at the first Rural Population point}[0.159, 0.668] with {the first Urban Population point}[0.159, 0.778], which one is higher?" }, { "from": "gpt", "value": "Rural Population." } ], "id_raw": 475 }, { "id": "BDw7D5Ixmt6NAJEJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the area located between [0.120, 0.726, 0.940, 0.864]." }, { "from": "gpt", "value": "This area contains the x-axis and the axis labels for years spanning from 2000 to 2020, arranged from left to right." } ], "id_raw": 475 }, { "id": "FFJ3U3uMPmFi1Z5y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within the region [0.058, 0.185, 0.974, 0.839]." }, { "from": "gpt", "value": "This region encompasses all the line plots representing Urban, Rural, and Total Population trend lines, including various data points marked along the lines." } ], "id_raw": 475 }, { "id": "f8ctK4TFQPbXMDx4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_15.png", "conversations": [ { "from": "human", "value": "\nWhat can you observe in the region starting from [0.006, 0.123] to [0.415, 0.277]?" }, { "from": "gpt", "value": "This region displays the sublegend for the graph showing Urban, Rural, and Total Population, along with color-coded markers beside each label to indicate their corresponding lines in the plot." } ], "id_raw": 475 }, { "id": "4i1FuYDD484t8zWP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at coordinates [0.814, 0.747, 0.969, 0.775] in the legend refer to?" }, { "from": "gpt", "value": "Interactive Learning." } ], "id_raw": 476 }, { "id": "Dms47YCzAl1TcVNz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_16.png", "conversations": [ { "from": "human", "value": "\nCan you identify the element located at [0.038, 0.039, 0.068, 0.067]?" }, { "from": "gpt", "value": "The y-axis tick label '100'." } ], "id_raw": 476 }, { "id": "Eq8KNJL9hJwWlwG1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the element at [0.079, 0.030, 0.983, 0.842]?" }, { "from": "gpt", "value": "The main plot area of the graph." } ], "id_raw": 476 }, { "id": "ki49xArCAA1UsnxP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_16.png", "conversations": [ { "from": "human", "value": "\nIs {the Direct Learning line}[0.120, 0.232, 0.942, 0.446] above or below {the Video Learning line}[0.120, 0.050, 0.942, 0.351] in 2009?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 476 }, { "id": "C9JmLdn6Yfdgt2FB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point of Interactive Learning in 2020}[0.942, 0.066] higher or lower than {the point of Direct Learning in the same year}[0.942, 0.446]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 476 }, { "id": "db0UfweyhaSTcRVc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_16.png", "conversations": [ { "from": "human", "value": "\nCompare {the point of Video Learning in 2007}[0.120, 0.351] with {the point of Interactive Learning in the same year}[0.120, 0.398]. Which one is higher?" }, { "from": "gpt", "value": "Interactive Learning." } ], "id_raw": 476 }, { "id": "X6LQDiCv2nqiRbSD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_16.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the boundaries [0.765, 0.694, 0.976, 0.828]." }, { "from": "gpt", "value": "This region contains the graph's legend, which includes: {Direct Learning with a blue color}[0.766, 0.709, 0.807, 0.726, 0.814, 0.705, 0.933, 0.733], {Interactive Learning with a green color}[0.766, 0.751, 0.807, 0.768, 0.814, 0.747, 0.969, 0.775], and {Video Learning with an orange color}[0.766, 0.793, 0.807, 0.809, 0.814, 0.789, 0.930, 0.817]." } ], "id_raw": 476 }, { "id": "w9mp3VTBbj27gcXN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_16.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects inside the box [0.017, 0.316, 0.032, 0.556]?" }, { "from": "gpt", "value": "The vertical area contains the y-axis label, which reads 'Success Rate (%)." } ], "id_raw": 476 }, { "id": "cu1LabgKlI78GDcK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_16.png", "conversations": [ { "from": "human", "value": "\nCould you describe the elements in the region [0.533, 0.242, 0.625, 0.352]?" }, { "from": "gpt", "value": "This region consists of part of the 'Direct Learning' and 'Interactive Learning' lines chart, including several points and lines between the years 2012 to 2015." } ], "id_raw": 476 }, { "id": "xRwal6TgwHeWWDTi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_18.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.721, 0.735, 0.966, 0.763] in the legend refer to?" }, { "from": "gpt", "value": "Renewable Energy Adoption." } ], "id_raw": 478 }, { "id": "DzEBIeGB1CpRVSJ5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_18.png", "conversations": [ { "from": "human", "value": "\nWhat's the label of line_2 depicted at coordinates [0.127, 0.332, 0.945, 0.456]?" }, { "from": "gpt", "value": "Population Growth." } ], "id_raw": 478 }, { "id": "i8O1ZHxTuohsrf9d", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_18.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label at [0.019, 0.435, 0.044, 0.477] denote?" }, { "from": "gpt", "value": "%." } ], "id_raw": 478 }, { "id": "PrU3AN6QBazm45Fr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_18.png", "conversations": [ { "from": "human", "value": "\nIs {the point '4'}[0.399, 0.612] on the 'Renewable Energy Adoption' trend line above or below {the point '4'}[0.399, 0.269] on the 'CO2 Emissions' trend line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 478 }, { "id": "LSORXyKcf0ATvUjL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_18.png", "conversations": [ { "from": "human", "value": "\nIn year 3, is the value of {Renewable Energy Adoption}[0.309, 0.664] less than, equal to, or greater than the value of {Population Growth}[0.309, 0.404]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 478 }, { "id": "vieFONyHFzX3j8z8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_18.png", "conversations": [ { "from": "human", "value": "\nIn year 6, did {CO2 Emissions}[0.581, 0.321] increase or decrease compared to {year 5}[0.490, 0.301]?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 478 }, { "id": "crAmea04DVwjMw4p", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.667, 0.739, 0.713, 0.755]." }, { "from": "gpt", "value": "This area contains a marker or an icon for the 'Renewable Energy Adoption' trend line in the color '#1f77b4'." } ], "id_raw": 478 }, { "id": "XbJxcCAfxStk9cA4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_18.png", "conversations": [ { "from": "human", "value": "\nProvide a description of region [0.121, 0.891, 0.132, 0.919]." }, { "from": "gpt", "value": "The area contains the label '1' for the x-axis tick." } ], "id_raw": 478 }, { "id": "irHg988nkdJSIsbH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_18.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.051, 0.341, 0.078, 0.369] represent?" }, { "from": "gpt", "value": "This region contains the '0.6' label for the y-axis tick." } ], "id_raw": 478 }, { "id": "1ZVq2MhPLYLQaf21", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_19.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.025, 0.280, 0.058, 0.621] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 479 }, { "id": "Sml3kGglMzUsGEum", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_19.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the third item in the legend?" }, { "from": "gpt", "value": "[0.199, 0.139, 0.525, 0.167]." } ], "id_raw": 479 }, { "id": "mLRWWmMz02OCepV3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_24_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the labeling of the {point}[0.221, 0.798] on the third line?" }, { "from": "gpt", "value": "2." } ], "id_raw": 479 }, { "id": "uK9HSOZ5mYJnsHO9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_19.png", "conversations": [ { "from": "human", "value": "\nIs the {point of 2015}[0.221, 0.798] on the 'AI Adoption in Health Sector' line above or below the {point of 2016}[0.329, 0.766] on the same line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 479 }, { "id": "BLBqYIBA9YHAj5jX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_19.png", "conversations": [ { "from": "human", "value": "\nIs the {percentage in 2015 for 'Rate of Digital Record Adoption'}[0.221, 0.556] higher or lower than the {percentage in 2015 for 'Increase in Medical Data Volume'}[0.221, 0.661]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 479 }, { "id": "b3k9i19y2EYnXFQB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_24_19.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher percentage in 2021, 'Increase in Medical Data Volume' or 'AI Adoption in Health Sector'?" }, { "from": "gpt", "value": "Increase in Medical Data Volume." } ], "id_raw": 479 }, { "id": "ioe3QMiZPqPiqKOj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region within [0.125, 0.044, 0.587, 0.178]?" }, { "from": "gpt", "value": "This region contains the legend with three items: {Rate of Digital Record Adoption}[0.199, 0.055, 0.563, 0.083], {Increase in Medical Data Volume}[0.199, 0.097, 0.577, 0.125], and {AI Adoption in Health Sector}[0.199, 0.139, 0.525, 0.167]. Each item has a color marker associated with it." } ], "id_raw": 479 }, { "id": "yVvY6aZsy5ZW0ish", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area enclosed by [0.515, 0.891, 0.573, 0.919]?" }, { "from": "gpt", "value": "The country\u2019s population information for the year {2018}[0.515, 0.891, 0.573, 0.919] is enclosed in this area." } ], "id_raw": 479 }, { "id": "Koch6UDAae6y8lKM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_24_19.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the boundary [0.221, 0.114, 0.867, 0.661]?" }, { "from": "gpt", "value": "The line 'Increase in Medical Data Volume' with its data points for each year from 2015 to 2021 is contained within the boundary." } ], "id_raw": 479 }, { "id": "hdh3KfOCCDhfYZjo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.337, 0.029, 0.631] on the chart?" }, { "from": "gpt", "value": "Number of Tourists ('000)" } ], "id_raw": 481 }, { "id": "Th1CbKMx2NAvTbDa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the point coordinate {for year 2012}[0.273, 0.661] on the blue line?" }, { "from": "gpt", "value": "[0.273, 0.661]" } ], "id_raw": 481 }, { "id": "i2GpdQxFo7wP6ksj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.492, 0.952, 0.521, 0.975]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 481 }, { "id": "wBQefqEHKnSEtKr5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point for year 2014}[0.429, 0.525] above or below {the point for year 2019}[0.817, 0.104] on the 'Tourism' line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 481 }, { "id": "SD1cy5upGr0VVkZ2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_1.png", "conversations": [ { "from": "human", "value": "\nIs {the Year 2018 point}[0.740, 0.167] in the red line higher or lower than {the Year 2018 point}[0.740, 0.185] in the blue line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 481 }, { "id": "UTXax0Bl5hRJAqhq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_1.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher point at {Year 2016}[0.584, 0.321], 'Tourism' or 'Facility Rating'?" }, { "from": "gpt", "value": "'Tourism'." } ], "id_raw": 481 }, { "id": "ugBe7R9MuHFrcRv4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.247, 0.025, 0.766, 0.058]." }, { "from": "gpt", "value": "The subtitle of the chart, \"Tourism and Hospitality Industry Data Analysis,\" is located here." } ], "id_raw": 481 }, { "id": "IPDxstS9CTkDJKq6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.079, 0.066, 0.934, 0.903]." }, { "from": "gpt", "value": "The area contains the plot of the chart features two intersecting lines: {the 'Tourism' line}[0.118, 0.104, 0.895, 0.865] indicated in blue, and {the 'Facility Rating' line}[0.118, 0.104, 0.895, 0.865] colored red, representing two different data trends over time." } ], "id_raw": 481 }, { "id": "kA0fYbZ6fKYgmJW7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.101, 0.919, 0.136, 0.942]?" }, { "from": "gpt", "value": "There is 1 object. The x-axis tick label '2010' is located at {coordinates}[0.101, 0.919, 0.136, 0.942]." } ], "id_raw": 481 }, { "id": "euDQG60Sy41iivkg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label for x-axis}[0.473, 0.942, 0.585, 0.975]?" }, { "from": "gpt", "value": "Risk Level." } ], "id_raw": 482 }, { "id": "YTybsNtFj0fl3Ur3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_2.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what object is located at [0.979, 1.002, 0.063, 1.002]?" }, { "from": "gpt", "value": "It's a Y-axis label, specifically -0.5." } ], "id_raw": 482 }, { "id": "2hFMspelq9CVCupG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the legend item}'s text at [0.124, 0.086, 0.198, 0.110]?" }, { "from": "gpt", "value": "Probability." } ], "id_raw": 482 }, { "id": "imS0ErYNputjT9lu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_2.png", "conversations": [ { "from": "human", "value": "\nDoes {Risk Level 'High' on 'Probability'}[0.529, 0.723] has a higher or a lower value than {Risk Level 'Medium' on 'Probability'}[0.321, 0.811]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 482 }, { "id": "8aCsqXX4C9IANCt1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_2.png", "conversations": [ { "from": "human", "value": "\nHow does {Impact's value for 'Medium' risk level}[0.321, 0.767] compared to {Probability's value for 'Medium' risk level}[0.321, 0.811]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 482 }, { "id": "dPPV0NColgYLAZdf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_2.png", "conversations": [ { "from": "human", "value": "\nIs {Risk Level 'Low' on 'Impact'}[0.114, 0.833] higher or lower than {Risk Level 'Low' on 'Probability'}[0.114, 0.855]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 482 }, { "id": "9h48kkfdf0C6wX9I", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_2.png", "conversations": [ { "from": "human", "value": "\nCan you provide some details about the objects in the area marked as [0.514, 0.909, 0.545, 0.932]?" }, { "from": "gpt", "value": "In this area, there is an X-axis tick label with the text 'High'." } ], "id_raw": 482 }, { "id": "rCq3ByfIouMiRYsB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents of the region [0.079, 0.077, 0.203, 0.154]." }, { "from": "gpt", "value": "This region contains the legend of the chart. It includes the texts for 'Probability' with {coordinates}[0.124, 0.086, 0.198, 0.110], and 'Impact' with {coordinates}[0.124, 0.121, 0.172, 0.145]." } ], "id_raw": 482 }, { "id": "JKQXilyaG23tC5g2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_2.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the region [0.334, 0.025, 0.723, 0.058]?" }, { "from": "gpt", "value": "Within this region, there is one object: the subtitle of the chart, which reads \"Risk Assessment and Management\"." } ], "id_raw": 482 }, { "id": "2oaoFV3ByxUhi1bM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the object at the coordinates [0.164, 0.813] referring to?" }, { "from": "gpt", "value": "Year 2001." } ], "id_raw": 483 }, { "id": "9LUB803OZflzivqd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-tick label at {the coordinates} [0.512, 0.902, 0.555, 0.932] correspond to?" }, { "from": "gpt", "value": "Year 2010." } ], "id_raw": 483 }, { "id": "roYKJ3tk0UgxUcAh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_3.png", "conversations": [ { "from": "human", "value": "\nWhat is assigned to the y-axis label in the coordinates [0.015, 0.366, 0.035, 0.586]?" }, { "from": "gpt", "value": "Count/Level." } ], "id_raw": 483 }, { "id": "c0BVnstrs76cHdcn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_3.png", "conversations": [ { "from": "human", "value": "\nAt {the coordinate} [0.883, 0.932, 0.963, 0.9323], which is greater, Integration_Level or Heritage_Count?" }, { "from": "gpt", "value": "Heritage_Count." } ], "id_raw": 483 }, { "id": "hQWtMk479OAm5N6o", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the point}[0.739, 0.845] of the Integration_Level line fall below or above the point at {the Heritage_Count line}[0.739, 0.543]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 483 }, { "id": "emGSMqGbNywCq5rf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_3.png", "conversations": [ { "from": "human", "value": "\nIs the Integration_Level at year {2000}[0.164, 0.813] lower or higher than year {2010}[0.533, 0.832]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 483 }, { "id": "ntxkymbCARsrZhY9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.41, 0.773, 0.48, 0.832]?" }, { "from": "gpt", "value": "This region contains the year 2007 along the x-axis and corresponding point of the Heritage_Count line." } ], "id_raw": 483 }, { "id": "nM0LV4oZYprwL94Y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.808, 0.077, 0.978, 0.154]." }, { "from": "gpt", "value": "The enclosed area consists of the legend for the line chart. It contains labels for Heritage_Count and Integration_Level along with their respective color codings." } ], "id_raw": 483 }, { "id": "DHViUZvim9jFokXU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_3.png", "conversations": [ { "from": "human", "value": "\nWhat are contained within the region [0.923, 0.902, 0.965, 0.932]?" }, { "from": "gpt", "value": "It contains the x-axis tick label '2020'." } ], "id_raw": 483 }, { "id": "1PoKbriVjh1gtrSi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label for the top chart in this line diagram?" }, { "from": "gpt", "value": "\"Active Chats\"" } ], "id_raw": 484 }, { "id": "tSjuZg8jmKsLA1ie", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of chart according to the chart_type attribute of the metadata?" }, { "from": "gpt", "value": "Line chart." } ], "id_raw": 484 }, { "id": "XTQnyuEHTbfK38XH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the ylabel {coordinates}[0.015, 0.391, 0.029, 0.537] in the second subplot?" }, { "from": "gpt", "value": "Tab:blue." } ], "id_raw": 484 }, { "id": "gVJYtVyoMwyAYzKI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_4.png", "conversations": [ { "from": "human", "value": "\nWhich line in the first subplot is higher at point {coordinates}[0.270, 0.065], '#d62728' or '#1f77b4'?" }, { "from": "gpt", "value": "'#d62728'." } ], "id_raw": 484 }, { "id": "as1hnEbSsOLgATt8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_4.png", "conversations": [ { "from": "human", "value": "\nIs the point with coordinates {coordinates}[0.895, 0.420] on the line '#d62728' above or below the point at {coordinates}[0.895, 0.672] on the line '#1f77b4' in the second subplot?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 484 }, { "id": "MfEXIDGbWSPDOyDU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the line color of the higher line at the point {coordinates}[0.348, 0.597] in the first subplot?" }, { "from": "gpt", "value": "'#d62728'." } ], "id_raw": 484 }, { "id": "REH3NqXjJIgk8rpo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.035, 0.965, 0.065, 0.988] in the second subplot." }, { "from": "gpt", "value": "The y-axis label \"0.8\" can be found here, styled in tab:blue color." } ], "id_raw": 484 }, { "id": "nG6vGZei8nXPZ5uL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_4.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.035, 0.836, 0.065, 0.860] on the y-axis in the second subplot?" }, { "from": "gpt", "value": "It contains the y-axis label \"1.0\", styled in tab:blue color." } ], "id_raw": 484 }, { "id": "OBV9oLuBW5jdT6nq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_4.png", "conversations": [ { "from": "human", "value": "\nWhat content can be found in region [0.971, 0.307, 0.985, 0.621] in the second subplot?" }, { "from": "gpt", "value": "The y-axis label \"Average Response Time (s)\" can be found here, styled in tab:blue color." } ], "id_raw": 484 }, { "id": "KPNCfpJnZWq0le8x", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label with coordinates [0.015, 0.117, 0.035, 0.841] represent in the chart?" }, { "from": "gpt", "value": "Average Heart Rate (beats per minute)." } ], "id_raw": 485 }, { "id": "HnCT3HJaWKssfhgb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fifth tick on the x-axis?" }, { "from": "gpt", "value": "[0.636, 0.909, 0.645, 0.932]" } ], "id_raw": 485 }, { "id": "hk5x7NFQa8OwT20l", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the {7th point of the 'Active Adults' line} at [0.564, 0.650]?" }, { "from": "gpt", "value": "79.7." } ], "id_raw": 485 }, { "id": "EVTUtMck3ui3juQW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_5.png", "conversations": [ { "from": "human", "value": "\nIs {the 3rd point on the 'Active Adults' line}[0.261, 0.855] higher or lower than {the 3rd point on the 'Sedentary Adults' line}[0.261, 0.267]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 485 }, { "id": "KmmOHK4qqXzvnIEL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_5.png", "conversations": [ { "from": "human", "value": "\nWhich group has a higher heart rate at the {tenth point} [0.792, 0.445] for 'Active Adults' and [0.792, 0.336] for 'Sedentary Adults'?" }, { "from": "gpt", "value": "Active Adults." } ], "id_raw": 485 }, { "id": "lbWluxEZWZRcPt90", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_5.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Active Adults' line}[[0.110, 0.295, 0.943, 0.855]] generally above or below {the 'Sedentary Adults' line}[[0.110, 0.103, 0.943, 0.513]]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 485 }, { "id": "hphJAPXMS8cjIvad", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.853, 0.121, 0.973, 0.145]." }, { "from": "gpt", "value": "This area hosts the label for Sedentary Adults, denoted with a color marker." } ], "id_raw": 485 }, { "id": "A8vFQtNt9GLghoJE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.853, 0.086, 0.944, 0.110]." }, { "from": "gpt", "value": "This area represents the label for Active Adults, along with a color marker." } ], "id_raw": 485 }, { "id": "qWirTWIOf9SaKDKc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.413, 0.103, 0.489, 0.719]?" }, { "from": "gpt", "value": "This region contains the 5th data points for both Active and Sedentary Adults represented on their respective lines." } ], "id_raw": 485 }, { "id": "S4ruzxptYyxjdKka", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object located at the coordinates [0.512, 0.942, 0.560, 0.975]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 486 }, { "id": "fdfbhheuZZv45Ahx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the eighth text on the x-axis?" }, { "from": "gpt", "value": "[0.583, 0.902, 0.625, 0.932]." } ], "id_raw": 486 }, { "id": "2I71mKtcmh3YP6Yy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the value shown by {the y-axis tick-label} at [0.041, 0.837, 0.077, 0.867]?" }, { "from": "gpt", "value": "30%." } ], "id_raw": 486 }, { "id": "6sIomsiQgpuxMobu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_6.png", "conversations": [ { "from": "human", "value": "\nComparing the year 1999, is {Industry 1's share}[0.740, 0.700] higher or lower than {Industry 2's share}[0.740, 0.252]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 486 }, { "id": "nsrPH8VlCO7b4WiS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_6.png", "conversations": [ { "from": "human", "value": "\nIs {the Industry 1 point for the year 2002}[0.944, 0.640] to the left or right of {the Industry 2 point for the year 2002}[0.944, 0.103]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 486 }, { "id": "bvIKmFbwFp9hsIhy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_6.png", "conversations": [ { "from": "human", "value": "\nWhich industry had a higher market share in 1996, Industry 1 at {coordinates}[0.536, 0.670] or Industry 2 at {coordinates}[0.536, 0.371]?" }, { "from": "gpt", "value": "Industry 1." } ], "id_raw": 486 }, { "id": "oOArUwFm2fXQjrAU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_6.png", "conversations": [ { "from": "human", "value": "\nWhat text is within the coordinates [0.342, 0.025, 0.730, 0.058]?" }, { "from": "gpt", "value": "The text is the subtitle of the chart: \"Industry Market Share Comparison\"." } ], "id_raw": 486 }, { "id": "2xGt8052bMgKBGFX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_6.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the area [0.094, 0.077, 0.214, 0.154]?" }, { "from": "gpt", "value": "The area contains the chart's legend, which includes {Industry 1}[0.138, 0.086, 0.209, 0.110] with its blue color marker at {coordinates} [0.096, 0.091, 0.131, 0.102], and {Industry 2}[0.138, 0.121, 0.209, 0.145] with its red color marker at {coordinates}[0.096, 0.126, 0.131, 0.137]." } ], "id_raw": 486 }, { "id": "fM82nXsMkWN3QZet", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_6.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements within [0.041, 0.091, 0.077, 0.121]?" }, { "from": "gpt", "value": "Within these coordinates, there is a y-axis tick label '55%'." } ], "id_raw": 486 }, { "id": "xnyFqv8Zw3KN0Tbb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_7.png", "conversations": [ { "from": "human", "value": "\nWhat's the label at the coordinate [0.814, 0.121, 0.954, 0.145] on the chart?" }, { "from": "gpt", "value": "Twitter Engagement." } ], "id_raw": 487 }, { "id": "AU3fpCnInil3wqFk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_7.png", "conversations": [ { "from": "human", "value": "\nWhat's the label at the point with coordinates [0.118, 0.154, 0.944, 0.682]?" }, { "from": "gpt", "value": "The label is \"Facebook Engagement\"." } ], "id_raw": 487 }, { "id": "YOWPvPvN8IrzeJHb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the Y-axis label}[0.015, 0.306, 0.035, 0.652]?" }, { "from": "gpt", "value": "Engagement Level." } ], "id_raw": 487 }, { "id": "nyRVtBEWrDitNJVA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_7.png", "conversations": [ { "from": "human", "value": "\nDoes the {Facebook Engagement} line [0.118, 0.154, 0.944, 0.682] have higher values than {Twitter Engagement} line [0.118, 0.294, 0.944, 0.858] in the early years (2011 - 2013)?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 487 }, { "id": "i3jMnLk1dFX9MhDt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_7.png", "conversations": [ { "from": "human", "value": "\nIs the {2011 point on the Facebook Engagement line}[0.118, 0.682] above {the 2011 point on the Twitter Engagement line}[0.118, 0.858] on the chart?" }, { "from": "gpt", "value": "No." } ], "id_raw": 487 }, { "id": "BBfJeIu9IRPghKDK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_7.png", "conversations": [ { "from": "human", "value": "\nWhich line drops more from 2018 to 2020: the {Facebook Engagement} line[0.760, 0.154, 0.944, 0.277] or the {Twitter Engagement} line[0.760, 0.470, 0.944, 0.294]?" }, { "from": "gpt", "value": "Facebook Engagement." } ], "id_raw": 487 }, { "id": "bO6XXWVShtaN4sk9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_7.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.770, 0.077, 0.978, 0.154]?" }, { "from": "gpt", "value": "This is the area where the two legend items are located: {Facebook Engagement} [0.814, 0.086, 0.972, 0.110] and {Twitter Engagement} [0.814, 0.121, 0.954, 0.145], each with their individual color markers." } ], "id_raw": 487 }, { "id": "Os86jmuH3EdkbmOV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region from [0.100, 0.909, 0.136, 0.932] to [0.926, 0.909, 0.961, 0.932]?" }, { "from": "gpt", "value": "This region contains the X-axis labels, representing the years from 2011 to 2020." } ], "id_raw": 487 }, { "id": "G38keYvxDr9epZga", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_7.png", "conversations": [ { "from": "human", "value": "\nList objects within region [0.041, 0.056, 0.067, 0.080]." }, { "from": "gpt", "value": "There is only one object, the y-axis tick label '135'." } ], "id_raw": 487 }, { "id": "RYlEqzn1xiuQ862E", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_8.png", "conversations": [ { "from": "human", "value": "\nCould you identify the object at coordinates [0.023, 0.319, 0.045, 0.642]?" }, { "from": "gpt", "value": "It's the y-axis label for the Air Quality Index (AQI)." } ], "id_raw": 488 }, { "id": "8Botr8r6B02AhnY4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_8.png", "conversations": [ { "from": "human", "value": "\nCould you tell me the coordinates of the 4th x-axis tick label?" }, { "from": "gpt", "value": "They are [0.385, 0.899, 0.440, 0.928]." } ], "id_raw": 488 }, { "id": "zZ21KSBXqUCPspwd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of {the y-axis label}[0.023, 0.319, 0.045, 0.642]?" }, { "from": "gpt", "value": "The y-axis label is \"Air Quality Index (AQI)\"." } ], "id_raw": 488 }, { "id": "8FBfWZdBZHFu5WdN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_8.png", "conversations": [ { "from": "human", "value": "\nComparing {point at year 2014}[0.412, 0.459] and {point at year 2020}[0.815, 0.118], which year has a higher AQI?" }, { "from": "gpt", "value": "Year 2014." } ], "id_raw": 488 }, { "id": "XR2ZzzOiYNem3Cz0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_8.png", "conversations": [ { "from": "human", "value": "\nIs the {population in 2020}[0.815, 0.118] higher or lower than the {population in 2010}[0.144, 0.842]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 488 }, { "id": "64oJMEOlyk4yPeFd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_8.png", "conversations": [ { "from": "human", "value": "\nIn reference to the AQI Line, does the {point in 2012}[0.278, 0.225] have a higher value than the {point in 2013}[0.345, 0.352]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 488 }, { "id": "79MzcIAmVZ8HOAQ7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_8.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content within the boundary [0.103, 0.031, 0.856, 0.073]?" }, { "from": "gpt", "value": "The area contains the subtitle \"AQI and Population changes over the years\"." } ], "id_raw": 488 }, { "id": "N4WZ62s8EHGXqkwa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the area located at [0.111, 0.082, 0.848, 0.879]?" }, { "from": "gpt", "value": "This area represents both the plots of AQI and Population changes over the years." } ], "id_raw": 488 }, { "id": "pVLQT7w24r8Mj9eR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_8.png", "conversations": [ { "from": "human", "value": "\nWhat is within the coordinates [0.144, 0.118, 0.815, 0.842]?" }, { "from": "gpt", "value": "These coordinates contain the plots for AQI and Population over the years from 2010 to 2020." } ], "id_raw": 488 }, { "id": "RaYecG4tsFW5tkpi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.507, 0.942, 0.555, 0.975]?" }, { "from": "gpt", "value": "It is the x-axis label." } ], "id_raw": 489 }, { "id": "ucZIMhYEZ81VW927", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the fifth tick on the y-axis?" }, { "from": "gpt", "value": "140." } ], "id_raw": 489 }, { "id": "vqQtPr7T0zxFMWzq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.015, 0.283, 0.035, 0.675] represent?" }, { "from": "gpt", "value": "It represents the y-axis label." } ], "id_raw": 489 }, { "id": "pRYrRJQCHDevEFMs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_9.png", "conversations": [ { "from": "human", "value": "\nDoes the value for {Medical Technology Spending}[0.118, 0.103, 0.944, 0.777] increase or decrease from 2005 to 2020?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 489 }, { "id": "WukdRSMfS1ZMaPVl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_9.png", "conversations": [ { "from": "human", "value": "\nIn 2005, did {Medical Technology Spending}[0.118, 0.777] or {Medical Research Spending}[0.118, 0.259] have a higher value?" }, { "from": "gpt", "value": "Medical Research Spending." } ], "id_raw": 489 }, { "id": "zcmYiaho3MlN1wwt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_9.png", "conversations": [ { "from": "human", "value": "\nIn 2020, was the {Medical Technology Spending}[0.944, 0.103] higher than or lower than the {Medical Research Spending}[0.944, 0.855]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 489 }, { "id": "c9pt8X7v379LIWzw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content located at [0.084, 0.077, 0.339, 0.154]." }, { "from": "gpt", "value": "This area contains the legend for the \"Medical Technology Spending\" with a blue marker and \"Medical Research Spending\" with a red marker." } ], "id_raw": 489 }, { "id": "PugOwZ2oC58zqJ8P", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is found in the area [0.106, 0.893, 0.944, 0.932]" }, { "from": "gpt", "value": "It contains x-axis tick labels indicating years from '2005' to '2020'." } ], "id_raw": 489 }, { "id": "beqO3AQfcAafyBBF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_9.png", "conversations": [ { "from": "human", "value": "\nDescribe what is visible in the area [0.041, 0.042, 0.067, 0.999]." }, { "from": "gpt", "value": "This area contains y-axis tick labels representing 'Spending (in billions)' from '60' to '240'." } ], "id_raw": 489 }, { "id": "yaT6gdoDpCBGYaMk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_10.png", "conversations": [ { "from": "human", "value": "\nWhat serves as the label of the y-axis within the chart?" }, { "from": "gpt", "value": "The label is \"Trend\", as observed at these coordinates: {ylabel}[0.015, 0.351, 0.035, 0.453]." } ], "id_raw": 490 }, { "id": "yJyQT0A334nI7bOV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_10.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the point at the coordinates [0.490, 0.300]?" }, { "from": "gpt", "value": "The point represents the trend score of \"Crop Tops\"." } ], "id_raw": 490 }, { "id": "DVPUuABjbWn8CbcQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis label in the bounding box [0.429, 0.942, 0.624, 0.975] represents?" }, { "from": "gpt", "value": "The x-axis label represents the \"Fashion Elements\"." } ], "id_raw": 490 }, { "id": "BGeytmpJF3uj52NA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_10.png", "conversations": [ { "from": "human", "value": "\nAre the trend scores for 'Skinny Jeans' higher or lower than that of 'Ripped Jeans'?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 490 }, { "id": "M3JQt69jp80DGwTq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_10.png", "conversations": [ { "from": "human", "value": "\nComparing the trend scores, is 'Off Shoulder' more or less trendy than 'Flare Skirts'?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 490 }, { "id": "pkBekW3HsGXFKqgv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_10.png", "conversations": [ { "from": "human", "value": "\nBetween 'Leather Jackets' and 'Animal Prints', which fashion element is more in trend?" }, { "from": "gpt", "value": "'Leather Jackets'." } ], "id_raw": 490 }, { "id": "Ef8o4ZXkVr8RimLt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region defined by this bounding box: [0.068, 0.066, 0.045, 0.739]." }, { "from": "gpt", "value": "This region contains the main plotting area of the chart where the trends of the different fashion elements are showcased as data points connected by line." } ], "id_raw": 490 }, { "id": "w2w54UMSExO0LJOV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_10.png", "conversations": [ { "from": "human", "value": "\nExplain what you see contained in this area: [0.429, 0.942, 0.624, 0.975]." }, { "from": "gpt", "value": "The x-axis label, \"Fashion Elements\", is located here. It's rendered in black and has no rotation." } ], "id_raw": 490 }, { "id": "tCazrIatQLWkwaTi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_10.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region bounded by the coordinates [0.015, 0.351, 0.035, 0.453]?" }, { "from": "gpt", "value": "This region contains the Y-axis label which denotes the 'Trend' of different fashion elements." } ], "id_raw": 490 }, { "id": "6keLvjD7Thn8ANau", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis in the chart?" }, { "from": "gpt", "value": "Mean Attendance." } ], "id_raw": 491 }, { "id": "MdSMHrV9rMMh4KiG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the legend item representing 'Local Winterfell'?" }, { "from": "gpt", "value": "[0.094, 0.161, 0.129, 0.172]" } ], "id_raw": 491 }, { "id": "3uhDvhLWXlxxfDao", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_11.png", "conversations": [ { "from": "human", "value": "\nWhat entity is presented at [0.094, 0.126, 0.129, 0.137] in the visualization?" }, { "from": "gpt", "value": "The handle for the 'International King's Landing' legend item." } ], "id_raw": 491 }, { "id": "GMFZJVnPoKHjhesy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_11.png", "conversations": [ { "from": "human", "value": "\nDoes the marker for {point 30}[0.944, 0.430] on the 'Local Winterfell' line plot above or below {point 30}[0.944, 0.743] on the 'International Winterfell' line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 491 }, { "id": "EJArpWKPAAtBrfLN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_11.png", "conversations": [ { "from": "human", "value": "\nIs the color for {the line representing 'Local King's Landing'}[0.126, 0.130, 0.944, 0.226] more similar to {the color for the handle representing 'Local Winterfell'}[0.094, 0.161, 0.129, 0.172] or {the color for the handle representing 'International Winterfell'}[0.094, 0.196, 0.129, 0.207]?" }, { "from": "gpt", "value": "Local Winterfell." } ], "id_raw": 491 }, { "id": "vleXp6uFd570dKFm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_11.png", "conversations": [ { "from": "human", "value": "\nWhich label appears farther right on the x-axis, {the label '15'}[0.453, 0.893] or {the label '25'}[0.781, 0.893]?" }, { "from": "gpt", "value": "25." } ], "id_raw": 491 }, { "id": "FKQZo63BLaYv1F6U", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area between [0.126, 0.743, 0.944, 0.839]." }, { "from": "gpt", "value": "This region contains the 'International King's Landing' line along with its respective plot points and markers shaped as squares." } ], "id_raw": 491 }, { "id": "SNEtQEza0x7ZJmtV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the bounding box [0.126, 0.412, 0.944, 0.430]?" }, { "from": "gpt", "value": "The 'Local Winterfell' line plot runs through this area, represented by a green line with triangle markers denoting various statistical points." } ], "id_raw": 491 }, { "id": "Ayx99Dk8jgCBB88d", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the region from [0.041, 0.271, 0.076, 0.909] include?" }, { "from": "gpt", "value": "The y-axis labels for numerical values ranging from 6000 to 9000 are located in this region." } ], "id_raw": 491 }, { "id": "Gnj8kGMq9WgHbqFQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart?" }, { "from": "gpt", "value": "\"International Trade and Globalization Trends\"[0.288, 0.025, 0.780, 0.058]." } ], "id_raw": 492 }, { "id": "M0f8mo9JnGc0Ndt2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_12.png", "conversations": [ { "from": "human", "value": "\nWhat object is represented by the color coordinates [0.091, 0.091, 0.126, 0.102]?" }, { "from": "gpt", "value": "This is the color marker for \"Trade Volume Index\" in the legend." } ], "id_raw": 492 }, { "id": "uGVDwF6GMyBappXE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_12.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.041, 0.351, 0.072, 0.381]?" }, { "from": "gpt", "value": "It is the y-axis label \"120\"." } ], "id_raw": 492 }, { "id": "dmxd44OCpq3DiX5N", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_12.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher index value at {year '2008'}[0.451, 0.902, 0.472, 0.932], the Trade Volume Index or the Globalization Index?" }, { "from": "gpt", "value": "The Trade Volume Index." } ], "id_raw": 492 }, { "id": "3sjWGDvhuj3l2EQu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_12.png", "conversations": [ { "from": "human", "value": "\nAt the year 2010, is the {Globalization Index}[0.534, 0.712] higher or lower than the {Trade Volume Index}[0.534, 0.100]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 492 }, { "id": "ddwPSxyEAZW00laf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_12.png", "conversations": [ { "from": "human", "value": "\nAre the points corresponding to the year 2012 for {Trade Volume Index}[0.616, 0.144] and {Globalization Index}[0.616, 0.694] located above or below the 100-level label on the y-axis [0.041, 0.525, 0.072, 0.555]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 492 }, { "id": "3dJsN9a1WUKSlGrj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the coordinates [0.089, 0.077, 0.276, 0.154]?" }, { "from": "gpt", "value": "This area contains the legend for the chart. The two items of the legend are {Trade Volume Index}[0.133, 0.086, 0.270, 0.110] and {Globalization Index}[0.133, 0.121, 0.267, 0.145] with their respective color markers precisely beside them." } ], "id_raw": 492 }, { "id": "CiuuCnmN4wlazlEE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the item at [0.041, 0.176, 0.072, 0.206]?" }, { "from": "gpt", "value": "This is the label \"140\" from the y-axis." } ], "id_raw": 492 }, { "id": "yHDgIsoEl0osGtkP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents located at [0.594, 0.902, 0.637, 0.932]." }, { "from": "gpt", "value": "The contents located at this coordinate are the x-axis label \"2012\"." } ], "id_raw": 492 }, { "id": "MihxTyTFV0itQDmM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label at the y-axis} at [0.015, 0.273, 0.035, 0.685]?" }, { "from": "gpt", "value": "Usage Percentage (%)." } ], "id_raw": 493 }, { "id": "n736gbDsoFrvOT29", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the fifth position on the x-axis, what are its coordinates?" }, { "from": "gpt", "value": "Field Studies, [0.891, 0.909, 0.979, 0.932]." } ], "id_raw": 493 }, { "id": "XxjakOONpkrW1MWB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_13.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the blue point} at [0.935, 0.633]?" }, { "from": "gpt", "value": "The usage percentage of Field Studies which is 63.8%." } ], "id_raw": 493 }, { "id": "j9QiFucXKHIpkPz9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_13.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Books'}[0.109, 0.125] at higher or lower percentage than {the point for 'Online Materials'}[0.316, 0.339]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 493 }, { "id": "IE0Aa7ZVdCry2AQ5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_13.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Experiments'}[0.729, 0.825] at higher or lower usage percentage than {the point for 'Peer Discussions'}[0.522, 0.510]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 493 }, { "id": "yCk8BVsLc0IxnAQd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_13.png", "conversations": [ { "from": "human", "value": "\nWhich resource type has a higher usage percentage at {the point} [0.522, 0.510], Books or Peer Discussions?" }, { "from": "gpt", "value": "Peer Discussions." } ], "id_raw": 493 }, { "id": "FKUsaRx2VxyKtLwQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.259, 0.025, 0.785, 0.058] contain?" }, { "from": "gpt", "value": "The region contains the subtitle of the chart : \"Education Resource Distribution and Utilization\"." } ], "id_raw": 493 }, { "id": "0OCZ0EapvqkW3rJ4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is contained in the area [0.808, 0.077, 0.969, 0.119]." }, { "from": "gpt", "value": "The area contains the legend for the chart. It includes the descriptor \"Resource Usage\" with {its indicator color symbol} [0.810, 0.091, 0.845, 0.102] in the color '#3498db'." } ], "id_raw": 493 }, { "id": "weSzsI8WZZRe9V8a", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_13.png", "conversations": [ { "from": "human", "value": "\nWhat information is in the region [0.088, 0.909, 0.979, 0.932]?" }, { "from": "gpt", "value": "There are five objects in this region. They are x-axis labels for 'Books' [0.088, 0.909, 0.130, 0.932], 'Online Materials' [0.259, 0.909, 0.372, 0.932], 'Peer Discussions' [0.464, 0.909, 0.580, 0.932], 'Experiments' [0.685, 0.909, 0.772, 0.932] and 'Field Studies' [0.891, 0.909, 0.979, 0.932], respectively." } ], "id_raw": 493 }, { "id": "1jI98VsQAkN5mCd5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the label with coordinates [0.342, 0.090, 0.683, 0.115] represent?" }, { "from": "gpt", "value": "It represents the subtitle of the chart, \"Rural and Urban Development Comparison\"." } ], "id_raw": 494 }, { "id": "IqYayZNojzriL4Re", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the {y-axis label}[0.082, 0.387, 0.095, 0.623] read as?" }, { "from": "gpt", "value": "\"Development Level\"." } ], "id_raw": 494 }, { "id": "XpHLYVo1li8XbSxw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at these coordinates [0.664, 0.902, 0.689, 0.920]?" }, { "from": "gpt", "value": "It's an x-axis tick label representing \"2013\"." } ], "id_raw": 494 }, { "id": "XKLBakZP9pq5iQzd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_14.png", "conversations": [ { "from": "human", "value": "\nIs the {point representing year 2010 for 'Rural'}[0.480, 0.486] higher than the {point representing year 2010 for 'Urban'}[0.480, 0.409]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 494 }, { "id": "pljkG3k9IqKmG1yH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_14.png", "conversations": [ { "from": "human", "value": "\nDoes the {line labeled 'Rural'}[0.160, 0.312, 0.865, 0.794] start or end on a higher value compared to the {line labeled 'Urban'}[0.160, 0.216, 0.865, 0.698]?" }, { "from": "gpt", "value": "It starts on a higher value." } ], "id_raw": 494 }, { "id": "sDdGa5G4MKcFFF55", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_14.png", "conversations": [ { "from": "human", "value": "\nWhich development level is higher in 2016, {Urban}[0.865, 0.216] or {Rural}[0.865, 0.794]?" }, { "from": "gpt", "value": "Urban." } ], "id_raw": 494 }, { "id": "O4OXQeryZPFxMogn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area with coordinates [0.829, 0.129, 0.895, 0.186]?" }, { "from": "gpt", "value": "The area contains a legend. This consists of two entries: {Rural}[0.861, 0.136, 0.887, 0.153] with a blue color marker and {Urban}[0.861, 0.162, 0.891, 0.179] with a red color marker." } ], "id_raw": 494 }, { "id": "tAbuWouvHyVwUMUI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in [0.096, 0.113, 0.480, 0.498]." }, { "from": "gpt", "value": "This area contains y-axis labels ranging from '70' to '90' with their associated tick marks on the y-axis. It specifically includes the labels '70', '75', '80', '85' and '90'." } ], "id_raw": 494 }, { "id": "oL9lDUpHbFSZvGs6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects can you find within [0.276, 0.902, 0.813, 0.920]?" }, { "from": "gpt", "value": "This area contains the x-axis tick labels for the years 2007 to 2015." } ], "id_raw": 494 }, { "id": "nMK6YoasqsrF4c5K", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the line labeled 'Process 1 Efficiency'}[0.128, 0.130, 0.944, 0.827] in the chart?" }, { "from": "gpt", "value": "It is a line representing the performance trend of the internal business process 1." } ], "id_raw": 495 }, { "id": "9IS0wvDkeVOaSRHv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fifth x-axis tick label?" }, { "from": "gpt", "value": "[0.294, 0.902, 0.305, 0.932]" } ], "id_raw": 495 }, { "id": "GBI6c0gZWsMs9heb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.386, 0.035, 0.566]?" }, { "from": "gpt", "value": "Efficiency." } ], "id_raw": 495 }, { "id": "9HLrUIpQG3zjYpQZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_15.png", "conversations": [ { "from": "human", "value": "\nWhat is higher at time 10, {the 'Process 1 Efficiency'}[0.514, 0.311] or {the 'Process 2 Efficiency'}[0.514, 0.465]?" }, { "from": "gpt", "value": "Process 1 Efficiency." } ], "id_raw": 495 }, { "id": "AMNF7lqzrTfHD0nZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_15.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Process 1 Efficiency'}[0.858, 0.723] above or below {the point for 'Process 2 Efficiency'}[0.858, 0.207] at time 18?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 495 }, { "id": "tCOIaYByZuycCpMy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_15.png", "conversations": [ { "from": "human", "value": "\nWhich point is higher at {time 3}[0.214, 0.336], Process 1 Efficiency or Process 2 Efficiency?" }, { "from": "gpt", "value": "Process 2 Efficiency." } ], "id_raw": 495 }, { "id": "HlleUZE71AR0D6A6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.345, 0.025, 0.727, 0.058]." }, { "from": "gpt", "value": "The region contains the sub-title of the chart, \"Internal Business Process Analysis,\" displayed in black color." } ], "id_raw": 495 }, { "id": "UWnyq8U7aJdxrcLf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.791, 0.077, 0.978, 0.154]." }, { "from": "gpt", "value": "The area contains the legend items: {Process 1 Efficiency}[0.835, 0.086, 0.972, 0.110] and {Process 2 Efficiency}[0.835, 0.121, 0.972, 0.145], each paired with a colored marker." } ], "id_raw": 495 }, { "id": "b7mcA0pqPhe9Qs1S", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_15.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.541, 0.902, 0.912, 0.932]?" }, { "from": "gpt", "value": "There are 4 objects: the x-axis tick labels '11', '13', '15', '19' at coordinates [0.547, 0.902, 0.568, 0.932], [0.633, 0.902, 0.654, 0.932], [0.719, 0.902, 0.740, 0.932], and [0.891, 0.902, 0.912, 0.932] respectively." } ], "id_raw": 495 }, { "id": "lPKheuDVGcMeUXGl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the main object in the [0.069, 0.465, 0.083, 0.545] coordinates correspond to?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 496 }, { "id": "uYVy0NMwrB3yDXtA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_16.png", "conversations": [ { "from": "human", "value": "\nHow many items are in the legend, and what are their coordinates?" }, { "from": "gpt", "value": "There are two items. One is \"Interactions\"[0.176, 0.141, 0.259, 0.164] and the other is \"Relationships\"[0.176, 0.176, 0.269, 0.199]." } ], "id_raw": 496 }, { "id": "gDdwYC4Li8jSWoar", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the {x-axis label}[0.495, 0.939, 0.529, 0.962] in the chart?" }, { "from": "gpt", "value": "Time." } ], "id_raw": 496 }, { "id": "4IYx0yPgT7UBD9hd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_16.png", "conversations": [ { "from": "human", "value": "\nAround the coordinate [0.264, 0.745], which value is greater, \"Interactions\" or \"Relationships\"?" }, { "from": "gpt", "value": "Interactions." } ], "id_raw": 496 }, { "id": "4wkmcc990Lmtu00k", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_16.png", "conversations": [ { "from": "human", "value": "\nAre the \"Interactions\" point coordinates at {Time '1'}[0.158, 0.824] higher or lower than the \"Relationship\" point coordinates at {Time '1'}[0.158, 0.856]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 496 }, { "id": "5KFqpqMs9mO3KYbD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_16.png", "conversations": [ { "from": "human", "value": "\nIs the point of \"Relationships\"[0.291, 0.774] at Time '6' above or below the point of \"Interactions\"[0.291, 0.729] at the same time?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 496 }, { "id": "Uk0Dbf4KPLlRLHu7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe what the objects are in the region [0.529, 0.939, 0.098, 0.161]?" }, { "from": "gpt", "value": "This region includes the x-axis label \"Time\", y-axis label \"Counts\", and the plot area which contains the line graphs for \"Interactions\" and \"Relationships\"." } ], "id_raw": 496 }, { "id": "NewPyjwkXRAPTTm3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.125, 0.120, 0.900, 0.890]." }, { "from": "gpt", "value": "This region contains the main chart area. It includes all plotted lines for \"Interactions\" and \"Relationships\" over time, together with the relevant x and y axes." } ], "id_raw": 496 }, { "id": "QO8Idyjs279ZjUp4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_16.png", "conversations": [ { "from": "human", "value": "\nWhat objects can be identified in the area of [0.264, 0.745, 0.370, 0.722]?" }, { "from": "gpt", "value": "There are two objects within this region. Both are data points in the line graph, one from the \"Interactions\" trend[0.264, 0.745] and the other from the \"Relationships\" trend[0.370, 0.722]." } ], "id_raw": 496 }, { "id": "CynbLPo34QZPnMPY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.117, 0.861, 0.160, 0.932]?" }, { "from": "gpt", "value": "The first x-axis tick label, '2010'." } ], "id_raw": 497 }, { "id": "yZscP3fS5YtS7YY6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the description for {the object}[0.660, 0.091, 0.694, 0.102] shown in the legend?" }, { "from": "gpt", "value": "It's the marker for the line showing 'Freight Volume (million ton-kilometers)'." } ], "id_raw": 497 }, { "id": "W0OjeUnAuHQEJDx1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_17.png", "conversations": [ { "from": "human", "value": "\nIs there a title for the legend, if so, what are its coordinates?" }, { "from": "gpt", "value": "No, there isn't a title for the legend." } ], "id_raw": 497 }, { "id": "qDmE4B6PxZwJTOKR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_17.png", "conversations": [ { "from": "human", "value": "\nIs the {point for '2015' on the 'Freight Volume (million ton-kilometers)'}[0.542, 0.704] higher or lower than the point for '2012' on the same line?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 497 }, { "id": "a2weFDUrQlnQCPRp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_17.png", "conversations": [ { "from": "human", "value": "\nWhich trend reaches a higher value at {the point for '2020'}[0.945, 0.702 for 'Freight Volume (million ton-kilometers)', 0.945, 0.441 for 'Passenger Miles (million)'], 'Freight Volume (million ton-kilometers)' or 'Passenger Miles (million)'?" }, { "from": "gpt", "value": "Freight Volume (million ton-kilometers)." } ], "id_raw": 497 }, { "id": "4lnPIVk2YtvCFXRC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_17.png", "conversations": [ { "from": "human", "value": "\nIs {the point 'Passenger Miles (million)' for '2018'}[0.783, 0.436] lower or higher than the point for '2011' on the same line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 497 }, { "id": "LVR9GrASf481ruNZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the rectangle [0.657, 0.077, 0.978, 0.154]." }, { "from": "gpt", "value": "This region contains the legend for the chart. Each label, 'Freight Volume (million ton-kilometers)' and 'Passenger Miles (million)', is alongside a marker that corresponds to the line color on the chart." } ], "id_raw": 497 }, { "id": "ARZU8Z8FrZICGtyt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the region located at [0.098, 0.066, 0.985, 0.845]." }, { "from": "gpt", "value": "This is the main chart area. It includes two segmented line plots displaying the trends of 'Freight Volume (million ton-kilometers)' and 'Passenger Miles (million)', for different years. The x-axis is labeled with years from 2010 to 2020." } ], "id_raw": 497 }, { "id": "OYzvX4E8CBPlioaD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region at coordinates [0.015, 0.121, 0.035, 0.790]." }, { "from": "gpt", "value": "This region contains the Y-axis label which is 'Million ton-kilometers / Million miles'." } ], "id_raw": 497 }, { "id": "O8bnnvvF75l6deyP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the point with label 'Phoenix'}[0.471, 0.453] on the January line?" }, { "from": "gpt", "value": "19.4." } ], "id_raw": 498 }, { "id": "LLwTIa2CzmiWM9df", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.025, 0.180, 0.041, 0.203] on the y-axis?" }, { "from": "gpt", "value": "Y-axis tick label '35'." } ], "id_raw": 498 }, { "id": "6RLG6a353S7ecdhQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_18.png", "conversations": [ { "from": "human", "value": "\nWhat temperature does {the point with label 'San Diego'}[0.757, 0.348] on the July line represent?" }, { "from": "gpt", "value": "25.6." } ], "id_raw": 498 }, { "id": "PxczPlPbpdcn5DJT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_18.png", "conversations": [ { "from": "human", "value": "\nWhich city has a higher temperature in January, {Philadelphia}[0.566, 0.728] or {San Antonio}[0.662, 0.524]?" }, { "from": "gpt", "value": "Philadelphia." } ], "id_raw": 498 }, { "id": "6HLFcv1d4V1O5c6G", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_18.png", "conversations": [ { "from": "human", "value": "\nDoes {Houston}[0.376, 0.194] have a higher or lower temperature in July than {San Antonio}[0.662, 0.181]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 498 }, { "id": "CVcqann44r3TNCDt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_18.png", "conversations": [ { "from": "human", "value": "\nIs the July temperature of {Los Angeles}[0.186, 0.300] higher or lower than that of {San Jose}[0.947, 0.294]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 498 }, { "id": "PScEqoG8Tn1QpIAH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.917, 0.058, 0.985, 0.115]." }, { "from": "gpt", "value": "This is the chart legend. It contains two items: {January}[0.947, 0.065, 0.982, 0.082] represented by a blue handle and {July}[0.947, 0.091, 0.963, 0.108], represented by a red handle." } ], "id_raw": 498 }, { "id": "IkukJaPfVTaHijU3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.048, 0.434, 0.994, 0.785]." }, { "from": "gpt", "value": "This area represents the January temperature line plot. It ranges from cities {New York City}[0.090, 0.748] to {San Jose}[0.947, 0.532], with variable temperature points for each city." } ], "id_raw": 498 }, { "id": "ILNHhOm4P3ddTLk5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_18.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.281, 0.289, 0.376, 0.785]?" }, { "from": "gpt", "value": "This region includes the January and July temperature data points for {Chicago} and {Houston}[0.281, 0.785] and [0.376, 0.194], respectively." } ], "id_raw": 498 }, { "id": "AGmVQqZZs0UIAJhs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the title of the chart}[0.360, 0.025, 0.667, 0.058]?" }, { "from": "gpt", "value": "Demographics (1980-2000)." } ], "id_raw": 499 }, { "id": "AVAL1Q4iGKmomyqN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the fig_texts} at [0.015, 0.752, 0.033, 0.775]?" }, { "from": "gpt", "value": "20." } ], "id_raw": 499 }, { "id": "v2FnfqM2WRX845vK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_25_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the X-Axis label} at [0.490, 0.942, 0.537, 0.975]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 499 }, { "id": "1mkMb9SPcy7LQ4RO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_19.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Population Growth Rate (%)'}[0.085, 0.849, 0.942, 0.855] generally above or below {the line for 'Life Expectancy (Years)'}[0.085, 0.103, 0.942, 0.170]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 499 }, { "id": "Puq0mAwvnPMQGwya", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_19.png", "conversations": [ { "from": "human", "value": "\nIn 1980, was the {Population Growth Rate (%)}[0.085, 0.849] higher or lower than {Life Expectancy (Years)}[0.085, 0.170]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 499 }, { "id": "80dNdSiAf6vncJhm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_25_19.png", "conversations": [ { "from": "human", "value": "\nWhich line reaches a higher y-value at its end point, {Population Growth Rate (%)}[0.942, 0.855] or {Life Expectancy (Years)}[0.942, 0.103]?" }, { "from": "gpt", "value": "Life Expectancy (Years)." } ], "id_raw": 499 }, { "id": "aB9EeJsaj0loU1Yd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.015, 0.050, 0.033, 0.074]" }, { "from": "gpt", "value": "The coordinate represents a Y-axis label '80'." } ], "id_raw": 499 }, { "id": "M5oQ5mm2k9lDxH7l", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.490, 0.942, 0.537, 0.975]." }, { "from": "gpt", "value": "The area contains the x-axis label: {Year}[0.490, 0.942, 0.537, 0.975]." } ], "id_raw": 499 }, { "id": "wvDoWrpT5a0irH5A", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_25_19.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.918, 0.909, 0.966, 0.932]?" }, { "from": "gpt", "value": "There is 1 object. The x-axis label '2000.0' is located within these coordinates." } ], "id_raw": 499 }, { "id": "3WpqdG3gRKgn2EVR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_1.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the label at [0.019, 0.296, 0.043, 0.646]?" }, { "from": "gpt", "value": "It represents the Player 1 annoyance level." } ], "id_raw": 501 }, { "id": "XgZpWAD0bGl7i2Rl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fifth x-axis tick label?" }, { "from": "gpt", "value": "[0.522, 0.918, 0.587, 0.943]" } ], "id_raw": 501 }, { "id": "gKcAcsHpWaTyyebn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.049, 0.461, 0.072, 0.476]?" }, { "from": "gpt", "value": "6." } ], "id_raw": 501 }, { "id": "HvbNDYUwSkabmFmI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the Player 1 annoyance level}[0.116, 0.207, 0.884, 0.669] decrease or increase over days?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 501 }, { "id": "qXjFP2dParVPTtre", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the Player 2 annoyance level}[0.116, 0.075, 0.884, 0.867] increase or decrease toward {Day 3}[0.336, 0.075] from {Day 0}[0.116, 0.515]?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 501 }, { "id": "ie585GTrrdDrx0SV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_1.png", "conversations": [ { "from": "human", "value": "\nWho has a higher annoyance level at {Day_2}[0.336, 0.405, 0.336, 0.075], Player 1 or Player 2?" }, { "from": "gpt", "value": "Player 1." } ], "id_raw": 501 }, { "id": "OmQcFpxsAa1pBiZj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.723, 0.045, 0.910, 0.136]." }, { "from": "gpt", "value": "This region contains the legend of the chart. The items are {Player 1}[0.801, 0.056, 0.900, 0.084] and {Player 2}[0.801, 0.097, 0.900, 0.125], each with a color handle indicating their line color on the graph." } ], "id_raw": 501 }, { "id": "8cz6txFN45hKWLHu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.194, 0.918, 0.258, 0.943]." }, { "from": "gpt", "value": "This area represents the x-axis tick label for {Day_1}[0.194, 0.918, 0.258, 0.943]." } ], "id_raw": 501 }, { "id": "RrdFvXTFYEPqUC50", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.303, 0.918, 0.368, 0.943]?" }, { "from": "gpt", "value": "There is 1 object. The x-axis tick label 'Day_2' is located." } ], "id_raw": 501 }, { "id": "3KfHWj8Pk28nIBVz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line representing {Product Success Rates}[0.114, 0.266, 0.880, 0.803] from the plotted data?" }, { "from": "gpt", "value": "Green (#008000)." } ], "id_raw": 503 }, { "id": "DobSmEBc3xqFzfmi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the shape used as marker for the {Employee Satisfaction Rates}[0.114, -2.790, 0.880, -2.253] line?" }, { "from": "gpt", "value": "Square." } ], "id_raw": 503 }, { "id": "rA3TMK70O5HaBwvq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label with coordinates [0.015, 0.441, 0.034, 0.509] represent?" }, { "from": "gpt", "value": "Rates." } ], "id_raw": 503 }, { "id": "qzlmg6mPOkSNGC7q", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_3.png", "conversations": [ { "from": "human", "value": "\nDoes the {Employee Satisfaction Rates}[0.114, -2.790, 0.880, -2.253] line have a higher or lower data point in 2014 than the {Product Success Rates}[0.114, 0.266, 0.880, 0.803] line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 503 }, { "id": "dRfffyNUF2hSlwSh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_3.png", "conversations": [ { "from": "human", "value": "\nHow is the position of the {Risk Management Efficacy}[0.114, 0.296, 0.880, 0.773] line data point in 2013 as compared to the {Product Success Rates}[0.114, 0.266, 0.880, 0.803] line data point corresponding to the same year?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 503 }, { "id": "naEHsoMgFywL6mn8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_3.png", "conversations": [ { "from": "human", "value": "\nHow does trend of {IP Registrations}[0.114, 0.117, 0.880, 0.743] compare to {Employee Satisfaction Rates}[0.114, -2.790, 0.880, -2.253] in the chart?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 503 }, { "id": "T15U95EC3c1BiKev", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the coordinates [0.076, 0.028, 0.919, 0.922]?" }, { "from": "gpt", "value": "This area contains the main chart with multiple graphical lines including 'Product Success Rates', 'Risk Management Efficacy', 'Employee Satisfaction Rates', and 'IP Registrations'. The x-axis and y-axis with respective labels 'Years' and 'Rates' are also included within this area." } ], "id_raw": 503 }, { "id": "xRR4HSUZWpVeI3jU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents enclosed in the legend area [0.083, 0.036, 0.331, 0.146]." }, { "from": "gpt", "value": "This area includes a color-coded legend. It contains representations for 'Product Success Rates', 'Risk Management Efficacy', 'Employee Satisfaction Rates', and 'IP Registrations'. The color codes used are green, purple, blue, and red respectively." } ], "id_raw": 503 }, { "id": "L2vM9VoCay2pCPzf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region [0.093, 0.928, 0.136, 0.951]." }, { "from": "gpt", "value": "This area contains the year '2013' label of the x-axis." } ], "id_raw": 503 }, { "id": "oBpmLk873KXuIBSD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.019, 0.324, 0.043, 0.617] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 504 }, { "id": "PVNlzk1eKFXIsf5E", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fourth x-axis label in the chart?" }, { "from": "gpt", "value": "[0.443, 0.928, 0.456, 0.951]" } ], "id_raw": 504 }, { "id": "PbF75MP8uNtzHVJ1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.049, 0.462, 0.076, 0.484]?" }, { "from": "gpt", "value": "15." } ], "id_raw": 504 }, { "id": "mSS7FNWzmWr2CmkL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_4.png", "conversations": [ { "from": "human", "value": "\nAre the {points labeled '1 and 2'}[0.123, 0.892] and [0.205, 0.862] increasing or decreasing?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 504 }, { "id": "MLCCEfupbN1WQwsO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '4'}[0.450, 0.561] above or below {the point labeled '5'}[0.532, 0.440]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 504 }, { "id": "tsuOJooScQTu476B", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_4.png", "conversations": [ { "from": "human", "value": "\nIs the {Creativity Level} at [0.286, 0.802] higher or lower than at [0.613, 0.320]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 504 }, { "id": "vIOSJ3ZlQb8SHiQo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.082, 0.019, 0.981, 0.922]." }, { "from": "gpt", "value": "The content in this region encompasses the entire line chart plot with the 'Creativity Level' and 'Design Innovation Level',which includes x and y axes with labels and ticks, and a line marked with different points of values ranging from 0 to 10 for 'Creativity Level' and 1 to 27 for 'Design Innovation Level'." } ], "id_raw": 504 }, { "id": "DouAQJ1HFHPKSm6K", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.436, 0.958, 0.627, 0.981]." }, { "from": "gpt", "value": "The area contains the x-axis label \"Creativity Level\"." } ], "id_raw": 504 }, { "id": "MqMHKQDi8yDtcvGp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.123, 0.109, 0.940, 0.892]?" }, { "from": "gpt", "value": "This region contains the entire plot of the line chart, covering values of 'Creativity Level' from 0 to 10 and their corresponding 'Design Innovation Level' ranging from 1 to 27." } ], "id_raw": 504 }, { "id": "vmS8IZaoHmmCcC8F", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.019, 0.326, 0.043, 0.606] of subplot 0?" }, { "from": "gpt", "value": "Creativity Score (%)." } ], "id_raw": 505 }, { "id": "ecolR73ovgTCkGa9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the underlying value for {the Shape data point}[0.245, 0.286] on the Creativity Scores line?" }, { "from": "gpt", "value": "90." } ], "id_raw": 505 }, { "id": "Pdw7KlMIrGG5zAp4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the point}[0.457, 0.622] on the Innovation Scores line representing?" }, { "from": "gpt", "value": "The innovation score for the Space design element." } ], "id_raw": 505 }, { "id": "E8IDfcap6qmIInlc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_5.png", "conversations": [ { "from": "human", "value": "\nAt {the Value design element}[0.775, 0.911], which score is higher, Creativity Score or Innovation Score?" }, { "from": "gpt", "value": "Creativity Score." } ], "id_raw": 505 }, { "id": "zgZj3SaHGKkJLIqV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_5.png", "conversations": [ { "from": "human", "value": "\nIs {the Form data point}[0.563, 0.062] for the Innovation Scores line higher or lower than {the Form data point}[0.563, 0.062] for the Creativity Scores line?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 505 }, { "id": "Y0Ykjhc2engG7Vfi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_5.png", "conversations": [ { "from": "human", "value": "\nAre {the Texture data point for Creativity Scores}[0.351, 0.331] and {the Texture data point for Innovation Scores}[0.351, 0.248] higher or lower than the average of their respective lines?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 505 }, { "id": "Upegx007SJbTjILi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_5.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the region [0.102, 0.021, 0.918, 0.911]?" }, { "from": "gpt", "value": "This region contains the main plot for both Creativity Score and Innovation Score lines, with x-axis representing Design Elements and y-axes representing the scores in %." } ], "id_raw": 505 }, { "id": "EYg5gtiHTnCqqfgf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_5.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the region [0.647, 0.033, 0.908, 0.116]?" }, { "from": "gpt", "value": "This region contains the legend for the two lines {Creativity Scores}[0.647, 0.045, 0.706, 0.064] and {Innovation Scores}[0.647, 0.082, 0.706, 0.102]." } ], "id_raw": 505 }, { "id": "zFR7ajihFb1buu0L", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within the coordinates[0.112, 0.918, 0.905, 0.943]?" }, { "from": "gpt", "value": "This area contains all the labels for the x-axis ticks representing the different Design Elements: Color, Shape, Texture, Space, Form, Line, Value, and Type." } ], "id_raw": 505 }, { "id": "f2PtUSiRvoVLRnql", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the top of {the y-axis}[0.015, 0.323, 0.034, 0.614]?" }, { "from": "gpt", "value": "GDP Growth Rate(%)." } ], "id_raw": 506 }, { "id": "zYedqSyyvFjVdgen", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_6.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the color of {the handle for 'Inflation Rate(%)'}[0.008, 0.061, 0.055, 0.081]?" }, { "from": "gpt", "value": "#ff7f00." } ], "id_raw": 506 }, { "id": "XfEIqTkVcFrqxYgN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_6.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis tick label at}[0.686, 0.923, 0.721, 0.943] represent?" }, { "from": "gpt", "value": "2018." } ], "id_raw": 506 }, { "id": "MSsvCpNGbzaDXwnu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_6.png", "conversations": [ { "from": "human", "value": "\nIs {the unemployment rate marker for 2008}[0.106, 0.567] higher or lower than {the GDP growth rate marker for the same year}[0.106, 0.206]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 506 }, { "id": "VubEPN1qjtSDCXcb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_6.png", "conversations": [ { "from": "human", "value": "\nWhich is earlier, {the x-tick '2010'}[0.225, 0.910] or {the x-tick '2012'}[0.345, 0.910]?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 506 }, { "id": "9C87sXILHTWsuGwa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the point for 2015 in the unemployment chart}[0.524, 0.633] have a higher or lower rate than {the point for 2016}[0.584, 0.685]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 506 }, { "id": "ivD3yQtcKeDQfpCi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.106, 0.068, 0.823, 0.870]." }, { "from": "gpt", "value": "This region contains line charts for GDP Growth Rate(%). It shows fluctuating data points for each year from 2008 to 2020, highlighted with a blue line and circular markers." } ], "id_raw": 506 }, { "id": "sGOzCzY1QyoIlZEC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_6.png", "conversations": [ { "from": "human", "value": "\nWhat can you describe about the region [0.008, 0.012, 0.261, 0.132]?" }, { "from": "gpt", "value": "This is the legend area of the chart with entries for GDP Growth Rate(%), Inflation Rate(%), and Unemployment Rate(%). Each entry includes a text label alongside a colored handle representing the respective data series in the chart." } ], "id_raw": 506 }, { "id": "D8Gzsx1BVxIc9NdQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_6.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements in [0.868, 0.022, 0.877, 0.996]?" }, { "from": "gpt", "value": "This vertical region includes the y-axis ticks for the inflation rate, ranging from -1 to 5, each accompanied by their respective labels." } ], "id_raw": 506 }, { "id": "nfcM8lC4EgogRH1U", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_7.png", "conversations": [ { "from": "human", "value": "\nWhat color is the line represented by {the coordinates of 'AI Usage'}[0.104, 0.216, 0.943, 0.887]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 507 }, { "id": "uQVblFrIaAHklFUh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at these coordinates [0.356, 0.019, 0.691, 0.041]?" }, { "from": "gpt", "value": "The subplot title." } ], "id_raw": 507 }, { "id": "EMEXYEY4e55LCPnY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis located at this position [0.015, 0.438, 0.029, 0.536]?" }, { "from": "gpt", "value": "Percentage." } ], "id_raw": 507 }, { "id": "3XRET3PWVix20fyj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_7.png", "conversations": [ { "from": "human", "value": "\nIs {the 'AI Usage'}[0.104, 0.216, 0.943, 0.887] line above or below {the 'Automation'}[0.104, 0.087, 0.943, 0.871] line in 2004?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 507 }, { "id": "FQN8qYSa43CuI1Gk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'AI Usage'}[0.104, 0.216, 0.943, 0.887] line or {the 'Automation'}[0.104, 0.087, 0.943, 0.871] line reach a higher point in the year 2008?" }, { "from": "gpt", "value": "Automation." } ], "id_raw": 507 }, { "id": "RC0Ee0uyPzr92qUG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_7.png", "conversations": [ { "from": "human", "value": "\nAre the points of {the 'AI Usage'}[0.104, 0.216, 0.943, 0.887] line in 2006 above or below the points of {the 'Automation'}[0.104, 0.087, 0.943, 0.871] line in the same year?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 507 }, { "id": "oPgzDJkkGbnIyAUf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the region located at these coordinates [0.069, 0.055, 0.200, 0.113]." }, { "from": "gpt", "value": "It contains the legend of the subplot. Two items are listed: 'AI Usage' with blue marker and 'Automation' with orange marker." } ], "id_raw": 507 }, { "id": "B1oddKp8wq9Z9jFY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_7.png", "conversations": [ { "from": "human", "value": "\nCan you provide details of the entities that lie within the region [0.104, 0.087, 0.943, 0.887]?" }, { "from": "gpt", "value": "This region includes the 'AI Usage' line plotted on the graph with several points along the line for different years from 2000 to 2012." } ], "id_raw": 507 }, { "id": "PLL8P6VBtlIVzQsq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_7.png", "conversations": [ { "from": "human", "value": "\nWhat lies in the coordinates [0.505, 0.964, 0.542, 0.981]?" }, { "from": "gpt", "value": "The x-axis label 'Years' is positioned within these coordinates." } ], "id_raw": 507 }, { "id": "zHlywaECgd8Qykui", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {ylabel}[0.019, 0.309, 0.043, 0.624] of subplot 0?" }, { "from": "gpt", "value": "#377eb8." } ], "id_raw": 508 }, { "id": "Eo7ErBRh1TXoZJDU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {x tick mark}[0.423, 0.911] in subplot 1?" }, { "from": "gpt", "value": "2014." } ], "id_raw": 508 }, { "id": "k4BXtr5xnuKLaexr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {marker}[0.595, 0.045, 0.654, 0.064] for 'Social Inequality Index' in subplot 0?" }, { "from": "gpt", "value": "#377eb8." } ], "id_raw": 508 }, { "id": "06NVTWuUW9JPxYq1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_8.png", "conversations": [ { "from": "human", "value": "\nIs the point for year 2012 on the {line}[0.116, 0.062, 0.884, 0.870] for 'Social Inequality Index' higher or lower than the point for year 2014?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 508 }, { "id": "AeZIzyVUWMosCQ0R", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_8.png", "conversations": [ { "from": "human", "value": "\nIs the point for year 2018 on the {line}[0.116, 0.062, 0.884, 0.870] for 'Rights Protection Index' located higher or lower compared to the point for year 2016?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 508 }, { "id": "g58VuSMSx4M5YcHN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_8.png", "conversations": [ { "from": "human", "value": "\nWhat change, on 'Rights Protection Index' line, can be kbserved from year 2011 to 2012, an increase or decrease?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 508 }, { "id": "jxJ2LQDH0NYFkCiV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area between the coordinates [0.595, 0.033, 0.912, 0.116] in subplot 0?" }, { "from": "gpt", "value": "This region contains the subplot legend, with two items. 'Social Inequality Index' text is located within this area at {coordinates}[0.662, 0.043, 0.897, 0.069] paired with a colored handle marker at {coordinates}[0.595, 0.045, 0.654, 0.064]. 'Rights Protection Index' text is situated in this region at {coordinates}[0.662, 0.080, 0.903, 0.106] with its related colored marker at {coordinates}[0.595, 0.082, 0.654, 0.102]." } ], "id_raw": 508 }, { "id": "eXokIRQITLkERGuM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects contained in the region [0.078, 0.021, 0.922, 0.911]." }, { "from": "gpt", "value": "This region houses the plotting space for subplot 0 and subplot 1, each containing their respective lines, xticks, and yticks. They are sharing the same xticks but own distinctive yticks. Also, subplot 0 has a ylabel and a sublegend." } ], "id_raw": 508 }, { "id": "ko1kZ0b8jQVuAZ81", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within coordinates [0.958, 0.305, 0.981, 0.628] in subplot 1." }, { "from": "gpt", "value": "The given coordinates host the ylabel of subplot 1, with the label text as 'Rights Protection Index'." } ], "id_raw": 508 }, { "id": "XESwUODSMLieDNHI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the object with coordinates [0.019, 0.207, 0.043, 0.735] in the chart represent?" }, { "from": "gpt", "value": "Y-axis label for the first half of the graph showing Facebook & WhatsApp Users." } ], "id_raw": 509 }, { "id": "IEK3TBWwvHI8OTXJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.094, 0.925, 0.138, 0.945]?" }, { "from": "gpt", "value": "The year '2016' on the x-axis." } ], "id_raw": 509 }, { "id": "Q3SLaqnepx4swgH2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_9.png", "conversations": [ { "from": "human", "value": "\nWhich color is represented by the marked handle {object} [0.685, 0.083, 0.741, 0.103]?" }, { "from": "gpt", "value": "The color for Twitter Users, pink." } ], "id_raw": 509 }, { "id": "b78qAGS807QNMEV4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_9.png", "conversations": [ { "from": "human", "value": "\n{Line for Facebook Users} [0.116, 0.090, 0.884, 0.386] is showing a decline or increase in user numbers from 2016 to 2021?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 509 }, { "id": "n2Td9f89kcuH2z11", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_9.png", "conversations": [ { "from": "human", "value": "\nIs the {coordinate of Instagram Users in 2016} [0.116, 0.647] more than the {coordinate of Facebook Users in 2017} [0.270, 0.304]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 509 }, { "id": "ef0vs67Sn4IqZxaG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_9.png", "conversations": [ { "from": "human", "value": "\nWhich has more users in 2020 based on the coordinates, Facebook ({Facebook Users 2020} [0.730, 0.178]) or Instagram ({Instagram Users 2020} [0.730, 0.339])?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 509 }, { "id": "A2BZW1sbOOuPGkGR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the region [0.686, 0.042, 0.912, 0.114]?" }, { "from": "gpt", "value": "This region comprises the sublegend of the chart. It contains labels for 'Instagram Users' and 'Twitter Users', each paired with a colored marker representing their distinct line in the chart." } ], "id_raw": 509 }, { "id": "rH2NckIVOBcjjKtC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_9.png", "conversations": [ { "from": "human", "value": "\nWhat details are in the region [0.958, 0.226, 0.981, 0.716]?" }, { "from": "gpt", "value": "This region contains the label for the second half of the Y-axis, indicating Instagram & Twitter Users (in billions)." } ], "id_raw": 509 }, { "id": "FZteFQjbuqlMHYal", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_9.png", "conversations": [ { "from": "human", "value": "\nWhat information is in the region [0.116, 0.090, 0.884, 0.386]?" }, { "from": "gpt", "value": "This region represents the line plot for Facebook Users from the years 2016 to 2021, showing an increase in their numbers." } ], "id_raw": 509 }, { "id": "LeOsnYHCUMsRlR9m", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the coordinates [0.116, 0.231] on the \"Peace Index\" line?" }, { "from": "gpt", "value": "The value '75.0'." } ], "id_raw": 510 }, { "id": "P8crDrUGugBtDKcb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {ylabel text}[0.019, 0.432, 0.043, 0.508]?" }, { "from": "gpt", "value": "The color of the ylabel text is '#377eb8'." } ], "id_raw": 510 }, { "id": "AZVcqG7ZF72bdk6B", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_10.png", "conversations": [ { "from": "human", "value": "\nIs there a blank label element located at [0.958, 0.352, 0.981, 0.587] in the figure?" }, { "from": "gpt", "value": "Yes, the ylabel of the second subplot \"Absolute Poverty Rate (%)\" is located at these coordinates." } ], "id_raw": 510 }, { "id": "BgbsYvGBcVZ9Jdpa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_10.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher at {the '2014' xtick}[0.457, 0.925, 0.484, 0.951] on the chart, {the 'Peace Index'}[0.457, 0.118] or {the 'Social Equality Index'}[0.457, 0.849]?" }, { "from": "gpt", "value": "Social Equality Index." } ], "id_raw": 510 }, { "id": "IX5kVbL3jnf361Fv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_10.png", "conversations": [ { "from": "human", "value": "\nIn 2016, does the {Peace Index}[0.628, 0.062] have a higher or lower value than the {Absolute Poverty Rate}[0.628, 0.714]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 510 }, { "id": "00GRLc7NzMbdDKro", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_10.png", "conversations": [ { "from": "human", "value": "\nIs the {Social Equality Index}[0.202, 0.872] in 2011 higher or lower than the {Absolute Poverty Rate}[0.202, 0.171] value in the same year?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 510 }, { "id": "Wxx8wkUoMH0XenEP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects belong within the area with coordinates [0.078, 0.849, 0.798, 0.877]?" }, { "from": "gpt", "value": "The area includes the 'Social Equality Index' line with its respective points plotted for each year from 2011 to 2019, with coordinates varying from around [0.116, 0.847] to [0.884, 0.842]." } ], "id_raw": 510 }, { "id": "PhnuHJPYZyfMvks1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the area from [0.078, 0.021, 0.922, 0.918]?" }, { "from": "gpt", "value": "This area contains two subplot areas, with each having their individual xticks, yticks, lines representing different indices and a label representing the name associated with the y-values. The first subplot represents \"Peace Index\" and \"Social Equality Index\" values from 2010 to 2019, while the second subplot represents the \"Absolute Poverty Rate\" in the same time period." } ], "id_raw": 510 }, { "id": "6PclBCIBxCvazPbm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within [0.087, 0.031, 0.342, 0.127]." }, { "from": "gpt", "value": "The area includes the legend for the first subplot with labels \"Peace Index\", \"Social Equality Index\", and \"Absolute Poverty Rate\" along with their respective colors representing each plot. The color markers are placed left of each index." } ], "id_raw": 510 }, { "id": "q4i5o2TcWbwne2ms", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object in the coordinates [0.958, 0.327, 0.981, 0.606]?" }, { "from": "gpt", "value": "This is the label on the secondary Y-axis, reading \"Approval Rating (%)\"." } ], "id_raw": 511 }, { "id": "ApBzATij50DLHObF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_11.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object located at [0.685, 0.816, 0.849, 0.844]?" }, { "from": "gpt", "value": "Yes, this is the legend text for \"Social Impact\"." } ], "id_raw": 511 }, { "id": "7WkGcN9vGr7Zr98f", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.100, 0.923, 0.143, 0.943]?" }, { "from": "gpt", "value": "The year \"2012\"." } ], "id_raw": 511 }, { "id": "jswGx3KJs9RoWdbK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_11.png", "conversations": [ { "from": "human", "value": "\nCan you compare the position of {the point '2016'}[0.500, 0.496] on the Policy Efficiency line and {the point '2016'}[0.500, 0.332] on the Approval Rating line? Which is higher?" }, { "from": "gpt", "value": "The point on the Policy Efficiency line is higher." } ], "id_raw": 511 }, { "id": "VRZMwdHTJGNmF5I3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_11.png", "conversations": [ { "from": "human", "value": "\nIs the {2019 point}[0.784, 0.511] in Policy Efficiency above or below the {2019 point}[0.784, 0.097] in Approval Rating?" }, { "from": "gpt", "value": "Above" } ], "id_raw": 511 }, { "id": "RcQBBHRh0qjh4nbE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_11.png", "conversations": [ { "from": "human", "value": "\nAt year 2020, which line has a higher value, {Policy Efficiency}[0.879, 0.481] or {Approval Rating}[0.879, 0.064]?" }, { "from": "gpt", "value": "Policy Efficiency" } ], "id_raw": 511 }, { "id": "eOjkoNj96ARIg6c8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the area within [0.607, 0.723, 0.904, 0.896]." }, { "from": "gpt", "value": "It houses the legend, featuring classifications like {Policy Efficiency}[0.685, 0.734, 0.881, 0.762], {Economic Impact}[0.685, 0.775, 0.894, 0.803], {Social Impact}[0.685, 0.816, 0.849, 0.844] and {Approval Rating}[0.685, 0.857, 0.879, 0.885], with various colors corresponding to each trend." } ], "id_raw": 511 }, { "id": "r50XOyJ2LWKimIbA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content of the region [0.084, 0.023, 0.916, 0.910]." }, { "from": "gpt", "value": "This region primarily encompasses the main chart area, displaying both the line trends for aspects like Policy Efficiency, Economic Impact, Social Impact, and Approval Rating from the year 2012 to 2020 as well as the tick labels for the years on x-axis and the corresponding ratings or scores on Y-axis." } ], "id_raw": 511 }, { "id": "E5CDIyUn6FW08MdL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.951, 0.923, 0.995, 0.943]?" }, { "from": "gpt", "value": "This segment belongs to the x-axis and holds the label of the last tick mark, representing the year \"2021\"." } ], "id_raw": 511 }, { "id": "5ChDgAafaDMMfJyT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label represent in the coordinates [0.019, 0.451, 0.036, 0.514]?" }, { "from": "gpt", "value": "Index." } ], "id_raw": 512 }, { "id": "Z1381sRCchAgBmfJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fourth year labeled on the x-axis?" }, { "from": "gpt", "value": "[0.590, 0.919, 0.634, 0.942]" } ], "id_raw": 512 }, { "id": "5TneVnlZebvts8dL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the point of 'Sustainable Practices'} at [0.940, 0.228]?" }, { "from": "gpt", "value": "2020,25." } ], "id_raw": 512 }, { "id": "U2xbzz0xJHmNEEYA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_12.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Corporate Responsibility'}[0.119, 0.419, 0.940, 0.865] generally above or below {the line for 'Environmental Protection'}[0.119, 0.100, 0.940, 0.546]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 512 }, { "id": "NdMH84YKozNCElAp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Sustainable Practices'}[0.119, 0.228, 0.940, 0.674] cross {the line for 'Environmental Protection'}[0.119, 0.100, 0.940, 0.546] over the years?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 512 }, { "id": "SQNvuGr3x0dLjVSj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_12.png", "conversations": [ { "from": "human", "value": "\nRegarding the y-value at {2019}[0.776, 0.903], which line is highest among Corporate Responsibility, Environmental Protection, and Sustainable Practices?" }, { "from": "gpt", "value": "Environmental Protection." } ], "id_raw": 512 }, { "id": "gWK8MHVh8eb4Q49F", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.086, 0.074, 0.369, 0.185]." }, { "from": "gpt", "value": "It is the legend of the chart containing the labels: {Corporate Responsibility}[0.142, 0.083, 0.354, 0.106],[0.088, 0.086, 0.133, 0.100], {Environmental Protection}[0.142, 0.118, 0.362, 0.141],[0.088, 0.121, 0.133, 0.135], and {Sustainable Practices}[0.142, 0.153, 0.327, 0.176],[0.088, 0.156, 0.133, 0.170], displayed with corresponding color handles indicating their representation on the chart." } ], "id_raw": 512 }, { "id": "gRvWWJzrfshNfwYu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.238, 0.025, 0.821, 0.055] contain?" }, { "from": "gpt", "value": "This area contains the subtitle of the chart: \"Trends in Social Responsibility and Sustainable Business.\"" } ], "id_raw": 512 }, { "id": "fW1fwCA1e0TDqvdl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.511, 0.952, 0.548, 0.975]?" }, { "from": "gpt", "value": "The x-axis label 'Year' is located in this region." } ], "id_raw": 512 }, { "id": "S2cBEgkbsiWBeR1b", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the element at the coordinates [0.015, 0.369, 0.034, 0.571]?" }, { "from": "gpt", "value": "Y-axis label: 'Growth Rate (%)'." } ], "id_raw": 513 }, { "id": "upIPcIg05oKjQT2R", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the {graph line}[0.806, 0.357, 0.944, 0.245] in the chart representing?" }, { "from": "gpt", "value": "It represents the '5G' trend." } ], "id_raw": 513 }, { "id": "Rg9GLRE3nSoaNV10", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the legend marker with coordinates [0.087, 0.042, 0.129, 0.053]?" }, { "from": "gpt", "value": "The color is '#377eb8' which represents 'Voice Recognition'." } ], "id_raw": 513 }, { "id": "ngKhKe6PfPecpBYq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_13.png", "conversations": [ { "from": "human", "value": "\nIs the growth rate of {Quantum Computing}[0.117, 0.516, 0.944, 0.877] higher or lower than {Blockchain}[0.117, 0.019, 0.944, 0.493] in the year 2022?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 513 }, { "id": "I23B2gXWnK7w0t7P", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_13.png", "conversations": [ { "from": "human", "value": "\nDoes {5G}[0.117, 0.245, 0.944, 0.922] have a higher growth rate than {AI}[0.117, 0.086, 0.944, 0.425] in the year 2023?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 513 }, { "id": "7XHdC7uT9oT16tj0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_13.png", "conversations": [ { "from": "human", "value": "\nIs the trend of {Voice Recognition}[0.117, 0.651, 0.944, 0.357] increasing or decreasing between the years 2018 and 2024?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 513 }, { "id": "BiRc90Qm2awipgWg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content included within the coordinates [0.050, 0.800, 0.071, 0.823]?" }, { "from": "gpt", "value": "This area contains the y-axis label \"50\"." } ], "id_raw": 513 }, { "id": "3b3lrNf5DBbYwIye", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the area enclosed by [0.076, 0.029, 0.318, 0.200]?" }, { "from": "gpt", "value": "This area contains the legend of the chart, representing 'Voice Recognition', 'Quantum Computing', 'Blockchain', '5G', and 'AI', each labeled with a color-coded marker." } ], "id_raw": 513 }, { "id": "xzauPmRTpV1iLc6f", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the area [0.372, 0.928, 0.414, 0.951]?" }, { "from": "gpt", "value": "This region contains the x-axis label for the year '2020'." } ], "id_raw": 513 }, { "id": "XazF0cXXWqg9knLi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the description provided in the area [0.958, 0.292, 0.981, 0.641]?" }, { "from": "gpt", "value": "Stock Market Growth (%)." } ], "id_raw": 514 }, { "id": "Yr39QCewqO1EehOQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the data indicated by {the point}[0.884, 0.070] on the red dotted line?" }, { "from": "gpt", "value": "It represents the Stock Market Growth (%) for the year 2019." } ], "id_raw": 514 }, { "id": "QcaahDDAPK83aQAe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_14.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is referred to by the coordinates [0.645, 0.046, 0.900, 0.074]?" }, { "from": "gpt", "value": "It refers to the label 'Stock Market Growth'." } ], "id_raw": 514 }, { "id": "0iaGPAarpGDk4e8c", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_14.png", "conversations": [ { "from": "human", "value": "\nComparing {2000's GDP Growth point}[0.116, 0.062] and {2000's Stock Market Growth point}[0.116, 0.292], which one was higher?" }, { "from": "gpt", "value": "Stock Market Growth." } ], "id_raw": 514 }, { "id": "upRg7szZKUIAexIo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_14.png", "conversations": [ { "from": "human", "value": "\nWere market conditions better in {2018}[0.843, 0.487] or in {2015}[0.722, 0.405] according to the Stock Market Growth shown?" }, { "from": "gpt", "value": "2015." } ], "id_raw": 514 }, { "id": "Jf6zwoIYG9JzFfwr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_14.png", "conversations": [ { "from": "human", "value": "\nWas the Stock Market Growth more positive in {2009}[0.480, 0.134] or in {2014}[0.682, 0.278]?" }, { "from": "gpt", "value": "2014." } ], "id_raw": 514 }, { "id": "sYEJjjZDqct0t3bK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_14.png", "conversations": [ { "from": "human", "value": "\nWhat is shown with the label at [0.116, 0.292, 0.884, 0.870] of the chart?" }, { "from": "gpt", "value": "This is the 'Stock Market Growth' line representing the actual percentage growth per year from 2000 to 2019." } ], "id_raw": 514 }, { "id": "mdW84MbJsh6ZteWQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region from [0.078, 0.021, 0.922, 0.911]?" }, { "from": "gpt", "value": "This region represents the entire span of the chart, including the lines representing 'GDP Growth' and 'Stock Market Growth', the x-axis labeled as 'Years' and y-axes labeled as 'GDP Growth (%)' and 'Stock Market Growth (%)'." } ], "id_raw": 514 }, { "id": "eJt03kg7o1Zp9G0l", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_14.png", "conversations": [ { "from": "human", "value": "\nWhat details can you provide within the coordinates [0.567, 0.035, 0.910, 0.085]?" }, { "from": "gpt", "value": "These coordinates define the chart legend, specifying {the label 'Stock Market Growth'}[0.645, 0.046, 0.900, 0.074] alongside its associated red-colored marker." } ], "id_raw": 514 }, { "id": "6SqbFoHVtj8C2qKo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the second line}[0.140, 0.556, 0.860, 0.830] in subplot_1?" }, { "from": "gpt", "value": "Economic Integration." } ], "id_raw": 515 }, { "id": "Z5AqwJaFI1W3A94l", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at {the location}[0.185, 0.188, 0.355, 0.217] in the sublegend of subplot_1?" }, { "from": "gpt", "value": "Tech Revolution." } ], "id_raw": 515 }, { "id": "yPelnBhgb5l7f3Bm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the tick label}[0.592, 0.888, 0.647, 0.918] on the x-axis in subplot_0?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 515 }, { "id": "bJsPsQPfiMZoEdbU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_15.png", "conversations": [ { "from": "human", "value": "\nIn subplot_0, does the line for {Economic Integration}[0.140, 0.556, 0.860, 0.830] start at a higher or lower point than the line for {Global Trade Volume}[0.140, 0.069, 0.860, 0.526]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 515 }, { "id": "iytIlz0Di5lrTSJc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_15.png", "conversations": [ { "from": "human", "value": "\nIn subplot_1, is the beginning of the {Tech Revolution} line[0.140, 0.521, 0.860, 0.830] above or below the {Market Globalization} line[0.140, 0.069, 0.860, 0.592]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 515 }, { "id": "anNucleQVkDxU3vy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_15.png", "conversations": [ { "from": "human", "value": "\nIn subplot_1, does the {Tech Revolution} line[0.140, 0.521, 0.860, 0.830] end at a higher or lower point than where it started?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 515 }, { "id": "jsVanBeFcB14EreO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.233, 0.888, 0.408, 0.918] of subplot_0." }, { "from": "gpt", "value": "The region contains two X-axis tick labels: '1995' and '2000', located at {coordinates}[0.233, 0.888, 0.288, 0.918] and {coordinates}[0.353, 0.888, 0.408, 0.918] respectively." } ], "id_raw": 515 }, { "id": "wzycZngUmkqq3xxb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_15.png", "conversations": [ { "from": "human", "value": "\nCan you explain what's in the region [0.062, 0.819, 0.938, 0.848] on subplot_1?" }, { "from": "gpt", "value": "The region includes the Y-axis tick label '5' at {coordinates}[0.911, 0.819, 0.924, 0.848] and a portion of the {Tech Revolution} line." } ], "id_raw": 515 }, { "id": "e6DIVdqIoJb3FxhW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.115, 0.046, 0.425, 0.229] of subplot_1." }, { "from": "gpt", "value": "This area contains the legend items for each line plot in subplot_1: {Global Trade Volume}[0.185, 0.057, 0.408, 0.086], {Economic Integration}[0.185, 0.101, 0.416, 0.130], {Market Globalization}[0.185, 0.145, 0.409, 0.174], and {Tech Revolution}[0.185, 0.188, 0.355, 0.217], each paired with a colored line marker." } ], "id_raw": 515 }, { "id": "MM7Jy1ICoftnTCB7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the {ylabel}[0.019, 0.394, 0.043, 0.549] in subplot_0 refer to?" }, { "from": "gpt", "value": "The ylabel refers to the CSR Rating." } ], "id_raw": 516 }, { "id": "Qz5j2OR4WDTEYWrj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the color denotation of the {legend}[0.482, 0.058, 0.547, 0.078] in subplot_1?" }, { "from": "gpt", "value": "The color denotes the Sustainable Business Trend." } ], "id_raw": 516 }, { "id": "srWR39mVmwJlHZR9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {xticks}[0.898, 0.918, 0.930, 0.943] in subplot_0?" }, { "from": "gpt", "value": "The label is \"7.0\"." } ], "id_raw": 516 }, { "id": "wog3WkrNZ7UrnzSE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_16.png", "conversations": [ { "from": "human", "value": "\nDoes the {CSR Trend}[0.116, 0.075, 0.874, 0.911] in subplot_0 tend to increase or decrease as the CSR Rating increases?" }, { "from": "gpt", "value": "The CSR Trend appears to decrease as the CSR Rating increases." } ], "id_raw": 516 }, { "id": "zYWhcSMAbbBqIEbk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_16.png", "conversations": [ { "from": "human", "value": "\nIs the marker style of {Sustainable Business Trend}[0.116, 0.031, 0.874, 0.911] in subplot_1 circle or triangle?" }, { "from": "gpt", "value": "The marker style is a triangle." } ], "id_raw": 516 }, { "id": "L17YpcfZeITYi8hZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_16.png", "conversations": [ { "from": "human", "value": "\nCompare the maximum point labeled values of {CSR Trend}[0.116, 0.075, 0.874, 0.911] and {Sustainable Business Trend}[0.116, 0.031, 0.874, 0.911]. Which is higher?" }, { "from": "gpt", "value": "CSR Trend has a higher maximum value." } ], "id_raw": 516 }, { "id": "J2pk5UBt56p69K5g", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.078, 0.031, 0.912, 0.911] for subplot_0." }, { "from": "gpt", "value": "The area contains a line graph representing the CSR Trend, with x-axis tick labels ranging from \"2.5\" to \"7.0\" and y-axis tick labels from \"2\" to \"7\". Also, it includes an x-label titled 'CSR Rating' along with a y-label 'CSR Rating', and a legend item 'CSR Trend' towards the lower left corner." } ], "id_raw": 516 }, { "id": "JeU2ADy0WBoemJwt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_16.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area in [0.078, 0.031, 0.912, 0.911] for subplot_1." }, { "from": "gpt", "value": "The area contains a line graph that represents the Sustainable Business Trend, with x-axis tick labels ranging from \"2.5\" to \"7.0\" and y-axis ranging from \"14\" to \"20\". Also, it includes a y-label titled 'Sustainable Business Rating' along with a legend item 'Sustainable Business Trend' towards the middle, lower part of the subplot." } ], "id_raw": 516 }, { "id": "7HzHFcl71yDk66FH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_16.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.918, 0.925, 0.930, 0.945] in subplot_0?" }, { "from": "gpt", "value": "The x-axis tick label '7.0' for CSR Trend located at {coordinates}[0.898, 0.918, 0.930, 0.943] is found in this region." } ], "id_raw": 516 }, { "id": "weF1R8JSmZuyqD50", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.019, 0.253, 0.043, 0.678] in the chart?" }, { "from": "gpt", "value": "y-axis label." } ], "id_raw": 517 }, { "id": "WnfenZvedaZhwVGr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_17.png", "conversations": [ { "from": "human", "value": "\nPlease tell me the coordinates of the third item's label in the legend." }, { "from": "gpt", "value": "[0.173, 0.128, 0.326, 0.156]" } ], "id_raw": 517 }, { "id": "BLkCgQWXgl8bc4WI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_17.png", "conversations": [ { "from": "human", "value": "\nCan you find the {x-label's tick} with coordinates [0.429, 0.923, 0.473, 0.943] and tell me what it is?" }, { "from": "gpt", "value": "It's the label '2017'." } ], "id_raw": 517 }, { "id": "VoTLNvELN7wqn8bi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_17.png", "conversations": [ { "from": "human", "value": "\nOn the year 2018, did {Cardiovascular Diseases} have a higher or lower count than {Diabetes} in my plot?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 517 }, { "id": "aKzdmuhahhIFihR3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_17.png", "conversations": [ { "from": "human", "value": "\nIs the {2015}[0.124, 0.243] data point for Cardiovascular Diseases higher or lower than the Lung Cancer data point in the same year?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 517 }, { "id": "MvlDIjfECbhc8I7d", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_17.png", "conversations": [ { "from": "human", "value": "\nComparing the lines for {Cardiovascular Diseases} and {Lung Cancer}, which one has a higher data point value for the year 2020?" }, { "from": "gpt", "value": "Cardiovascular Diseases." } ], "id_raw": 517 }, { "id": "98P6WciXOFplzwTM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the chart for the region with coordinates [0.096, 0.035, 0.495, 0.167]." }, { "from": "gpt", "value": "This region holds the legend for your chart, with labels for Cardiovascular Diseases, Diabetes, and Lung Cancer, each paired with a color representing them in the chart." } ], "id_raw": 517 }, { "id": "d1bEL8BLxBOgkRN4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the area restricted by these coordinates [0.124, 0.110, 0.940, 0.243]?" }, { "from": "gpt", "value": "This area contains a line graph representing the trend of Cardiovascular Diseases from 2015 to 2020." } ], "id_raw": 517 }, { "id": "eIv3WNfgbVWxJUlp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_17.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the region from [0.124, 0.243, 0.940, 0.466]?" }, { "from": "gpt", "value": "This region is dedicated to the line graph showing Lung Cancer trends from 2015 to 2020." } ], "id_raw": 517 }, { "id": "rj0Emu0n2vcJm9rY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_19.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.019, 0.353, 0.043, 0.589] of the first subplot represent?" }, { "from": "gpt", "value": "Programming(%)." } ], "id_raw": 519 }, { "id": "T0LgiQSOsmsmHzVV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_19.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the marker at {coordinates}[0.692, 0.597]?" }, { "from": "gpt", "value": "The data point of Programming trend in year 2018." } ], "id_raw": 519 }, { "id": "HKTRGNeXXNA8sZAo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_26_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the Year corresponding to the x-axis tick label at {coordinates}[0.404, 0.911] in the second subplot?" }, { "from": "gpt", "value": "2015." } ], "id_raw": 519 }, { "id": "tKsuhjI8xRJTaT3t", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_19.png", "conversations": [ { "from": "human", "value": "\nIn 2019, does {Leadership trend}[0.788, 0.383] have a higher or lower value compared to {Team Building trend}[0.788, 0.107] in the second subplot?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 519 }, { "id": "DeObI2Fa62VpkprT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_19.png", "conversations": [ { "from": "human", "value": "\nIn the first subplot, is the data point {in 2015}[0.404, 0.383] for Programming Trend above or below {the point in 2016}[0.500, 0.471]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 519 }, { "id": "tBDveweilC7CQgfL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_26_19.png", "conversations": [ { "from": "human", "value": "\nIn the second subplot, is {Leadership trend}[0.115, 0.028] generally above or below {Team Building trend}[0.117, 0.056] in 2012?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 519 }, { "id": "8xIL8u81PUbgZaDz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents inside this region [0.656, 0.056, 0.863, 0.084]." }, { "from": "gpt", "value": "The region contains a text part of the subplot's legend indicating 'Leadership trend'." } ], "id_raw": 519 }, { "id": "PFQYhm4BnZDrk7W5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents in the area [0.656, 0.097, 0.900, 0.125]." }, { "from": "gpt", "value": "This area contains a text part of the subplot's legend representing 'Team Building trend'." } ], "id_raw": 519 }, { "id": "oIBRMKFIUpcCibHx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_26_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects located in the area [0.116, 0.283, 0.884, 0.785]." }, { "from": "gpt", "value": "This region includes a line representing Programming Trend with points marking every year from 2012 to 2020 in the first subplot." } ], "id_raw": 519 }, { "id": "KEeByxXHpVkEbeOH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_0.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is represented by {the entire plot}[0.0, 0.0, 1.0, 1.0]?" }, { "from": "gpt", "value": "Line." } ], "id_raw": 520 }, { "id": "6uIDYt1NDtjqE4U2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing Instagram}[0.403, 0.183, 0.634, 0.706]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 520 }, { "id": "uA0aReZdA4ehlh1q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_0.png", "conversations": [ { "from": "human", "value": "\nWhat marker symbol is used for {the LinkedIn growth trend line}[0.732, 0.611, 0.964, 0.849]?" }, { "from": "gpt", "value": "x." } ], "id_raw": 520 }, { "id": "p2SdfKSBNyxvmAlg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_0.png", "conversations": [ { "from": "human", "value": "\nWhich {Facebook growth point}[0.073, 0.658] corresponds to the first month?" }, { "from": "gpt", "value": "Point 1." } ], "id_raw": 520 }, { "id": "KlPllM1Mbk0gQO3C", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_0.png", "conversations": [ { "from": "human", "value": "\nIs {the first point on the Twitter line}[0.732, 0.777] at a higher growth percentage compared to {the first point on the LinkedIn line}[0.732, 0.849]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 520 }, { "id": "KS9C8NF9Yke1QVVh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the label at position}[0.283, 0.912, 0.289, 0.936] denote the eighth month?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 520 }, { "id": "mjlEo0KfgtLOfC2q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line representing {Microsoft Corp.}'s stock price trend in the chart?" }, { "from": "gpt", "value": "#2ecc71" } ], "id_raw": 521 }, { "id": "qn9bt4tmfJ7MlfeF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the marker style for {Amazon Inc.}'s line on the graph?" }, { "from": "gpt", "value": "x" } ], "id_raw": 521 }, { "id": "tDg2fEve81pcmPby", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_1.png", "conversations": [ { "from": "human", "value": "\nWhat label is found at coordinates {year label for 2017}[0.885, 0.825, 0.924, 0.871]?" }, { "from": "gpt", "value": "2017" } ], "id_raw": 521 }, { "id": "I4DqAkDVqNcHloBT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_1.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at the year marked by {2019}[0.924, 0.825, 0.963, 0.871], {Microsoft Corp.}[0.440, 0.753, 0.636, 0.787] or {Amazon Inc.}[0.748, 0.128, 0.983, 0.792]?" }, { "from": "gpt", "value": "Amazon Inc." } ], "id_raw": 521 }, { "id": "DBfBFuKrexRfzE7m", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_1.png", "conversations": [ { "from": "human", "value": "\nIs the stock value for {Apple Inc.} at the {2015}[0.190, 0.825, 0.230, 0.871] tick mark above or below 150?" }, { "from": "gpt", "value": "Below" } ], "id_raw": 521 }, { "id": "gDF2UkegXipnRZul", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_1.png", "conversations": [ { "from": "human", "value": "\nIn which year does {Amazon Inc.}'s graph cross the 2500 stock price mark?" }, { "from": "gpt", "value": "2018" } ], "id_raw": 521 }, { "id": "My1XwYLx6e8tJcWR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_1.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the area [0.151, 0.183, 0.237, 0.737]?" }, { "from": "gpt", "value": "This area includes the y-axis label \"Stock Price (in USD)\" for the second subplot, showing Google Inc. and Microsoft Corp. stock prices." } ], "id_raw": 521 }, { "id": "C4ruzdfhePeaxuWo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within [0.420, 0.128, 0.600, 0.304]." }, { "from": "gpt", "value": "This region contains the legend for the second subplot, specifying color-coded line representations for Google Inc. and Microsoft Corp." } ], "id_raw": 521 }, { "id": "p3gb2hRy3MZ1VReW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_1.png", "conversations": [ { "from": "human", "value": "\nWhat is represented between [0.093, 0.792, 0.327, 0.871]?" }, { "from": "gpt", "value": "This area corresponds to the x-axis tracking years from 2010 to 2020 for Apple Inc.'s stock price subplot." } ], "id_raw": 521 }, { "id": "a32BeN1tGQR2Hf1m", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the second subplot?" }, { "from": "gpt", "value": "Production (in 1000 metric tons)." } ], "id_raw": 522 }, { "id": "TPc3YQ3ic5i6kMy1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the color of the handle} for the legend marked 'Meat Production'?" }, { "from": "gpt", "value": "#f39c12[0.434, 0.260, 0.465, 0.260]." } ], "id_raw": 522 }, { "id": "AHtXBH5C4D6x7OeW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_2.png", "conversations": [ { "from": "human", "value": "\nHow is {the x-axis tick labeled '2020'}{r>[0.935, 0.825, 0.973, 0.871] on the last subplot?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 522 }, { "id": "0ArLq1ttmRr3rTTt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the point at '2010' for Cereal Production Line}[0.240, 0.369] have a higher or lower coordinate than {the point at '2010' for Dairy Production Line}[0.895, 0.490] on the y-axis?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 522 }, { "id": "Gnz2y4phynHQHU1B", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_2.png", "conversations": [ { "from": "human", "value": "\nAre {the labels for the y-axis tick '2000'}{r>[0.371, 0.351, 0.410, 0.398] in the second subplot at the same height as {the labels for the y-axis tick '2000'}{r>[0.699, 0.351, 0.737, 0.398] in the third subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 522 }, { "id": "72JAZpYS6QfwO2CH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_2.png", "conversations": [ { "from": "human", "value": "\nWhich has higher y-coordinate in 1995, {Vegetable Production Line}[0.479, 0.651] or {Meat Production Line}[0.479, 0.672]?" }, { "from": "gpt", "value": "Meat Production Line[0.479, 0.672]." } ], "id_raw": 522 }, { "id": "iJHcgigcvPVDj9FH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the region {bounded by axes lines}[0.093, 0.128, 0.328, 0.792] in the first subplot." }, { "from": "gpt", "value": "This region displays two line charts, representing Cereal and Fruit Production metrics. X-axis labels range from '1990' to '2020', y-axis shows production volume from '750' to '2750' metric tons." } ], "id_raw": 522 }, { "id": "RMHCRkgcaxx9XA88", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_2.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the chart region {bounded}[0.420, 0.128, 0.656, 0.792] of the second subplot?" }, { "from": "gpt", "value": "The region contains two line charts indicating vegetable and meat production over years from 1990 to 2020 as well as x and y axes with corresponding tick marks and labels." } ], "id_raw": 522 }, { "id": "rNZbShDWgfO7INou", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_2.png", "conversations": [ { "from": "human", "value": "\nCan you explain the contents within the chart area {r>[0.748, 0.128, 0.983, 0.792] for the third subplot?" }, { "from": "gpt", "value": "The chart details the dairy production line showcasing the increase in production from the year '1990' to '2020'. It includes the x-axis with year intervals and the y-axis with production values, both featuring their respective ticks and labels." } ], "id_raw": 522 }, { "id": "dsz4FMu4l8WgkEzi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object at [0.058, 0.895, 0.064, 0.923]?" }, { "from": "gpt", "value": "X-axis tick label." } ], "id_raw": 523 }, { "id": "632M4Gn85sSyzTEI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_3.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {the 'Infrastructure' category}[0.756, 0.108, 0.819, 0.136] in the legend?" }, { "from": "gpt", "value": "Purple." } ], "id_raw": 523 }, { "id": "vLFlyoUyuXZK8GzZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object located at [0.058, 0.895, 0.064, 0.923]?" }, { "from": "gpt", "value": "X-axis tick label." } ], "id_raw": 523 }, { "id": "yF3LD6snLFmnghtm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_3.png", "conversations": [ { "from": "human", "value": "\nIs {the first data point for 'Policy'}[0.721, 0.875] higher than {the first data point for 'Infrastructure'}[0.721, 0.840] on the graph?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 523 }, { "id": "JPh8mhlmf8ZQS16T", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_3.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher data point at {x = 5}[0.838, 0.875], 'Policy' or 'Infrastructure'?" }, { "from": "gpt", "value": "Policy." } ], "id_raw": 523 }, { "id": "qapmlRh5fRAA8yam", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_3.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '6000'}[0.421, 0.375] above or below {the point labeled '8000'}[0.450, 0.208] for 'Startups'?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 523 }, { "id": "yjRd7hUIrJG0gUje", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_3.png", "conversations": [ { "from": "human", "value": "\nWhat features are contained within [0.361, 0.041, 0.655, 0.875]?" }, { "from": "gpt", "value": "This area spans an entire subplot which includes axes labels for 'Time' and 'Values', marked data points for the categories 'Startups' and 'Research', with corresponding lines and markers, and tick labels." } ], "id_raw": 523 }, { "id": "fXWv3rxmkPCkFJVl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects located in [0.690, 0.030, 0.984, 0.198]." }, { "from": "gpt", "value": "This region hosts y-axis labels for values 0 to 8000 along with corresponding grid lines, and parts of visual elements like lines and plus markers representing the 'Infrastructure' category." } ], "id_raw": 523 }, { "id": "xOrg78L7jJZwvqYE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.058, 0.863, 0.384, 0.926] encompass?" }, { "from": "gpt", "value": "It includes several x-axis tick labels from '1' to '4', as well as the y-axis label '0'." } ], "id_raw": 523 }, { "id": "AGCHGL7pQVn1eOww", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in plot 3 according to the coordinates [0.671, 0.317, 0.686, 0.623]?" }, { "from": "gpt", "value": "Progress Index." } ], "id_raw": 524 }, { "id": "GmQcMxEFQIuaIeLF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the fourth marker on the x-axis}[0.575, 0.869, 0.589, 0.904] in the second plot?" }, { "from": "gpt", "value": "60." } ], "id_raw": 524 }, { "id": "1R1wEu1zhZ6UcqJr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing 'Emails'}[0.728, 0.395, 0.972, 0.716] according to the data?" }, { "from": "gpt", "value": "#2ecc71." } ], "id_raw": 524 }, { "id": "XoV99fwmPnc17p7S", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_4.png", "conversations": [ { "from": "human", "value": "\nIs {the progress of 'Emails'}[0.728, 0.395, 0.972, 0.716] higher or lower than {the progress of 'Social Media'}[0.728, 0.481, 0.972, 0.737] at the 80th Decomposition IO Norm?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 524 }, { "id": "FubP5qdkgYXg4v5y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_4.png", "conversations": [ { "from": "human", "value": "\nAre {the points for 'Desktops'}[0.399, 0.310, 0.643, 0.673] above or below {the points for 'Smartphones'}[0.070, 0.181, 0.314, 0.630]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 524 }, { "id": "HMrKmXZ3n2dP2LEp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_4.png", "conversations": [ { "from": "human", "value": "\nDoes {the line representing 'Social Media'}[0.728, 0.481, 0.972, 0.737] have a steeper slope than {the line representing 'Emails'}[0.728, 0.395, 0.972, 0.716]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 524 }, { "id": "LGxk2bAqPJT3xls8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content inside this bounding box [0.101, 0.037, 0.284, 0.085]." }, { "from": "gpt", "value": "It contains the title of the first subplot, which is \"Smartphones Progress\", written in black color." } ], "id_raw": 524 }, { "id": "wmgkIFWXhlKn9pne", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_4.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects in the bounding box [0.205, 0.764, 0.323, 0.827]?" }, { "from": "gpt", "value": "The box contains the legend for the first subplot. It represents {Smartphones}[0.242, 0.778, 0.319, 0.813] with a colored marker at these coordinates [0.203, 0.776, 0.238, 0.811], colored in red." } ], "id_raw": 524 }, { "id": "fyovddLl4GC82JLp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_4.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the coordinates [0.867, 0.712, 0.982, 0.827]?" }, { "from": "gpt", "value": "The area captures the legend for the third subplot. It includes two categories: {Emails}[0.904, 0.726, 0.942, 0.761] represented by a green marker at [0.866, 0.724, 0.901, 0.758] and {Social Media}[0.904, 0.778, 0.977, 0.813] represented with an orange marker at [0.866, 0.776, 0.901, 0.811]." } ], "id_raw": 524 }, { "id": "sPPy4AplM4iA8HUh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_5.png", "conversations": [ { "from": "human", "value": "\nWhat is referred to by the text with coordinates [0.400, 0.025, 0.685, 0.057] in the chart?" }, { "from": "gpt", "value": "This is the subtitle of the chart: 'Policy Rate vs KPI'." } ], "id_raw": 525 }, { "id": "1wwxeoDFMUKtI0cL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.848, 0.085, 0.954, 0.108]?" }, { "from": "gpt", "value": "It's an item in the legend denoted as 'KPI Trend'." } ], "id_raw": 525 }, { "id": "JYXL1b6f3WpAkQdH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the content at the coordinates [0.917, 0.912, 0.954, 0.936]?" }, { "from": "gpt", "value": "This is an x-axis tick label with the value '2.5'." } ], "id_raw": 525 }, { "id": "1rpSNsEjSsHIAjAu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_5.png", "conversations": [ { "from": "human", "value": "\nAre the points on the {KPI Trend line}[0.149, 0.147, 0.936, 0.813] generally increasing or decreasing?" }, { "from": "gpt", "value": "Decreasing and then increasing again." } ], "id_raw": 525 }, { "id": "0E05wpcsJAf1KgVk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_5.png", "conversations": [ { "from": "human", "value": "\nIs the {KPI value at '1.0'}[0.346, 0.397] higher or lower than the {KPI value at '0.5'}[0.149, 0.813]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 525 }, { "id": "3ukh4GKlyfc0mNt9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_5.png", "conversations": [ { "from": "human", "value": "\nIs the {KPI point at '2.0'}[0.739, 0.147] right or left of the {KPI point at '2.5'}[0.936, 0.314]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 525 }, { "id": "9YA4m3x5kno21UM3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.774, 0.075, 0.963, 0.117]." }, { "from": "gpt", "value": "This is the legend denoted by the title 'KPI Trend' in black, with a colored handle signifying the color code used for the line in the chart." } ], "id_raw": 525 }, { "id": "YZGTGrS15tJVAjw1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the area [0.149, 0.147, 0.936, 0.813]?" }, { "from": "gpt", "value": "This region contains the plotted points of the 'KPI Trend' line." } ], "id_raw": 525 }, { "id": "IkWlIAS2KpDBofUL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.064, 0.138, 0.094, 0.161]." }, { "from": "gpt", "value": "It contains a y-axis label '58' with a tick corresponding to it." } ], "id_raw": 525 }, { "id": "99sUCWsm1dTN7xiZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_6.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.178, 0.890, 0.223, 0.950] in the subplot_0?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 526 }, { "id": "ToEmUMumiwqEb8Kc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the Y-axis label}[0.017, 0.307, 0.037, 0.613] in subplot_0?" }, { "from": "gpt", "value": "\"Stock Price\"." } ], "id_raw": 526 }, { "id": "mC3PEzErltvOhkZo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_6.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the 80th x-axis tick label in subplot_1?" }, { "from": "gpt", "value": "[0.620, 0.825, 0.640, 0.871]" } ], "id_raw": 526 }, { "id": "HBbYQWX1NrzBtIA2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_6.png", "conversations": [ { "from": "human", "value": "\nIs {the fourth point of 'Steady Stock'}[0.251, 0.371] higher than {the first point of 'Decreasing Stock'}[0.754, 0.349] in terms of the stock price?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 526 }, { "id": "ukuJLxUcHT9SDSJ9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_6.png", "conversations": [ { "from": "human", "value": "\nComparing in subplot_2, does {the 'Erratic Stock' line at point 40}[0.856, 0.515] have a stock price greater than {the 'Decreasing Stock' line at point 40}[0.856, 0.515]?" }, { "from": "gpt", "value": "Equal." } ], "id_raw": 526 }, { "id": "YXqTVQNDHdKKaeCi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_6.png", "conversations": [ { "from": "human", "value": "\nWhich subplot's stock line has a higher value at time 60, {the 'Volatile Stock'}[0.579, 0.405] in subplot_1 or {the 'Steady Stock'}[0.251, 0.371] in subplot_0?" }, { "from": "gpt", "value": "\"Volatile Stock\" in subplot_1." } ], "id_raw": 526 }, { "id": "3mIjuHEVTnZtJVse", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.163, 0.686, 0.320, 0.769] in subplot_0?" }, { "from": "gpt", "value": "This is the legend of subplot_0 that contains the label '{Steady Stock}'[0.212, 0.704, 0.314, 0.751] paired with a colored marker." } ], "id_raw": 526 }, { "id": "EcUKkPahipacb0L6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content inside [0.673, 0.307, 0.693, 0.613] in subplot_2?" }, { "from": "gpt", "value": "This region primarily contains the y-axis label of subplot_2 which is '{Stock Price}'[0.672, 0.307, 0.692, 0.613]." } ], "id_raw": 526 }, { "id": "CF6C1MJuGI3Rmi5S", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within region [0.749, 0.825, 0.759, 0.871] in subplot_2." }, { "from": "gpt", "value": "This region contains the x-axis label '0' with its {coordinates}[0.749, 0.825, 0.759, 0.871]." } ], "id_raw": 526 }, { "id": "tBF43TL2UGm6VVSh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object positioned at [0.017, 0.384, 0.037, 0.536] in the chart?" }, { "from": "gpt", "value": "Y-axis label for the Climate Change Index subplot." } ], "id_raw": 527 }, { "id": "XLevQdUEZZV18252", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_7.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the coordinates of the Year label for the Sea Level subplot?" }, { "from": "gpt", "value": "[0.838, 0.890, 0.877, 0.950]" } ], "id_raw": 527 }, { "id": "yUEz4ZIasSzgE2F4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the 1990 x-axis tick label} at [0.733, 0.825, 0.772, 0.871] referring to in the plot?" }, { "from": "gpt", "value": "The year 1990." } ], "id_raw": 527 }, { "id": "5RWT2te61j6jbq2o", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point representing the year 2000 in the Climate Change Index subplot}[0.167, 0.294] higher or lower than {the point representing the year 2010 in the same subplot}[0.237, 0.322]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 527 }, { "id": "Ri8jBn5CbAwz2Tx0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Sea Level' line}[0.753, 0.756, 0.963, 0.765] have a higher or lower ending point than {the 'Climate Change Index' line}[0.097, 0.239, 0.308, 0.355]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 527 }, { "id": "V21tXzO16ofcHfcM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_7.png", "conversations": [ { "from": "human", "value": "\nCould you tell me whether the {the 'Air Quality' line start point}[0.425, 0.349] is above or below its endpoint {the 'Air Quality' line end point}[0.635, 0.671]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 527 }, { "id": "kKHfVK1GuWCz35Q4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements present in the region [0.739, 0.128, 0.977, 0.792]." }, { "from": "gpt", "value": "This region contains a line plot which represents the sea level change over time. It has coordinates for x and y axes labels, x and y ticks, and an individual line plot with markers at specific years." } ], "id_raw": 527 }, { "id": "RQHN8FQOCj85CHlE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_7.png", "conversations": [ { "from": "human", "value": "\nWhat can you find in the region [0.411, 0.128, 0.650, 0.792]?" }, { "from": "gpt", "value": "This area includes the subplot for 'Air Quality', comprising x and y-axis labels, tick marks for both axes, and a dashed line plot showing the change in Air Quality over the years." } ], "id_raw": 527 }, { "id": "CEVGvjBuUrQwZlFR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.083, 0.128, 0.322, 0.792]?" }, { "from": "gpt", "value": "This region includes the 'Climate Change Index' subplot. The elements within this region include x and y axes labels, tick labels for both the axes, the subplot title, and a line representing the Climate Change Index over the years." } ], "id_raw": 527 }, { "id": "mxEVu9XWTwJBFilr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the label refer to at these coordinates [0.053, 0.776, 0.077, 0.819]?" }, { "from": "gpt", "value": "Y-axis label, '0.4'." } ], "id_raw": 529 }, { "id": "2Hvr9V3theLdiPJC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the location of the 4th label on the x-axis?" }, { "from": "gpt", "value": "The coordinates are [0.580, 0.824, 0.601, 0.870]" } ], "id_raw": 529 }, { "id": "txJMeaQwOlboW9KR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_9.png", "conversations": [ { "from": "human", "value": "\nWhat is at this point {the marker}[0.969, 0.701, 0.909, 0.748] in the Biotech subplot?" }, { "from": "gpt", "value": "A legend marker." } ], "id_raw": 529 }, { "id": "upaCzntngGjdppI6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_9.png", "conversations": [ { "from": "human", "value": "\nIs {the point at TRL 60 for ICT}[0.262, 0.325] above or below {the point at TRL 60 for Nanotech}[0.262, 0.208] in ICT & Nanotech subplot?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 529 }, { "id": "0LTKaZcqxwjWS1Ih", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_9.png", "conversations": [ { "from": "human", "value": "\nIs {the ICT line}[0.429, 0.325, 0.643, 0.525] generally above or below {the Nanotech line}[0.429, 0.160, 0.643, 0.458] in ICT & Nanotech subplot?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 529 }, { "id": "h16gCLYRdWC0NmpO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_9.png", "conversations": [ { "from": "human", "value": "\nWhich technology level at {TRL 40}[0.207, 0.420] has a higher Innovation Success, Advanced Manufacturing or Cleantech?" }, { "from": "gpt", "value": "Cleantech." } ], "id_raw": 529 }, { "id": "sENMRNpOpHachmel", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the items located in the region [0.864, 0.688, 0.977, 0.769]." }, { "from": "gpt", "value": "There is only one legend item: {Biotech}[0.909, 0.706, 0.969, 0.753], with a colored marker associated with it." } ], "id_raw": 529 }, { "id": "0Afg8cWqaNwL6ZS6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the details of the area [0.147, 0.819, 0.166, 0.870]." }, { "from": "gpt", "value": "The area includes one x-axis label: {20}[0.148, 0.824, 0f.167, 0.870] in the first subplot (Advanced Manufacturing & Cleantech)." } ], "id_raw": 529 }, { "id": "bRhQcIvzrUuiqIMB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.677, 0.132, 1.011, 0.195]?" }, { "from": "gpt", "value": "There are 3 objects: {the ICT label}[0.683, 0.165, 0.707, 0.209], {the & label}[0.781, 0.166, 0.797, 0.209], and {the Nanotech label}[0.981, 0.166, 1.058, 0.209] in the second subplot's legend (ICT & Nanotech)." } ], "id_raw": 529 }, { "id": "zzWzRbtafw2zYgo0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color associated with {the line representing 'Product Sales'}[0.763, 0.290, 0.941, 0.571] in the 'Cloud Technology' subplot?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 530 }, { "id": "ZJEq7runwo9xYCdo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the linestyle used for {the line representing 'Investment'}[0.106, 0.511, 0.284, 0.641] in the 'AI Research' subplot?" }, { "from": "gpt", "value": "Solid." } ], "id_raw": 530 }, { "id": "vJHQmAgd6xb813e9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the shape of the marker for {the line labeled 'Product Sales'}[0.435, 0.576, 0.612, 0.770] in the 'CyberSecurity' subplot?" }, { "from": "gpt", "value": "Square." } ], "id_raw": 530 }, { "id": "Wj24AFAev1b7ckrY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_10.png", "conversations": [ { "from": "human", "value": "\nWhich line starts at a lower y-coordinate at x = 2020 between {the 'Investment'}[0.763, 0.258, 0.941, 0.582] and {the 'Product Sales'}[0.763, 0.290, 0.941, 0.571] in 'Cloud Technology'?" }, { "from": "gpt", "value": "Investment." } ], "id_raw": 530 }, { "id": "Qkfos4NqnVqAt41Z", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_10.png", "conversations": [ { "from": "human", "value": "\nIn terms of height at x = 2019, which dataset is displayed at a higher position on {the graph}: {the 'Investment Sales'}[0.763, 0.258, 0.941, 0.582] or {the 'Product Sales'}[0.763, 0.290, 0.941, 0.571]?" }, { "from": "gpt", "value": "Product Sales." } ], "id_raw": 530 }, { "id": "ONTrb3chwuXalV6b", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_10.png", "conversations": [ { "from": "human", "value": "\nLooking at the initial values [0.106], which line begins higher on the plot for 'AI Research', {the 'Investment'}[0.106, 0.511, 0.284, 0.641] or {the 'Product Sales'}[0.106, 0.490, 0.284, 0.630]?" }, { "from": "gpt", "value": "Product Sales." } ], "id_raw": 530 }, { "id": "BwPEK9eQwzQloQVp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the area enclosed by [0.390, 0.086, 0.511, 0.155]." }, { "from": "gpt", "value": "This region contains the sublegend for 'CyberSecurity' with handles colored in orange and green representing 'Investment' and 'Product Sales' respectively." } ], "id_raw": 530 }, { "id": "bYatBxTJWO8S6JbW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_10.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the region [0.343, 0.278, 0.357, 0.680]?" }, { "from": "gpt", "value": "This area corresponds to the y-axis of the 'CyberSecurity' subplot, featuring labels for Investment/Sales values." } ], "id_raw": 530 }, { "id": "pq8ETwl4TDC9EQHh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the section spanned by [0.180, 0.942, 0.210, 0.970]." }, { "from": "gpt", "value": "This narrow region holds the x-axis label of the 'Year' for the 'AI Research' subplot, presented in black." } ], "id_raw": 530 }, { "id": "xXbS753z5TjuEqhi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.098, 0.890, 0.318, 0.950] in subplot_0?" }, { "from": "gpt", "value": "Decomposition IO Norm." } ], "id_raw": 531 }, { "id": "OoVxyH4QbuVhwlXT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_11.png", "conversations": [ { "from": "human", "value": "\nWhich legend item does {the marker}[0.155, 0.701, 0.202, 0.748] in subplot_0 correspond to?" }, { "from": "gpt", "value": "BigData (10k)." } ], "id_raw": 531 }, { "id": "eZgXK9NpWAqBgJ1N", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of {the point}[0.101, 0.194] on the line in subplot_0?" }, { "from": "gpt", "value": "It represents that when the Decomposition IO Norm is 0, the accuracy of BigData (10k) is 0.78." } ], "id_raw": 531 }, { "id": "AC9MomwrOD7IIcXU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_11.png", "conversations": [ { "from": "human", "value": "\nBetween BigData (5k) represented by {the line}[0.757, 0.227, 0.970, 0.493] and Hive (5k) represented by {the line}[0.757, 0.360, 0.970, 0.659], which one has a higher accuracy at Decomposition IO Norm of 40 in subplot_2?" }, { "from": "gpt", "value": "BigData (5k)." } ], "id_raw": 531 }, { "id": "c8tTtLiffvkV746u", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_11.png", "conversations": [ { "from": "human", "value": "\nCompared to {BigData (10k) at point '40'}[0.208, 0.394] in subplot_0, is {Hive (10k) at point '40'}[0.536, 0.527] in subplot_1 higher or lower?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 531 }, { "id": "tUDlxrUJmz03YRHm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_11.png", "conversations": [ { "from": "human", "value": "\nIs the accuracy of {BigData (10k) at point '80'}[0.314, 0.593] in subplot_0 higher or lower than {Hive (5K) at point '80'}[0.970, 0.360] in subplot_2?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 531 }, { "id": "TyfaUahgDbgXRZpp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.822, 0.616, 0.976, 0.769] in subplot_2 of the chart?" }, { "from": "gpt", "value": "This area contains the legend items for BigData (5k) and Hive (5k) which are colored and styled with square and cross markers respectively. Their corresponding colors are green and orange." } ], "id_raw": 531 }, { "id": "vIS7YvOtHFFiHN6q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_11.png", "conversations": [ { "from": "human", "value": "\nTell me about the area [0.822, 0.616, 0.976, 0.769] in subplot_2." }, { "from": "gpt", "value": "This region contains two lines of legend. BigData (5k), marked with a green square, and Hive (5k), marked with an orange cross." } ], "id_raw": 531 }, { "id": "icXZqwB2wURC5gpM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_11.png", "conversations": [ { "from": "human", "value": "\nWhat object is featured in the [0.763, 0.050, 0.963, 0.113] area of subplot_2?" }, { "from": "gpt", "value": "Subplot_2's subtitle, \"BigData & Hive 5K,\" populates this area." } ], "id_raw": 531 }, { "id": "mhfImqZqL995geKx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of the coordinates [0.810, 0.890, 0.903, 0.950] in the given chart?" }, { "from": "gpt", "value": "These are the coordinates for the x-axis title \"Risk Level\" for the 'High Risk' subplot." } ], "id_raw": 532 }, { "id": "VqDJKlBOzgQ8Yb0E", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second point on the Risk Management Efficiency line in the 'Low Risk' subplot?" }, { "from": "gpt", "value": "[0.201, 0.645]" } ], "id_raw": 532 }, { "id": "JnCyjAMvmCzgSp0Q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label for subplot_2 with the coordinates [0.672, 0.326, 0.692, 0.594]?" }, { "from": "gpt", "value": "Efficiency." } ], "id_raw": 532 }, { "id": "4FfJcIVZbIXcYOIA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_12.png", "conversations": [ { "from": "human", "value": "\nIs the {point at '100'}[0.968, 0.541] in {line_0 of subplot_2} above or below the {point at '100'}[0.968, 0.571] in {line_1 of subplot_2}?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 532 }, { "id": "haiRA4mT1equ4bdV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_12.png", "conversations": [ { "from": "human", "value": "\nDoes the highest value on the y-axis for the subplot with coordinates '[0.734, 0.128, 0.979, 0.792]' match with the point at '0' on {line_0} in that subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 532 }, { "id": "ZJwQxGNK6BSmzaBg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_12.png", "conversations": [ { "from": "human", "value": "\nIn the subplot with coordinates '[0.406, 0.128, 0.651, 0.792]', which label is associated with the 'o' marker and line color '#3498db'?" }, { "from": "gpt", "value": "Risk Management Efficiency." } ], "id_raw": 532 }, { "id": "QSrgh51jtN1BXVpC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_12.png", "conversations": [ { "from": "human", "value": "\nWhat's included in the region [0.806, 0.050, 0.907, 0.113]?" }, { "from": "gpt", "value": "The title of the 'High Risk' subplot is located here with font color black and zero rotation." } ], "id_raw": 532 }, { "id": "fuF9UN4l1bbXDB6E", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the region located at [0.698, 0.616, 0.971, 0.769]." }, { "from": "gpt", "value": "Two labels: {Risk Management Efficiency}[0.747, 0.634, 0.965, 0.681] and {Risk Assessment Efficiency}[0.747, 0.704, 0.955, 0.751] in the legend of the subplot 'High Risk'. Each label is paired with a color handle for visual representation." } ], "id_raw": 532 }, { "id": "T7zEheFm9H5HwZcu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the region located at [0.734, 0.128, 0.979, 0.792] contain?" }, { "from": "gpt", "value": "This is the area occupied by the 'High Risk' subplot. It includes the title 'High Risk', risk levels as x-axis labels, 'Efficiency' as the y-axis label, and various line series." } ], "id_raw": 532 }, { "id": "6N2B092Phzv7iQuj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the subtitle for the plot at coordinates [0.642, 0.050, 1.052, 0.113]?" }, { "from": "gpt", "value": "The subtitle is \"Robotics and IoT Development Trends\"." } ], "id_raw": 533 }, { "id": "mkVpUUy5zlIzdw9u", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_13.png", "conversations": [ { "from": "human", "value": "\nAt what coordinate is the label '2010' on the x-axis in the first subplot located?" }, { "from": "gpt", "value": "The coordinate is [0.144, 0.825, 0.183, 0.871]." } ], "id_raw": 533 }, { "id": "3vXI03HfkYVKUOjb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label associated with {the tick on the Y-Axis}[0.043, 0.442, 0.062, 0.488]?" }, { "from": "gpt", "value": "30." } ], "id_raw": 533 }, { "id": "UGMdPxwRUdZR8Vz7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_13.png", "conversations": [ { "from": "human", "value": "\nIs {the 'AI'} trend at [0.300, 0.175] higher or lower than {the 'Blockchain'} trend at [0.619, 0.460]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 533 }, { "id": "tPqPk3ygKX6BVd2B", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_13.png", "conversations": [ { "from": "human", "value": "\nIs the {marker for AI in subplot_0}[0.078, 0.155, 0.104, 0.178] the same color as the {marker for IoT in subplot_2}[0.716, 0.199, 0.742, 0.222]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 533 }, { "id": "nF9F76BHoqoqtGWo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_13.png", "conversations": [ { "from": "human", "value": "\nIs the {line for IoT}[0.757, 0.318, 0.938, 0.745] climbing steeper than the {line for Robotics}[0.757, 0.318, 0.938, 0.697]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 533 }, { "id": "VV7vDwTWl0aDE89q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the chart content within [0.392, 0.128, 0.664, 0.792]." }, { "from": "gpt", "value": "This region contains the subplot showing the \"Blockchain and AR/VR Development Trends\". The region includes the X-axis and Y-axis, labeled 'Years' and varied numerical values respectively. There are lines depicting the development trends for Blockchain and AR/VR." } ], "id_raw": 533 }, { "id": "wBKmlYZuUDHLG16G", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_13.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.644, 0.824, 0.961, 0.871]?" }, { "from": "gpt", "value": "The region contains the X-axis labels for the Year in the third subplot. The labels are for the years from 2010 to 2025." } ], "id_raw": 533 }, { "id": "MQtK6wOOwVJTqSsN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within [0.397, 0.142, 0.481, 0.238]." }, { "from": "gpt", "value": "This region contains the legend for the second subplot. The legend shows two entries, one for Blockchain and one for AR/VR, each with their respective color-coded markers." } ], "id_raw": 533 }, { "id": "2MOCbTQo7H5syER8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the first data point of the 'Homeless Rate' line plot in the third subplot?" }, { "from": "gpt", "value": "{0}[0.743, 0.762]" } ], "id_raw": 534 }, { "id": "58ZmYHfPurqmJEfS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_14.png", "conversations": [ { "from": "human", "value": "\nWhat visible string is at {coordinates}[0.903, 0.825, 0.922, 0.871]?" }, { "from": "gpt", "value": "60" } ], "id_raw": 534 }, { "id": "mR4aBVL9Locubf7c", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_14.png", "conversations": [ { "from": "human", "value": "\nWhat chart lays under these coordinates [0.073, 0.128, 0.328, 0.792]?" }, { "from": "gpt", "value": "First subplot" } ], "id_raw": 534 }, { "id": "3qSkGc46RBz6QDuJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_14.png", "conversations": [ { "from": "human", "value": "\nIs the 'High Income Households' line plot at coordinates [0.846, 0.825, 0.866, 0.871] above or below the 'Single Parent Households' line plot at the same x-value?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 534 }, { "id": "QGLfh5Tf8ZAZhZvz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_14.png", "conversations": [ { "from": "human", "value": "\nAt x-value '80', does the 'Poverty Rate' or 'Foreigner Households' have a higher y-value?" }, { "from": "gpt", "value": "Poverty Rate." } ], "id_raw": 534 }, { "id": "IAVXEZc2Gbpq6wM8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point '0'} of Homeless Rate [0.743, 0.762] higher or lower than {the point '20'} of Homeless Rate [0.799, 0.765] ?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 534 }, { "id": "KzsbOIB141CQtm7g", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within this region [0.729, 0.128, 0.983, 0.792]." }, { "from": "gpt", "value": "This region contains the third subplot. It's a line plot representing 'Homeless Rate'. The x-axis label is 'Decomposition IO Norm' and the y-axis label is 'Percentage'. The tick values for x-axis are '0', '20', '40', '60', '80' while those for the y-axis are '0', '2', '4', '6', '8', '10', '12'." } ], "id_raw": 534 }, { "id": "Neyoa4bPq03WUyhf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_14.png", "conversations": [ { "from": "human", "value": "\nDescribe what's in the bounded box [0.401, 0.128, 0.656, 0.792]." }, { "from": "gpt", "value": "This region covers the second subplot of the chart. It consists of two line plots for 'Single Parent Households' and 'High Income Households' both plotted against 'Decomposition IO Norm'. The y-axis is labeled as 'Percentage'." } ], "id_raw": 534 }, { "id": "OOaowOmRfsHvYyoX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.337, 0.170, 0.727, 0.862] include?" }, { "from": "gpt", "value": "It includes part of the first subplot and the entire second subplot. The first subplot's line graph for 'Foreigner Households' and 'Poverty Rate' is cutoff, whereas the second subplot's line graph for 'Single Parent Households' and 'High Income Households' is fully visible." } ], "id_raw": 534 }, { "id": "qsaSGqarkAGHpZMQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the label associated with the x-axis in the first subplot?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 535 }, { "id": "P5FkNEY2g9pGdMt3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_15.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {the line labeled 'LYRAA'}[0.765, 0.197, 0.962, 0.540]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 535 }, { "id": "Q5deB0na4ktYqVwk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label shown in {subplot_3}[0.743, 0.128, 0.983, 0.792]?" }, { "from": "gpt", "value": "Effectiveness." } ], "id_raw": 535 }, { "id": "c9IEvN7WD7GUltVy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_15.png", "conversations": [ { "from": "human", "value": "\nIs {the trend for 'ALBA'}[0.110, 0.197, 0.306, 0.724] increasing or decreasing from 2010 to 2015?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 535 }, { "id": "9T8xZAXIUtE91Fog", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_15.png", "conversations": [ { "from": "human", "value": "\nWhich line has a lower start point at the year 2006, {the LYRAA trend}[0.765, 0.265] or {the MAPLE trend}[0.765, 0.449]?" }, { "from": "gpt", "value": "MAPLE." } ], "id_raw": 535 }, { "id": "ccIAG5kq58ruoR3z", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_15.png", "conversations": [ { "from": "human", "value": "\nDoes the {label \"IOTA\" from the legend}[0.606, 0.704, 0.642, 0.751] match the {marker color}[0.555, 0.701, 0.602, 0.748] in its corresponding subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 535 }, { "id": "cDKoTesT7J4UwiCB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_15.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.088, 0.128, 0.328, 0.792] display in the chart?" }, { "from": "gpt", "value": "This area shows a line graph representing the growth of 'ALBA', with points marked for each year along the y-axis labeled 'Effectiveness' and the x-axis marked by years." } ], "id_raw": 535 }, { "id": "swgKwaodyEDLg3Tt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements located in the region [0.416, 0.128, 0.656, 0.792]." }, { "from": "gpt", "value": "This section contains the 'IOTA Growth' subplot, illustrating its performance over years with a dotted line, and marked measurements along the y-axis denoting 'Effectiveness', similar to x-axis labels for each year." } ], "id_raw": 535 }, { "id": "QePPjkRrTfR1V4ti", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the chart region [0.743, 0.128, 0.983, 0.792]." }, { "from": "gpt", "value": "This segment of the chart depicts the combined decline of 'LYRAA & MAPLE' shown by two distinct line styles for each in varied colors, with corresponding x and y-axis labels for 'Year' and 'Effectiveness' respectively." } ], "id_raw": 535 }, { "id": "olMXOZuG0U0VdNHb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the marker at {coordinates}[0.106, 0.416] represent?" }, { "from": "gpt", "value": "Type_A Expenditure for 2009." } ], "id_raw": 536 }, { "id": "xhzSa81Lb67pcZVA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_16.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the Type_B Expenditure as seen at {the handle}[0.080, 0.200, 0.109, 0.223]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 536 }, { "id": "jcD8K2KgIZKqdI96", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_16.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is used for {the line for Type_C Expenditure}[0.434, 0.588, 0.624, 0.774]?" }, { "from": "gpt", "value": "Square." } ], "id_raw": 536 }, { "id": "3PyE6WiGmBMJ8hBu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_16.png", "conversations": [ { "from": "human", "value": "\nIs {data point '2010' for Type_D Expenditure}[0.793, 0.685] above or below {data point '2015' for Type_D Expenditure}[0.952, 0.544]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 536 }, { "id": "RtRwN87omn7cj3cq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_16.png", "conversations": [ { "from": "human", "value": "\nWhich expenditure type has a higher expenditure in 2011, {Type_D}[0.825, 0.678] or {Type_E}[0.825, 0.281]?" }, { "from": "gpt", "value": "Type_E." } ], "id_raw": 536 }, { "id": "BqCUEpKb9sxm5pyt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_16.png", "conversations": [ { "from": "human", "value": "\nDoes {the first tick label '2009' for Type E Expenditure}[0.742, 0.851, 0.781, 0.898] align with {the first point of Type E Expenditure}[0.761, 0.351] on the X-axis?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 536 }, { "id": "Z7MBbU0ppzpvE5Yb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_16.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the subset area [0.000, 0.312, 0.392, 0.956] for subplot 0?" }, { "from": "gpt", "value": "This region includes elements like {'xlabel for years'}[0.185, 0.917, 0.217, 0.950], {'ylabel for expenditure in $'}[0.017, 0.314, 0.028, 0.620], x-axis and y-axis numerical labels, and a line graph depicting Type A and B Expenditures with respective data points and a small legend displaying their color codes." } ], "id_raw": 536 }, { "id": "1gzhf3McMHEgUUQO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the content present in the area [0.678, 0.050, 0.983, 0.974] for subplot 2?" }, { "from": "gpt", "value": "This section encompasses {'the subtitle labeling Type D and E Expenditure'}[0.757, 0.050, 0.956, 0.097], {'xlabel and ylabel for years and expenditure'}[0.840, 0.917, 0.873, 0.950][0.672, 0.314, 0.683, 0.620], the associated x-axis and y-axis tick labels and two line graphs showing the expenditures for Types D and E, along with their legend entries." } ], "id_raw": 536 }, { "id": "qCq2TxqU1gH1K6zp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_16.png", "conversations": [ { "from": "human", "value": "\nWhat features are contained in the region [0.074, 0.114, 0.656, 0.819] that spans two subplots?" }, { "from": "gpt", "value": "The region includes {'the axes of subplot 0 and 1'}[0.074, 0.114, 0.328, 0.819][0.402, 0.114, 0.656, 0.819] which cover the data visualizations for Type A, B, and C expenditures, their x and y axes labels, tick marks, and a part of their legends." } ], "id_raw": 536 }, { "id": "nh3bCUtWaVtSTox9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the object positioned at [0.011, 0.422, 0.023, 0.530] on the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 537 }, { "id": "cQ8Yy2uo6TBXRYxc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the second x-axis tick label, which is also placed at [0.107, 0.875]?" }, { "from": "gpt", "value": "Data_Analysis." } ], "id_raw": 537 }, { "id": "XKfOm2QARg159Wq8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_17.png", "conversations": [ { "from": "human", "value": "\nWhat is located at these coordinates [0.045, 0.113]?" }, { "from": "gpt", "value": "The point representing the value of 'Network_Length' in the 'Marginal Growth' trend." } ], "id_raw": 537 }, { "id": "85Cpn6eYJyvTluMx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_17.png", "conversations": [ { "from": "human", "value": "\nLooking at the points {Network_Length on Marginal Growth}[0.048, 0.839] and {Data_Analysis on Marginal Growth}[0.107, 0.113] which one has the higher value?" }, { "from": "gpt", "value": "Network_Length." } ], "id_raw": 537 }, { "id": "FaspuCVrMY0Iq1bq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_17.png", "conversations": [ { "from": "human", "value": "\nFor the 'Minimal Change' trend, is the value for {Data_Quality}[0.706, 0.234] higher or lower than the value for {Data_Variables}[0.765, 0.839]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 537 }, { "id": "QdST6mVcHDCbNyiE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_17.png", "conversations": [ { "from": "human", "value": "\nLooking at {Network_Length on Marginal Growth}[0.048, 0.839] and {Data_Quality on Minimal Change}[0.706, 0.234], which one has a greater value?" }, { "from": "gpt", "value": "Network_Length." } ], "id_raw": 537 }, { "id": "Bxq6gLifPAjxeozy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the coordinate [0.150, 0.093, 0.281, 0.156]." }, { "from": "gpt", "value": "There is a legend item labeled 'Marginal Growth', associated with a marker colored in '#e74c3c' located at {coordinates}[0.149, 0.109, 0.181, 0.137]." } ], "id_raw": 537 }, { "id": "lqwRKwmpN35kPThP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the region between [0.374, 0.077, 0.615, 0.875] contain?" }, { "from": "gpt", "value": "It's a subplot with the title 'Data Preprocessing Overview'. This region contains x and y-axis labels 'Categories' and 'Values' respectively, a line plotting for the trend 'Minimal Change', and x/y-axis tick labels." } ], "id_raw": 537 }, { "id": "7AWy4dS92KDKMVDF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_17.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area from [0.669, 0.422, 0.681, 0.530]?" }, { "from": "gpt", "value": "I see a y-axis label 'Values' in this area." } ], "id_raw": 537 }, { "id": "YniZK28RgHqh8E5J", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.159, 0.822, 0.192, 0.842] represent in the chart?" }, { "from": "gpt", "value": "Handle color for 'Public Trust in Government'." } ], "id_raw": 539 }, { "id": "ssUbWZFz9RNUTVKP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_19.png", "conversations": [ { "from": "human", "value": "\nIdentify the main element at [0.559, 0.828, 0.582, 0.842]." }, { "from": "gpt", "value": "Handle color for 'Trust in Media'." } ], "id_raw": 539 }, { "id": "mUMKW6EdlpMzTUnF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_27_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.837, 0.822, 0.982, 0.850]?" }, { "from": "gpt", "value": "Label text for 'Public Approval of Government'." } ], "id_raw": 539 }, { "id": "dnX4W3eEIZUkhS08", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_19.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher start point on their respective y-axis, {the trend line marked 'Public Trust in Government'}[0.082, 0.512] or {the trend line marked 'Trust in Media'}[0.412, 0.469]?" }, { "from": "gpt", "value": "Trust in Media." } ], "id_raw": 539 }, { "id": "XGrcFVNjLPcimhVE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_19.png", "conversations": [ { "from": "human", "value": "\nCompare the end points of {the 'Political Engagement' trend}[0.955, 0.353] with {the 'Public Approval of Government' trend}[0.955, 0.440] on the y-axis. Which is higher?" }, { "from": "gpt", "value": "Political Engagement." } ], "id_raw": 539 }, { "id": "MSraPEutbEAheyju", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_27_19.png", "conversations": [ { "from": "human", "value": "\nIs the start point of {the line 'Public Trust in Government'}[0.082, 0.512] higher or lower than the end point of the same line[0.295, 0.803]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 539 }, { "id": "kBQ5WzjTznZRu4ZB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_19.png", "conversations": [ { "from": "human", "value": "\nWhat information is within the bounds [0.777, 0.030, 0.897, 0.068]?" }, { "from": "gpt", "value": "This region contains the subtitle, \"Political Trends.\"" } ], "id_raw": 539 }, { "id": "f8XUXGGa9hmALQKa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the significance of the elements found between [0.808, 0.769, 0.985, 0.862]." }, { "from": "gpt", "value": "This region holds the sublegend for the plot, including items for 'Political Engagement' and 'Public Approval of Government', each with their respective color indications." } ], "id_raw": 539 }, { "id": "NKu26P2RjBW4RlJV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_27_19.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.157, 0.811, 0.325, 0.862]?" }, { "from": "gpt", "value": "It is the sublegend area displaying the 'Public Trust in Government' along with its color handle used in the line graph." } ], "id_raw": 539 }, { "id": "zzfqlP96Z9Uw42Ng", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_1.png", "conversations": [ { "from": "human", "value": "\nWhat information is represented by the line with coordinates '[0.179, 0.352, 0.932, 0.733]' in the chart?" }, { "from": "gpt", "value": "The performance of the Base Model." } ], "id_raw": 541 }, { "id": "JByFBuFy2Omw8mh6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_1.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis tick label} at '[0.529, 0.838, 0.582, 0.885]' represent?" }, { "from": "gpt", "value": "Training Epoch 100." } ], "id_raw": 541 }, { "id": "rgjm3vuViqfjFh3H", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_1.png", "conversations": [ { "from": "human", "value": "\nWhat is described by the object with the coordinates '[0.141, 0.124, 0.970, 0.806]'?" }, { "from": "gpt", "value": "The main chart area." } ], "id_raw": 541 }, { "id": "IXsyBGXySlXC7Sou", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_1.png", "conversations": [ { "from": "human", "value": "\nAt {Training Epoch 80}[0.480, 0.461], which model has a higher accuracy - the Base Model or the Improved Model?" }, { "from": "gpt", "value": "Improved Model." } ], "id_raw": 541 }, { "id": "qiJbpA57t7aH01XU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_1.png", "conversations": [ { "from": "human", "value": "\nAt {Training Epoch 150}[0.744, 0.206], is the accuracy of the {Base Model} higher or lower than the Improved Model?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 541 }, { "id": "OdfCo7PtPa7YEqMZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_1.png", "conversations": [ { "from": "human", "value": "\nAre the {Base Model}[0.932, 0.733] and {Improved Model}[0.932, 0.179] performances closer together or further apart at {Training Epoch 200}[0.932, 0.806]?" }, { "from": "gpt", "value": "Further apart." } ], "id_raw": 541 }, { "id": "Vj1OSMCwkuMSAslP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_1.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is inside the area, specifically '[0.155, 0.629, 0.479, 0.783]'?" }, { "from": "gpt", "value": "The area contains the chart's legend. It has entries for the Base Model [0.159, 0.656, 0.229, 0.680] and the Improved Model [0.159, 0.726, 0.229, 0.749] respectively." } ], "id_raw": 541 }, { "id": "PtEHMkpWk1pxEEbr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within '[0.312, 0.838, 0.385, 0.885]'?" }, { "from": "gpt", "value": "The label for the x-axis tick '75' is located here." } ], "id_raw": 541 }, { "id": "KH5UL1IYKTgIKkV6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content inside the area '[0.231, 0.050, 0.881, 0.110]'" }, { "from": "gpt", "value": "This area contains the subtitle of the chart, which is \"Model Accuracy across Training Epochs\"." } ], "id_raw": 541 }, { "id": "H75lJF8ux4peLYJi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the object located at coordinates [0.030, 0.402, 0.058, 0.529] in the plot?" }, { "from": "gpt", "value": "Value" } ], "id_raw": 542 }, { "id": "WtYeunxeO53eMQVU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the handle of the Social Networks legend}[0.482, 0.245, 0.551, 0.268]?" }, { "from": "gpt", "value": "#ffa500" } ], "id_raw": 542 }, { "id": "SVz9byKEo6mBjkOL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-axis tick label} at [0.069, 0.242, 0.131, 0.289]?" }, { "from": "gpt", "value": "0.70" } ], "id_raw": 542 }, { "id": "7QzMYRyOwVf6V8iE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point at time 0.0 for 'Interpersonal Relationships'}[0.187, 0.261] above or below {the point at time 1.0526315789473684 for 'Interpersonal Relationships'}[0.227, 0.288]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 542 }, { "id": "rJDBmHY7EBsoyCGq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point at time 0.0 for 'Social Networks'}[0.187, 0.397] to the left or right of {the point at time 0.0 for 'Interpersonal Relationships'}[0.187, 0.261]?" }, { "from": "gpt", "value": "Neither, they are vertically aligned." } ], "id_raw": 542 }, { "id": "HSeOlREuUBajvCq5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_2.png", "conversations": [ { "from": "human", "value": "\nAt time 15.789473684210526, which line, 'Interpersonal Relationships' or 'Social Networks', is at a higher value {Interpersonal Relationships point}[0.776, 0.533] or {Social Networks point}[0.776, 0.397]?" }, { "from": "gpt", "value": "Interpersonal Relationships." } ], "id_raw": 542 }, { "id": "Z7wIszvAl9QeiAwR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content contained in the box with coordinates [0.478, 0.148, 0.956, 0.301]." }, { "from": "gpt", "value": "It contains the legends for the lines. The first item is the line labeled 'Interpersonal Relationships', represented by a green marker at {coordinates}[0.482, 0.175, 0.551, 0.198]. The label of 'Interpersonal Relationships' is found at {coordinates}[0.567, 0.166, 0.945, 0.213]. The second item is the line labeled 'Social Networks', represented by an orange marker at {coordinates}[0.482, 0.245, 0.551, 0.268]. The label of 'Social Networks' is found at {coordinates}[0.567, 0.236, 0.788, 0.283]." } ], "id_raw": 542 }, { "id": "DJqmTuRbWZ1OmSLN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.079, 0.050, 1.041, 0.110]?" }, { "from": "gpt", "value": "This area contains the chart\u2019s subtitle 'Trends in Interpersonal Relationships and Social Networks' appearing in black color with no rotation." } ], "id_raw": 542 }, { "id": "sBydzJrf2qQQUFNC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_2.png", "conversations": [ { "from": "human", "value": "\nWhat are the labels and colors of the elements located in the box [0.165, 0.838, 0.302, 0.885]" }, { "from": "gpt", "value": "There are two x-axis labels located in this region: '0.0' at {[0.165, 0.838, 0.209, 0.885]}, and '2.5' at {[0.259, 0.838, 0.302, 0.885]}. Both labels are rendered in black." } ], "id_raw": 542 }, { "id": "EZDDd7epqQqli9ay", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the object located at [0.015, 0.295, 0.029, 0.663] represent?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 543 }, { "id": "bqvzGNFXG70gJfME", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the tick} at [0.831, 0.903, 0.866, 0.931]?" }, { "from": "gpt", "value": "2018." } ], "id_raw": 543 }, { "id": "bgFzCv0tN7Xp9Dwa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing 'Company A'}[0.096, 0.191, 0.943, 0.847]?" }, { "from": "gpt", "value": "Green (#008000)." } ], "id_raw": 543 }, { "id": "Mtp0gWgxx5gIpPou", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_3.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Company B'}[0.096, 0.367, 0.943, 0.751] generally above or below {the line for 'Company A'}[0.096, 0.191, 0.943, 0.847]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 543 }, { "id": "IZCqUF8SYVHiHqgu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Company C'}[0.096, 0.111, 0.943, 0.431] experience an increase or decrease between 2010 and 2019?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 543 }, { "id": "jNzyYNKz6t3KGxPm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_3.png", "conversations": [ { "from": "human", "value": "\nWhich company has a higher Social Responsibility Index in 2017, Company A or Company B?" }, { "from": "gpt", "value": "Company A." } ], "id_raw": 543 }, { "id": "hYLf6RBI62UYHfIO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is located in the region [0.060, 0.089, 0.190, 0.223]?" }, { "from": "gpt", "value": "This area contains the legend for the lines. It includes the companies: {Company A}[0.105, 0.100, 0.185, 0.128] with green as its line color, {Company B}[0.105, 0.142, 0.185, 0.170] with red as its line color, and {Company C}[0.105, 0.183, 0.185, 0.211] with blue as its line color." } ], "id_raw": 543 }, { "id": "9UyhJUTpA4G2U71j", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_3.png", "conversations": [ { "from": "human", "value": "\nWhat's in the region [0.266, 0.903, 0.301, 0.931]?" }, { "from": "gpt", "value": "It contains the x-axis tick label '2012'." } ], "id_raw": 543 }, { "id": "HpupJGRadKH2M703", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is found within the coordinates [0.096, 0.191, 0.943, 0.847]." }, { "from": "gpt", "value": "This area represents the Social Responsibility Index trend line for {Company A}[0.096, 0.191, 0.943, 0.847], which shows the company's performance from 2010 to 2019." } ], "id_raw": 543 }, { "id": "xMne6VtUeffFoaHA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.015, 0.427, 0.029, 0.543]?" }, { "from": "gpt", "value": "Trend Value." } ], "id_raw": 544 }, { "id": "l0C7ev2lUPDqa1wF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the xlabel?" }, { "from": "gpt", "value": "[0.504, 0.959, 0.547, 0.979]" } ], "id_raw": 544 }, { "id": "xoaD8TOLVMksAafg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_4.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.846, 0.063, 0.978, 0.129] in the chart?" }, { "from": "gpt", "value": "Legend." } ], "id_raw": 544 }, { "id": "5ExWH4pmHSFisdxo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_4.png", "conversations": [ { "from": "human", "value": "\nIs the {point 'January'}[0.108, 0.701] for 'Yoga Trends' higher or lower in value than the {point 'January'}[0.108, 0.399] for 'Gym Trends'?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 544 }, { "id": "NLsxAfG9EYvKrWf5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_4.png", "conversations": [ { "from": "human", "value": "\nWhere is {the 'Yoga Trends'}[0.943, 0.830] located in relation to {the 'Gym Trends'}[0.943, 0.312] in December?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 544 }, { "id": "0oBw6llZmRrSClhr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_4.png", "conversations": [ { "from": "human", "value": "\nAre the Gym Trends higher or lower in value at {July}[0.640, 0.796] compared to the Yoga Trends?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 544 }, { "id": "NPv3RmtwJP8VWl8M", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.846, 0.063, 0.978, 0.129]." }, { "from": "gpt", "value": "This region contains the chart legend. The legend is made up of two entries: {Yoga Trends}[0.890, 0.071, 0.972, 0.091] marked in color #00bfff and {Gym Trends}[0.890, 0.101, 0.972, 0.121] marked in color #b22222." } ], "id_raw": 544 }, { "id": "D3MSpPum6in7M8f9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_4.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area [0.104, 0.931, 0.728, 0.951]?" }, { "from": "gpt", "value": "In this area, we have the X-axis tick labels. These tick labels indicate the months of the year from January to December." } ], "id_raw": 544 }, { "id": "rjnbEpHwLhq4Y4qe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_4.png", "conversations": [ { "from": "human", "value": "\nWhat information can be found within [0.035, 0.909, 0.205, 0.929]?" }, { "from": "gpt", "value": "Within this region, the first two Y-axis ticks are present. They correspond to the values of {0.0}[0.035, 0.909, 0.057, 0.929] and {0.5}[0.035, 0.801, 0.057, 0.821]." } ], "id_raw": 544 }, { "id": "eETZreaV9jnrngqw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.030, 0.325, 0.058, 0.605]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 545 }, { "id": "qSs9ktG4pSW8G8lu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.308, 0.838, 0.378, 0.885]?" }, { "from": "gpt", "value": "2012." } ], "id_raw": 545 }, { "id": "ZEZA0EDwxxBbVo2c", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} located at [0.087, 0.651, 0.139, 0.698]?" }, { "from": "gpt", "value": "200." } ], "id_raw": 545 }, { "id": "XQblierVl1MbyFms", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_5.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '2013'}[0.417, 0.458] in the 'Startups Registered' line above or below {the point labeled '2013'}[0.417, 0.760] in the 'Innovation Index' line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 545 }, { "id": "Xn7bZV05SC0SbD4Y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_5.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '2015'}[0.564, 0.397] in the 'Startups Registered' line higher or lower than {the point labeled '2014'}[0.491, 0.428] in the same line?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 545 }, { "id": "IW63fI4WLmrdW5Mm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_5.png", "conversations": [ { "from": "human", "value": "\nWhat has a higher value at {the point labeled '2020'}[0.933, 0.187], Startups Registered or Innovation Index?" }, { "from": "gpt", "value": "Startups Registered." } ], "id_raw": 545 }, { "id": "1HNzAyXcMrHAXRUC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.156, 0.110, 1, 0.806]." }, { "from": "gpt", "value": "This area contains the main graph showing two trend lines. {The 'Startups Registered'}[0.196, 0.187, 0.933, 0.571] line is in light blue, and {the 'Innovation Index'}[0.196, 0.748, 0.933, 0.766] line is in light grey. Both lines are marked with markers at each year from 2010 to 2020." } ], "id_raw": 545 }, { "id": "giN7SllvCIK2fUgc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.262, 0.166, 0.537, 0.213]." }, { "from": "gpt", "value": "This area houses {the title of the first line in the graph, 'Startups Registered'}[0.262, 0.166, 0.537, 0.213], placed next to a light blue symbol representing the line color in the chart." } ], "id_raw": 545 }, { "id": "pBHqRFZv5JMo3LU0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.087, 0.050, 1.041, 0.110]." }, { "from": "gpt", "value": "The region features the subtitle \"Entrepreneurship and Innovation Ecosystem (2010-2020)\" rendered in black color." } ], "id_raw": 545 }, { "id": "L21BbaDUY5juoj26", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_8.png", "conversations": [ { "from": "human", "value": "\nHow would you describe {the Y-axis label}[0.021, 0.377, 0.041, 0.581]?" }, { "from": "gpt", "value": "The Y-axis label is \"Adoption Stats\"." } ], "id_raw": 548 }, { "id": "X3YhsM1njmd7jYCx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the coordinates [0.175, 0.100, 0.354, 0.128]?" }, { "from": "gpt", "value": "It's the 'Traditional EdTech' text in the legend." } ], "id_raw": 548 }, { "id": "GQWzgjtimFUJ9HLP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_8.png", "conversations": [ { "from": "human", "value": "\nWhat does {the seventh x-axis label}[0.707, 0.903, 0.751, 0.931] represent?" }, { "from": "gpt", "value": "It represents the number '15.0'." } ], "id_raw": 548 }, { "id": "5qzgihfkevlsBbfZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_8.png", "conversations": [ { "from": "human", "value": "\nDuring which week do {Traditional EdTech}[0.270, 0.390, 0.450, 0.385] and {Innovative EdTech}[0.270, 0.460, 0.450, 0.455] have the same adoption stats?" }, { "from": "gpt", "value": "Week 1." } ], "id_raw": 548 }, { "id": "qULiSbz3DUMMd88n", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_8.png", "conversations": [ { "from": "human", "value": "\nIs the {Adoption Stat for Innovative EdTech}[0.771, 0.209] higher or lower than {Adoption Stat for Traditional EdTech}[0.771, 0.763] at week 16?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 548 }, { "id": "uKcJaTEioZEtSxen", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_8.png", "conversations": [ { "from": "human", "value": "\nHow does the {Innovative EdTech}[0.435, 0.425] compare with the {Traditional EdTech}[0.435, 0.683] at week 8?" }, { "from": "gpt", "value": "It is greater." } ], "id_raw": 548 }, { "id": "RVfSbiHeu1Tw54Z3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the area marked by the coordinates [0.111, 0.089, 0.362, 0.181] contain?" }, { "from": "gpt", "value": "The area contains the labels for 'Traditional EdTech' and 'Innovative EdTech', along with color markers for each in a sublegend section." } ], "id_raw": 548 }, { "id": "eTLLFyFo6IIHWpgc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area defined by [0.141, 0.614, 0.939, 0.803]?" }, { "from": "gpt", "value": "This area contains the 'Traditional EdTech' line plot with plotted points, showing the adoption trends over 20 weeks." } ], "id_raw": 548 }, { "id": "iZuPpGQSLwTLV05m", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the attributes observed in the region [0.348, 0.030, 0.732, 0.066]." }, { "from": "gpt", "value": "This region contains the subtitle for the chart, stating \"EdTech Adoption Over 20 Weeks\"." } ], "id_raw": 548 }, { "id": "4hstCuStdqWROKiZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_9.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object at coordinates [0.181, 0.142, 0.460, 0.170] in the chart?" }, { "from": "gpt", "value": "The label for \"Innovation Ecosystem Index\"." } ], "id_raw": 549 }, { "id": "Oqrv78JXnJIFZDr4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_9.png", "conversations": [ { "from": "human", "value": "\nCan you identify the {y-axis label}[0.021, 0.398, 0.041, 0.560]?" }, { "from": "gpt", "value": "Yes, it's \"Index Value\"." } ], "id_raw": 549 }, { "id": "iH1PMQw3oxJ3F0PN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the {x-axis tick label} at [0.650, 0.903, 0.700, 0.931]?" }, { "from": "gpt", "value": "2016." } ], "id_raw": 549 }, { "id": "SJroBXjDprvwJKI5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_9.png", "conversations": [ { "from": "human", "value": "\nFor the year 2018, is the {Global Entrepreneurship Index} point[0.851, 0.165] higher or lower in value than the {Innovation Ecosystem Index} point[0.851, 0.218]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 549 }, { "id": "uCh68N0upKP78fNM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_9.png", "conversations": [ { "from": "human", "value": "\nIs {the point for the year 2017 on the Global Entrepreneurship Index line}[0.763, 0.218] higher or lower than {the point for the year 2017 on the Innovation Ecosystem Index line}[0.763, 0.236]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 549 }, { "id": "BFHMVv7ArtKULgSj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the first Index to reach a value of 0.8, {Global Entrepreneurship Index} or {Innovation Ecosystem Index}?" }, { "from": "gpt", "value": "Global Entrepreneurship Index." } ], "id_raw": 549 }, { "id": "yete5oMD3y4AL6TG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_9.png", "conversations": [ { "from": "human", "value": "\nCould you please describe the area located at [0.117, 0.089, 0.491, 0.181]?" }, { "from": "gpt", "value": "This area contains the sublegend of the chart. There are two items: {Global Entrepreneurship Index} with coordinates [0.181, 0.100, 0.483, 0.128] and handle color blue, and {Innovation Ecosystem Index} with coordinates [0.181, 0.142, 0.460, 0.170] and handle color green." } ], "id_raw": 549 }, { "id": "rn31oMH8TECvoWh2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_9.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the region [0.474, 0.903, 0.524, 0.931]?" }, { "from": "gpt", "value": "The x-axis tick label {2014}[0.474, 0.903, 0.524, 0.931] is situated here." } ], "id_raw": 549 }, { "id": "pYyM8ob52xzzGAYH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_9.png", "conversations": [ { "from": "human", "value": "\nWhat contents can be found within [0.147, 0.129, 0.939, 0.758]?" }, { "from": "gpt", "value": "This area contains two line graphs. The {Global Entrepreneurship Index} line with coordinates [0.147, 0.129, 0.939, 0.650] and color blue, and the {Innovation Ecosystem Index} line with coordinates [0.147, 0.165, 0.939, 0.758] and color green." } ], "id_raw": 549 }, { "id": "yOl30Eux7XCIfCGQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the y-axis label in the chart figure?" }, { "from": "gpt", "value": "The y-axis label \"Scores\" is located at {coordinates}[0.030, 0.388, 0.058, 0.542]." } ], "id_raw": 550 }, { "id": "bg5vqecUeGq62l7I", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object found at [0.069, 0.702, 0.113, 0.749]?" }, { "from": "gpt", "value": "It is the y-axis tick labeled '0.6'." } ], "id_raw": 550 }, { "id": "VHfNAnPBaKA4NY7k", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the plot found at [0.167, 0.210, 0.842, 0.550]?" }, { "from": "gpt", "value": "It is the line plot for the 'Base Model'." } ], "id_raw": 550 }, { "id": "fHqTPwDk6vgQEfBo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_10.png", "conversations": [ { "from": "human", "value": "\nWhich of the two plots, {Base Model}[0.167, 0.210, 0.842, 0.550] or {Our Model}[0.167, 0.261, 0.842, 0.584], has a higher score on the 'Time per Unit' factor?" }, { "from": "gpt", "value": "Base Model." } ], "id_raw": 550 }, { "id": "p3pyMCQF1XxksFHT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_10.png", "conversations": [ { "from": "human", "value": "\nAt the 'Fraction Rejected' factor, are the scores of {Our Model}[0.167, 0.261] above or below {Base Model}[0.167, 0.210]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 550 }, { "id": "CFSr6jdVNFqrw9ME", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_10.png", "conversations": [ { "from": "human", "value": "\nFor the 'Scrap Rate' factor, is the score of {Our Model}[0.617, 0.584] greater than or less than the {Base Model}'s score[0.617, 0.550]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 550 }, { "id": "cvWNhnEisSUFbnkZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the region [0.112, 0.050, 0.896, 0.110]." }, { "from": "gpt", "value": "The subtitle of the graph, 'Manufacturing Production Efficiency Evaluation', is within this region." } ], "id_raw": 550 }, { "id": "s4ctg0CLsGouD7hj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the region [0.601, 0.148, 0.861, 0.301]." }, { "from": "gpt", "value": "It contains the legend for the two line plots, with labels for the 'Base Model' and 'Our Model', each associated with a color marker." } ], "id_raw": 550 }, { "id": "ZINho1M6a02AdzZv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_10.png", "conversations": [ { "from": "human", "value": "\nWhat can be found between the coordinates [0.454, 0.903, 0.554, 0.950]?" }, { "from": "gpt", "value": "The x-axis label \"Factors\" is located between these coordinates." } ], "id_raw": 550 }, { "id": "ejqeNMv3FJgi7Zdx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.095, 0.060, 0.902, 0.908]?" }, { "from": "gpt", "value": "Chart area." } ], "id_raw": 551 }, { "id": "9XqseXOIjhI12MgN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the year indicated by {x-axis tick label}[0.109, 0.919, 0.153, 0.942]?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 551 }, { "id": "dRGK7Qbe7AYvRsOX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_11.png", "conversations": [ { "from": "human", "value": "\nWhich category does the {line}[0.131, 0.100, 0.869, 0.865] belong to in the chart?" }, { "from": "gpt", "value": "Carbon Footprint." } ], "id_raw": 551 }, { "id": "S6ZiQZ66IeITR4sL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_11.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Corporate Giving'}[0.131, 0.865, 0.213, 0.769] generally increasing or decreasing over time in the chart?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 551 }, { "id": "fl8jTQhJfKberQKC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_11.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher at the year 2014, {Corporate Giving}[0.459, 0.530] or {Carbon Footprint}[0.459, 0.394]?" }, { "from": "gpt", "value": "Corporate Giving." } ], "id_raw": 551 }, { "id": "x3u2whMxmnkz2v5S", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_11.png", "conversations": [ { "from": "human", "value": "\nAt {year 2011}[0.213], is Corporate Giving or Carbon Footprint higher?" }, { "from": "gpt", "value": "Corporate Giving." } ], "id_raw": 551 }, { "id": "W2rvHtwwRkno3nRK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in the area with coordinates [0.258, 0.025, 0.742, 0.055]?" }, { "from": "gpt", "value": "It's the subtitle of the chart \"Social Responsibility and Sustainable Business\"." } ], "id_raw": 551 }, { "id": "BnOUBOFPgP8pgnAl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.094, 0.062, 0.906, 0.903]." }, { "from": "gpt", "value": "This is the main chart area. It contains two lines plotting: {Corporate Giving}[0.131, 0.865, 0.213, 0.769] and {Carbon Footprint}[0.131, 0.100, 0.213, 0.218]. Also, x-axis tick labels indicating years and y-axis tick labels indicating values." } ], "id_raw": 551 }, { "id": "2n6ta7yzVdd0t8hV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the area defined by [0.019, 0.275, 0.036, 0.690]." }, { "from": "gpt", "value": "This is where the y-axis label \"Corporate Giving / Carbon Footprint\" is located." } ], "id_raw": 551 }, { "id": "aLecux932oJdN856", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.030, 0.309, 0.058, 0.621] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 552 }, { "id": "K6u0kZ1GcQeoUtke", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first label in the legend?" }, { "from": "gpt", "value": "[0.763, 0.166, 0.945, 0.213]." } ], "id_raw": 552 }, { "id": "BBbvMVcuo5h832XV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.916, 0.838, 0.951, 0.885]?" }, { "from": "gpt", "value": "12." } ], "id_raw": 552 }, { "id": "fkW4l6QJyIDVNvjJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Our Product'}[0.866, 0.707] above or below {the point of 'Base Product'}[0.866, 0.654] in terms of sales unit at the 11th month?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 552 }, { "id": "dZX3z4CD2ahbx3Ra", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_12.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Our Product'}[[0.196, 0.200, 0.933, 0.745]] generally above or below {the line for 'Base Product'}[[0.196, 0.200, 0.933, 0.692]]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 552 }, { "id": "27LNKhjEycEPOKGT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_12.png", "conversations": [ { "from": "human", "value": "\nWhich product has a higher sales unit at the {end of the 12th month}[0.933, 0.692, 0.933, 0.745], Our product or Base product?" }, { "from": "gpt", "value": "Our product." } ], "id_raw": 552 }, { "id": "qATMnFNKNsZeMx5u", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.295, 0.050, 0.834, 0.110]." }, { "from": "gpt", "value": "The subtitle of the chart, \"Product Sales Trend Observation,\" is located here, styled in black color." } ], "id_raw": 552 }, { "id": "0axKETff30Lj8ZQv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.674, 0.148, 0.956, 0.301]." }, { "from": "gpt", "value": "This area contains the legend items: {Base Product}[0.763, 0.166, 0.945, 0.213] and {Our Product}[0.763, 0.236, 0.928, 0.283], each paired with a colored marker." } ], "id_raw": 552 }, { "id": "isMGGg7DtLbn3XuT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.781, 0.838, 0.817, 0.885]?" }, { "from": "gpt", "value": "There is 1 object. The x-axis tick label '10' is located at {coordinates}[0.781, 0.838, 0.817, 0.885]." } ], "id_raw": 552 }, { "id": "Y4uQNe3CfnZNiVrB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the {xlabel}[0.509, 0.942, 0.555, 0.970]?" }, { "from": "gpt", "value": "Years." } ], "id_raw": 553 }, { "id": "xt1xiYQhPzvu49uF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_13.png", "conversations": [ { "from": "human", "value": "\nCan you identify the graph title from the given coordinates [0.328, 0.030, 0.736, 0.066]?" }, { "from": "gpt", "value": "Yes, it's \"Culture and Arts Industry Development\"." } ], "id_raw": 553 }, { "id": "x2uBTOhksTZceGd0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.748, 0.147, 0.791, 0.161] in the legend?" }, { "from": "gpt", "value": "It's the color marker for 'Trendy Support'." } ], "id_raw": 553 }, { "id": "us3gnCCkK5Z6IpGM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_13.png", "conversations": [ { "from": "human", "value": "\nAt the year 2012 marked at coordinate [0.305, 0.883], which line represents a higher Level of Support - {Traditional Support}[0.305, 0.645] or {Trendy Support}[0.305, 0.628]?" }, { "from": "gpt", "value": "Traditional Support." } ], "id_raw": 553 }, { "id": "FP4BQGdvcrHJ72c6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_13.png", "conversations": [ { "from": "human", "value": "\nWhich shows a greater increase in support from 2010 at coordinate [0.124, 0.883] to 2011 at coordinate [0.214, 0.883], {Tradition Support}[0.124, 0.585, 0.214, 0.620] or {Trendy Support}[0.124, 0.713, 0.214, 0.688]?" }, { "from": "gpt", "value": "Trendy Support." } ], "id_raw": 553 }, { "id": "FHVQfgiZfjEMqnLY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_13.png", "conversations": [ { "from": "human", "value": "\nDoes the {Traditional Support} line[0.124, 0.543, 0.940, 0.679] rise or fall overall from 2010 to 2019?" }, { "from": "gpt", "value": "Rise." } ], "id_raw": 553 }, { "id": "ePGpVnT6RxdMIwtI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the [0.745, 0.089, 0.973, 0.181] region." }, { "from": "gpt", "value": "It contains the legend of the chart. It includes the labels for 'Traditional Support' {marker}[0.748, 0.105, 0.791, 0.119] and 'Trendy Support' {marker}[0.748, 0.147, 0.791, 0.161], each with color markers." } ], "id_raw": 553 }, { "id": "ASZblF5SK5YqtWMa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_13.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.396, 0.679, 0.487, 0.628]?" }, { "from": "gpt", "value": "It is a part of the {Traditional Support} line[0.396, 0.679 into 0.487, 0.628], indicating the decline in support between 2013 and 2014." } ], "id_raw": 553 }, { "id": "PxmqEipSZR7r6U8G", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.759, 0.245, 0.850, 0.202]" }, { "from": "gpt", "value": "This area contains a segment of the {Trendy Support} line[0.759, 0.245, 0.850, 0.202], representing the increased support from 2017 to 2018." } ], "id_raw": 553 }, { "id": "BhQo3jeW8ZTLX8pt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_15.png", "conversations": [ { "from": "human", "value": "\nWhat element lies within these coordinates: [0.108, 0.200, 0.943, 0.658]?" }, { "from": "gpt", "value": "The line representing Global Average Temperature." } ], "id_raw": 555 }, { "id": "SPMSirBFLumyjxzC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the text within the box with these coordinates: [0.774, 0.859, 0.890, 0.882]?" }, { "from": "gpt", "value": "Global Sea Level." } ], "id_raw": 555 }, { "id": "FLVDgHvb7ajje7Cl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_15.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis label, located at [0.507, 0.952, 0.544, 0.975], reads as?" }, { "from": "gpt", "value": "Years." } ], "id_raw": 555 }, { "id": "e5aY40ksUkUtALfq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_15.png", "conversations": [ { "from": "human", "value": "\nBetween {the point in 'Global Average Temperature'} at [0.630, 0.353] and {the point in 'Global Sea Level'} at [0.630, 0.773], which one is higher?" }, { "from": "gpt", "value": "Global Sea Level." } ], "id_raw": 555 }, { "id": "f7mnJKPAaUNwRQGa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_15.png", "conversations": [ { "from": "human", "value": "\nIs {the point in 'Global Average Temperature'} at [0.734, 0.276] higher or lower than {the point in 'Global Sea Level'} at [0.734, 0.758]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 555 }, { "id": "cNSWc44UFzdQTYOx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_15.png", "conversations": [ { "from": "human", "value": "\nWhich line shows a higher value at the year 2000 - {Global Average Temperature}[0.526, 0.452] or {Global Sea Level}[0.526, 0.788]?" }, { "from": "gpt", "value": "Global Sea Level." } ], "id_raw": 555 }, { "id": "bTjYSI0F9cxiM7HA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_15.png", "conversations": [ { "from": "human", "value": "\nCould you provide information on what is present within the region [0.066, 0.200, 0.985, 0.658]?" }, { "from": "gpt", "value": "This region contains two lines. One is representing the Global Average Temperature with points ranging from 1980 to 2020. The other one illustrates the Global Sea Level in the same year range." } ], "id_raw": 555 }, { "id": "5dA1VfUvIbSDmUIT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe what the area [0.729, 0.815, 0.978, 0.891] includes." }, { "from": "gpt", "value": "The area incorporates the legend for the chart, including 'Global Average Temperature' with its corresponding color marker, and 'Global Sea Level' with its color marker." } ], "id_raw": 555 }, { "id": "YsFe4uy0k84550hx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within [0.066, 0.062, 0.985, 0.903]." }, { "from": "gpt", "value": "This region captures the full graph plot, including the x and y-axes, their labels, the coordinates, and the plot area itself. The region also covers the lines for 'Global Average Temperature' and 'Global Sea Level', as well as their respective cordinates." } ], "id_raw": 555 }, { "id": "We5J01oSm8QZe3cM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the top left corner coordinates [0.373, 0.025, 0.674, 0.055]?" }, { "from": "gpt", "value": "Global Financial System and Policies." } ], "id_raw": 558 }, { "id": "ecNuQpn8JH2kDQ39", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the coordinate {line}[0.104, 0.124, 0.943, 0.499]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 558 }, { "id": "0YGj43Re0Z2s3r3N", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of {the legend item}[0.113, 0.083, 0.192, 0.106]?" }, { "from": "gpt", "value": "Global GDP." } ], "id_raw": 558 }, { "id": "a1RzqWVm49IKLbDg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_18.png", "conversations": [ { "from": "human", "value": "\nWhich crosses the Y-axis first: {Global GDP}[0.104, 0.124, 0.943, 0.499] or {Global Inflation Rate}[0.104, 0.804, 0.943, 0.867]?" }, { "from": "gpt", "value": "Global GDP." } ], "id_raw": 558 }, { "id": "34UXQWrkTyevvVt3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_18.png", "conversations": [ { "from": "human", "value": "\nWhich value is greater in the year 2006: {Global GDP}[0.104, 0.124, 0.943, 0.499] or {Global Inflation Rate}[0.104, 0.804, 0.943, 0.867]?" }, { "from": "gpt", "value": "Global GDP." } ], "id_raw": 558 }, { "id": "0OTuuZa0lx0DMd5b", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_18.png", "conversations": [ { "from": "human", "value": "\nDoes the {Inflation Rate}[0.104, 0.804, 0.943, 0.867] rise or fall from 2002 to 2003?" }, { "from": "gpt", "value": "Rise." } ], "id_raw": 558 }, { "id": "VOccb3L4sJwOa9pR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_19.png", "conversations": [ { "from": "human", "value": "\nWhat kind of cuisine does the corresponding object at coordinates [0.162, 0.100, 0.302, 0.128] represent?" }, { "from": "gpt", "value": "Italian Cuisine." } ], "id_raw": 559 }, { "id": "ARLTn7JJmnJA0BC8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_19.png", "conversations": [ { "from": "human", "value": "\nWhat does {the ylabel}[0.021, 0.367, 0.041, 0.591] tell us about the chart?" }, { "from": "gpt", "value": "It represents the Popularity Scale." } ], "id_raw": 559 }, { "id": "mThcrLNwtTOOmCd5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_28_19.png", "conversations": [ { "from": "human", "value": "\nWhat trend does the blue line represent?" }, { "from": "gpt", "value": "It represents the trend of Italian Cuisine." } ], "id_raw": 559 }, { "id": "TEc4216KuFGx00Ja", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_19.png", "conversations": [ { "from": "human", "value": "\nIs {the point of Italian Cuisine at 2005}[0.635, 0.571] higher or lower than {the point of Greek Cuisine at the same year}[0.635, 0.329]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 559 }, { "id": "hy3RUcV9wKVszwC2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_19.png", "conversations": [ { "from": "human", "value": "\nHas {Italian cuisine}[0.162, 0.100, 0.302, 0.128] grown more popular than {Greek cuisine}[0.162, 0.142, 0.300, 0.170] over the years?" }, { "from": "gpt", "value": "No." } ], "id_raw": 559 }, { "id": "KLwM7XxbX3l9BJJW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_28_19.png", "conversations": [ { "from": "human", "value": "\nWhich cuisine has a higher popularity scale at {year 2020}[0.938, 0.903, 0.963, 0.931], Italian or Greek?" }, { "from": "gpt", "value": "Greek." } ], "id_raw": 559 }, { "id": "5IPy43ZOZUVBcIAq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_19.png", "conversations": [ { "from": "human", "value": "\nCould you describe the region [0.104, 0.903, 0.154, 0.931]?" }, { "from": "gpt", "value": "The region contains the x-axis tick label '1980'." } ], "id_raw": 559 }, { "id": "IPrz8dzTasymZvHS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.205, 0.903, 0.255, 0.931]." }, { "from": "gpt", "value": "It includes the x-axis tick label for the year '1985'." } ], "id_raw": 559 }, { "id": "MIQmnUEBy8W4vo2J", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_28_19.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the area [0.711, 0.903, 0.761, 0.931]?" }, { "from": "gpt", "value": "This region includes an x-axis tick label for the year '2010'." } ], "id_raw": 559 }, { "id": "ioBAuekvlNOBk4mF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the colored rectangle located at {coordinates}[0.090, 0.056, 0.139, 0.078] in the subplot legend?" }, { "from": "gpt", "value": "It is the colored marker for the \"Western Cuisine Trend\" line in the chart." } ], "id_raw": 565 }, { "id": "zJ2hMxfmY8huVyjR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_5.png", "conversations": [ { "from": "human", "value": "\nWhat's the object at coordinates {coordinates}[0.506, 0.934, 0.557, 0.970] of the figure?" }, { "from": "gpt", "value": "It's the label for the x-axis which is 'Time'." } ], "id_raw": 565 }, { "id": "meDMTgg3H5lFB1Ve", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_5.png", "conversations": [ { "from": "human", "value": "\nWhat line does this {point with coordinates}[0.850, 0.135] belong to?" }, { "from": "gpt", "value": "The point belongs to the 'Asian Cuisine Trend' line." } ], "id_raw": 565 }, { "id": "G6TCH9tSQ35ahwuM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_5.png", "conversations": [ { "from": "human", "value": "\nAt {time point 2}[0.214, 0.681], is the 'Western Cuisine Trend' value above or below 'Asian Cuisine Trend' value at {points}[0.214, 0.383]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 565 }, { "id": "fluApstgk7ntlHTx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_5.png", "conversations": [ { "from": "human", "value": "\nDoes the 'Western Cuisine Trend' at {point}[0.850, 0.251] have a higher or lower value than the 'Asian Cuisine Trend' at {point}[0.850, 0.135]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 565 }, { "id": "HH1IDFU2wsaXsz0k", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_5.png", "conversations": [ { "from": "human", "value": "\nWhich line segment does this {point}[0.759, 0.317] belong to, 'Western Cuisine Trend' or 'Asian Cuisine Trend'?" }, { "from": "gpt", "value": "'Western Cuisine Trend'." } ], "id_raw": 565 }, { "id": "WeebJ9mscr8BKge6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_5.png", "conversations": [ { "from": "human", "value": "\nDescribe what the coordinates [0.123, 0.069, 0.940, 0.433] contain?" }, { "from": "gpt", "value": "The area contains the 'Asian Cuisine Trend' line with square markers. The line is colored blue and follows a dashed pattern." } ], "id_raw": 565 }, { "id": "tsSvDQQ7qbD2Qt7t", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_5.png", "conversations": [ { "from": "human", "value": "\nWhat elements are present in the box defined by [0.090, 0.044, 0.346, 0.136]?" }, { "from": "gpt", "value": "This represents the legend box. Inside it, we have two labels and their corresponding color markers. The labels are 'Western Cuisine Trend' at {coordinates}[0.146, 0.055, 0.339, 0.083] and 'Asian Cuisine Trend' at {coordinates}[0.146, 0.097, 0.315, 0.125]. The color markers for each label are found at {coordinates} [0.090, 0.056, 0.139, 0.078] for 'Western Cuisine Trend' and at {coordinates}[0.090, 0.098, 0.139, 0.120] for 'Asian Cuisine Trend'." } ], "id_raw": 565 }, { "id": "EKRLcml9s1redfV4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are encompassed within the area defined by coordinates [0.123, 0.185, 0.940, 0.846]?" }, { "from": "gpt", "value": "The area includes all the individual plot points and the entire line for the 'Western Cuisine Trend', which is drawn with a solid pattern and is decorated with circle-shaped markers." } ], "id_raw": 565 }, { "id": "URmPvTpthTOWpdvh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend {subplot_0's sublegend item with color '#0392fb'}[0.750, 0.098, 0.798, 0.120] represent?" }, { "from": "gpt", "value": "Midstream." } ], "id_raw": 570 }, { "id": "t38w8MA7QLnS8iQZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis?" }, { "from": "gpt", "value": "Phase Value." } ], "id_raw": 570 }, { "id": "jvewCffZYshZxZQQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the x-axis?" }, { "from": "gpt", "value": "Value Chain Phase." } ], "id_raw": 570 }, { "id": "zNvPfrY8MC7shjcu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_10.png", "conversations": [ { "from": "human", "value": "\nIs the point on the \"Upstream\" line at {xlabel 'Significant decrease'}[0.547, 0.895, 0.723, 0.923] above or below the threshold line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 570 }, { "id": "1GLSscpj6iHTlzp7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_10.png", "conversations": [ { "from": "human", "value": "\nWhich line has a greater value at {xlabel 'Sharp rise'}[0.334, 0.895, 0.422, 0.923], Upstream or Midstream?" }, { "from": "gpt", "value": "Midstream." } ], "id_raw": 570 }, { "id": "jqv7ActeuvHEdAEi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_10.png", "conversations": [ { "from": "human", "value": "\nAt which stage does the line \"Downstream\" cross the threshold line?" }, { "from": "gpt", "value": "Abrupt increase." } ], "id_raw": 570 }, { "id": "hHDAHtWBEvNV5Pce", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the items in the legend located at [0.750, 0.044, 0.922, 0.220]." }, { "from": "gpt", "value": "The legend contains four items: {Upstream}[0.805, 0.055, 0.890, 0.083] with a handle color of '#ff8b26', {Midstream}[0.805, 0.097, 0.897, 0.125] with a handle color of '#0392fb', {Downstream}[0.805, 0.139, 0.915, 0.167] with a handle color of '#a3a101', and {Threshold}[0.805, 0.181, 0.891, 0.209] with a handle color of '#ff0000'." } ], "id_raw": 570 }, { "id": "Y0En0IugHRY1vcTf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the y-axis labels located at [0.048, 0.058, 0.076, 0.086]." }, { "from": "gpt", "value": "The label here is '1.2', indicating a 1.2 score of Phase Value at this point on the y-axis. It is represented in black color." } ], "id_raw": 570 }, { "id": "1V8rvQIjs0unN4xR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.082, 0.030, 0.931, 0.885]." }, { "from": "gpt", "value": "This region represents the primary area of the chart, showcasing line charts of \"Upstream,\" \"Midstream,\" \"Downstream,\" and the 'Threshold'. Included here are x-axis and y-axis with their respective labels and tick marks showing different stages of the Value Chain Phase and their corresponding Phase Values." } ], "id_raw": 570 }, { "id": "A1iOdRsYURnvkDok", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_14.png", "conversations": [ { "from": "human", "value": "\nWhat is being represented by the object with coordinates [0.838, 0.021, 0.988, 0.044] in the chart?" }, { "from": "gpt", "value": "It's the text label for Average Temperature in the legend." } ], "id_raw": 574 }, { "id": "HecmajtT9tVoWqAl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_14.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.793, 0.057, 0.832, 0.075] refer to in this chart?" }, { "from": "gpt", "value": "It refers to the handle for 'Average Rainfall' in the legend." } ], "id_raw": 574 }, { "id": "s3W6bIDZWS35EuCB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the {ylabel}[0.015, 0.281, 0.033, 0.642] in the plot?" }, { "from": "gpt", "value": "It is the label of the Y-axis, representing 'Average Temperature (\u00b0C)'." } ], "id_raw": 574 }, { "id": "OeVwJelxMZ94E1BS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_14.png", "conversations": [ { "from": "human", "value": "\nIs the point at {coordinates}[0.662, 0.277] for 'Average Temperature' higher or lower than the point at {coordinates}[0.502, 0.699]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 574 }, { "id": "YleCnwkYqztgnU5o", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_14.png", "conversations": [ { "from": "human", "value": "\nComparing {the 'Average Rainfall' point}[0.662, 0.512] and {the 'Average Temperature' point}[0.878, 0.067], which one is higher on the chart?" }, { "from": "gpt", "value": "The 'Average Rainfall' point is higher." } ], "id_raw": 574 }, { "id": "cvaPCUcnlGWFZ2M5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_14.png", "conversations": [ { "from": "human", "value": "\nIs the 'Average Rainfall' at {point}[0.878, 0.188] higher or lower than the 'Average Temperature' at {point}[0.105, 0.857]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 574 }, { "id": "LiNTvJMYiyxzqG0P", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area with the coordinates [0.793, 0.012, 0.993, 0.088]?" }, { "from": "gpt", "value": "It is the area that contains the chart's legend. There are two entries: 'Average Temperature' with a color marker '#ff8b26' at [0.793, 0.022, 0.832, 0.040], and 'Average Rainfall' with color marker '#0392fb' at [0.793, 0.057, 0.832, 0.075]." } ], "id_raw": 574 }, { "id": "GawptYp9yDKr2qyU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_14.png", "conversations": [ { "from": "human", "value": "\nCan you tell me about the area defined by the coordinates [0.066, 0.027, 0.916, 0.896]?" }, { "from": "gpt", "value": "This is the area where the chart is plotted. It contains all the lines showing average temperature and rainfall, labels, markers, and tick labels for the X and Y axes." } ], "id_raw": 574 }, { "id": "LfMMHJ10vpt8cKqU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_14.png", "conversations": [ { "from": "human", "value": "\nWhat's in the area covered by the coordinates [0.966, 0.572, 1.0, 0.917]?" }, { "from": "gpt", "value": "This area contains the Y-axis label for the second subplot 'Average Rainfall (mm)'." } ], "id_raw": 574 }, { "id": "JKiKoH36IBAj0oyO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the label corresponding to the coordinates [0.505, 0.945, 0.541, 0.975]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 575 }, { "id": "oqbaLVLD5qcPS0v5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of Industry 2's line in the chart?" }, { "from": "gpt", "value": "Blue, with the RGB value #0000ff." } ], "id_raw": 575 }, { "id": "xdaqd00hN7zFN78e", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the coordinates [0.015, 0.331, 0.033, 0.598]?" }, { "from": "gpt", "value": "The y-axis label indicating 'Market Share 1 (%)'." } ], "id_raw": 575 }, { "id": "RUIU88jh8UTXP02T", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_15.png", "conversations": [ { "from": "human", "value": "\nWhich industry's market share was higher in 2013, Industry 1 or Industry 2?" }, { "from": "gpt", "value": "Industry 2." } ], "id_raw": 575 }, { "id": "4EtBC0izcrh0ztZG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_15.png", "conversations": [ { "from": "human", "value": "\nDid Industry 1 have a higher market share in 2020 than in 2012?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 575 }, { "id": "m8cJeQLRE7rjZ7EH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_15.png", "conversations": [ { "from": "human", "value": "\nIn 2019, were {Industry 1's market share}[0.838, 0.447] greater or lesser than {Industry 2's market share}[0.838, 0.456]?" }, { "from": "gpt", "value": "Lesser." } ], "id_raw": 575 }, { "id": "8UzdBObwEzfYW8X4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is happening in the area [0.523, 0.712, 0.733, 0.557]?" }, { "from": "gpt", "value": "This region contains points representing the market share of Industry 1 from the year 2016 to 2018. The market share is increasing over time in this period." } ], "id_raw": 575 }, { "id": "o4b7gzEhnf9K0aFC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_15.png", "conversations": [ { "from": "human", "value": "\nWhat content is found within [0.208, 0.103, 0.418, 0.173]?" }, { "from": "gpt", "value": "This area captures the market share of Industry 2 between 2013 and 2015. A slight decline in market share is observable over this period." } ], "id_raw": 575 }, { "id": "6fE56Z7ytMiiGXBb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content inside [0.858, 0.037, 0.978, 0.113]." }, { "from": "gpt", "value": "Inside this box is the chart subplot's legend with two entries \u2013 Industry 1 and Industry 2, each with their associated colors." } ], "id_raw": 575 }, { "id": "bomiHehElbtMiqaS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis} at [0.019, 0.287, 0.041, 0.628] in our chart?" }, { "from": "gpt", "value": "Global Trade Volume." } ], "id_raw": 576 }, { "id": "EBnsBPUCfjzWJgJh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_16.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the location of {the xlabel} 'Month'?" }, { "from": "gpt", "value": "[0.467, 0.934, 0.532, 0.970]." } ], "id_raw": 576 }, { "id": "4wfrifjs9q9001Zl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of this object on the x-axis: {xlabel}[0.727, 0.895, 0.749, 0.923]?" }, { "from": "gpt", "value": "10." } ], "id_raw": 576 }, { "id": "rz3XuYG2QS4O374y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Global Trade Volume'} at [0.193, 0.743] higher or lower than {the point for 'Supply Chain Risk'} at [0.193, 0.458]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 576 }, { "id": "5OTOtc5R0QvCS2aY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_16.png", "conversations": [ { "from": "human", "value": "\nCompare the point of {the 'Global Trade Volume'} at [0.329, 0.743] with {the 'Supply Chain Risk'} at [0.329, 0.069], which is higher?" }, { "from": "gpt", "value": "Global Trade Volume." } ], "id_raw": 576 }, { "id": "0sEFY3oExwWPcHEy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Supply Chain Risk'} at [0.670, 0.652] located above or below {the point for 'Global Trade Volume'} at [0.670, 0.587]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 576 }, { "id": "Z1qRE62RrPeXmJ2a", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents within [0.662, 0.044, 0.903, 0.136]?" }, { "from": "gpt", "value": "The area encompasses the legends of the two line graphs. The first one, {Global Trade Volume}[0.718, 0.055, 0.897, 0.083], is paired with an orange marker. The second, {Supply Chain Risk}[0.718, 0.097, 0.874, 0.125], is associated with a blue marker." } ], "id_raw": 576 }, { "id": "iPQOga3AMSayn58y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.087, 0.030, 0.912, 0.885] contain?" }, { "from": "gpt", "value": "This region contains the entire chart plot. It includes all points, lines, axes, labels, marks, and grid lines of the two line charts, 'Global Trade Volume' and 'Supply Chain Risk'." } ], "id_raw": 576 }, { "id": "SclAasqGn8Epm2fD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.924, 0.252, 0.952, 0.280]?" }, { "from": "gpt", "value": "It contains the y-axis label '5.0' for the 'Supply Chain Risk' chart." } ], "id_raw": 576 }, { "id": "RIyQHQwAi6M1sOHB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis for the plot?" }, { "from": "gpt", "value": "Number of Startups - Sector A." } ], "id_raw": 577 }, { "id": "OTHPPzjcH66V4Z19", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_17.png", "conversations": [ { "from": "human", "value": "\nWhere is {the x-tick for '2016'} located?" }, { "from": "gpt", "value": "[0.264, 0.895, 0.308, 0.923]." } ], "id_raw": 577 }, { "id": "V94uFLlr9lplXnsN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_17.png", "conversations": [ { "from": "human", "value": "\nWhat is found at these coordinates [0.048, 0.991, 0.076, 1.019]?" }, { "from": "gpt", "value": "A y-axis tick label '1.5'." } ], "id_raw": 577 }, { "id": "xdgEud3GKOT253BW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_17.png", "conversations": [ { "from": "human", "value": "\nIs {the Startups - Sector B data point for the year 2020}[0.940, 0.069] higher or lower than {the corresponding Startups - Sector A data point}[0.940, 0.406]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 577 }, { "id": "tMhDk7X76Y5UP46b", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_17.png", "conversations": [ { "from": "human", "value": "\nAt the year 2017, is the value of {Startups - Sector A}[0.450, 0.509] greater or less than the {Startups - Sector B}[0.450, 0.587]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 577 }, { "id": "Wlw3Pd9mbu4CzLsz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_17.png", "conversations": [ { "from": "human", "value": "\nLooking at the year 2019, which startup sector had a higher number, {Startups - Sector A}[0.777, 0.121] or {Startups - Sector B}[0.777, 0.328]?" }, { "from": "gpt", "value": "Startups - Sector B." } ], "id_raw": 577 }, { "id": "1CRffuv4DUwKdNdk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_17.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the area [0.048, 0.991, 0.076, 1.019]?" }, { "from": "gpt", "value": "A y-axis tick label '1.5' is displayed in this area." } ], "id_raw": 577 }, { "id": "2Te1Rl7sZYHqWbuO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area within [0.286, 0.769, 0.940, 0.069]?" }, { "from": "gpt", "value": "The area contains the data line for \"Startups - Sector B\". Markers for the line are represented by squares. The line is dashed and colored \"#0392fb\"." } ], "id_raw": 577 }, { "id": "RfK9BKRcPnV292wr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_17.png", "conversations": [ { "from": "human", "value": "\nDescribe what is located within these coordinates [0.286, 0.380, 0.940, 0.406]." }, { "from": "gpt", "value": "The data line for \"Startups - Sector A\" is located in this area. The data points are represented by circles. The line is connected with a solid line and colored \"#ff8b26\"." } ], "id_raw": 577 }, { "id": "74kyzfH0kxEPg53F", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_19.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at the coordinates [0.019, 0.326, 0.041, 0.580]?" }, { "from": "gpt", "value": "Y-axis label for the subplot depicting \"Startup Growth\"." } ], "id_raw": 579 }, { "id": "vUgsZUskcpjfmL4e", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the innovation index point}[0.375, 0.666] in the line graph?" }, { "from": "gpt", "value": "0.5" } ], "id_raw": 579 }, { "id": "jh5oWh11vwRT51Pp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_29_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line for \"VC Funding\"}[0.02ad24]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 579 }, { "id": "qiiEcX4K6yE1yaL9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_19.png", "conversations": [ { "from": "human", "value": "\nAt the year 2019, which index is higher, {Innovation Index}[0.625, 0.410] or {VC Funding}[0.625, 0.282]?" }, { "from": "gpt", "value": "VC Funding." } ], "id_raw": 579 }, { "id": "cHKYjcGafRZQi4AI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_19.png", "conversations": [ { "from": "human", "value": "\nIn 2020, did {Startup Growth}[0.750, 0.410] increase or decrease compared to the previous year?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 579 }, { "id": "BQLem4sN7OnHvkkr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_29_19.png", "conversations": [ { "from": "human", "value": "\nComparing 2017 and 2021, did {the VC Funding}[0.875, 0.325] increase, decrease, or stay the same?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 579 }, { "id": "iBAiSegMUyo5dvJ3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_19.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the regional coordinates [0.097, 0.044, 0.304, 0.178]?" }, { "from": "gpt", "value": "This area contains the figure's legend, which includes the labels: {Startup Growth}[0.152, 0.055, 0.285, 0.083], {Innovation Index}[0.152, 0.097, 0.297, 0.125], and {VC Funding}[0.152, 0.139, 0.251, 0.167], each marked with a different color." } ], "id_raw": 579 }, { "id": "AMvrg15LapXysub1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the region with coordinates [0.088, 0.030, 0.912, 0.875]." }, { "from": "gpt", "value": "This area contains two subplot line graphs showcasing \"Startup Growth\" and \"Innovation Index\" versus \"VC Funding\". The graphs contain x-axis labels for the years 2015 through 2021 and y-axis labels for relevant index values. Both graphs include line markers for respective indices." } ], "id_raw": 579 }, { "id": "7vetHtejgK90MeXQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_29_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the area [0.125, 0.068, 0.875, 0.453]?" }, { "from": "gpt", "value": "This area contains a line graph of VC Funding from the years 2015 through 2021. The graph includes data points for each year, with markers denoting the funding amounts." } ], "id_raw": 579 }, { "id": "oV9J5XWz2vnqQYj0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_1.png", "conversations": [ { "from": "human", "value": "\nWhat is being displayed at the coordinate [0.041, 0.431, 0.059, 0.569]?" }, { "from": "gpt", "value": "The Y-axis label 'Return Rate' is displayed." } ], "id_raw": 581 }, { "id": "AG6sj8vcRuLyBugA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_1.png", "conversations": [ { "from": "human", "value": "\nFind the coordinate of the subtitle of the first subplot." }, { "from": "gpt", "value": "[0.566, 0.011, 0.566, 0.011]" } ], "id_raw": 581 }, { "id": "RZXE5B7Spo72VE1v", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the coordinates [0.244, 0.896, 0.299, 0.920] in subplot 1?" }, { "from": "gpt", "value": "The label is '20000'." } ], "id_raw": 581 }, { "id": "03HisuKQ3D77r44q", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point at '50000' for Fixed Rateturn}[0.524, 0.217] above or below {the point at '50000' for Random Rate}[0.524, 0.217]?" }, { "from": "gpt", "value": "They are at the same level." } ], "id_raw": 581 }, { "id": "l8CcBsEwMpLz6LkE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_1.png", "conversations": [ { "from": "human", "value": "\nDoes the {Fixed Rateturn}[0.188, 0.042, 0.943, 0.392] start lower than the {Random Rate}[0.188, 0.217, 0.943, 0.392]?" }, { "from": "gpt", "value": "No, they start at the same level." } ], "id_raw": 581 }, { "id": "2iUuoKToBReyJEyj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_1.png", "conversations": [ { "from": "human", "value": "\nAt which point does {Random Rate}[0.188, 0.217, 0.943, 0.392] cross {Fixed Rateturn}[0.188, 0.042, 0.943, 0.392]?" }, { "from": "gpt", "value": "At point '50000'." } ], "id_raw": 581 }, { "id": "ztm1Uy5vqb5EiMS7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area within the coordinates [0.150, 0.495, 0.981, 0.880]." }, { "from": "gpt", "value": "This area contains Subplot 1. It contains multiple 'X' and 'Y' axis ticks and labels. The 'Y' axis label says 'Predictive Analysis Return'. There is a line chart inside which represents 'Predictive' analysis data points." } ], "id_raw": 581 }, { "id": "53pBfqccmGQ8mVt3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_1.png", "conversations": [ { "from": "human", "value": "\nWhat contents are within the region [0.150, 0.025, 0.981, 0.410]?" }, { "from": "gpt", "value": "This area includes Subplot 0, which carries the label of 'Fixed vs Random Return' on the Y-axis. It contains two line charts, one for 'Fixed Rateturn' and the other for 'Random Rate', with markers at data points '10000', '20000', '50000', and '100000'." } ], "id_raw": 581 }, { "id": "wBMeCyAxAaBE2MJ9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.453, 0.430, 0.678, 0.472] encompass?" }, { "from": "gpt", "value": "This region encompasses the legend for Subplot 1, containing one item labeled 'Predictive Analysis'." } ], "id_raw": 581 }, { "id": "4JnRoHSusrYUkf0z", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_2.png", "conversations": [ { "from": "human", "value": "\nOn the chart, what does the label at position [0.086, 0.213, 0.100, 0.322] represent?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 582 }, { "id": "1Q8HC5fZQDvdQlGk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the 7th x-axis tick label on the chart?" }, { "from": "gpt", "value": "[0.776, 0.451]." } ], "id_raw": 582 }, { "id": "i1EZToTcnt9pBOZ1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_2.png", "conversations": [ { "from": "human", "value": "\nWhat is identified by {the legend at}[0.429, 0.031, 0.488, 0.054]?" }, { "from": "gpt", "value": "Baseline." } ], "id_raw": 582 }, { "id": "cKL5ytVT5l1cPS3x", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_2.png", "conversations": [ { "from": "human", "value": "\nLooking at the trend lines for Baseline and SoftMoE, which trend line sits higher at {the 16 user point}[0.555, 0.610]?" }, { "from": "gpt", "value": "Baseline." } ], "id_raw": 582 }, { "id": "fH6WSLqICpUVYXsD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the Baseline point at}[0.947, 0.833] sit above or below {the Top1-MoE point at}[0.947, 0.610]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 582 }, { "id": "qchGObXWWbbXBthj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_2.png", "conversations": [ { "from": "human", "value": "\nAt the third tick on the x-axis, is {the SoftmoE line}[0.261, 0.555] above or below {the Top1-MoE line}[0.261, 0.778]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 582 }, { "id": "oCEknFNrwptscOrA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.385, 0.021, 0.750, 0.063] contain?" }, { "from": "gpt", "value": "This region contains the sublegend which includes Baseline, SoftMoE, and Top1-MoE line markers." } ], "id_raw": 582 }, { "id": "A7Ilv4n4QYhEYKvc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe what can be seen in the region spanning [0.150, 0.482, 0.985, 0.850]?" }, { "from": "gpt", "value": "The area is a subplot showing data for Twitter user counts. It includes y-ticks ranging from '4000' to '4800', x-ticks from '\u22125' to '35', and lines representing Baseline, SoftMoE, and Top1-MoE trends." } ], "id_raw": 582 }, { "id": "wPWgQOPYOm8QhhUv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained within the region [0.386, 0.034, 0.422, 0.048]?" }, { "from": "gpt", "value": "This region contains a color marker for the Baseline line in the legend." } ], "id_raw": 582 }, { "id": "zFzhIi0nFX6l82mI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.532, 0.938, 0.568, 0.962]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 583 }, { "id": "RKuBNsAtCLEYG95r", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of {the tenth tick on the x-axis}[0.943, 0.880]?" }, { "from": "gpt", "value": "[0.943, 0.880]" } ], "id_raw": 583 }, { "id": "6JgsBJf4fJJupT6n", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label in [0.091, 0.585, 0.109, 0.785] represent in the second subplot?" }, { "from": "gpt", "value": "Single Parent (%)" } ], "id_raw": 583 }, { "id": "Ip53W4ceL2qBPazl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_3.png", "conversations": [ { "from": "human", "value": "\nIs the point in 2000 for {the 'Rural'}[0.188, 0.700] line of the second subplot higher than the 'Urban' line?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 583 }, { "id": "38tgxzbKHp24WTNz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_3.png", "conversations": [ { "from": "human", "value": "\nWhere does {the 'Urban'}[0.566, 0.760] line stand in comparison to {the 'Rural'}[0.566, 0.640] line in 2010 on the percentage of single parents graph?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 583 }, { "id": "ADacaOVZ57JmpnAn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_3.png", "conversations": [ { "from": "human", "value": "\nAt the last data point of 2020, which line is higher in the second subplot, {the 'Urban'}[0.943, 0.640] or {the 'Rural'}[0.943, 0.550]?" }, { "from": "gpt", "value": "Urban." } ], "id_raw": 583 }, { "id": "4YWz0RlLU5kCAlls", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is depicted in the area from [0.188, 0.640, 0.943, 0.850]?" }, { "from": "gpt", "value": "It's the 'Urban' line of the second subplot, which shows the change in the percentage of single parents from 2000 to 2020." } ], "id_raw": 583 }, { "id": "gJavmvjgRR4bkhv0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.188, 0.550, 0.943, 0.700] within the second subplot?" }, { "from": "gpt", "value": "In this region, the 'Rural' line of the second subplot is plotted, indicating the percentage of single parents from the years 2000 to 2020." } ], "id_raw": 583 }, { "id": "s9kUzCH92AhDm2Iz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_3.png", "conversations": [ { "from": "human", "value": "\nAre there any key features within the region [0.110, 0.131, 0.138, 0.154] of the chart?" }, { "from": "gpt", "value": "Yes, the y-axis tick label '3.0' for the 'Family Size' plot is located within this region." } ], "id_raw": 583 }, { "id": "hEB68qkEXLNUwHDs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the text element located at [0.041, 0.409, 0.059, 0.591]?" }, { "from": "gpt", "value": "Y-axis label for the second subplot, 'Patent Expenses (in millions)'." } ], "id_raw": 584 }, { "id": "eBjbbhshOStDOvis", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_4.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the point representing number of patents '7' on the line graph?" }, { "from": "gpt", "value": "[0.836, 0.112]" } ], "id_raw": 584 }, { "id": "O6kgJLgWROBFAFm4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the description of {the x-axis tick}[0.620, 0.880] in the second subplot?" }, { "from": "gpt", "value": "5." } ], "id_raw": 584 }, { "id": "omwEfxeySBlnwChm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_4.png", "conversations": [ { "from": "human", "value": "\nDoes the point for number of patents '5' have a higher or lower innovation score than the point for number of patents '6'?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 584 }, { "id": "MQjDu931w8TY507T", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_4.png", "conversations": [ { "from": "human", "value": "\nIs the x-axis tick '3' to the left or right of the x-axis tick '2' in the first subplot?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 584 }, { "id": "kaWyn4FOKzkbhsSy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_4.png", "conversations": [ { "from": "human", "value": "\nIs the innovation score for number of patents '4' higher or lower than the score for number of patents '3'?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 584 }, { "id": "1NIv58DbnrzPJVqS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content inside the box [0.469, 0.938, 0.631, 0.962]." }, { "from": "gpt", "value": "It contains the chart title 'Number of Patents', set in black color." } ], "id_raw": 584 }, { "id": "IEf44n903FoYCBBo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_4.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the area [0.150, 0.031, 0.981, 0.413]?" }, { "from": "gpt", "value": "This area contains the first subplot which is a line graph showing 'Innovation Score' as y-axis label with y-axis tick labels like '3', '4', '5', '6', '7', '8'." } ], "id_raw": 584 }, { "id": "yqRyqBn1g17FhQ9S", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_4.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.150, 0.499, 0.981, 0.880]?" }, { "from": "gpt", "value": "This area contains the second subplot displaying a line graph showing 'Patent Expenses (in millions)' as y-axis label with y-axis tick labels like '1.0', '1.5', '2.0', '2.5', '3.0', '3.5', '4.0', '4.5' and an x-axis with tick labels ranging from '0' to '9'." } ], "id_raw": 584 }, { "id": "pE8tcPllXRU9k05q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.041, 0.418, 0.059, 0.582]?" }, { "from": "gpt", "value": "Transportation Volume." } ], "id_raw": 585 }, { "id": "wbb9lbILcX2qCqCg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the x-axis label}[0.543, 0.412, 0.588, 0.435]?" }, { "from": "gpt", "value": "Black." } ], "id_raw": 585 }, { "id": "b0Jw34wOyfYSqHqL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the marker style} for {the line representing Aviation Transport}[0.188, 0.570, 0.943, 0.821]?" }, { "from": "gpt", "value": "Circle." } ], "id_raw": 585 }, { "id": "z2EKZ6aULx09BE1w", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_5.png", "conversations": [ { "from": "human", "value": "\nIs {the first point on the Truck Transport line}[0.188, 0.266] above or below {the first point on the Marine Transport line}[0.188, 0.340]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 585 }, { "id": "NhuhxCiDnT5jMEFK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_5.png", "conversations": [ { "from": "human", "value": "\nBetween {the 5th point of Marine Transport}[0.524, 0.266] and {the 5th point of Logistics Service}[0.524, 0.674], which one has a higher value?" }, { "from": "gpt", "value": "Logistics Service." } ], "id_raw": 585 }, { "id": "iy8Tkt6nmf6JPGDy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_5.png", "conversations": [ { "from": "human", "value": "\nIs {the last point on Marine Transport}[0.943, 0.172] higher than {the last point on Aviation Transport}[0.943, 0.570]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 585 }, { "id": "7LsZEvbhO84h8Nag", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.069, 0.079, 0.087, 0.340]." }, { "from": "gpt", "value": "These coordinates contain the y-axis labels for Transportation Volume, with all labels rotated 90 degrees and styled in black." } ], "id_raw": 585 }, { "id": "XCtzogF55ejqwSXT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.342, 0.449, 0.789, 0.490]." }, { "from": "gpt", "value": "This region contains sublegend items {Aviation Transport}[0.397, 0.458, 0.555, 0.481] and {Logistics Service}[0.638, 0.458, 0.783, 0.481], where each label is paired with a color representation but no legend keys are visible." } ], "id_raw": 585 }, { "id": "p3VgEnMJN8SCYvC1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.188, 0.079, 0.943, 0.228]?" }, { "from": "gpt", "value": "This area contains {line for Goods Delivery}[0.188, 0.079, 0.943, 0.228], showing points from the first to the last year depicted by circle markers sequentially placed along this straight line. The end points are {year 1}[0.188, 0.228] and {year 10}[0.943, 0.079]." } ], "id_raw": 585 }, { "id": "hrvLOVSJOIInpaor", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.188, 0.319] in the graph?" }, { "from": "gpt", "value": "It's a data point on the Baseline line." } ], "id_raw": 586 }, { "id": "psXVZlXcyiZL1Dmk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_6.png", "conversations": [ { "from": "human", "value": "\nWhat exactly is represented by {the object} on the left end of {the line} in [0.188, 0.633]?" }, { "from": "gpt", "value": "This is the data point that represents the Success Score for SoftMoE when the Number of Patients is 500." } ], "id_raw": 586 }, { "id": "YvEtZfi2QPpBm3xA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_6.png", "conversations": [ { "from": "human", "value": "\nWhat content can we find at [0.471, 0.445, 0.542, 0.468]?" }, { "from": "gpt", "value": "The word \"SoftMoE\" as a legend item." } ], "id_raw": 586 }, { "id": "b6WSI1u80yNA6hd7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_6.png", "conversations": [ { "from": "human", "value": "\nComparing the 'Baseline' score of 500 patients at {coordinate1}[0.188, 0.319] with the 'SoftMoE' score of 500 patients at {coordinate2}[0.188, 0.823], which technique is more successful?" }, { "from": "gpt", "value": "SoftMoE." } ], "id_raw": 586 }, { "id": "N5yolqL761tEFeCh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_6.png", "conversations": [ { "from": "human", "value": "\nAt [0.692, 0.300], is the Baseline score less than, equal to, or greater than the SoftMoE score at [0.692, 0.709]?" }, { "from": "gpt", "value": "Less than." } ], "id_raw": 586 }, { "id": "yqFzYg0vsr6f7nO6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_6.png", "conversations": [ { "from": "human", "value": "\nIs the 'Top1-MoE' score at [0.943, 0.500] higher or lower than the Baseline score at [0.943, 0.129] when the Number of Patients is 2000?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 586 }, { "id": "oPuZP0Vy7q55oGjm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content found in the region [0.091, 0.175, 0.109, 0.273]." }, { "from": "gpt", "value": "The region contains the y-axis label \"Baseline\" for the subplot." } ], "id_raw": 586 }, { "id": "BJ3kZNh2SOyTS7cR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_6.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents within the area [0.416, 0.436, 0.716, 0.477]?" }, { "from": "gpt", "value": "The area hosts the legend for the subplot, with the labels: {SoftMoE}[0.471, 0.445, 0.542, 0.468] and {Top1-MoE}[0.626, 0.445, 0.709, 0.468], each paired with a colored marker." } ], "id_raw": 586 }, { "id": "HWdHvwU3Sb92wrlz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's inside the region [0.116, 0.025, 0.138, 0.048]?" }, { "from": "gpt", "value": "Within this region, we find the y-axis tick label '95' for the 'SoftMoE' subplot." } ], "id_raw": 586 }, { "id": "dlEd6e1UC7RU92Tf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object positioned at [0.041, 0.353, 0.059, 0.647] in the chart?" }, { "from": "gpt", "value": "The y-axis label of the top subplot." } ], "id_raw": 588 }, { "id": "YENE1mSf1ChV6eYS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-value of the point located at [0.374, 0.206]?" }, { "from": "gpt", "value": "Strategy 1." } ], "id_raw": 588 }, { "id": "xIoFSalt12qsrMef", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label of the line}[0.187, 0.513, 0.934, 0.595] in the second subplot?" }, { "from": "gpt", "value": "Strategy 2." } ], "id_raw": 588 }, { "id": "5aCR5kmUtyE86qRl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_8.png", "conversations": [ { "from": "human", "value": "\nIn the top subplot, are the points for Strategy 1 plotted higher or lower than those of Strategy 4?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 588 }, { "id": "2JrA0fNgIC5ObDRf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_8.png", "conversations": [ { "from": "human", "value": "\nFor the second subplot, is the line of Strategy 1 plotted above or below the line of Strategy 2?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 588 }, { "id": "DbKYDreb0BKBWdmi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_8.png", "conversations": [ { "from": "human", "value": "\nIn the top subplot, between Baseline and Strategy 3, whose points are plotted higher?" }, { "from": "gpt", "value": "Baseline." } ], "id_raw": 588 }, { "id": "Y0Mo6EP5a0npomCA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is situated in the region [0.075, 0.548, 0.093, 0.827]?" }, { "from": "gpt", "value": "This area contains the y-axis label for the second subplot, \"Policy B & C (in Millions)\"." } ], "id_raw": 588 }, { "id": "78Qco1DaLUSETEr6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_8.png", "conversations": [ { "from": "human", "value": "\nTell me about the objects within [0.888, 0.896, 0.979, 0.920]." }, { "from": "gpt", "value": "This region includes the x-axis label for the last strategy, Strategy 4, in the bottom subplot." } ], "id_raw": 588 }, { "id": "yUqPU904TQZatxWl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_8.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.150, 0.495, 0.971, 0.880]?" }, { "from": "gpt", "value": "This is the area of the second subplot displaying the line graphs of Strategy 1 and Strategy 2 for Policy B & C." } ], "id_raw": 588 }, { "id": "biT96cspKMxNtFpL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.463, 0.942, 0.537, 0.965] in our chart?" }, { "from": "gpt", "value": "The main chart's title \"Methods\"." } ], "id_raw": 589 }, { "id": "BApU7nBg8QWXU7SL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the bottom most ytick label on the first subplot (Innovative Ideas)?" }, { "from": "gpt", "value": "\"2.4\" located at [0.111, 0.098, 0.138, 0.121]." } ], "id_raw": 589 }, { "id": "b1NmjzDp7fzZJPJ7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_9.png", "conversations": [ { "from": "human", "value": "\nWhat color is the line plot found in the second subplot {at these coordinates}[0.184, 0.550, 0.866, 0.864]?" }, { "from": "gpt", "value": "Green (#008000)." } ], "id_raw": 589 }, { "id": "UYLH9XM0IAoT9IiN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_9.png", "conversations": [ { "from": "human", "value": "\nWhich Method Score, Method 1 or Method 2, achieved a higher score for 'Innovative Ideas' based on the coordinates of {Method 1} at [0.184, 0.450] and {Method 2} at [0.525, 0.136]?" }, { "from": "gpt", "value": "Method 2." } ], "id_raw": 589 }, { "id": "fLO9rYrsbJcvsTP2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_9.png", "conversations": [ { "from": "human", "value": "\nIs the score for 'Innovative Ideas' of {Method 1}[0.184, 0.450] higher or lower than the score of 'Design Quality' of {Method 1}[0.184, 0.864]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 589 }, { "id": "qV6nBuLUG6uOprI5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_9.png", "conversations": [ { "from": "human", "value": "\nComparing the 'Design Quality' scores, which of {method 2}[0.525, 0.550] or {method 3}[0.866, 0.676] scored better?" }, { "from": "gpt", "value": "Method 3." } ], "id_raw": 589 }, { "id": "Rch2HdBfKgObzgjm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the region [0.145, 0.896, 0.905, 0.920]." }, { "from": "gpt", "value": "This region comprises of the x-label of the second subplot which contains 3 method labels: {Method 1}[0.145, 0.896, 0.223, 0.920], {Method 2} [0.486, 0.896, 0.564, 0.920], and {Method 3} [0.827, 0.896, 0.905, 0.920]." } ], "id_raw": 589 }, { "id": "XRPm1hEk6PorUhxz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_9.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area spanning [0.111, 0.098, 0.138, 0.931]?" }, { "from": "gpt", "value": "This area contains all the y-axis tick labels from 2.4 (lowermost) to 3.75 (uppermost) for the second subplot, 'Design Quality'." } ], "id_raw": 589 }, { "id": "uw1ohkY8v64ZU3hS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_9.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects present within the coordinates [0.150, 0.120, 0.900, 0.465]?" }, { "from": "gpt", "value": "This region is the first subplot, containing a line chart for 'Innovative Ideas' with three points at {Method 1}[0.184, 0.450], {Method 2}[0.525, 0.136] and {Method 3}[0.866, 0.278], with corresponding y-axis ticks labels ranging from 2.4 to 1.0." } ], "id_raw": 589 }, { "id": "DukMuYZd2XT3H98i", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the y-axis}[0.081, 0.513, 0.098, 0.862] in the second subplot?" }, { "from": "gpt", "value": "Data Breaches & Online Fraud" } ], "id_raw": 590 }, { "id": "eFSKsMt8MfzHtLLE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the line representing 'Data Breaches' in the second subplot?" }, { "from": "gpt", "value": "[0.188, 0.611, 0.943, 0.718]" } ], "id_raw": 590 }, { "id": "RHjvXmW2O11Dsn4F", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.105, 0.640, 0.138, 0.663] in the second subplot?" }, { "from": "gpt", "value": "350" } ], "id_raw": 590 }, { "id": "5GVJn2FUNoWOqhnJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_10.png", "conversations": [ { "from": "human", "value": "\nIs {the first point for 'Data Breaches'}[0.188, 0.649] higher or lower than {the first point for 'Online Fraud'}[0.188, 0.572] in the second subplot?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 590 }, { "id": "sCEB3tcBKnoEcXP8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_10.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Data Breaches'}[0.188, 0.611, 0.943, 0.718] above or below {the line for 'Online Fraud'}[0.188, 0.572, 0.943, 0.634] in the second subplot?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 590 }, { "id": "xBYvOiamueBtWUAz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_10.png", "conversations": [ { "from": "human", "value": "\nWhich incident has a higher count at {point 4}[0.943, 0.634], Data Breaches or Online Fraud?" }, { "from": "gpt", "value": "Data Breaches." } ], "id_raw": 590 }, { "id": "0uJdla83ecAqo22k", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.374, 0.430, 0.757, 0.472]." }, { "from": "gpt", "value": "This area contains the subplot legend items: {Data Breaches}[0.429, 0.439, 0.557, 0.463] and {Online Fraud}[0.640, 0.439, 0.750, 0.463], each with an associated marker color." } ], "id_raw": 590 }, { "id": "rXTJKO9j3GqUiCyd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.105, 0.794, 0.138, 0.817]." }, { "from": "gpt", "value": "This area contains the y-axis tick label {250} in the second subplot." } ], "id_raw": 590 }, { "id": "X8ecc9EgGzYTO70A", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.188, 0.611, 0.943, 0.718]?" }, { "from": "gpt", "value": "This region consists of {the line for 'Data Breaches'} in the second subplot, which spans points at 1, 2, 3 and 4 on the x-axis." } ], "id_raw": 590 }, { "id": "WUZExLyDq0IrjveF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.477, 0.942, 0.523, 0.965] in the chart?" }, { "from": "gpt", "value": "It's a text indicating 'Years'." } ], "id_raw": 591 }, { "id": "9Vx0jDc4gwhNgIyY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the bar of 'Year 2018 under Unpredictable decline'} at [0.130, 0.181, 0.190, 0.930]?" }, { "from": "gpt", "value": "It's a data bar representing the value 60." } ], "id_raw": 591 }, { "id": "ZFS79hsaHdNADs3g", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_11.png", "conversations": [ { "from": "human", "value": "\nWhat is at these coordinates [0.091, 0.659, 0.113, 0.682] located in the chart?" }, { "from": "gpt", "value": "It's a y-axis tick label indicating '70'." } ], "id_raw": 591 }, { "id": "tYYLKRA9XO8yYfSh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_11.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Year 2019 under Oscillating trend'}[0.395, 0.295] above or below {the point for 'Year 2019 under Unpredictable decline'}[0.395, 0.250]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 591 }, { "id": "wvuQVC8cvRI5eoku", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_11.png", "conversations": [ { "from": "human", "value": "\nIs {the value for 'Year 2020 under Substantial decrease'}[0.630, 0.696] higher or lower than {the value for 'Year 2020 under Sustained decline'}[0.630, 0.640]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 591 }, { "id": "JfY4qTqfimSspdmn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_11.png", "conversations": [ { "from": "human", "value": "\nIn {Year 2021}[0.865, 0.181, 0.865, 0.687], which trend has a higher value, Oscillating trend or Accelerated growth?" }, { "from": "gpt", "value": "Accelerated growth." } ], "id_raw": 591 }, { "id": "rqAtDWaHMT0u7nIU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.145, 0.482, 0.880, 0.523]." }, { "from": "gpt", "value": "This area contains the legend items: {Substantial decrease}[0.201, 0.491, 0.383, 0.514], {Sustained decline}[0.466, 0.491, 0.620, 0.514], and {Accelerated growth}[0.704, 0.491, 0.873, 0.514], each with their respective colored markers." } ], "id_raw": 591 }, { "id": "0I72tc7ZJNF8wWPj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.125, 0.540, 0.900, 0.890]." }, { "from": "gpt", "value": "The region contains a line plot graph with three lines representing 'Substantial decrease', 'Sustained decline', and 'Accelerated growth' with data points for the years 2018 to 2021. The x-axis displays the years and the y-axis displays the scores." } ], "id_raw": 591 }, { "id": "B4iLy6cJld7E01yW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_11.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.273, 0.062, 0.752, 0.103]?" }, { "from": "gpt", "value": "It's the legend area for first subplot. It contains legend items: {Oscillating trend}[0.329, 0.071, 0.472, 0.094] and {Unpredictable decline}[0.555, 0.071, 0.745, 0.094], each with their respective colored markers." } ], "id_raw": 591 }, { "id": "ilsYc01aqzGixmsF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.086, 0.018, 0.103, 0.568] in the first subplot?" }, { "from": "gpt", "value": "Established Democracies & Young Democracies." } ], "id_raw": 592 }, { "id": "gy5BuJIDIamusBBB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fourth x-axis tick in the second subplot?" }, { "from": "gpt", "value": "[0.563, 0.880]" } ], "id_raw": 592 }, { "id": "EDeQkSDj8ckQsQmG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the value at {the point}[0.411, 0.326] on the line in the first subplot?" }, { "from": "gpt", "value": "4.9" } ], "id_raw": 592 }, { "id": "f4O6191yEHnqeuTt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_12.png", "conversations": [ { "from": "human", "value": "\nIs the trend of {established democracies}[0.184, 0.147, 0.866, 0.416] in the first subplot going up or down?" }, { "from": "gpt", "value": "Up." } ], "id_raw": 592 }, { "id": "i2AUaziiwaIKrqnR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_12.png", "conversations": [ { "from": "human", "value": "\nWhich is higher on the y-axis at {x coordinate 9}[0.639, 0.192, 0.639, 0.394] in the first subplot, established democracies or young democracies?" }, { "from": "gpt", "value": "Established democracies." } ], "id_raw": 592 }, { "id": "lQgFseS3UDYPCDR3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_12.png", "conversations": [ { "from": "human", "value": "\nWhich is larger at {x coordinate 6}[0.411, 0.326, 0.411, 0.693], established democracies in the first subplot or autocracies in the second subplot?" }, { "from": "gpt", "value": "Established democracies." } ], "id_raw": 592 }, { "id": "q7zCDYBZzESJDstt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.086, 0.639, 0.103, 0.775] in the second subplot." }, { "from": "gpt", "value": "It contains the y-axis label. The y-axis label is \"Autocracies\" stylized with black font color and rotated 90 degrees." } ], "id_raw": 592 }, { "id": "4o1sfjuXbV3kGGFT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.150, 0.120, 0.900, 0.465] in the first subplot." }, { "from": "gpt", "value": "This area contains the two lines representing the trends of established democracies and young democracies. The trend line for established democracies is colored in #3178bd and the trend line for young democracies is colored in #469c76. Both trend lines are marked with circles at each data point." } ], "id_raw": 592 }, { "id": "zLGO4OFJc1sSOg5G", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.263, 0.880, 0.603, 0.920] in the second subplot?" }, { "from": "gpt", "value": "There are three objects here which correspond to x-axis labels '4', '6', and '8'." } ], "id_raw": 592 }, { "id": "YAEm1XtsTy3bDPQB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.069, 0.100, 0.087, 0.334] in the chart?" }, { "from": "gpt", "value": "Y-axis label of first subplot." } ], "id_raw": 593 }, { "id": "gSzikQgkSYK0ZYTI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_13.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the last element in the 'Export' line of the first subplot?" }, { "from": "gpt", "value": "[0.943, 0.102]" } ], "id_raw": 593 }, { "id": "Z5iOe5NiI8o1QbP9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the year at {the x-axis tick label}[0.922, 0.896, 0.965, 0.920] in the second subplot?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 593 }, { "id": "BE0aiVye0wJFMXva", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_13.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'CPI'}[0.188, 0.389, 0.943, 0.392] generally above or below {the line for 'Export'}[0.188, 0.094, 0.943, 0.145] in the first subplot?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 593 }, { "id": "KzxYBO6OvfTpAtSv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_13.png", "conversations": [ { "from": "human", "value": "\nTowards the end of 2020, does the 'Export'[0.943, 0.102] or 'Import'[0.943, 0.042] line have a higher value in the first subplot?" }, { "from": "gpt", "value": "Export." } ], "id_raw": 593 }, { "id": "vlspXuQvHy9ofUPe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_13.png", "conversations": [ { "from": "human", "value": "\nIn the second subplot, is {the point for the year '2015'}[0.566, 0.759] on the 'Unemployment_rate' line above or below the {point for the year '2020'}[0.943, 0.633]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 593 }, { "id": "DFKbWxN3m2KTZ70x", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.446, 0.430, 0.686, 0.472] of the second subplot." }, { "from": "gpt", "value": "The area contains the sublegend items: {Unemployment_rate}[0.501, 0.439, 0.679, 0.463] which is paired with a colored marker." } ], "id_raw": 593 }, { "id": "5MAUDictK7hora2U", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.150, 0.401, 0.350, 0.430] in the first subplot." }, { "from": "gpt", "value": "The area contains three X-axis tick labels '2008', '2010', and '2012' without explicitly given coordinates. It also includes the start of the 'CPI', 'Export', and 'Import' lines." } ], "id_raw": 593 }, { "id": "aCXJ7VYGVOlFfjre", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.150, 0.495, 0.981, 0.880] in the second subplot?" }, { "from": "gpt", "value": "The main elements within these coordinates are the axes of the plot, x-grid, y-grid, and the 'Unemployment_rate' line. It also contains the X and Y axis labels and tick labels." } ], "id_raw": 593 }, { "id": "mQ1qNJaIFTOt5zpn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.124, 0.057] in the first subplot?" }, { "from": "gpt", "value": "It's a datapoint on the 'Security Risk' line." } ], "id_raw": 594 }, { "id": "uNLSv3oUt3YOjRHd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_14.png", "conversations": [ { "from": "human", "value": "\nWhat is located at coordinates [0.919, 0.583] on the x-axis?" }, { "from": "gpt", "value": "It's the label '8'." } ], "id_raw": 594 }, { "id": "dUM1dyTMLntYAfHT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_14.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the point [0.019, 0.161, 0.036, 0.311]?" }, { "from": "gpt", "value": "It's the 'Security Risk' y-axis label in the first subplot." } ], "id_raw": 594 }, { "id": "iUDOwQUGlujHNYkv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Legal Risk' at timestep 5}[0.487, 0.769] higher or lower than {the point for 'Financial Risk' at timestep 5}[0.487, 0.688]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 594 }, { "id": "GBuiyT1wJs94e3tm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_14.png", "conversations": [ { "from": "human", "value": "\nWhat trend is {the point at coordinates [0.940, 0.415]}[0.940, 0.415] on, 'Legal Risk' or 'Security Risk'?" }, { "from": "gpt", "value": "Security Risk." } ], "id_raw": 594 }, { "id": "P84yKvERsMv0TE3K", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_14.png", "conversations": [ { "from": "human", "value": "\nWhich risk is at a higher level at {point at timestep 3}[0.305, 0.137], 'Security Risk' or 'Legal Risk'?" }, { "from": "gpt", "value": "Legal Risk." } ], "id_raw": 594 }, { "id": "GyPOdZH4DUyE6Vhy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects located within coordinates [0.209, 0.919, 0.583, 0.942]." }, { "from": "gpt", "value": "It's the section of x-axis labels from '2' to '6'." } ], "id_raw": 594 }, { "id": "hvgZdKrQh7T5OP4T", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_14.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the area specified by the coordinates [0.432, 0.501, 0.951, 0.942]?" }, { "from": "gpt", "value": "It contains datapoints on both the 'Legal Risk' and 'Financial Risk' lines between the time periods of '4' to '10'." } ], "id_raw": 594 }, { "id": "RE3ZrdXmQRxpbKrE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the section [0.043, 0.379, 0.071, 0.403]?" }, { "from": "gpt", "value": "It is the '2.5' y-axis label of the first subplot." } ], "id_raw": 594 }, { "id": "owrzT5KufZ4YY6WU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_15.png", "conversations": [ { "from": "human", "value": "\nWhat does the label with coordinates [0.527, 0.938, 0.573, 0.962] in the chart represent?" }, { "from": "gpt", "value": "X-axis label, which is \"Years\"." } ], "id_raw": 595 }, { "id": "qqIljPW3HaTOPpB9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_15.png", "conversations": [ { "from": "human", "value": "\nCould you tell me the coordinates of the 4th x-axis tick in Subplot 1?" }, { "from": "gpt", "value": "[0.636, 0.880]" } ], "id_raw": 595 }, { "id": "wafoS9M61P3HVUCh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.086, 0.454, 0.103, 0.914] in the lower chart?" }, { "from": "gpt", "value": "\"Number of Patent Citations (in 10,000s)\"" } ], "id_raw": 595 }, { "id": "tn3bDhg5gx20jrmP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_15.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Number of Patents}[0.150, 0.129, 0.959, 0.391] in 2014 higher or lower than its value in 2012?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 595 }, { "id": "MMhsoRT4xzmS3ppA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_15.png", "conversations": [ { "from": "human", "value": "\nIn 2015, does {Number of Patent Citations}[0.150, 0.586, 0.959, 0.782] have a higher or lower value compared to {Number of Patents}[0.150, 0.129, 0.959, 0.391]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 595 }, { "id": "G7hvWjyqKAGpgAOo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_15.png", "conversations": [ { "from": "human", "value": "\nAre the 'Number of Patent Citations' values increasing or decreasing from 2010 to 2015?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 595 }, { "id": "esREI5VxEPNKOHC2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_15.png", "conversations": [ { "from": "human", "value": "\nCould you describe the region with coordinates [0.150, 0.487, 0.959, 0.880]?" }, { "from": "gpt", "value": "This region contains the second subplot, showing a line plot titled 'Number of Patent Citations (in 10,000s)', with values ranging from 5.0 to 6.0. The x-axis has labels for the years from 2010 to 2015." } ], "id_raw": 595 }, { "id": "eNpROjuVbfuIXeyX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.111, 0.120, 0.138, 0.143]?" }, { "from": "gpt", "value": "This area contains a label on the y-axis. The label is \"3.0\" and is positioned towards the lower part of the axis." } ], "id_raw": 595 }, { "id": "9aFjASbch8rTifQu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_15.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what's in the area with coordinates [0.774, 0.896, 0.820, 0.920]?" }, { "from": "gpt", "value": "This area contains an x-axis label in Subplot 1. The label is \"2014\"." } ], "id_raw": 595 }, { "id": "uF9IhJvuBowCDDt8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart object located at coordinates [0.059, 0.086, 0.076, 0.252]?" }, { "from": "gpt", "value": "It is the y-axis label of the first subplot, labeled as 'Base Capacity'." } ], "id_raw": 596 }, { "id": "wkJ7xpnK5JQonH0u", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.883, 0.612, 1.028, 0.930]?" }, { "from": "gpt", "value": "There is no chart element at these coordinates." } ], "id_raw": 596 }, { "id": "ScnjZirH0oCWK6n4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the year represented by the x-axis tick located at coordinates [0.943, 0.880]?" }, { "from": "gpt", "value": "The year is 2024.0." } ], "id_raw": 596 }, { "id": "LmK6tS527NZxgtwR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_16.png", "conversations": [ { "from": "human", "value": "\nIs the point at {coordinates}[0.188, 0.773] in subplot_2 higher or lower compared to the point at {coordinates}[0.377, 0.811]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 596 }, { "id": "SSGPzwrkOsJuuim2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_16.png", "conversations": [ { "from": "human", "value": "\nAre the plotted points for 'College' generally above or below the points for 'Graduate School'?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 596 }, { "id": "O4KSsVIkWsM0ecEn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_16.png", "conversations": [ { "from": "human", "value": "\nIs the data for 'High School' at {coordinates}[0.188, 0.527] in subplot_1 higher or lower than the 'Base Capacity' at {coordinates}[0.188, 0.103] in subplot_0?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 596 }, { "id": "YQIlvsmF9yCF1Gjc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.150, 0.342, 0.981, 0.585]." }, { "from": "gpt", "value": "This describes the second subplot which displays data for 'High School'. It includes a linear chart line, x and y grids, y-axis label, x and y tick marks with labels, and the legend for 'High School'." } ], "id_raw": 596 }, { "id": "zYGvgNPdAmTo8qBt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.150, 0.637, 0.981, 0.880] contain?" }, { "from": "gpt", "value": "This region contains the third subplot in the figure which represents data for 'Post High School' categories 'College' and 'Graduate School'. Among other things, it includes a the data lines for 'College' and 'Graduate School' represented with marker 'o', labels on the y-axis, tick marks on the x and y axes, and a legend for 'College' and 'Graduate School'." } ], "id_raw": 596 }, { "id": "WjRUQRRBJhU42Gh8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the region [0.188, 0.773, 0.943, 0.831]?" }, { "from": "gpt", "value": "This region represents the data line on the third subplot for 'College', with plotted lines and 'o' markers located at various points along the line." } ], "id_raw": 596 }, { "id": "Lzjx3oiRyIz7Frzs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_17.png", "conversations": [ { "from": "human", "value": "\nWhat type of efficiency is represented by the y-axis at [0.091, 0.066, 0.109, 0.369]?" }, { "from": "gpt", "value": "Operational Efficiency." } ], "id_raw": 597 }, { "id": "FCPpgONH5IEllPHE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_17.png", "conversations": [ { "from": "human", "value": "\nWhat specific point does these coordinates [0.188, 0.255] refer to in the line diagram?" }, { "from": "gpt", "value": "It refers to the point on the 'Baseline' line when the number of employees is 20 and the Operational Efficiency is 80%." } ], "id_raw": 597 }, { "id": "BZnBOLZDmdnR9EhD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the third y-axis label in subplot_1 at [0.116, 0.769, 0.138, 0.792] refer to?" }, { "from": "gpt", "value": "This label refers to the value '65' on the y-axis." } ], "id_raw": 597 }, { "id": "nbDmMq6qza6Iq9HD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_17.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher y-value at x = 50, is it {Method A line's point}[0.362, 0.230] or the {Method B line's point}[0.362, 0.130]?" }, { "from": "gpt", "value": "Method A." } ], "id_raw": 597 }, { "id": "A5GovtT8Dbi53aoP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_17.png", "conversations": [ { "from": "human", "value": "\nConsidering the {the 'Method A' point at x=20}[0.188, 0.192] and the {the 'Method B' point at x=150}[0.943, 0.167], which point is higher on the scale?" }, { "from": "gpt", "value": "The 'Method A' point at x=20." } ], "id_raw": 597 }, { "id": "84HFnfu86ub29qL9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_17.png", "conversations": [ { "from": "human", "value": "\nFor {Method A}[0.188, 0.658, 0.943, 0.742] and {Method B}[0.188, 0.537, 0.943, 0.646], which one generally has a higher y-coordinate value?" }, { "from": "gpt", "value": "Method A." } ], "id_raw": 597 }, { "id": "WuqEMBdXg7S1YF26", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is contained in the coordinates [0.332, 0.430, 0.800, 0.472]." }, { "from": "gpt", "value": "This region contains the legend of subplot_1, which has the labels 'Baseline', 'Method A' and 'Method B', each paired with a colored marker." } ], "id_raw": 597 }, { "id": "MMTGWHqHbtV5jfNO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_17.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the region [0.150, 0.495, 0.981, 0.880]?" }, { "from": "gpt", "value": "This region includes the entire subplot_1, which demonstrates 'Functional Efficiency' with y-axis labels ranging from '55' to '90', x-axis labels that represent 'Number of Employees', and three line graphs that represent 'Baseline', 'Method A', and 'Method B'." } ], "id_raw": 597 }, { "id": "mxznhem1N5LqiUF3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the region defined by [0.150, 0.025, 0.981, 0.410] consist of?" }, { "from": "gpt", "value": "This region contains subplot_0 that represents 'Operational Efficiency' on the y-axis, 'Number of Employees' on the x-axis, and three line graphs for 'Baseline', 'Method A', and 'Method B'." } ], "id_raw": 597 }, { "id": "nz0O9Cf17umqHCIb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.069, 0.091, 0.087, 0.354] on the chart?" }, { "from": "gpt", "value": "Internet Users (Million)." } ], "id_raw": 598 }, { "id": "u4M3QJD7guNX2gkL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_18.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.566, 0.020, 0.566, 0.020] in the subplot 0?" }, { "from": "gpt", "value": "The subplot's subtitle." } ], "id_raw": 598 }, { "id": "EYuHcNs4iZxVBAIG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the fourth marker on the line in subplot 1 at the coordinates [0.755, 0.663] indicating?" }, { "from": "gpt", "value": "The number of Mobile Users (Million) in 2015." } ], "id_raw": 598 }, { "id": "1Vg3myOWWKwJ3FT2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_18.png", "conversations": [ { "from": "human", "value": "\nIn subplot 0, is the point at {4th year}[0.566, 0.259] higher or lower than the point at {the 2nd year}[0.377, 0.333]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 598 }, { "id": "lkt6jGqUYq6oIZXQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_18.png", "conversations": [ { "from": "human", "value": "\nIs the Internet Users (Million) in the {year 2005}[0.377, 0.333] higher or lower than in the {year 2010}[0.566, 0.259]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 598 }, { "id": "hjAss8GvP14C2Dk1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_18.png", "conversations": [ { "from": "human", "value": "\nIn subplot 1, which year has more Mobile Users (Million), {the year 2000}[0.188, 0.873] or {the year 2005}[0.377, 0.860]?" }, { "from": "gpt", "value": "The year 2000." } ], "id_raw": 598 }, { "id": "cgvkVr8lMVHNCqCK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.150, 0.503, 0.981, 0.880]." }, { "from": "gpt", "value": "This area contains subplot 1 which includes the graph for Mobile Users (Million) over different years and the y-axis labels ranging from 0 to 7000." } ], "id_raw": 598 }, { "id": "JBZqG6MBcPBDABSv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.150, 0.034, 0.981, 0.411]." }, { "from": "gpt", "value": "This area contains subplot 0 which includes the plot for Internet Users (Million) over different years and the y-axis labels ranging from 0 to 5000." } ], "id_raw": 598 }, { "id": "KJZt2sK2XxSZApnR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_18.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.535, 0.896, 0.596, 0.920]?" }, { "from": "gpt", "value": "This area contains the X-axis tick label \"2010.0\" and its tick on the X-axis in subplot 1." } ], "id_raw": 598 }, { "id": "fxA0vsj8rLMPSu5E", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis represent according to the coordinate information [0.019, 0.202, 0.036, 0.324]?" }, { "from": "gpt", "value": "User score." } ], "id_raw": 599 }, { "id": "4eOH6uKsJdnvF62G", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the trend of {the blue line}[0.124, 0.224, 0.940, 0.382] referring to in the first subplot?" }, { "from": "gpt", "value": "Household Robots." } ], "id_raw": 599 }, { "id": "RToeQ4lfhX2E5gNW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_30_19.png", "conversations": [ { "from": "human", "value": "\nWhat is at this coordinate [0.729, 0.659] on the chart?" }, { "from": "gpt", "value": "It's a point on the line representing the performance of the 'Smart Home' category in the second subplot." } ], "id_raw": 599 }, { "id": "gOROkLEH7fgA3Pqh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_19.png", "conversations": [ { "from": "human", "value": "\nAt {point 4}[0.396, 0.342], is the score of {Household Robots} higher or lower than in {point 3}[0.305, 0.224]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 599 }, { "id": "H0x33kvIdFBOPtTy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_19.png", "conversations": [ { "from": "human", "value": "\nWhich one reaches the maximum value faster; {Smart Home}[0.124, 0.620, 0.940, 0.659] or {AI Services}[0.124, 0.596, 0.940, 0.793]?" }, { "from": "gpt", "value": "AI Services." } ], "id_raw": 599 }, { "id": "wWJ5mDNpuOQOujnq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_30_19.png", "conversations": [ { "from": "human", "value": "\nOver the timespan, does the {Household Robots}[0.124, 0.224, 0.940, 0.382] perform better or worse than the {Android Assistants}[0.124, 0.171, 0.940, 0.303] in the first subplot?" }, { "from": "gpt", "value": "Worse." } ], "id_raw": 599 }, { "id": "71CPWf5aYCznZR5Z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content located within [0.596, 0.028, 0.756, 0.051]?" }, { "from": "gpt", "value": "This area of the chart contains the legend label for {Android Assistants}[0.596, 0.028, 0.756, 0.051]." } ], "id_raw": 599 }, { "id": "TebeU3mx5H3RMB5h", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area within [0.357, 0.493, 0.707, 0.535]?" }, { "from": "gpt", "value": "The area contains the label {Smart Home}[0.412, 0.502, 0.522, 0.526] and {AI Services}[0.605, 0.502, 0.700, 0.526] in the legend of second subplot, each associated with a different color." } ], "id_raw": 599 }, { "id": "tHrBsVZiuMlJmNWL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_30_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.209, 0.952, 0.220, 0.975] correspond to?" }, { "from": "gpt", "value": "It corresponds to an x-axis label of '2' in the second subplot." } ], "id_raw": 599 }, { "id": "waNQO4ds5iRRvNSV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.012, 0.484, 0.029, 0.582] in the chart?" }, { "from": "gpt", "value": "Y-axis title." } ], "id_raw": 600 }, { "id": "PVSKTOJWX10qw8Zd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_0.png", "conversations": [ { "from": "human", "value": "\nWhat kind of object is located at [0.079, 0.367, 0.958, 0.643]?" }, { "from": "gpt", "value": "Line chart representing Mobiles sales." } ], "id_raw": 600 }, { "id": "t76Z0HVq6H0WoKky", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.484, 0.942, 0.552, 0.975]?" }, { "from": "gpt", "value": "Months." } ], "id_raw": 600 }, { "id": "E97y6udjn7EZRdwI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_0.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Laptops'}[0.079, 0.275, 0.958, 0.533] generally above or below {the line for 'TVs'}[0.079, 0.202, 0.958, 0.386]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 600 }, { "id": "QcGwNOAhCMuwPSoR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_0.png", "conversations": [ { "from": "human", "value": "\nWhich product shows a higher value in October, {Mobiles}[0.798, 0.404] or {Headphones}[0.798, 0.570]?" }, { "from": "gpt", "value": "Headphones." } ], "id_raw": 600 }, { "id": "ovuRc9XomtQ5mf37", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_0.png", "conversations": [ { "from": "human", "value": "\nWhich representation uses a '--' linestyle, {Mobiles}[0.079, 0.367, 0.958, 0.643] or {Headphones}[0.079, 0.496, 0.958, 0.791]?" }, { "from": "gpt", "value": "Mobiles." } ], "id_raw": 600 }, { "id": "NoHfZmWo3k5j9drO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.394, 0.128, 0.643, 0.158]." }, { "from": "gpt", "value": "This area contains the chart subtitle, \"Consumer Behaviour Trend Analysis\", in black color." } ], "id_raw": 600 }, { "id": "0qBEWR2fYgbEGXWq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.464, 0.032, 0.951, 0.117]." }, { "from": "gpt", "value": "This area contains the legend of the graph. It includes four items: Mobiles, Laptops, TVs, and Headphones. Each element is represented with a different color and specific marker." } ], "id_raw": 600 }, { "id": "9kJZEB8FakCQfv12", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.464, 0.032, 0.951, 0.117]?" }, { "from": "gpt", "value": "This region contains the legend items: {Mobiles}[0.517, 0.060, 0.570, 0.090] with color #2171b5, {Laptops}[0.637, 0.060, 0.691, 0.090] with color #6baed6, {TVs}[0.758, 0.060, 0.783, 0.090] with color #42cc46, and {Headphones}[0.850, 0.060, 0.937, 0.090] with color #9dfa9f." } ], "id_raw": 600 }, { "id": "9aBNxrhC1XhoiWDB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.034, 0.245, 0.048, 0.263]?" }, { "from": "gpt", "value": "Y-axis tick label \"70\"." } ], "id_raw": 601 }, { "id": "Oc5RkVtxiTz3tVCY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the text located at [0.034, 0.913, 0.048, 0.930] correspond to?" }, { "from": "gpt", "value": "The Y-axis tick label \"30\"." } ], "id_raw": 601 }, { "id": "fondgzdR6Jz1yUmL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_1.png", "conversations": [ { "from": "human", "value": "\nWhat does {the legend}[0.671, 0.012, 0.966, 0.076] represent on the chart?" }, { "from": "gpt", "value": "The legend denotes different line styles and their corresponding color according to the markers used in the chart." } ], "id_raw": 601 }, { "id": "KgumKHB70aPb4E8Y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_1.png", "conversations": [ { "from": "human", "value": "\nAt year 2020, which color represents a higher indicator? \n{line_0 color} [0.973, 0.394] or {line_3 color} [0.973, 0.306]?" }, { "from": "gpt", "value": "Line with marker color '#2171b5'." } ], "id_raw": 601 }, { "id": "yjbt1OCmRQTobfcb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_1.png", "conversations": [ { "from": "human", "value": "\nIs {the line style '--'}[0.973, 0.806] performance better or worse than {the line style '-'}[0.973, 0.306] in 2020?" }, { "from": "gpt", "value": "Worse." } ], "id_raw": 601 }, { "id": "sYftBHtI7twDqQL6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the line style '-.'}[0.973, 0.546] show an overall increase or decrease over the years compared to {the line style ':'}[0.973, 0.391]?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 601 }, { "id": "wBxIRx0gOucFAiVu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_1.png", "conversations": [ { "from": "human", "value": "\nCan you provide details about the region [0.551, 0.932, 0.580, 0.949]?" }, { "from": "gpt", "value": "This region contains the X-axis tick label indicating the year 2016." } ], "id_raw": 601 }, { "id": "Mt4RwZ6B8EasmWVm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_1.png", "conversations": [ { "from": "human", "value": "\nWhat information is included within the area [0.671, 0.012, 0.966, 0.076]?" }, { "from": "gpt", "value": "This region contains the chart's legend which helps explain the styling of the lines used in the chart. Specifically, it includes four items with different line styles and colors." } ], "id_raw": 601 }, { "id": "irkC42oDJNQJuDyt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_1.png", "conversations": [ { "from": "human", "value": "\nWhat content is in the area [0.034, 0.078, 0.048, 0.096]?" }, { "from": "gpt", "value": "The region contains the Y-axis tick label \"80\"." } ], "id_raw": 601 }, { "id": "l8W4CUKcm4eL03Sc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the label indicate at coordinates [0.092, 0.909, 0.127, 0.932]?" }, { "from": "gpt", "value": "The year 2016." } ], "id_raw": 602 }, { "id": "TuJqnfpKTretfXHZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the handle of {Online Learning's} legend[0.783, 0.140, 0.830, 0.163]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 602 }, { "id": "zoI8CShNW3OvPvfK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the axis label at the coordinates [0.503, 0.942, 0.550, 0.975]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 602 }, { "id": "zrv8TDfX25FLXj8m", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_2.png", "conversations": [ { "from": "human", "value": "\nBased on the graph, did {Group Study}[0.110, 0.340, 0.943, 0.438] increase or decrease from 2016 to 2021?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 602 }, { "id": "1p1UOXRPZGNRJNHN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_2.png", "conversations": [ { "from": "human", "value": "\nIs the {Individual Study}[0.110, 0.064, 0.943, 0.084] line above or below the {Group Study}[0.110, 0.340, 0.943, 0.438] line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 602 }, { "id": "OKjinyeWAWgJGeAY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_2.png", "conversations": [ { "from": "human", "value": "\nWas there a greater percentage of people engaged in {Group Study}[0.943, 0.438] or {Online Learning}[0.943, 0.634] in 2021?" }, { "from": "gpt", "value": "Group Study." } ], "id_raw": 602 }, { "id": "ks57sDvc7I2ocETi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the coordinates [0.783, 0.039, 0.977, 0.223]?" }, { "from": "gpt", "value": "This area contains a legend for the chart, with four entries including 'Individual Study', 'Group Study', 'Online Learning', and 'Practical Work', each with a corresponding color marker." } ], "id_raw": 602 }, { "id": "1TtFt92XaE15n2v8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_2.png", "conversations": [ { "from": "human", "value": "\nDescribe what can be seen within the coordinates [0.068, 0.025, 0.985, 0.893]." }, { "from": "gpt", "value": "This is the main plot area of the chart. It contains y-axis from \u221210 to 60 in increment of 10, x-axis from 2016 to 2021 and lines representing four different study types: 'Individual Study', 'Group Study', 'Online Learning', and 'Practical Work'. Each of these lines has distinct styles and color as represented in the legend." } ], "id_raw": 602 }, { "id": "JEyeDsUbNzvPSvte", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_2.png", "conversations": [ { "from": "human", "value": "\nWhat visual components are within the coordinates [0.759, 0.909, 0.794, 0.932]?" }, { "from": "gpt", "value": "This area contains an x-axis label '2020'." } ], "id_raw": 602 }, { "id": "Um3OfSGLjLbI6bi5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.435, 0.021, 0.977, 0.106]?" }, { "from": "gpt", "value": "It is the legend of the chart." } ], "id_raw": 604 }, { "id": "xEf5tCzXPiiLSSBi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {ylabel}[0.744, 0.362, 0.764, 0.628]?" }, { "from": "gpt", "value": "Average Index." } ], "id_raw": 604 }, { "id": "wiORzYyrMcuf656i", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at {[0.797, 0.088, 0.985, 0.901]}?" }, { "from": "gpt", "value": "It is the main plotting area or axes of the chart." } ], "id_raw": 604 }, { "id": "1MuJaFy4qPk7VdOk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_4.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at \"Outliers\", {ICT line}[0.797, 0.332] or {Biotech line}[0.797, 0.115]?" }, { "from": "gpt", "value": "Biotech." } ], "id_raw": 604 }, { "id": "w6zKiUTTRIZx8swn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_4.png", "conversations": [ { "from": "human", "value": "\nBetween {the Startups line}[0.797, 0.765] and {the Patents line}[0.797, 0.224] at the point \"Outliers\", which one is lower?" }, { "from": "gpt", "value": "Startups." } ], "id_raw": 604 }, { "id": "HaU2yPmVRnV7t4h0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_4.png", "conversations": [ { "from": "human", "value": "\nRegarding the Biotech and ICT trends, is the {Biotech point}[0.609,0.413] or the {ICT point}[0.609, 0.224] higher in the \"Trends\" category?" }, { "from": "gpt", "value": "Biotech." } ], "id_raw": 604 }, { "id": "hDVFXvJhawCntpRx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region from [0.770, 0.079, 0.787, 0.102]." }, { "from": "gpt", "value": "This region contains the y-axis tick label \"90\"." } ], "id_raw": 604 }, { "id": "QE3h1kKUA0QaLmjy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area within [0.609, 0.115, 0.797, 0.765]?" }, { "from": "gpt", "value": "This area includes all the points of the ICT, Biotech, Startups, and Patents trend lines for the \"Trends\" and \"Outliers\" categories." } ], "id_raw": 604 }, { "id": "mmaYkUlSqUTTKIT9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_4.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the region [0.744, 0.250, 0.764, 0.515]?" }, { "from": "gpt", "value": "The region contains a part of the y-axis label \"Average Index\"." } ], "id_raw": 604 }, { "id": "KLxEmjK3D3tCNmOb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object indicated by {the Y-axis label}[0.012, 0.301, 0.029, 0.625]?" }, { "from": "gpt", "value": "Investment Value." } ], "id_raw": 605 }, { "id": "3wOeH6hMVcMxa7Jy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_5.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis label}[0.514, 0.942, 0.554, 0.975] represent in the chart?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 605 }, { "id": "nnl7zSUOn1enawaO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of {the tick mark for the year '2014'}[0.740, 0.901]?" }, { "from": "gpt", "value": "[0.740, 0.901]" } ], "id_raw": 605 }, { "id": "n2H5teGmUwXVurSx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_5.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Startup Investments' for 2008}[0.245, 0.701] higher than {the point for 'Real Estate' in the same year}[0.245, 0.526]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 605 }, { "id": "CJ4ZVV9srAvsvW6Y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_5.png", "conversations": [ { "from": "human", "value": "\nWhich is closer to {the top of the chart}, {the 'Stock Market' point in 2013}[0.658, 0.213] or {the 'Bank Savings' point in 2016}[0.905, 0.325]?" }, { "from": "gpt", "value": "Stock Market." } ], "id_raw": 605 }, { "id": "u6gem6osMEmAuoSq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_5.png", "conversations": [ { "from": "human", "value": "\nIs {the trend line for 'Bank Savings' in 2013}[0.658, 0.369] above or below {the 'Real Estate' line at the same point}[0.658, 0.576]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 605 }, { "id": "zYzvcwCaqALF3Fdt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the region around [0.163, 0.400, 0.905, 0.801]." }, { "from": "gpt", "value": "This region contains the trend line for the 'Stock Market', marked in blue with circles, showing significant fluctuations." } ], "id_raw": 605 }, { "id": "QLWAZefNZc2dHm6o", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_5.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region described by [0.041, 0.266, 0.070, 0.915]?" }, { "from": "gpt", "value": "The region includes the y-axis tick labels ranging from 6000 to 11500, aligned vertically." } ], "id_raw": 605 }, { "id": "n4BpiwnXVjnlEJcU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the segment annotated by [0.089, 0.057, 0.273, 0.212]." }, { "from": "gpt", "value": "This area houses the chart legend, including labels and symbols for 'Stock Market', 'Real Estate', 'Startup Investments', and 'Bank Savings' with respective colors and shapes." } ], "id_raw": 605 }, { "id": "2zFTrrasVPkNXbrF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart title at the coordinates [0.502, 0.942, 0.542, 0.975]?" }, { "from": "gpt", "value": "The chart title is \"Task\"." } ], "id_raw": 606 }, { "id": "uW46qWP0AbZZXgwE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_6.png", "conversations": [ { "from": "human", "value": "\nCan you provide the color of the line labeled as 'Raw Materials'?" }, { "from": "gpt", "value": "The color of the line labeled 'Raw Materials' is '#6baed6'." } ], "id_raw": 606 }, { "id": "ghqaY8q2RRaKNbWb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.012, 0.352, 0.029, 0.637]?" }, { "from": "gpt", "value": "The y-axis label is \"Percentage (%)\"." } ], "id_raw": 606 }, { "id": "Xefm1ONUOiCMTHEL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Raw Materials' trend}[0.064, 0.328, 0.980, 0.611] show a generally increasing or decreasing trend from task 1 to task 10?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 606 }, { "id": "5Cvjl548xLulZlwC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_6.png", "conversations": [ { "from": "human", "value": "\nBetween {the 'Production' trend}[0.064, 0.702, 0.980, 0.833] and {the 'Sales' trend}[0.064, 0.599, 0.980, 0.777], which one shows a faster rate of increase?" }, { "from": "gpt", "value": "'Sales' trend." } ], "id_raw": 606 }, { "id": "1epLHMUoubp6ulQv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_6.png", "conversations": [ { "from": "human", "value": "\nWhat's the percentage value of {the 'Stakeholders' trend}[0.064, 0.266, 0.980, 0.395] at the point with coordinates [0.166, 0.394]?" }, { "from": "gpt", "value": "62.31%." } ], "id_raw": 606 }, { "id": "JZo7LmRyraRyfwLZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects in [0.282, 0.021, 0.973, 0.106]." }, { "from": "gpt", "value": "The area contains the legend of the graph, including labels for 'Stakeholders', 'Raw Materials', 'Production', 'Logistics', and 'Sales', each associated with a unique colored marker." } ], "id_raw": 606 }, { "id": "BUbe2AkdVhpNsT1u", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.064, 0.088, 0.980, 0.901]." }, { "from": "gpt", "value": "This is the main plotting area of the graph where all the trends for 'Stakeholders', 'Raw Materials', 'Production', 'Logistics', and 'Sales' are plotted in different lines with various colors and styles. The x-axis represents 'Tasks' and y-axis represents 'Percentage (%)'." } ], "id_raw": 606 }, { "id": "gICZu7Ap3wlA0U6V", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_6.png", "conversations": [ { "from": "human", "value": "\nWhat is located between the coordinates [0.060, 0.909, 0.269, 0.932]?" }, { "from": "gpt", "value": "Within these coordinates, we have the x-axis tick labels '1' and '2' placed at {coordinates}[0.060, 0.909, 0.068, 0.932] and {coordinates}[0.162, 0.909, 0.169, 0.932] respectively." } ], "id_raw": 606 }, { "id": "dm4CiEGZq8hVr0GR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the legend item for 'Instagram'}[0.918, 0.814, 0.977, 0.837]?" }, { "from": "gpt", "value": "Pink." } ], "id_raw": 607 }, { "id": "yslZ2GCZbj58xAJg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the location of the y-axis tick label for '20'?" }, { "from": "gpt", "value": "[0.034, 0.684, 0.048, 0.708]" } ], "id_raw": 607 }, { "id": "cFUvNJ1OrRLSQxGS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the text} at [0.346, 0.025, 0.698, 0.058]?" }, { "from": "gpt", "value": "Social Media Active Users: 2010-2019." } ], "id_raw": 607 }, { "id": "rZxvEj6283DrFgKs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_7.png", "conversations": [ { "from": "human", "value": "\nWhich social media portrayed by the legend has a higher number of monthly active users in 2019, {Twitter}[0.945, 0.486] or {LinkedIn}[0.945, 0.449]?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 607 }, { "id": "SGvunx3rNfs49R2V", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point representing Facebook's users in 2011}[0.193, 0.391] above or below {the point representing Twitter's users in 2011}[0.193, 0.653]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 607 }, { "id": "ljB91HyY2vsVfaKW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_7.png", "conversations": [ { "from": "human", "value": "\nHow does {LinkedIn's active user count}[0.663, 0.493] in 2016 compare to {Instagram's active user count}[0.663, 0.728] the same year?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 607 }, { "id": "XGlEPNFUfsqFOPaW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region defined by [0.178, 0.909, 0.866, 0.932]." }, { "from": "gpt", "value": "This is the x-axis of the graph labeled with the years from 2011 to 2018." } ], "id_raw": 607 }, { "id": "5ynq8hwlEqALYNFN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the area in [0.881, 0.735, 0.982, 0.881] comprise of?" }, { "from": "gpt", "value": "This area contains the chart legend, featuring colorful markers corresponding to social media platforms like Facebook, Twitter, Instagram, and LinkedIn." } ], "id_raw": 607 }, { "id": "SLHMzdmRhMsoIyG6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the space [0.034, 0.143, 0.048, 0.888] entail?" }, { "from": "gpt", "value": "This region includes the y-axis of the graph ranging from 0 to 80, representing Monthly Active Users (M) in millions." } ], "id_raw": 607 }, { "id": "Eo4OVWo1XSxOgNlz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object represented at [0.434, 0.050, 0.483, 0.080]?" }, { "from": "gpt", "value": "Legend label for 'Benefit'." } ], "id_raw": 608 }, { "id": "k5vgWGql5wqGGD1s", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_8.png", "conversations": [ { "from": "human", "value": "\nIdentify the x-axis label within the coordinates [0.505, 0.942, 0.541, 0.975]?" }, { "from": "gpt", "value": "Age." } ], "id_raw": 608 }, { "id": "FQfCYOAJoXZt54yc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_8.png", "conversations": [ { "from": "human", "value": "\nWhat data does the mark at [0.197, 0.474] in the chart represent?" }, { "from": "gpt", "value": "Net Health Benefit at age 30." } ], "id_raw": 608 }, { "id": "P5YDllK8MIGxBuLb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_8.png", "conversations": [ { "from": "human", "value": "\nIs {the point}[0.654, 0.775] at age 65 associated with higher value {than the point}[0.458, 0.627] at age 50?" }, { "from": "gpt", "value": "No." } ], "id_raw": 608 }, { "id": "WlNvmq48TVVXqJ4O", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_8.png", "conversations": [ { "from": "human", "value": "\nConsidering the plot lines, does {the Admin Cost line at}[0.654, 0.534] position higher than {the Net Health Benefit line}[0.654, 0.775] for age 65?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 608 }, { "id": "TPShnRfLYN3CJk0D", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_8.png", "conversations": [ { "from": "human", "value": "\nWhich variable is indicated higher at {age 55}[0.523, 0.611], Admin Cost or Patient Fee?" }, { "from": "gpt", "value": "Patient Fee." } ], "id_raw": 608 }, { "id": "gLqCIbRGyaZk69cY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the region [0.390, 0.039, 0.973, 0.091]." }, { "from": "gpt", "value": "This region contains the subplot legend with labels such as {Benefit}[0.434, 0.050, 0.483, 0.080], {Patient Fee}[0.550, 0.050, 0.627, 0.080], {Admin Cost}[0.694, 0.050, 0.773, 0.080], and {Net Health Benefit}[0.840, 0.050, 0.968, 0.080] coupled with their colored markers respectively." } ], "id_raw": 608 }, { "id": "o4ULHH3moJ0mlWZA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you see within the space [0.034, 0.071, 0.058, 0.915]?" }, { "from": "gpt", "value": "This area encompasses the y-axis label {Values}[0.012, 0.402, 0.029, 0.524] and y-axis tick labels such as {'\u221240'}[0.034, 0.892, 0.058, 0.915] down to {'110'}[0.036, 0.071, 0.058, 0.094]." } ], "id_raw": 608 }, { "id": "K6A9r7rCh5JebbxP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the information between [0.434, 0.000, 0.896, 1.000]." }, { "from": "gpt", "value": "This broad area includes multiple line plots of {Benefit}[0.066, 0.189, 0.980, 0.271], {Patient Fee}[0.066, 0.271, 0.980, 0.572], {Admin Cost}[0.066, 0.463, 0.980, 0.638], and {Net Health Benefit}[0.066, 0.370, 0.980, 0.863], identified at various points along their respective x-coordinates." } ], "id_raw": 608 }, { "id": "1ACsEngd6c21QEOC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object represented by {line_2}[0.087, 0.548, 0.698, 0.631]?" }, { "from": "gpt", "value": "Line graph indicating \"Legal Risk\"" } ], "id_raw": 611 }, { "id": "FZ5hohzwHVz56UXc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at {coordinates} [0.788, 0.117, 0.974, 0.147]?" }, { "from": "gpt", "value": "\"Persistent Market Changes\"" } ], "id_raw": 611 }, { "id": "WIxgl2OReDCdRQZN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_11.png", "conversations": [ { "from": "human", "value": "\nWhat label is at {coordinate} [0.034, 0.646, 0.048, 0.670]?" }, { "from": "gpt", "value": "Y-axis label \"40\"" } ], "id_raw": 611 }, { "id": "YV0tykF7gnCDf38C", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_11.png", "conversations": [ { "from": "human", "value": "\nComparing the line indicators for \"Legal Risk\" {coordinates} [0.087, 0.548, 0.698, 0.631] and \"Natural Disasters\" {coordinates} [0.087, 0.774, 0.698, 0.856] in the \"Shrinking\" scenario, which risk is higher?" }, { "from": "gpt", "value": "Legal Risk" } ], "id_raw": 611 }, { "id": "7Zlj71QErC2CQUrS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_11.png", "conversations": [ { "from": "human", "value": "\nIs the \"Persistent Market Changes\" line {coordinates}[0.087, 0.113, 0.698, 0.388] on top or below the \"Natural Disasters\" line {coordinates}[0.087, 0.774, 0.698, 0.856] at the \"Expanding\" scenario?" }, { "from": "gpt", "value": "Below" } ], "id_raw": 611 }, { "id": "M1NkcUmCiEFAfFS3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_11.png", "conversations": [ { "from": "human", "value": "\nAre the \"Major Regulatory Changes\" {coordinates} [0.087, 0.278, 0.698, 0.408] points generally higher or lower than the \"Legal Risk\" {coordinates} [0.087, 0.548, 0.698, 0.631] points?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 611 }, { "id": "I19w7829NnHDRTL6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the region [0.736, 0.089, 0.987, 0.307]?" }, { "from": "gpt", "value": "The region contains the chart's legend, with four entries: \"Persistent Market Changes\", \"Major Regulatory Changes\", \"Legal Risk\", and \"Natural Disasters\". Each entry has a unique color and style of line to represent it on the chart." } ], "id_raw": 611 }, { "id": "0fCeJHosv57M3Gc6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_11.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the area [0.027, 0.008, 0.048, 0.031]?" }, { "from": "gpt", "value": "The y-axis label \"100\" is located in this area." } ], "id_raw": 611 }, { "id": "w5VLsUpvR5HArV8e", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_11.png", "conversations": [ { "from": "human", "value": "\nDescribe what is included within the region of coordinates [0.061, 0.909, 0.114, 0.932]." }, { "from": "gpt", "value": "The x-axis label \"Stagnant\" is located within this region." } ], "id_raw": 611 }, { "id": "9BvnqucEjAIUfd9L", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the object represented by these coordinates [0.013, 0.282, 0.029, 0.599]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 612 }, { "id": "hm3RDDSNnHpkty44", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the data point corresponding to the coordinates [0.790, 0.201]?" }, { "from": "gpt", "value": "It represents the value 1.68 in the Rapid Increase trend for the Energy sector." } ], "id_raw": 612 }, { "id": "SvX0fcPsKAl6KtUi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_12.png", "conversations": [ { "from": "human", "value": "\nWhat chart element do the following coordinates correspond to: [0.034, 0.431, 0.052, 0.454]?" }, { "from": "gpt", "value": "A y-tick label showing the value '1.0'." } ], "id_raw": 612 }, { "id": "q8hOz6ikCtonOkUP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Health sector in the Steady Recovery line}[0.206, 0.651] higher or lower than {the point for the same sector in the Rapid Increase line}[0.206, 0.556]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 612 }, { "id": "QUugzLx8g5wgAzIe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_12.png", "conversations": [ { "from": "human", "value": "\nIs the y-coordinate of {the point for Education sector in Steady Recovery line}[0.352, 0.581] greater or less than {the point in Rapid Increase line for the same sector}[0.352, 0.468]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 612 }, { "id": "vTv7KgNjFI3o0fmw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_12.png", "conversations": [ { "from": "human", "value": "\nIn the Finance sector, does {the Rapid Increase trend}[0.060, 0.644] have a higher or lower data point value than {the Steady Recovery trend}[0.060, 0.704]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 612 }, { "id": "U3oz5shOiOfLP4qg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the specific range [0.577, 0.032, 0.929, 0.117]?" }, { "from": "gpt", "value": "This area contains the legend of the chart, providing keys for \"Steady Recovery\" and \"Rapid Increase,\" each with their own particular color and shape marker." } ], "id_raw": 612 }, { "id": "yc73CHLK0lOiQLso", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within these coordinates:[0.468, 0.942, 0.528, 0.975]?" }, { "from": "gpt", "value": "The region houses the xlabel of the chart, which reads 'Sector'." } ], "id_raw": 612 }, { "id": "Ls4qK432Vkbv3xQc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_12.png", "conversations": [ { "from": "human", "value": "\nWhat is located in this specific range: [0.324, 0.800, 0.380, 0.878]?" }, { "from": "gpt", "value": "This region encompasses the x-tick label for the 'Education' sector." } ], "id_raw": 612 }, { "id": "Y4sxBefBJf3XpvmA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis}[0.013, 0.343, 0.029, 0.638] in the chart?" }, { "from": "gpt", "value": "Scenario Values." } ], "id_raw": 613 }, { "id": "HxqaskOhXjMyWUMC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing 'VR Technology'}[0.064, 0.132, 0.973, 0.796]?" }, { "from": "gpt", "value": "The color is #6baed6." } ], "id_raw": 613 }, { "id": "umwoVUJWaM0hw853", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_13.png", "conversations": [ { "from": "human", "value": "\nWhat does {the point}[0.973, 0.401] on the 'Climate Change' line represent?" }, { "from": "gpt", "value": "The value of Climate Change in the year 2100." } ], "id_raw": 613 }, { "id": "VTEEcQZDjxbNyEBG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_13.png", "conversations": [ { "from": "human", "value": "\nWhich scenario has a higher value in the year 2030 - {Urbanization}[0.178, 0.275] or {VR Technology}[0.178, 0.695]?" }, { "from": "gpt", "value": "Urbanization." } ], "id_raw": 613 }, { "id": "oMgyiCgK7fY3s1B8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_13.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Climate Change'}[0.064, 0.152, 0.973, 0.401] decreasing or increasing over the years?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 613 }, { "id": "J2LNBnnvhppayeru", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_13.png", "conversations": [ { "from": "human", "value": "\nDoes {AI Adoption} at [0.746, 0.428] have a higher value than {VR Technology} at [0.746, 0.297] in the year 2080?" }, { "from": "gpt", "value": "No." } ], "id_raw": 613 }, { "id": "rwmKuEuRfOwSfBIi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_13.png", "conversations": [ { "from": "human", "value": "\nProvide a description of the area bounded by [0.789, 0.022, 0.966, 0.239]." }, { "from": "gpt", "value": "The plotted area consists of a legend, with labels 'Urbanization', 'VR Technology', 'AI Adoption', and 'Climate Change' each paired with a colored marker squared, diamond, and circular respectively." } ], "id_raw": 613 }, { "id": "PMzYaOzqEGaqkkTM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you observe in the region from [0.064, 0.132, 0.973, 0.796]?" }, { "from": "gpt", "value": "The area illustrates the plotted lines for the four scenarios, 'Urbanization', 'VR Technology', 'AI Adoption', and 'Climate Change' over the decades." } ], "id_raw": 613 }, { "id": "fmJh1150dPxBndxf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_13.png", "conversations": [ { "from": "human", "value": "\nCould you describe the data represented by x-axis ticks and their coordinates within the region [0.064, 0.893, 0.973, 0.893]?" }, { "from": "gpt", "value": "This region delineates the decade labels ranging from 2020 to 2100 on the x-axis, mapped sequentially at coordinates starting from [0.064, 0.893] ending at [0.973, 0.893]." } ], "id_raw": 613 }, { "id": "AcFEeQZ9Rxw8T82u", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the element of the graph at coordinates [0.978, 0.501, 0.993, 0.515]?" }, { "from": "gpt", "value": "Y-axis tick label." } ], "id_raw": 615 }, { "id": "V1FmrHKi4gAdQRcl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_15.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object at these coordinates [0.843, 0.141, 0.933, 0.158]?" }, { "from": "gpt", "value": "It is the label for {AI Adoption}[0.843, 0.141, 0.933, 0.158] in the legend." } ], "id_raw": 615 }, { "id": "0gqLisjz9GYMEsz1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_15.png", "conversations": [ { "from": "human", "value": "\nWhat's the label of {the x-axis tick} at [0.843, 0.909, 0.872, 0.932]?" }, { "from": "gpt", "value": "2090." } ], "id_raw": 615 }, { "id": "B0PyG7WGc87aQZgA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the relationship between the data on {AI Adoption}[0.842, 0.137, 0.921, 0.167] and {Urbanization}[0.842, 0.049, 0.930, 0.079] in 2100?" }, { "from": "gpt", "value": "AI Adoption is lower." } ], "id_raw": 615 }, { "id": "rjZWqMXOP2UAyeb9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_15.png", "conversations": [ { "from": "human", "value": "\nIn 2060, is the scenario value for Urbanization represented by {the line for 'Urbanization'}[0.064, 0.179, 0.973, 0.297] greater or less than the scenario value for VR Technology represented by {the line for 'VR Technology'}[0.064, 0.132, 0.973, 0.796]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 615 }, { "id": "TPqfSc84FIfBHNEB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_15.png", "conversations": [ { "from": "human", "value": "\nBetween {Climate Change}[0.842, 0.181, 0.952, 0.211] and {AI Adoption}[0.842, 0.137, 0.921, 0.167], which scenario had a lower value in 2040?" }, { "from": "gpt", "value": "Climate Change." } ], "id_raw": 615 }, { "id": "i3KEjc0msh1ICCEy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the coordinates [0.789, 0.022, 0.966, 0.239]." }, { "from": "gpt", "value": "This area contains the legend of the graph with labels and color markers representing four different scenarios: {Urbanization}[0.842, 0.049, 0.930, 0.079], {VR Technology}[0.842, 0.093, 0.943, 0.123], {AI Adoption}[0.842, 0.137, 0.921, 0.167], and {Climate Change}[0.842, 0.181, 0.952, 0.211]." } ], "id_raw": 615 }, { "id": "FHldDp6JEYGkt9vp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region from [0.013, 0.343, 0.029, 0.638]?" }, { "from": "gpt", "value": "This region contains the Y-axis label, which is titled {Scenario Values}[0.013, 0.343, 0.029, 0.638]." } ], "id_raw": 615 }, { "id": "CB95vlcxYEHGQpYs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_15.png", "conversations": [ { "from": "human", "value": "\nDescribe what's inside the area defined by [0.958, 0.909, 0.988, 0.932]." }, { "from": "gpt", "value": "There is one object - the last {X-axis tick label}[0.958, 0.909, 0.988, 0.932], it is the year '2100'." } ], "id_raw": 615 }, { "id": "FSRlAfgCxEOy06QT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at coordinates [0.086, 0.397, 0.098, 0.613] refer to?" }, { "from": "gpt", "value": "Y-axis label which is \"Global Trend Value\"." } ], "id_raw": 616 }, { "id": "jufxvA8lKmUt2MZi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_16.png", "conversations": [ { "from": "human", "value": "\nCan you identify what is located at the coordinates [0.708, 0.838]?" }, { "from": "gpt", "value": "It corresponds to a data point on the graph representing the year 2018 for Global Temperature." } ], "id_raw": 616 }, { "id": "8JgpNcvIw7JqWDGU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_16.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.102, 0.658, 0.117, 0.681]?" }, { "from": "gpt", "value": "10." } ], "id_raw": 616 }, { "id": "vpCgVo2k6rI7OXuK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_16.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value in 2020, {Global Temperature}[0.865, 0.832] or {Sea Level Rise}[0.865, 0.667]?" }, { "from": "gpt", "value": "Global Temperature." } ], "id_raw": 616 }, { "id": "PLUmdtd2md5R9VKN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_16.png", "conversations": [ { "from": "human", "value": "\nIn 2013, is the value for {Renewable Energy Consumption}[0.317, 0.492] higher or lower than the {Sea Level Rise}[0.317, 0.803]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 616 }, { "id": "syZmTidMxJKhY8af", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_16.png", "conversations": [ { "from": "human", "value": "\nIn 2014, which one is higher, {global temperature}[0.395, 0.840] or {renewable energy consumption}[0.395, 0.470]?" }, { "from": "gpt", "value": "Global temperature." } ], "id_raw": 616 }, { "id": "rPYz3XwqNpfn3Lcr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within [0.168, 0.211, 0.356, 0.234]?" }, { "from": "gpt", "value": "The area contains the legend item: {Renewable Energy Consumption}[0.168, 0.211, 0.356, 0.234]. It is paired with a green square marker." } ], "id_raw": 616 }, { "id": "C7z26pTMsPPefTYK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_16.png", "conversations": [ { "from": "human", "value": "\nCan you provide a description for the region defined by [0.102, 0.283, 0.117, 0.306]?" }, { "from": "gpt", "value": "This area contains the Y-axis label for the value '30' for the Global Trend Value." } ], "id_raw": 616 }, { "id": "SiSsU3pta8DMYDKr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.160, 0.832, 0.865, 0.842] contain?" }, { "from": "gpt", "value": "This region contains the line for the Global Temperature trend from 2011 to 2020." } ], "id_raw": 616 }, { "id": "MVjumKCEnXFNuSc6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.091, 0.397, 0.108, 0.592] on this chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 617 }, { "id": "GfQp4fYmlM36CfDn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_17.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the text field of the fourth legend item?" }, { "from": "gpt", "value": "[0.799, 0.048, 0.952, 0.078]." } ], "id_raw": 617 }, { "id": "HAAPNxv4G3ZHP0Nq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_17.png", "conversations": [ { "from": "human", "value": "\nWhat year corresponds to the x-axis tick label positioned at [0.795, 0.909, 0.824, 0.932]?" }, { "from": "gpt", "value": "2008." } ], "id_raw": 617 }, { "id": "QkqjmqrvBTJQVA75", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_17.png", "conversations": [ { "from": "human", "value": "\nIs the point {in 2002}[0.320, 0.514] for 'Moving within the city' higher or lower than the point for the same year for 'Moving to another city'[0.320, 0.822]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 617 }, { "id": "zkXF1uEb3tBxVNwi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_17.png", "conversations": [ { "from": "human", "value": "\nIs the {point at 2000}[0.157, 0.527] on 'Moving within the city' line placed above or below the {first point}[0.157, 0.230] on the 'Not moving' line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 617 }, { "id": "ERexlb3Mi75JKuOG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_17.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {2006}[0.647, 0.495], 'Moving within the city' or 'Moving to another city'[0.647, 0.821]?" }, { "from": "gpt", "value": "'Moving within the city'." } ], "id_raw": 617 }, { "id": "yRqyno3VAJiBRtUA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects found in the area [0.053, 0.021, 0.966, 0.106]." }, { "from": "gpt", "value": "The region contains the chart legend. The legend includes the items: 'Moving within the city' {with the associated marker in blue square}[0.061, 0.051, 0.101, 0.074], 'Moving to another city' {with the associated marker in light-blue triangle}[0.281, 0.051, 0.320, 0.074], 'Moving to another country' {with the associated marker in green diamond}[0.504, 0.051, 0.543, 0.074], and another item labeled 'Moving within the city' {with the associated marker in light-green circle}[0.754, 0.051, 0.794, 0.074]." } ], "id_raw": 617 }, { "id": "IdSWbENnDGgEU30R", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.545, 0.942, 0.585, 0.975]." }, { "from": "gpt", "value": "This is where the x-axis label 'Year' is located." } ], "id_raw": 617 }, { "id": "wLVAy3bR47JbJIis", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_17.png", "conversations": [ { "from": "human", "value": "\nChoose an area and describe what it contains [0.112, 0.171, 0.149, 0.194]." }, { "from": "gpt", "value": "This area encloses the y-axis tick label '55000'." } ], "id_raw": 617 }, { "id": "oxF4Hqrp7GmurcjM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_18.png", "conversations": [ { "from": "human", "value": "\nWhat does the object with coordinate [0.013, 0.297, 0.029, 0.692] stand for in the chart?" }, { "from": "gpt", "value": "It is the y-axis label." } ], "id_raw": 618 }, { "id": "K4DckLpJ031lYpL4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the object marked at these coordinates [0.064, 0.781]?" }, { "from": "gpt", "value": "It is the first data point of the 'AI in Healthcare' line." } ], "id_raw": 618 }, { "id": "PGaz3nB8Cc1MqsrG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_18.png", "conversations": [ { "from": "human", "value": "\nWhat information does the coordinate [0.468, 0.901] correspond to?" }, { "from": "gpt", "value": "It corresponds to the x-axis tick for the year 2026." } ], "id_raw": 618 }, { "id": "tzfmN44vVIgJCUpG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_18.png", "conversations": [ { "from": "human", "value": "\nWhich line lies above at the 2026 comparison point: {AI in Healthcare}[0.468, 0.627] or {Telemedicine}[0.468, 0.445]?" }, { "from": "gpt", "value": "AI in Healthcare." } ], "id_raw": 618 }, { "id": "Zg3nZ5bJRg08xh2U", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_18.png", "conversations": [ { "from": "human", "value": "\nBy 2031, which line indicates greater projected growth: {Electronic Health Records}[0.973, 0.373] or {Precision Medicine}[0.973, 0.446]?" }, { "from": "gpt", "value": "Precision Medicine." } ], "id_raw": 618 }, { "id": "ORqZReeluXl96HrZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_18.png", "conversations": [ { "from": "human", "value": "\nBetween {AI in Healthcare}[0.872, 0.286] and {Electronic Health Records}[0.872, 0.360], who had a higher projected growth in 2030?" }, { "from": "gpt", "value": "Electronic Health Records." } ], "id_raw": 618 }, { "id": "869N94OJbbDL8ctb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within [0.064, 0.161, 0.973, 0.525]." }, { "from": "gpt", "value": "The area includes four line graphs: {AI in Healthcare}[0.064, 0.161, 0.973, 0.781], {Telemedicine}[0.064, 0.332, 0.973, 0.525], {Electronic Health Records}[0.064, 0.169, 0.973, 0.373], and {Precision Medicine}[0.064, 0.234, 0.973, 0.446] that represent the projected growth in their respective sectors." } ], "id_raw": 618 }, { "id": "ol3r8nm8KNKfAmc9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region with coordinates [0.193, 0.021, 0.966, 0.106]." }, { "from": "gpt", "value": "The area includes the chart legend with labels {AI in Healthcare}[0.245, 0.048, 0.356, 0.078], {Telemedicine}[0.422, 0.048, 0.513, 0.078], {Electronic Health Records}[0.580, 0.048, 0.757, 0.078], and {Precision Medicine}[0.824, 0.048, 0.952, 0.078], each paired with a colored iconic representation." } ], "id_raw": 618 }, { "id": "cMvlcycSPihOZKGF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_18.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects located within the region [0.064, 0.088, 0.973, 0.901]?" }, { "from": "gpt", "value": "This region includes the whole plotting area of the line chart containing the line graphs, the tick labels on both the x and y-axis and their respective labels." } ], "id_raw": 618 }, { "id": "tt2y6CZEm2pJaxB9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_19.png", "conversations": [ { "from": "human", "value": "\nWhat does {the text associated with the coordinate} [0.265, 0.049, 0.413, 0.079] refer to in the legend?" }, { "from": "gpt", "value": "Executive Leadership." } ], "id_raw": 619 }, { "id": "PdmEJST8zJQmZZRG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} [0.502, 0.942, 0.542, 0.975]?" }, { "from": "gpt", "value": "Task." } ], "id_raw": 619 }, { "id": "70u1WoUTlPsdlXQF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_31_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the point on the 'Business Analysis' line} [0.980, 0.783]?" }, { "from": "gpt", "value": "13.7." } ], "id_raw": 619 }, { "id": "7oWvUXLduiVa8HF6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_19.png", "conversations": [ { "from": "human", "value": "\nIs {the data point for 'Strategic Planning'}[0.267, 0.356] below or above the corresponding point in {the 'Executive Leadership' line}[0.267, 0.219]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 619 }, { "id": "ketp4JG7e1ZSK0to", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_19.png", "conversations": [ { "from": "human", "value": "\nWhere does {the point for 'Team Building'}[0.675, 0.645] lie in relation to {the point for 'Business Analysis'}[0.675, 0.765] on the chart?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 619 }, { "id": "WIuQeX8XXO3AYVsy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_31_19.png", "conversations": [ { "from": "human", "value": "\nDoes the {Task 8 point for 'Executive Leadership'}[0.777, 0.368] indicate a higher or lower performance than the {Task 8 point for 'Strategic Planning'}[0.777, 0.491]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 619 }, { "id": "m8nEgnX4CdLKE3TD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_19.png", "conversations": [ { "from": "human", "value": "\nDescribe what is in the area with the bounding box [0.212, 0.022, 0.973, 0.107]." }, { "from": "gpt", "value": "This area contains the legend of the chart with colors and labels indicating the different lines: {Executive Leadership}[0.220, 0.051, 0.260, 0.074], {Strategic Planning}[0.435, 0.051, 0.474, 0.074], {Team Building}[0.628, 0.051, 0.667, 0.074], and {Business Analysis}[0.792, 0.051, 0.831, 0.074]." } ], "id_raw": 619 }, { "id": "Emff6ZXSPSqvCb4W", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is located in the box [0.064, 0.088, 0.980, 0.893]." }, { "from": "gpt", "value": "The area contains the main chart plotting area with lines representing the different categories: {Executive Leadership}[0.064, 0.126, 0.980, 0.424], {Strategic Planning}[0.064, 0.286, 0.980, 0.519], {Team Building}[0.064, 0.552, 0.980, 0.696], and {Business Analysis}[0.064, 0.731, 0.980, 0.783]. Also, the x-axis and y-axis with their respective labels and tick labels are located within this bounding box." } ], "id_raw": 619 }, { "id": "Ng0PzHqdBqjqZUho", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_31_19.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the box defined by [0.048, 0.884, 0.056, 0.907]?" }, { "from": "gpt", "value": "This region holds the label '0' from the y-axis tick labels of the chart." } ], "id_raw": 619 }, { "id": "eb8Y9iS5uiVb8Asu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.344, 0.019, 0.741, 0.041] represent?" }, { "from": "gpt", "value": "It is the subtitle of the first subplot, \"Automobile Positive Emotion\"." } ], "id_raw": 621 }, { "id": "xDTm8b3io6AAQ4Qa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the nature of the object found at coordinates [0.025, 0.620, 0.048, 0.845]?" }, { "from": "gpt", "value": "It is the Y-axis label of the second subplot." } ], "id_raw": 621 }, { "id": "Yg3NrqfsDjjenqlq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_1.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.827, 0.939, 0.864, 0.957] correspond to in the chart?" }, { "from": "gpt", "value": "The label '0.9' on the x-axis of the second subplot." } ], "id_raw": 621 }, { "id": "w70HE14c7kLx8y3l", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point at '0.5'}[0.500, 0.320] higher or lower than {the point at '0.7'}[0.672, 0.359] on the 'Automobile' line?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 621 }, { "id": "Hlh0PVFzoqpaiPdB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_1.png", "conversations": [ { "from": "human", "value": "\nIn the second subplot, comparing {the point at '0.9'}[0.845, 0.693] and {the point at '1.0'}[0.932, 0.700], which one is higher on the 'Public Transportation' line?" }, { "from": "gpt", "value": "The point at '1.0'." } ], "id_raw": 621 }, { "id": "HNyBsuAWLCR86cOk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_1.png", "conversations": [ { "from": "human", "value": "\nThe line of 'Automobile Positive Emotion' at {Ratio_0.2}[0.240, 0.352], is it above or below the 'Public Transportation Negative Emotion' line at {Ratio_0.3}[0.327, 0.609]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 621 }, { "id": "FTNIQYdLuxBbjPyp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area defined by [0.269, 0.509, 0.816, 0.532]?" }, { "from": "gpt", "value": "The area contains the subtitle of the second subplot, \"Public Transportation Negative Emotion\"." } ], "id_raw": 621 }, { "id": "NR7Lq2EocGKOK3ad", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents within [0.122, 0.055, 0.338, 0.087]?" }, { "from": "gpt", "value": "The area has the legend for the first subplot. It includes {the label 'Automobile'}[0.196, 0.062, 0.328, 0.080] and its corresponding marker." } ], "id_raw": 621 }, { "id": "HLMmP6SHlrw4lk34", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_1.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region defined by [0.135, 0.939, 0.172, 0.957] and [0.913, 0.939, 0.950, 0.957]?" }, { "from": "gpt", "value": "These regions contain the x-axis tick labels '0.1' and '1.0', respectively, for the second subplot." } ], "id_raw": 621 }, { "id": "pSYbEYXejly7AW5Y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at coordinate [0.218, 0.553, 0.277, 0.570] indicate in the chart?" }, { "from": "gpt", "value": "That text indicates '2021'." } ], "id_raw": 622 }, { "id": "opWpF9mnku0Y1naY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_2.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.175, 0.905] in 'subplot_1' refer to on the chart?" }, { "from": "gpt", "value": "These coordinates refer to the data point for the year 2021 in the first month (0)." } ], "id_raw": 622 }, { "id": "7ovK8r3VW80gVymZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel} at [0.025, 0.709, 0.048, 0.755]?" }, { "from": "gpt", "value": "Sales." } ], "id_raw": 622 }, { "id": "hc6GODtYhEmBDTMp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_2.png", "conversations": [ { "from": "human", "value": "\nDoes the sales graph of 2021 start at a higher or lower point compared to 2020, considering respective starting points {2020}[0.175, 0.395] and {2021}[0.175, 0.905]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 622 }, { "id": "MCRuqrEl0d5YQLUW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_2.png", "conversations": [ { "from": "human", "value": "\nBy comparing the final sales points of {2020}[0.933, 0.395] and {2021}[0.933, 0.545], which year ended with higher sales?" }, { "from": "gpt", "value": "2021." } ], "id_raw": 622 }, { "id": "4Kxwlqx1W7plzYIl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_2.png", "conversations": [ { "from": "human", "value": "\nDuring the 5th month, comparing the sales levels of {2020}[0.596, 0.410] and {2021}[0.596, 0.710], which year had higher sales?" }, { "from": "gpt", "value": "2021." } ], "id_raw": 622 }, { "id": "EKuhczFel3hOS31V", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the region [0.518, 0.509, 0.589, 0.532]." }, { "from": "gpt", "value": "This region contains the subtitle for the second plot, stating the label '2021.'" } ], "id_raw": 622 }, { "id": "b1J4lBujdujsCTKN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the region between [0.144, 0.546, 0.286, 0.577]." }, { "from": "gpt", "value": "This region contains the legend for the sales chart of 2021. The text '2021' is present, with its corresponding marker." } ], "id_raw": 622 }, { "id": "e8atLSVdPx5MGl1N", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_2.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.167, 0.939, 0.182, 0.957]?" }, { "from": "gpt", "value": "This region contains the label for the 0th month on the x-axis of the sales chart for 2021." } ], "id_raw": 622 }, { "id": "lxNTsP6sRUlV9LiK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_3.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what {the object}[0.417, 0.509, 0.669, 0.532] is in the chart?" }, { "from": "gpt", "value": "It's the subtitle of a subplot, which reads \"Dividends_Growth\"." } ], "id_raw": 623 }, { "id": "ggZL74ocf9omuiZJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_3.png", "conversations": [ { "from": "human", "value": "\nWhat object can be found at the coordinates [0.189, 0.132, 0.896, 0.398]?" }, { "from": "gpt", "value": "This is the line for 'Earnings_Yield' in the chart." } ], "id_raw": 623 }, { "id": "LhtidiE7IGbVGdTb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_3.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the chart at {coordinates}[0.875, 0.939, 0.912, 0.957]?" }, { "from": "gpt", "value": "The x-axis tick label '1.0' is located here." } ], "id_raw": 623 }, { "id": "NL7hKaMN6c3Rz3PI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_3.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Dividends_Growth'}[0.896, 0.912] at a higher or lower value than {the point for 'Earnings_Yield'}[0.896, 0.398] at the same Index?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 623 }, { "id": "YqlgNWeUqks9lCfc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Earnings_Yield'}[0.189, 0.132, 0.896, 0.398] experience more or less growth than {the line for 'Dividends_Growth'}[0.189, 0.553, 0.896, 0.912] across the Index?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 623 }, { "id": "BmCgGWIpTHPpMyHr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_3.png", "conversations": [ { "from": "human", "value": "\nIs the last x-axis tick in the {sublot_0}[0.110, 0.047, 0.975, 0.437] above or below the first x-axis tick in the {subplot_1}[0.110, 0.537, 0.975, 0.927]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 623 }, { "id": "olWwQzmgzPbJtCIU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe what you see in the region [0.110, 0.537, 0.975, 0.927]." }, { "from": "gpt", "value": "This area contains the second subplot which is titled 'Dividends_Growth'. It includes a line for 'Dividends_Growth' which is marked by a '--' linestyle and 'o' marker. There are labels at the top and bottom, as well as a grid, and tick labels along the x and y axis." } ], "id_raw": 623 }, { "id": "I1fllFHM8Ubld6Wa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_3.png", "conversations": [ { "from": "human", "value": "\nCan you tell me about the region between [0.110, 0.047, 0.975, 0.437]?" }, { "from": "gpt", "value": "This area contains the first subplot titled 'Earnings_Yield'. The plot includes a line to represent 'Earnings_Yield' in blue color with a '--' linestyle and 'o' markers. It also has x and y axis labels and tick labels with a visible grid." } ], "id_raw": 623 }, { "id": "UIblmjMWIIddWShZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects inside [0.485, 0.449, 0.522, 0.466] in the chart." }, { "from": "gpt", "value": "Within these coordinates, one of the x-axis tick labels '0.5' is located." } ], "id_raw": 623 }, { "id": "WvnXBfUMYBNp92x4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_4.png", "conversations": [ { "from": "human", "value": "\nWhat type of label is at {position} [0.478, 0.320, 0.586, 0.330]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 624 }, { "id": "6mltiOSJxeNcqTtU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the x-axis label} at [0.478, 0.650, 0.586, 0.660]?" }, { "from": "gpt", "value": "Black." } ], "id_raw": 624 }, { "id": "hsnu2HlGVlNPLP4b", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_4.png", "conversations": [ { "from": "human", "value": "\nWhat details are specified for {the ylabel} at [0.019, 0.422, 0.036, 0.563]?" }, { "from": "gpt", "value": "'Ratio of Usage/Consumption' in black color and rotated 90 degrees." } ], "id_raw": 624 }, { "id": "Uw5OZwGTh4TJsm7k", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_4.png", "conversations": [ { "from": "human", "value": "\nHow does {the point at [0.757, 0.445]} compare in value to {the point at [0.846, 0.450]} on the energy use line for Oil?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 624 }, { "id": "UNC1xE3TKsmabS5b", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_4.png", "conversations": [ { "from": "human", "value": "\nWhich {point}[0.936, 0.070] has a higher y value, from Energy Use or Resource Consumption lines for Coal?" }, { "from": "gpt", "value": "Energy Use." } ], "id_raw": 624 }, { "id": "mEkYFgR05U5a2wqY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_4.png", "conversations": [ { "from": "human", "value": "\nWhich of these points, {at [0.846, 0.796]} or {at [0.846, 0.822]}, marks a higher level of resource consumption, in Renewable energy data?" }, { "from": "gpt", "value": "[0.846, 0.822]." } ], "id_raw": 624 }, { "id": "WtmmCeDudQzKh1Cy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.478, 0.979, 0.586, 0.989]." }, { "from": "gpt", "value": "The x-axis label which reads \"Time (Years)\" in black text is situated here." } ], "id_raw": 624 }, { "id": "CN1MIYPQN8J7LXYg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.019, 0.092, 0.036, 0.233]." }, { "from": "gpt", "value": "This section contains the y-axis label 'Ratio of Usage/Consumption' presented in black and rotated 90 degrees." } ], "id_raw": 624 }, { "id": "wF6m34boYRFO1jdQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_4.png", "conversations": [ { "from": "human", "value": "\nWhat elements are observed within [0.712, 0.324, 0.966, 0.390]?" }, { "from": "gpt", "value": "It comprises the legend for the subplot, with labels 'Energy Use' at {coordinates}[0.766, 0.365, 0.864, 0.375] and 'Resource Consumption' at {coordinates}[0.766, 0.380, 0.966, 0.390], associated with markers that are colored blue and green respectively." } ], "id_raw": 624 }, { "id": "vX4bBNWymCH3NiRf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_5.png", "conversations": [ { "from": "human", "value": "\nWhat type of marks are used for the data points in the 'High Risk' series?" }, { "from": "gpt", "value": "'o'." } ], "id_raw": 625 }, { "id": "S6H04dWWzUfBD9nq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the text located at [0.461, 0.012, 0.607, 0.027]?" }, { "from": "gpt", "value": "SPRisk-0.5." } ], "id_raw": 625 }, { "id": "S4Z1LKaTNs04P73c", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_5.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the line representing 'Low Risk'}[0.110, 0.143, 0.957, 0.162]?" }, { "from": "gpt", "value": "#2ca02c." } ], "id_raw": 625 }, { "id": "NXyUBbwh1WllqhRL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_5.png", "conversations": [ { "from": "human", "value": "\nWhich trend, 'High Risk' or 'Low Risk', starts at a higher point in the first subplot?" }, { "from": "gpt", "value": "High Risk." } ], "id_raw": 625 }, { "id": "e6LiLDOIENYf6beR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_5.png", "conversations": [ { "from": "human", "value": "\nAre the data points for {the 'High Risk' series}[0.110, 0.847, 0.957, 0.865] above or below {the data points for the 'Low Risk' series}[0.110, 0.884, 0.957, 0.903] in the third subplot?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 625 }, { "id": "R4r30EXxbL6qKICQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_5.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, the start of {the 'Low Risk' line}[0.110, 0.143] or the start of {the 'High Risk' line}[0.110, 0.087] in the first subplot?" }, { "from": "gpt", "value": "High Risk." } ], "id_raw": 625 }, { "id": "BkzLJZRsT9uKY9Xh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents and appearance in the region [0.110, 0.031, 0.957, 0.329]." }, { "from": "gpt", "value": "Contains x-axis labeled '{Policy}'[0.500, 0.318, 0.567, 0.329], y-axis labeled '{Performance Score}'[0.025, 0.107, 0.048, 0.217], and multiple grid lines, along with the plot of two series 'High Risk' and 'Low Risk'." } ], "id_raw": 625 }, { "id": "Ux9lBMG1paJeOKX7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained within [0.110, 0.360, 0.957, 0.658]?" }, { "from": "gpt", "value": "The subplot includes axes labels 'Policy' at [0.500, 0.647, 0.567, 0.658] and 'Performance Score' at [0.025, 0.437, 0.048, 0.546], x-axis tick labels ranging from '0.1' to '0.5', y-axis tick labels from '0.3' to '0.9', and the plotted data for two trend lines labeled 'High Risk' and 'Low Risk'." } ], "id_raw": 625 }, { "id": "mhivAEVErd4j9RfO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_5.png", "conversations": [ { "from": "human", "value": "\nWhat features are observable in the area [0.092, 0.301, 0.975, 0.313]?" }, { "from": "gpt", "value": "The area includes the x-axis tick labels from '0.1' to '0.5' for the first subplot along with its respective grid lines." } ], "id_raw": 625 }, { "id": "nKABtzHXelXPPLkC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_6.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.025, 0.364, 0.048, 0.584] read?" }, { "from": "gpt", "value": "Performance." } ], "id_raw": 626 }, { "id": "RlEfFnelUBo9yuMU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the text represented by the coordinates [0.211, 0.125, 0.347, 0.160] in the legend?" }, { "from": "gpt", "value": "AI Research." } ], "id_raw": 626 }, { "id": "uVXPCEOiNnBFtc6R", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label at the coordinates [0.512, 0.927, 0.588, 0.963]?" }, { "from": "gpt", "value": "Ratio r." } ], "id_raw": 626 }, { "id": "og0CdzM6aYEWdtd0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point with x being 0.7}[0.678, 0.496] for the line representing 'AI Research' higher or lower than {the corresponding point}[0.678, 0.389] for the 'AI Healthcare'?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 626 }, { "id": "Mq0zi6MdLhKSgps9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the line representing 'AI Research'}[0.168, 0.254, 0.933, 0.810] increase or decrease from the point {x being 0.5}[0.508, 0.630] to the point {x being 0.9}[0.848, 0.281]?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 626 }, { "id": "1zLauDEiwvFZdWNW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_6.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {x being 0.3}, {the line representing 'AI Research'}[0.338, 0.747] or {the line representing 'AI Healthcare'}[0.338, 0.630]?" }, { "from": "gpt", "value": "AI Research." } ], "id_raw": 626 }, { "id": "r390dJoLq964OJ1f", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_6.png", "conversations": [ { "from": "human", "value": "\nWhat contents are seen in the area with coordinates [0.137, 0.111, 0.376, 0.226]?" }, { "from": "gpt", "value": "This area includes a legend with two items: {AI Research with a 'o' marker}[0.211, 0.125, 0.347, 0.160] in '#1f77b4' color, and {AI Healthcare with a '^' marker}[0.211, 0.177, 0.367, 0.212] in '#2ca02c' color." } ], "id_raw": 626 }, { "id": "KTwE4EcDMpwhUDu1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's within the coordinates [0.125, 0.093, 0.975, 0.854]?" }, { "from": "gpt", "value": "This area primarily comprises of the main body of the chart which includes two colorful lines representing 'AI Research' and 'AI Healthcare', the x-axis and y-axis with their respective tick labels ranging from 0.1-1.0 and 0.15-0.95 respectively, and the grid overlay." } ], "id_raw": 626 }, { "id": "Qs9oq2EDgtrhqfRL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents in the region [0.501, 0.037, 0.599, 0.083]?" }, { "from": "gpt", "value": "The region includes a subtitle that reads \"Ratio 1\" in black color." } ], "id_raw": 626 }, { "id": "xn5wLNW3iNoDs03s", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.025, 0.112, 0.048, 0.212] within subplot_0?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 627 }, { "id": "2umZ3MFsWo3JSnFr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_7.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the '2019' x-axis label in subplot_1?" }, { "from": "gpt", "value": "[0.495, 0.630, 0.554, 0.642]" } ], "id_raw": 627 }, { "id": "xz9lgqz5aAm1MTWl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-axis tick label} at [0.058, 0.114, 0.087, 0.126] in subplot_0?" }, { "from": "gpt", "value": "40." } ], "id_raw": 627 }, { "id": "kx5wb1clYNV3kbxd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 2018}[0.103, 0.118] in subplot_0 above or below {the point for 2019}[0.524, 0.075] for 'Ind-A'?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 627 }, { "id": "lDumfDEMc6DQf6kM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 2018}[0.103, 0.491] in subplot_1 higher or lower than {the point for 2020}[0.946, 0.448] for 'Ind-B'?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 627 }, { "id": "7wN7lbz9XeIvcDLH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_7.png", "conversations": [ { "from": "human", "value": "\nWhich year in subplot_2 has a higher market share for 'Ind-C', 2018 at {point}[0.103, 0.908] or 2019 at {point}[0.524, 0.908]?" }, { "from": "gpt", "value": "Equal." } ], "id_raw": 627 }, { "id": "QZRBhO1dSMzD75Tt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.103, 0.360, 0.946, 0.622]." }, { "from": "gpt", "value": "This area represents subplot_1 showcasing the trend of 'Ind-B'. It includes markers for the years 2018-2020 on the x-axis, marked percentage values for the y-axis, and the corresponding line graph in green, indicating the change in 'Market Share (%)' for the respective years." } ], "id_raw": 627 }, { "id": "NjlC6rMEKOs2AZFb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_7.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.058, 0.201, 0.087, 0.213] in subplot_0?" }, { "from": "gpt", "value": "The y-axis tick label '30' is located here." } ], "id_raw": 627 }, { "id": "mty0JKBywxSPTBrR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_7.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within the area [0.103, 0.689, 0.946, 0.951]?" }, { "from": "gpt", "value": "This area represents subplot_2 for 'Ind-C'. The objects within this area are the line graph representing market share percentages across 2018 to 2020, along with the x-axis labels of the three years and the y-axis labels representing percentage values." } ], "id_raw": 627 }, { "id": "R3FhSJJ5O0uRGQqf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_8.png", "conversations": [ { "from": "human", "value": "\nWhat does {the ylabel text}[0.024, 0.118, 0.038, 0.208] on the first subplot indicate?" }, { "from": "gpt", "value": "Yield in Million Tons." } ], "id_raw": 628 }, { "id": "1v3QiBqHpthevbTX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_8.png", "conversations": [ { "from": "human", "value": "\nWhat chart type is displayed at {subplot coordinates}[0.075, 0.025, 0.985, 0.301]?" }, { "from": "gpt", "value": "Line chart." } ], "id_raw": 628 }, { "id": "wXhVqhNNTeOwOyZb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_8.png", "conversations": [ { "from": "human", "value": "\nWhat year corresponds to {the x-axis tick}[0.116, 0.301] in the first subplot?" }, { "from": "gpt", "value": "2000." } ], "id_raw": 628 }, { "id": "7c6gmTUFTqbpd8pD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_8.png", "conversations": [ { "from": "human", "value": "\nIs {the point on 'Yield Trend for SPGrocery-0.5'}[0.208, 0.215] higher or lower than {the last point on the same trend}[0.944, 0.037]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 628 }, { "id": "feQ8f35OGHBwRzQB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_8.png", "conversations": [ { "from": "human", "value": "\nAre {the y-axis tick label at}[0.035, 0.963, 0.065, 0.972] and {the y-axis tick label at}[0.035, 0.783, 0.065, 0.793] from the same subplot?" }, { "from": "gpt", "value": "No." } ], "id_raw": 628 }, { "id": "v8fQfRifUdQOHUG5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the first point on the line}[0.116, 0.619] align vertically above or below {the corresponding x-axis tick}[0.116, 0.631]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 628 }, { "id": "uWywhxOrK7hwCUfP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_8.png", "conversations": [ { "from": "human", "value": "\nDetail the elements within the region [0.075, 0.025, 0.985, 0.301]." }, { "from": "gpt", "value": "This region contains the plot for 'Yield Trend for SPGrocery-0.5' including y-axis with labels {2.4 to 3.4}[0.043, 0.314, 0.065, 0.028] and x-axis marking the years from {2000 to 2009}[0.099, 0.308, 0.961, 0.317]." } ], "id_raw": 628 }, { "id": "LZz5yp8vzu3GZtzb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content shown in the region [0.075, 0.355, 0.985, 0.631]." }, { "from": "gpt", "value": "This area accommodates the 'Yield Trend for SPGrocery-0.7' chart, featuring a y-axis with varied yield values from {2.50 to 2.90}[0.035, 0.637, 0.065, 0.342] and x-axis marked with years {2000 to 2009}[0.099, 0.638, 0.961, 0.647] associated with specific yield points along a line chart." } ], "id_raw": 628 }, { "id": "bnsVzRWXmu17dUeG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_8.png", "conversations": [ { "from": "human", "value": "\nCould you explain what's included in the area [0.075, 0.685, 0.985, 0.961]?" }, { "from": "gpt", "value": "Within this region, the chart titled 'Yield Trend for SPGrocery-0.9' is present, showcasing a ylabel {Yield in Million Tons}[0.015, 0.778, 0.029, 0.868] and an x-axis extending from {2000}[0.099, 0.968, 0.134, 0.977] through {2009}[0.926, 0.968, 0.961, 0.977], with the corresponding line graph plotting yield points throughout the years." } ], "id_raw": 628 }, { "id": "Jrtiq8rZKM5EjIBe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of {marker}[0.857, 0.862, 0.893, 0.876] for Lifestyle B in the chart legend?" }, { "from": "gpt", "value": "Triangle marker." } ], "id_raw": 629 }, { "id": "2xfqzDYJVIjXohJP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_9.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the line with coordinates [0.116, 0.550, 0.944, 0.865] in the chart?" }, { "from": "gpt", "value": "Lifestyle B." } ], "id_raw": 629 }, { "id": "jnB6tDCgYyADmnxT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label at the coordinates [0.476, 0.952, 0.585, 0.975]?" }, { "from": "gpt", "value": "Social Activities." } ], "id_raw": 629 }, { "id": "hisc2yHhQ1wgPYZo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_9.png", "conversations": [ { "from": "human", "value": "\nAt the Social Activity labeled 'Stay at Home', is {Lifestyle A}[0.116, 0.550] higher or lower than {Lifestyle B}[0.116, 0.775]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 629 }, { "id": "rN5zbiXIUXAvQRcY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_9.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at 'Outdoor Activities', {Lifestyle A}[0.323, 0.325] or {Lifestyle B}[0.323, 0.685]?" }, { "from": "gpt", "value": "Lifestyle A." } ], "id_raw": 629 }, { "id": "GK6qqTIoUJ3xci3b", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_9.png", "conversations": [ { "from": "human", "value": "\nComparing the lifestyle activities at 'Online Socialisation', is {Lifestyle A}[0.530, 0.460] more represented than {Lifestyle B}[0.530, 0.730]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 629 }, { "id": "yJJPteOs0vJzfvoZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within [0.856, 0.815, 0.978, 0.891]." }, { "from": "gpt", "value": "This is the legend area of the chart. It contains information about 'Lifestyle A' with a blue circular marker at {position}[0.857, 0.827, 0.893, 0.841], and 'Lifestyle B' with a green triangular marker at {position}[0.857, 0.862, 0.893, 0.876]." } ], "id_raw": 629 }, { "id": "Viu9FvOYkkKzuzpd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_9.png", "conversations": [ { "from": "human", "value": "\nCan you provide a description of the area [0.075, 0.062, 0.985, 0.903]?" }, { "from": "gpt", "value": "This is the main plotting area of the line chart. It contains two lines representing 'Lifestyle A' and 'Lifestyle B'. The plot also includes x-axis labels indicating different social activities and y-axis labels showing time spent, with tick marks at regular intervals." } ], "id_raw": 629 }, { "id": "np5OrHfGBbm3twuU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.035, 0.204, 0.065, 0.227]" }, { "from": "gpt", "value": "This region includes a y-axis label of 17.5." } ], "id_raw": 629 }, { "id": "XeStbrYqr8f6yiAo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.058, 0.079, 0.094, 0.091] indicate in the chart?" }, { "from": "gpt", "value": "Y-axis value." } ], "id_raw": 630 }, { "id": "x9jjEgtePynlr3N1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_10.png", "conversations": [ { "from": "human", "value": "\nWhat is shown at position [0.154, 0.293, 0.154, 0.293] in the chart?" }, { "from": "gpt", "value": "X-axis tick mark." } ], "id_raw": 630 }, { "id": "J22Sls2hnlBYI0Wc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.827, 0.630, 0.864, 0.642]?" }, { "from": "gpt", "value": "X-axis tick label." } ], "id_raw": 630 }, { "id": "FqFDu6IuHOZcl8L4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_10.png", "conversations": [ { "from": "human", "value": "\nIs {the point at [0.240, 0.267]} higher or lower than {the point at [0.240, 0.267]}?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 630 }, { "id": "0wmu54vLzCGwGw8s", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_10.png", "conversations": [ { "from": "human", "value": "\nWhich line is above at {point}[0.927, 0.084] between RPiS and CNN lines?" }, { "from": "gpt", "value": "CNN." } ], "id_raw": 630 }, { "id": "2LcyqvrNHCkGR2Vy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_10.png", "conversations": [ { "from": "human", "value": "\nCompare the slope direction from the second point to the third point of {line labeled 'RPiS'}[0.240, 0.251, 0.327, 0.230] with that of {line labeled 'CNN'}[0.240, 0.267, 0.327, 0.241]?" }, { "from": "gpt", "value": "Steeper." } ], "id_raw": 630 }, { "id": "Z5Wh3XrwkabHVTw4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found in the coordinates [0.025, 0.053, 0.256, 0.110]." }, { "from": "gpt", "value": "The legend box containing the items {RPiS}[0.196, 0.042, 0.247, 0.053] with a blue marker and {CNN}[0.196, 0.059, 0.247, 0.071] with a green marker are located here." } ], "id_raw": 630 }, { "id": "yTPcFQ2d9SzTOTJN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_10.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the bounding box from [0.504, 0.976, 0.581, 0.988]?" }, { "from": "gpt", "value": "The x-axis label \"Ratio r\" styled in black color can be observed within this region." } ], "id_raw": 630 }, { "id": "5zbBri4YIGnhkwse", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content between [0.110, 0.301, 0.975, 0.329]?" }, { "from": "gpt", "value": "This area encompasses the x-axis, where labels ranging from '0.1' at [0.135, 0.301, 0.172, 0.313] to '1.0' at [0.913, 0.301, 0.950, 0.313] appear, with corresponding tick marks from {coordinates}[0.154, 0.293] to {coordinates}[0.932, 0.293]." } ], "id_raw": 630 }, { "id": "TCwFkYwK26iGAAVp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_11.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis label}[0.504, 0.318, 0.581, 0.329] display in the chart?" }, { "from": "gpt", "value": "Ratio r." } ], "id_raw": 631 }, { "id": "ozJkhsJRTy4nH7ba", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the fifth point on the PNA line}[0.500, 0.132]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 631 }, { "id": "aR35TK8KyUar4vAO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_11.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at {coordinates}[0.058, 0.114, 0.094, 0.126] on the y-axis?" }, { "from": "gpt", "value": "0.8." } ], "id_raw": 631 }, { "id": "EWJnP3b4JXRu3JfR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_11.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at {point 0.5}[0.500, 0.132], {PNA}[0.500, 0.132] or {GIN}[0.500, 0.197]?" }, { "from": "gpt", "value": "PNA." } ], "id_raw": 631 }, { "id": "duztuAVYeGoafrF7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the label '0.9'}[0.827, 0.960, 0.864, 0.971] represent a higher or lower value than {the label '0.2'}[0.222, 0.960, 0.258, 0.971] on the x-axis?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 631 }, { "id": "S7UKDRbTckGOC2ME", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_11.png", "conversations": [ { "from": "human", "value": "\nIs {the 0.9 tick mark on the x-axis}[0.845, 0.951] to the left or right of {the 0.3 tick mark}[0.327, 0.951]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 631 }, { "id": "ETPwv0rIvZ07Di7K", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen within [0.122, 0.695, 0.252, 0.734]?" }, { "from": "gpt", "value": "The subplot legend for \"SPMotif-0.9\" featuring {PNA text}[0.196, 0.700, 0.243, 0.712] and {GIN text}[0.196, 0.717, 0.238, 0.729] with respective color markers." } ], "id_raw": 631 }, { "id": "X0iSh6jY6AFRWnSc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.110, 0.031, 0.975, 0.293] contain?" }, { "from": "gpt", "value": "It consists of a line chart with labels such as {PNA line}[0.154, 0.132, 0.932, 0.180] and {GIN line}[0.154, 0.145, 0.932, 0.241], and includes both axes labels and tick marks." } ], "id_raw": 631 }, { "id": "mzUA6o7MOSK4jWwj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found in [0.058, 0.070, 0.094, 0.082]." }, { "from": "gpt", "value": "This area contains the y-axis tick label {0.9}[0.058, 0.070, 0.094, 0.082], colored in black." } ], "id_raw": 631 }, { "id": "K2WSJHu4eQTUkdaE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_13.png", "conversations": [ { "from": "human", "value": "\nWhat type of mark represents {the point at time '0.7'}[0.672, 0.254] in the chart for \"Hunter-gatherer Eras\"?" }, { "from": "gpt", "value": "Circle." } ], "id_raw": 633 }, { "id": "tS33tBD2jcWgFd5L", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing 'Agricultural development'}[0.154, 0.426, 0.932, 0.600]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 633 }, { "id": "9i6bSo1SE42jIMs7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_13.png", "conversations": [ { "from": "human", "value": "\nWhat represents the label at {coordinate}[0.308, 0.301, 0.345, 0.313] on the x-axis?" }, { "from": "gpt", "value": "0.3." } ], "id_raw": 633 }, { "id": "pskorTCNVvczZIlT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_13.png", "conversations": [ { "from": "human", "value": "\nIs the development value depicted at {the point 0.5}[0.500, 0.539] in 'Agricultural development' higher than at {the point 0.3}[0.327, 0.206] in 'Hunter-gatherer Eras'?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 633 }, { "id": "K6W0jQt2iRX4FgFs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_13.png", "conversations": [ { "from": "human", "value": "\nDoes the start or end have a higher development index for {the line 'Industrial Revolution'}[0.154, 0.694, 0.932, 0.951]?" }, { "from": "gpt", "value": "End." } ], "id_raw": 633 }, { "id": "Am3UR42smf60Rmeo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_13.png", "conversations": [ { "from": "human", "value": "\nBetween {the mark '0.4'}[0.413, 0.548] and {the mark '0.8'}[0.759, 0.469] in 'Agricultural development', which is higher on the graph?" }, { "from": "gpt", "value": "0.4." } ], "id_raw": 633 }, { "id": "ex5SRoi6yo3rT43W", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_13.png", "conversations": [ { "from": "human", "value": "\nWhat do the labels surrounding the area [0.058, 0.247, 0.094, 0.300] on the y-axis within the subplot 'Hunter-gatherer Eras' represent?" }, { "from": "gpt", "value": "The range consistently lists numerical development values from 0.4 at the top to 0.3 at the bottom." } ], "id_raw": 633 }, { "id": "eCZyCn6jp8xV1FW7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found in the horizontal span across [0.385, 0.976, 0.700, 0.988] at the bottom of the chart." }, { "from": "gpt", "value": "The x-axis label \"Time (from ancient to now)\" for the 'Industrial Revolution' subplot is located here." } ], "id_raw": 633 }, { "id": "94FGlUYQg0AKSPeX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the section [0.058, 0.618, 0.094, 0.629] on the third subplot represent?" }, { "from": "gpt", "value": "It includes a y-axis tick label displaying the value 0.3 in the subplot 'Agricultural development'." } ], "id_raw": 633 }, { "id": "1u5mgtizWbiAoBm1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_14.png", "conversations": [ { "from": "human", "value": "\nWhat is marked by {the point}[0.172, 0.273] on the chart?" }, { "from": "gpt", "value": "GDP for 2007." } ], "id_raw": 634 }, { "id": "qLKUgsqDVwVVzMak", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_14.png", "conversations": [ { "from": "human", "value": "\nWhat is located at {coordinates}[0.204, 0.050, 0.253, 0.064] in the subplot area?" }, { "from": "gpt", "value": "Legend text \"GDP\"." } ], "id_raw": 634 }, { "id": "rKt9hzok7bLaGeZ9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_14.png", "conversations": [ { "from": "human", "value": "\nIdentify the label of {the x-axis tick}[0.172, 0.285] in the chart." }, { "from": "gpt", "value": "2007." } ], "id_raw": 634 }, { "id": "5tiOcswgMRwUf3ah", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_14.png", "conversations": [ { "from": "human", "value": "\nIs {year 2012's GDP point}[0.439, 0.600] positioned higher or lower than {year 2012's CPI point}[0.439, 0.424]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 634 }, { "id": "q3WYL5Ca97fhwQf9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_14.png", "conversations": [ { "from": "human", "value": "\nWhich is located farther to the right, {the last GDP point}[0.921, 0.923] or {the last CPI point}[0.921, 0.722] in the subplot?" }, { "from": "gpt", "value": "Both are at the same position." } ], "id_raw": 634 }, { "id": "f5q6F3e8CKttQe9Q", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_14.png", "conversations": [ { "from": "human", "value": "\nAre {the line labeled 'GDP'}[0.172, 0.283] and {the line labeled 'CPI'}[0.172, 0.095] plotted in the same color?" }, { "from": "gpt", "value": "No." } ], "id_raw": 634 }, { "id": "dpVIZggJ23PgTorE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_14.png", "conversations": [ { "from": "human", "value": "\nCharacterize the area defined by [0.118, 0.037, 0.975, 0.285]." }, { "from": "gpt", "value": "The lower part of the chart displays two line graphs representing GDP (in blue) and CPI (in green), flavored with x-grid and y-grid lines, having tick labels marking the years from 2007 to 2011, and y-axis values ranging from 0 to 160, located in respective tick mark positions." } ], "id_raw": 634 }, { "id": "IUAvfafZKH6ij9O4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_14.png", "conversations": [ { "from": "human", "value": "\nWhat can you describe about the region within [0.118, 0.366, 0.975, 0.613]?" }, { "from": "gpt", "value": "This middle portion of the chart features the continuation of the GDP and CPI lines, now marking the years from 2012 to 2016, with y-axis values stretching up to 280, and colored lines (blue and green, similarly) continuing the map of the economic metrics' trends." } ], "id_raw": 634 }, { "id": "spS1guWW343F2vDZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the graphic elements and details within the location [0.118, 0.694, 0.975, 0.942]." }, { "from": "gpt", "value": "The upper section encompasses line graphs for GDP and CPI over the years 2017 through 2021, illustrated using blue and green dashes and markers respectively. It includes x-axis and y-axis indicators, both offering detailed labels of years and economic metric values up to 280." } ], "id_raw": 634 }, { "id": "lp6zzhLLZeGUqZs6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_15.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the label at coordinates [0.058, 0.879, 0.094, 0.886]?" }, { "from": "gpt", "value": "Y-axis tick label for 0.9." } ], "id_raw": 635 }, { "id": "UdixCYakxnm8b0h6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_15.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the \"Innovation\" line as seen at {handle}[0.760, 0.363, 0.820, 0.367]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 635 }, { "id": "lf9FHdxeb1JFAVae", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_15.png", "conversations": [ { "from": "human", "value": "\nIs there a marker at the point [0.845, 0.432] on the plot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 635 }, { "id": "AwvWUfNFL0U6yXUU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_15.png", "conversations": [ { "from": "human", "value": "\nIs the line at [0.240, 0.313] positioned higher on the graph than the line at [0.240, 0.467] at the same x-coordinate?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 635 }, { "id": "mtF3AN4Q9JLEcpf4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_15.png", "conversations": [ { "from": "human", "value": "\nWhich x-axis tick corresponds to {the point at}[0.500, 0.127] situated on the line graph?" }, { "from": "gpt", "value": "0.5." } ], "id_raw": 635 }, { "id": "0QYTp9cLpDB0YujN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_15.png", "conversations": [ { "from": "human", "value": "\nDoes the line point at {coordinate}[0.327, 0.462] have a higher performance metric than the point at {coordinate}[0.327, 0.319]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 635 }, { "id": "IGd75ebVoozWD6VY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area inside [0.758, 0.161, 0.963, 0.173]?" }, { "from": "gpt", "value": "This region contains the legend title for the subplot, which is unlabeled yet includes a color-coded handle indicating the \"Innovation\" line style in blue." } ], "id_raw": 635 }, { "id": "I26WxxGdyiS3Pgqm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is contained within the region [0.058, 0.879, 0.094, 0.886]." }, { "from": "gpt", "value": "This area displays a y-axis tick label, specifically the label '0.9', marked in black." } ], "id_raw": 635 }, { "id": "5kQyYpIDfyvn2G7I", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_15.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region [0.135, 0.182, 0.950, 0.189]?" }, { "from": "gpt", "value": "It contains x-axis tick labels comprising numerical values from '0.1' to '1.0', evenly spaced along the axis." } ], "id_raw": 635 }, { "id": "bSRQ2aTPS2H8AtbT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the xlabel} in the 'Interpersonal Relationships' subplot?" }, { "from": "gpt", "value": "Ratio r[0.504, 0.647, 0.581, 0.658]." } ], "id_raw": 638 }, { "id": "mH696rCgM9SXB5t8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_18.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the color of {the point marker for 'Family'} in the third subplot?" }, { "from": "gpt", "value": "#d62728[0.154, 0.786]." } ], "id_raw": 638 }, { "id": "a7FhIWydQhOir2TS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel} for the second subplot?" }, { "from": "gpt", "value": "Performance[0.025, 0.455, 0.048, 0.528]." } ], "id_raw": 638 }, { "id": "4KluB3FlP4HP3gbp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_18.png", "conversations": [ { "from": "human", "value": "\nWhich line plot begins its y-coordinate at a higher position, {the line labeled 'Friends'} or {the line labeled 'Facebook'}?" }, { "from": "gpt", "value": "Facebook[0.154, 0.786]." } ], "id_raw": 638 }, { "id": "le9EQ2e36sUahG5x", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_18.png", "conversations": [ { "from": "human", "value": "\nIs {the y-coordinate of the last tick on the y-axis}[0.058, 0.737, 0.094, 0.749] higher or lower than {the first point of 'Family' line}[0.154, 0.786]?" }, { "from": "gpt", "value": "Lower[0.110, 0.742]." } ], "id_raw": 638 }, { "id": "KeKQIQa0zlPBMPLq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_18.png", "conversations": [ { "from": "human", "value": "\nWhich plot shows a higher performance at 0.8 ratio r, {Facebook or Family} in the second subplot?" }, { "from": "gpt", "value": "Family[0.759, 0.731]." } ], "id_raw": 638 }, { "id": "hhPCELJWQDHThzGb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the lower section of the 'Interpersonal Relationships' labeled area." }, { "from": "gpt", "value": "It contains the axes labels 'Ratio r' and 'Performance' styled in black color, x-axis and y-axis tick labels like '0.1', '0.4', all in black color, and a visible grid with lines styled as dotted and colored '#b0b0b0'." } ], "id_raw": 638 }, { "id": "svkRaCNtNSyM3jHm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the middle section indication in the chart." }, { "from": "gpt", "value": "This area captures the subplot titled 'Comparison' along with labeled lines for 'Facebook', 'Twitter', 'Friends', and 'Family' in various colors and markers, including the y-axis with performance percentages." } ], "id_raw": 638 }, { "id": "M6aMQ6ynzItAcUwe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_18.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region spanning the majority of the plot along the top and labeled 'Social Networks'?" }, { "from": "gpt", "value": "It features grid lines, plotted line indicating data trend of 'Facebook' and 'Twitter', x-axis and y-axis markers and labels with 'Ratio r' and 'Performance' respectively, and colored differently marked data points showing a detailed trend over a specified range." } ], "id_raw": 638 }, { "id": "2H7vE36ellacHsPC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.147, 0.886, 0.931, 0.886] in the third subplot?" }, { "from": "gpt", "value": "Line for Male in Home Activities." } ], "id_raw": 639 }, { "id": "YBCDG2kcHzS77to7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_19.png", "conversations": [ { "from": "human", "value": "\nHow is {the sixth x-axis tick mark}[0.583, 0.293] labeled in the Social Interact subplot?" }, { "from": "gpt", "value": "0.6." } ], "id_raw": 639 }, { "id": "iwenNrjo6CW8waIk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_32_19.png", "conversations": [ { "from": "human", "value": "\nWhat does {the colored marker}[0.799, 0.372, 0.859, 0.379] in the legend of the second subplot represent?" }, { "from": "gpt", "value": "Male." } ], "id_raw": 639 }, { "id": "VJkPkOdqrHY5YIyW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_19.png", "conversations": [ { "from": "human", "value": "\nIs {the point marked at '0.2' ratio}[0.234, 0.188] in the Home Activities subplot for males higher or lower than for females?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 639 }, { "id": "hNyQdKHVL1ZsVqEM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_19.png", "conversations": [ { "from": "human", "value": "\nWhich gender has a higher value for the last data point in the chart, {Male}[0.931, 0.886] or {Female}[0.931, 0.834] in the Home Activities subplot?" }, { "from": "gpt", "value": "Female." } ], "id_raw": 639 }, { "id": "p1h6VkwXLfqJggNS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_32_19.png", "conversations": [ { "from": "human", "value": "\nIs the initial point for the Outdoor Activities trend lower for {Male}[0.147, 0.622] compared to {Female}[0.147, 0.557]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 639 }, { "id": "10wERDrUzZi9Euor", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.025, 0.776, 0.087, 0.865] in the chart represent?" }, { "from": "gpt", "value": "This region contains the y-axis label, which reads \"Percentage (%)\", for the Home Activities subplot, styled in black color and a 90\u00b0 rotation." } ], "id_raw": 639 }, { "id": "Gg2mWBsVftxlLqNT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the segment [0.103, 0.951, 0.975, 0.976] in the chart." }, { "from": "gpt", "value": "This region encompasses the x-axis for the Home Activities subplot, displaying labels for the ratios from '0.1' to '1.0', complete with corresponding tick marks, under a subtitle labeled \"Ratio over time\"." } ], "id_raw": 639 }, { "id": "QmgzOC2Mdu1VU7V2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_32_19.png", "conversations": [ { "from": "human", "value": "\nWhat is illustrated in the area [0.796, 0.037, 0.963, 0.075]?" }, { "from": "gpt", "value": "The area contains the legend for the Social Interact subplot, indicating the color-coded labels for Male and Female, associated with their respective colored handles. Male is represented with a blue line, while Female is indicated by a red line." } ], "id_raw": 639 }, { "id": "q0Sb16dlyapL1EOf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at coordinates {the ylabel's label}[0.015, 0.337, 0.034, 0.623] represent?" }, { "from": "gpt", "value": "Values in $million" } ], "id_raw": 640 }, { "id": "ztAmzVIaH6fGKW53", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis tick label at {x-axis tick label}[0.057, 0.911, 0.077, 0.934]?" }, { "from": "gpt", "value": "Q1" } ], "id_raw": 640 }, { "id": "7TuV28r8nypq084d", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_0.png", "conversations": [ { "from": "human", "value": "\nWhat lies at coordinates [0.076, 0.095, 0.121, 0.113] in the chart?" }, { "from": "gpt", "value": "Color handle for Revenue" } ], "id_raw": 640 }, { "id": "OmX7LejzH23ACCG6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_0.png", "conversations": [ { "from": "human", "value": "\nIs {the Q3 Expense point}[0.430, 0.547] above or below {the Q3 Revenue point}[0.430, 0.460]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 640 }, { "id": "IdKtiayEvrC3SnnM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the Revenue line}[0.067, 0.132, 0.975, 0.560] end at a higher or lower position than {the Expense line}[0.067, 0.373, 0.975, 0.661]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 640 }, { "id": "ELW2iYDc7KS42GN0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_0.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at point Q3, {the Revenue value}[0.430, 0.460] or {the Net Profit value}[0.430, 0.808]?" }, { "from": "gpt", "value": "Net Profit." } ], "id_raw": 640 }, { "id": "cBdrlPyBaVOqu2dx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content at coordinates [0.075, 0.079, 0.214, 0.219]." }, { "from": "gpt", "value": "This area contains the legend for the chart. It includes the labels for Revenue, Expenses, and Net Profit, each paired with a marker of different color and shape." } ], "id_raw": 640 }, { "id": "Zr1v7M7UwG84qNRn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.430, 0.460, 0.612, 0.393]?" }, { "from": "gpt", "value": "In this region, there are points from the Revenue line from Q3 to Q4." } ], "id_raw": 640 }, { "id": "QuPRdizkQE0YA2Uy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.076, 0.095, 0.121, 0.113]" }, { "from": "gpt", "value": "This area contains a color handle for Revenue in the chart's legend where the color is sky blue." } ], "id_raw": 640 }, { "id": "QKpnlnFcTsIMyBUF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.019, 0.236, 0.043, 0.684]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 641 }, { "id": "7bdimLWdb1cbxsqY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at these coordinates [0.095, 0.182, 0.138, 0.205]?" }, { "from": "gpt", "value": "The handle of the legend for Machine A." } ], "id_raw": 641 }, { "id": "0AN1EmFEvVMMm9E5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the text of {the x-tick label}[0.239, 0.821, 0.277, 0.868]?" }, { "from": "gpt", "value": "2.00." } ], "id_raw": 641 }, { "id": "sfB0wTZhKIS3N7l4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_1.png", "conversations": [ { "from": "human", "value": "\nDid {Machine A}[0.084, 0.218, 0.957, 0.443] perform better or worse over time, compared to its initial performance?" }, { "from": "gpt", "value": "Better." } ], "id_raw": 641 }, { "id": "oKNeOmEWQbfJGbnd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_1.png", "conversations": [ { "from": "human", "value": "\nWhich machine had the highest efficiency at {time 10.00}[0.932, 0.821, 0.981, 0.868], {Machine A}[0.957, 0.218], {Machine B}[0.957, 0.477], or {Machine C}[0.957, 0.512]?" }, { "from": "gpt", "value": "Machine A." } ], "id_raw": 641 }, { "id": "rB5Mj64sLxevyLap", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_1.png", "conversations": [ { "from": "human", "value": "\nBetween {Machine B}[0.084, 0.408, 0.957, 0.529] and {Machine C}[0.084, 0.477, 0.957, 0.599], which one showed a better efficiency trend over time?" }, { "from": "gpt", "value": "Machine B." } ], "id_raw": 641 }, { "id": "llqX5tSyMtkjCR6e", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects found in this region [0.092, 0.154, 0.245, 0.378]." }, { "from": "gpt", "value": "This area contains three legend items for three machines, {Machine A}[0.148, 0.173, 0.238, 0.219] with a orange square handle[0.095, 0.182, 0.138, 0.205], {Machine B}[0.148, 0.243, 0.238, 0.289] with a green diamond handle[0.095, 0.251, 0.138, 0.275], and {Machine C}[0.148, 0.312, 0.238, 0.359] with a blue square handle[0.095, 0.321, 0.138, 0.344]." } ], "id_raw": 641 }, { "id": "5stR0d44wKY7l4FG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_1.png", "conversations": [ { "from": "human", "value": "\nTell me about the content in coordinates [0.115, 0.050, 0.926, 0.117]." }, { "from": "gpt", "value": "The content here is the subtitle of the chart and says \"{Manufacturing Production Efficiency Evaluation Over Time}\"[0.115, 0.050, 0.926, 0.117], which is colored black." } ], "id_raw": 641 }, { "id": "QHllI5AgRZUPlRsk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within [0.084, 0.789, 0.271, 0.868]?" }, { "from": "gpt", "value": "Two x-axis tick labels with values '0.00' at coordinates {position}[0.064, 0.821, 0.103, 0.868] and '1.00' at coordinates {position}[0.152, 0.821, 0.190, 0.868] are within this region.." } ], "id_raw": 641 }, { "id": "wrSY3mZvDkhl2Lbr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_2.png", "conversations": [ { "from": "human", "value": "\nWhat object stands at [0.507, 0.915, 0.558, 0.963] in the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 642 }, { "id": "MpGhhkbK8vHDY4K4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_2.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.183, 0.136, 0.376, 0.181]?" }, { "from": "gpt", "value": "The text label for Precipitation (mm)." } ], "id_raw": 642 }, { "id": "DiBe4NsRKzJrE0cd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.837, 0.121] on the chart?" }, { "from": "gpt", "value": "The data point for the year 2010 belonging to the line representing 'Precipitation (mm)'." } ], "id_raw": 642 }, { "id": "k5u8Tp6Dg3LdPdHR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_2.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {the 2000 data point}[0.593, 0.841], 'Temperature Increase (\u00b0C)' or 'Sea Level Rise (cm)'?" }, { "from": "gpt", "value": "Neither. Both are at the same level." } ], "id_raw": 642 }, { "id": "VkHufm4gLGpLuIsm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_2.png", "conversations": [ { "from": "human", "value": "\nIs the {''}[0.183, 0.136, 0.376, 0.181] higher or lower than the {'Sea Level Rise (cm) label'}[0.183, 0.267, 0.388, 0.312]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 642 }, { "id": "AHPM2SRN3U95IlM0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_2.png", "conversations": [ { "from": "human", "value": "\nIs the data point for 'Precipitation (mm)' at 2005 higher or lower than its preceding year?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 642 }, { "id": "B06RuLgCfopCOZky", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.116, 0.119, 0.465, 0.329]?" }, { "from": "gpt", "value": "This region contains the legend for the graph. There are 3 items: {Precipitation (mm)}[0.183, 0.136, 0.376, 0.181], {Temperature Increase (\u00b0C)}[0.183, 0.202, 0.456, 0.247], and {Sea Level Rise (cm)}[0.183, 0.267, 0.388, 0.312]. Each item is accompanied by a colored symbol." } ], "id_raw": 642 }, { "id": "dYYKt7cWlhN3Zv4e", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the items in the region [0.572, 0.866, 0.615, 0.901]." }, { "from": "gpt", "value": "This region contains the x-axis label for the year 2000." } ], "id_raw": 642 }, { "id": "vmtB0Zvj2Hb9Jt6J", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the area covering the coordinates [0.106, 0.099, 0.959, 0.310]." }, { "from": "gpt", "value": "This area contains the line data for 'Precipitation (mm)'. It covers different data points from the year 1980 to 2015." } ], "id_raw": 642 }, { "id": "jGBcPJ3MSuAdupfr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the item being represented inside the bounding box [0.859, 0.357, 0.933, 0.417] in the chart figure?" }, { "from": "gpt", "value": "This represents the 'Steroid' item in the legend." } ], "id_raw": 643 }, { "id": "4RDsdllH5kGAJNjj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_3.png", "conversations": [ { "from": "human", "value": "\nPlease provide the coordinate points of the y-axis label in the chart figure." }, { "from": "gpt", "value": "The coordinates of the y-axis label are [0.019, 0.304, 0.043, 0.616]." } ], "id_raw": 643 }, { "id": "OrlCorChVDZG1n84", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.970, 0.821, 0.981, 0.868]?" }, { "from": "gpt", "value": "8." } ], "id_raw": 643 }, { "id": "kozRDc0nxicBdoo9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_3.png", "conversations": [ { "from": "human", "value": "\nIn the plotted lines, is {the point for 'Steroid'}[0.593, 0.360] above or below {the point for 'Antibiotic'}[0.593, 0.503] at the same dose?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 643 }, { "id": "RaNvqWl5hvb7QXcZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_3.png", "conversations": [ { "from": "human", "value": "\nFor {the dose labeled '4'}[0.466, 0.145], which treatment has a higher response, 'Antibiotic' at [0.466, 0.145] or 'Steroid' at [0.466, 0.145]?" }, { "from": "gpt", "value": "Both have the same response." } ], "id_raw": 643 }, { "id": "ZMvUSu7gztGgPztF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_3.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Control' point at Dose_7}[0.848, 0.732] showing a higher or lower response than {the 'Steroid' point at dose_7}[0.848, 0.575]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 643 }, { "id": "5SJa9D3THVz7NG00", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content enclosed in [0.784, 0.157, 0.964, 0.437]." }, { "from": "gpt", "value": "This area contains the legend of the chart. It includes 3 legend items: {Control with blue color}[0.859, 0.181, 0.934, 0.241], {Antibiotic with red color}[0.859, 0.269, 0.957, 0.329], and {Steroid with green color}[0.859, 0.357, 0.933, 0.417]. Each legend is paired with a colored marker--a circle, a square, and a triangle respectively--that corresponds with the color and marker style of each line plot." } ], "id_raw": 643 }, { "id": "RTspqLbnOCj68Fak", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_3.png", "conversations": [ { "from": "human", "value": "\nCan you explain the objects located in the box [0.499, 0.887, 0.560, 0.950] in the chart figure." }, { "from": "gpt", "value": "This area contains the x-label of the chart, which is 'Dose'." } ], "id_raw": 643 }, { "id": "SRma25aaBmFPsXTs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the items inside the bounding box [0.333, 0.821, 0.344, 0.868] and [0.460, 0.821, 0.471, 0.868] in the chart figure?" }, { "from": "gpt", "value": "These objects represent the x-axis tick labels for the chart, specifically {Dose_3}[0.333, 0.821, 0.344, 0.868] and {Dose_4}[0.460, 0.821, 0.471, 0.868]." } ], "id_raw": 643 }, { "id": "r0BqPzkw8PwS1AyB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_4.png", "conversations": [ { "from": "human", "value": "\nWhat data does the tick label at [0.925, 0.893, 0.985, 0.921] represent?" }, { "from": "gpt", "value": "2021-Q4." } ], "id_raw": 644 }, { "id": "NmyuvyV4hUumBSf6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color used for {the line for 'Tour Agency'}[0.071, 0.238, 0.955, 0.436] in the plotted chart?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 644 }, { "id": "2hAnqafiA05YMyZo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the orientation of {the label}[0.015, 0.371, 0.034, 0.581] in the chart?" }, { "from": "gpt", "value": "Vertical." } ], "id_raw": 644 }, { "id": "JoS5b1UZMAUVebc0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_4.png", "conversations": [ { "from": "human", "value": "\nIn the plotted lines, which industry has a higher data point for 2021-Q2, {the Hotel Industry} line[r][0.071, 0.556, 0.955, 0.754] or {the Air Travel} line[r][0.071, 0.118, 0.955, 0.476]?" }, { "from": "gpt", "value": "Air Travel." } ], "id_raw": 644 }, { "id": "7eXrF3spY6bbykdy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_4.png", "conversations": [ { "from": "human", "value": "\nConsider the data points for 2021-Q1, is {the Tour Agency} at [0.576, 0.357] higher or lower than {the Hotel Industry} at [0.576, 0.675]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 644 }, { "id": "LgW0QnR2FDOXJY6X", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_4.png", "conversations": [ { "from": "human", "value": "\nWhich industry at {the point}[0.198, 0.714] in the chart has lower income, Air Travel or Hotel Industry?" }, { "from": "gpt", "value": "Hotel Industry." } ], "id_raw": 644 }, { "id": "dtYC1XRPxEStFRwn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is found within the coordinates [0.769, 0.095, 0.947, 0.263]?" }, { "from": "gpt", "value": "This is the area of the chart that contains the legend for the three plotted lines. It includes the labels for \"Hotel Industry\", \"Air Travel\", and \"Tour Agency\", each associated with a color, representing their corresponding lines in the chart." } ], "id_raw": 644 }, { "id": "XGBQocOjIMuFVix8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_4.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.071, 0.079, 0.955, 0.873]?" }, { "from": "gpt", "value": "This region contains the primary plot area of the line chart, displaying three lines, each representing a different sector in Tourism and Hospitality: {Hotel Industry}[0.071, 0.556, 0.955, 0.754], {Air Travel}[0.071, 0.118, 0.955, 0.476], {Tour Agency}[0.071, 0.238, 0.955, 0.436]. The x-axis and y-axis with their labels and ticks are also within this area." } ], "id_raw": 644 }, { "id": "uSLTlaEzzcZs05Di", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_4.png", "conversations": [ { "from": "human", "value": "\nDetailed explanation required for the area demarcated by [0.283, 0.030, 0.743, 0.070]?" }, { "from": "gpt", "value": "This area holds the subtitle of the chart, which reads \"Tourism and Hospitality Industry Analysis\"." } ], "id_raw": 644 }, { "id": "jZorgoQkTL4enrWv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the description corresponding to the colored marker at the coordinates [0.610, 0.278, 0.666, 0.315]?" }, { "from": "gpt", "value": "Sea Level Rise (inches)." } ], "id_raw": 646 }, { "id": "rmvncylMdpMa3aV0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.342, 0.282, 0.342, 0.282] represent?" }, { "from": "gpt", "value": "It is a point in the 'CO2 Emissions' line that represents the value for the year 2000." } ], "id_raw": 646 }, { "id": "A7iKfgREZXxwJvHC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the purpose of the title at coordinates [0.231, 0.050, 0.823, 0.117]?" }, { "from": "gpt", "value": "It's the subtitle of the chart, which is \"Climate Change Trends from 1990 to 2025\"." } ], "id_raw": 646 }, { "id": "alWN9t5p8P0l23Zo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_6.png", "conversations": [ { "from": "human", "value": "\nCompare the data points for the year 2005 on the 'CO2 Emissions (ppm)' line [0.465, 0.268] and 'Temperature Change (\u00b0C)' line [0.465, 0.788]. Which one is higher on the chart?" }, { "from": "gpt", "value": "The point on the 'Temperature Change (\u00b0C)' line is higher." } ], "id_raw": 646 }, { "id": "BQwIYTQpKcet9tLk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_6.png", "conversations": [ { "from": "human", "value": "\nIs the tick label '200' [0.050, 0.360, 0.082, 0.406] above or below the 'Temperature Change (\u00b0C)' line data point for the year 2020 [0.836, 0.787]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 646 }, { "id": "bp04g42ueLlfNiTN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_6.png", "conversations": [ { "from": "human", "value": "\nIn the subtitles [0.676, 0.181, 0.899, 0.241], [0.676, 0.269, 0.916, 0.329] and [0.676, 0.357, 0.941, 0.417], which one is positioned at the top of the others?" }, { "from": "gpt", "value": "The subtitle 'CO2 Emissions (ppm)' is positioned at the top." } ], "id_raw": 646 }, { "id": "uSdwgxH4Mwvlelkf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content of the region with these coordinates [0.609, 0.159, 0.949, 0.439]." }, { "from": "gpt", "value": "This region contains a legend that classifies three different trends: {CO2 Emissions (ppm)}[0.676, 0.181, 0.899, 0.241], {Sea Level Rise (inches)}[0.676, 0.269, 0.916, 0.329], and {Temperature Change (\u00b0C)}[0.676, 0.357, 0.941, 0.417], along with their respective colored markers." } ], "id_raw": 646 }, { "id": "YyLbJhV1gWyMspM8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_6.png", "conversations": [ { "from": "human", "value": "\nWhat elements are present in the area between [0.342, 0.282, 0.836, 0.787]?" }, { "from": "gpt", "value": "This area contains part of three trend lines from the years 2000 to 2020: {CO2 Emissions (ppm)}[0.342, 0.282, 0.836, 0.186], {Sea Level Rise (inches)}[0.342, 0.784, 0.836, 0.784] and {Temperature Change (\u00b0C)}[0.342, 0.788, 0.836, 0.787]." } ], "id_raw": 646 }, { "id": "YuwvwGvoDT65b8WD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_6.png", "conversations": [ { "from": "human", "value": "\nWhat can you say about the region from [0.095, 0.159, 0.959, 0.789]?" }, { "from": "gpt", "value": "This region encompasses the area that represents the plotted information on the climate change trends from three parameters ('CO2 Emissions', 'Sea Level Rise', and 'Temperature Change') over the years from 1990 to 2025." } ], "id_raw": 646 }, { "id": "v3IEFmiW7RkJKFTn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_7.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the given coordinates [0.019, 0.327, 0.043, 0.594] in the chart figure?" }, { "from": "gpt", "value": "It is the Y-axis label which is \"Latitude\"." } ], "id_raw": 647 }, { "id": "Z6q7BGoAbOCyu7hl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.762, 0.181, 0.938, 0.241] in the chart figure?" }, { "from": "gpt", "value": "It is the label of the chart's legend, denoted as \"Geographic Data\"." } ], "id_raw": 647 }, { "id": "gtiwQ6AtDBuL7tjI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_7.png", "conversations": [ { "from": "human", "value": "\nWhat item can I find at these coordinates [0.467, 0.747] in the chart figure?" }, { "from": "gpt", "value": "It is a data point on the line representing Geographic Data, where Longitude is \"-0.165\" and Latitude is \"1.018\"." } ], "id_raw": 647 }, { "id": "FdcJkxVB0fHma0CI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_7.png", "conversations": [ { "from": "human", "value": "\nComparing the position of {the point for Longitude '-1.934'}[0.100, 0.625] and {the point for Longitude '-1.387'}[0.222, 0.542], which point is higher on the graph?" }, { "from": "gpt", "value": "{The point for Longitude '-1.934'}[0.100, 0.625] is higher." } ], "id_raw": 647 }, { "id": "7Eh5QkuBIhXLiQlY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Longitude '0.319'}[0.589, 0.768] located to the left or right of {the point for Longitude '0.921'}[0.712, 0.747]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 647 }, { "id": "5z6p7BR10xzpUEZt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_7.png", "conversations": [ { "from": "human", "value": "\nWhich geographic point is located further to the right, {the point for Longitude '1.674'}[0.834, 0.542] or {the point for Longitude '2.538'}[0.957, 0.172]?" }, { "from": "gpt", "value": "The point for Longitude '2.538'." } ], "id_raw": 647 }, { "id": "yGQWaP15MtUPyKXk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is found in this region [0.696, 0.159, 0.946, 0.263] in the chart figure?" }, { "from": "gpt", "value": "This region contains the chart's legend. The legend includes the label \"Geographic Data\" at {coordinates}[0.762, 0.181, 0.938, 0.241] with its corresponding color marker at {coordinates}[0.697, 0.190, 0.753, 0.227]." } ], "id_raw": 647 }, { "id": "jtCL37vuI1cTlMCq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_7.png", "conversations": [ { "from": "human", "value": "\nProvide a description for the content in the area [0.100, 0.172, 0.957, 0.768] of the chart figure." }, { "from": "gpt", "value": "This area represents the main graph of the chart. It consists of a line representing 'Geographic Data' with the plotted points for various longitudes and latitudes." } ], "id_raw": 647 }, { "id": "Z3714C8XP7cKmMnv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_7.png", "conversations": [ { "from": "human", "value": "\nWhat details can you provide about the objects within the coordinates [0.195, 0.821, 0.495, 0.868]?" }, { "from": "gpt", "value": "This region accommodates two labels for x-axis ticks corresponding to the Longitude values '-1.387' at {coordinates}[0.195, 0.821, 0.250, 0.868] and '-0.165' at {coordinates}[0.439, 0.821, 0.495, 0.868]." } ], "id_raw": 647 }, { "id": "DC8WQ75poMnd63el", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at these coordinates [0.851, 0.181, 0.949, 0.241]?" }, { "from": "gpt", "value": "Legend text for 'Facebook'." } ], "id_raw": 649 }, { "id": "sqrCNh7iT5lq2zUr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_9.png", "conversations": [ { "from": "human", "value": "\nCan you specify the object at location [0.206, 0.821, 0.217, 0.868]?" }, { "from": "gpt", "value": "It's the x-tick label '1'." } ], "id_raw": 649 }, { "id": "8VkULvIV7pnjy9JF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_9.png", "conversations": [ { "from": "human", "value": "\nWhich social media's line does the handle with coordinates [0.786, 0.278, 0.842, 0.315] represent?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 649 }, { "id": "PqGh5BhjLtwiWeoP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_9.png", "conversations": [ { "from": "human", "value": "\nIs the third point of the {Twitter line}[0.339, 0.387] higher on the y-axis than the third point of the {Facebook line}[0.339, 0.239]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 649 }, { "id": "DIzLkJztYAoeLaXQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_9.png", "conversations": [ { "from": "human", "value": "\nComparing the seventh points of both lines, which is higher, the {Instagram line}[0.848, 0.348] or the {Facebook line}[0.848, 0.317]?" }, { "from": "gpt", "value": "Instagram." } ], "id_raw": 649 }, { "id": "TwVWVdPFFO8q9coj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_9.png", "conversations": [ { "from": "human", "value": "\nBetween the first points of the {Facebook line}[0.084, 0.170] and the {Instagram line}[0.084, 0.518], which is on the left of the line chart?" }, { "from": "gpt", "value": "Both are on the same leftmost point on the x-axis." } ], "id_raw": 649 }, { "id": "N0jTocx0DNHM8n5L", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_9.png", "conversations": [ { "from": "human", "value": "\nCan you please describe the objects and their contents present in this area: [0.785, 0.159, 0.965, 0.439]?" }, { "from": "gpt", "value": "This is the chart legend containing three social platforms: {Facebook}[0.851, 0.181, 0.949, 0.241] in blue, {Twitter}[0.851, 0.269, 0.921, 0.329] in red, and {Instagram}[0.851, 0.357, 0.957, 0.417] in green." } ], "id_raw": 649 }, { "id": "XODhbSBS9wUkFS89", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_9.png", "conversations": [ { "from": "human", "value": "\nWhat information can you gather about the area [0.318, 0.050, 0.741, 0.117] depicted in the chart?" }, { "from": "gpt", "value": "It contains the subtitle of the chart named {Social Media Usage Over Time}[0.318,0.050,0.741,0.117] in black color." } ], "id_raw": 649 }, { "id": "mpAEYmqwWa8aX9LK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_9.png", "conversations": [ { "from": "human", "value": "\nBased on your observations, what can you tell me about the objects present in the area [0.084, 0.159, 0.976, 0.768]?" }, { "from": "gpt", "value": "This area mainly contains the main plot including the timeline on the x-axis, percentage usage on the y-axis, and visual representation of usage stats for 3 social platforms: Facebook, Twitter, and Instagram along with their markers plotted along the timeline." } ], "id_raw": 649 }, { "id": "VADdq9mOO445fZia", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_10.png", "conversations": [ { "from": "human", "value": "\nWhat is located within these coordinates [0.754, 0.159, 0.949, 0.439]?" }, { "from": "gpt", "value": "The legend." } ], "id_raw": 650 }, { "id": "MlTAfy4STTQCoI5b", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the {y-axis label}[0.019, 0.077, 0.043, 0.843]?" }, { "from": "gpt", "value": "Venture Capital Activity." } ], "id_raw": 650 }, { "id": "tNhTggIiA1oO7Qd6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the item in the legend located at [0.820, 0.269, 0.941, 0.329]?" }, { "from": "gpt", "value": "Fundraising." } ], "id_raw": 650 }, { "id": "JurK6EjEdMdEu3D7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_10.png", "conversations": [ { "from": "human", "value": "\nWhich line is the {point '2017'}[0.521, 0.350] a part of, Startup or Investment?" }, { "from": "gpt", "value": "Startup." } ], "id_raw": 650 }, { "id": "B3Yq9dlXLqCtFvNQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the Fundraising line}[0.084, 0.183, 0.959, 0.371] have a higher or lower value in 2019 than {the Investment line}[0.084, 0.142, 0.959, 0.277]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 650 }, { "id": "mWTkwNMdPlUbkZkW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_10.png", "conversations": [ { "from": "human", "value": "\nWhich line type color is darker at {the caption}[0.754, 0.159, 0.949, 0.439], the one for Startup or the one for Investment?" }, { "from": "gpt", "value": "Startup." } ], "id_raw": 650 }, { "id": "qsOCVk5S3Xl79XMy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the layout within [0.062, 0.821, 0.106, 0.868]." }, { "from": "gpt", "value": "The x-axis tick label '2013' is located here." } ], "id_raw": 650 }, { "id": "n7E1lHFnrMTX7R9Q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.223, 0.050, 0.820, 0.117] contain?" }, { "from": "gpt", "value": "The subtitle of the chart, \"Venture Capital Activity from 2013 to 2021,\" is placed within these coordinates." } ], "id_raw": 650 }, { "id": "RvPonb10d6v7JNVZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within the coordinates [0.019, 0.077, 0.043, 0.843]?" }, { "from": "gpt", "value": "The y-axis label 'Venture Capital Activity' is displayed within these coordinates." } ], "id_raw": 650 }, { "id": "QfeSJHU8BukmiYsj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.019, 0.092, 0.043, 0.828]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 651 }, { "id": "Xk20UAW1zIbKI09o", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_11.png", "conversations": [ { "from": "human", "value": "\n{The '2010' x-axis label}[0.051, 0.821, 0.095, 0.868], can you tell me what coordinates represent its tick on the axis grid?" }, { "from": "gpt", "value": "[0.073, 0.789]" } ], "id_raw": 651 }, { "id": "WMM9xKSAjhhnt4HI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the information of the object drawn in the area defined by the coordinates [0.865, 0.357, 0.931, 0.417] in the legend?" }, { "from": "gpt", "value": "It's the legened label for 'Cardio'." } ], "id_raw": 651 }, { "id": "aqIunNhqvWp4IL6Z", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_11.png", "conversations": [ { "from": "human", "value": "\nIs the 'Yoga' trend line higher or lower than the 'Cycling' trend line at the year 2013?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 651 }, { "id": "ujTj1DfqcD7qqP4r", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_11.png", "conversations": [ { "from": "human", "value": "\nWhich line among {the 'Yoga'}[0.073, 0.225, 0.959, 0.507], {the 'Cycling'}[0.073, 0.131, 0.959, 0.601], and {the 'Cardio'}[0.073, -0.104, 0.959, 0.648] trend lines reach the highest point in the year 2016?" }, { "from": "gpt", "value": "Cardio." } ], "id_raw": 651 }, { "id": "U2H9TfkZC3INSO1B", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_11.png", "conversations": [ { "from": "human", "value": "\nIn the legend, is {the 'Yoga' label}[0.865, 0.181, 0.913, 0.241] above or below {the 'Cycling' label}[0.865, 0.269, 0.941, 0.329]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 651 }, { "id": "KeHXSMK6ppw5CKHQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are visible in the area marked by coordinates [0.798, 0.159, 0.949, 0.439]?" }, { "from": "gpt", "value": "In this area, the legend for the chart is placed. It consists of labels and icons for the categories 'Yoga', 'Cycling', and 'Cardio'." } ], "id_raw": 651 }, { "id": "ISomRqqGyGBOh33x", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content visible inside the coordinates [0.073, 0.131, 0.959, 0.789]." }, { "from": "gpt", "value": "This area shows the main chart plotted with x and y axes. It displays three diverse trend lines each representing 'Yoga', 'Cycling', 'Cardio' for the years between 2010 and 2017, with year labels and tick-marks along the X-axis and numerical markers along the Y-axis." } ], "id_raw": 651 }, { "id": "Al5tb4PldrUu4BkF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within the coordinates [0.153, 0.050, 0.879, 0.117]." }, { "from": "gpt", "value": "The coordinates contains the chart subtitle \"Fitness and Sports Health Trends from 2010 to 2017\"." } ], "id_raw": 651 }, { "id": "xyMrFSonWCNWtFGr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_12.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by {coordinates} at [0.019, 0.257, 0.043, 0.663]?" }, { "from": "gpt", "value": "The Y-axis label." } ], "id_raw": 652 }, { "id": "VwM1EkZYJJBeUnO1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_12.png", "conversations": [ { "from": "human", "value": "\nCan you identify the item located at these [0.680, 0.357, 0.837, 0.417] coordinates?" }, { "from": "gpt", "value": "It's the legend label for 'Public Facilities'." } ], "id_raw": 652 }, { "id": "gropO6oBgaK7loHx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis label at [0.484, 0.887, 0.548, 0.950] represent?" }, { "from": "gpt", "value": "It represents 'Years'." } ], "id_raw": 652 }, { "id": "Nf47o2wXJAr2a5SH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_12.png", "conversations": [ { "from": "human", "value": "\nIs {the 2020 point of the line for 'Residential Development'}[0.579, 0.219] higher or lower than {the 2020 point of the line for 'Business Development'}[0.579, 0.394]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 652 }, { "id": "E8dI8lhimlzQKUS9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Residential Development' line}[0.073, 0.219, 0.959, 0.701] end higher or lower than {the 'Business Development' line}[0.073, 0.394, 0.959, 0.736]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 652 }, { "id": "zpdu6ykT92aYbe4I", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_12.png", "conversations": [ { "from": "human", "value": "\nWhich line, 'Residential Development' or 'Business Development', has a higher value at {the '2025' x-ticks}[0.706, 0.789]?" }, { "from": "gpt", "value": "Residential Development." } ], "id_raw": 652 }, { "id": "yZmFDAzR7fcej9TG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the space located at [0.613, 0.159, 0.949, 0.439]." }, { "from": "gpt", "value": "It's the chart legend. It contains labels for 'Residential Development' {coordinates}[0.680, 0.181, 0.941, 0.241], 'Business Development' {coordinates}[0.680, 0.269, 0.918, 0.329], and 'Public Facilities' {coordinates}[0.680, 0.357, 0.837, 0.417], each label is associated with a different color marker." } ], "id_raw": 652 }, { "id": "VdN3dEmLxPpF7Nmj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_12.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area with coordinates [0.177, 0.821, 0.221, 0.868]?" }, { "from": "gpt", "value": "It is an x-axis tick label, representing the year 2005." } ], "id_raw": 652 }, { "id": "XhXdBLc9RIwwTrCg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_12.png", "conversations": [ { "from": "human", "value": "\nCould you describe the contents within the region [0.073, 0.219, 0.959, 0.701]?" }, { "from": "gpt", "value": "This region includes the 'Residential Development' line, which details the growth rate of residential development from 2000 to 2035 through a series of connected points." } ], "id_raw": 652 }, { "id": "M45jn4H9UUmcCzim", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of {the object}[0.019, -0.063, 0.043, 0.983] on the y-axis?" }, { "from": "gpt", "value": "Trade Volume & Economic Index." } ], "id_raw": 655 }, { "id": "ZFqLkZwEO3GZ66Xs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_15.png", "conversations": [ { "from": "human", "value": "\nWhere is {the text \"GDP Growth\"} located?" }, { "from": "gpt", "value": "[0.800, 0.269, 0.926, 0.329]." } ], "id_raw": 655 }, { "id": "zmtSVGluRoTXoQjo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-value that corresponds to {the object}[0.266, 0.601]?" }, { "from": "gpt", "value": "2000." } ], "id_raw": 655 }, { "id": "8oRSKGiM3WA4QiK1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_15.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at the point labeled '2010', {Trade Volume}[0.405, 0.538] or {GDP Growth}[0.405, 0.789]?" }, { "from": "gpt", "value": "GDP Growth." } ], "id_raw": 655 }, { "id": "UQKNiAsVoR2HnrxN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_15.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Trade Volume'}[0.127, 0.162, 0.959, 0.648] generally increasing or decreasing over time?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 655 }, { "id": "No4G9tNWLuPEHU0L", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_15.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value in 2050, {GDP Growth}[0.959, 0.789] or {Gini Index}[0.959, 0.789]?" }, { "from": "gpt", "value": "GDP Growth." } ], "id_raw": 655 }, { "id": "apgdyOihWKr8ZzVh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the area [0.733, 0.159, 0.949, 0.439]." }, { "from": "gpt", "value": "This area represents the legend and contains three items: {Trade Volume}[0.800, 0.181, 0.941, 0.241] marked with the color '#4169e1', {GDP Growth}[0.800, 0.269, 0.926, 0.329] marked with the color '#dc143c', and {Gini Index}[0.800, 0.357, 0.904, 0.417] marked with the color '#32cd32'." } ], "id_raw": 655 }, { "id": "unYp6h6z5fBM2hDJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_15.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area [0.019, -0.063, 0.043, 0.983]?" }, { "from": "gpt", "value": "The y-axis label \"Trade Volume & Economic Index\" is within the mentioned region." } ], "id_raw": 655 }, { "id": "QOxQtVvAuno57keg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content enclosed in the zone [0.236, 0.050, 0.851, 0.117]." }, { "from": "gpt", "value": "This area contains the subtitle of the chart, \"International Trade and Globalization Trends\"." } ], "id_raw": 655 }, { "id": "ybQQvNbyRXfJhOKE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the first x-axis tick label with these coordinates [0.076, 0.893, 0.111, 0.921]?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 657 }, { "id": "TLw5xk6OaxglrdXT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_17.png", "conversations": [ { "from": "human", "value": "\nWhat's the name of {the line graph}[0.093, 0.611, 0.968, 0.677] traced with a '--' linestyle and square styled marker?" }, { "from": "gpt", "value": "Wheat." } ], "id_raw": 657 }, { "id": "fRefi0a5GNhEuNLa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of {the label}[0.015, 0.054, 0.034, 0.898] on the y-axis with color 'black' and rotation 90.0?" }, { "from": "gpt", "value": "Production Quantity (thousands of bushels)." } ], "id_raw": 657 }, { "id": "rDLqqJ51SLBnEAZQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_17.png", "conversations": [ { "from": "human", "value": "\nAt which year did {the Corn production}[0.093, 0.079, 0.968, 0.324] and {the Wheat production}[0.093, 0.611, 0.968, 0.677] have almost the same y-axis position on the chart?" }, { "from": "gpt", "value": "2012." } ], "id_raw": 657 }, { "id": "wLaVVlYiXhM40qUy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_17.png", "conversations": [ { "from": "human", "value": "\nIn comparison to {the Corn production}[0.093, 0.079, 0.968, 0.324], did {the Barley Production}[0.093, 0.783, 0.968, 0.815] generally produce higher or lower results from 2010 to 2020?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 657 }, { "id": "8vboh5mvNoZZ6o6c", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_17.png", "conversations": [ { "from": "human", "value": "\nWhich crop has the production marker at the lowest position on the y-axis in 2020, {Corn}[0.968, 0.079], {Wheat}[0.968, 0.611], or {Barley}[0.968, 0.815]?" }, { "from": "gpt", "value": "Corn." } ], "id_raw": 657 }, { "id": "xz819AJnF6R2DLCa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects found within the bounding box [0.101, 0.095, 0.215, 0.263]?" }, { "from": "gpt", "value": "The bounding box corresponds to a legend with three entries. The first entry is for {Corn}[0.155, 0.109, 0.194, 0.145] and has a marker in '#6495ed' color. The second entry, for {Wheat}[0.155, 0.161, 0.209, 0.197], has a marker in '#f5deb3' color. And the third entry is for {Barley}[0.155, 0.214, 0.208, 0.250] with a marker in '#b8860b' color." } ], "id_raw": 657 }, { "id": "6Z9e10E462DgIv4c", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_17.png", "conversations": [ { "from": "human", "value": "\nCould you provide information on the area between [0.093, 0.079, 0.968, 0.324]?" }, { "from": "gpt", "value": "This region of the chart includes the plot for {Corn}[0.093, 0.079, 0.968, 0.324] production from 2010 to 2020 in the food and agriculture industry, plotted as a solid line with color '#6495ed' and 'o' styled markers." } ], "id_raw": 657 }, { "id": "GzKgphwJtJJBqxqs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents of the chart in the area with the following coordinates [0.093, 0.611, 0.968, 0.815]." }, { "from": "gpt", "value": "This area of the chart contains the graphical representation of {Barley}[0.093, 0.611, 0.968, 0.815] from 2010 to 2020 in the food and agriculture industry. The line is trace in a ':' style, the color used is '#b8860b' and '^' styled markers have been used to mark the points on the line." } ], "id_raw": 657 }, { "id": "Joy0pN739QevZ3Cw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.019, 0.119, 0.043, 0.801] displayed in the chart figure?" }, { "from": "gpt", "value": "Quantity (in millions)." } ], "id_raw": 658 }, { "id": "ceR2WgdTpQWGwwht", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_18.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the fifth tick on the x-axis?" }, { "from": "gpt", "value": "[0.589, 0.789]" } ], "id_raw": 658 }, { "id": "UlB1q79EURDJc2OG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the legend text}[0.159, 0.312, 0.317, 0.359] in the subplot?" }, { "from": "gpt", "value": "Vegetables Export." } ], "id_raw": 658 }, { "id": "jYtCNDkQ0vOglSrZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_18.png", "conversations": [ { "from": "human", "value": "\nIs the line representing {Grains Production}[0.095, 0.620, 0.959, 0.699] predominantly above or below the line representing {Fruits Imports}[0.095, 0.577, 0.959, 0.718]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 658 }, { "id": "9YiE8wNuwA7jcVE2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_18.png", "conversations": [ { "from": "human", "value": "\nWhich trend exhibits a higher value at the year 2000, {Grains Production}[0.342, 0.651] or {Fruits Imports}[0.342, 0.704]?" }, { "from": "gpt", "value": "Fruits Imports." } ], "id_raw": 658 }, { "id": "8kyEjIJH01VUPZsx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_18.png", "conversations": [ { "from": "human", "value": "\nIn 2025, is {Vegetables Export}[0.959, 0.443] higher or lower than {Grains Production}[0.959, 0.699]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 658 }, { "id": "Efbu5BNPu9iPaN5a", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.258, 0.050, 0.796, 0.117]." }, { "from": "gpt", "value": "The area contains the subtitle of the chart, \"Food and Agriculture Industry Analysis.\"" } ], "id_raw": 658 }, { "id": "wRATSEPWFY7WgOLo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is included within [0.103, 0.154, 0.324, 0.378]?" }, { "from": "gpt", "value": "This area consists of the legend items: {Grains Production}[0.159, 0.173, 0.313, 0.219], {Fruits Imports}[0.159, 0.243, 0.278, 0.289], and {Vegetables Export}[0.159, 0.312, 0.317, 0.359], each paired with a distinct marker." } ], "id_raw": 658 }, { "id": "EKkM3r3XqTHMhvxg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_18.png", "conversations": [ { "from": "human", "value": "\nWhat objects can be located between [0.218, 0.641, 0.836, 0.683]?" }, { "from": "gpt", "value": "This area includes several data points from the {Grains Production}[0.218, 0.641] to [0.836, 0.683] line representing the years 1995 to 2020." } ], "id_raw": 658 }, { "id": "82Dqojmdi7pC34h6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object between [0.878, 0.091, 0.932, 0.121] in the chart?" }, { "from": "gpt", "value": "Legend label 'Rural'." } ], "id_raw": 659 }, { "id": "txezQcWvkUlE129t", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in the box defined by [0.939, 0.911, 0.981, 0.934]?" }, { "from": "gpt", "value": "The x-axis label '2020'." } ], "id_raw": 659 }, { "id": "IFsDXYN39OOROGe2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_33_19.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the bounding box [0.019, 0.338, 0.043, 0.623]?" }, { "from": "gpt", "value": "The Y-axis label \"Development (%)\" is located here." } ], "id_raw": 659 }, { "id": "7ydeZQG2Tbz8faGW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_19.png", "conversations": [ { "from": "human", "value": "\nConsidering {the point in 2000 for 'Rural'}[0.073, 0.685] and {the point in 2000 for 'Urban'}[0.073, 0.563], which category had higher development in the year 2000?" }, { "from": "gpt", "value": "Rural." } ], "id_raw": 659 }, { "id": "HXaBc0p3G8XUYZWs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_19.png", "conversations": [ { "from": "human", "value": "\nIn the year 2010, the development rate of which area is greater, judging by {the point on the Rural line}[0.516, 0.205] and {the point on the Urban line}[0.516, 0.223]?" }, { "from": "gpt", "value": "Urban." } ], "id_raw": 659 }, { "id": "jrUZI6IgWQNETWKT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_33_19.png", "conversations": [ { "from": "human", "value": "\nWho has experienced a greater growth rate in the year 2020, when considering {the 'Rural' data point}[0.959, 0.170] vs {the 'Urban' data point}[0.959, 0.153]?" }, { "from": "gpt", "value": "Urban." } ], "id_raw": 659 }, { "id": "X3bjadM57mjVkLd9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_19.png", "conversations": [ { "from": "human", "value": "\nCan you provide information about the objects residing within the region defined by [0.811, 0.079, 0.949, 0.176]?" }, { "from": "gpt", "value": "This region contains the legend for the chart. Two legends items are included here - {Rural}[0.878, 0.091, 0.932, 0.121] and {Urban}[0.878, 0.134, 0.941, 0.164] each with their respective color markers." } ], "id_raw": 659 }, { "id": "Mdaj7ZVRs3uaEvdH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_19.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the region defined by bounding box [0.273, 0.911, 0.316, 0.934]?" }, { "from": "gpt", "value": "This region contains the x-axis tick label, which represents the year '2005'." } ], "id_raw": 659 }, { "id": "JZw2XF58RNDUlEkc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_33_19.png", "conversations": [ { "from": "human", "value": "\nWhat is found within region [0.049, 0.362, 0.061, 0.385]?" }, { "from": "gpt", "value": "Within this area, we can find the y-axis tick label representing the value '6'." } ], "id_raw": 659 }, { "id": "FJentr9mnFq7FyA8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the signified content of {xlabel}[0.398, 0.828, 0.466, 0.863]?" }, { "from": "gpt", "value": "Hours." } ], "id_raw": 660 }, { "id": "dY1m4lnfXsJ59ab7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_0.png", "conversations": [ { "from": "human", "value": "\nWhat color does {the handle of 'Twitter Usage'}[0.754, 0.904, 0.815, 0.925] have?" }, { "from": "gpt", "value": "#ff7f0e." } ], "id_raw": 660 }, { "id": "epA6PBal1hc5c3lm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the numerical label at {yticks coordinate}[0.025, 0.585, 0.054, 0.620]?" }, { "from": "gpt", "value": "10." } ], "id_raw": 660 }, { "id": "yUXhX9ar1fZtVGYA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_0.png", "conversations": [ { "from": "human", "value": "\nBased on {Point 1 for Instagram Usage}[0.062, 0.430] and {Point 1 for Twitter Usage}[0.062, 0.598], which platform is used more at hour 1?" }, { "from": "gpt", "value": "Instagram." } ], "id_raw": 660 }, { "id": "Qs8Kd9Dtgf8btet0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_0.png", "conversations": [ { "from": "human", "value": "\nAt point 6, is {Instagram Usage}[0.432, 0.329] below or above {Twitter Usage}[0.432, 0.413]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 660 }, { "id": "sDjp4gSxXi8sqO9o", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_0.png", "conversations": [ { "from": "human", "value": "\nBetween {the last point of Instagram Usage}[0.802, 0.279] and {the last point of Twitter Usage}[0.802, 0.295], which one has a higher usage?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 660 }, { "id": "ReqombTcAaIrXBv3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the area [0.444, 0.885, 0.993, 0.948]?" }, { "from": "gpt", "value": "The area contains the chart legend. It has two items, {Instagram Usage}[0.518, 0.899, 0.715, 0.934] with a blue color marker, and {Twitter Usage}[0.826, 0.899, 0.984, 0.934] with an orange color marker." } ], "id_raw": 660 }, { "id": "10mzVLpVtTcsujhn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_0.png", "conversations": [ { "from": "human", "value": "\nWhat is present in region [0.062, 0.178, 0.802, 0.430]?" }, { "from": "gpt", "value": "This region shows the line representing Instagram Usage, plotted as a sequence of datum points from hour 1 to 11." } ], "id_raw": 660 }, { "id": "8TsioYKFqwMDQ6rU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.062, 0.211, 0.802, 0.598]?" }, { "from": "gpt", "value": "This region contains the line for Twitter usage over time, depicted as a series of points from hour 1 to 11." } ], "id_raw": 660 }, { "id": "fJUkQjwEIG4wuz23", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the {line at coordinates}[0.142, 0.661, 0.234, 0.612]?" }, { "from": "gpt", "value": "It is a segment of the AI Trend line." } ], "id_raw": 661 }, { "id": "RhNxsS5CVIfgi6yq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_1.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is the {tick label}[0.393, 0.879, 0.437, 0.903] on X axis?" }, { "from": "gpt", "value": "The tick label is 2024." } ], "id_raw": 661 }, { "id": "wlwsHyOttS4diXF4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_1.png", "conversations": [ { "from": "human", "value": "\nWhich technology does the {legend item}[0.415, 0.948, 0.485, 0.972] refer to?" }, { "from": "gpt", "value": "The legend item refers to the AI Trend." } ], "id_raw": 661 }, { "id": "eyxTeIfgOSDFoTWH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_1.png", "conversations": [ { "from": "human", "value": "\nWhich technology prediction, {AI Trend}[0.052, 0.135, 0.959, 0.709] or {VR Trend}[0.052, 0.645, 0.959, 0.871], has a higher value for the year 2022?" }, { "from": "gpt", "value": "AI Trend." } ], "id_raw": 661 }, { "id": "Am9ldRngjUSyaoOE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point of AI Trend}[0.415, 0.483] in 2024 above or below {the point of VR Trend}[0.415, 0.734] in the same year?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 661 }, { "id": "pdya4ymMCBK9TOy7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_1.png", "conversations": [ { "from": "human", "value": "\nBetween AI Trend and VR Trend, which one starts lower in {2020}[0.052, 0.709] and [0.052, 0.645] respectively?" }, { "from": "gpt", "value": "VR Trend." } ], "id_raw": 661 }, { "id": "mHrrxFS4VqaHerHh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within this region [0.359, 0.939, 0.652, 0.981]?" }, { "from": "gpt", "value": "The region contains the chart legend with two items. The first one, {AI Trend}[0.415, 0.948, 0.485, 0.972], is marked with the color '#1f77b4', and the second one, {VR Trend}[0.568, 0.948, 0.645, 0.972], is marked with the color '#ff7f0e'." } ], "id_raw": 661 }, { "id": "nab2IeHfiaxYOeaV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_1.png", "conversations": [ { "from": "human", "value": "\nWhat is denoted by the area given by the coordinates [0.052, 0.062, 0.959, 0.871]?" }, { "from": "gpt", "value": "This area represents the main plot of the chart, containing the line graphs of the AI Trend and VR Trend data plotted over time." } ], "id_raw": 661 }, { "id": "p7Q138QLmcGU1E6D", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_1.png", "conversations": [ { "from": "human", "value": "\nCould you depict what's in the region [0.734, 0.779, 0.869, 0.839]?" }, { "from": "gpt", "value": "This region has points from the AI Trend and VR Trend lines for the year 2028. The AI Trend is plotted at a higher value compared to the VR trend for this year." } ], "id_raw": 661 }, { "id": "LnfczhW6YFDPn0mK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinate {the label}[0.488, 0.879, 0.532, 0.914]?" }, { "from": "gpt", "value": "2016." } ], "id_raw": 662 }, { "id": "BwWZE2PYTKEjrryl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the handle with {the handle's coordinates}[0.071, 0.128, 0.118, 0.152] in the legend?" }, { "from": "gpt", "value": "#1f77b4." } ], "id_raw": 662 }, { "id": "pRy1Tur3DDrhTNcA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_2.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the line with the first point at {coordinates}[0.061, 0.147] and the last point at {coordinates}[0.959, 0.858]?" }, { "from": "gpt", "value": "Tesla Return%." } ], "id_raw": 662 }, { "id": "PIyIfi1AjUxie65P", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_2.png", "conversations": [ { "from": "human", "value": "\nLooking at the line plots, does 'Tesla Return%' {line's points}[0.061, 0.823], [0.959, 0.147] generally have higher returns compared to 'AAPL Return%' {line's points}[0.061, 0.818], [0.959, 0.768]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 662 }, { "id": "jO7MLfhL40ZKRSXY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_2.png", "conversations": [ { "from": "human", "value": "\nIs the point for 2016 on the 'Tesla Return%' line {point 2016}[0.510, 0.805] above or below 2016 point on the 'AAPL Return%' line {point 2016}[0.510, 0.834]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 662 }, { "id": "Gla19QqhJOiynLvu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_2.png", "conversations": [ { "from": "human", "value": "\nAt what point does 'AAPL Return%' {points coordinates}[0.847, 0.766], [0.959, 0.768] surpass 'Tesla Return%' {points coordinates}[0.847, 0.823], [0.959, 0.147]?" }, { "from": "gpt", "value": "2019." } ], "id_raw": 662 }, { "id": "5ASVnbDR3h8GAJw9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within {region coordinates}[0.125, 0.125, 0.249, 0.212]?" }, { "from": "gpt", "value": "That region includes the legend labels for 'Tesla Return%' and 'AAPL Return%', and their corresponding color markers." } ], "id_raw": 662 }, { "id": "pII6LZl65ApmpVtA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained within the area {area coordinates}[0.264, 0.879, 0.308, 0.914]?" }, { "from": "gpt", "value": "It contains the x-axis label for '2014'." } ], "id_raw": 662 }, { "id": "Vdpekx3kpUosf9N8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the information shown in the region {region}[0.033, 0.758, 0.055, 0.793]." }, { "from": "gpt", "value": "It contains the y-axis label '80'." } ], "id_raw": 662 }, { "id": "6nlpJvM2RaJybyP0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart type depicted in this figure?" }, { "from": "gpt", "value": "Line chart." } ], "id_raw": 663 }, { "id": "NvwDCbTyapfXKNTo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the line with color '#1f77b4' represent in the legend?" }, { "from": "gpt", "value": "Startup Count." } ], "id_raw": 663 }, { "id": "YAiXAvy7HcRoPky3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_3.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of {the point labeled '2020'} for the 'innovation_index' line?" }, { "from": "gpt", "value": "[0.946, 0.142]." } ], "id_raw": 663 }, { "id": "uD8Gf8rpcodymoMF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_3.png", "conversations": [ { "from": "human", "value": "\nAt {the point labeled '2018'}[0.769, 0.190], is the value of 'innovation_index' lower or higher than the value of 'startup_count'?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 663 }, { "id": "HMZPbeBczF87Z7ms", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_3.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '2010'} for the 'startup_count' line[0.062, 0.866] above or below {the point labeled '2010'} for the 'innovation_index' line[0.062, 0.383]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 663 }, { "id": "viw3eiBe7enFreYI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_3.png", "conversations": [ { "from": "human", "value": "\nIs the 'innovation_index' line generally ascending or descending between {the point labeled '2012'}[0.239, 0.364] and {the point labeled '2020'}[0.946, 0.142]?" }, { "from": "gpt", "value": "Ascending." } ], "id_raw": 663 }, { "id": "yIq9fUGZaAgGoHeV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_3.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects found in [0.062, 0.866, 0.946, 0.721]?" }, { "from": "gpt", "value": "A line graph labeled 'startup_count' is present here with points spread across the years from 2010 to 2020, each connected by a blue colored line." } ], "id_raw": 663 }, { "id": "xAz4oMYY9WzEbSdi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_3.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.062, 0.142, 0.946, 0.383]?" }, { "from": "gpt", "value": "Another line graph, labeled 'innovation_index', is found within this region. It also has points spread across the years from 2010 to 2020, connected by an orange colored line." } ], "id_raw": 663 }, { "id": "drG28iopQP1VbsMF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_3.png", "conversations": [ { "from": "human", "value": "\nWhat information is located at [0.197, 0.037, 0.811, 0.083]?" }, { "from": "gpt", "value": "The subtitle of the chart, 'Entrepreneurship and Innovation Ecosystem', is positioned in this region." } ], "id_raw": 663 }, { "id": "4xXrvLDPV8TnTNPf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_4.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.015, 0.305, 0.029, 0.660] in the chart?" }, { "from": "gpt", "value": "The Y-axis label." } ], "id_raw": 664 }, { "id": "jvpBy1bP8nizWnpy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.124, 0.118, 0.936, 0.833]?" }, { "from": "gpt", "value": "It's the Workplace Development Trend graph line." } ], "id_raw": 664 }, { "id": "FTNoboKk5W0gASoM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_4.png", "conversations": [ { "from": "human", "value": "\nWhich x-axis tick label is located at coordinates [0.346, 0.919, 0.390, 0.942]?" }, { "from": "gpt", "value": "It's the 'Senior' label." } ], "id_raw": 664 }, { "id": "9FJ4eB38M8Iavc4H", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_4.png", "conversations": [ { "from": "human", "value": "\nIs the {point 'Management'}[0.449, 0.728] above or below the {point 'Senior'}[0.368, 0.679] on the line graph?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 664 }, { "id": "Iiqlnh849cByaSOD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point 'Intermediate'}[0.206, 0.756] located to the left or right of the {point 'Experienced'}[0.287, 0.693]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 664 }, { "id": "aPCwXONJr4VEP1xK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_4.png", "conversations": [ { "from": "human", "value": "\nDoes the {point 'Health'}[0.692, 0.118] on the trend line have a higher or lower value than the {point 'Novice'}[0.124, 0.833] in the Workplace Development Trend?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 664 }, { "id": "DQdYT5xwuynzTesJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region at [0.298, 0.025, 0.762, 0.055] on the chart?" }, { "from": "gpt", "value": "This region contains the subtitle of the chart, which is \"Workplace Development across Different Career Stages.\"" } ], "id_raw": 664 }, { "id": "E0ffM8RHQuSXdTfT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_4.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.706, 0.074, 0.969, 0.116]?" }, { "from": "gpt", "value": "The region contains a legend item, {Workplace Development Trend}[0.751, 0.083, 0.964, 0.106], with a color marker of blue at {coordinates}[0.708, 0.086, 0.744, 0.100]." } ], "id_raw": 664 }, { "id": "qVTTza5U1s4b5eX5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_4.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region defined by [0.081, 0.053, 0.216, 0.917]?" }, { "from": "gpt", "value": "This region encompasses the Y-axis and includes respective tick labels and marks. For instance, the label '0.004' at {coordinates}[0.035, 0.894, 0.074, 0.917] and '0.006' at {coordinates}[0.035, 0.754, 0.074, 0.777]." } ], "id_raw": 664 }, { "id": "iKLBEpCZ4uVKT9qf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the label with coordinates [0.015, 0.358, 0.029, 0.608] represent in the chart?" }, { "from": "gpt", "value": "Y-axis label representing \"Energy Allocation (%)\"." } ], "id_raw": 665 }, { "id": "AZakQJcnzJu98jES", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of x-axis tick with coordinates [0.407, 0.919, 0.442, 0.942]?" }, { "from": "gpt", "value": "The label is \"2024\"." } ], "id_raw": 665 }, { "id": "glnwUcfl7CNb7jDF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line plot {Biofuels}\\[0.062, 0.716, 0.968, 0.903]\\?" }, { "from": "gpt", "value": "The color of the line plot is blue (#0000ff)." } ], "id_raw": 665 }, { "id": "lLFn1JIOY9T4ak4t", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_5.png", "conversations": [ { "from": "human", "value": "\nIs the data point for Hydroelectric at {Year 2021}[0.152, 0.118]\\ higher or lower than the data point for Biofuels at {Year 2021}[0.152, 0.753]\\?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 665 }, { "id": "B85WodN2Wd51CGue", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_5.png", "conversations": [ { "from": "human", "value": "\nIn Year 2020, is the {Energy Allocation} for Biofuels [0.035, 0.707, 0.052, 0.730]\\ less than that for Hydroelectric [0.062, 0.156]\\?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 665 }, { "id": "uxiI2oqOsl2Dd0g2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_5.png", "conversations": [ { "from": "human", "value": "\nDoes the energy allocation for {Biofuels}[0.862, 0.083, 0.917, 0.106]\\ decrease or increase from 2020 to 2030 [0.062, 0.716, 0.968, 0.903]\\?" }, { "from": "gpt", "value": "Decreases." } ], "id_raw": 665 }, { "id": "C1KVeoRDip2IaB3f", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.818, 0.074, 0.961, 0.151]." }, { "from": "gpt", "value": "This region contains the legend of the graph. It includes the labels for Biofuels and Hydroelectric, each associated with a color marker of blue and green, respectively." } ], "id_raw": 665 }, { "id": "52vmLNqTORG5XS1b", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_5.png", "conversations": [ { "from": "human", "value": "\nCould you elaborate what's inside [0.500, 0.952, 0.529, 0.975]?" }, { "from": "gpt", "value": "This region contains the x-axis label, which is \"Year\"." } ], "id_raw": 665 }, { "id": "xKbS8oeUpxrsw4jN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_5.png", "conversations": [ { "from": "human", "value": "\nWhat sort of content lies within the area [0.354, 0.025, 0.675, 0.055]?" }, { "from": "gpt", "value": "The subtitle \"Future Energy and Resource Allocation\" is located within this area." } ], "id_raw": 665 }, { "id": "kFulXmjFt72o4W2U", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at coordinates [0.025, 0.381, 0.048, 0.461]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 666 }, { "id": "RoDEYF04UtzlyyJL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_6.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object at coordinates [0.339, 0.879, 0.541, 0.914] in the chart?" }, { "from": "gpt", "value": "Legend for GDP Growth Rate." } ], "id_raw": 666 }, { "id": "rYEIMSGGms50wixQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.916, 0.761, 0.975, 0.796]?" }, { "from": "gpt", "value": "2018." } ], "id_raw": 666 }, { "id": "puWGXv5ZyCVOBNp4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_6.png", "conversations": [ { "from": "human", "value": "\nIs {the GDP Growth Rate} at point [0.622, 0.144] higher or lower than {the Inflation Rate} at point [0.622, 0.226]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 666 }, { "id": "oZpKZTUZLLzeMgsy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Inflation Rate} at [0.784, 0.213] above or below {the point for GDP Growth Rate} at [0.784, 0.128]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 666 }, { "id": "Mv8cRTiybc7U2D53", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_6.png", "conversations": [ { "from": "human", "value": "\nWhich rate is higher at the year '2014', GDP Growth Rate or Inflation Rate?" }, { "from": "gpt", "value": "GDP Growth Rate." } ], "id_raw": 666 }, { "id": "EfcFfpCusnfrmDLd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_6.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the region [0.265, 0.865, 0.817, 0.928]?" }, { "from": "gpt", "value": "It contains the legend of the chart describing two items: {GDP Growth Rate}[0.339, 0.879, 0.541, 0.914] with a blue color marker, and {Inflation Rate}[0.652, 0.879, 0.807, 0.914] with an orange color marker." } ], "id_raw": 666 }, { "id": "uQKpiU7pmEWxIp9T", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_6.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is present in the area [0.136, 0.093, 0.946, 0.749]?" }, { "from": "gpt", "value": "That region contains the main plot of the chart displaying two line graphs. The first one represents the {GDP Growth Rate}[0.136, 0.110, 0.946, 0.695] and the second line represents the {Inflation Rate}[0.136, 0.196, 0.946, 0.244]." } ], "id_raw": 666 }, { "id": "crzmJAABFDATXisX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_6.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained within [0.290, 0.037, 0.792, 0.083]?" }, { "from": "gpt", "value": "The subtitle of the chart, labeled \"Global Financial System and Policies\", is located at this region." } ], "id_raw": 666 }, { "id": "TjijUiS75WQz9HLK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.016, 0.421, 0.016, 0.421] in the chart?" }, { "from": "gpt", "value": "Y-axis." } ], "id_raw": 667 }, { "id": "qGEo5NT0XGhgF0Zh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the label '6' on the x-axis?" }, { "from": "gpt", "value": "[0.614, 0.761, 0.629, 0.796]." } ], "id_raw": 667 }, { "id": "3bAlL1hbCN6Ry2l4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the {ylabel} at [0.016, 0.421, 0.016, 0.421]?" }, { "from": "gpt", "value": "It's empty, no label is given." } ], "id_raw": 667 }, { "id": "GO67oXOJUWOP9YM0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Layer 2'}[0.283, 0.281] for 'Social Responsibility' above or below {the point for 'Layer 2'}[0.283, 0.636] for 'Sustainable Business'?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 667 }, { "id": "JqsXtl60Nr4fmX1l", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Layer 5'}[0.537, 0.327] for 'Social Responsibility' to the left or right of {the point for 'Layer 5'}[0.537, 0.674] for 'Sustainable Business'?" }, { "from": "gpt", "value": "Neither, they align vertically." } ], "id_raw": 667 }, { "id": "T6SbayG2tHxjgWhf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_7.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at {Layer 6}[0.622, 0.356] for 'Social Responsibility' or {Layer 6}[0.622, 0.636] for 'Sustainable Business'?" }, { "from": "gpt", "value": "Social Responsibility." } ], "id_raw": 667 }, { "id": "JSbT2O323QT1fpp5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.195, 0.865, 0.879, 0.928]." }, { "from": "gpt", "value": "This area contains the legend for the lines representing 'Social Responsibility' at {coordinates}[0.198, 0.884, 0.258, 0.905] and 'Sustainable Business' at {coordinates}[0.549, 0.884, 0.609, 0.905], each with a corresponding color marker." } ], "id_raw": 667 }, { "id": "j4nRa1Yn4YHJW2wZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.154, 0.037, 0.920, 0.083]." }, { "from": "gpt", "value": "This area contains the subtitle of the chart: 'Social Responsibility & Sustainable Business Over Time', in black color." } ], "id_raw": 667 }, { "id": "OxGDvmJhP91x5G3X", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.945, 0.761, 0.975, 0.796]?" }, { "from": "gpt", "value": "The x-axis tick label '10' is located here." } ], "id_raw": 667 }, { "id": "aC0oEfidb84wJlRG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the x-axis tick} at [0.839, 0.763, 0.865, 0.791]?" }, { "from": "gpt", "value": "16." } ], "id_raw": 668 }, { "id": "4k0NWlW5a1pEVP89", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_8.png", "conversations": [ { "from": "human", "value": "\nWhat type of line is represented by the {coordinates} [0.113, 0.195, 0.966, 0.557]?" }, { "from": "gpt", "value": "Creativity-Level." } ], "id_raw": 668 }, { "id": "swzJpXGACaqJ5y5l", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the y-axis tick} at [0.050, 0.214, 0.106, 0.242]?" }, { "from": "gpt", "value": "0.070." } ], "id_raw": 668 }, { "id": "3FnXIlC9GlyFgAZf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_8.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Creativity-Level} at [0.852, 0.451] higher or lower than {Design-Innovation-Level} at [0.852, 0.602]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 668 }, { "id": "4f9xOKBcn8jDSBTK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_8.png", "conversations": [ { "from": "human", "value": "\nIs the {Creativity-Level point} at [0.625, 0.376] above or below the {Design-Innovation-Level point} at [0.625, 0.617]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 668 }, { "id": "guOtdkk2fiCYMQQ9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_8.png", "conversations": [ { "from": "human", "value": "\nWhich level is higher at Layer 4, {Creativity-Level} at [0.170, 0.542] or {Design-Innovation-Level} at [0.170, 0.708]?" }, { "from": "gpt", "value": "Creativity-Level." } ], "id_raw": 668 }, { "id": "bYnsQ2D04nDw9elh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the region [0.498, 0.763, 0.523, 0.791]." }, { "from": "gpt", "value": "In this region, there is a tick label '10' on the x-axis." } ], "id_raw": 668 }, { "id": "P8kNA0E7lprxKnsM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.322, 0.903, 0.477, 0.931] represent?" }, { "from": "gpt", "value": "This region contains the text 'Creativity-Level', which is part of the chart legend and is associated with a line on the chart." } ], "id_raw": 668 }, { "id": "HcAVVga6xzzHlZlE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you say about the area [0.454, 0.557, 0.454, 0.632]?" }, { "from": "gpt", "value": "This area indicates the 9th points on both Creativity-Level and Design-Innovation-Level lines." } ], "id_raw": 668 }, { "id": "zixm2qnQ8N6zAvn3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the xlabel}[0.483, 0.927, 0.548, 0.963] indicating?" }, { "from": "gpt", "value": "Layer." } ], "id_raw": 669 }, { "id": "rhrEYHQyEOHQLoeh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the title of the chart}[0.259, 0.037, 0.772, 0.083]?" }, { "from": "gpt", "value": "Social Inequality vs Rights Protection." } ], "id_raw": 669 }, { "id": "ieJQSfAq7iEgKPT5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the significance of {the point}[0.062, 0.577] on the 'Social Inequality' line?" }, { "from": "gpt", "value": "It indicates that the Social Inequality at Layer 1 is 5.0." } ], "id_raw": 669 }, { "id": "ac5A9vyvSX5ZlAeL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_9.png", "conversations": [ { "from": "human", "value": "\nAt {Layer 6}[0.515, 0.721], is the Social Inequality score higher or lower than the Rights Protection score [0.515, 0.287]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 669 }, { "id": "sAVAJuFh1LO7pljW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_9.png", "conversations": [ { "from": "human", "value": "\nIs {the point representing Layer 8 in the Rights Protection trend}[0.696, 0.190] above or below {the point representing Layer 8 in the Social Inequality trend}[0.696, 0.789]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 669 }, { "id": "1J7wEwFxH1IeAxp7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_9.png", "conversations": [ { "from": "human", "value": "\nWhich value is greater at {Layer 2}, the Social Inequality score [0.153, 0.596] or the Rights Protection score [0.153, 0.441]?" }, { "from": "gpt", "value": "Rights Protection." } ], "id_raw": 669 }, { "id": "PF53HSCO6b0z2JL5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within this area [0.244, 0.625, 0.334, 0.673]." }, { "from": "gpt", "value": "The area creates a segment of the 'Social Inequality' trend line going from Layer 3 at the value of 4.5< r>[0.244, 0.625] to Layer 4 at the value of 4.0< r>[0.334, 0.673]." } ], "id_raw": 669 }, { "id": "SuAt0UkbFy2A019S", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content inside these coordinates [0.224, 0.864, 0.807, 0.926]?" }, { "from": "gpt", "value": "The area contains the chart's legend items: {Social Inequality}[0.226, 0.882, 0.286, 0.903] and {Rights Protection}[0.528, 0.882, 0.588, 0.903], each with their corresponding color marker." } ], "id_raw": 669 }, { "id": "coU35na1vXto4262", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_9.png", "conversations": [ { "from": "human", "value": "\nPlease define the content in [0.600, 0.878, 0.798, 0.913]." }, { "from": "gpt", "value": "This area contains the text label {Rights Protection}[0.600, 0.878, 0.798, 0.913], which is one of the line labels in the plot's legend." } ], "id_raw": 669 }, { "id": "1JHAeh11ayhhvNtM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.025, 0.419, 0.048, 0.541] referring to?" }, { "from": "gpt", "value": "The y-axis label." } ], "id_raw": 670 }, { "id": "2CKUwygPlJCuJ9ho", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object found in these coordinates [0.594, 0.734, 0.934, 0.849]?" }, { "from": "gpt", "value": "The subplot legend." } ], "id_raw": 670 }, { "id": "uD0nVHHdIwO8YB5E", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_10.png", "conversations": [ { "from": "human", "value": "\nBased on provided coordinates, what is indicated by [0.073, 0.879, 0.132, 0.914]?" }, { "from": "gpt", "value": "The x-axis tick label for the year 1980." } ], "id_raw": 670 }, { "id": "hbgClWThzwDXFPeW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_10.png", "conversations": [ { "from": "human", "value": "\nAround year 2030, between {Inequality Index line}[0.946, 0.535] and {Right Protection Index line}[0.946, 0.149], which index has a higher value?" }, { "from": "gpt", "value": "Inequality Index." } ], "id_raw": 670 }, { "id": "NxYl6agvuZj9aKWH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_10.png", "conversations": [ { "from": "human", "value": "\nAt {the point}[0.777, 0.248], is the Right Protection Index higher or lower compared to the {previous point}[0.693, 0.270]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 670 }, { "id": "wN9VjwgEDD7g3CJN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_10.png", "conversations": [ { "from": "human", "value": "\nBased on the coordinates of each point in 2000 for {Inequality Index line}[0.440, 0.712] and {Right Protection Index line}[0.440, 0.336], which index has a higher value?" }, { "from": "gpt", "value": "Inequality Index." } ], "id_raw": 670 }, { "id": "wA0KTiG52RB6kPN2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_10.png", "conversations": [ { "from": "human", "value": "\nPlease provide a description for the details within coordinates [0.594, 0.734, 0.934, 0.849]." }, { "from": "gpt", "value": "This region contains the legend of the subplot. It lists two items: {Inequality Index}[0.669, 0.748, 0.854, 0.783] in blue and {Right Protection Index}[0.669, 0.800, 0.925, 0.835] in orange." } ], "id_raw": 670 }, { "id": "lmnQ8ojcZtSLlXEt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_10.png", "conversations": [ { "from": "human", "value": "\nWhat is located within [0.411, 0.879, 0.469, 0.914]?" }, { "from": "gpt", "value": "This is the x-axis tick label for the year '2000'." } ], "id_raw": 670 }, { "id": "UoVTTdqjTmqMKrAe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the section at [0.102, 0.535, 0.946, 0.811]?" }, { "from": "gpt", "value": "This section presents the line for the Inequality Index, with data points represented by blue circles marking the index values from the years 1980 to 2030." } ], "id_raw": 670 }, { "id": "a4d6zUkGbVEym9Na", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the object located at [0.019, 0.400, 0.036, 0.573] in the chart?" }, { "from": "gpt", "value": "Freight Volume." } ], "id_raw": 671 }, { "id": "Tid639GOKranhEPg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe what the object at [0.093, 0.835, 0.138, 0.849] represents?" }, { "from": "gpt", "value": "It's the color marker for E-commerce Freight Volume in the legend." } ], "id_raw": 671 }, { "id": "wex10NwJjpUNRfSV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_11.png", "conversations": [ { "from": "human", "value": "\nIdentify the label of the x-axis tick mark at [0.586, 0.919, 0.631, 0.942]." }, { "from": "gpt", "value": "2016." } ], "id_raw": 671 }, { "id": "h09YLsq0lixnaeDh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_11.png", "conversations": [ { "from": "human", "value": "\nIn the year 2016, which value is higher, {E-commerce Freight Volume}[0.609, 0.422] or {Heavy Industrial Transport Volume}[0.609, 0.631]?" }, { "from": "gpt", "value": "Heavy Industrial Transport Volume." } ], "id_raw": 671 }, { "id": "QTheLto0CePGaqeb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_11.png", "conversations": [ { "from": "human", "value": "\nAt the year {2018}[0.762, 0.919, 0.806, 0.942], is the {E-commerce Freight Volume}[0.784, 0.252] higher or lower than {Heavy Industrial Transport Volume}[0.784, 0.661]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 671 }, { "id": "a4VJZ7PP3g9LdhHr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_11.png", "conversations": [ { "from": "human", "value": "\nComparing the first point of the {E-commerce Freight Volume}[0.082, 0.861] and the last point of the {Heavy Industrial Transport Volume}[0.959, 0.711], which one is higher?" }, { "from": "gpt", "value": "E-commerce Freight Volume." } ], "id_raw": 671 }, { "id": "pCXaSdeNPh4zbDA2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area enclosed by the coordinates [0.091, 0.823, 0.452, 0.899]." }, { "from": "gpt", "value": "The area contains the legend of the chart. It includes two entries: {E-commerce Freight Volume}[0.146, 0.832, 0.390, 0.855] and {Heavy Industrial Transport Volume}[0.146, 0.867, 0.445, 0.890], with their respective handle color markers at [0.093, 0.835, 0.138, 0.849] and [0.093, 0.870, 0.138, 0.884]." } ], "id_raw": 671 }, { "id": "ngRJbOjFLE0MJhu1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the area [0.292, 0.025, 0.750, 0.055]?" }, { "from": "gpt", "value": "Within this area, the chart's subheading \"Transportation and Logistics Industry Trends\" is found." } ], "id_raw": 671 }, { "id": "1xVLENAApIGusbI7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_11.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the area defined by [0.082, 0.062, 0.959, 0.911]?" }, { "from": "gpt", "value": "This large area includes all of the plot's content. This includes the x and y axises, the plot title, all of the data points and lines corresponding to {E-commerce Freight Volume} and {Heavy Industrial Transport Volume}, as well as the legend." } ], "id_raw": 671 }, { "id": "EgABpdQ28vmRERpP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_12.png", "conversations": [ { "from": "human", "value": "\nWhat is at this coordinate range in the chart: [0.362, 0.919, 0.444, 0.943]?" }, { "from": "gpt", "value": "The label for 'FMCG Sales' in the legend." } ], "id_raw": 672 }, { "id": "41dIOMME9Za8h4rY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis described by this bounding box [0.015, 0.378, 0.029, 0.440]?" }, { "from": "gpt", "value": "The label is 'Sales'." } ], "id_raw": 672 }, { "id": "idwWRlMa8DnGDEKk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the x-axis tick at the coordinate [0.075, 0.756]?" }, { "from": "gpt", "value": "The label is '2000'." } ], "id_raw": 672 }, { "id": "bjXyk5yjXstpFEZT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_12.png", "conversations": [ { "from": "human", "value": "\nWhich sales trend, {FMCG Sales}[0.075, 0.302, 0.967, 0.724] or {Consumer Discretionary Sales}[0.075, 0.113, 0.967, 0.362], experienced a larger decline from 2000 to 2012?" }, { "from": "gpt", "value": "FMCG Sales." } ], "id_raw": 672 }, { "id": "aAlnkiheTplL3yVJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_12.png", "conversations": [ { "from": "human", "value": "\nBased on the values for the year 2008, {FMCG Sales}[0.670, 0.488] or {Consumer Discretionary Sales}[0.670, 0.210], which one was higher?" }, { "from": "gpt", "value": "Consumer Discretionary Sales." } ], "id_raw": 672 }, { "id": "6u87nzqofqWe2vom", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_12.png", "conversations": [ { "from": "human", "value": "\nAre the {FMCG Sales}[0.075, 0.302, 0.967, 0.724] generally higher or lower than {Consumer Discretionary Sales}[0.075, 0.113, 0.967, 0.362] throughout the years 2000 to 2012?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 672 }, { "id": "eFBmy8AYagPqSUeJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the following region: [0.317, 0.910, 0.725, 0.952]." }, { "from": "gpt", "value": "This region contains the legend for the chart. It includes labels for 'FMCG Sales' and 'Consumer Discretionary Sales', each paired with a color marker." } ], "id_raw": 672 }, { "id": "TvVxScybf64jVhik", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the area bounded by [0.075, 0.062, 0.967, 0.756]?" }, { "from": "gpt", "value": "This region contains the plot of the chart, including two lines representing 'FMCG Sales' and 'Consumer Discretionary Sales', as well as the axes with their corresponding labels and ticks." } ], "id_raw": 672 }, { "id": "DMOVH35x8q9VXv05", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_12.png", "conversations": [ { "from": "human", "value": "\nWhat information can be found in the area [0.510, 0.919, 0.719, 0.943]?" }, { "from": "gpt", "value": "This area contains the legend item for 'Consumer Discretionary Sales', paired with a colored marker." } ], "id_raw": 672 }, { "id": "1t2t4o3VOoIJVHMf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_13.png", "conversations": [ { "from": "human", "value": "\nWhich line in the chart corresponds to the Charity Fund?" }, { "from": "gpt", "value": "{The blue line}[0.093, 0.108, 0.959, 0.447]" } ], "id_raw": 673 }, { "id": "3zDTD5SQtNVx7dx5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the marker for {Direct Aid}[0.551, 0.923, 0.596, 0.937] in the legend?" }, { "from": "gpt", "value": "#ff7f0e" } ], "id_raw": 673 }, { "id": "tQ9xubcMOvXL8uhu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the value at {the point}[0.286, 0.293] on the Charity Fund line?" }, { "from": "gpt", "value": "7000" } ], "id_raw": 673 }, { "id": "PF6Ys671fQszg3cZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_13.png", "conversations": [ { "from": "human", "value": "\nBetween 2011 and 2020, which fund had more money in 2017, the Charity Fund or Direct Aid?" }, { "from": "gpt", "value": "Charity Fund." } ], "id_raw": 673 }, { "id": "bnJOndRp8kjnKd8l", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_13.png", "conversations": [ { "from": "human", "value": "\nIs the {Charity Fund line}[0.093, 0.108, 0.959, 0.447] higher or lower than the {Direct Aid line}[0.093, 0.170, 0.959, 0.709] in 2015?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 673 }, { "id": "bt8hmr3U2kcVl0Uy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_13.png", "conversations": [ { "from": "human", "value": "\nWhich fund was allocating more money in 2014, the Charity Fund or Direct Aid?" }, { "from": "gpt", "value": "Charity Fund." } ], "id_raw": 673 }, { "id": "vMfR8JIKLADWE80b", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_13.png", "conversations": [ { "from": "human", "value": "\nWhat's in this region [0.355, 0.910, 0.697, 0.952]?" }, { "from": "gpt", "value": "The area represents the chart legend. It includes two entries: {Charity Fund}[0.411, 0.919, 0.521, 0.943] represented by a blue marker and {Direct Aid}[0.605, 0.919, 0.690, 0.943] represented by an orange marker." } ], "id_raw": 673 }, { "id": "W8XCeLhcpVbjEFWL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.093, 0.062, 0.959, 0.756]." }, { "from": "gpt", "value": "This region contains the plot of the chart, including the X and Y axis, the line graphs representing the Charity Fund and Direct Aid, and the axis tickmarks for years and amount of money." } ], "id_raw": 673 }, { "id": "sW2F7zK4G2EMTssK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_13.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region [0.819, 0.123, 0.884, 0.947]?" }, { "from": "gpt", "value": "This region encompasses the last two points of each line chart for the year 2020." } ], "id_raw": 673 }, { "id": "fvfzDOVMqB80r6XU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_14.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinates [0.017, 0.328, 0.032, 0.500] on the graph?" }, { "from": "gpt", "value": "The Y-axis label, indicating \"User Growth\"." } ], "id_raw": 674 }, { "id": "qwpFJSrPt6C2h3La", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_14.png", "conversations": [ { "from": "human", "value": "\nWhich item is located at [0.478, 0.903, 0.578, 0.931] in the legend?" }, { "from": "gpt", "value": "The label of \"Twitter Users\"." } ], "id_raw": 674 }, { "id": "xfTlzk4urADcNfGA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the text {the x-axis tick label} at [0.800, 0.763, 0.839, 0.791]?" }, { "from": "gpt", "value": "The year 2018." } ], "id_raw": 674 }, { "id": "5MYpb62QCzZ2np11", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_14.png", "conversations": [ { "from": "human", "value": "\nIs the {Twitter Users}[0.243, 0.210, 0.964, 0.482] growth line above or below the {Facebook Users}[0.243, 0.256, 0.964, 0.617] growth line at the year 2013?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 674 }, { "id": "MWnypDbmh8M1hiYE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_14.png", "conversations": [ { "from": "human", "value": "\nWhat growth line is at higher value at {year 2019}[0.892, 0.753], Twitter Users or Facebook Users?" }, { "from": "gpt", "value": "Twitter Users." } ], "id_raw": 674 }, { "id": "U1eqKQIugRe5mRqi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_14.png", "conversations": [ { "from": "human", "value": "\nAt {year 2015}[0.603, 0.753], does the line for Twitter Users or Facebook Users project a higher user growth?" }, { "from": "gpt", "value": "Twitter Users." } ], "id_raw": 674 }, { "id": "9QqlJWSubgkTK4nZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area between [0.429, 0.892, 0.778, 0.942]." }, { "from": "gpt", "value": "This area contains the legend for the chart, with labels and symbols for {Twitter Users}[0.478, 0.903, 0.578, 0.931] and {Facebook Users}[0.652, 0.903, 0.772, 0.931]. A color symbol is assigned to each user group." } ], "id_raw": 674 }, { "id": "phUDRCiglDrmU5zP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_14.png", "conversations": [ { "from": "human", "value": "\nWhat elements are found in the area [0.243, 0.075, 0.964, 0.753]?" }, { "from": "gpt", "value": "This area primarily includes the main plot of the chart. It contains two line plots representing the growth of Twitter and Facebook users. It also contains the Y-axis tick labels and the X-axis tick labels for each year from 2010 to 2020." } ], "id_raw": 674 }, { "id": "wbFMNHNY9iaO0JCU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in the region from [0.485, 0.030, 0.722, 0.066]?" }, { "from": "gpt", "value": "The region contains the chart's subtitle: \"Social Media User Growth\"." } ], "id_raw": 674 }, { "id": "Gj8BTlibaSGGWWRl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_15.png", "conversations": [ { "from": "human", "value": "\nWhat object is located within these coordinates [0.025, 0.311, 0.048, 0.531]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 675 }, { "id": "bQlVsvb5tMVKTm1d", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_15.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object at the coordinates [0.278, 0.884, 0.338, 0.905] in the legend?" }, { "from": "gpt", "value": "It is the color handle for {Recursion Slice}[0.278, 0.884, 0.338, 0.905]." } ], "id_raw": 675 }, { "id": "K19sy6EfGdf8Vmj1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_15.png", "conversations": [ { "from": "human", "value": "\nWhat label is linked with the coordinates [0.946, 0.761, 0.975, 0.796] on the x-axis?" }, { "from": "gpt", "value": "The label is {12}[0.946, 0.761, 0.975, 0.796]." } ], "id_raw": 675 }, { "id": "qzJWB3nQJ2V0ZGzY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_15.png", "conversations": [ { "from": "human", "value": "\nComparing {point 12 of Recursion Slice}[0.960, 0.146] with {point 12 of Chained List}[0.960, 0.251], which algorithm performed better in the 12th month?" }, { "from": "gpt", "value": "Recursion Slice." } ], "id_raw": 675 }, { "id": "RL9FmiMP91iBWwS9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_15.png", "conversations": [ { "from": "human", "value": "\nAt {point 1}[0.103, 0.723], did Chained List outperform Recursion Slice?" }, { "from": "gpt", "value": "No." } ], "id_raw": 675 }, { "id": "Jyxu8qSFWotppx6a", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_15.png", "conversations": [ { "from": "human", "value": "\nWhat performance was greater at {the 6th month point}[0.492, 0.500], Recursion Slice or Chained List?" }, { "from": "gpt", "value": "Recursion Slice." } ], "id_raw": 675 }, { "id": "w8Pc7dYW6lv2JPbu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_15.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the objects located around these coordinates [0.275, 0.865, 0.788, 0.928]?" }, { "from": "gpt", "value": "The region includes the chart legend. Two legends are present: {Recursion Slice}[0.349, 0.879, 0.524, 0.914] with the color blue, and {Chained List}[0.635, 0.879, 0.778, 0.914] with the color orange, each associated with a color handle indicating their respective lines." } ], "id_raw": 675 }, { "id": "y26RZPCv8i71CptU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_15.png", "conversations": [ { "from": "human", "value": "\nWhat's in the area within the coordinates [0.349, 0.879, 0.524, 0.914]?" }, { "from": "gpt", "value": "It is the legend item labeled {Recursion Slice}[0.349, 0.879, 0.524, 0.914]." } ], "id_raw": 675 }, { "id": "kU80pj4OLnAmiT4E", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.635, 0.879, 0.778, 0.914]?" }, { "from": "gpt", "value": "There's a legend item here labeled as {Chained List}[0.635, 0.879, 0.778, 0.914]." } ], "id_raw": 675 }, { "id": "HymW4VOWMz7SMvNY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_17.png", "conversations": [ { "from": "human", "value": "\nWhat is {the Y-axis label}[0.016, 0.154, 0.016, 0.154]?" }, { "from": "gpt", "value": "There is no text for the Y-axis label." } ], "id_raw": 677 }, { "id": "6nAj6Q6C95G8GMUf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_17.png", "conversations": [ { "from": "human", "value": "\nWhat text is presented at this coordinate [0.302, 0.037, 0.750, 0.083]?" }, { "from": "gpt", "value": "Economic Indicator Presentation." } ], "id_raw": 677 }, { "id": "9dlsVPBYkfMOEol4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the fourth tick label on the X-axis?" }, { "from": "gpt", "value": "{The label is 2016}[0.581, 0.227, 0.639, 0.262]." } ], "id_raw": 677 }, { "id": "97yI3KNN5hpeKCDG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_17.png", "conversations": [ { "from": "human", "value": "\nConsidering the GDP Growth Rate and Inflation Rate, which indicator had higher value in 2015?" }, { "from": "gpt", "value": "{GDP Growth Rate}[0.526, 0.123] was higher than {Inflation Rate}[0.526, 0.177]." } ], "id_raw": 677 }, { "id": "IlcwUmQqAC0JBrG6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_17.png", "conversations": [ { "from": "human", "value": "\nAmong the line datapoints, is the value of Inflation Rate in year 2012 lesser or greater than its value in 2019." }, { "from": "gpt", "value": "The value in 2012[0.274, 0.142] was greater than in 2019[0.862, 0.175]." } ], "id_raw": 677 }, { "id": "CXrNxqy5h3tt0DX6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_17.png", "conversations": [ { "from": "human", "value": "\nCompare the performance of GDP Growth Rate between 2018 and 2020. Which year had a better performance?" }, { "from": "gpt", "value": "The performance in 2018[0.778, 0.161] was better than 2020[0.946, 0.332]." } ], "id_raw": 677 }, { "id": "N2zlz475kFGsYqqr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe any gui elements at the area [0.250, 0.171, 0.802, 0.234]?" }, { "from": "gpt", "value": "It is the legend area and it has two entries: {Inflation Rate}[0.324, 0.185, 0.480, 0.220] with a blue marker and {GDP Growth Rate}[0.591, 0.185, 0.793, 0.220] with an orange marker." } ], "id_raw": 677 }, { "id": "XhVO2WKu6a63I74C", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_17.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the content placed within the coordinates [0.502, 0.276, 0.551, 0.311]?" }, { "from": "gpt", "value": "The label 'Year' is positioned at these coordinates, indicating the x-axis label." } ], "id_raw": 677 }, { "id": "z3N20RxNTJxoZs4X", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the coordinates [0.025, 0.080, 0.098, 0.115]." }, { "from": "gpt", "value": "There is a single object. The Y-axis tick label '3.50%' is located at these coordinates." } ], "id_raw": 677 }, { "id": "fn9yw6FFOchDxBy4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_18.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.723, 0.125, 0.966, 0.160] in the chart?" }, { "from": "gpt", "value": "The label for 'Government Support'." } ], "id_raw": 678 }, { "id": "NmTgWwzWGdhHWWIb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinate [0.454, 0.879, 0.484, 0.914]?" }, { "from": "gpt", "value": "The label '16' on the x-axis." } ], "id_raw": 678 }, { "id": "Tya57yIEuq1865lU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_18.png", "conversations": [ { "from": "human", "value": "\nWhat object is at {the position}[0.651, 0.130, 0.711, 0.150]?" }, { "from": "gpt", "value": "The color marker for 'Government Support' in the legend." } ], "id_raw": 678 }, { "id": "TnP8iPWNwnGzXnVT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_18.png", "conversations": [ { "from": "human", "value": "\nIs the {data point for 'Government Support'}[0.133, 0.564] above or below the {data point for 'Private Investment'}[0.133, 0.557] in Grade 10?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 678 }, { "id": "lQqxIH9BVX4Rr6mY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_18.png", "conversations": [ { "from": "human", "value": "\nFor the {x-value}14, is the {y-value for 'Government support'}[0.357, 0.654] higher or lower than the {y-value for 'Private Investment'}[0.357, 0.422]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 678 }, { "id": "6mzxlS85Fs2kG2ej", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_18.png", "conversations": [ { "from": "human", "value": "\nBetween 'Government Support' and 'Private Investment', which has a higher value at {x-value}19?" }, { "from": "gpt", "value": "'Private Investment'." } ], "id_raw": 678 }, { "id": "Hs7lW6pkotlQYKLu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the range [0.649, 0.111, 0.975, 0.226]?" }, { "from": "gpt", "value": "This section contains the legend items for 'Government Support' {located in the box}[0.723, 0.125, 0.966, 0.160] and 'Private Investment' {located in the box}[0.723, 0.177, 0.942, 0.212], each associated with a respective color handle, the 'Private Investment' color handle {is at}[0.651, 0.182, 0.711, 0.203] and the 'Government Support' color handle {is at}[0.651, 0.130, 0.711, 0.150]." } ], "id_raw": 678 }, { "id": "uXppcs3UfPAfvGxd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_18.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the coordinates [0.077, 0.544, 0.637, 0.834]?" }, { "from": "gpt", "value": "This region traces the path of the 'Government Support' line plot with multiple intersecting points, including such {points}[0.077, 0.544] and {points}[0.637, 0.834]." } ], "id_raw": 678 }, { "id": "gqifnzYx6U9RnAeL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_18.png", "conversations": [ { "from": "human", "value": "\nWhat can we see in the region defined by [0.077, 0.126, 0.637, 0.577]?" }, { "from": "gpt", "value": "The region contains the 'Private Investment' line plot extending from {grade 9}[0.077, 0.577] to {grade 19}[0.637, 0.126]." } ], "id_raw": 678 }, { "id": "x2NMwFfH0Lt80AWJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {this area}[0.252, 0.025, 0.788, 0.055]?" }, { "from": "gpt", "value": "Public Support vs. Military Expenditure (2005-2015)" } ], "id_raw": 679 }, { "id": "jrIVgV5wED7dnV9H", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in {this point}[0.702, 0.392] of the Public Support line?" }, { "from": "gpt", "value": "The year 2012 with Public Support value at 0.61." } ], "id_raw": 679 }, { "id": "OcKb9sKt6bGbLHbW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_34_19.png", "conversations": [ { "from": "human", "value": "\nWhat color is the series labeled {Military Expenditure}[0.507, 0.923, 0.552, 0.937] in the sublegend?" }, { "from": "gpt", "value": "Orange" } ], "id_raw": 679 }, { "id": "gTrK79wvPyrDbMoc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_19.png", "conversations": [ { "from": "human", "value": "\nWhich of the two lines, {Public Support}[0.063, 0.392, 0.976, 0.582], or {Military Expenditure}[0.063, 0.149, 0.976, 0.669], shows a higher measurement at the year 2009?" }, { "from": "gpt", "value": "Public Support" } ], "id_raw": 679 }, { "id": "d3Lf7dya9zu0jB8o", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_19.png", "conversations": [ { "from": "human", "value": "\nIs the {Public Support line point}[0.793, 0.513] in 2013 above or below the {Military Expenditure line point}[0.793, 0.236]?" }, { "from": "gpt", "value": "Above" } ], "id_raw": 679 }, { "id": "OpbqmytcNFPNQ3vx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_34_19.png", "conversations": [ { "from": "human", "value": "\nIs the 2007 {point}[0.246, 0.426] on the Public Support line higher or lower than the 2007 point on the Military Expenditure line?" }, { "from": "gpt", "value": "Higher" } ], "id_raw": 679 }, { "id": "AUYndPUKXkXj5UEu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_19.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in {this region}[0.501, 0.796, 0.538, 0.820]?" }, { "from": "gpt", "value": "The label \"Year\" on the x-axis is shown in this region." } ], "id_raw": 679 }, { "id": "QrA2IRaHsbSPlpb8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_19.png", "conversations": [ { "from": "human", "value": "\nPlease provide details for {this section}[0.296, 0.910, 0.743, 0.952]." }, { "from": "gpt", "value": "This section comprises the chart legend, including {Public Support}[0.352, 0.919, 0.477, 0.943] denoted in blue color, and {Military Expenditure}[0.561, 0.919, 0.736, 0.943] depicted in orange color." } ], "id_raw": 679 }, { "id": "9Vz0XbUT0rVEYbrZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_34_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within {these coordinates}[0.063, 0.392, 0.976, 0.582]?" }, { "from": "gpt", "value": "This region includes the line representing the Public Support data from the years 2005 to 2015." } ], "id_raw": 679 }, { "id": "kDq76nseQfVjLCqJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart according to the coordinates you see?" }, { "from": "gpt", "value": "The title is \"Ecological Environment Sustainability Assessment\" [-0.255, 0.025, 0.444, 0.072]." } ], "id_raw": 680 }, { "id": "eGqkByKmc25y7ypY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_0.png", "conversations": [ { "from": "human", "value": "\nHow many lines are there in the chart? Could you tell me the color of the first line?" }, { "from": "gpt", "value": "There are 5 lines in the chart. The color of the first line is black." } ], "id_raw": 680 }, { "id": "vuxaYFeJPJFJE0YA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_0.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis} label[0.015, 0.382, 0.035, 0.578] represent?" }, { "from": "gpt", "value": "The y-axis label represents the Magnitude." } ], "id_raw": 680 }, { "id": "s5D7XZ4wz4p7GMWQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Human Population Growth' line}[0.088, 0.863, 0.101, 0.871] rise or fall across the chart?" }, { "from": "gpt", "value": "Rises." } ], "id_raw": 680 }, { "id": "UrayO8Gfhnv7p38Z", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_0.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Deforestation rate' line}[0.088, 0.750, 0.101, 0.786] above or below {the 'Invention of Sustainable Technologies' line}[0.088, 0.864, 0.101, 0.881]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 680 }, { "id": "gbCVReyr7LzXiGFU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_0.png", "conversations": [ { "from": "human", "value": "\nBetween {the 'CO2 in atmosphere'}[0.088, 0.156, 0.091, 0.156] and {the 'Polar Ice Caps Melting Rate'}[0.088, 0.868, 0.101, 0.873], which has a steeper slope?" }, { "from": "gpt", "value": "Polar Ice Caps Melting Rate." } ], "id_raw": 680 }, { "id": "PLFg26drkWpTVB3d", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.096, 0.818, 1.746, 0.871]." }, { "from": "gpt", "value": "This is the legend area with 5 items, including the items CO2 in atmosphere, Deforestation rate, Polar Ice Caps Melting Rate, Human Population Growth, and Invention of Sustainable Technologies, each paired with a different colored marker." } ], "id_raw": 680 }, { "id": "r0eOt6iDW0FeF4by", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_0.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.041, 0.423, 0.078, 0.454]?" }, { "from": "gpt", "value": "The y-axis tick label '250' is located in this area." } ], "id_raw": 680 }, { "id": "Y9R6AOry3TjBBp2h", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_0.png", "conversations": [ { "from": "human", "value": "\nWhat do the objects within [0.068, 0.901, 0.088, 0.933] represent?" }, { "from": "gpt", "value": "These objects are x-axis tick labels '3', '10', '15', '30' and '40'." } ], "id_raw": 680 }, { "id": "DZtew3RbsHnFllKP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the object}[0.025, 0.199, 0.058, 0.620]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 681 }, { "id": "5WUYKxnXgMRQ0bxO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of {the text labeled 'Music'} in the legend?" }, { "from": "gpt", "value": "[0.572, 0.571, 0.651, 0.631]" } ], "id_raw": 681 }, { "id": "x7ChKMuPhojf8Z9H", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label}[0.068, 0.398, 0.119, 0.461]?" }, { "from": "gpt", "value": "0.4." } ], "id_raw": 681 }, { "id": "NDWcwPTChSyDNqFs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_1.png", "conversations": [ { "from": "human", "value": "\nIn the chart, is {the line for 'Theatre'}[0.164, 0.249, 0.733, 0.379] generally higher or lower than {the line for 'Music'}[0.164, 0.336, 0.733, 0.249]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 681 }, { "id": "uVAm51HiUj9L0hux", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point in 1990 for 'Photography'}[0.164, 0.379] higher or lower than {the point in 2010 for 'Photography'}[0.733, 0.466]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 681 }, { "id": "93CsCG1Vs0Bp71Bv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_1.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {the year 2010 tick}[0.733, 0.769], 'Theatre' or 'Music'?" }, { "from": "gpt", "value": "Music." } ], "id_raw": 681 }, { "id": "O9pWUkF1J9Up2Yjr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_1.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content in the region [0.149, 0.549, 1.124, 0.741]?" }, { "from": "gpt", "value": "This area contains the chart's legend, describing all the different lines represented in the chart. It includes the labels {GPT-Neo}[0.238, 0.571, 0.356, 0.631], {Theatre}[0.238, 0.659, 0.345, 0.719], {Music}[0.572, 0.571, 0.651, 0.631], {Visual Arts}[0.572, 0.659, 0.719, 0.719], {Photography}[0.936, 0.571, 1.112, 0.631], and {Dance}[0.936, 0.659, 1.024, 0.719]." } ], "id_raw": 681 }, { "id": "Yzikb2VlyyaBYUsV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the area between [0.164, 0.163, 0.733, 0.466]." }, { "from": "gpt", "value": "This region includes several line plots representing different art categories' growth. The lines included are {Theatre}[0.164, 0.249, 0.733, 0.379], {Music}[0.164, 0.249, 0.733, 0.336], {Visual Arts}[0.164, 0.163, 0.733, 0.206], {Photography}[0.164, 0.379, 0.733, 0.466] and {Dance}[0.164, 0.423, 0.733, 0.509]." } ], "id_raw": 681 }, { "id": "WZZ8LkBbzrkBNwUl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.123, 0.801, 0.774, 0.865]?" }, { "from": "gpt", "value": "This region contains the labels for the x axis ticks, which are the '1990' and '2010' year markers, respectively located at {coordinates}[0.123, 0.801, 0.205, 0.865] and {coordinates}[0.692, 0.801, 0.774, 0.865]." } ], "id_raw": 681 }, { "id": "jboRuU4UXz5ZfQPI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.050, 0.535, 0.083, 0.565] in the chart?" }, { "from": "gpt", "value": "Y-axis tick label 1.6." } ], "id_raw": 682 }, { "id": "xYQwC5YAKP61PMmU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {line_1}[0.135, 0.334, 0.941, 0.589]?" }, { "from": "gpt", "value": "Aviation." } ], "id_raw": 682 }, { "id": "4KgLJFhn0yqkxCvo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the text}[0.815, 0.831, 0.867, 0.861] located in [0.752, 0.838, 0.804, 0.852]?" }, { "from": "gpt", "value": "Road." } ], "id_raw": 682 }, { "id": "gVcONuLeSrExin88", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Shipping'}[0.135, 0.122, 0.941, 0.334] have higher or lower Growth Rate than {the line for 'Road'}[0.135, 0.674, 0.941, 0.844] by the end of quarter 4?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 682 }, { "id": "ilWF1Kf9CVzDLWgC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_2.png", "conversations": [ { "from": "human", "value": "\nIs {the third point on the 'Aviation' line}[0.672, 0.334] above or below the {third point on the 'Shipping' line}[0.672, 0.207]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 682 }, { "id": "H2o9RaPFoDKi0WNL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_2.png", "conversations": [ { "from": "human", "value": "\nWhich quarter had higher Growth Rate for {Aviation}, the {second quarter}[0.404, 0.461] or the {fourth quarter}[0.941, 0.504]?" }, { "from": "gpt", "value": "Fourth quarter." } ], "id_raw": 682 }, { "id": "7mXp1UQfIzcnxqGC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region at [0.815, 0.656, 0.906, 0.817]." }, { "from": "gpt", "value": "This region includes the label for 'Shipping' with its respective color marker, and the label for 'Road' with its color marker." } ], "id_raw": 682 }, { "id": "g6C5OGXOCo3URdlo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.050, 0.195, 0.083, 0.395]." }, { "from": "gpt", "value": "These are the y-axis tick labels '2.0' and '2.4'." } ], "id_raw": 682 }, { "id": "QkuLZS056FXtacHh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.135, 0.334, 0.941, 0.589]?" }, { "from": "gpt", "value": "Within this region, there are the line plotted for 'Aviation' and its corresponding point markers." } ], "id_raw": 682 }, { "id": "zm2uM01oxHwVSULw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_3.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is displayed with the dimensions {figure size}[10.0, 5.0]?" }, { "from": "gpt", "value": "Line chart." } ], "id_raw": 683 }, { "id": "iZdDa4NoqulvboAz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line labeled {Facebook}[0.119, 0.055, 0.185, 0.083] in the legend?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 683 }, { "id": "cOgvbspqFWvRCGiF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-coordinate corresponding to {the line for 'LinkedIn'}[0.108, 0.406, 0.919, 0.833] at the 'Active Users in Billions' point?" }, { "from": "gpt", "value": "0.833." } ], "id_raw": 683 }, { "id": "HE4wpa2l8xryuvGr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_3.png", "conversations": [ { "from": "human", "value": "\nBetween {Facebook}[0.108, 0.551, 0.919, 0.776] and {Twitter}[0.108, 0.334, 0.919, 0.824], which one has the higher value at 'Growth Rate in %'?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 683 }, { "id": "T6VTCxYFvoRtB7Sm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_3.png", "conversations": [ { "from": "human", "value": "\nIs the growth rate of {LinkedIn}[0.919, 0.406] higher or lower than that of {Instagram}[0.919, 0.068]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 683 }, { "id": "L6CiBpnW3PqiQZYZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_3.png", "conversations": [ { "from": "human", "value": "\nWhich company has a higher number of 'Active Users in Billions', {Facebook}[0.108, 0.776] or {Instagram}[0.108, 0.809]?" }, { "from": "gpt", "value": "Instagram." } ], "id_raw": 683 }, { "id": "1Rd4FqThSkPj42qL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the details in the box {coordinates}[0.068, 0.030, 0.960, 0.871]?" }, { "from": "gpt", "value": "It's where the plot occurs. This area includes the x and y axes, with gridlines, data lines labeled as 'Facebook', 'Twitter', 'Instagram' and 'LinkedIn', as well as the x-axis labels 'Active Users in billions' and 'Growth Rate in %', and y-axis labels ranging from -5 to 35." } ], "id_raw": 683 }, { "id": "R9nwgq0Qql6mXx9Q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_3.png", "conversations": [ { "from": "human", "value": "\nWhat information is included in the legend box with {coordinates}[0.075, 0.044, 0.196, 0.220]?" }, { "from": "gpt", "value": "The legend contains 4 items. These items are the labels for each dataset: 'Facebook' with a red marker, 'Twitter' with a blue marker, 'Instagram' with a green marker, and 'LinkedIn' with a yellow marker." } ], "id_raw": 683 }, { "id": "O6QngqmF2GwD5mqU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region with these {coordinates}[0.450, 0.930, 0.577, 0.970]?" }, { "from": "gpt", "value": "The region contains the x-axis label, which reads 'Parameters'." } ], "id_raw": 683 }, { "id": "C8E52gd1pITqXQ9J", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the 'Milk' text in the legend?" }, { "from": "gpt", "value": "[0.860, 0.396, 0.916, 0.456]" } ], "id_raw": 684 }, { "id": "PBmAgad8di2Lr2a0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.025, 0.022, 0.058, 0.797]?" }, { "from": "gpt", "value": "Number of units sold." } ], "id_raw": 684 }, { "id": "3cAOOiYueLWk88Gi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label}[0.917, 0.801, 0.958, 0.865]?" }, { "from": "gpt", "value": "18." } ], "id_raw": 684 }, { "id": "BbuE8YrVI18HDbyQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_4.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Milk' sales line}[0.183, 0.267, 0.937, 0.334] above or below {the 'Water' sales line}[0.183, 0.167, 0.937, 0.612]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 684 }, { "id": "4wtHyrRWQlb4hcxZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_4.png", "conversations": [ { "from": "human", "value": "\nAt {point 18}[0.937, 0.334], is 'Coffee' sales higher or lower than 'Milk' sales?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 684 }, { "id": "wf7w3qPZwFhT0pjs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_4.png", "conversations": [ { "from": "human", "value": "\nComparing {the 'Tea' sales line}[0.183, 0.201, 0.937, 0.468] and {the 'Juice' sales line}[0.183, 0.100, 0.937, 0.602], which one generally has a higher value?" }, { "from": "gpt", "value": "Juice." } ], "id_raw": 684 }, { "id": "GkGPJvgUhvOr9LzK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.771, 0.286, 0.961, 0.741]." }, { "from": "gpt", "value": "This area contains a legend for the chart with 5 entries: {Coffee}[0.860, 0.308, 0.950, 0.368], {Milk}[0.860, 0.396, 0.916, 0.456], {Tea}[0.860, 0.484, 0.906, 0.544], {Juice}[0.860, 0.571, 0.926, 0.631], and {Water}[0.860, 0.659, 0.942, 0.719]. Each label is accompanied by a color handle situated from [0.775, 0.321, 0.845, 0.349] to [0.775, 0.672, 0.845, 0.700], each in a unique color." } ], "id_raw": 684 }, { "id": "rf3rvmHyEx1INtBO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.146, 0.050, 0.975, 0.769]." }, { "from": "gpt", "value": "This is the main area of the chart, featuring a white background and gridlines in light grey. There are five line plots representing sales data for five different items: Coffee, Milk, Tea, Juice, and Water at two time points (6 and 18). The area also includes x-axis and y-axis labels, multiple tick labels, and their respective coordinates." } ], "id_raw": 684 }, { "id": "PErwuhOs9XcMPXQ6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.183, 0.334, 0.937, 0.435]?" }, { "from": "gpt", "value": "There are two objects. These are two points representing sales of 'Coffee' at time 6 and time 18, located at {coordinates}[0.183, 0.435] and {coordinates}[0.937, 0.334], respectively." } ], "id_raw": 684 }, { "id": "LAJZgq6zaJokiCTd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.329, 0.035, 0.563] written on the chart?" }, { "from": "gpt", "value": "Population." } ], "id_raw": 685 }, { "id": "ArPPgmTRZT8aJsDz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fifth legend text?" }, { "from": "gpt", "value": "[0.154, 0.808, 0.200, 0.836]." } ], "id_raw": 685 }, { "id": "t98vbwxpWOMGG8lh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of {legend item}[0.154, 0.808, 0.200, 0.836]?" }, { "from": "gpt", "value": "Area E." } ], "id_raw": 685 }, { "id": "jI8OR6NsPg33Ls1e", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_5.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Area B'}[0.143, 0.177, 0.945, 0.372] generally above or below {the line for 'Area C'}[0.143, 0.079, 0.945, 0.226]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 685 }, { "id": "NiAIKgHklGjjp69P", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_5.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Area D'} at 2000 on top of or below the {point of 'Area E'} at 2000?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 685 }, { "id": "ZcMyU6X9UdkXIVRJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_5.png", "conversations": [ { "from": "human", "value": "\nWhich area has a higher population in 2010, {Area A}[0.945, 0.813] or {Area E}[0.945, 0.617]?" }, { "from": "gpt", "value": "Area E." } ], "id_raw": 685 }, { "id": "6iE4GyJMHYD7zYgA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.110, 0.588, 0.272, 0.848]." }, { "from": "gpt", "value": "This area contains the chart legend with labels for 2000 Population, Area A, Area B, Area C, Area D, and Area E, each paired with a color marker." } ], "id_raw": 685 }, { "id": "1i75SKLIjCLBdByS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.515, 0.930, 0.573, 0.970] contain?" }, { "from": "gpt", "value": "The region includes the label for the x-axis, which is \"Years\"." } ], "id_raw": 685 }, { "id": "e32fqYdx0O15OTHO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects present within the range [0.119, 0.881, 0.970, 0.919]." }, { "from": "gpt", "value": "There are x-axis labels '2000' and '2010' found at {coordinates}[0.119, 0.881, 0.168, 0.919] and {coordinates}[0.920, 0.881, 0.970, 0.919] respectively within this range." } ], "id_raw": 685 }, { "id": "qBw7aLpRCtSDVfV0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_6.png", "conversations": [ { "from": "human", "value": "\nWhat object is positioned at the coordinates [0.019, 0.394, 0.044, 0.528]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 686 }, { "id": "S3l1g7XHGFFrMgiF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.508, 0.942, 0.567, 0.975]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 686 }, { "id": "ZEXCPQrHRyyPRXvk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the yticks label }[0.066, 0.872, 0.082, 0.904]?" }, { "from": "gpt", "value": "0." } ], "id_raw": 686 }, { "id": "OweFPVtdsNOO7MaF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_6.png", "conversations": [ { "from": "human", "value": "\nIn 2009, which trend had a higher growth, {telecom}[0.171, 0.062, 0.256, 0.093] or {hardware}[0.171, 0.150, 0.271, 0.180]?" }, { "from": "gpt", "value": "Hardware." } ], "id_raw": 686 }, { "id": "ksChC3nM8VN4cbw7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_6.png", "conversations": [ { "from": "human", "value": "\nDid the growth trend of {cloud services}[0.371, 0.062, 0.525, 0.093] increase or decrease from 2009 to 2019?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 686 }, { "id": "iu87r3EYRGJrODu7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_6.png", "conversations": [ { "from": "human", "value": "\nIs the growth trend of {software}[0.171, 0.106, 0.262, 0.136] in 2019 higher or lower than the {internet services}[0.371, 0.106, 0.548, 0.136]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 686 }, { "id": "IsMkkpJn82pQRCMK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents located in the region [0.104, 0.051, 0.556, 0.191]?" }, { "from": "gpt", "value": "The region contains the legend of the chart with five entries including Telecom, Software, Hardware, Cloud Services, and Internet Services, each paired with a colored marker." } ], "id_raw": 686 }, { "id": "K46KGlNnfkZ9WvKA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_6.png", "conversations": [ { "from": "human", "value": "\nWhat content is present in the area marked by coordinates [0.094, 0.037, 0.981, 0.885]?" }, { "from": "gpt", "value": "This region encompasses the main area of the chart where the five lines representing Telecom, Software, Hardware, Cloud Services, and Internet Services growth over the years 2009 and 2019 are plotted." } ], "id_raw": 686 }, { "id": "9G9aT124kYBFxFf9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content contained within the coordinates [0.134, 0.567, 0.941, 0.673]." }, { "from": "gpt", "value": "There is a line plot within this area representing the growth trend of the Telecom industry from the year 2009 to 2019." } ], "id_raw": 686 }, { "id": "ALNvT1B98H3WU1wm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-tick labeled at coordinates [0.153, 0.851, 0.192, 0.899]?" }, { "from": "gpt", "value": "Q1." } ], "id_raw": 687 }, { "id": "B6sN2REwfPBpG5Rq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the legend item whose handle has the coordinates [0.822, 0.674, 0.870, 0.694]?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 687 }, { "id": "CLnFb6KjjPEi5cfe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_7.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the line marked with these coordinates [0.172, 0.629, 0.940, 0.745]?" }, { "from": "gpt", "value": "Chart line for Amazon's earnings." } ], "id_raw": 687 }, { "id": "O21gmzUEFbN2PwR1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_7.png", "conversations": [ { "from": "human", "value": "\nReading from the diagram, is {the 'Google' line}[0.172, 0.498, 0.940, 0.613] over the {line labeled 'Facebook'}[0.172, 0.416, 0.940, 0.531] in Q3?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 687 }, { "id": "WtaVqckyau1mwrdV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_7.png", "conversations": [ { "from": "human", "value": "\nComparing the second quarter, is {Apple's line}[0.428, 0.218] higher or lower than {IBM's line}[0.428, 0.120]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 687 }, { "id": "My5ORWRksZrlYiUa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_7.png", "conversations": [ { "from": "human", "value": "\nAmong {the 'IBM'}[0.940, 0.366] and {the 'Google'}[0.940, 0.498] lines, which has a higher value in Q4?" }, { "from": "gpt", "value": "Google." } ], "id_raw": 687 }, { "id": "PID2ZKzdnzNDzKLN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe the region with coordinates [0.021, 0.084, 0.050, 0.780]?" }, { "from": "gpt", "value": "The ylabel of the chart is located in this area, labeled \"Earnings per Share (in $)\". The label is colored black and is rotated at 90 degrees." } ], "id_raw": 687 }, { "id": "iVd0fs9bwOYsRQOK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_7.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects found in the area [0.619, 0.657, 0.970, 0.811]?" }, { "from": "gpt", "value": "These are the legend items in the chart: {Apple}[0.676, 0.670, 0.727, 0.702], {IBM}[0.676, 0.718, 0.709, 0.751], {Google}[0.676, 0.766, 0.739, 0.799], {Facebook}[0.878, 0.670, 0.963, 0.702], {Amazon}[0.878, 0.718, 0.950, 0.751] with corresponding color markers." } ], "id_raw": 687 }, { "id": "w78QTpZk9QIlQMkL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects in the region with coordinates [0.134, 0.038, 0.979, 0.827]?" }, { "from": "gpt", "value": "This region contains the entire plot region of the line chart. It includes the x and y-axes ticks and labels, line plots for the companies: Apple, IBM, Google, Facebook, and Amazon each representing their earnings per quarter." } ], "id_raw": 687 }, { "id": "trQPGwlvlvSZ1Uom", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_8.png", "conversations": [ { "from": "human", "value": "\nWhat element does the coordinate [0.019, 0.355, 0.043, 0.532] represent in the chart?" }, { "from": "gpt", "value": "It represents the y-axis label." } ], "id_raw": 688 }, { "id": "SZeBCBfYdlTD4fs5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_8.png", "conversations": [ { "from": "human", "value": "\nWhere is the label for the second point on the 'Knowledge Economy' line located on the chart?" }, { "from": "gpt", "value": "The coordinate is [0.404, 0.172]." } ], "id_raw": 688 }, { "id": "XPX9c20ZhaZdk7yk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.519, 0.766, 0.700, 0.801] refer to in the chart?" }, { "from": "gpt", "value": "It refers to the 'Knowledge Economy' label in the legend." } ], "id_raw": 688 }, { "id": "Nz7EWotWdRzBVpwI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_8.png", "conversations": [ { "from": "human", "value": "\nAt point '3', is {the point on the 'Knowledge Economy' line}[0.135, 0.210] above or below {the point on the 'Innovation Industries' line}[0.135, 0.443]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 688 }, { "id": "5Yue1TYVFaRSRDNV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_8.png", "conversations": [ { "from": "human", "value": "\nAt point '30', is {the point on the 'Innovation Industries' line}[0.672, 0.288] above or below {the point on the 'Knowledge Economy' line}[0.672, 0.366]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 688 }, { "id": "LB5YvWN7nNz30dpc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_8.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at point '100', Knowledge Economy or Innovation Industries?" }, { "from": "gpt", "value": "Innovation Industries." } ], "id_raw": 688 }, { "id": "Bcj3IIFMgtlyww8C", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_8.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.463, 0.752, 0.973, 0.814] of the chart?" }, { "from": "gpt", "value": "This region contains the legend of the chart. There are two items: {Knowledge Economy}[0.519, 0.766, 0.700, 0.801] labeled with a blue marker, and {Innovation Industries}[0.783, 0.766, 0.966, 0.801] labeled with a green marker." } ], "id_raw": 688 }, { "id": "6tlvG67ss4ln5FrG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content of the region [0.049, 0.659, 0.082, 0.704] on the chart?" }, { "from": "gpt", "value": "This is the area for the label of the y-axis tick mark '0.2'." } ], "id_raw": 688 }, { "id": "owB6r4PXUG8kW3AH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_8.png", "conversations": [ { "from": "human", "value": "\nWhat contents are included in the area [0.095, 0.055, 0.981, 0.832]?" }, { "from": "gpt", "value": "This is the region for the main plot of the chart, including the lines representing 'Knowledge Economy' and 'Innovation Industries', their respective points, as well as the x and y-axis labels and tick labels." } ], "id_raw": 688 }, { "id": "CNdLF10vg5otMB1z", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis title} located at [0.025, 0.116, 0.058, 0.703]?" }, { "from": "gpt", "value": "Efficiency Value" } ], "id_raw": 689 }, { "id": "a10xRKfHIaFbOuHC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_9.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.174, 0.672, 0.244, 0.700] in the chart figure?" }, { "from": "gpt", "value": "The legend item representing the 'Learned Meaning' efficiency level" } ], "id_raw": 689 }, { "id": "w5C7DsqKc2p5ezta", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the {x-axis item} at coordinates [0.881, 0.801, 0.974, 0.865]?" }, { "from": "gpt", "value": "Theta" } ], "id_raw": 689 }, { "id": "9OZiMfRoYmlIYNVr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_9.png", "conversations": [ { "from": "human", "value": "\nWhich is higher on the 'Efficiency Levels' axis: {Theta in line for 'Literal Meaning'}[0.927, 0.721] or {Theta in line for 'Learned Meaning'}[0.927, 0.601]?" }, { "from": "gpt", "value": "Literal Meaning" } ], "id_raw": 689 }, { "id": "GxnEuraEnVWe3NAv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_9.png", "conversations": [ { "from": "human", "value": "\nIs {Epsilon for 'Literal Meaning'}[0.743, 0.649] located higher or lower than {Epsilon for 'Learned Meaning'}[0.743, 0.529]?" }, { "from": "gpt", "value": "Higher" } ], "id_raw": 689 }, { "id": "LZRVyAqC5LXFvjID", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_9.png", "conversations": [ { "from": "human", "value": "\nWhich has greater efficiency in the Alpha category: Literal Meaning [0.193, 0.457] or Learned Meaning [0.193, 0.170]?" }, { "from": "gpt", "value": "Learned Meaning" } ], "id_raw": 689 }, { "id": "wfSGlD8hhCuthUWR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.170, 0.549, 0.509, 0.741]?" }, { "from": "gpt", "value": "This area contains the chart legend. It has two items: 'Literal Meaning' with a red marker at {coordinates}[0.174, 0.585, 0.244, 0.612], and 'Learned Meaning' with a blue marker at {coordinates}[0.174, 0.672, 0.244, 0.700]." } ], "id_raw": 689 }, { "id": "ysEX7cpnPVKnUMeJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the information in the region [0.068, 0.265, 0.140, 0.328]?" }, { "from": "gpt", "value": "This region includes a label for the '1.00' value on the y-axis." } ], "id_raw": 689 }, { "id": "T6hxeom5ijeu72CB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_9.png", "conversations": [ { "from": "human", "value": "\nPlease explain the objects in the area [0.193, 0.170, 0.927, 0.721]." }, { "from": "gpt", "value": "This region includes the plotted line for both 'Literal Meaning' and 'Learned Meaning' marking levels of efficiency across the 'Alpha', 'Beta', 'Delta', 'Epsilon' and 'Theta' efficiency levels." } ], "id_raw": 689 }, { "id": "VNwNbrcNEBkV8ugP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the Y-axis label}[0.015, 0.392, 0.035, 0.514]?" }, { "from": "gpt", "value": "Value." } ], "id_raw": 690 }, { "id": "iuIG6EyxSHmonjOj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the X-axis label}[0.519, 0.930, 0.566, 0.970]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 690 }, { "id": "xYzN3pr7mqYCOfgp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.151, 0.112, 0.257, 0.140] in the chart?" }, { "from": "gpt", "value": "The label for the line representing 'Patent Granted'." } ], "id_raw": 690 }, { "id": "RFYIcg75Yo0fEzsf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_10.png", "conversations": [ { "from": "human", "value": "\nIs the {innovation index trend line}[0.140, 0.858, 0.945, 0.860] above or below the {patent expired trend line}[0.140, 0.691, 0.945, 0.827]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 690 }, { "id": "xOjwJDJvFhyvXHX7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_10.png", "conversations": [ { "from": "human", "value": "\nWhich line value is higher in the year 2020, the {patent granted line}[0.140, 0.453, 0.945, 0.644] or the {patent transferred line}[0.140, 0.725, 0.945, 0.841]?" }, { "from": "gpt", "value": "Patent transferred line." } ], "id_raw": 690 }, { "id": "UGFBasIeLaK00V3v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_10.png", "conversations": [ { "from": "human", "value": "\nIs the {patent filed trend line}[0.140, 0.113, 0.945, 0.487] increasing or decreasing over time?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 690 }, { "id": "8Q9uUkQIPa5xy605", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in [0.106, 0.059, 0.284, 0.277]." }, { "from": "gpt", "value": "This area contains the chart legend, listing out each of the categories plotted on the graph. These categories include Patent Filed with a red line, Patent Granted with a blue line, Patent Expired with a green line, Innovation Index in yellow, and Patent Transferred in black." } ], "id_raw": 690 }, { "id": "2b6h4RIxPoQILAyj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_10.png", "conversations": [ { "from": "human", "value": "\nWhat content is within the bounding box [0.920, 0.881, 0.969, 0.919]?" }, { "from": "gpt", "value": "The x-axis label for the year '2020' is located in this region." } ], "id_raw": 690 }, { "id": "6wwesVuydpCF7Mf4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the information contained in [0.140, 0.725, 0.945, 0.841]." }, { "from": "gpt", "value": "This area describes the line showing the trend of Patents Transferred from the year 2000 to 2020, which is represented by the black line and triangular markers." } ], "id_raw": 690 }, { "id": "Na5brtZlVNhEKUek", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel}[0.012, 0.274, 0.029, 0.635] associated with on the chart?" }, { "from": "gpt", "value": "Measurement Units." } ], "id_raw": 691 }, { "id": "LiZLM2qqJyQNhwOW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_11.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker represents {the dataset for 'CO2(ppm)'}[0.621, 0.084, 0.651, 0.098] on the chart?" }, { "from": "gpt", "value": "Circle." } ], "id_raw": 691 }, { "id": "sjiUZxsHkqWSWTek", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of {the x-axis label}[0.495, 0.942, 0.565, 0.975]?" }, { "from": "gpt", "value": "[0.495, 0.942, 0.565, 0.975]." } ], "id_raw": 691 }, { "id": "lDAw4UAE5QlO7a3A", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_11.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 2000 on the 'CO2(ppm)' graph}[0.114, 0.127] higher or lower than {the point for 2000 on 'Methane(ppb)' graph}[0.114, -2.809]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 691 }, { "id": "H73Tt2VAkMbqUqXc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_11.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Global temperature(Celsius)'}[0.114, 0.853, 0.946, 0.857] above or below {the line for 'CO2(ppm)'}[0.114, 0.029, 0.946, 0.127] at the 2010 mark?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 691 }, { "id": "CmIPCvUiLKigAi5E", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_11.png", "conversations": [ { "from": "human", "value": "\nWhich line starts at a higher point in 2000, {the 'Nitrogen Oxide(ppb)' line}[0.114, 0.240] or {the 'Global temperature(Celsius)' line}[0.114, 0.853]?" }, { "from": "gpt", "value": "Global temperature(Celsius)." } ], "id_raw": 691 }, { "id": "l46HLweVsmQZiTse", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the data presented in the region [0.073, 0.025, 0.988, 0.885]." }, { "from": "gpt", "value": "The region encompasses axes, grid lines, and lines representing trends for 'Global temperature(Celsius)', 'CO2(ppm)', 'Methane(ppb)', and 'Nitrogen Oxide(ppb)', each with distinct markers and colors." } ], "id_raw": 691 }, { "id": "BmLTWlsM26tXj6uQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_11.png", "conversations": [ { "from": "human", "value": "\nWhat is described in the area [0.620, 0.037, 0.982, 0.113]?" }, { "from": "gpt", "value": "This region contains the subplot legend titled 'key environmental metrics' with items labeled 'Global temperature(Celsius)', 'CO2(ppm)', 'Methane(ppb)', and 'Nitrogen Oxide(ppb)', each associated with a colored marker." } ], "id_raw": 691 }, { "id": "UiHNUWaKN2stVq8R", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_11.png", "conversations": [ { "from": "human", "value": "\nExplain what is visualized between the coordinates [0.073, 0.885, 0.988, 0.932]." }, { "from": "gpt", "value": "This area features the x-axis with labels for the years from 2000 to 2019, with corresponding tick marks at regular intervals, facilitating a chronological view of the data." } ], "id_raw": 691 }, { "id": "NHtd6dmhKdmSPasH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_12.png", "conversations": [ { "from": "human", "value": "\nWhat type of chart is this one?" }, { "from": "gpt", "value": "Line chart." } ], "id_raw": 692 }, { "id": "9EfvW5KWQB7gayA0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_12.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the coordinates [0.553, 0.113, 0.678, 0.149]?" }, { "from": "gpt", "value": "Legend for SME Growth line." } ], "id_raw": 692 }, { "id": "hQ6XiJMcY1PVKzsC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_12.png", "conversations": [ { "from": "human", "value": "\nWhat is plotted at {the point}[0.748, 0.363] on the chart?" }, { "from": "gpt", "value": "It's a data point for the \"Startup Registration\" line corresponding to the year 2016." } ], "id_raw": 692 }, { "id": "KyNMt41fTTevdLxR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_12.png", "conversations": [ { "from": "human", "value": "\n'Foreign Investment Line' and 'Startup Registration' both have points at year 2013. Which one has higher value at that year?" }, { "from": "gpt", "value": "Startup Registration." } ], "id_raw": 692 }, { "id": "4ywvMNqFyjYGJEKb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_12.png", "conversations": [ { "from": "human", "value": "\nIs the {line for 'Patents Registered'}[0.164, 0.462, 0.942, 0.595] above or below the {line for 'SME Growth'}[0.164, 0.662, 0.942, 0.762]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 692 }, { "id": "W0w0VEyM30IHyYE8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_12.png", "conversations": [ { "from": "human", "value": "\nAt the year of 2017, which trend has a higher value, Patents Registered or SME Growth?" }, { "from": "gpt", "value": "Patents Registered." } ], "id_raw": 692 }, { "id": "yanKif2DJtRhQ88E", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is found in the coordinates [0.125, 0.030, 0.981, 0.861]?" }, { "from": "gpt", "value": "This area includes the full scope of the plotted lines. All data trends, including Foreign Investment, Startup Registration, Patents Registered, and SME Growth are located here." } ], "id_raw": 692 }, { "id": "N9AHLfQFHxueih6n", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the information in the area [0.135, 0.047, 0.687, 0.215]?" }, { "from": "gpt", "value": "It contains the chart legend items including {Foreign Investment Line}[0.202, 0.060, 0.453, 0.096], {Startup Registration}[0.202, 0.113, 0.410, 0.149], {Patents Registered}[0.202, 0.165, 0.397, 0.201], and two times {SME Growth}[0.553, 0.060, 0.678, 0.096],[0.553, 0.113, 0.678, 0.149]. Each entry is paired with a colored marker." } ], "id_raw": 692 }, { "id": "q9JbmXIRpKD223Wt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_12.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the region [0.911, 0.881, 0.973, 0.919]?" }, { "from": "gpt", "value": "This region contains the x-axis label for the year '2017'." } ], "id_raw": 692 }, { "id": "VB683sOb68YXKXgG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.019, 0.392, 0.044, 0.514]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 693 }, { "id": "LS4lX1w3Y8womU59", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first x-axis tick label?" }, { "from": "gpt", "value": "[0.118, 0.881, 0.179, 0.919]." } ], "id_raw": 693 }, { "id": "fUdUGZuT8v8lHibu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the {the trend line for Infrastructure Projects}[0.149, 0.208, 0.228, 0.208] represent in the plot?" }, { "from": "gpt", "value": "It represents the changes in the trend of Infrastructure Projects over the years." } ], "id_raw": 693 }, { "id": "IJRlgCGUhYHCpcdG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_13.png", "conversations": [ { "from": "human", "value": "\nIs {the trend line for Building Materials}[0.149, 0.233, 0.942, 0.453] above or below {the trend line for Residential Buildings}[0.149, 0.167, 0.942, 0.412]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 693 }, { "id": "7HRNU5tbJ3yAmJXk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_13.png", "conversations": [ { "from": "human", "value": "\nWhich appears first in the vertical direction, {the legend for Residential Buildings}[0.186, 0.233, 0.402, 0.269] or {the legend for Building Materials}[0.186, 0.128, 0.372, 0.164]?" }, { "from": "gpt", "value": "Building Materials." } ], "id_raw": 693 }, { "id": "084iYWhzJp5XHayJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_13.png", "conversations": [ { "from": "human", "value": "\nWhich trend shows an increase in years from 2012 to 2015, {Real Estate Agencies}[0.149, 0.167, 0.942, 0.372] or {Commercial Buildings}[0.149, 0.265, 0.942, 0.519]?" }, { "from": "gpt", "value": "Both." } ], "id_raw": 693 }, { "id": "a4zbg2skXqzXDxte", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the region [0.119, 0.062, 0.431, 0.388]." }, { "from": "gpt", "value": "This region contains the legend for the six different trend lines in the graph: Infrastructure Projects, Building Materials, Real Estate Agencies, Residential Buildings, Commercial Buildings, and Public Sector Buildings." } ], "id_raw": 693 }, { "id": "3vWxqcipv9onvvst", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the area [0.149, 0.233, 0.942, 0.453]?" }, { "from": "gpt", "value": "This area contains the trend line for Building Materials. It shows how the trend has changed from year 2009 to 2019." } ], "id_raw": 693 }, { "id": "LBAOGdUbCJeksTn6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_13.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the region [0.081, 0.846, 0.097, 0.884]?" }, { "from": "gpt", "value": "The y-axis tick label '0' is located in this region." } ], "id_raw": 693 }, { "id": "YmeBxX9STKdZsNw1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_14.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.234, 0.102, 0.300, 0.130] indicate in the chart?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 694 }, { "id": "aMchm3ayQYF1fuNX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_14.png", "conversations": [ { "from": "human", "value": "\nCan you tell what the color of {the line associated with Twitter}[0.116, 0.370, 0.944, 0.875] is on the graph?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 694 }, { "id": "0IiwNiMZgDOowBFo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the last x-axis tick label}[0.871, 0.895, 0.906, 0.923] representing?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 694 }, { "id": "HYvNOv8T1Is9YEN8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the Twitter line}[0.116, 0.370, 0.944, 0.875] end above or below {the Netflix line}[0.116, 0.117, 0.944, 0.875]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 694 }, { "id": "ztjNMNxuSn6nzDOc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_14.png", "conversations": [ { "from": "human", "value": "\nWhat point is higher on the y-axis, {the 2019 point on the Google line}[0.833, 0.343] or {the 2019 point on the Facebook line}[0.833, 0.543]?" }, { "from": "gpt", "value": "Google." } ], "id_raw": 694 }, { "id": "S22DwnBIIkfknby6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_14.png", "conversations": [ { "from": "human", "value": "\nWhich line crosses the sixth y-tick label, {Netflix}[0.116, 0.117, 0.944, 0.875] or {Twitter}[0.116, 0.370, 0.944, 0.875]?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 694 }, { "id": "iKIYkB5ndZd14odF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements located in [0.126, 0.102, 0.300, 0.213]." }, { "from": "gpt", "value": "This region contains 'Google' and 'Facebook' text labels for the legend, along with their respective color handles." } ], "id_raw": 694 }, { "id": "9X6YuLLMEsYj6M1F", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within [0.039, 0.066, 0.065, 0.892]?" }, { "from": "gpt", "value": "The y-axis labels ranging from '0' to '600', and tick marks corresponding to these labels." } ], "id_raw": 694 }, { "id": "UnFqs5hGWDviO7SU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of [0.098, 0.895, 0.189, 0.923]." }, { "from": "gpt", "value": "The x-axis tick labels for '2006' and '2007' including their corresponding tick marks." } ], "id_raw": 694 }, { "id": "sRka0DfRWCvNa62Q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the object marked with the coordinates [0.065, 0.872, 0.077, 0.904]?" }, { "from": "gpt", "value": "The y-axis label '0'." } ], "id_raw": 695 }, { "id": "7KRBBgSaSdHvF3lt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the graphical entity located at [0.944, 0.837, 0.944, 0.837]?" }, { "from": "gpt", "value": "The point representing the 'Electrical Vehicles' in year '2020'." } ], "id_raw": 695 }, { "id": "Zu7AYQWeehyeg4g0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_15.png", "conversations": [ { "from": "human", "value": "\nWhat is associated with the coordinate range [0.567, 0.150, 0.682, 0.180]?" }, { "from": "gpt", "value": "The label 'Two Wheelers' in the legend." } ], "id_raw": 695 }, { "id": "EW8K81oZDrPqWRiP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_15.png", "conversations": [ { "from": "human", "value": "\nIs {the value for 'Commercial Vehicles' in 2019}[0.740, 0.613] higher or lower than the {value for 'Passenger Vehicles' in 2019}[0.740, 0.274]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 695 }, { "id": "nEBlESSVKsAfS2wl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_15.png", "conversations": [ { "from": "human", "value": "\nDoes the {point on 'Electric Vehicles' line in 2016}[0.128, 0.878] have higher or lower value in comparison with the {point on 'Two Wheelers' line in 2016}[0.128, 0.075]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 695 }, { "id": "77WsnsO2fjbPQPt4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_15.png", "conversations": [ { "from": "human", "value": "\nComparatively, is the {point for 'Passenger Vehicles' in 2017}[0.332, 0.270] above or below the {Point for 'Passenger Vehicles' in 2020}[0.944, 0.305]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 695 }, { "id": "Wdt34qjEQStOu7X7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe what the [0.514, 0.051, 0.977, 0.191] region encompasses?" }, { "from": "gpt", "value": "The region includes the chart's legend with labels 'Passenger Vehicles', 'Commercial Vehicles', 'Two Wheelers', 'Three Wheelers', and 'Electrical Vehicles' along with their respective color markers." } ], "id_raw": 695 }, { "id": "9p8kaORxm7CqdwfX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents located within the coordinates [0.041, 0.025, 0.077, 0.057]." }, { "from": "gpt", "value": "The area contains the lowermost y-axis label '100'." } ], "id_raw": 695 }, { "id": "ZuENbR8f4dlEq5d0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_15.png", "conversations": [ { "from": "human", "value": "\nWhat components are present within the region defined by coordinates [0.715, 0.901, 0.765, 0.932]?" }, { "from": "gpt", "value": "This area contains the '2019' x-axis label." } ], "id_raw": 695 }, { "id": "c5IW2MjFRB5Q9Vcz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with label coordinates [0.823, 0.125, 0.880, 0.185] in the chart?" }, { "from": "gpt", "value": "It is the \"USA\" label in the legend." } ], "id_raw": 697 }, { "id": "kR0DXS0s2DJXNX1x", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of {Germany's line} on the chart?" }, { "from": "gpt", "value": "It is at [0.971, 0.353, 0.975, 0.408]." } ], "id_raw": 697 }, { "id": "dO40yRVL8zAsanJ8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_17.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object at [0.025, 0.155, 0.058, 0.690] in the chart?" }, { "from": "gpt", "value": "It is the Y-axis label, which represents '% GDP in R&D'." } ], "id_raw": 697 }, { "id": "CGu0Dj6QyBUf47MD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_17.png", "conversations": [ { "from": "human", "value": "\nComparing the lines for {USA}[0.971, 0.165, 0.975, 0.290] and {Germany}[0.971, 0.353, 0.975, 0.408], which country had a higher % GDP in R&D in 2006?" }, { "from": "gpt", "value": "USA." } ], "id_raw": 697 }, { "id": "EljXcu09qMLJ9W4s", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_17.png", "conversations": [ { "from": "human", "value": "\nDid the % GDP in R&D for {Brazil}[0.971, 0.471, 0.975, 0.526] increase or decrease from 2000 to 2010?" }, { "from": "gpt", "value": "Decreased." } ], "id_raw": 697 }, { "id": "mLfwoPlEOSk2pT5b", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_17.png", "conversations": [ { "from": "human", "value": "\nWhich data point is lower on the chart, {China's data point in 2002}[0.971, 0.658] or {India's data point in 2002}[0.971, 0.547]?" }, { "from": "gpt", "value": "India's data point in 2002." } ], "id_raw": 697 }, { "id": "Jbcf3sToZ9pnh9lH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.734, 0.103, 0.961, 0.558]." }, { "from": "gpt", "value": "It contains the chart's legend, with labels for the USA, China, India, Germany, and Brazil, each paired with a unique color marker." } ], "id_raw": 697 }, { "id": "KIM6fIsNQ5kZCyEG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_17.png", "conversations": [ { "from": "human", "value": "\nWhat analysis is presented in the plot area with coordinates [0.125, 0.075, 0.975, 0.769]?" }, { "from": "gpt", "value": "It is a line chart showing the % GDP spent on R&D by the USA, China, India, Germany, and Brazil from 2000 to 2010." } ], "id_raw": 697 }, { "id": "dBWSITP3P9HhebXd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_17.png", "conversations": [ { "from": "human", "value": "\nWhat's within the area [0.971, 0.165, 0.975, 0.290]?" }, { "from": "gpt", "value": "It holds the line for USA, indicating the trend of % GDP spent on R&D over the years from 2000 to 2010." } ], "id_raw": 697 }, { "id": "fe3lahL9YBRxxKsj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_18.png", "conversations": [ { "from": "human", "value": "\nWhich policy is represented by the line located at these coordinates: [0.116, 0.225, 0.930, 0.592]?" }, { "from": "gpt", "value": "Monetary Policy." } ], "id_raw": 698 }, { "id": "Za3LMuRZlBxyP45f", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_18.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at these coordinates: [0.137, 0.690, 0.268, 0.726]?" }, { "from": "gpt", "value": "The text 'Monetary Policy'." } ], "id_raw": 698 }, { "id": "4bv1Qv7mmn6iN8Q0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the ylabel at these coordinates: [0.015, 0.305, 0.035, 0.601]?" }, { "from": "gpt", "value": "Policy Impact." } ], "id_raw": 698 }, { "id": "cbP8yiA0AcT9F2KP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_18.png", "conversations": [ { "from": "human", "value": "\nAt the coordinate point [0.930, 0.592], which policy has a greater impact, {Monetary Policy}[0.116, 0.225, 0.930, 0.592] or {Fiscal Policy}[0.116, 0.176, 0.930, 0.551]?" }, { "from": "gpt", "value": "Fiscal Policy." } ], "id_raw": 698 }, { "id": "RPMiIPTyOYgjCDNS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_18.png", "conversations": [ { "from": "human", "value": "\nIs the {Monetary Policy line}[0.116, 0.225, 0.930, 0.592] generally above or below the {Investment Policy line}[0.116, 0.306, 0.930, 0.617]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 698 }, { "id": "ckrPcaOq62yzqJEL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_18.png", "conversations": [ { "from": "human", "value": "\nWhich policy begins at a higher point on the y-axis at the 'Decade50' point, {Tax Policy}[0.116, 0.470, 0.930, 0.763] or {Trade Policy}[0.116, 0.421, 0.930, 0.657]?" }, { "from": "gpt", "value": "Tax Policy." } ], "id_raw": 698 }, { "id": "7p6PGHYJUzwoseTk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects located within [0.053, 0.846, 0.065, 0.884]?" }, { "from": "gpt", "value": "This region contains a y-axis tick label '0'." } ], "id_raw": 698 }, { "id": "d1IEdTjKBpfCj4GQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.083, 0.677, 0.502, 0.845]." }, { "from": "gpt", "value": "This region contains the legend for the chart with labels such as {Monetary Policy}[0.137, 0.690, 0.268, 0.726], {Fiscal Policy}[0.137, 0.743, 0.235, 0.779], {Trade Policy}[0.137, 0.795, 0.235, 0.831], {Tax Policy}[0.348, 0.690, 0.429, 0.726] and {Investment Policy}[0.348, 0.743, 0.496, 0.779]." } ], "id_raw": 698 }, { "id": "Bml4mFVxJbGjZp8V", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within [0.041, 0.030, 0.065, 0.068]." }, { "from": "gpt", "value": "There is a y-axis tick label '10' within this region." } ], "id_raw": 698 }, { "id": "frFxSZPnJMAxWlqF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_19.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.017, 0.390, 0.038, 0.520]?" }, { "from": "gpt", "value": "The y-axis label." } ], "id_raw": 699 }, { "id": "7qXVD049eAWpHYXS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_19.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the marker at the coordinates: [0.079, 0.702, 0.125, 0.719]?" }, { "from": "gpt", "value": "The marker corresponds to the color representation of the 'Average Health Score' line in the plot legend." } ], "id_raw": 699 }, { "id": "XTHxSxQCcj77YOo2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_35_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label at [0.496, 0.932, 0.553, 0.970]?" }, { "from": "gpt", "value": "Years." } ], "id_raw": 699 }, { "id": "eCq7fm20lgZU0KsK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_19.png", "conversations": [ { "from": "human", "value": "\nWhich data series has a higher score value at {point 5}[0.479, 0.249], the 'Average Health Score' line or the 'Medical Consultations' line?" }, { "from": "gpt", "value": "Average Health Score." } ], "id_raw": 699 }, { "id": "6VQpnDk3PevUJrpc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_19.png", "conversations": [ { "from": "human", "value": "\nCompare the values of {the 'Exercise Frequency' line at point 3}[0.293, 0.426] and {the 'Stress Level' line at point 3}[0.293, 0.301]. Which one is higher on the chart?" }, { "from": "gpt", "value": "Stress Level." } ], "id_raw": 699 }, { "id": "f6A69ploorhHb87k", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_35_19.png", "conversations": [ { "from": "human", "value": "\nIs {the point 10 on the 'Disease Incidence' line}[0.942, 0.383] lower or higher in the chart than {the point 10 on the 'Exercise Frequency' line}[0.942, 0.301]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 699 }, { "id": "sCfcG38eRSissQy1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_19.png", "conversations": [ { "from": "human", "value": "\nWhat content is displayed in the area [0.135, 0.694, 0.335, 0.730]?" }, { "from": "gpt", "value": "This area contains the 'Average Health Score' legend text color-coded with a certain marker." } ], "id_raw": 699 }, { "id": "2SkiOhtnxfH9fffZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.461, 0.747, 0.663, 0.783]." }, { "from": "gpt", "value": "The area includes a colored marker and the corresponding label in the legend, which reads 'Medical Consultations'." } ], "id_raw": 699 }, { "id": "s9rcLqj4FwvhP7Nr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_35_19.png", "conversations": [ { "from": "human", "value": "\nWhat information is found in the region [0.044, 0.030, 0.056, 0.066]?" }, { "from": "gpt", "value": "This region contains the y-axis tick label '8'." } ], "id_raw": 699 }, { "id": "atgtxyhPfSCeNT4r", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object identified by the coordinates [0.432, 0.062, 0.488, 0.080] in the chart?" }, { "from": "gpt", "value": "Sublegend label for Solar Energy." } ], "id_raw": 700 }, { "id": "WrDpar7qATE11OtT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the bounded area [0.510, 0.449, 0.524, 0.466] indicate in the subplot about Wind Energy?" }, { "from": "gpt", "value": "X-axis tick label \"0.0\"." } ], "id_raw": 700 }, { "id": "wkisJTd6Ixh7AkGi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_0.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the label positioned at the exact coordinates [0.517, 0.847, 0.536, 0.864] in the fourth subplot?" }, { "from": "gpt", "value": "Y-axis tick label \"0.05\"." } ], "id_raw": 700 }, { "id": "jkPRGNYxVORymroT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_0.png", "conversations": [ { "from": "human", "value": "\nIs the value for {the x-axis tick label '1.0'}[0.475, 0.437] greater or lesser than {the x-axis tick label '1.2'}[0.566, 0.437] in the Solar Energy subplot?" }, { "from": "gpt", "value": "Lesser." } ], "id_raw": 700 }, { "id": "bM2heA0yfrsrot8K", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_0.png", "conversations": [ { "from": "human", "value": "\nAre the energy figures for Nuclear Power at {point 1}[0.563, 0.564] higher or lower compared to Hydroelectric Power at {point 1}[0.067, 0.577]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 700 }, { "id": "ogjaxWlSecd58bME", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_0.png", "conversations": [ { "from": "human", "value": "\nWhich trend bends more steeply downward, {Solar Energy at point 5}[0.393, 0.115] or {Wind Energy at point 5}[0.889, 0.117]?" }, { "from": "gpt", "value": "Solar Energy." } ], "id_raw": 700 }, { "id": "vAYQ6QhhQuRnNdSZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_0.png", "conversations": [ { "from": "human", "value": "\nDescribe what can be found within the bounding box [0.0, 0.449, 0.1, 0.466]." }, { "from": "gpt", "value": "This area encompasses the first x-axis tick label '0.0' in the subplot for Solar Energy, specifically located at {the tick label for '0.0'}[0.015, 0.449, 0.029, 0.466]." } ], "id_raw": 700 }, { "id": "zQfnGtwGa5I5LPJD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_0.png", "conversations": [ { "from": "human", "value": "\nWhat contents lay within the region specified by [0.5, 0.6, 0.9, 0.65]?" }, { "from": "gpt", "value": "This region includes part of the plotting area for wind and nuclear energy showing trend lines and points, including the {point 4 of Wind Energy}[0.807, 0.156] and {point 5 of Nuclear Power}[0.889, 0.891]." } ], "id_raw": 700 }, { "id": "KB0f0O9MiAX7y8tV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_0.png", "conversations": [ { "from": "human", "value": "\nWhat features or elements are encompassed in the section [0.2, 0.4, 0.3, 0.5]?" }, { "from": "gpt", "value": "This section contains the y-axis label \"Energy Allocation\", the 5th y-axis tick labeled '0.4' for Solar Energy, and a segment of {Solar Energy's trend line}[0.287, 0.449, 0.301, 0.466]." } ], "id_raw": 700 }, { "id": "OSpBZZjtGbgkAYkl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object located at coordinates [0.523, 0.279, 0.548, 0.297] in the chart?" }, { "from": "gpt", "value": "Y-axis tick label." } ], "id_raw": 701 }, { "id": "I1pty2tpY2yGd4oB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_1.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.576, 0.419, 0.654, 0.330] in the subplot for Upstream Manufacturing?" }, { "from": "gpt", "value": "Point." } ], "id_raw": 701 }, { "id": "4PI5DAw7I23P2Eut", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.688, 0.019, 0.856, 0.041]?" }, { "from": "gpt", "value": "Subtitle." } ], "id_raw": 701 }, { "id": "JVXic8kEsAOXMDGy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_1.png", "conversations": [ { "from": "human", "value": "\nIs {the y-axis tick labeled '1.90'}[0.530, 0.058, 0.548, 0.075] higher or lower than {the y-axis tick labeled '1.95'}[0.530, 0.013, 0.548, 0.031]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 701 }, { "id": "D6DgkFtrx8WkuJjz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_1.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {the point at '0'}[0.498, 0.437] or {the point at '6'}[0.968, 0.437] in the Downstream Distribution plot?" }, { "from": "gpt", "value": "Equal." } ], "id_raw": 701 }, { "id": "0sb8He5uB5jFL2Z2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_1.png", "conversations": [ { "from": "human", "value": "\nIs {the first tick label on the x-axis}[0.004, 0.927] to the left or right of {the last tick label on the x-axis}[1.046, 0.927] for the Downstream Distribution subplot?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 701 }, { "id": "IwjbAqlgClFPsWxV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_1.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.000, 0.439, 0.549, 0.466]?" }, { "from": "gpt", "value": "This region encompasses all the x-axis tick labels and their respective tick marks for the Midstream Packaging subplot." } ], "id_raw": 701 }, { "id": "JcdkbS6nwlhMuIk5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects present in the area [0.494, 0.040, 0.988, 0.473]." }, { "from": "gpt", "value": "This region contains all elements of the Upstream Manufacturing subplot including axes labels, subtitle, x and y tick labels, and data points." } ], "id_raw": 701 }, { "id": "3yL9HTBtNdrxfEPc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you find in the region spanning from [0.063, 0.530, 0.494, 0.927]?" }, { "from": "gpt", "value": "This space includes the entirety of the Midstream Packaging subplot's axes, grid lines, labels, legend, and its line series data points." } ], "id_raw": 701 }, { "id": "jChDmiAtXz0CBi9Y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the point corresponding to 'Twitter' on the Facebook subplot}[0.208, 0.290]?" }, { "from": "gpt", "value": "Purple." } ], "id_raw": 704 }, { "id": "XHtgSKL2708I5RKc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_4.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform corresponds to {the x-axis tick}[0.703, 0.927]?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 704 }, { "id": "b9wHRNwW2yBUVBXg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the count represented at {the LinkedIn point in the Instagram subplot}[0.969, 0.804]?" }, { "from": "gpt", "value": "3893." } ], "id_raw": 704 }, { "id": "nKJCshtFkBRo5XpT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_4.png", "conversations": [ { "from": "human", "value": "\nIs {the Facebook data point in the first subplot}[0.075, 0.262] higher or lower than {the Facebook data point in the second subplot}[0.570, 0.101]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 704 }, { "id": "htYyRA3e6n6JIPLi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_4.png", "conversations": [ { "from": "human", "value": "\nWhich has more entries: {the Instagram point under 'Stable Engagement'}[0.341, 0.848] or {the Instagram point under 'Volatile Feedbacks'}[0.836, 0.821]?" }, { "from": "gpt", "value": "Stable Engagement." } ], "id_raw": 704 }, { "id": "6XgxNkgaogCyCZX0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_4.png", "conversations": [ { "from": "human", "value": "\nIs {the first subplot's zero y-tick label}[0.055, 0.457] at a higher position than {the third subplot's zero y-tick label}[0.055, 0.947]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 704 }, { "id": "RqpqMOjrUxBC2BJI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_4.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the region [0.522, 0.772, 0.987, 0.927]?" }, { "from": "gpt", "value": "This area includes the LinkedIn subplot's x-grid, y-grid, axes, y-ticks (labeled 4000, 6000, 8000), x-tick labels (Facebook, Twitter, Instagram, LinkedIn), and a subtitle 'LinkedIn'." } ], "id_raw": 704 }, { "id": "B8lWin437GLgw140", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the section between [0.022, 0.002, 0.049, 0.503]." }, { "from": "gpt", "value": "This signifies the range of the y-ticks and corresponding labels on the left side of the first subplot, encompassing 0 to 12000." } ], "id_raw": 704 }, { "id": "v3uQKUWGC1W4fTvM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the components found within the region [0.318, 0.080, 0.363, 0.957]." }, { "from": "gpt", "value": "The region encompasses Instagram related x-tick labels and line points from all four subplots, indicating data and labels for Instagram across all data categories shown." } ], "id_raw": 704 }, { "id": "ExDdPPyjC8FayVLp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the line chart with the color '#800080'?" }, { "from": "gpt", "value": "Procedure A." } ], "id_raw": 706 }, { "id": "gi1rGrtG8Jzfezsh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label for the subplot with coordinates '[0.542, 0.047, 0.991, 0.927]'?" }, { "from": "gpt", "value": "Performance." } ], "id_raw": 706 }, { "id": "zBQfIEKoByLUut8I", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the item marked with the orange color ('#ffa500') in the legend?" }, { "from": "gpt", "value": "Procedure B." } ], "id_raw": 706 }, { "id": "jO3azgnbFhfbXY8V", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_6.png", "conversations": [ { "from": "human", "value": "\nIn the line chart for Manufacturing Procedure A, is the point at 'r' value of 0.64 above or below the point at 'r' value of 0.82?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 706 }, { "id": "eouzFgUnHjjQnB2H", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_6.png", "conversations": [ { "from": "human", "value": "\nFor Manufacturing Procedure B, is the performance at 'r' value 0.82 higher or lower than at 'r' value 0.64?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 706 }, { "id": "AYCYIC6IstgWltxD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_6.png", "conversations": [ { "from": "human", "value": "\nComparatively, does Manufacturing Procedure A or B have a higher performance at 'r' value 1.0?" }, { "from": "gpt", "value": "Procedure B." } ], "id_raw": 706 }, { "id": "0wwynRHDBPetzWSf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.752, 0.964, 0.781, 0.981]." }, { "from": "gpt", "value": "It is the label \"Ratio r\" for the x-axis of the chart showing Manufacturing Procedure B's performance by ratio 'r'." } ], "id_raw": 706 }, { "id": "GXr6RhAYVRm2ykmw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.903, 0.055, 0.986, 0.087]." }, { "from": "gpt", "value": "This area holds the legend for the Manufacting Procedure B's line chart. The color marked '#ffa500' represents Procedure B." } ], "id_raw": 706 }, { "id": "QYTQ1oORVVAKXLXr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.563, 0.116, 0.970, 0.850]?" }, { "from": "gpt", "value": "The line chart for Procedure B is located here, showing performance at different 'r' ratios. Points are marked at 0.1, 0.28, 0.4599, 0.64, 0.82 and 1.0 'r' values." } ], "id_raw": 706 }, { "id": "gRbwszT7jwM0T7Kg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the first subplot?" }, { "from": "gpt", "value": "Performance[0.009, 0.187, 0.018, 0.297]." } ], "id_raw": 708 }, { "id": "lCLllPoqLBuXJuhU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_8.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the \"Interactive Media\" line in the first subplot?" }, { "from": "gpt", "value": "Orange[0.385, 0.091, 0.407, 0.101]." } ], "id_raw": 708 }, { "id": "1CNN6fIpJW2TM2d2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label in the third subplot?" }, { "from": "gpt", "value": "Trends[0.260, 0.964, 0.288, 0.981]." } ], "id_raw": 708 }, { "id": "3kQMituZrno55IAh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_8.png", "conversations": [ { "from": "human", "value": "\nIs {the measurement point 5 on the line for 'Video Content'}[0.072, 0.555] above or below {the measurement point 5 on the line for 'Interactive Media'}[0.072, 0.585]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 708 }, { "id": "StOG0Nvz0c7jHSRN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_8.png", "conversations": [ { "from": "human", "value": "\nIs the Y-value for {x=1 on 'Video Content'}[0.072, 0.419] higher than for {x=1 on 'Interactive Media'}[0.072, 0.242]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 708 }, { "id": "SFdHrL7QrTx9Ghe0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_8.png", "conversations": [ { "from": "human", "value": "\nDoes point {7 on the 'Video Content'}[0.568, 0.555] have a higher trend measurement than point {7 on 'Interactive Media'}[0.568, 0.618]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 708 }, { "id": "Ary0QM6EWkKqgEay", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.548, 0.537, 0.991, 0.927]." }, { "from": "gpt", "value": "This region includes a subplot titled \"Rapid Decline vs Unstable Behavior,\" with axes labeled 'Trends' and 'Performance' and a legend that includes the items 'Video Content' and 'Interactive Media' with their associated colors of purple and orange." } ], "id_raw": 708 }, { "id": "ZNQdZ959oQI2Vjnr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.052, 0.537, 0.495, 0.927]." }, { "from": "gpt", "value": "The region encompasses a subplot highlighting 'Steady Decline vs Consistent Expansion'. It contains x-axis labeled by trends with tick labels from '5' to '6' and y-axis labeled by performance ranging from '0.0' to '0.8'. It features a legend itemizing 'Video Content' and 'Interactive Media' displayed with purple and orange markers respectively." } ], "id_raw": 708 }, { "id": "XKb9QfuTb0PVKPf4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained within the lower half of the chart area [0.548, 0.049, 0.991, 0.437]?" }, { "from": "gpt", "value": "The area includes a subplot titled \"Dramatic Decrease vs Minimal Decline\" along with its axes describing 'Trends' and 'Performance.' The x-axis holds tick labels '3' and '4' whereas the y-axis details levels from '0.675' to '0.900'. The subplot's legend showcases 'Video Content' and 'Interactive Media' with their respective colors and markers." } ], "id_raw": 708 }, { "id": "npjuRdCDXld4VD3r", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_9.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the \"Urban Plan.\" series on the plot?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 709 }, { "id": "DiGxwknnMHXjTOUT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.014, 0.706, 0.021, 0.765]?" }, { "from": "gpt", "value": "Measure." } ], "id_raw": 709 }, { "id": "MnXCdZmE8FFqe1na", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_9.png", "conversations": [ { "from": "human", "value": "\nWhat does {the marker for 'Com. Dev.'}[0.426, 0.544, 0.444, 0.553] look like?" }, { "from": "gpt", "value": "Square." } ], "id_raw": 709 }, { "id": "QPsEbD2xXfddCb1x", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_9.png", "conversations": [ { "from": "human", "value": "\nIs {point 0.5 on 'Com. Dev.'}[0.240, 0.222] above or below {point 0.5 on 'Urban Plan.'}[0.739, 0.174]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 709 }, { "id": "q01BqXtSgRXIEoPe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_9.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher final point, {the 'Inexorable Rise'}[0.473, 0.556] series or {the 'Minimal Change'}[0.972, 0.642] series?" }, { "from": "gpt", "value": "Inexorable Rise." } ], "id_raw": 709 }, { "id": "sIb49GOqr62ETqq8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_9.png", "conversations": [ { "from": "human", "value": "\nDoes {the second point of 'Gradual Decline'}[0.599, 0.115] represent a higher or lower value than {the second point of 'Slow Growth'}[0.101, 0.368]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 709 }, { "id": "0BWdi9fMBMW15X6B", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements in the region[0.553, 0.037, 0.993, 0.449]." }, { "from": "gpt", "value": "This region contains the subplot for 'Gradual Decline,' which includes axes with coordinate labels, grid lines, x-axis labels positioned at the bottom and y-axis labels along the left side, displaying two line graphs representing 'Com. Dev.' and 'Urban Plan.' with corresponding legends." } ], "id_raw": 709 }, { "id": "nBduU8IOQe6JmcyX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_9.png", "conversations": [ { "from": "human", "value": "\nWhat can you detail about the subplot in[0.033, 0.530, 0.494, 0.942]?" }, { "from": "gpt", "value": "The subplot labeled 'Inexorable Rise' features a plot area with vertical and horizontal grid lines, axes titled with measurements and observations, ticks for data points, and lines ascending sharply representing two categories, all depicted against a white background." } ], "id_raw": 709 }, { "id": "uGhWmKMUnEQ8a8pt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_9.png", "conversations": [ { "from": "human", "value": "\nWhat features are visible in the area[0.532, 0.530, 0.993, 0.942]?" }, { "from": "gpt", "value": "This area encompasses a subplot identified as 'Minimal Change' containing a white plot surface, grid lines, numerical labels on both x and y axes, minimal fluctuating lines for two distinct series displayed with shaped point markers, and legend entries reflecting the series titles." } ], "id_raw": 709 }, { "id": "Ye5eyEbfelnUyLbJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_10.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the 'Creativity' line in the subplot titled 'Graph-SST2'?" }, { "from": "gpt", "value": "Purple." } ], "id_raw": 710 }, { "id": "JSmrngc7oDpCSCUP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the marker shape used for the 'Design Innovation' line in the subplot titled 'SPMotif-0.5'?" }, { "from": "gpt", "value": "Square." } ], "id_raw": 710 }, { "id": "zmY0cjmCKOECO2Ip", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_10.png", "conversations": [ { "from": "human", "value": "\nWhat axis label is located at [0.752, 0.964, 0.781, 0.981]?" }, { "from": "gpt", "value": "Ratio r." } ], "id_raw": 710 }, { "id": "WIV0RnM6nlG45vu0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_10.png", "conversations": [ { "from": "human", "value": "\nIs the {y-value of the first point on the 'Creativity' line}{r}[0.563, 0.823] higher than the {y-value of the last point on the 'Design Innovation' line}{r}[0.970, 0.571] in the 'SPMotif-0.5' subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 710 }, { "id": "GdyaWt23d2UTB8uF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_10.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher endpoint in the subplot titled 'MNIST-75SP', {the 'Creativity' line}{r}[0.475, 0.598] or {the 'Design Innovation' line}{r}[0.475, 0.571]?" }, { "from": "gpt", "value": "Creativity." } ], "id_raw": 710 }, { "id": "xTIdSlrTONlMu61i", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the y-value of the middle point on the 'Creativity' line}{r}[0.726, 0.868] exceed that of {the middle point on the 'Design Innovation' line}{r}[0.726, 0.850] in the subplot labeled 'Graph-SST2'?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 710 }, { "id": "lUdvtqXXzCVe7a4j", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the coordinates [0.047, 0.537, 0.495, 0.012]." }, { "from": "gpt", "value": "This region contains the chart title \"MNIST-75SP\" styled in black color." } ], "id_raw": 710 }, { "id": "wd4cHfY443mJG0s5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_10.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.542, 0.047, 0.991, 0.473]?" }, { "from": "gpt", "value": "This area includes the axes, grid lines, and points for the subplot titled 'Graph-SST2'. Both 'Creativity' and 'Design Innovation' lines along with their respective data points and trend lines are present." } ], "id_raw": 710 }, { "id": "Ag3nUqCCkxLvHDKw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the region between [0.381, 0.546, 0.491, 0.604]?" }, { "from": "gpt", "value": "This area contains the legend for the subplot titled 'MNIST-75SP'. It includes labels for 'Creativity' and 'Design Innovation' with associated colored markers." } ], "id_raw": 710 }, { "id": "v9zA1E113TAd1yU0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.018, 0.219, 0.026, 0.265] for the second subplot?" }, { "from": "gpt", "value": "Sales." } ], "id_raw": 711 }, { "id": "Uw2Ror4YrbvvPxCr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_11.png", "conversations": [ { "from": "human", "value": "\nWhat color is used for {the line representing Product 2}[0.561, 0.064, 0.970, 0.419]?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 711 }, { "id": "KSxFEfrYDT1vuXMK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_11.png", "conversations": [ { "from": "human", "value": "\nWhich month corresponds to {the x-axis tick}[0.253, 0.937, 0.267, 0.954] in the third subplot?" }, { "from": "gpt", "value": "Jun." } ], "id_raw": 711 }, { "id": "frC2h2gKHpLkYydD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_11.png", "conversations": [ { "from": "human", "value": "\nIs {the point for June sales}[0.747, 0.242] higher or lower than {the point for June}[0.253, 0.768] in the plotted line for Product 1 vs Product 3?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 711 }, { "id": "n2bYDdxuchXaRFXr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_11.png", "conversations": [ { "from": "human", "value": "\nIs {the first point on the axis for Product 4}[0.561, 0.639] located above or below {the first point for Product 1}[0.067, 0.419]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 711 }, { "id": "YS4UjYOHM5t6Cb8B", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_11.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher starting sales value, {Product 1}[0.067, 0.419] or {Product 2}[0.561, 0.419]?" }, { "from": "gpt", "value": "Product 2." } ], "id_raw": 711 }, { "id": "vHFirJeCbF3MHoXM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in {the region}[0.541, 0.047, 0.991, 0.437]." }, { "from": "gpt", "value": "This region contains the second subplot chart showing sales data for Product 2, with a grid and axis for each month and various sales figures plotted as a colored line." } ], "id_raw": 711 }, { "id": "Q4HynwY8YrtQtqJx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within {the region}[0.047, 0.537, 0.497, 0.927]?" }, { "from": "gpt", "value": "The area includes the third subplot displaying Product 3's monthly sales, marked by an axis for months, sales values on the y-axis, and a green line tracking the sales trend." } ], "id_raw": 711 }, { "id": "lLPlW4EgH1Ur4aHG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within {the region}[0.258, 0.473, 0.285, 0.491] and {the region}[0.752, 0.473, 0.779, 0.491]?" }, { "from": "gpt", "value": "These areas both contain the x-axis labels 'Month' for the sales charts of Product 1 and Product 2 respectively." } ], "id_raw": 711 }, { "id": "xmFUUJVMBms0moXy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_12.png", "conversations": [ { "from": "human", "value": "\nWhat is labeled at {the y-axis}[0.009, 0.374, 0.018, 0.600]?" }, { "from": "gpt", "value": "Percentage of Households." } ], "id_raw": 712 }, { "id": "qp10N9s3tUolfK7Q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the handle color} for the legend item 'PNA + ours' at [0.415, 0.065, 0.438, 0.075]?" }, { "from": "gpt", "value": "Purple." } ], "id_raw": 712 }, { "id": "2Nod4pXnBC9UnNl0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-tick label value at {the y-tick}[0.521, 0.643, 0.532, 0.661]?" }, { "from": "gpt", "value": "45." } ], "id_raw": 712 }, { "id": "K1rKoQ4YbMRlMdAB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_12.png", "conversations": [ { "from": "human", "value": "\nIs {the line 'PNA + ours' at point 5}[0.250, 0.369] above or below {the line 'GIN + ours' at point 5}[0.742, 0.739]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 712 }, { "id": "mVPXewID27D7csGS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_12.png", "conversations": [ { "from": "human", "value": "\nDo {the y-axis label}[0.009, 0.374, 0.018, 0.600] and {the y-axis label}[0.509, 0.374, 0.518, 0.600] have different rotations?" }, { "from": "gpt", "value": "No." } ], "id_raw": 712 }, { "id": "cPM4wV8X8Hr0ToiG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_12.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at {point 3}, 'PNA + ours' or 'GIN + GSAT'?" }, { "from": "gpt", "value": "PNA + ours." } ], "id_raw": 712 }, { "id": "EE8zsofTcHtXyo3n", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within [0.018, 0.930, 0.041, 0.957]." }, { "from": "gpt", "value": "Includes two y-tick labels and their corresponding y-ticks, one labeled '62.5' at {coordinates}[0.022, 0.150, 0.041, 0.168] and the other labeled '65.0' at {coordinates}[0.022, 0.033, 0.041, 0.050]." } ], "id_raw": 712 }, { "id": "qt5DBK62jqCiTc7s", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_12.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.559, 0.442, 0.970, 0.887]?" }, { "from": "gpt", "value": "This area contains a set of points marked on the line labeled 'GIN + GSAT', including point coordinates at {coordinate}[0.559, 0.857], {coordinate}[0.970, 0.442], among others, representing a downward trend in data." } ], "id_raw": 712 }, { "id": "WGwWrNm6ilOC68Zx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_12.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the chart area [0.067, 0.134, 0.479, 0.840]?" }, { "from": "gpt", "value": "Contains the lines for 'PNA + ours' and 'GIN + ours' across multiple points, with markers indicating data points for 'Urban Households' over the time span." } ], "id_raw": 712 }, { "id": "v7yVP5pU2ifRnus3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the color of the line representing 'Erratic growth'}[0.067, 0.064, 0.478, 0.400] in the first subplot?" }, { "from": "gpt", "value": "#1f77b4." } ], "id_raw": 713 }, { "id": "T3wro9ZXOO6v6KkK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_13.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis label}[0.261, 0.473, 0.284, 0.491] in the second subplot read?" }, { "from": "gpt", "value": "Ratio." } ], "id_raw": 713 }, { "id": "BUsr3n1XSHotsfQd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the figure associated with {the label 'Marginal growth'}[0.572, 0.631, 0.642, 0.649] in the legend of the fourth subplot?" }, { "from": "gpt", "value": "a colored marker." } ], "id_raw": 713 }, { "id": "xVRk8xlVtka8nwQU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_13.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at a 'Ratio' of 0.55, {Erratic growth}[0.272, 0.307] or {Subdued decline}[0.272, 0.344] in the first subplot?" }, { "from": "gpt", "value": "Subdued decline." } ], "id_raw": 713 }, { "id": "pGjAxqq4WYFYJTZW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_13.png", "conversations": [ { "from": "human", "value": "\nIn the third subplot, which line's point at a ratio of 1.0 is closer to {the x-axis}[0.261, 0.964, 0.284, 0.981], 'Erratic growth' or 'Inconsistent decline'?" }, { "from": "gpt", "value": "Erratic growth." } ], "id_raw": 713 }, { "id": "wNSEpAi8PjdEBjH5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_13.png", "conversations": [ { "from": "human", "value": "\nComparing {the second last x-tick}[0.790, 0.449, 0.795, 0.466] to {the last x-tick}[0.963, 0.449, 0.977, 0.466] in the second subplot, which has a higher corresponding y-coordinate for 'Marginal growth'?" }, { "from": "gpt", "value": "last x-tick." } ], "id_raw": 713 }, { "id": "fz0OKRh9zVEZesaz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the area [0.050, 0.065, 0.200, 0.165]." }, { "from": "gpt", "value": "This region contains the legend for the first subplot. It includes items for 'Erratic growth', 'Inconsistent decline', 'Subdued decline', and 'Marginal growth', each with a distinct colored marker." } ], "id_raw": 713 }, { "id": "Sjgxzo7l4zqomk2q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_13.png", "conversations": [ { "from": "human", "value": "\nWhat content resides within the coordinates [0.260, 0.440, 0.470, 0.460]?" }, { "from": "gpt", "value": "The area includes part of the x-axis, displaying multiple x-tick labels such as '0.2', '0.4', '0.6', '0.8', '1.0', and their corresponding tick marks." } ], "id_raw": 713 }, { "id": "ysLo1cczFp57GeUX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_13.png", "conversations": [ { "from": "human", "value": "\nPlease provide details about the area [0.250, 0.500, 0.330, 0.530]." }, { "from": "gpt", "value": "The region displays the subtitle 'Atmospheric Emission' at the top of the third subplot in black font." } ], "id_raw": 713 }, { "id": "YbBP8pnmDeK9RMaB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the value for the first y-axis tick}[0.022, 0.464, 0.046, 0.480] on the first subplot?" }, { "from": "gpt", "value": "102.5." } ], "id_raw": 714 }, { "id": "gvpHbqQQ1nGiYHbS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_14.png", "conversations": [ { "from": "human", "value": "\nCould you identify {the value on the second x-axis tick mark}[0.128, 0.454, 0.139, 0.470] in the second subplot?" }, { "from": "gpt", "value": "2021." } ], "id_raw": 714 }, { "id": "QJ8F74THnYWHTnCV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_14.png", "conversations": [ { "from": "human", "value": "\nWhat label is associated with {the third x-axis tick mark}[0.841, 0.454, 0.862, 0.470] in the third subplot?" }, { "from": "gpt", "value": "2022." } ], "id_raw": 714 }, { "id": "GAtUVt6Bxw54fdaR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_14.png", "conversations": [ { "from": "human", "value": "\nIs the value of {point 4}[0.254, 0.188] higher or lower than {point 5}[0.317, 0.060] on the 'Anonymous Country' line in the first subplot?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 714 }, { "id": "HLiXfkLr9GbJ9BiI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the '2020' tick mark}[0.395, 0.444] appear before or after {the '2021' tick mark}[0.458, 0.444] on the x-axis in subplot 5?" }, { "from": "gpt", "value": "Before." } ], "id_raw": 714 }, { "id": "WOVMsGaGYGkUeHU5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_14.png", "conversations": [ { "from": "human", "value": "\nIs the value at {point 3}[0.191, 0.771] on the line chart for 'Russia' in subplot 3 above or below {point 4}[0.254, 0.661]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 714 }, { "id": "Jo6mwvyOhFWgReOF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within [0.052, 0.454, 0.330, 0.470]." }, { "from": "gpt", "value": "This region contains x-axis tick labels for the years 2020 to 2024 in subplot 0, with each label followed by corresponding tick marks {year 2020}[0.065, 0.444], {year 2021}[0.128, 0.444], {year 2022}[0.191, 0.444], {year 2023}[0.254, 0.444], {year 2024}[0.317, 0.444]." } ], "id_raw": 714 }, { "id": "4t6owzokTnnxU2W7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the components found within [0.682, 0.449, 0.713, 0.464]." }, { "from": "gpt", "value": "This region includes the y-axis tick label '102.5' along with its corresponding tick mark {y-tick 102.5}[0.713, 0.455] in subplot 6." } ], "id_raw": 714 }, { "id": "Y4kXU9stpOeOUiEc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_14.png", "conversations": [ { "from": "human", "value": "\nCan you specify what exists in [0.348, 0.966, 0.377, 0.982] in the last subplot?" }, { "from": "gpt", "value": "This region contains the y-axis tick label '100' and its y-tick at {tick location}[0.383, 0.972] in subplot 5." } ], "id_raw": 714 }, { "id": "rc3n9JP4MueOGNm2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the object represented at the point found at coordinates [0.439, 0.129]?" }, { "from": "gpt", "value": "Point label '11'." } ], "id_raw": 715 }, { "id": "fDhp9G2mLUC0Fams", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_15.png", "conversations": [ { "from": "human", "value": "\nWhat does {the mark}[0.934, 0.406] on the line chart in subplot 1 represent?" }, { "from": "gpt", "value": "Value 805000." } ], "id_raw": 715 }, { "id": "vpT406fnJr1lRuIj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_15.png", "conversations": [ { "from": "human", "value": "\nWhat content is found at the location identified by [0.544, 0.048]?" }, { "from": "gpt", "value": "Y-axis tick label '42000'." } ], "id_raw": 715 }, { "id": "RJnLIqzqMsoVTJxb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_15.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at {point}[0.403, 0.170], the plot value in subplot 0 or subplot 1?" }, { "from": "gpt", "value": "subplot 1." } ], "id_raw": 715 }, { "id": "aOt1PLroH6tiNJ0Z", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_15.png", "conversations": [ { "from": "human", "value": "\nIs {the value}[0.439, 0.799] in the subplot 2 higher than {the value}[0.934, 0.867] in subplot 3?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 715 }, { "id": "1hsXjUqCSdOaeTpu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_15.png", "conversations": [ { "from": "human", "value": "\nWhich axis label is closer to {the origin point}[0.039, 0.468], the x-axis label or {the y-axis label}[0.009, 0.232, 0.018, 0.260]?" }, { "from": "gpt", "value": "x-axis label." } ], "id_raw": 715 }, { "id": "khuNNpesyfVQmmz0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.244, 0.301, 0.475, 0.315]." }, { "from": "gpt", "value": "This region holds the x-axis labels for the subplot 0, specifically housing the labels from '0' to '10' along the measured time frame." } ], "id_raw": 715 }, { "id": "coGFaPR57v4uYfsc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_15.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area extending from [0.530, 0.450, 0.971, 0.495]?" }, { "from": "gpt", "value": "This area holds various x-axis labels ranging from '4' to '12' in subplot 1 indicating time in months, aligned with the associated tick marks." } ], "id_raw": 715 }, { "id": "H5xnbxgVQo6Gd08m", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_15.png", "conversations": [ { "from": "human", "value": "\nWhat elements are present inside the bounds [0.244, 0.970, 0.475, 0.991]?" }, { "from": "gpt", "value": "The described region contains the lower edge of the chart, displaying the x-axis label 'Time (months)' for subplot 2." } ], "id_raw": 715 }, { "id": "HkoJmvsLMMQmWvZT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.022, 0.301, 0.035, 0.319] represent on the chart?" }, { "from": "gpt", "value": "Y-axis tick." } ], "id_raw": 716 }, { "id": "PihOIY1ssdYFl5N6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the feature at [0.022, 0.168, 0.035, 0.186]?" }, { "from": "gpt", "value": "Y-axis tick label." } ], "id_raw": 716 }, { "id": "97KAox1g6OwtYDFM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_16.png", "conversations": [ { "from": "human", "value": "\nCould you tell me the color of the marker at [0.870, 0.143, 0.892, 0.154]?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 716 }, { "id": "11gVLNODxwjUnoWl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_16.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {the tick at point '0.6'}[0.041, 0.735] or {the tick at point '0.7'}[0.041, 0.688]?" }, { "from": "gpt", "value": "0.7." } ], "id_raw": 716 }, { "id": "NnFNlB7rhuq1HFfe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_16.png", "conversations": [ { "from": "human", "value": "\nIs {the line labeled 'AI and Machine Learning'}[0.516, 0.776] above or below {the line labeled 'Quantum Technology'}[0.516, 0.250] at the 'Minimal Change' position?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 716 }, { "id": "aqMPxsqLMGNfCd46", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_16.png", "conversations": [ { "from": "human", "value": "\nCompares the colors of {the handle at 'Steady Decline', AI and Machine Learning}[0.870, 0.660, 0.892, 0.671] and {the handle at 'Steady Decline', Quantum Technology}[0.870, 0.169, 0.892, 0.180]." }, { "from": "gpt", "value": "Same." } ], "id_raw": 716 }, { "id": "QTliPsATH29DspAM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.480, 0.932, 0.980, 0.957]." }, { "from": "gpt", "value": "The area includes x-axis tick labels for 'Minimal Change', 'Incremental Growth', and 'Steady Decline' with their respective tick points {Minimal Change}[0.516, 0.927], {Incremental Growth}[0.732, 0.927], {Steady Decline}[0.947, 0.927]." } ], "id_raw": 716 }, { "id": "46cidr2lDV1yEXQw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_16.png", "conversations": [ { "from": "human", "value": "\nWhat crucial chart elements are within the area [0.480, 0.449, 0.984, 0.466]?" }, { "from": "gpt", "value": "This region contains x-axis labels for 'Minimal Change', 'Incremental Growth', and 'Steady Decline', clearly identifying the trends in Quantum Technology." } ], "id_raw": 716 }, { "id": "cmN0U5LvHYQKIQCl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.22, 0.3, 0.30, 0.4] encompass?" }, { "from": "gpt", "value": "The area houses the y-axis tick label '0.2' and part of the grid line, helping visualize the scale of 'Performance' in the Quantum Technology chart." } ], "id_raw": 716 }, { "id": "YVP4jSgGTifab4qw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart located in [0.427, 0.509, 0.607, 0.532]?" }, { "from": "gpt", "value": "Influence of CNN on Public Opinion." } ], "id_raw": 717 }, { "id": "3WVJdHqauoWAVaRe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_17.png", "conversations": [ { "from": "human", "value": "\nWhat are the x label and their coordinates on the second chart?" }, { "from": "gpt", "value": "The x label is \"Scenarios\",[0.496, 0.964, 0.538, 0.981]." } ], "id_raw": 717 }, { "id": "cwU3s6kdWvyvgl1w", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-Axis label described at {coordinates}[0.009, 0.162, 0.018, 0.321] in the first chart?" }, { "from": "gpt", "value": "Influence Measure." } ], "id_raw": 717 }, { "id": "RLXSqrep80mPbWfJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_17.png", "conversations": [ { "from": "human", "value": "\nIs the value of {BBC}[0.095, 0.098, 0.940, 0.391] higher or lower than {CNN}[0.095, 0.568, 0.940, 0.898] in the 'Steady increase' scenario?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 717 }, { "id": "p7jjAPbgiYCpCVvQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_17.png", "conversations": [ { "from": "human", "value": "\nIn the 'Rapid expansion' scenario, which media outlet, {BBC}[0.095, 0.098, 0.940, 0.391] or {CNN}[0.095, 0.568, 0.940, 0.898] has more influence?" }, { "from": "gpt", "value": "BBC." } ], "id_raw": 717 }, { "id": "MKoXlkvPWM8MWaWG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_17.png", "conversations": [ { "from": "human", "value": "\nIs the influence measure of {BBC}[0.095, 0.098, 0.940, 0.391] higher or lower than {CNN}[0.095, 0.568, 0.940, 0.898] in the 'Accelerating decline' scenario?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 717 }, { "id": "e9DaYvhyD6GUPEWN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content located within the region [0.959, 0.555, 0.975, 0.570]." }, { "from": "gpt", "value": "This region contains the label for CNN, which is represented with purple color." } ], "id_raw": 717 }, { "id": "bIXt3Cd8vyaKUc0d", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within [0.764, 0.949, 0.833, 0.967]?" }, { "from": "gpt", "value": "This area contains the 'Steady increase' label on the x-axis of the second chart." } ], "id_raw": 717 }, { "id": "DBYOSUDASC4nYvGH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_17.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region [0.033, 0.946, 0.046, 0.964]?" }, { "from": "gpt", "value": "The region contains an y-axis tick label '0.3' on the second chart." } ], "id_raw": 717 }, { "id": "AsEPKDwxfa3nuwvQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label for the subplot showing data on Acid Rain pH?" }, { "from": "gpt", "value": "Time[0.507, 0.473, 0.528, 0.491]." } ], "id_raw": 718 }, { "id": "Ktt4HKVwYabq1Wv1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_18.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the 'SuddenDrop' label in the chart legend?" }, { "from": "gpt", "value": "#ff7f0e[0.228, 0.091, 0.250, 0.101]." } ], "id_raw": 718 }, { "id": "EVLWkzrrFH19uwdW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_36_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the subplot with Surfaces Temperature data?" }, { "from": "gpt", "value": "Value[0.670, 0.218, 0.679, 0.265]." } ], "id_raw": 718 }, { "id": "cHoFfFRTEE38psOZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_18.png", "conversations": [ { "from": "human", "value": "\nIs {the point on the line for 'MarinePollution'}[0.439, 0.904] higher than {the point for 'RiverContamination'}[0.770, 0.904]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 718 }, { "id": "Vi2vx53tEVclvaU9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_18.png", "conversations": [ { "from": "human", "value": "\nDoes {the y-grid line at}[0.044, 0.230] align with {the y-tick marked '60'}[0.044, 0.230] in the Atmospheric PM2.5 subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 718 }, { "id": "bktZep76NRj5af13", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_36_18.png", "conversations": [ { "from": "human", "value": "\nIs {the first point on the TrendSteadying line}[0.057, 0.909] lower than {the last point on the same line}[0.317, 0.905] in the Atmospheric PM2.5 subplot?" }, { "from": "gpt", "value": "No." } ], "id_raw": 718 }, { "id": "cSfPGuxSTpUocjYl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region [0.044, 0.537, 0.330, 0.927]." }, { "from": "gpt", "value": "This region contains the subplot for Atmospheric PM2.5, which includes axis labels: Time (Horizontal), Value (Vertical), y-tick marks from \u221220 to 120, x-tick marks from \u22121 to 6, and two line plots named TrendSteadying and SuddenDrop with markers." } ], "id_raw": 718 }, { "id": "uVGM2RxGduIYYZxC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_18.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.659, 0.927, 0.991, 0.957] contain in the RiverContamination subplot?" }, { "from": "gpt", "value": "This area displays the x-axis tick labels ranging from -1 to 6 for the RiverContamination data, aligned along the bottom of the subplot." } ], "id_raw": 718 }, { "id": "AFzxW9K6IzCKSgXm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_36_18.png", "conversations": [ { "from": "human", "value": "\nWhat features are included in the area [0.705, 0.047, 0.991, 0.437]?" }, { "from": "gpt", "value": "This space encompasses the Surfaces Temperature subplot, including y-tick marks from -20 to 120, x-axis labels for time, a y-label indicating value, and two line plots representing environmental trends." } ], "id_raw": 718 }, { "id": "uNkh2xqpC8xgUn6O", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object corresponding to {coordinates}[0.658, 0.116, 0.694, 0.132]?" }, { "from": "gpt", "value": "legend handle for \"Polynomial Trend (from Fuel Efficiency)\"." } ], "id_raw": 720 }, { "id": "fw18ZN2hcwpwvDRw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_0.png", "conversations": [ { "from": "human", "value": "\nWhat does {coordinates}[0.468, 0.819] represent in the chart?" }, { "from": "gpt", "value": "Point for \"Fuel Efficiency\" at 1000 steps." } ], "id_raw": 720 }, { "id": "GFvzH8qtqo2QFNJi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_0.png", "conversations": [ { "from": "human", "value": "\nWhat feature is displayed at {coordinates}[0.263, 0.344, 0.274, 0.372]?" }, { "from": "gpt", "value": "Text label for a point on the \"Energy Efficiency\" line graph." } ], "id_raw": 720 }, { "id": "xVwzdEbjeSoaU9ZX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Energy Efficiency' at 500 steps}[0.263, 0.394] higher or lower than {the point for 'Fuel Efficiency' at the same step}[0.263, 0.606]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 720 }, { "id": "K6USFnvqGG8vCthW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_0.png", "conversations": [ { "from": "human", "value": "\nIs {the starting point on the line for 'Energy Efficiency'}[0.099, 0.494] above or below {the starting point on the line for 'Fuel Efficiency'}[0.099, 0.556]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 720 }, { "id": "XpzD2hR55vTiveUC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_0.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value at the 900th step, {Fuel Efficiency}[0.427, 0.806] or {Overall Efficiency in subplot 1}[0.924, 0.753]?" }, { "from": "gpt", "value": "Overall Efficiency." } ], "id_raw": 720 }, { "id": "XIcPtLWnyZvwDUQi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.087, 0.059, 0.257, 0.151]." }, { "from": "gpt", "value": "The region showcases the legends {Energy Efficiency}[0.132, 0.070, 0.252, 0.098] with a {handle}[0.089, 0.074, 0.125, 0.090], colored orange, and {Fuel Efficiency}[0.132, 0.112, 0.232, 0.140] with a {handle}[0.089, 0.116, 0.125, 0.132], colored blue." } ], "id_raw": 720 }, { "id": "UINkJsyKVoWtX27S", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_0.png", "conversations": [ { "from": "human", "value": "\nWhat elements are included in the coordinate space [0.577, 0.045, 0.983, 0.856]?" }, { "from": "gpt", "value": "The area presents a plot with a labeled y-axis 'Polynomial Value' and multiple x-axis text labels for step increments from 100 to 1000 paired with respective plots for labels {Overall Efficiency}[0.701, 0.070, 0.818, 0.098] and {Polynomial Trend (from Fuel Efficiency)}[0.701, 0.112, 0.971, 0.140]." } ], "id_raw": 720 }, { "id": "pBtyInjAvpFjKW1q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_0.png", "conversations": [ { "from": "human", "value": "\nHighlight the components in the rectangle [0.523, 0.081, 0.553, 0.880]." }, { "from": "gpt", "value": "This region contains y-axis tick labels ranging from 0.45 to 0.85 for the second subplot, informing someone on various polynomial values plotted against steps." } ], "id_raw": 720 }, { "id": "IQspZz5PihpBjpqB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.035, 0.532, 0.079, 0.560] indicate on the y-axis?" }, { "from": "gpt", "value": "48500." } ], "id_raw": 721 }, { "id": "yBNJKLJzHTpYfbbo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_1.png", "conversations": [ { "from": "human", "value": "\nWhich label is present at [0.154, 0.055, 0.342, 0.083] in the sublegend?" }, { "from": "gpt", "value": "Public School Loan Amount." } ], "id_raw": 721 }, { "id": "SaUvMl0J3ycfQgba", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_1.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the point at [0.242, 0.481] on the Public School Loan Amount line chart?" }, { "from": "gpt", "value": "The year 2012 loan amount." } ], "id_raw": 721 }, { "id": "rGGBEjwelwQWQzF5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Public School in 2013}[0.283, 0.329] higher than {the point for Government School in 2013}[0.283, 0.677]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 721 }, { "id": "OQXbgugmaWhKXG0M", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the trend line for 'Number of Software Destroyed'} cover a larger range vertically compared to {the trend line for 'Number of Hurricanes'} within the subplot coordinates?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 721 }, { "id": "FUsdyXrBcZBHBjBr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_1.png", "conversations": [ { "from": "human", "value": "\nWhich color marker represents a greater loan amount in 2018, {Public School}[0.486, 0.068] or {Government School}[0.486, 0.391]?" }, { "from": "gpt", "value": "Public School." } ], "id_raw": 721 }, { "id": "VZxohUc1A4tLFwvl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents and visual arrangement in [0.035, 0.256, 0.079, 0.370]." }, { "from": "gpt", "value": "This region contains the y-axis labels {68500}[0.035, 0.256, 0.079, 0.284] and {63500}[0.035, 0.325, 0.079, 0.353], each displayed with black text and vertically spaced." } ], "id_raw": 721 }, { "id": "6nQDFnNGxpLQtd0J", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the coordinates [0.711, 0.097, 0.971, 0.176]?" }, { "from": "gpt", "value": "This region includes part of the legend for the subplot, specifically containing the label for 'Number of Hurricanes' with it's corresponding colored marker indicator." } ], "id_raw": 721 }, { "id": "BhtyVuGZVPWnZVBt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents of the region [0.582, 0.903, 0.617, 0.931]?" }, { "from": "gpt", "value": "This region includes {the x-axis label '2009'}[0.582, 0.903, 0.617, 0.931] on the subplot showing 'Number of Software Destroyed' and 'Number of Hurricanes'." } ], "id_raw": 721 }, { "id": "iFE8MY48sDFPOWnR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the handle represent at [0.448, 0.681, 0.485, 0.698] in the legend?" }, { "from": "gpt", "value": "Procurement." } ], "id_raw": 724 }, { "id": "MRfW7oEl31PFYoU9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the content at the coordinates [0.939, 0.903, 0.947, 0.931]?" }, { "from": "gpt", "value": "x-axis tick label 5." } ], "id_raw": 724 }, { "id": "vEZS8pbuomrghyUo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_4.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis tick label} at [0.035, 0.127, 0.057, 0.155] represent?" }, { "from": "gpt", "value": "1.5." } ], "id_raw": 724 }, { "id": "xsa3YK4XxubDyl28", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_4.png", "conversations": [ { "from": "human", "value": "\nIs the {point from 'After-sale Service'}[0.533, 0.425] higher or lower than the {point from 'Procurement'} at the same x-axis position?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 724 }, { "id": "SUusWOYCpnBO0HmT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_4.png", "conversations": [ { "from": "human", "value": "\n{At 'Quarter 5'}[0.944], is the {value of 'Supply Chain'}[0.944, 0.568] higher or lower than the {value of 'After-sale Service'}[0.944, 0.209]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 724 }, { "id": "mkQrvyA0CJ6O1Szb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_4.png", "conversations": [ { "from": "human", "value": "\nAt {x=2}, is the {point of 'Delivery'}[0.327, 0.712] higher or lower than the {point of 'Manufacturing'}[0.327, 0.066]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 724 }, { "id": "UNlApsSxHf2zykRE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content between the coordinates [0.447, 0.624, 0.618, 0.842]." }, { "from": "gpt", "value": "This region contains the legend of the chart. It includes labels for the lines 'Supply Chain', 'Procurement', 'Manufacturing', 'Delivery', and 'After-sale Service'. These labels are associated with different colored markers representing each line in the chart." } ], "id_raw": 724 }, { "id": "TX1PX1oGeXatri90", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_4.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the attributes of the region [0.080, 0.030, 0.985, 0.856]?" }, { "from": "gpt", "value": "This region represents the axes of the chart, where the historical data are plotted. It contains the gridlines along the x and y axes, the x-axis labeled as 'Quarters (x Phenomenon)', indicating the different quarters, the y-axis labeled as 'Efficiency', reflecting the efficiency metrics, and all the plotted lines representing 'Supply Chain', 'Procurement', 'Manufacturing', 'Delivery', and 'After-sale Service'." } ], "id_raw": 724 }, { "id": "wOdc7sv6hzJe5Wiw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are in the region [0.448, 0.633, 0.613, 0.664]?" }, { "from": "gpt", "value": "This region contains two parts of the legend, specifically the labels and icons for 'Supply Chain' and 'Procurement'. Each label is accompanied by a colored symbol." } ], "id_raw": 724 }, { "id": "HHicC6me2OYJXAxF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_6.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the 'Stocks' line on the chart?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 726 }, { "id": "KpVIGfkjQCqbxv5V", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the the y-axis label's text?" }, { "from": "gpt", "value": "Investment Value." } ], "id_raw": 726 }, { "id": "IymN4h8h1bG58cqF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_6.png", "conversations": [ { "from": "human", "value": "\nWhat label is applied to the x-axis at {the coordinate}[0.480, 0.856]?" }, { "from": "gpt", "value": "12." } ], "id_raw": 726 }, { "id": "QUnt4IomGnMIaegh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point at month 6 on the 'Stocks' line}[0.294, 0.109] higher than {the corresponding point on the 'Bonds' line}[0.294, 0.443]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 726 }, { "id": "anVmx3Wqy599XVpk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_6.png", "conversations": [ { "from": "human", "value": "\nWhich line has a point located lower at {month 10}[0.418, 0.276]." }, { "from": "gpt", "value": "Stocks." } ], "id_raw": 726 }, { "id": "NUCmSK5MTUXuNRdM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_6.png", "conversations": [ { "from": "human", "value": "\nIs {the first 'Gold' line point}[0.593, 0.215] higher in value compared to {the first 'Bonds' line point}[0.108, 0.651]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 726 }, { "id": "Ry5O5HknEMgCSvbt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.520, 0.060, 0.780, 0.130]." }, { "from": "gpt", "value": "Contains y-axis labels ranging from '95.0' to '100.0' along with their tick marks integrated into the plot; labels have black text." } ], "id_raw": 726 }, { "id": "DCMVXo7dsww6No8Q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.085, 0.030, 0.495, 0.858]." }, { "from": "gpt", "value": "The region includes the primary plot area for the 'Investment Value' graph, showcasing data lines for 'Stocks' and 'Bonds', grids, and axis labels." } ], "id_raw": 726 }, { "id": "JQmdGd5BKWhXlX2q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_6.png", "conversations": [ { "from": "human", "value": "\nWhat is encompassed within the area [0.576, 0.044, 0.847, 0.136]?" }, { "from": "gpt", "value": "The legend for the subplot containing `Gold` and `Polynomial Trend (from Bonds)` items, described with respective color markers (green and magenta)." } ], "id_raw": 726 }, { "id": "wzfoe1WIlGgZUsQ7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the text within the coordinates [0.770, 0.942, 0.799, 0.970] in the right-hand chart?" }, { "from": "gpt", "value": "The X-axis label, \"Year\"." } ], "id_raw": 728 }, { "id": "shCKpPOTg57fzyxI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_8.png", "conversations": [ { "from": "human", "value": "\nWhich text is located at the coordinates [0.145, 0.097, 0.191, 0.125] within the left chart?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 728 }, { "id": "eabqLPdL4hktyXTq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at {the y-axis} at [0.035, 0.421, 0.070, 0.449] in the left-hand plot?" }, { "from": "gpt", "value": "1500." } ], "id_raw": 728 }, { "id": "VDiLO8XN5k17EEFK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_8.png", "conversations": [ { "from": "human", "value": "\nIn the right-hand plot, is {the WhatsApp data point at 2020}[0.965, 0.058] higher or lower than {the Instagram data point at 2020}[0.965, 0.317]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 728 }, { "id": "YIG32CVLmhZHxNWx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_8.png", "conversations": [ { "from": "human", "value": "\nIn the left-hand plot, do {the line representing Facebook}[0.111, 0.065, 0.473, 0.419] and {the line representing Twitter}[0.111, 0.760, 0.473, 0.766] intersect?" }, { "from": "gpt", "value": "No." } ], "id_raw": 728 }, { "id": "pvoROKb6irLD0s09", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_8.png", "conversations": [ { "from": "human", "value": "\nAre the user numbers for {Facebook in 2016}[0.111, 0.419] higher or lower than the user numbers for {Twitter in 2016}[0.111, 0.766]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 728 }, { "id": "ux0GbPlTTmWYhWCh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the indicators given in the keys located at the coordinates [0.093, 0.044, 0.216, 0.136]." }, { "from": "gpt", "value": "There are two social networks, Facebook and Twitter, represented by different colors and marker symbols. The Facebook key is represented with a color of '#4169e1' and a round marker. Twitter is represented with color '#2e8b57' and a square marker." } ], "id_raw": 728 }, { "id": "NYZ3LFRdkSC5O4f4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_8.png", "conversations": [ { "from": "human", "value": "\nExplain the content in the area marked by [0.508, 0.337, 0.522, 0.549]." }, { "from": "gpt", "value": "This area contains the Y-Axis label, 'Users (Millions)', for the right-hand plot. The label is in a black color and is rotated at a 90 degree angle." } ], "id_raw": 728 }, { "id": "5S5CAFvn2iwku2ef", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_8.png", "conversations": [ { "from": "human", "value": "\nWhat information lies between the bounding box of [0.586, 0.030, 0.984, 0.856]?" }, { "from": "gpt", "value": "This box encompasses a plot featuring the change in user counts for both WhatsApp and Instagram from 2016 to 2020. The counts are measured in Millions and denoted on the y-axis, and the years are denoted on the x-axis. A key is also located in this bounding box, defining the color and marker for WhatsApp and Instagram." } ], "id_raw": 728 }, { "id": "GREhd2elsPGqF1Oh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.010, 0.345, 0.019, 0.559] in the chart?" }, { "from": "gpt", "value": "Y-axis label of the first subplot." } ], "id_raw": 731 }, { "id": "nXiNtxNAeXrSMBUQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the information represented by the line located at coordinates [0.075, 0.081, 0.456, 0.750]?" }, { "from": "gpt", "value": "It represents the Citation Counts of different Academic Fields." } ], "id_raw": 731 }, { "id": "tzzi2fYAp64uKfto", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_11.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the tick label at coordinates [0.173, 0.903, 0.232, 0.931]?" }, { "from": "gpt", "value": "The academic field Mathematics." } ], "id_raw": 731 }, { "id": "bFyilu2eOo3wZqN0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_11.png", "conversations": [ { "from": "human", "value": "\nCompare {the data point for 'Physics'}[0.245, 0.222] and {the data point for 'Mathematics'}[0.202, 0.152], which one has higher citation counts?" }, { "from": "gpt", "value": "Mathematics." } ], "id_raw": 731 }, { "id": "i0cXHX1sfmJY1Dlw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_11.png", "conversations": [ { "from": "human", "value": "\nIs {the data point for 'Health Science'}[0.621, 0.596] in the second subplot higher or lower compared to {the data point for 'Statistics'}[0.578, 0.819]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 731 }, { "id": "NxxkHRTfoBphC3ke", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the data point for 'Biology'}[0.329, 0.257] in the first subplot have more or less citations than {the data point for 'Engineering'}[0.372, 0.081]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 731 }, { "id": "sjGQ9QG5ttU50KkG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the section between coordinates [0.559, 0.048, 0.978, 0.856]." }, { "from": "gpt", "value": "This is the second subplot of the chart that represents the polynomial trend value of citation counts of different academic fields. The line plot shows both the outliers and polynomial trend." } ], "id_raw": 731 }, { "id": "aGvin66DhSWElaQH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_11.png", "conversations": [ { "from": "human", "value": "\nCan you provide some details about the region [0.862, 0.062, 0.974, 0.154]?" }, { "from": "gpt", "value": "This region contains the legend for the second subplot. It includes a green symbol representing outliers and a purple symbol representing the polynomial trend." } ], "id_raw": 731 }, { "id": "LYRkmjMvMAnoP911", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.056, 0.048, 0.475, 0.856]." }, { "from": "gpt", "value": "This area corresponds to the first subplot of the chart, which is a line plot illustrating the citation counts across a range of academic fields." } ], "id_raw": 731 }, { "id": "tHPQu6MTlKAqWeGn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_12.png", "conversations": [ { "from": "human", "value": "\nIn the first subplot, what is the label for the object with coordinates [0.149, 0.055, 0.427, 0.083]?" }, { "from": "gpt", "value": "The label is \"Population Density (Persons per sq. km)\"." } ], "id_raw": 732 }, { "id": "fCfb108ZXqqRSez1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label in the first subplot?" }, { "from": "gpt", "value": "It is labelled as \"Value\"." } ], "id_raw": 732 }, { "id": "kDcsrsawceJk3E1Y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the text for {the x-axis tick label}[0.662, 0.903, 0.697, 0.931]?" }, { "from": "gpt", "value": "The text is \"2021\"." } ], "id_raw": 732 }, { "id": "mZRdhnBV3l9HurbL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_12.png", "conversations": [ { "from": "human", "value": "\nComparing {the point for the year '2023'}[0.330, 0.803] and {the point for the year '2024'}[0.402, 0.794], which year has a higher 'Population Density (Persons per sq. km)' value?" }, { "from": "gpt", "value": "Year '2023'." } ], "id_raw": 732 }, { "id": "LbAjEBn4JifN5em8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point for year '2025'}[0.473, 0.068] above or below {the point for year '2024'}[0.894, 0.403] in their Renewable Energy Production values?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 732 }, { "id": "KqpfvC6RhMqQuWqF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_12.png", "conversations": [ { "from": "human", "value": "\nFor 'Renewable Energy Production (in PJ)', is the value for {year '2020'}[0.116, 0.294] greater than, equal to, or less than the value for {year '2021'}[0.187, 0.272]?" }, { "from": "gpt", "value": "Less than." } ], "id_raw": 732 }, { "id": "8AdU2YoXxRg9VFBJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.527, 0.423, 0.567, 0.643]." }, { "from": "gpt", "value": "The region contains two y-axis tick labels of the second subplot: {487.8}[0.527, 0.615, 0.567, 0.643] and {540.2}[0.527, 0.807, 0.567, 0.835]." } ], "id_raw": 732 }, { "id": "KTwYQnLwM8XErEyb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area with coordinates [0.527, 0.044, 0.977, 0.094]." }, { "from": "gpt", "value": "This area includes the legend associated with the second subplot. It represents {the 'Polynomial Trend (from Renewable Energy Production)'}[0.593, 0.055, 0.971, 0.083] graph line." } ], "id_raw": 732 }, { "id": "lP2DlZ155W2epFb2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.098, 0.303, 0.473, 0.818] of the first subplot?" }, { "from": "gpt", "value": "This region contains all the data points for the lines representing 'Population Density (Persons per sq. km)' and 'Renewable Energy Production (in PJ)'." } ], "id_raw": 732 }, { "id": "8TtxOgX4YHOnPSz8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis} at [0.015, 0.383, 0.029, 0.503]?" }, { "from": "gpt", "value": "Intensity." } ], "id_raw": 733 }, { "id": "H1FAzrBeXk26A47L", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing 'Rights Protection'} with points at coordinates like [0.098, 0.065, 0.461, 0.737]?" }, { "from": "gpt", "value": "Blue (Hex color code: #0000ff)." } ], "id_raw": 733 }, { "id": "xIHpn4SOVz539yNO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_13.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is used for {the line representing 'Rights Protection'} within the coordinates [0.098, 0.421, 0.461, 0.757]?" }, { "from": "gpt", "value": "Square (represented by 's' in matplotlib)." } ], "id_raw": 733 }, { "id": "xEEabHSFikAyl6ld", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_13.png", "conversations": [ { "from": "human", "value": "\nBetween {the trend of 'Social Inequality'}[0.098, 0.065, 0.461, 0.737] and {the trend of 'Rights Protection'}[0.098, 0.421, 0.461, 0.757], which one dips lowest at the 200 months mark?" }, { "from": "gpt", "value": "Social Inequality." } ], "id_raw": 733 }, { "id": "HMCqNAA8XsNUHRq3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_13.png", "conversations": [ { "from": "human", "value": "\nAt the start point (0 months), does {the trend of 'Social Inequality'}[0.098, 0.065, 0.461, 0.737] start higher or lower than {the trend of 'Rights Protection'}[0.098, 0.421, 0.461, 0.757]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 733 }, { "id": "FCGt87gUPRjWhj4v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_13.png", "conversations": [ { "from": "human", "value": "\nBetween {the line of 'Rights Protection'}[0.604, 0.068, 0.967, 0.800] and {the line of 'Polynomial Trend (from Rights Protection)'}[0.604, 0.105, 0.967, 0.818], which ends higher at 300 months?" }, { "from": "gpt", "value": "Polynomial Trend (from Rights Protection)." } ], "id_raw": 733 }, { "id": "MZJhKF7eflmIjY04", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the area with coordinates [0.586, 0.030, 0.985, 0.856]?" }, { "from": "gpt", "value": "Sure, it represents a subplot that displays a polynomial trend of 'Rights Protection' over time. It includes a detailed x-axis labeled as 'Time (months)', and a y-axis labeled as 'Polynomial Trend of Rights Protection'. The plot includes two different lines, one for the exact data of 'Rights Protection' and the other representing the polynomial trend derived from it. A legend is present to distinguish between the two lines." } ], "id_raw": 733 }, { "id": "6zkXuEe2OI6Yc7AB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_13.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area [0.087, 0.044, 0.256, 0.136]?" }, { "from": "gpt", "value": "This area contains the legend items for the subplot. Specifically, it includes labels and color markers for the 'Social Inequality' and 'Rights Protection' trends." } ], "id_raw": 733 }, { "id": "i4qOmOq56FfXXmsA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.494, 0.184, 0.508, 0.702] in the chart figure?" }, { "from": "gpt", "value": "Within these coordinates is the y-axis label for the second subplot of the chart, reading as 'Polynomial Trend of Rights Protection'. The label is placed vertically and colored in black." } ], "id_raw": 733 }, { "id": "0ygjmqFXPFiz3xyS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis denote in the plot on the left side of the figure?" }, { "from": "gpt", "value": "On Fire Value" } ], "id_raw": 734 }, { "id": "YEEjI4dwHcAqyITe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the object residing in the coordinates [0.035, 0.192, 0.052, 0.220] in the left subplot of the chart?" }, { "from": "gpt", "value": "80" } ], "id_raw": 734 }, { "id": "giA49tyOHovcD1wZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the data label of the object located at [0.369, 0.031, 0.384, 0.059] in the left subplot?" }, { "from": "gpt", "value": "95" } ], "id_raw": 734 }, { "id": "GpR8xvnW8qY0fyPU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_14.png", "conversations": [ { "from": "human", "value": "\nIn the left subplot of the chart, is {the point labeled '73'}[0.095, 0.261] above or below {the point labeled '32'}[0.188, 0.595] in the 'On Fire' line?" }, { "from": "gpt", "value": "Below" } ], "id_raw": 734 }, { "id": "4whdflFW5LDw8LfM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_14.png", "conversations": [ { "from": "human", "value": "\nIn the right subplot, looking at the 'Ice Cool Trend' line, is {the point at the third time interval}[0.779, 0.689] higher or lower than {the point at the first time interval}[0.591, 0.383]?" }, { "from": "gpt", "value": "Higher" } ], "id_raw": 734 }, { "id": "cSWfkPJSWDsjbT3p", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_14.png", "conversations": [ { "from": "human", "value": "\nIn the right subplot of the chart, which trend value is higher at the 5th time interval: 'Ice Cool Trend' or 'Polynomial Trend (from Ice Cool)'?" }, { "from": "gpt", "value": "Polynomial Trend (from Ice Cool)" } ], "id_raw": 734 }, { "id": "oMi6lVMw4QzFoFYB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_14.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area in the coordinates [0.087, 0.063, 0.182, 0.114] in the left subplot?" }, { "from": "gpt", "value": "The area contains a colored marker and the term {On Fire}[0.127, 0.074, 0.176, 0.102] from the legend." } ], "id_raw": 734 }, { "id": "yZEIGCwmQjScglXL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_14.png", "conversations": [ { "from": "human", "value": "\nPlease detail the content of the area in [0.729, 0.942, 0.828, 0.970] in the right subplot of the chart?" }, { "from": "gpt", "value": "The x-axis label {Time Intervals}[0.729, 0.942, 0.828, 0.970] of the second subplot is located in this area." } ], "id_raw": 734 }, { "id": "yQ4fUcOHpDe55fJV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_14.png", "conversations": [ { "from": "human", "value": "\nCan you provide a description of the area [0.573, 0.049, 0.985, 0.856] in the right subplot?" }, { "from": "gpt", "value": "The area is occupied by a labeled grid chart. The grid chart has x-ticks for 'Time Intervals' running 1 through 5 and y-ticks running from 0 to 140 marking the 'Ice Cool Value'. The chart indicates two lines - Ice Cool Trend and Polynomial Trend (from Ice Cool) along with their points marked at the time intervals and their respective trend values." } ], "id_raw": 734 }, { "id": "EKS4o9poZIil3r2R", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis represent in the chart according to the label at [0.760, 0.942, 0.811, 0.970]?" }, { "from": "gpt", "value": "Months." } ], "id_raw": 737 }, { "id": "m50WbyjvWOVJCsOd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_17.png", "conversations": [ { "from": "human", "value": "\nWhat product is represented by the line with coordinates [0.604, 0.068, 0.967, 0.818] in the second sub-plot?" }, { "from": "gpt", "value": "Product C." } ], "id_raw": 737 }, { "id": "8AOjNVwfQLAZ22QM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.527, 0.932, 0.562, 0.960] on the y-axis of the second subplot?" }, { "from": "gpt", "value": "1500." } ], "id_raw": 737 }, { "id": "BHZ7gu3vpDdNPtc6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_17.png", "conversations": [ { "from": "human", "value": "\nBetween {Product A}[0.111, 0.643, 0.474, 0.818] and {Product B}[0.111, 0.068, 0.474, 0.393] in the first subplot, which product has declining sales?" }, { "from": "gpt", "value": "Product A" } ], "id_raw": 737 }, { "id": "UBAC7Sd9dRufs25L", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_17.png", "conversations": [ { "from": "human", "value": "\nDoes {Product C}[0.604, 0.068, 0.967, 0.818] in the second subplot has higher sales in {Month 12}[0.958, 0.903, 0.976, 0.931] compared to {Month 1}[0.600, 0.903, 0.608, 0.931]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 737 }, { "id": "MeMcyfxH8Dio7wJu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_17.png", "conversations": [ { "from": "human", "value": "\nComparing {Product A}[0.111, 0.643, 0.474, 0.818] and {Product C}[0.604, 0.068, 0.967, 0.818], which product shows more fluctuation in sales?" }, { "from": "gpt", "value": "Product C" } ], "id_raw": 737 }, { "id": "zc4cu0u0vgsYyz8h", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the coordinates range [0.527, 0.058, 0.562, 0.022]." }, { "from": "gpt", "value": "This area contains the lower section of the y-axis on the second subplot containing labels '3250' and '3750'." } ], "id_raw": 737 }, { "id": "IgslB3u1vwO0l7oH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_17.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen between the coordinates [0.278, 0.088, 0.849, 0.130]?" }, { "from": "gpt", "value": "This region signifies the legend items in the first subplot, which include the labels for 'Product A - Rapid Decline' and 'Product B - Gradual Improvement' with their respective color markers." } ], "id_raw": 737 }, { "id": "3Ad0p7uSlPZPux3g", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the area within [0.145, 0.055, 0.321, 0.083]." }, { "from": "gpt", "value": "This region in the first subplot contains a legend item indicating the color marker and label for 'Product A - Rapid Decline'." } ], "id_raw": 737 }, { "id": "UmBRRhnN6iV1JklG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis}[0.015, 0.226, 0.029, 0.671] in the first subplot?" }, { "from": "gpt", "value": "R&D Investment (in Billion USD)." } ], "id_raw": 738 }, { "id": "1agu2DVyix5RFWSk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the line representing 'Revenue Increase' in the second subplot?" }, { "from": "gpt", "value": "[0.587, 0.066, 0.966, 0.556]" } ], "id_raw": 738 }, { "id": "QkPyjuk10U58mVPc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_18.png", "conversations": [ { "from": "human", "value": "\nWhat value is signified by {the y-axis tick label} at [0.527, 0.210, 0.545, 0.238]?" }, { "from": "gpt", "value": "11." } ], "id_raw": 738 }, { "id": "qR2ER8jm1Udq5HQz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_18.png", "conversations": [ { "from": "human", "value": "\nAt year 3, is the R&D investment higher or lower than the revenue increase?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 738 }, { "id": "um7H37uFxusRylq9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_18.png", "conversations": [ { "from": "human", "value": "\nIs the value of {R&D Investment}[0.179, 0.345, 0.474, 0.093] higher or lower at year 2 compared to year 9?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 738 }, { "id": "NSSw6yIsvUHNJdQJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_18.png", "conversations": [ { "from": "human", "value": "\nComparing the initial and final years, did the {Revenue Increase}[0.587, 0.066, 0.966, 0.556] trend increase or decrease over time?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 738 }, { "id": "KHimnOM32jcsPnQu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the region [0.068, 0.041, 0.490, 0.860]?" }, { "from": "gpt", "value": "This region contains a plot representing the R&D investment over a period of 10 years. It includes a light-colored grid, axis labels mentioning Time Period (in Years) and R&D Investment (in Billion USD), multiple tick labels, and a line plot with circular markers representing the trend." } ], "id_raw": 738 }, { "id": "jCPJcnIbaXNBEqzS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.555, 0.046, 0.993, 0.857]." }, { "from": "gpt", "value": "This region contains a plot displaying the increase in revenue over ten years. Similar to the first, it has a light-colored grid, axis labels detailing Time Period (in Years) and Revenue Increase (in Billion USD), multiple tick labels, and a line plot, but with square markers for the data points." } ], "id_raw": 738 }, { "id": "3yJZy8mibZ9nmNtr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_18.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the rectangle [0.565, 0.048, 0.962, 0.580]?" }, { "from": "gpt", "value": "In this region, the right side of the graph showing 'Revenue Increase' is located. It represents the revenue increase trend with square markers over a period of 10 years, with associated x and y-axis labels and ticks." } ], "id_raw": 738 }, { "id": "EBzCBnV63sv7Lo9X", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_19.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the Charity Index in the line chart?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 739 }, { "id": "7Vtvjvrn5Fugqfb2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis}[0.015, 0.675, 0.029, 0.768] in the second subplot?" }, { "from": "gpt", "value": "Charity Index." } ], "id_raw": 739 }, { "id": "kadNP7tmykNuqa9K", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_37_19.png", "conversations": [ { "from": "human", "value": "\nWhere is the legend for the first subplot located?" }, { "from": "gpt", "value": "[0.087, 0.031, 0.232, 0.056]." } ], "id_raw": 739 }, { "id": "Kz6ou6CCynTuUTY3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_19.png", "conversations": [ { "from": "human", "value": "\nIs {the line point for 2017}[0.645, 0.570] higher than {the point for 2020}[0.869, 0.680] in the second subplot?" }, { "from": "gpt", "value": "No." } ], "id_raw": 739 }, { "id": "u9Cu50klT3juYgeD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_19.png", "conversations": [ { "from": "human", "value": "\nWhich is lower, {the welfare index for 2016}[0.570, 0.159] or {the charity index for 2016}[0.570, 0.652]?" }, { "from": "gpt", "value": "Welfare Index." } ], "id_raw": 739 }, { "id": "G8G7Z82MnPd3ESDs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_37_19.png", "conversations": [ { "from": "human", "value": "\nWhich of the points for 2021 is higher on the chart, {the welfare index}[0.944, 0.043] or {the charity index}[0.944, 0.735]?" }, { "from": "gpt", "value": "Welfare Index." } ], "id_raw": 739 }, { "id": "bs3OAsiGyUL6uKH3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the area within the coordinates [0.121, 0.437, 0.794, 0.475]." }, { "from": "gpt", "value": "This area primarily includes x-axis labels for years 2010 through 2019 in the first subplot, presented in black text, \"2010\" at [0.104, 0.461, 0.139, 0.475] through \"2019\" at [0.777, 0.461, 0.812, 0.475]." } ], "id_raw": 739 }, { "id": "lvqQBtrs3GTxnB0w", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_19.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen within the area [0.676, 0.522, 0.978, 0.568]?" }, { "from": "gpt", "value": "The area contains legends for the second subplot displaying the 'Charity Index' and 'Polynomial Trend (For Charity Index)' colored blue and green respectively with labels placed at [0.720, 0.527, 0.813, 0.541] and [0.720, 0.548, 0.972, 0.562]." } ], "id_raw": 739 }, { "id": "GlHo2NzGZhClozLA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_37_19.png", "conversations": [ { "from": "human", "value": "\nDescribe what is found within [0.080, 0.024, 0.985, 0.437]." }, { "from": "gpt", "value": "This is the first subplot which includes an axes area for plotting, grid lines in gray, and labels indicating the 'Welfare Index' on the y-axis and 'Years' on the x-axis. The line for the 'Welfare Index' runs across this region." } ], "id_raw": 739 }, { "id": "bPE4Ygk5xH9lKtXX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the {Y-Axis}[0.025, 0.386, 0.058, 0.529] in the given chart?" }, { "from": "gpt", "value": "Ratio." } ], "id_raw": 740 }, { "id": "eXi4cpHtKcGNjWux", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the {X-Axis}[0.382, 0.912, 0.692, 0.963]?" }, { "from": "gpt", "value": "Innovation Index." } ], "id_raw": 740 }, { "id": "erKH3XqamWHMILUY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the color of the {legend entry's label}[0.210, 0.319, 0.518, 0.364] represent on the chart?" }, { "from": "gpt", "value": "Innovation Index 6.6." } ], "id_raw": 740 }, { "id": "lMHNggSAi4VhhQoS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_0.png", "conversations": [ { "from": "human", "value": "\nIn the line plot, does the point marked with {Innovation Index 1.2}[0.167, -78290.554, 0.167, 0.815] lie to the left or right of the line marked with {Innovation Index 2}[0.281, -78290.554, 0.281, 0.815]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 740 }, { "id": "QVeaEkbLrcIjrtFb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_0.png", "conversations": [ { "from": "human", "value": "\nIs the {Start-Up Density line}[0.138, 0.110, 0.935, 0.541] generally above or below the {Government Support line}[0.138, 0.760, 0.935, 0.805]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 740 }, { "id": "eOnOekajASREalHh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_0.png", "conversations": [ { "from": "human", "value": "\nWhat is higher, the {point indicating Start-Up Density}[0.281, 0.110] or {Government Support}[0.281, 0.805] at the innovation index of 2?" }, { "from": "gpt", "value": "Government Support." } ], "id_raw": 740 }, { "id": "XzDtLmJ8GJcmTd77", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_0.png", "conversations": [ { "from": "human", "value": "\nWhat content is present in the area specified by these coordinates: [0.114, 0.098, 0.537, 0.517]?" }, { "from": "gpt", "value": "This area contains the chart legend, which includes labels for Innovation Index 1, Innovation Index 1.2, Innovation Index 2, Innovation Index 6.6, Start-Up Density, and Government Support, each with their corresponding color markings." } ], "id_raw": 740 }, { "id": "3oi3GAwSMzQVY9Kg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_0.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the location defined by [0.138, -78290.554, 0.935, 0.815]?" }, { "from": "gpt", "value": "Within this range, there are multiple lines drawn to represent Innovation Index 1, Innovation Index 1.2, Innovation Index 2, and Innovation Index 6.6." } ], "id_raw": 740 }, { "id": "DsOhruBNhO9Nix8Z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.210, 0.386, 0.459, 0.431]." }, { "from": "gpt", "value": "This area contains the label for 'Start-Up Density' in the legend section of the chart, along with its associated color marker." } ], "id_raw": 740 }, { "id": "AmRBWZ5SiO0UzZY2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_1.png", "conversations": [ { "from": "human", "value": "\nIn which area is {the y-axis label}[0.021, 0.292, 0.041, 0.612] located?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 741 }, { "id": "ya1lqsVVxHRXTtR6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_1.png", "conversations": [ { "from": "human", "value": "\nWhat type of object is at these coordinates [0.105, 0.044, 0.245, 0.220]?" }, { "from": "gpt", "value": "Legend." } ], "id_raw": 741 }, { "id": "HC3m0dANrm5FrPLQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label associated with {the point}[0.768, 0.784]?" }, { "from": "gpt", "value": "Natural gas." } ], "id_raw": 741 }, { "id": "XoEWIIlKG4g1lTtF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_1.png", "conversations": [ { "from": "human", "value": "\nWhich resource consumption is higher at the point with {coordinates}[0.938, 0.066], Coal or Nuclear?" }, { "from": "gpt", "value": "Nuclear." } ], "id_raw": 741 }, { "id": "6tVUGKhfN2UnYnb0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_1.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Solar'}[0.257, 0.695, 0.257, 0.874] steeper than {the line for 'Biofuel'}[0.500, 0.695, 0.500, 0.874]?" }, { "from": "gpt", "value": "They are of the same steepness." } ], "id_raw": 741 }, { "id": "9w6r0zhryiLRb9ej", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_1.png", "conversations": [ { "from": "human", "value": "\nWhich resource has a lower energy use, {the point labeled 'Wind'}[0.378, 0.874] or {the point labeled 'Coal'}[0.622, 0.874]?" }, { "from": "gpt", "value": "Wind." } ], "id_raw": 741 }, { "id": "K9Mj7Benf5XVYCrj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_1.png", "conversations": [ { "from": "human", "value": "\nCan you descibe the data provided within {the region}[0.168, 0.055, 0.237, 0.167]?" }, { "from": "gpt", "value": "It refers to the legend label 'Biofuel', associated with a color handle." } ], "id_raw": 741 }, { "id": "80tlrHOCeWqv22bj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.480, 0.942, 0.593, 0.970]." }, { "from": "gpt", "value": "This region contains the x-axis label 'Energy Use'." } ], "id_raw": 741 }, { "id": "xI29KMNPxDncZjVt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_1.png", "conversations": [ { "from": "human", "value": "\nWhat is located at coordinates [0.050, 0.145, 0.081, 0.173]?" }, { "from": "gpt", "value": "There is a y-axis tick label '4.0' located at these coordinates." } ], "id_raw": 741 }, { "id": "u5FHhmQRmiseDofx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the identifier of the object located at [0.446, 0.675, 0.948, 0.720]?" }, { "from": "gpt", "value": "'Maximum Performance Efficiency' sublegend label." } ], "id_raw": 742 }, { "id": "1n69tGGYHmS8IsXq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis label at [0.434, 0.913, 0.661, 0.963] represent?" }, { "from": "gpt", "value": "Cost Quality." } ], "id_raw": 742 }, { "id": "f6ptyv6ASJMAMM0Y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the figure, as given by the forward-most text in {the title field}[0.025, 0.113, 0.058, 0.752]?" }, { "from": "gpt", "value": "There is no title given in the provided coordinates." } ], "id_raw": 742 }, { "id": "W5FPm96LInyrq4Jz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_2.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Best Cost' line}[0.159, 0.075, 0.159, 1.957] to the left or right of {the 'Average Cost' line}[0.315, 0.075, 0.315, 1.957]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 742 }, { "id": "1bpEZLrIFAXUbTKS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_2.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Best Cost' line}[0.159, 0.075, 0.159, 1.957] above or below {the 'Maximum Performance Efficiency' line}[-308.406, 0.075, 2.334, 0.075]?" }, { "from": "gpt", "value": "It is not applicable because the lines are vertical and horizontal, respectively." } ], "id_raw": 742 }, { "id": "DRX9pI8qtE1CQevL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_2.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Average Cost' line}[0.315, 0.075, 0.315, 1.957] to the left or right of {the 'Worst Cost' line}[0.781, 0.075, 0.781, 1.957]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 742 }, { "id": "q3oZMXkiIYq2mBXF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [[0.446, 0.472, 0.588, 0.517], [0.446, 0.539, 0.645, 0.584], [0.446, 0.607, 0.608, 0.652], [0.446, 0.675, 0.948, 0.720], [0.446, 0.742, 0.778, 0.787]]." }, { "from": "gpt", "value": "This region contains the labels of the sublegend: {Best Cost}[0.446, 0.472, 0.588, 0.517], {Average Cost}[0.446, 0.539, 0.645, 0.584], {Worst Cost}[0.446, 0.607, 0.608, 0.652], {Maximum Performance Efficiency}[0.446, 0.675, 0.948, 0.720], and {Value Chain Efficiency}[0.446, 0.742, 0.778, 0.787]." } ], "id_raw": 742 }, { "id": "EWCpu9XDZUKBU4P5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained within the region [0.352, 0.747, 0.431, 0.775]?" }, { "from": "gpt", "value": "It contains the colored handle of the {Value Chain Efficiency}[0.352, 0.747, 0.431, 0.775] in the sublegend." } ], "id_raw": 742 }, { "id": "x3emkEEXE9t5F11l", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_2.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the coordinates [0.159, 0.075, 0.936, 0.640]?" }, { "from": "gpt", "value": "It outlines the coordinates of the 'Value Chain Efficiency' line chart." } ], "id_raw": 742 }, { "id": "WCWz2oqDBhzhFbE3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the line with the marker 'o' and color '#0173b2' represent in the chart?" }, { "from": "gpt", "value": "Health management trend." } ], "id_raw": 743 }, { "id": "PkUHy83LsWL0MHTR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label in the chart?" }, { "from": "gpt", "value": "Search window size ratio." } ], "id_raw": 743 }, { "id": "Ydupchurl5SVLHU6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_3.png", "conversations": [ { "from": "human", "value": "\nBased on the coordinates, what does the entry with points {coordinates}[0.141, 0.290, 0.215, 0.310] in the legend key represent?" }, { "from": "gpt", "value": "Health management trend." } ], "id_raw": 743 }, { "id": "2UulCvWHOcKclsLc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_3.png", "conversations": [ { "from": "human", "value": "\nIs the trend for {Initiation of Diabetes trend}[0.421, 0.670, 0.421, 0.828] above or below the trend for {Abrupt drop in Diabetes trend}[0.604, 0.670, 0.604, 0.828]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 743 }, { "id": "umNcaXh0ShB5kciR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_3.png", "conversations": [ { "from": "human", "value": "\nWhich trend reaches the highest value, {Expansion in Virus Spread}[0.920, 0.670, 0.920, 0.828] or {Health management trend}[0.159, 0.196, 0.933, 0.670]?" }, { "from": "gpt", "value": "Health management trend." } ], "id_raw": 743 }, { "id": "abjnkMd4twuPwVVj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_3.png", "conversations": [ { "from": "human", "value": "\nIn this chart, is the point at {coordinates}[0.604, 0.828] for {Abrupt drop in Diabetes trend}[0.604, 0.828] greater or lesser than the point for {Expansion in Virus Spread}[0.920, 0.828]?" }, { "from": "gpt", "value": "Lesser." } ], "id_raw": 743 }, { "id": "5W9LbOOw2tVyv2Zs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.136, 0.060, 0.694, 0.344]?" }, { "from": "gpt", "value": "It contains the legend entries for the chart's lines: {Initiation of Diabetes trend}[0.148, 0.097, 0.208, 0.097], {Abrupt drop in Diabetes trend}[0.148, 0.165, 0.208, 0.165], {Expansion in Virus Spread}[0.148, 0.232, 0.208, 0.232], and {Health management trend}[0.141, 0.290, 0.215, 0.310]." } ], "id_raw": 743 }, { "id": "VNP6NxoqqFlaEReK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the region [0.376, 0.913, 0.716, 0.963]?" }, { "from": "gpt", "value": "The region contains the x-axis label, which is \"Quantization error.\"" } ], "id_raw": 743 }, { "id": "OlAcBKLGiivz4y3r", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_3.png", "conversations": [ { "from": "human", "value": "\nDescribe what is located within the area bounds [0.120, 0.037, 0.972, 0.828]." }, { "from": "gpt", "value": "This region encompasses the plot itself, including four plotted line trends styled with different colors and styles: the Initiation of Diabetes trend, the Abrupt drop in Diabetes trend, the Expansion in Virus Spread, and the Health management trend. It also includes the x-tick and y-tick labels." } ], "id_raw": 743 }, { "id": "OAG3XVYe6JmekUu7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the object with coordinates [0.015, 0.350, 0.034, 0.606] represent in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 744 }, { "id": "LheuowYzY28mgiXy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the location of the third x-axis tick label?" }, { "from": "gpt", "value": "[0.261, 0.911, 0.296, 0.934]" } ], "id_raw": 744 }, { "id": "8scKGEkyK8Fr8Esx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_4.png", "conversations": [ { "from": "human", "value": "\nCan you identify what {the point on line 'Public Service Cost'} at [0.528, 0.304] indicates?" }, { "from": "gpt", "value": "The cost of Public Service in 2005." } ], "id_raw": 744 }, { "id": "VsxylUugsGNVPFh0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Public Service Cost' in 2003}[0.362, 0.362] higher or lower than {the point for 'Basic Welfare Cost' in 2003}[0.362, 0.769]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 744 }, { "id": "WhRnJV5dnHAxGlEN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_4.png", "conversations": [ { "from": "human", "value": "\nWhich cost was higher in 2009, {Public Service Cost at point}[0.860, 0.158] or {Basic Welfare Cost at point}[0.860, 0.537]?" }, { "from": "gpt", "value": "Basic Welfare Cost." } ], "id_raw": 744 }, { "id": "zRueoymBw0WouxD1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_4.png", "conversations": [ { "from": "human", "value": "\nIn 2010, did the {Public Service Cost}[0.943, 0.100] exceed the {Basic Welfare Cost}[0.943, 0.449]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 744 }, { "id": "hMFZ5Ezm1zrxziEs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within [0.080, 0.077, 0.317, 0.176]?" }, { "from": "gpt", "value": "This is the legend section. It consists of two items: {Public Service Cost}[0.138, 0.089, 0.310, 0.119], represented with a color code of '#1f77b4', and {Basic Welfare Cost}[0.138, 0.134, 0.306, 0.164], represented with a color code of '#ff7f0e'." } ], "id_raw": 744 }, { "id": "SLs9FFEHHi3cHp4T", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.508, 0.943, 0.549, 0.975]." }, { "from": "gpt", "value": "This area contains the x-axis label, which reads {\u201cYear\u201d}[0.508, 0.943, 0.549, 0.975]." } ], "id_raw": 744 }, { "id": "LOw2TbizA7XqH820", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_4.png", "conversations": [ { "from": "human", "value": "\nWhat elements exist within [0.261, 0.911, 0.795, 0.934]?" }, { "from": "gpt", "value": "Within this area, there are 4 x-axis tick labels for the years 2002{[0.261, 0.911, 0.296, 0.934]}, 2004{[0.428, 0.911, 0.463, 0.934]}, 2006{[0.594, 0.911, 0.629, 0.934]} and 2008{[0.760, 0.911, 0.795, 0.934]}." } ], "id_raw": 744 }, { "id": "Rxkb3ubSwNsjveyu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the label {at these coordinates}[0.172, 0.912, 0.310, 0.936] represent?" }, { "from": "gpt", "value": "Simulation Platform." } ], "id_raw": 746 }, { "id": "4xa5F3semUsBQD84", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line for 'Before Pandemic'}[0.110, 0.465, 0.898, 0.858]?" }, { "from": "gpt", "value": "#1f77b4." } ], "id_raw": 746 }, { "id": "Gj6c5EPYuuM9FYq5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-axis tick label}[0.039, 0.849, 0.061, 0.873]?" }, { "from": "gpt", "value": "0.5." } ], "id_raw": 746 }, { "id": "Q9ZzZXTK9T0OWM3P", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_6.png", "conversations": [ { "from": "human", "value": "\nBetween the points {for 'Gamification Tools'}[0.635, 0.607, 0.635, 0.339], which has a higher 'Usage Ratio', 'Before Pandemic' or 'During Pandemic'?" }, { "from": "gpt", "value": "During Pandemic." } ], "id_raw": 746 }, { "id": "mrOsbpJDEOh0fJ3k", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point 'Before Pandemic' for 'Learning Analytics Tools'}[0.898, 0.733] above or below {the point 'During Pandemic' for 'Learning Analytics Tools'}[0.898, 0.465]" }, { "from": "gpt", "value": "Above." } ], "id_raw": 746 }, { "id": "mdmWmGI7jPtVxOPb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'During Pandemic' for 'e-Reading'}[0.372, 0.103] higher or lower than {the point for 'During Pandemic' for 'Online Assessment Tools'}[0.766, 0.103]?" }, { "from": "gpt", "value": "They are at the same level." } ], "id_raw": 746 }, { "id": "VoEb6QdbK1RWwkgT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the visualization within the region [0.110, 0.103, 0.898, 0.858]." }, { "from": "gpt", "value": "The area includes two line graphs, one representing the usage ratio of EdTech modules before the pandemic and the other during the pandemic. Each module is represented by a unique point on the line." } ], "id_raw": 746 }, { "id": "VkAuBLeY2Sl4NHkl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_6.png", "conversations": [ { "from": "human", "value": "\nCan you tell me more about the region [0.079, 0.079, 0.280, 0.176]?" }, { "from": "gpt", "value": "This region is the legend of the line chart. It includes two entries: 'Before Pandemic' with a blue line symbol and 'During Pandemic' with an orange line symbol." } ], "id_raw": 746 }, { "id": "Q5uq48C9vefO4E10", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.322, 0.025, 0.685, 0.058] represent?" }, { "from": "gpt", "value": "It contains the subtitle of the graph titled 'Growth Trend of EdTech Modules'." } ], "id_raw": 746 }, { "id": "ro9oTGoqhH4rOL7T", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the line with coordinates [0.653, 0.717, 0.653, 0.828] represent in the chart?" }, { "from": "gpt", "value": "Investment A." } ], "id_raw": 747 }, { "id": "6p3H0zr9HwaQDlH1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_7.png", "conversations": [ { "from": "human", "value": "\nAt what coordinate can we find the label 'Risk Return Trade-off'?" }, { "from": "gpt", "value": "[0.210, 0.295, 0.524, 0.340]." } ], "id_raw": 747 }, { "id": "JpzHfTRbURnGEURk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label}[0.068, 0.259, 0.082, 0.294]?" }, { "from": "gpt", "value": "5." } ], "id_raw": 747 }, { "id": "cYG8ZAuRVns6cYeV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_7.png", "conversations": [ { "from": "human", "value": "\nFor {the point}[0.653, 0.828] on the 'Investment A' line, is it on the left or right side compared to {the point}[0.767, 0.828] on the 'Investment B' line?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 747 }, { "id": "Sl9zmDeOoVPSBsPH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_7.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher risk factor at the 0.4 return ratio, {Investment A}[0.653, 0.828] or {Investment B}[0.767, 0.828]?" }, { "from": "gpt", "value": "Investment B." } ], "id_raw": 747 }, { "id": "uX1WR3PFpu6RbCEL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_7.png", "conversations": [ { "from": "human", "value": "\nFor {the point}[0.767, 0.051] on the 'Investment B' line, is it above or below {the point}[0.935, 0.051] on the 'Investment C' line?" }, { "from": "gpt", "value": "Same level." } ], "id_raw": 747 }, { "id": "RNM8ALhmxX7lEHfc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in between the coordinates [0.099, 0.051, 0.975, 0.828]." }, { "from": "gpt", "value": "This contains the entire chart, including the axes labels, the tick labels, the legend, and the plot lines for Investment A, B, C and Risk Return Trade-off." } ], "id_raw": 747 }, { "id": "cfFZYo66M3SKuXD4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_7.png", "conversations": [ { "from": "human", "value": "\nWhat's in the region enclosed by the coordinates [0.114, 0.074, 0.536, 0.358]?" }, { "from": "gpt", "value": "In this region, you can find the legend of the chart: {Investment A}[0.210, 0.092, 0.410, 0.137], {Investment B}[0.210, 0.159, 0.410, 0.204], {Investment C}[0.210, 0.227, 0.410, 0.272], and {Risk Return Trade-off}[0.210, 0.295, 0.524, 0.340]." } ], "id_raw": 747 }, { "id": "zDT27YukO8zpHlCL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the portion defined by [0.537, 0.030, 0.537, 0.030] include?" }, { "from": "gpt", "value": "This region is the area where the subtitle of the chart should be located. However, in this particular chart, the subtitle is absent." } ], "id_raw": 747 }, { "id": "o2iNh9htXR1YuLdr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the {ylabel}[0.023, 0.394, 0.045, 0.557] of the chart?" }, { "from": "gpt", "value": "Percentage." } ], "id_raw": 748 }, { "id": "vuVjfTuTKQwmx3uN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_8.png", "conversations": [ { "from": "human", "value": "\nWhat color is the {Unemployment Rate marker}[0.606, 0.050, 0.662, 0.050] in the legend?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 748 }, { "id": "UmXUBRoiY3yEp2w5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_8.png", "conversations": [ { "from": "human", "value": "\nWhat's the value of {the x tick label}[0.269, 0.940, 0.283, 0.969]?" }, { "from": "gpt", "value": "1." } ], "id_raw": 748 }, { "id": "W1iPmef1tWV6rcNv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_8.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Unemployment Rate'}[0.138, 0.072] line above or below {the 'Median Income'}[0.828, 0.072] at X =-1?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 748 }, { "id": "AiNiwtSGoobiIxDJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_8.png", "conversations": [ { "from": "human", "value": "\nAre the {Median Income points}[0.138, 0.879, 0.828, 0.072] rising or falling overall?" }, { "from": "gpt", "value": "Falling." } ], "id_raw": 748 }, { "id": "hf356Ly8oXuIOr9T", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_8.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher at X=1, Unemployment Rate or Median Income?" }, { "from": "gpt", "value": "Median Income." } ], "id_raw": 748 }, { "id": "KXWiavzRl5kmxqmh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is presented in the region [0.685, 0.034, 0.975, 0.071]." }, { "from": "gpt", "value": "It is the text 'Unemployment Rate' from the chart legend, paired with a red marker." } ], "id_raw": 748 }, { "id": "y0v5BcvKziAyiy8y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_8.png", "conversations": [ { "from": "human", "value": "\nDiscuss the contents within the coordinates [0.877, 0.262, 0.946, 0.291]." }, { "from": "gpt", "value": "The y label '81000' is located in this region." } ], "id_raw": 748 }, { "id": "00SlmDDM9Wk3Sl1p", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_8.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area of [0.104, 0.072, 0.828, 0.879]?" }, { "from": "gpt", "value": "This area contains the plotted lines for both 'Unemployment Rate' and 'Median Income' with their corresponding data points." } ], "id_raw": 748 }, { "id": "fDI3Wf13oalm4OXV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the detail at [0.329, 0.913, 0.760, 0.963]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 749 }, { "id": "TwrXOoxrZ5y4UHFy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_9.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.025, 0.115, 0.058, 0.750] represent?" }, { "from": "gpt", "value": "Improvement Potential." } ], "id_raw": 749 }, { "id": "55r7fAE4ywYaTgse", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_9.png", "conversations": [ { "from": "human", "value": "\nWhat object is indicated at {the coordinates}[0.153, 0.792, 0.153, 0.828]?" }, { "from": "gpt", "value": "Baseline-Efficiency-100 line." } ], "id_raw": 749 }, { "id": "JYOlHdqspXah8aKG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_9.png", "conversations": [ { "from": "human", "value": "\nAt {point at x=1}[0.153, 0.828], is the value of Baseline-Efficiency-100 the same as {the Efficiency Improvement point}[0.153, 0.109]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 749 }, { "id": "iM1VqifhXnHZQwwj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_9.png", "conversations": [ { "from": "human", "value": "\nAt {point coordinates}[0.936, 0.720], is Efficiency Improvement line above or below {the Baseline-Efficiency-1024 line}[0.936, 0.037]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 749 }, { "id": "Ln15e3qE7Fm6R5ZF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_9.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at {point 6.6}[0.936, 0.828], Baseline-Efficiency-1024 or Efficiency Improvement?" }, { "from": "gpt", "value": "Baseline-Efficiency-1024." } ], "id_raw": 749 }, { "id": "iUj2z7n68099Qf6N", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_9.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements present within the [0.128, 0.060, 0.606, 0.411] region?" }, { "from": "gpt", "value": "The area includes descriptions for multiple trends: Baseline-Efficiency-100, Baseline-Efficiency-256, Baseline-Efficiency-512, Baseline-Efficiency-1024, and Efficiency Improvement." } ], "id_raw": 749 }, { "id": "JHMJnxrZztrnMIat", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_9.png", "conversations": [ { "from": "human", "value": "\nCan you provide information about the elements located at [0.153, 0.109, 0.936, 0.720]?" }, { "from": "gpt", "value": "This region covers part of the Efficiency Improvement line, which is marked with blue color and circular markers, and includes multiple points from x = 1.0 to x = 6.6." } ], "id_raw": 749 }, { "id": "LFW7oip7p1OsnQr1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_9.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements in the region [0.225, 0.078, 0.575, 0.123]?" }, { "from": "gpt", "value": "This area contains the text label for the Baseline-Efficiency-100 trend." } ], "id_raw": 749 }, { "id": "jsyPJ0buAPbNVqZx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the text with coordinates [0.015, 0.389, 0.033, 0.579] referring to?" }, { "from": "gpt", "value": "Y-axis label \"Spending (in USD)\"." } ], "id_raw": 751 }, { "id": "Tr1mXEPu7WlMbGzS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_11.png", "conversations": [ { "from": "human", "value": "\nWhat specific data does the point at the coordinates [0.276, 0.500] represent?" }, { "from": "gpt", "value": "Spending of Public Schools." } ], "id_raw": 751 }, { "id": "2715LBjDQPTeIKFw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.454, 0.959, 0.582, 0.981]?" }, { "from": "gpt", "value": "Institution Type." } ], "id_raw": 751 }, { "id": "jrIvUq5o2923FQxV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_11.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Graduate Schools'}[0.599, 0.249] above or below {the point for 'Training Centers'}[0.760, 0.365] on the line chart?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 751 }, { "id": "JTLGDwR9lQGQbmJi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_11.png", "conversations": [ { "from": "human", "value": "\nDid the spending increase or decrease from {the point of 'Colleges'}[0.437, 0.529] to {the point of 'Graduate Schools'}[0.599, 0.249]?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 751 }, { "id": "U9qQL6FYN0fbnAtC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_11.png", "conversations": [ { "from": "human", "value": "\nBetween {the point for 'Private Schools'}[0.114, 0.882] and {the point for 'Online platforms'}[0.922, 0.086], which one represents a higher spending level?" }, { "from": "gpt", "value": "Private Schools." } ], "id_raw": 751 }, { "id": "TkiCtaza1jZIjF78", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region with coordinates [0.081, 0.055, 0.401, 0.087]." }, { "from": "gpt", "value": "This region contains the legend, which is labeled as \"Education and Training Industry Trends\", along with the respective color indicator." } ], "id_raw": 751 }, { "id": "tZFrxQT4oWP4jRPi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_11.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained in the area with coordinates [0.226, 0.934, 0.325, 0.952]?" }, { "from": "gpt", "value": "The x-axis tick label \"Public Schools\" is located in this specific region." } ], "id_raw": 751 }, { "id": "AMi9N2ip4EIYBwPa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.114, 0.086, 0.922, 0.882]?" }, { "from": "gpt", "value": "The {line chart representing 'Education and Training Industry Trends'}[0.114, 0.086, 0.922, 0.882] occupies this area, with specific points marking the spending levels of different types of educational institutions." } ], "id_raw": 751 }, { "id": "IEFIuUKr2lSBUuci", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the handle at coordinates [0.126, 0.165, 0.186, 0.165] represent?" }, { "from": "gpt", "value": "Training Technology." } ], "id_raw": 752 }, { "id": "Lyue03QFjJGdc0jF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line associated with {the object with coordinates [0.210, 0.078, 0.537, 0.123]}?" }, { "from": "gpt", "value": "#d86810." } ], "id_raw": 752 }, { "id": "uVdp9wXwAYH7J7Um", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_12.png", "conversations": [ { "from": "human", "value": "\nWhat marks the point corresponding to the handle at [0.126, 0.097, 0.186, 0.097]?" }, { "from": "gpt", "value": "Education Technology." } ], "id_raw": 752 }, { "id": "5myIhtQ1fY1oD3dz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point at [0.935, 0.046]} above or below {the point at [0.138, 0.527]} on the 'empirical relat.' line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 752 }, { "id": "Di6fnTMYRkQP1mQn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_12.png", "conversations": [ { "from": "human", "value": "\nIn which direction does {the 'Average (Education Technology)' line at coordinates [0.666, 0.750, 0.666, 0.839]} run relative to {the 'Empirical relat.' line at coordinates [0.138, 0.046, 0.935, 0.527]}?" }, { "from": "gpt", "value": "Vertical." } ], "id_raw": 752 }, { "id": "YQ3XgaqdjMWA24dW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_12.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Training Technology' line at coordinates [0.787, 0.750, 0.787, 0.839]} to the left or the right of {the 'Education Technology' line at coordinates [0.509, 0.750, 0.509, 0.839]}?" }, { "from": "gpt", "value": "To the right." } ], "id_raw": 752 }, { "id": "CiQZNtGUnJLdfrlC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents inside [0.114, 0.060, 0.706, 0.411]." }, { "from": "gpt", "value": "The region contains the legend items of the chart. The text and handle of each item are represented in pairs. The labels are 'Education Technology', 'Training Technology', 'Average (Education Technology)', 'Average (Training Technology)', and 'empirical relat.'. Each label is paired with a colored handle that matches the color of corresponding line in the chart." } ], "id_raw": 752 }, { "id": "YoZ3hA2NJ8u7ml1f", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_12.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in [0.138, 0.046, 0.935, 0.527]?" }, { "from": "gpt", "value": "This area contains the 'empirical relat.' line that represents the correlation between 'Time' and 'Innovation Index'. It begins from {an x-value of 3.3 and a y-value of 3.5}[0.138, 0.527] and ends at {an x-value of 7.6 and a y-value of 8.9}[0.935, 0.046]." } ], "id_raw": 752 }, { "id": "KSJMKH2FMvIF65mx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_12.png", "conversations": [ { "from": "human", "value": "\nWhat might be located within the region [0.509, 0.750, 0.509, 0.839]?" }, { "from": "gpt", "value": "This represents the 'Education Technology' line. It is a vertical line indicating that the time series of 'Education Technology' is constant at a value of 5.3." } ], "id_raw": 752 }, { "id": "XdAOe9AHiE3sxDwV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.019, 0.289, 0.044, 0.638]?" }, { "from": "gpt", "value": "Y-axis label" } ], "id_raw": 753 }, { "id": "5OH3Il3gIqIuDd71", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the benchmark for {Risk Moderation}[0.157, 0.061, 0.474, 0.091]?" }, { "from": "gpt", "value": "70" } ], "id_raw": 753 }, { "id": "80iuZDOa1JyxK9dp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {Supply Chain Integration benchmark line}[0.940, 0.770, 0.940, 0.893]?" }, { "from": "gpt", "value": "#314e73" } ], "id_raw": 753 }, { "id": "C3ManJAzLNWxlJbH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the performance index of {Risk Moderation} at end of the analysis period {at x=5}[0.161, 0.427]?" }, { "from": "gpt", "value": "3.8" } ], "id_raw": 753 }, { "id": "T6HTww49xo99jegm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_13.png", "conversations": [ { "from": "human", "value": "\nDoes the {Supply Chain Integration line}[0.115, 0.034, 0.161, 0.169] finish higher than the {Risk Moderation line}[0.115, 0.280, 0.161, 0.427]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 753 }, { "id": "IbDrTrr7ApG9yohB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_13.png", "conversations": [ { "from": "human", "value": "\nIs the {Supply Chain Integration benchmark line}[0.940, 0.770, 0.940, 0.893] to the right of the {Risk Moderation benchmark line}[0.757, 0.770, 0.757, 0.893]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 753 }, { "id": "VEFv3oZxMmq2CeKN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.085, 0.049, 0.510, 0.238]." }, { "from": "gpt", "value": "This region includes the benchmark indicators and actual performance lines of \"Risk Moderation\" and \"Supply Chain Integration\", each identified with a color." } ], "id_raw": 753 }, { "id": "tm1PfNC9wj3byxpM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.446, 0.942, 0.610, 0.975]." }, { "from": "gpt", "value": "The text \"Time Period\" is located in this area, serving as the x-axis label." } ], "id_raw": 753 }, { "id": "AqjXbKol82D4pgyQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe all objects found in the area [0.094, 0.164, 0.139, 0.209]?" }, { "from": "gpt", "value": "This area contains markers for the lines on the chart: the markers for Risk Moderation and Supply Chain Integration are found here, colored #1738b2 and #7ccd44 respectively." } ], "id_raw": 753 }, { "id": "PdLuYLBuvHY9CZw7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the text on the y-axis in the chart?" }, { "from": "gpt", "value": "The y-axis label is 'Score' {ylabel}[0.025, 0.385, 0.055, 0.511]." } ], "id_raw": 755 }, { "id": "ZMYwiBbO1wV4jiHa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the text located at coordinates [0.707, 0.083, 0.826, 0.118]?" }, { "from": "gpt", "value": "In the chart, the text at that location is 'CSR Score'." } ], "id_raw": 755 }, { "id": "QXUqTD3j7irsv2ZO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_15.png", "conversations": [ { "from": "human", "value": "\nWhat color is the marker associated with 'Economic' in the legend?" }, { "from": "gpt", "value": "The marker for 'Economic' is colored '#de8f05' {Economic}[0.707, 0.187, 0.819, 0.222]." } ], "id_raw": 755 }, { "id": "lrz9ewEQXI8BtYEU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_15.png", "conversations": [ { "from": "human", "value": "\nIs the line for 'CSR Score' generally above or below the line for 'Economic'?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 755 }, { "id": "sAgR2vK7tOwtvIyE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_15.png", "conversations": [ { "from": "human", "value": "\nWhat does the line with color '#0173b2' represent?" }, { "from": "gpt", "value": "Sustainable Business." } ], "id_raw": 755 }, { "id": "geK4etIu99NPLB7I", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_15.png", "conversations": [ { "from": "human", "value": "\nIs the point for 'CSR Score' at x=3.0 higher or lower than the point for 'Economic' at the same x-value?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 755 }, { "id": "fqj9Mdw1icU1r2SE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area between [0.249, 0.869, 0.408, 0.904]?" }, { "from": "gpt", "value": "This area contains two labels on the x-axis, '0' and '1', located at the respective coordinates {0}[0.132, 0.869, 0.146, 0.904] and {1}[0.219, 0.869, 0.234, 0.904]." } ], "id_raw": 755 }, { "id": "ctr8Xa8ohKSnjTni", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_15.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.633, 0.069, 0.960, 0.341]?" }, { "from": "gpt", "value": "In this region, the chart legend is observed. It holds the information on five different lines represented in the chart: CSR Score {CSR Score}[0.707, 0.083, 0.826, 0.118], Environment {Environment}[0.707, 0.135, 0.855, 0.170], Economic {Economic}[0.707, 0.187, 0.819, 0.222], Employee Wellness {Employee Wellness}[0.707, 0.240, 0.930, 0.275], and Sustainable Business {Sustainable Business}[0.707, 0.292, 0.951, 0.327], each with a color marker." } ], "id_raw": 755 }, { "id": "WGT6bFAo0x0v4nvj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_15.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area from [0.139, 0.148, 0.575, 0.393]?" }, { "from": "gpt", "value": "This area contains the line plot for 'Sustainable Business' tracked over years 0.0 to 5.0. The y-coordinates of the points at each year indicate the scores for that year." } ], "id_raw": 755 }, { "id": "D3c7jgmtdVuqoPXR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_16.png", "conversations": [ { "from": "human", "value": "\nWhat is being represented by {the label} at [0.210, 0.295, 0.461, 0.340]?" }, { "from": "gpt", "value": "BioTech Startups." } ], "id_raw": 756 }, { "id": "ROBESLLWzW916agX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the legend item} at [0.126, 0.449, 0.186, 0.449]?" }, { "from": "gpt", "value": "#367ae9." } ], "id_raw": 756 }, { "id": "eNUjAMGTGfdKY9Qh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the {y-axis tick label} at [0.068, 0.814, 0.082, 0.849]?" }, { "from": "gpt", "value": "0." } ], "id_raw": 756 }, { "id": "hUpPRC3xSLOUo5lb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point for High-Tech Startups}[0.654, 0.634, 0.654, 0.828] to the left or right of {the point for QuantumTech Startups}[0.935, 0.634, 0.935, 0.828]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 756 }, { "id": "Ew6qukiY4oST9kQx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_16.png", "conversations": [ { "from": "human", "value": "\nIs {the line for AI startups}[0.701, 0.634, 0.701, 0.828] above or below {the line for Empirical Recovery}[0.138, 0.168, 0.935, 0.634]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 756 }, { "id": "xfFsgfLdp4jhAQ22", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_16.png", "conversations": [ { "from": "human", "value": "\nWhich startup has a higher y-coordinate at {point on the x-axis where x=100000}[0.841, 0.634]?" }, { "from": "gpt", "value": "Traditional Innovation." } ], "id_raw": 756 }, { "id": "30puD7XDlTfzl9Nu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within [0.210, 0.092, 0.536, 0.137]." }, { "from": "gpt", "value": "The area contains the label \"Traditional Innovation\" next to a colored marker." } ], "id_raw": 756 }, { "id": "J3rt6pcNoZ40EBq3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_16.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.068, 0.037, 0.082, 0.073]." }, { "from": "gpt", "value": "This area contains the y-axis tick label '4'." } ], "id_raw": 756 }, { "id": "pQtIfmB0ilYOeTo9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_16.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.841, 0.051, 0.841, 0.828]?" }, { "from": "gpt", "value": "The area contains two lines, line_0 representing 'Traditional Innovation' and line_6 representing 'Empirical Recovery'." } ], "id_raw": 756 }, { "id": "QA4KwMg5G5UL82vB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_17.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the object at these coordinates [0.025, 0.221, 0.058, 0.658]?" }, { "from": "gpt", "value": "It's the Y-axis label." } ], "id_raw": 757 }, { "id": "5poSCVMx102Lop1K", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at this coordinate [0.210, 0.295, 0.647, 0.340]?" }, { "from": "gpt", "value": "It's the label for \"Outdoor Recreational Spaces\"." } ], "id_raw": 757 }, { "id": "mtf2orQKNzLn4ssl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.068, 0.259, 0.082, 0.294] on the chart?" }, { "from": "gpt", "value": "It's the Y-axis tick label '5'." } ], "id_raw": 757 }, { "id": "TJUD9nNsUqvbQpmM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_17.png", "conversations": [ { "from": "human", "value": "\nIs the point representing \"Social Aging Care\" at {coordinates}[0.872, 0.828] positioned higher or lower than the point for \"Outdoor Recreational Spaces\" at {coordinates}[0.825, 0.828]?" }, { "from": "gpt", "value": "They are at the same level." } ], "id_raw": 757 }, { "id": "TVkB6SOrNv38IwFi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_17.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Policy Trend'}[0.138, 0.096, 0.935, 0.711] mostly to the left or right of the {line for 'Public Transport Accessibility'}[0.682, 0.717, 0.682, 0.828]?" }, { "from": "gpt", "value": "It is mostly to the right." } ], "id_raw": 757 }, { "id": "xqHtjLSHKgKK8YsI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_17.png", "conversations": [ { "from": "human", "value": "\nWhich trend has a higher point at 25 units, \"Social Aging Care\" or \"Policy Trend\"?" }, { "from": "gpt", "value": "They both have the same value." } ], "id_raw": 757 }, { "id": "FofITSIKHh7ee29l", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_17.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the content in the region [0.349, 0.913, 0.724, 0.963]?" }, { "from": "gpt", "value": "This is the region for the X-axis label, which is \"Average Time Spent\". It uses black color font." } ], "id_raw": 757 }, { "id": "008DxQyvGR7lO7t0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_17.png", "conversations": [ { "from": "human", "value": "\nWhat can we find in the area [0.114, 0.074, 0.659, 0.425]?" }, { "from": "gpt", "value": "There are 5 legend items in this area: {Social Aging Care}[0.210, 0.092, 0.474, 0.137], {Public Transport Accessibility}[0.210, 0.159, 0.645, 0.204], {Mental Health Services}[0.210, 0.227, 0.557, 0.272], {Outdoor Recreational Spaces}[0.210, 0.295, 0.647, 0.340], and {Policy Trend}[0.210, 0.362, 0.389, 0.407], each with a different color marker." } ], "id_raw": 757 }, { "id": "kvRauMJh6GfGBC45", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the bounding box [0.138, 0.096, 0.935, 0.711]?" }, { "from": "gpt", "value": "This region contains the line for the \"Policy Trend\" from point 5 (represented as {'x': 5, 'y': 6.6}) to point 25 (represented as {'x': 25, 'y': 1.2}). The line is marked with a blue color and 'o' markers." } ], "id_raw": 757 }, { "id": "oYXiGip5Vi5yYhYC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_18.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the {line_0}[0.180, 0.109, 0.937, 0.821] within the chart?" }, { "from": "gpt", "value": "Model Performance." } ], "id_raw": 758 }, { "id": "9QSPJZaYFEUUPifs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_18.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by {the x-axis label}[0.460, 0.913, 0.658, 0.963]" }, { "from": "gpt", "value": "Error Ratio" } ], "id_raw": 758 }, { "id": "IycMAt3eVzGhoX8H", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_18.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.025, 0.183, 0.058, 0.682] portray?" }, { "from": "gpt", "value": "Model Complexity" } ], "id_raw": 758 }, { "id": "nLHFpOWh8yiPyu2u", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_18.png", "conversations": [ { "from": "human", "value": "\nDoes {the second point}[0.445, 0.792] in the 'Model Performance' line have higher or lower value compared to {the first point}[0.180, 0.821]?" }, { "from": "gpt", "value": "Higher" } ], "id_raw": 758 }, { "id": "43ZfTDkEMfPsgzd9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_18.png", "conversations": [ { "from": "human", "value": "\nIs {the fourth point}[0.625, 0.720] in the 'Model Performance' line above or below {the third point}[0.559,0.756]?" }, { "from": "gpt", "value": "Below" } ], "id_raw": 758 }, { "id": "hNMas08O7fBlAqe9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_18.png", "conversations": [ { "from": "human", "value": "\nWhich point in the 'Model Performance' line has lesser value, is it {the fifth point}[0.823, 0.469] or {the sixth point}[0.937, 0.109]?" }, { "from": "gpt", "value": "The sixth point" } ], "id_raw": 758 }, { "id": "HH8fFeSmGmmykf6u", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements included within [0.157, 0.060, 0.556, 0.141] region?" }, { "from": "gpt", "value": "This region shares the legend for the 'Model Performance' line, with its corresponding colored marker." } ], "id_raw": 758 }, { "id": "N29z9OzqqTldO4M5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within this section of the chart [0.142, 0.038, 0.975, 0.828]." }, { "from": "gpt", "value": "The encompassed region contains the main plot of the chart, including the 'Model Performance' line and the respective x and y grids." } ], "id_raw": 758 }, { "id": "wjzAJnWlMjoXqmsv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_18.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in this region [0.068, 0.096, 0.126, 0.131] in the chart?" }, { "from": "gpt", "value": "Within this region, the '1000' y-axis tick label is located." } ], "id_raw": 758 }, { "id": "hvzxGWBv0gGOUwUb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object referred to by the coordinate on the Y axis [0.025, -0.113, 0.058, 1.003]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 759 }, { "id": "lWnD5e5L1SkBIjBP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_19.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.521, 0.502]?" }, { "from": "gpt", "value": "Point on 'Paid User Ratio' graph line." } ], "id_raw": 759 }, { "id": "rZHN0QObj9IcDnyl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_38_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the line} at [0.294, 0.164, 0.899, 0.614]?" }, { "from": "gpt", "value": "Paid User Ratio." } ], "id_raw": 759 }, { "id": "9l8GY9jmhHXrDthr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_19.png", "conversations": [ { "from": "human", "value": "\nDoes {the point on 'Paid User Ratio'}[0.672, 0.389] have a more significant increase compared to {the point on 'Revenue Generated'}[0.824, 0.839] between Year 2015 and 2020?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 759 }, { "id": "faEM6nEZfv3TuCdd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_19.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Revenue Generated'}[[0.294, 0.838, 0.899, 0.839]] located above or below {the line for 'Paid User Ratio'}[[0.294, 0.164, 0.899, 0.614]]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 759 }, { "id": "XaiW3TnO3vKD6vTU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_38_19.png", "conversations": [ { "from": "human", "value": "\nWhat trend shows more variation, 'Paid User Ratio' or 'Revenue Generated'?" }, { "from": "gpt", "value": "Paid User Ratio." } ], "id_raw": 759 }, { "id": "dxEgmmPbxW5snA6S", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_19.png", "conversations": [ { "from": "human", "value": "\nProvide a detailed description of the objects contained within [0.157, 0.074, 0.566, 0.560]." }, { "from": "gpt", "value": "This area contains the seriale of the chart, with each different year represented by a different line color. The years 2000, 2005, 2010, 2015, and 2020 are represented, as well as a legend entry for 'Paid User Ratio' and 'Revenue Generated'." } ], "id_raw": 759 }, { "id": "YsP5Zqk6gjkuzivC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content of the region [0.124, 0.864, 0.918, 0.899]." }, { "from": "gpt", "value": "These are the labels on the x-axis identifying the types of game development scores from 0.0 to 1.2." } ], "id_raw": 759 }, { "id": "Ynf68YAVbXl1PPQt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_38_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is in the area with the coordinates [0.294, 0.164, 0.899, 0.614]." }, { "from": "gpt", "value": "This region contains the line graph for the 'Paid User Ratio' from 0.2 game development scores to 1.0 showing a significant increase in the ratio." } ], "id_raw": 759 }, { "id": "bjhoQh5PDPAaZJKd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.025, 0.403, 0.048, 0.542] representing?" }, { "from": "gpt", "value": "Y-axis label" } ], "id_raw": 760 }, { "id": "f6yVXMWoPcljlWgv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the data point on the \"Steady Increase\" line} at [0.643, 0.414] representing?" }, { "from": "gpt", "value": "Value of the \"Steady Increase\" for \"Solar Power\"." } ], "id_raw": 760 }, { "id": "zbFcsADTdMXJ2viB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_0.png", "conversations": [ { "from": "human", "value": "\nHow many categories on x-axis are there in the chart and what are their coordinates?" }, { "from": "gpt", "value": "There are 4 categories. {AI}[0.129, 0.919, 0.151, 0.942], {Smart Grid}[0.329, 0.919, 0.455, 0.942], {Solar Power}[0.575, 0.919, 0.712, 0.942], {Hydroelectric}[0.818, 0.919, 0.972, 0.942]." } ], "id_raw": 760 }, { "id": "nZtB9FSzbWOYGX3U", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_0.png", "conversations": [ { "from": "human", "value": "\nIs the {data point for \"Solar Power\"}[0.643, 0.414] on the \"Steady Increase\" line is above or below the {data point for \"Solar Power\"}[0.643, 0.443] on the \"Moderate Growth\" line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 760 }, { "id": "kiIEX8ZJ20Cp0hHZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_0.png", "conversations": [ { "from": "human", "value": "\nIn terms of \"AI\" technology, does \"Moderate Growth\" or \"Steady Increase\" have higher investment?" }, { "from": "gpt", "value": "\"Steady Increase\"." } ], "id_raw": 760 }, { "id": "aKe7LCCL51vUioMd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_0.png", "conversations": [ { "from": "human", "value": "\nWhich line ends higher on the Y-axis, {the line for \"Moderate Growth\"}[0.140, 0.268, 0.895, 0.794] or {the line for \"Steady Increase\"}[0.140, 0.180, 0.895, 0.736]?" }, { "from": "gpt", "value": "\"Moderate Growth\"." } ], "id_raw": 760 }, { "id": "ykJZFYPUfPYhDwZJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within coordinates [0.114, 0.046, 0.695, 0.157]." }, { "from": "gpt", "value": "The area contains the legend titled 'New Energy and Clean Technology Development'. There are two lines mentioned in the legend, {Moderate growth}[0.338, 0.090, 0.536, 0.113] and {Steady Increase}[0.338, 0.125, 0.524, 0.148]. Both of these legend items are associated with different marker types." } ], "id_raw": 760 }, { "id": "ibCLgWeCzsb0XQig", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_0.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.129, 0.919, 0.972, 0.942]?" }, { "from": "gpt", "value": "This contains all the labels of x-axis: {AI}[0.129, 0.919, 0.151, 0.942], {Smart Grid}[0.329, 0.919, 0.455, 0.942], {Solar Power}[0.575, 0.919, 0.712, 0.942], {Hydroelectric}[0.818, 0.919, 0.972, 0.942]" } ], "id_raw": 760 }, { "id": "j8z0mGO2sY7JfSQU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in coordinates [0.058, 0.025, 0.094, 0.048]." }, { "from": "gpt", "value": "It contains the smallest y-axis tick label which is {0.0}[0.058, 0.025, 0.094, 0.048]." } ], "id_raw": 760 }, { "id": "WKaOCsymgGZL9cHe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the tick label representing 'Weight Training' on the x-axis?" }, { "from": "gpt", "value": "[0.216, 0.919, 0.396, 0.942]." } ], "id_raw": 761 }, { "id": "NephM0Ui9hB4VZ57", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_1.png", "conversations": [ { "from": "human", "value": "\nWhat represents {the line} at [0.155, 0.253, 0.912, 0.779]?" }, { "from": "gpt", "value": "Fitness trend." } ], "id_raw": 761 }, { "id": "bXX1h2Fr2THSXgjn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at {position} [0.771, 0.125, 0.929, 0.148] in the 'Exercise Trends' legend represent?" }, { "from": "gpt", "value": "Sports Health." } ], "id_raw": 761 }, { "id": "qWP1phu5rolaSiVg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_1.png", "conversations": [ { "from": "human", "value": "\nIs the 'Fitness' level higher or lower for 'HIIT' than it is for 'Yoga'?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 761 }, { "id": "RTDPMpujRwecixir", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_1.png", "conversations": [ { "from": "human", "value": "\nIs the {point at coordinate} [0.912, 0.867] above or below the {point at coordinate} [0.912, 0.779]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 761 }, { "id": "oks1fN2XuziWixLY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_1.png", "conversations": [ { "from": "human", "value": "\nIs the 'Sports Health' level for 'Pilates' at {coordinate} [0.761, 0.779] higher or lower than the 'Fitness' trend at {coordinate} [0.761, 0.692]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 761 }, { "id": "gfGwrjcBiknTJ20L", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area bounded by [0.117, 0.034, 0.950, 0.911]?" }, { "from": "gpt", "value": "The area contains the main body of the line chart plotting 'Fitness' and 'Sports Health' against various 'Types of Exercise'." } ], "id_raw": 761 }, { "id": "94Ppy0CxmzXY1ng5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_1.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the region [0.697, 0.046, 0.938, 0.157]?" }, { "from": "gpt", "value": "This area houses the legend titled 'Exercise Trends' with entries for 'Fitness' and 'Sports Health'." } ], "id_raw": 761 }, { "id": "2xoJZCQ3xMxivq20", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_1.png", "conversations": [ { "from": "human", "value": "\nWhat lies within the area [0.216, 0.919, 0.396, 0.942]?" }, { "from": "gpt", "value": "In this area, the X-axis label 'Weight Training' is located." } ], "id_raw": 761 }, { "id": "fY9CzWVCtB54WE7Q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_2.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the line with the square points represents in the chart?" }, { "from": "gpt", "value": "It represents the Conflict Index. The coordinates of one of its many data points are here: {Conflict Index Point}[0.102, 0.207]." } ], "id_raw": 762 }, { "id": "142uDpHQVMr58bAe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-value of this point on the Development Index line {Development Index Point}[0.267, 0.824]?" }, { "from": "gpt", "value": "The value is 0.6." } ], "id_raw": 762 }, { "id": "vr99SFdCrWWPfh2t", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_2.png", "conversations": [ { "from": "human", "value": "\nWhat age does this x-axis tick label represent {the x-axis tick label}[0.544, 0.939, 0.650, 0.957]?" }, { "from": "gpt", "value": "Middle Ages." } ], "id_raw": 762 }, { "id": "FnEVvxYksxu5DlZY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_2.png", "conversations": [ { "from": "human", "value": "\nIs {the Digital Age point on the Development Index line} [0.927, -0.301] higher or lower than {the Digital Age point on the Conflict Index line} [0.927, 0.788]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 762 }, { "id": "B8CaT6dM8UjLGfCs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the Development Index line} [0.102, -0.301, 0.927, 0.897] show an overall increase or decrease from Stone Age to Digital Age?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 762 }, { "id": "y93W28ZT47C13PyZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_2.png", "conversations": [ { "from": "human", "value": "\nIs {the Middle Ages point on the Conflict Index line} [0.597, 0.461] at a higher or lower index than {the Middle Ages point on the Development Index line} [0.597, 0.516]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 762 }, { "id": "ixH4bGSaKcVkScek", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is within the area [0.702, 0.939, 0.822, 0.957]." }, { "from": "gpt", "value": "In the area specified, there's the x-axis tick label 'Industrial Age'." } ], "id_raw": 762 }, { "id": "8zm4fqEFHVbtN4DK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_2.png", "conversations": [ { "from": "human", "value": "\nWhat visuals can be seen in the region [0.102, 0.207, 0.927, 0.897]?" }, { "from": "gpt", "value": "This region includes most of the chart itself, therefore it contains several points and lines for both the {Development Index}[0.102, -0.301, 0.927, 0.897] and the {Conflict Index}[0.102, 0.207, 0.927, 0.788] plotted across different eras." } ], "id_raw": 762 }, { "id": "POQTEiQCqyR7nqGG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_2.png", "conversations": [ { "from": "human", "value": "\nCould you please describe this particular region [0.727, 0.034, 0.960, 0.118]?" }, { "from": "gpt", "value": "This region includes the legend of the chart, having two items labeled as {Development Index}[0.783, 0.067, 0.953, 0.085] and {Conflict Index}[0.783, 0.094, 0.902, 0.111], and it has the title 'Human History' as well." } ], "id_raw": 762 }, { "id": "p5B8kzRZSUWmsGXQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the following coordinates: [0.025, 0.367, 0.048, 0.569]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 763 }, { "id": "fIxgmUaNQnwI9UOE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at coordinates [0.226, 0.832, 0.370, 0.855] in the legend stand for?" }, { "from": "gpt", "value": "Gold Values." } ], "id_raw": 763 }, { "id": "jePMdFcZeoBe6h8J", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the number represented by the x-axis tick label at this coordinate [0.377, 0.919, 0.391, 0.942]?" }, { "from": "gpt", "value": "2." } ], "id_raw": 763 }, { "id": "bgEsBVykB9Layow8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_3.png", "conversations": [ { "from": "human", "value": "\nAt {0th time interval}[0.163, 0.638], which investment strategy has a higher value, Gold or Stocks?" }, { "from": "gpt", "value": "Stocks." } ], "id_raw": 763 }, { "id": "EHZ9QW91vEAOVIJn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_3.png", "conversations": [ { "from": "human", "value": "\nAt the {6th time interval}[0.826, 0.639] of Gold Values, is it below or above its starting value at {0th time interval}[0.163, 0.638]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 763 }, { "id": "luTBSLaNueBv7J8t", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_3.png", "conversations": [ { "from": "human", "value": "\nBetween the {3rd time interval}[0.494, 0.461] and the {7th time interval}[0.936, 0.863] of Stocks Values, which one has a higher value?" }, { "from": "gpt", "value": "3rd time interval." } ], "id_raw": 763 }, { "id": "C95vobm6bmPWDA5z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_3.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.136, 0.788, 0.411, 0.899]?" }, { "from": "gpt", "value": "The area contains the chart's legend. The legend includes two labels, {Gold Values}[0.226, 0.832, 0.370, 0.855] and {Stocks Values}[0.226, 0.867, 0.385, 0.890], each paired with a colored marker icon." } ], "id_raw": 763 }, { "id": "jSoz3vpDGLibtyZw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are in the region [0.163, 0.093, 0.936, 0.863]?" }, { "from": "gpt", "value": "This section contains two line charts. The one indicated by gold, cubic markers represents the trend of Gold Values while the one symbolized by blue, square markers represents the Stocks Values trend over time." } ], "id_raw": 763 }, { "id": "VMyDWg1WMlLLKIWm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_3.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what's inside the area [0.058, 0.766, 0.116, 0.789]?" }, { "from": "gpt", "value": "There is a y-axis tick label in this region. It is labeled '1000'." } ], "id_raw": 763 }, { "id": "uoN4mYU220YyCr0D", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart element found at the coordinates [0.025, 0.289, 0.048, 0.656]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 764 }, { "id": "slSjriFF1nTYcLuZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label located at [0.415, 0.952, 0.669, 0.975]?" }, { "from": "gpt", "value": "Years of Development." } ], "id_raw": 764 }, { "id": "2fyAoRVIOSHkv10H", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_4.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.149, 0.174, 0.936, 0.701] refer to in the chart?" }, { "from": "gpt", "value": "Line plotting the Sales data." } ], "id_raw": 764 }, { "id": "z0wWoYJJGpdjKSw7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_4.png", "conversations": [ { "from": "human", "value": "\nIs the {Sales line}[0.149, 0.174, 0.936, 0.701] higher or lower than the {Profits line}[0.149, 0.315, 0.936, 0.806] at the year 1?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 764 }, { "id": "K0ZyyKtpP52LXrw8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_4.png", "conversations": [ { "from": "human", "value": "\nIs {the Profits line}[0.149, 0.315, 0.936, 0.806] increasing or decreasing from the 5th year to the 10th year?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 764 }, { "id": "HdfqL1pSokhxGAPx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_4.png", "conversations": [ { "from": "human", "value": "\nWhere is {the Sales line}[0.149, 0.174, 0.936, 0.701] positioned compared to the {the Profits line}[0.149, 0.315, 0.936, 0.806] at the year 10?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 764 }, { "id": "O7yl3wptrLvrnf0Y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_4.png", "conversations": [ { "from": "human", "value": "\nCan you characterize the area within the coordinates [0.121, 0.046, 0.695, 0.157]?" }, { "from": "gpt", "value": "The area contains the chart legend titled \"Entertainment & Gaming Industry Development\". Two legend items are present: \"Sales\", marked in light blue, and \"Profits\", marked in yellow." } ], "id_raw": 764 }, { "id": "AfiuM4ZWlsmGhKWl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_4.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region with coordinates [0.415, 0.952, 0.669, 0.975]?" }, { "from": "gpt", "value": "The x-axis label, \"Years of Development\", is situated within this area." } ], "id_raw": 764 }, { "id": "SW4DQGPs1AVHOCW5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content located within the coordinates [0.149, 0.174, 0.936, 0.701]." }, { "from": "gpt", "value": "This area predominantly houses the \"Sales\" line plot. The line indicates the sales revenue over different years." } ], "id_raw": 764 }, { "id": "g3i6nXPvEZZj99ub", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the marker at {the point on the line for Rights Protection}[0.581, 0.451]?" }, { "from": "gpt", "value": "#000000." } ], "id_raw": 765 }, { "id": "Bik5tZnq8Um3mZsK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_5.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.025, 0.388, 0.048, 0.557] indicate as a measure?" }, { "from": "gpt", "value": "Measure Value." } ], "id_raw": 765 }, { "id": "GqbpQVKQSA5xGmfb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label of the fifth tick}[0.645, 0.919, 0.660, 0.942] on the x-axis?" }, { "from": "gpt", "value": "8." } ], "id_raw": 765 }, { "id": "1w4NDDWHrJrdvqaF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_5.png", "conversations": [ { "from": "human", "value": "\nIs {the point at}[0.511, 0.451] higher or lower than {the point at}[0.652, 0.341] in the chart?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 765 }, { "id": "mGVFEMwpuQVGrC5z", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_5.png", "conversations": [ { "from": "human", "value": "\nDoes {the point at}[0.511, 0.451] belong to the same trend as {the curve starting at}[0.156, 0.188]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 765 }, { "id": "hc6zADxbSuC7oH5W", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_5.png", "conversations": [ { "from": "human", "value": "\nWhich is further to the right on the x-axis, {the point at}[0.298, 0.297] or {the point at}[0.440, 0.429]?" }, { "from": "gpt", "value": "[0.440, 0.429]." } ], "id_raw": 765 }, { "id": "OWIWpPfEOKMRl8KC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content located in [0.058, 0.025, 0.109, 0.048]." }, { "from": "gpt", "value": "This contains the y-axis tick labeled '2.00'." } ], "id_raw": 765 }, { "id": "gt3K0rDP6Sp8zpdO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.156, 0.122, 0.936, 0.613]." }, { "from": "gpt", "value": "This region contains points from two distinct data trends, Rights Protection and another trend with no label, displayed with black markers on blue and red lines respectively." } ], "id_raw": 765 }, { "id": "OvaOIsodLJW9jnNQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_5.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the area [0.058, 0.025, 0.951, 0.942]?" }, { "from": "gpt", "value": "The area includes the entire scale of the x-axis and y-axis, containing all 12 x-axis ticks and 9 y-axis ticks, plotted against the data trends shown on the graph." } ], "id_raw": 765 }, { "id": "csTvLLCsrRLUKhSr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with the coordinates [0.058, 0.902, 0.094, 0.925] on the chart?" }, { "from": "gpt", "value": "The y-axis tick label \"0.0\"." } ], "id_raw": 766 }, { "id": "wOzqLL9xlQE6sE2t", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object {at this location}[0.440, 0.952, 0.611, 0.975]?" }, { "from": "gpt", "value": "The x-axis label \"Market Growth\"." } ], "id_raw": 766 }, { "id": "Ufyi0z05cac09bUd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_6.png", "conversations": [ { "from": "human", "value": "\nWhat is at {these coordinates}[0.141, 0.379]?" }, { "from": "gpt", "value": "The point representing the \"Strong Growth\" market share for \"Sporty\" automotive." } ], "id_raw": 766 }, { "id": "yrIOFlJ3UQgv9t6a", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the relationship in position between {Sporty automotive at 'No Growth'}[0.910, 0.616] and {Luxury automotive at 'No Growth'}[0.910, 0.793]?" }, { "from": "gpt", "value": "The \"Sporty\" point is below the \"Luxury\" point." } ], "id_raw": 766 }, { "id": "W84azy5C5Fp4wUtl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_6.png", "conversations": [ { "from": "human", "value": "\nAre the market shares of {Sporty}[0.141, 0.143] higher or lower than that of {Luxury}[0.141, 0.379] in the \"Strong Growth\" segment?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 766 }, { "id": "AoMj8JkK8PUeqmW7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_6.png", "conversations": [ { "from": "human", "value": "\nWhich automotive type has higher market share at {\"No Growth\"}[0.910, 0.616], Sporty or Luxury?" }, { "from": "gpt", "value": "Luxury." } ], "id_raw": 766 }, { "id": "svhlLA3XoLLd2xRc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_6.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the region [0.114, 0.788, 0.693, 0.899]?" }, { "from": "gpt", "value": "This area contains the chart's legend titled \"Automotive and Transportation Market Research\". There are two legend items: a blue-colored 'Sporty' and a red-colored 'Luxury'. They are located at {coordinates}[0.397, 0.832, 0.473, 0.855] and {coordinates}[0.397, 0.867, 0.475, 0.890] respectively." } ], "id_raw": 766 }, { "id": "4DrcJW7zt3iAwDed", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the region of [0.057, 0.919, 0.225, 0.942]." }, { "from": "gpt", "value": "This region contains the x-axis tick label for 'Strong Growth'." } ], "id_raw": 766 }, { "id": "InvXosBPudeqdQ06", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_6.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.058, 0.666, 0.094, 0.689]?" }, { "from": "gpt", "value": "The y-axis tick label '0.4' is located within this region." } ], "id_raw": 766 }, { "id": "eFTE7I1rLI24PadS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_7.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker represents the values on the 'Post-Covid' data line?" }, { "from": "gpt", "value": "Square." } ], "id_raw": 767 }, { "id": "MMWDxTuQIJ6Xi6jz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_7.png", "conversations": [ { "from": "human", "value": "\nWhich label is found at the coordinate [0.455, 0.952, 0.588, 0.975]?" }, { "from": "gpt", "value": "Economic Indicator." } ], "id_raw": 767 }, { "id": "Lxgo8OMcZxpaSGSo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_7.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.015, 0.435, 0.029, 0.510] represent?" }, { "from": "gpt", "value": "Values." } ], "id_raw": 767 }, { "id": "rrXCQrBPkSxmY2tp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point at 'Employment Rate' for Pre-Covid}[0.107, 0.104] higher or lower than {the point at 'Employment Rate' for Post-Covid}[0.107, 0.148]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 767 }, { "id": "PBCNqXxCDyp3Dubk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_7.png", "conversations": [ { "from": "human", "value": "\nWhich economic indicator shows a higher value in the Post-Covid era between {Interest Rate}[0.660, 0.907] and {Inflation Rate}[0.384, 0.904]?" }, { "from": "gpt", "value": "Inflation Rate." } ], "id_raw": 767 }, { "id": "8LOZTIU5PcmIqDED", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_7.png", "conversations": [ { "from": "human", "value": "\nBetween {the points for 'GDP Growth' for Pre-Covid}[0.936, 0.893] and {the points for 'Employment Rate' for Post-Covid}[0.107, 0.148], which is positioned lower on the chart?" }, { "from": "gpt", "value": "GDP Growth." } ], "id_raw": 767 }, { "id": "ruQEMcP9hIxWpyvA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content found in [0.015, 0.435, 0.029, 0.510]." }, { "from": "gpt", "value": "This region contains the y-axis label, \"Values\", which is aligned vertically and displayed in black color." } ], "id_raw": 767 }, { "id": "0ropaMRJIk4UZhwA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.107, 0.104, 0.107, 0.148] include?" }, { "from": "gpt", "value": "It encompasses the data points for 'Employment Rate' for both Pre-Covid and Post-Covid, positioned at {Pre-Covid}[0.107, 0.104] and {Post-Covid}[0.107, 0.148]." } ], "id_raw": 767 }, { "id": "JDs7zQDubx0kKobo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_7.png", "conversations": [ { "from": "human", "value": "\nWhat are the key elements contained in the region defined by [0.836, 0.832, 0.909, 0.890]?" }, { "from": "gpt", "value": "The key elements in this area are the legend items for Pre-Covid, marked with blue color, and Post-Covid, marked with red color. The legend texts are: {Pre-Covid}[0.836, 0.832, 0.902, 0.855] and {Post-Covid}[0.836, 0.867, 0.909, 0.890]." } ], "id_raw": 767 }, { "id": "CZyevvtvjx68A0ZV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the labeling on the y-axis of the chart?" }, { "from": "gpt", "value": "\"Effectiveness\"{ylabel}[0.025, 0.395, 0.048, 0.550]" } ], "id_raw": 768 }, { "id": "xdV70GcrWakVWSxU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the last event on the x-axis?" }, { "from": "gpt", "value": "\"Book Drive\"{xticks label}[0.845, 0.919, 0.973, 0.942]" } ], "id_raw": 768 }, { "id": "Nor78sJ7Zeamf8FB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of the event that corresponds to the tick mark at [0.456, 0.911] on the x-axis?" }, { "from": "gpt", "value": "\"Fun Run\"." } ], "id_raw": 768 }, { "id": "kpCgG7AJpZVqLfyR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_8.png", "conversations": [ { "from": "human", "value": "\nIs the Participation of Volunteer higher or lower than the Increase of Donation on 'Clothing Drive'?" }, { "from": "gpt", "value": "Lower. {Donation Increasement's point on 'Clothing Drive'}[0.758, 0.166] {Volunteer Participation's point on 'Clothing Drive'}[0.758, 0.473]" } ], "id_raw": 768 }, { "id": "jOhZcGINZz7tuDS6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_8.png", "conversations": [ { "from": "human", "value": "\nWhich is higher on 'Book Drive', Volunteer Participation or Donation Increasement?" }, { "from": "gpt", "value": "Donation Increasement. {Donation Increasement's point on 'Book Drive'}[0.909, 0.253] {Volunteer Participation's point on 'Book Drive'}[0.909, 0.604]" } ], "id_raw": 768 }, { "id": "g2A2VjtdlpLxt4UD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_8.png", "conversations": [ { "from": "human", "value": "\nWhich has higher effectiveness in 'Fun Run', the increase in donations or the participation of volunteers?" }, { "from": "gpt", "value": "Volunteer Participation. {Volunteer Participation's point on 'Fun Run'}[0.456, 0.341] {Donation Increasement's point on 'Fun Run'}[0.456, 0.253]" } ], "id_raw": 768 }, { "id": "zpdBC80mwkqDPNwj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_8.png", "conversations": [ { "from": "human", "value": "\nWhat is defined in the region [0.129, 0.788, 0.541, 0.899]?" }, { "from": "gpt", "value": "This area contains the legend, titled 'Social Welfare & Charity Activities', with two items: 'Donation Increasement' and 'Volunteer Participation' along with their color identifiers." } ], "id_raw": 768 }, { "id": "lEk6TnI9KutPIsxr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the features present in the space [0.845, 0.919, 0.973, 0.942]." }, { "from": "gpt", "value": "This region represents the final point on the x-axis, labeled as 'Book Drive'." } ], "id_raw": 768 }, { "id": "OdxWmR4vXGVXuZ9i", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_8.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the region [0.025, 0.395, 0.048, 0.550]?" }, { "from": "gpt", "value": "This area contains the label for the y-axis, which is labeled as 'Effectiveness'." } ], "id_raw": 768 }, { "id": "pv2zSbQEZ2oGIA4d", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_9.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis label} at [0.477, 0.964, 0.581, 0.981] represent in the chart?" }, { "from": "gpt", "value": "Career Field." } ], "id_raw": 769 }, { "id": "rg48pyVvF9Aohww8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_9.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label} at [0.019, 0.363, 0.036, 0.596] represent in this chart?" }, { "from": "gpt", "value": "Career Development Index." } ], "id_raw": 769 }, { "id": "b2qqI6KIYXWgeCg6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_9.png", "conversations": [ { "from": "human", "value": "\nWhat element is referenced by the coordinates [0.843, 0.067, 0.956, 0.085] in the legend?" }, { "from": "gpt", "value": "Early Career." } ], "id_raw": 769 }, { "id": "IkGMBvmtIySAR6kQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_9.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Tech' in 'Early Career'}[0.118, 0.337] higher or lower than {the point for 'Tech' in 'Mid Career'}[0.118, 0.544]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 769 }, { "id": "3mEYPZMr80rATNAg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_9.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Sports' in 'Early Career'}[0.940, 0.233] above or below {the point for 'Sports' in 'Mid Career'}[0.940, 0.363]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 769 }, { "id": "I0Ad6sIe3Egartgt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_9.png", "conversations": [ { "from": "human", "value": "\nComparing the 'Early Career' stages, which one is higher on the chart: {Tech}[0.118, 0.337] or {Medicine}[0.323, 0.492]?" }, { "from": "gpt", "value": "Medicine." } ], "id_raw": 769 }, { "id": "6KTejHyFXt0r0z4W", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_9.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the region [0.043, 0.926, 0.071, 0.944]?" }, { "from": "gpt", "value": "The numeric label '0.0' for the Career Development Index is represented in this region." } ], "id_raw": 769 }, { "id": "M7M7jVPScHI3K2WW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_9.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the content in the region [0.735, 0.285, 0.940, 0.363]?" }, { "from": "gpt", "value": "This region contains the 'Mid Career' points for 'Arts' and 'Sports', which are represented with a marker style of square and the color green." } ], "id_raw": 769 }, { "id": "MzCpe4gnpQrecUlW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_9.png", "conversations": [ { "from": "human", "value": "\nDescribe what is found in the area [0.118, 0.233, 0.940, 0.544]." }, { "from": "gpt", "value": "This region contains all plotted points representing 'Early Career' and 'Mid Career' stages for five different fields: 'Tech', 'Medicine', 'Education', 'Arts', and 'Sports'. Different career stages are marked with different color codes and different marker styles." } ], "id_raw": 769 }, { "id": "7wnLAw1gC593R0JR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_10.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the given coordinates [0.279, 0.832, 0.450, 0.855] in the chart?" }, { "from": "gpt", "value": "The text \"Inexorable rise\" from the legend." } ], "id_raw": 770 }, { "id": "nyu7oPs1MEgvnAtb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object represented by these coordinates [0.154, 0.385] in the plot?" }, { "from": "gpt", "value": "It is a point on the line labeled \"Inexorable rise\" representing the value of \"Income inequality\"." } ], "id_raw": 770 }, { "id": "342kZa3QxrGorYQT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the text located at [0.279, 0.867, 0.450, 0.890] represent?" }, { "from": "gpt", "value": "It represents the \"Slight Increase\" label in the legend." } ], "id_raw": 770 }, { "id": "jvBbxMoFYjavBoq0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_10.png", "conversations": [ { "from": "human", "value": "\nIs the {point of 'Healthcare cost'}[0.646, 0.429] on the 'Inexorable rise' line higher or lower than the {point of 'Public spending'}[0.400, 0.560]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 770 }, { "id": "1IP1a0u3f3nfttIK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_10.png", "conversations": [ { "from": "human", "value": "\nComparing the points on the lines for 'Inexorable rise' and 'Slight Increase', which line has a higher value for 'Income inequality'?" }, { "from": "gpt", "value": "'Slight Increase'." } ], "id_raw": 770 }, { "id": "QdQVezg4dhm7Dxnh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_10.png", "conversations": [ { "from": "human", "value": "\nWhich trendline values are higher at the 'Social welfare' category, 'Inexorable rise' or 'Slight Increase'?" }, { "from": "gpt", "value": "'Slight Increase'." } ], "id_raw": 770 }, { "id": "ysCxEC9fZCcae9pf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area defined by [0.129, 0.788, 0.537, 0.899] on the chart?" }, { "from": "gpt", "value": "This area contains the chart legend titled \"Socioeconomic and Policy Making\". It includes two entries: {Inexorable rise}[0.279, 0.832, 0.450, 0.855] marked in black color, and {Slight Increase}[0.279, 0.867, 0.450, 0.890] marked in red color." } ], "id_raw": 770 }, { "id": "rGU6LuzVLqeuw5iG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the area marked by the coordinates [0.117, 0.034, 0.929, 0.911] on the plot." }, { "from": "gpt", "value": "This area encloses the entire plotted area consisting of x-axis labeled 'Categories', y-axis labeled 'Values', associated tick labels, and two plotted lines - 'Inexorable rise' and 'Slight Increase'. Each line contains four data points each representing a category." } ], "id_raw": 770 }, { "id": "qlMGhqcDjPg2r477", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements contained within the coordinates [0.058, 0.025, 0.109, 0.048]." }, { "from": "gpt", "value": "This contains the y-axis tick label '2.00'." } ], "id_raw": 770 }, { "id": "ybyhIiOEop460Ll2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_11.png", "conversations": [ { "from": "human", "value": "\nWhat does {the ylabel}[0.025, 0.441, 0.048, 0.504] represent?" }, { "from": "gpt", "value": "Index." } ], "id_raw": 771 }, { "id": "HFpKRKl8H3b8dH86", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the object}[0.929, 0.902, 0.975, 0.925] on the y-axis of the secondary chart?" }, { "from": "gpt", "value": "-1.5." } ], "id_raw": 771 }, { "id": "HW3T7ng9UcwugmKF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the point}[0.866, 0.708] on the primary line chart?" }, { "from": "gpt", "value": "Stagnant Markets." } ], "id_raw": 771 }, { "id": "SRTQvs5I7qcIsEaO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_11.png", "conversations": [ { "from": "human", "value": "\nAre the error bars for 'Emerging Markets' higher or lower level than the point itself on the secondary chart?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 771 }, { "id": "rZNZuePEOdhJSn4z", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_11.png", "conversations": [ { "from": "human", "value": "\nWhich x-axis label is situated further right, {Emerging Markets} at [0.031, 0.919, 0.241, 0.942] or {Stagnant Markets} at [0.764, 0.919, 0.969, 0.942]?" }, { "from": "gpt", "value": "Stagnant Markets." } ], "id_raw": 771 }, { "id": "qsYwFJm3U64WM9e3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_11.png", "conversations": [ { "from": "human", "value": "\nBetween {'Developed Markets'}[0.380, 0.204] and {'Frontier Markets'}[0.623, 0.367], which point on the primary line chart is located higher?" }, { "from": "gpt", "value": "Developed Markets." } ], "id_raw": 771 }, { "id": "Bwv0lZdqQx88LnXu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_11.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.100, 0.034, 0.903, 0.911]?" }, { "from": "gpt", "value": "This area corresponds to the entire chart, including both the primary and secondary line charts. It includes all markers, lines, labels, and tick markers." } ], "id_raw": 771 }, { "id": "RCuLKj3n5u0dtQrw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the region [0.111, 0.046, 0.318, 0.123]." }, { "from": "gpt", "value": "In this area is the legend for one of the subplot charts. There is one item in the legend, labeled {GP Growth}[0.186, 0.090, 0.309, 0.113], along with the matching black square marker." } ], "id_raw": 771 }, { "id": "e3TGaaVZLrWU84ta", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_11.png", "conversations": [ { "from": "human", "value": "\nWhat information is found in [0.431, 0.952, 0.572, 0.975]?" }, { "from": "gpt", "value": "This region contains the xlabel for the plot, which is 'Market Type'." } ], "id_raw": 771 }, { "id": "JxgP4zA7bHizwxh7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.505, 0.952, 0.550, 0.975]?" }, { "from": "gpt", "value": "Tool." } ], "id_raw": 772 }, { "id": "dVIYnwS5XxgUyO9G", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_12.png", "conversations": [ { "from": "human", "value": "\nWhich entry in the dataset is represented by the point having the coordinates {0.527, 0.166}?" }, { "from": "gpt", "value": "MegaDrill from the optimization set." } ], "id_raw": 772 }, { "id": "iUDCDFfX7E4SdcJQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_12.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the Y-axis label at these coordinates: [0.025, 0.382, 0.048, 0.563]?" }, { "from": "gpt", "value": "Efficiency Level." } ], "id_raw": 772 }, { "id": "CbogjrUqnA22RAiz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point}[0.334, 0.359] of HyperDrill in 'Optimization' above or below the baseline point {the point}[0.334, 0.429]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 772 }, { "id": "LRe3ScTH8Z3ydJuo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_12.png", "conversations": [ { "from": "human", "value": "\nIs the efficiency level for {the point 'MegaDrill'}[0.527, 0.210] in 'Optimization' higher or lower than in the baseline {the point 'MegaDrill'}[0.527, 0.210]?" }, { "from": "gpt", "value": "The same." } ], "id_raw": 772 }, { "id": "VpKpBOYHWEUMESVT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_12.png", "conversations": [ { "from": "human", "value": "\nComparing the point of {SuperDrill in Baseline}[0.141, 0.341] to {PowerDrill in Optimization}[0.913, 0.227], which one has a higher efficiency level?" }, { "from": "gpt", "value": "SuperDrill in Baseline." } ], "id_raw": 772 }, { "id": "eW2PAqm0s62kJ0ES", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the chart within {coordinates}[0.102, 0.034, 0.952, 0.911]?" }, { "from": "gpt", "value": "This region depicts a line chart showing the efficiency level of various tools, both in baseline and after optimization. It includes lines with markers representing both scenarios for each tool, i.e., SuperDrill, HyperDrill, MegaDrill, EconoDrill, and PowerDrill. Along with this, the y-axis labels from 0 to 1 representing the efficiency level, and the x-axis labels for the names of the tools are also present." } ], "id_raw": 772 }, { "id": "9jCMocXw7hCiZHMH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area enclosed within the given coordinates [0.084, 0.919, 0.972, 0.942]." }, { "from": "gpt", "value": "This area includes the x-axis labels for the tools: SuperDrill, HyperDrill, MegaDrill, EconoDrill, and PowerDrill." } ], "id_raw": 772 }, { "id": "ZWvLXJMdZY6eIp6R", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_12.png", "conversations": [ { "from": "human", "value": "\nDescribe what is inside the region [0.141, 0.210, 0.913, 0.560]." }, { "from": "gpt", "value": "This region contains the lines representing the baseline and optimization data for the tools (SuperDrill, HyperDrill, MegaDrill, EconoDrill, and PowerDrill) on the chart, with each tool's point denoted." } ], "id_raw": 772 }, { "id": "kb4A9SLbDQq05wQM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line with square markers}[0.120, 0.613, 0.903, 0.823] in the chart?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 773 }, { "id": "l0TyfStmDfezwagW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of {Speculative under Investment Styles}[0.836, 0.919, 0.971, 0.942] in the x-axis?" }, { "from": "gpt", "value": "Fourth." } ], "id_raw": 773 }, { "id": "d1DZAnoxVFyxh7me", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at {the y-axis coordinate}[0.058, 0.726, 0.072, 0.750]?" }, { "from": "gpt", "value": "1." } ], "id_raw": 773 }, { "id": "BSFv2iKomj63cVSx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_13.png", "conversations": [ { "from": "human", "value": "\nWhich one is higher {High Risk at Aggressive}[0.381, 0.297] or {Low Risk at Aggressive}[0.381, 0.823]?" }, { "from": "gpt", "value": "Low Risk." } ], "id_raw": 773 }, { "id": "Xl8XFlWQAMZNxvtf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_13.png", "conversations": [ { "from": "human", "value": "\nAre the {Risk Levels} in chart depicted by [#000000] or by color [#ff0000]?" }, { "from": "gpt", "value": "#ff0000." } ], "id_raw": 773 }, { "id": "D4xmbhIYcH4TQfYl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_13.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value at the fourth point, {High Risk}[0.903, 0.122] or {Low Risk}[0.903, 0.788]?" }, { "from": "gpt", "value": "Low Risk." } ], "id_raw": 773 }, { "id": "n6J8mRLgIzuRfU1J", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within [0.317, 0.919, 0.444, 0.942]." }, { "from": "gpt", "value": "It contains the label 'Aggressive' of the x-axis with black color, regular font style." } ], "id_raw": 773 }, { "id": "2MZxBwfWiErf7uMD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_13.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is located in the region [0.092, 0.788, 0.291, 0.899]?" }, { "from": "gpt", "value": "This area contains the legend titled 'Finance', indicating the two types of Investment Styles: {High Risk}[0.166, 0.832, 0.282, 0.855] with black color marker, and {Low Risk}[0.166, 0.867, 0.267, 0.890] with a red color marker." } ], "id_raw": 773 }, { "id": "7dIGZYLv1tV1CYDk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are in the region [0.120, 0.122, 0.903, 0.455]?" }, { "from": "gpt", "value": "This region contains a black line marked with circle points indicating 'High Risk' investment style from 'Conservative' to 'Speculative'. At each marked point, a horizontal line is created representing the variations." } ], "id_raw": 773 }, { "id": "T7ZJsR1fsoKvAepB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.012, 0.367, 0.024, 0.569] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 774 }, { "id": "78NQj6cQKIJAa0nv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the x-axis tick label '8'?" }, { "from": "gpt", "value": "[0.771, 0.919, 0.778, 0.942]" } ], "id_raw": 774 }, { "id": "BQlO41FWkH3SLV5J", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the legend located at [0.057, 0.788, 0.241, 0.899] on the chart?" }, { "from": "gpt", "value": "Human Life Quality Over Time." } ], "id_raw": 774 }, { "id": "hfcKQN5qusyYqTyq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_14.png", "conversations": [ { "from": "human", "value": "\nIs {the Urban Life Quality line}[0.094, 0.244, 0.945, 0.438] above or below {the Rural Life Quality line}[0.094, 0.427, 0.945, 0.557] at Time (x-axis) equal to 6?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 774 }, { "id": "DiqraU8fyOl0IknR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the Urban Life Quality line}[0.094, 0.244, 0.945, 0.438] cross {the Rural Life Quality line}[0.094, 0.427, 0.945, 0.557]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 774 }, { "id": "apnyGVsuML2xR2gY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_14.png", "conversations": [ { "from": "human", "value": "\nIs the value of {the Urban Life Quality line}[0.094, 0.244, 0.945, 0.438] at Time (x-axis) equal to 10 greater than or less than the {Rural Life Quality line}[0.094, 0.427, 0.945, 0.557]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 774 }, { "id": "32CaG8Ye2JlZRp3Z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.094, 0.244, 0.945, 0.557]." }, { "from": "gpt", "value": "The region encompasses two lines, the Urban Life Quality line and Rural Life Quality line, ranging in time (x-axis) from 0 to 10 years. The values on the y-axis represent the Life Quality Index for both urban and rural areas." } ], "id_raw": 774 }, { "id": "hehDe1aGWq83Uc76", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area in the range [0.029, 0.075, 0.047, 0.098]." }, { "from": "gpt", "value": "The area contains the y-axis tick label '1.4', which represents a Life Quality Index of 1.4." } ], "id_raw": 774 }, { "id": "A514XmXtEyn5A92c", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are found within [0.179, 0.244, 0.945, 0.557]?" }, { "from": "gpt", "value": "There are two main objects found, these are points on the Urban Life Quality line and Rural Life Quality line tracing from Time (x-axis) equal to 1 to 10 years." } ], "id_raw": 774 }, { "id": "immuqpwDtWPoWW9Q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the object at coordinates [0.025, 0.363, 0.048, 0.582]?" }, { "from": "gpt", "value": "Performance Score." } ], "id_raw": 775 }, { "id": "pqlR3Q2sAFRnK8fw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the {ylabel} at coordinates [0.025, 0.363, 0.048, 0.582]?" }, { "from": "gpt", "value": "Performance Score." } ], "id_raw": 775 }, { "id": "RxmfbdYqJR2i1kXN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_15.png", "conversations": [ { "from": "human", "value": "\nWhat is located at coordinates [0.465, 0.952, 0.572, 0.975] in the chart?" }, { "from": "gpt", "value": "The x-axis label, which is \"Channels\"." } ], "id_raw": 775 }, { "id": "cesYgYDcO8W1405u", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_15.png", "conversations": [ { "from": "human", "value": "\nComparing {the point for 'New Creation'}[0.140, 0.239] and {the point for 'Digital Content'}[0.140, 0.472], which has a higher value on 'Search Engine'?" }, { "from": "gpt", "value": "Digital Content." } ], "id_raw": 775 }, { "id": "GVcds9AVN5NXARGL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_15.png", "conversations": [ { "from": "human", "value": "\nAt the {point}[0.897, 0.356] on the 'Social Media' channel, is 'New Creation' or 'Digital Content' higher?" }, { "from": "gpt", "value": "New Creation." } ], "id_raw": 775 }, { "id": "k2ngepKxHTT0tQb3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_15.png", "conversations": [ { "from": "human", "value": "\nIs the {New Creation}[0.897, 0.356] above or below {Digital Content}[0.897, 0.297] for 'Social Media' channel?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 775 }, { "id": "BEcvQaDrfZekeUkq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is within the region [0.114, 0.787, 0.372, 0.899]." }, { "from": "gpt", "value": "This corresponds to the legend of the chart showing two items: {New Creation}[0.188, 0.832, 0.344, 0.855] and {Digital Content}[0.188, 0.867, 0.363, 0.890], each with a different color marker." } ], "id_raw": 775 }, { "id": "yMLpb0dAzGJdV7mI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_15.png", "conversations": [ { "from": "human", "value": "\nWhat can I find in the area [0.140, 0.239, 0.897, 0.472]?" }, { "from": "gpt", "value": "It contains the data lines for both 'New Creation' and 'Digital Content' for the 'Search Engine' and 'Social Media' channels." } ], "id_raw": 775 }, { "id": "ZxUjMfBoLaL6EgDv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within the area [0.465, 0.952, 0.572, 0.975]?" }, { "from": "gpt", "value": "This area contains the X-axis label, which is \"Channels\"." } ], "id_raw": 775 }, { "id": "9jNoNTpoRPByBHGm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_16.png", "conversations": [ { "from": "human", "value": "\nWhich media type is at position [0.875, 0.939, 0.976, 0.957]?" }, { "from": "gpt", "value": "Radio News." } ], "id_raw": 776 }, { "id": "Yj9dHASMXDjlLXX5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_16.png", "conversations": [ { "from": "human", "value": "\nWhat's the y-axis label for coordinates [0.019, 0.414, 0.036, 0.545]?" }, { "from": "gpt", "value": "Influence Level." } ], "id_raw": 776 }, { "id": "gmkqUPHXPVCeBOjB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_16.png", "conversations": [ { "from": "human", "value": "\nWhat's the text of the last entry in the legend at [0.716, 0.094, 0.840, 0.111]?" }, { "from": "gpt", "value": "Public Opinion." } ], "id_raw": 776 }, { "id": "ghkYmllPzGFYohgF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_16.png", "conversations": [ { "from": "human", "value": "\nIs the influence level of {Social Media}[0.112, 0.135] higher than {News Websites}[0.315, 0.280] in Trust in Media?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 776 }, { "id": "h0HKrDGbpEVVYTYz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_16.png", "conversations": [ { "from": "human", "value": "\nWhat has a higher value on the y-axis, {TV News}[0.722, 0.588] or {Radio News}[0.925, 0.443], in terms of Trust in Media?" }, { "from": "gpt", "value": "TV News." } ], "id_raw": 776 }, { "id": "eroGSKwn2FKzKPhS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_16.png", "conversations": [ { "from": "human", "value": "\nIs {Print Newspapers}'s influence level[0.519, 0.743] higher or lower than {Radio News}[0.925, 0.643] according to Public Opinion?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 776 }, { "id": "jEJJIO3BQqkEsaOs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.071, 0.026, 0.966, 0.933]." }, { "from": "gpt", "value": "This region includes all the information of the chart: the x-axis labeled with different Media Types, the y-axis demonstrating Influence Level, two lines each representing Trust in Media and Public Opinion with corresponding markers for each media type." } ], "id_raw": 776 }, { "id": "Ow1MorYi7qVe3Q29", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_16.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area defined by [0.551, 0.034, 0.957, 0.118]?" }, { "from": "gpt", "value": "It contains the legend which has the title \"Media Influence and Public Opinion Guidance\" with descriptions of each line: {Trust in Media}[0.716, 0.067, 0.837, 0.085] and {Public Opinion}[0.716, 0.094, 0.840, 0.111]." } ], "id_raw": 776 }, { "id": "30WKgieynYHl5AHc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_16.png", "conversations": [ { "from": "human", "value": "\nDescribe what can be found in the region [0.543, 0.939, 0.595, 0.957]." }, { "from": "gpt", "value": "This area includes the x-label for {Print Newspapers}[0.543, 0.939, 0.595, 0.957]." } ], "id_raw": 776 }, { "id": "lkfZD5Vx2Xfz1tSD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of {the marker for 'E-Learning Platform'}[inf, inf, -inf, -inf] in the legend?" }, { "from": "gpt", "value": "Square." } ], "id_raw": 777 }, { "id": "kFLtm2V7tG8dbcXG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_17.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, what is {the y-value for 'Accelerated decline'}[0.861, 0.736] in line_3?" }, { "from": "gpt", "value": "0.4." } ], "id_raw": 777 }, { "id": "O8V6vgEXPk3S5I0v", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of {the ylabel}[0.025, 0.325, 0.048, 0.620] of the subplot?" }, { "from": "gpt", "value": "Innovation/Adoption Rate." } ], "id_raw": 777 }, { "id": "0MKvusjCKgKWjONi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_17.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Accelerated decline'}[0.861, 0.736] in line_3 higher or lower than {the point for 'Abrupt decline'}[0.152, 0.210] in line_0?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 777 }, { "id": "5m8qZRCogLEyoXJB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_17.png", "conversations": [ { "from": "human", "value": "\nDo both {the line for 'AI Innovation'}[0.152, 0.210, 0.861, 0.604] and {the line for 'E-Learning Platform'}[0.152, 0.385, 0.861, 0.736] show a decrease or an increase in trend?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 777 }, { "id": "Rtwpj43IgIy1rdLb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_17.png", "conversations": [ { "from": "human", "value": "\nBesides color, what is the difference between {the line for 'AI Innovation'}[0.152, 0.210, 0.861, 0.604] and {the line for 'E-Learning Platform'}[0.152, 0.385, 0.861, 0.736] based on their parameters?" }, { "from": "gpt", "value": "Marker style." } ], "id_raw": 777 }, { "id": "dd8MufeP2HKEAywg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_17.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.334, 0.046, 0.884, 0.157]?" }, { "from": "gpt", "value": "This is the legend of the chart, titled as \"Education and Training Technology Innovation\". It contains two items, \"AI Innovation\" at {coordinates}[0.527, 0.090, 0.680, 0.113] and \"E-Learning Platform\" at {coordinates}[0.527, 0.125, 0.756, 0.148], along with their corresponding color marks." } ], "id_raw": 777 }, { "id": "PrUedYomcGoYF0IP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_17.png", "conversations": [ { "from": "human", "value": "\nDescribe what's within the region [0.117, 0.034, 0.896, 0.911]." }, { "from": "gpt", "value": "This area represents the main plot region of the chart. It contains several lines representing the trends of \"AI Innovation\" and \"E-Learning Platform\", along with labeled ticks on both the x and y-axes." } ], "id_raw": 777 }, { "id": "oaE7azJgoEL0chyb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_17.png", "conversations": [ { "from": "human", "value": "\nWhat is found within the bounding box [0.469, 0.952, 0.544, 0.975]?" }, { "from": "gpt", "value": "This region contains the xlabel of the chart, which says \"Trends\"." } ], "id_raw": 777 }, { "id": "ku1K7UCmLyLOIbB6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the object corresponding to the coordinates [0.061, 0.919, 0.216, 0.942]?" }, { "from": "gpt", "value": "X-axis tick label 'Remote Work'." } ], "id_raw": 778 }, { "id": "XXlwJomeRcyOVpsd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_18.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what {the y-axis label}[0.025, 0.382, 0.048, 0.554] is?" }, { "from": "gpt", "value": "'Trend Intensity'." } ], "id_raw": 778 }, { "id": "YPISGbPJm01NGYJC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_18.png", "conversations": [ { "from": "human", "value": "\nWhat is marked at the point [0.855, 0.261] on the chart?" }, { "from": "gpt", "value": "Career trend 'Continuous Learning'." } ], "id_raw": 778 }, { "id": "qDDsxnN2N6KKt110", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_18.png", "conversations": [ { "from": "human", "value": "\nHow does the trend 'Digital Literacy' compare between 2020 and 2021? The point for 2020 is {the point}[0.676, 0.320] and for 2021 is {the point}[0.676, 0.438]." }, { "from": "gpt", "value": "It declined." } ], "id_raw": 778 }, { "id": "N8x10bEFNoMHOhZb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_18.png", "conversations": [ { "from": "human", "value": "\nWhich career trend had a higher intensity in 2020, {Remote Work}[0.138, 0.202] or {Continuous Learning}[0.855, 0.379]?" }, { "from": "gpt", "value": "Remote Work." } ], "id_raw": 778 }, { "id": "P8eFbJBW2YKSrimm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_18.png", "conversations": [ { "from": "human", "value": "\nBetween {Green Jobs}[0.317, 0.616] and {Technological Advancement}[0.497, 0.557], which career trend had more intensity in 2020?" }, { "from": "gpt", "value": "Green Jobs." } ], "id_raw": 778 }, { "id": "r0twiCob0lql4G7R", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the region [0.736, 0.919, 0.974, 0.942]?" }, { "from": "gpt", "value": "The last x-axis tick label 'Continuous Learning' is located within this region." } ], "id_raw": 778 }, { "id": "Kbu6r63eiXw4JPZE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_18.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements included in the bounding box with coordinates [0.138, 0.084, 0.855, 0.498]?" }, { "from": "gpt", "value": "It contains the line and points representing the career trend intensities of 2021 for all careers, which are 'Remote Work', 'Green Jobs', 'Technological Advancement', 'Digital Literacy', 'Continuous Learning'." } ], "id_raw": 778 }, { "id": "gglmSrD3atzZIdLj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_18.png", "conversations": [ { "from": "human", "value": "\nCan you detail what is within the box with coordinates [0.114, 0.788, 0.500, 0.899]?" }, { "from": "gpt", "value": "The box contains a legend for the year 2020 and 2021 with their respective identifiers." } ], "id_raw": 778 }, { "id": "V9dVxQUC8qmtCLXZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.025, 0.386, 0.048, 0.559] represent?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 779 }, { "id": "zNDSGGZQMuLVUUrJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.510, 0.952, 0.567, 0.975] represent?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 779 }, { "id": "vou6Cl5yJZIBorcP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_39_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the {Y-axis tick}[0.058, 0.573, 0.094, 0.596]?" }, { "from": "gpt", "value": "2.5" } ], "id_raw": 779 }, { "id": "0APD3y0Y2qX81d08", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_19.png", "conversations": [ { "from": "human", "value": "\nAt the 5th time point, does the {water quality indicator}[0.495, 0.341] have a higher or lower value than the {deforestation indicator}[0.495, 0.385]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 779 }, { "id": "OXpMHwKXvb5JqqAn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_19.png", "conversations": [ { "from": "human", "value": "\nAt the 6th time point, is the {water quality indicator}[0.583, 0.582] placed above or below the {deforestation indicator}[0.583, 0.845]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 779 }, { "id": "XxC3GuZXm5AWgvJE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_39_19.png", "conversations": [ { "from": "human", "value": "\nIs the value of the {water quality indicator}[0.935, 0.560] rising or falling between the 8th and 10th time points compared to the {deforestation indicator}[0.935, 0.779]?" }, { "from": "gpt", "value": "Rising." } ], "id_raw": 779 }, { "id": "RWjvHrEwRhmpK8y9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_19.png", "conversations": [ { "from": "human", "value": "\nWhat objects are present in the region given by [0.142, 0.210, 0.935, 0.648]?" }, { "from": "gpt", "value": "This region describes the trend of Water Quality over time. It is represented by a line with circle markers." } ], "id_raw": 779 }, { "id": "SmCHsCWIwheOBrBq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the range [0.142, 0.341, 0.935, 0.867]?" }, { "from": "gpt", "value": "This range represents the Deforestation over time. The trend is represented by a line with square markers." } ], "id_raw": 779 }, { "id": "ZtfXhp8sQOnlAWyT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_39_19.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what's within the coordinates [0.114, 0.788, 0.714, 0.899]?" }, { "from": "gpt", "value": "It is the chart legend and it includes two items: {Water Quality}[0.363, 0.832, 0.529, 0.855] and {Deforestation}[0.363, 0.867, 0.521, 0.890]. Each is represented by different markers with respective colors." } ], "id_raw": 779 }, { "id": "Mv3fg5PdzlofCvC4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label associated with the object at [0.858, 0.393, 0.952, 0.415] in the chart's legend?" }, { "from": "gpt", "value": "Minimal Change." } ], "id_raw": 780 }, { "id": "N7k67vjGqYyEBu2U", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the y-axis label corresponding to the coordinates [0.035, 0.789, 0.061, 0.812]?" }, { "from": "gpt", "value": "110." } ], "id_raw": 780 }, { "id": "MjyIemcTv8b7VLsK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the sixth text in the x-axis tick labels?" }, { "from": "gpt", "value": "2080." } ], "id_raw": 780 }, { "id": "86Vomf5WYn4mVFg5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_0.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Minimal Change}[0.112, 0.825, 0.943, 0.865] trend higher or lower than {Inexorable Rise}[0.112, 0.100, 0.943, 0.333] at year 2100?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 780 }, { "id": "xoh8jbTP2Y8HBwjP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_0.png", "conversations": [ { "from": "human", "value": "\nCould you compare the trend value in 2070 between {Upward Trend}[0.112, 0.200, 0.943, 0.599] and {Subdued Expansion}[0.112, 0.300, 0.943, 0.532]?" }, { "from": "gpt", "value": "Upward Trend is higher." } ], "id_raw": 780 }, { "id": "uIUoUeApO7hWKuY8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_0.png", "conversations": [ { "from": "human", "value": "\nIs {Gradual Decline}[0.112, 0.200, 0.943, 0.665] trend increasing or decreasing between 2030 and 2100?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 780 }, { "id": "1Vp1mwxoE2ySFhIO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content and structure of the area [0.821, 0.386, 0.979, 0.579]?" }, { "from": "gpt", "value": "This area contains the chart's legend, with each trend line name accompanied by a colored marker, these include Minimal Change, Gradual Decline, Upward Trend, Accelerated Decline, Inexorable Rise, and Subdued Expansion." } ], "id_raw": 780 }, { "id": "hDB4fuQA57vpYwhh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_0.png", "conversations": [ { "from": "human", "value": "\nCan you comment on the object layout in the region [0.035, 0.324, 0.061, 0.347]?" }, { "from": "gpt", "value": "In this region, there is a y-value of '180' from the y-axis label." } ], "id_raw": 780 }, { "id": "jwT3CUOpbHq92Swd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the region [0.372, 0.025, 0.684, 0.055]?" }, { "from": "gpt", "value": "This region contains the chart's subtitle, which reads \"Future Forecast and Trend Predictions.\"" } ], "id_raw": 780 }, { "id": "mxxhtfCKKR4W6BaK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the chart?" }, { "from": "gpt", "value": "No. of Published Papers." } ], "id_raw": 781 }, { "id": "V2SRy33yUas6EwNg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the current position of the fourth point in the {line series for 'Deep Learning'}[0.687, 0.495]?" }, { "from": "gpt", "value": "2015" } ], "id_raw": 781 }, { "id": "wtimpgwJuhoI3wiA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_1.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the line for 'IVF'}[0.254, 0.083, 0.831, 0.527]?" }, { "from": "gpt", "value": "#ffa500" } ], "id_raw": 781 }, { "id": "AtXIObNo7Dj7H10d", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_1.png", "conversations": [ { "from": "human", "value": "\nWhich of the two has more points at year 2020: {the line for 'IVF'}[0.831, 0.083] or {the line for 'Deep Learning'}[0.831, 0.368]?" }, { "from": "gpt", "value": "Deep Learning" } ], "id_raw": 781 }, { "id": "VhBBWOboi2kWTg1h", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_1.png", "conversations": [ { "from": "human", "value": "\nIs {The paper published in 2010 according to the 'Nanotechnology' line}[0.542, 0.479] more or less than the {paper according to the 'Biotechnology' line}[0.542, 0.368]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 781 }, { "id": "RYxzSXFNRPdzD7Op", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_1.png", "conversations": [ { "from": "human", "value": "\nWhich field has a higher publication rate according to the lines in 2015, {IVF}[0.687, 0.178], or {Deep Learning}[0.687, 0.495]?" }, { "from": "gpt", "value": "Deep Learning." } ], "id_raw": 781 }, { "id": "rjmNZHa2K2wGSZ3q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe what information is contained within these coordinates [0.724, 0.564, 0.767, 0.609]?" }, { "from": "gpt", "value": "It contains the legend identifier 'IVF' and its respective line color represented by a square colored with the hex value #ffa500." } ], "id_raw": 781 }, { "id": "nmHNAPdtZDGCYz1O", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_1.png", "conversations": [ { "from": "human", "value": "\nPlease explain how the contents of the area enclosed within [0.064, 0.355, 0.094, 0.390] contribute to the chart." }, { "from": "gpt", "value": "The enclosed area contains the y-axis label '30', indicating that this is the value mapped along the y-axis at this grid height." } ], "id_raw": 781 }, { "id": "NO5nji4suQNyEZ8I", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_1.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the region [0.254, 0.083, 0.831, 0.527]?" }, { "from": "gpt", "value": "The region contains the entirety of the 'IVF' line plot data, which includes points corresponding to the years 2000, 2005, 2010, 2015, and 2020." } ], "id_raw": 781 }, { "id": "8vRDrzybsZSTeKjG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_2.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinate [0.015, 0.399, 0.033, 0.549] in the chart figure?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 782 }, { "id": "Dw3vIx8D1W6NY7ld", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fourth text in the legend?" }, { "from": "gpt", "value": "[0.136, 0.143, 0.355, 0.166]" } ], "id_raw": 782 }, { "id": "SGvRvskwfd7i05r6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_2.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis tick} at coordinate [0.362, 0.934, 0.396, 0.952] represent?" }, { "from": "gpt", "value": "The year 2013." } ], "id_raw": 782 }, { "id": "JkI57tzO9mY3STc2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_2.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {point}[0.909, 0.841], line for 'Game Sales (in Millions)' or line for 'Game Releases'?" }, { "from": "gpt", "value": "Game Sales (in Millions)." } ], "id_raw": 782 }, { "id": "FICmpfBYUYeqscV1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'New Gaming Platforms' at year 2019}[0.833, 0.919] higher or lower than {the point for 'New Gaming Technologies' at year 2019}[0.833, 0.901]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 782 }, { "id": "HFUh0fd87LKFto0U", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_2.png", "conversations": [ { "from": "human", "value": "\nWhich line decreases more steeply between {year 2014}[0.454, 0.922] and {year 2015}[0.530, 0.922], line for 'Game Releases' or line for 'Game Sales (in Millions)'?" }, { "from": "gpt", "value": "Game Releases." } ], "id_raw": 782 }, { "id": "bcOlZTCklEfG0KvY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents in the region [0.075, 0.026, 0.509, 0.981]." }, { "from": "gpt", "value": "This major portion of the chart contains multiple line graphs representing game releases, game sales, new gaming platforms and new gaming technologies plotted over various years from 2010 to 2020. The lines for each of these data series are represented by various markers and colors." } ], "id_raw": 782 }, { "id": "2ZUfiQ02Zk3WxrJC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the zone [0.083, 0.036, 0.361, 0.174]?" }, { "from": "gpt", "value": "This area contains the chart legend with labels 'Game Releases', 'Game Sales (in Millions)', 'New Gaming Platforms', and 'New Gaming Technologies'. Each label is accompanied by a unique color and marker." } ], "id_raw": 782 }, { "id": "fXd6IOEKi22prLZj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the area [0.151, 0.735, 0.909, 0.919]?" }, { "from": "gpt", "value": "The area includes points from the 'Game Sales (in Millions)' and 'New Gaming Platforms' lines for the years from 2010 to 2020." } ], "id_raw": 782 }, { "id": "11g9RcpH43KMDp69", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.057, 0.769, 0.107, 0.797] represent in this line chart?" }, { "from": "gpt", "value": "Y-axis tick label indicating '4000'." } ], "id_raw": 783 }, { "id": "1zjlHoCYtNdc0WUI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.120, 0.077, 0.901, 0.311], a line with points on the chart, represent?" }, { "from": "gpt", "value": "It represents the Sushi trend line." } ], "id_raw": 783 }, { "id": "saSoYp2hqHGtDCxo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the legend item} at [0.136, 0.121, 0.195, 0.137]?" }, { "from": "gpt", "value": "Sashimi." } ], "id_raw": 783 }, { "id": "mg4IqM6taVIOE2JI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_3.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Sushi} at [0.901, 0.124] higher or lower than {the point for Sashimi} at [0.901, 0.358] for the trend category 'Rapid Recovery'?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 783 }, { "id": "U2t6alTbGd0GIthD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_3.png", "conversations": [ { "from": "human", "value": "\nWhich trend line, {Sushi} or {Tempura}, starts at a higher count, judging by the points at [0.120, 0.311] and [0.120, 0.780] respectively?" }, { "from": "gpt", "value": "Tempura." } ], "id_raw": 783 }, { "id": "eO9UIJXphmxghqH8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_3.png", "conversations": [ { "from": "human", "value": "\nComparing the two points [0.706, 0.077] for {Sushi} and [0.706, 0.545] for {Tempura}, which has a higher count in the 'Rapid Expansion' category?" }, { "from": "gpt", "value": "Sushi." } ], "id_raw": 783 }, { "id": "3t4bxRLarxBkuIMm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area between [0.132, 0.047, 0.321, 0.215]." }, { "from": "gpt", "value": "This area contains the chart legend. There are entries for three categories: {Sushi}[0.136, 0.068, 0.195, 0.085], {Sashimi}[0.136, 0.121, 0.195, 0.137], and {Tempura}[0.136, 0.173, 0.195, 0.190]. Each entry is paired with a distinct color code." } ], "id_raw": 783 }, { "id": "6s01gKFeSBLlnwgq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the region [0.120, 0.873, 0.901, 0.921]." }, { "from": "gpt", "value": "This region covers all the labels for the x-axis replicating the trend categories: {Dramatic Decrease}[0.025, 0.893, 0.216, 0.921], {Fluctuating Growth}[0.221, 0.893, 0.410, 0.921], {Gradual Downturn}[0.420, 0.893, 0.602, 0.921], {Rapid Expansion}[0.623, 0.893, 0.788, 0.921], and {Rapid Recovery}[0.824, 0.893, 0.979, 0.921]." } ], "id_raw": 783 }, { "id": "kHnklc367UR2gjrA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_3.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects situated within [0.120, 0.030, 0.901, 0.873]?" }, { "from": "gpt", "value": "The area hosts the main line chart with the trend lines for Sushi, Sashimi, and Tempura along with all the associated data points. It also includes the x and y-axes with their corresponding labels and ticks." } ], "id_raw": 783 }, { "id": "NCMv8ROxkRa4s0A4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the content denoted by the coordinates [0.015, 0.378, 0.033, 0.582] in the chart?" }, { "from": "gpt", "value": "It's the y-axis label, displaying 'Revenue ($bn)'." } ], "id_raw": 784 }, { "id": "udrWqbBELOxIB2hG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_4.png", "conversations": [ { "from": "human", "value": "\nCould you identify the text at these coordinates [0.922, 0.912, 0.958, 0.936]?" }, { "from": "gpt", "value": "It's an x-axis tick label, displaying '2018'." } ], "id_raw": 784 }, { "id": "fQVHVrL62zxg3mee", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_4.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the point}[0.129, 0.493] in the chart?" }, { "from": "gpt", "value": "This is the first data point for the 'Knowledge Economy' line corresponding to the year 2000." } ], "id_raw": 784 }, { "id": "TAOI7NDcsL1CUTsl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_4.png", "conversations": [ { "from": "human", "value": "\nComparing the data lines of {Knowledge Economy}[0.129, 0.493, 0.940, 0.829] and {Internet Economy}[0.129, 0.091, 0.940, 0.859], which one generally has higher revenues?" }, { "from": "gpt", "value": "The Internet Economy." } ], "id_raw": 784 }, { "id": "CZAchLiS4XGRHqcL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_4.png", "conversations": [ { "from": "human", "value": "\nIs the point for 'Knowledge Economy' in {2002}[0.219, 0.766] above or below the corresponding point of 'Smart Manufacturing' in {2002}[0.219, 0.848]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 784 }, { "id": "Nhm1gkha6S7qQq17", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_4.png", "conversations": [ { "from": "human", "value": "\nBased on the data lines of {Smart Manufacturing}[0.129, 0.413, 0.940, 0.891] and {Innovation Industries}[0.129, 0.118, 0.940, 0.614], which one has higher revenue in 2018?" }, { "from": "gpt", "value": "Innovation Industries." } ], "id_raw": 784 }, { "id": "bwyjtDgrY7iP5dqO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_4.png", "conversations": [ { "from": "human", "value": "\nCould you provide a description of the area [0.092, 0.078, 0.327, 0.262] on the chart?" }, { "from": "gpt", "value": "This area contains the chart legend representing four economic sectors: {Knowledge Economy}[0.145, 0.089, 0.318, 0.119], {Innovation Industries}[0.145, 0.133, 0.320, 0.163], {Smart Manufacturing}[0.145, 0.177, 0.321, 0.207], and {Internet Economy}[0.145, 0.221, 0.293, 0.251]. Each sector is represented by a different color and marker symbol." } ], "id_raw": 784 }, { "id": "v2iY2ArBG9zfQE6F", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_4.png", "conversations": [ { "from": "human", "value": "\nWhat elements are within the region [0.291, 0.916, 0.958, 0.936]?" }, { "from": "gpt", "value": "This region contains x-axis tick labels of the years, ranging from {2004}[0.291, 0.912, 0.326, 0.936] to {2018}[0.922, 0.912, 0.958, 0.936]." } ], "id_raw": 784 }, { "id": "olhO8EOCvsqxNHXB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents found within the coordinates, [0.039, 0.162, .074, 0.266]." }, { "from": "gpt", "value": "In this region, there are y-axis tick labels: {1500}[0.039, 0.243, 0.074, 0.266] and {1650}[0.039, 0.162, 0.074, 0.185], representing billions in revenue." } ], "id_raw": 784 }, { "id": "4iFr89E5xV7xWfuH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_6.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the line with color #ff4500}[0.097, 0.321, 0.959, 0.730] in the chart?" }, { "from": "gpt", "value": "%Change on Restaurant Revenue." } ], "id_raw": 786 }, { "id": "vtxW3mvxrfDqSyjT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.048, 0.531, 0.085, 0.554]?" }, { "from": "gpt", "value": "-10." } ], "id_raw": 786 }, { "id": "kuik1qzwDAQyNgI3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.174, 0.841, 0.475, 0.871] in the legend?" }, { "from": "gpt", "value": "%Change on Airline Revenue." } ], "id_raw": 786 }, { "id": "SxXEErsgs6qZokRo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 2015 on the Hotel Occupancy line}[0.384, 0.155] higher or lower than {the point for 2016 on the Hotel Occupancy line}[0.480, 0.172]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 786 }, { "id": "ZQ4NOxdsau1lQfBI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_6.png", "conversations": [ { "from": "human", "value": "\nWhich trend has a higher value at {point for the year 2020}[0.863, 0.867], %Change on Restaurant Revenue or %Change on Travel Agency Revenue?" }, { "from": "gpt", "value": "None." } ], "id_raw": 786 }, { "id": "StNL5q6NutcKuTgL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point for the year 2014 on the '%Change on Restaurant Revenue' line}[0.289, 0.380] positioned higher or lower than {the point for the same year on the '%Change on Airline Revenue' line}[0.289, 0.463]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 786 }, { "id": "sXUNRviybGBxeQE8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in [0.107, 0.698, 0.563, 0.882]." }, { "from": "gpt", "value": "The area contains the legend items: {Hotel Occupancy}[0.174, 0.710, 0.350, 0.740], {%Change on Restaurant Revenue}[0.174, 0.753, 0.523, 0.783], {%Change on Travel Agency Revenue}[0.174, 0.797, 0.554, 0.827], and {%Change on Airline Revenue}[0.174, 0.841, 0.475, 0.871], each paired with a colored marker." } ], "id_raw": 786 }, { "id": "FaJ4ymIiXJVtGq0B", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.260, 0.025, 0.797, 0.058]." }, { "from": "gpt", "value": "It is the subtitle of the chart, which reads \"Tourism and Hospitality Industry Analysis.\"" } ], "id_raw": 786 }, { "id": "r21858oOqsAZDhyl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.862, 0.474, 0.864, 0.482]?" }, { "from": "gpt", "value": "This region contains a line segment with identifier _child4, whose color code is #000000 and whose linestyle is --." } ], "id_raw": 786 }, { "id": "ZvvblslGXW2PYOTs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label associated with the coordinates [0.025, 0.102, 0.055, 0.793]?" }, { "from": "gpt", "value": "Income/Expenses/Risk_Controller." } ], "id_raw": 787 }, { "id": "uurprw8qEbp9FxhN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.513, 0.917, 0.586, 0.963]?" }, { "from": "gpt", "value": "Years." } ], "id_raw": 787 }, { "id": "qarpZLqrGONTfY5S", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the legend item} at [0.257, 0.695, 0.465, 0.741]?" }, { "from": "gpt", "value": "Risk_Controller." } ], "id_raw": 787 }, { "id": "k0sJQTIa2tBPF9qm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_7.png", "conversations": [ { "from": "human", "value": "\nIs {the Income line point '2020'}[0.946, 0.165] above or below {the Expenses line point '2020'}[0.946, 0.334]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 787 }, { "id": "3RsGcemnWcrHtMUC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_7.png", "conversations": [ { "from": "human", "value": "\nIs {the Income line point '2015'}[0.154, 0.278] higher or lower than {the Expenses line point '2015'}[0.154, 0.391]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 787 }, { "id": "y1GuuQssd679nct9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_7.png", "conversations": [ { "from": "human", "value": "\nIs {the Income line point '2020'}[0.946, 0.165] to the left or right of {the Income line point '2015'}[0.154, 0.278]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 787 }, { "id": "KYbw2GrTs3juQBWR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.257, 0.629, 0.389, 0.674]." }, { "from": "gpt", "value": "This area contains the text for the \"Expenses\" legend item, which is associated with a red color marker." } ], "id_raw": 787 }, { "id": "0cnYJoblpVdsvHEb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.257, 0.695, 0.465, 0.741]." }, { "from": "gpt", "value": "The area contains the legend item: {Risk_Controller}[0.257, 0.695, 0.465, 0.741], which is paired with a blue square marker." } ], "id_raw": 787 }, { "id": "lllqntRG6HjbRr1o", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.787, 0.210, 0.946, 0.165]?" }, { "from": "gpt", "value": "There are 2 points. They are the Income line points for the years 2019 and 2020, respectively. The 2019 point is located at {coordinates}[0.787, 0.210] and the 2020 point at {coordinates}[0.946, 0.165]." } ], "id_raw": 787 }, { "id": "uLfD8dETTs8xi3qb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object mapped at the coordinate [0.015, 0.388, 0.033, 0.533]?" }, { "from": "gpt", "value": "It's the Y-axis label which is Population." } ], "id_raw": 788 }, { "id": "5Jdl7KMfPstPTLbr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_8.png", "conversations": [ { "from": "human", "value": "\nCould you identify what {the colored marker}[0.832, 0.804, 0.873, 0.818] in the legend stands for?" }, { "from": "gpt", "value": "It represents the chart line for 'Boomers'." } ], "id_raw": 788 }, { "id": "gwiAOccl9VA4yKSr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the X-axis label at the [0.490, 0.945, 0.578, 0.975] coordinates stand for?" }, { "from": "gpt", "value": "Age Group." } ], "id_raw": 788 }, { "id": "lkYlo8h92WP7vpcy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_8.png", "conversations": [ { "from": "human", "value": "\nIs {GenX's line}[0.212, 0.265, 0.856, 0.722] above or below {Boomers' line}[0.212, 0.047, 0.856, 0.831]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 788 }, { "id": "uU1R1mpOyRYiQHiO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_8.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher population count at age '60-70', {Millennials'}[0.727, 0.613] or {GenZ}[0.727, 0.853]?" }, { "from": "gpt", "value": "Millennials." } ], "id_raw": 788 }, { "id": "KkRS6h1Xb32tmLhx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_8.png", "conversations": [ { "from": "human", "value": "\nAt the age group '30-40', does {GenX}[0.341, 0.635] or {Boomers}[0.341, 0.657] have a higher population?" }, { "from": "gpt", "value": "Boomers." } ], "id_raw": 788 }, { "id": "S8BoSWOc4BLgJKFO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_8.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area at the [0.829, 0.698, 0.977, 0.882] coordinates?" }, { "from": "gpt", "value": "This rectangular area contains the chart legend with item labels: {Millennials}[0.882, 0.710, 0.970, 0.740], {GenX}[0.882, 0.753, 0.927, 0.783], {Boomers}[0.882, 0.797, 0.956, 0.827], and {GenZ}[0.882, 0.841, 0.927, 0.871]. Each label is accompanied by a matching colored handle specifying their respective colors." } ], "id_raw": 788 }, { "id": "wtk0310ESn8BEhKg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the content in area [0.048, 0.865, 0.074, 0.889] represent?" }, { "from": "gpt", "value": "This corresponds to the Y-axis label '200'." } ], "id_raw": 788 }, { "id": "HKkB6nMCEJZl1w23", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_8.png", "conversations": [ { "from": "human", "value": "\nWhat's within the rectangular region [0.450, 0.912, 0.490, 0.936]?" }, { "from": "gpt", "value": "It's an X-axis tick label representing the '40-50' age group." } ], "id_raw": 788 }, { "id": "BApJGVKoNTBMnrfz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the seventh y-axis tick label contained in {the Y-axis label}[0.019, 0.220, 0.041, 0.711] in the chart?" }, { "from": "gpt", "value": "35." } ], "id_raw": 789 }, { "id": "haTsqHyEhIncmB3r", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the {the legend's third item tag}[0.159, 0.147, 0.308, 0.177] show?" }, { "from": "gpt", "value": "DNA Structure." } ], "id_raw": 789 }, { "id": "0fRAe7Jxe1xjRQYi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the second point on the \"Protein Analysis\" line}[0.307, 0.276] correspond to?" }, { "from": "gpt", "value": "Year 2016." } ], "id_raw": 789 }, { "id": "WmpQeYbX6h5RFNDq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_9.png", "conversations": [ { "from": "human", "value": "\nIs the line for {Protein Analysis}[0.157, 0.207, 0.906, 0.551] above or below the line for {DNA Structure}[0.157, 0.207, 0.906, 0.551]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 789 }, { "id": "2Jo8HdqoSuS6FU9I", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_9.png", "conversations": [ { "from": "human", "value": "\nIs {the point for \"Cell Membrane Research\" representing the year 2020}[0.906, 0.172] higher or lower than {the point for \"Anaerobic Digestion\" representing the year 2020}[0.906, 0.276]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 789 }, { "id": "kH0wznlOGuUrwoPe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_9.png", "conversations": [ { "from": "human", "value": "\nWhich research area had a higher degree of focus in the industry in the year 2015, {Protein Analysis}[0.157, 0.207], or {DNA Structure}[0.157, 0.551]?" }, { "from": "gpt", "value": "Protein Analysis." } ], "id_raw": 789 }, { "id": "jSoNZ6yge0klGXXN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the region [0.093, 0.048, 0.426, 0.232]." }, { "from": "gpt", "value": "This area contains the legend items for the graph, which include {Protein Analysis}[0.159, 0.059, 0.325, 0.089], {Cell Membrane Research}[0.159, 0.103, 0.418, 0.133], {DNA Structure}[0.159, 0.147, 0.308, 0.177], and {Anaerobic Digestion}[0.159, 0.191, 0.368, 0.221], each paired with a colored marker indicating the line color on the chart." } ], "id_raw": 789 }, { "id": "vhdWy0V9Hw3mnGLR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are found within the region [0.135, 0.912, 0.179, 0.936]?" }, { "from": "gpt", "value": "In this region, there's an x-axis label showing the year 2015." } ], "id_raw": 789 }, { "id": "Y8sCliJ9iIKZKdD7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_9.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.159, 0.191, 0.368, 0.221]?" }, { "from": "gpt", "value": "The legend item for {Anaerobic Digestion}[0.159, 0.191, 0.368, 0.221] is contained within this region." } ], "id_raw": 789 }, { "id": "hu1DWIdJr50ZAnrk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the seventh x-axis label according to the coordinates on the chart?" }, { "from": "gpt", "value": "70." } ], "id_raw": 790 }, { "id": "1LGVWSdnnJVhgYnh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line labeled as {Global Trade Flow}[0.616, 0.696, 0.861, 0.741]?" }, { "from": "gpt", "value": "#ffa500." } ], "id_raw": 790 }, { "id": "UPl5VQ12wAhEcYOc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.025, 0.189, 0.055, 0.707]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 790 }, { "id": "7BsA4qI9nG96Jegm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_10.png", "conversations": [ { "from": "human", "value": "\nIs point 7 of the {Global Trade Flow}[0.174, 0.117, 0.933, 0.712] above or below point 7 of the {Supply Chain Disruption}[0.174, 0.117, 0.933, 0.712]?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 790 }, { "id": "BGCAofPNTFeD3F8C", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_10.png", "conversations": [ { "from": "human", "value": "\nIs {the point at '40'}[0.427, 0.448] for 'Global Trade Flow' higher or lower than {the point at '40'}[0.427, 0.448] for 'Supply Chain Disruption'?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 790 }, { "id": "7wwmz7aUBiGJW3zX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_10.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {x=100}[0.933, 0.712], 'Global Trade Flow' or 'Supply Chain Disruption'?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 790 }, { "id": "fj9vd2LPyimXWNyF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects present within the region [0.328, 0.869, 0.357, 0.904]?" }, { "from": "gpt", "value": "The region contains the x-axis label '30'." } ], "id_raw": 790 }, { "id": "t456uCVAuOD3XoJQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.528, 0.679, 0.961, 0.823]." }, { "from": "gpt", "value": "The area contains the legend items: {Global Trade Flow}[0.616, 0.696, 0.861, 0.741] with a color marker in #ffa500 and {Supply Chain Disruption}[0.616, 0.762, 0.950, 0.807] with a color marker in #ff4500." } ], "id_raw": 790 }, { "id": "WV3rIoRoSNAzcRHQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_10.png", "conversations": [ { "from": "human", "value": "\nWhat features are found within the coordinates [0.412, 0.869, 0.441, 0.904]?" }, { "from": "gpt", "value": "This region includes the x-axis label '40'." } ], "id_raw": 790 }, { "id": "PJ9NU29CaetUkrRp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_11.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinates [0.041, 0.517, 0.019, 0.441] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 791 }, { "id": "qc7iPCvkyYt4VRhi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_11.png", "conversations": [ { "from": "human", "value": "\nWhere is the legend for 'Accumulated Knowledge' located?" }, { "from": "gpt", "value": "[0.115, 0.121, 0.160, 0.135]" } ], "id_raw": 791 }, { "id": "H05o9ej85Fh1btS3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_11.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by this {point}[0.939, 0.866] on the Social Equality line?" }, { "from": "gpt", "value": "The year 2050." } ], "id_raw": 791 }, { "id": "n8edpsEwqwgwRdYr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_11.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Accumulated Knowledge}[0.939, 0.096] higher than the {Human Population}[0.939, -3245074.638] in the year 2050?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 791 }, { "id": "C91wDVVhwH4DTtZg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_11.png", "conversations": [ { "from": "human", "value": "\nBetween the years 1950 and 2000, does the {Social Equality line}[0.522, 0.884, 0.731, 0.876] increase or decrease?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 791 }, { "id": "aVg2a4gSoPfbzdz9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_11.png", "conversations": [ { "from": "human", "value": "\nAre the values of {Warfare Intensity}[0.522, 0.866] and {Social Equality}[0.522, 0.884] in 1950 close to each other?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 791 }, { "id": "Dj96HuDlZ2XhzgGm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the area within [0.113, 0.074, 0.390, 0.220] of the chart." }, { "from": "gpt", "value": "This region contains the chart's legend. It lists four essential factors of human history and civilization development: Human Population with a yellow handle, Accumulated Knowledge with a red handle, Warfare Intensity with a royal blue handle, and Social Equality with a dark blue handle." } ], "id_raw": 791 }, { "id": "BSfGxOkEhwKHe8zh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_11.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.081, 0.053, 0.092, 0.910]?" }, { "from": "gpt", "value": "It's the y-axis tick labels: {0}[0.081, 0.887, 0.092, 0.910], {500}[0.059, 0.720, 0.092, 0.744], {1000}[0.048, 0.553, 0.092, 0.577], {1500}[0.048, 0.387, 0.092, 0.410], {2000}[0.048, 0.220, 0.092, 0.243], and {2500}[0.048, 0.053, 0.092, 0.076]." } ], "id_raw": 791 }, { "id": "G60Y6IdfSLOe9hLk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_11.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content within [0.522, 0.866, 0.731, 0.871]?" }, { "from": "gpt", "value": "This part of the chart corresponds to the lines of Warfare Intensity and Social Equality between the years 1950 and 2000. The lines show a slight increase in Warfare Intensity and a more marked increase in Social Equality." } ], "id_raw": 791 }, { "id": "2AJacyaju9yWSREy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label located at [0.025, 0.205, 0.055, 0.691]?" }, { "from": "gpt", "value": "No. of Reforms/Projects." } ], "id_raw": 793 }, { "id": "maGZmOHyXFv2xKcq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label located at [0.491, 0.917, 0.564, 0.963]?" }, { "from": "gpt", "value": "Years." } ], "id_raw": 793 }, { "id": "aIyS4C5JW1n5VRu3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the first legend item located at [0.693, 0.327, 0.843, 0.372]?" }, { "from": "gpt", "value": "Healthcare." } ], "id_raw": 793 }, { "id": "yIW2Mmhiqtuz0vaT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_13.png", "conversations": [ { "from": "human", "value": "\nComparing {the 'Healthcare' line}[0.110, 0.131, 0.946, 0.567] and {the 'Social Security' line}[0.110, 0.250, 0.946, 0.368], which one generally has higher values?" }, { "from": "gpt", "value": "Social Security." } ], "id_raw": 793 }, { "id": "tkSjs9M61c3QfyVr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_13.png", "conversations": [ { "from": "human", "value": "\nAt the point {2019}[0.946, 0.686], is {the 'Public Transport' line}[0.528, 0.567] above or below the 'Public Education' line [0.946, 0.567]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 793 }, { "id": "FNUnw9986UIhlbLh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_13.png", "conversations": [ { "from": "human", "value": "\nIn 2015, is the 'Healthcare' or the 'Public Education' department initiating more reforms/projects based on points {Healthcare[0.110, 0.448]} and {Public Education[0.110, 0.368]}?" }, { "from": "gpt", "value": "Healthcare." } ], "id_raw": 793 }, { "id": "E0adMRhfZ7MRxlyG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within [0.604, 0.310, 0.932, 0.586]?" }, { "from": "gpt", "value": "The legend is located here including the labels {Healthcare}[0.693, 0.327, 0.843, 0.372], {Public Education}[0.693, 0.392, 0.921, 0.437], {Public Transport}[0.693, 0.458, 0.913, 0.503], {Social Security}[0.693, 0.524, 0.897, 0.569], each paired with a colored marker." } ], "id_raw": 793 }, { "id": "ZnHxctRnx8afKKgY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_13.png", "conversations": [ { "from": "human", "value": "\nCan you provide a brief description of the area [0.498, 0.869, 0.557, 0.904]?" }, { "from": "gpt", "value": "The x-axis year label '2017' is located in this area." } ], "id_raw": 793 }, { "id": "aXmAnCavwn5ky3a7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content found within the region [0.079, 0.632, 0.094, 0.667]." }, { "from": "gpt", "value": "The y-axis label '5' is located here." } ], "id_raw": 793 }, { "id": "kC2oS72zmaGfDBRb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.025, 0.383, 0.055, 0.499]?" }, { "from": "gpt", "value": "Score" } ], "id_raw": 794 }, { "id": "R31qRvSYwtukaRaf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_14.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the line for 'Start-Up Spirit'}[0.132, 0.138, 0.946, 0.441]?" }, { "from": "gpt", "value": "#008000" } ], "id_raw": 794 }, { "id": "JqwJUBYqpMwm3fy8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.509, 0.917, 0.568, 0.963]?" }, { "from": "gpt", "value": "Year" } ], "id_raw": 794 }, { "id": "aIzeCUIeJYM5uBYK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_14.png", "conversations": [ { "from": "human", "value": "\nIs {the line representing 'Disruption'}[0.132, 0.401, 0.946, 0.764] above or below {the line representing 'Large Corporations'}[0.132, 0.078, 0.946, 0.401]?" }, { "from": "gpt", "value": "Above" } ], "id_raw": 794 }, { "id": "8yTJPUDYKzpBr8te", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point for the year 2005 on the 'Start-Up Spirit' line}[0.335, 0.340] higher or lower than {the point for the same year on the 'Innovation' line}[0.335, 0.441]?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 794 }, { "id": "yQRuARSRd6W9puQS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_14.png", "conversations": [ { "from": "human", "value": "\nWhich line is at a higher point in the year 2015, 'Disruption' or 'Large Corporations'?" }, { "from": "gpt", "value": "Disruption" } ], "id_raw": 794 }, { "id": "MjMd1EHXIpumIMQl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.568, 0.548, 0.932, 0.823]?" }, { "from": "gpt", "value": "This area contains a legend for the chart, with labels for {Start-Up Spirit}[0.656, 0.564, 0.853, 0.609] in green, {Innovation}[0.656, 0.630, 0.803, 0.675] in orange, {Disruption}[0.656, 0.696, 0.800, 0.741] in red, and {Large Corporations}[0.656, 0.762, 0.921, 0.807] in blue. Each label is accompanied by a color marker." } ], "id_raw": 794 }, { "id": "Bo9L8Bk6Dlslvh8V", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents of [0.064, 0.125, 0.115, 0.866]." }, { "from": "gpt", "value": "It contains the y-axis tick labels ranging from 0.20 to 0.55." } ], "id_raw": 794 }, { "id": "5KV9NRFUKqa7SMWl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_14.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the area [0.102, 0.869, 0.975, 0.904]?" }, { "from": "gpt", "value": "The area houses the x-axis labels, marking the years from 2000 to 2020." } ], "id_raw": 794 }, { "id": "QTYz3DMmgQ1mvhrS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the coordinates [0.047, 0.419, 0.082, 0.442]?" }, { "from": "gpt", "value": "7000." } ], "id_raw": 795 }, { "id": "0fWi7Rruf6wGeo6c", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.099, 0.075, 0.319, 0.222]?" }, { "from": "gpt", "value": "Subplot legend." } ], "id_raw": 795 }, { "id": "18Go3VVKQ62kVHEY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the marker for {Trademark Filings}[0.100, 0.158, 0.137, 0.172] in the legend?" }, { "from": "gpt", "value": "Blue (#0343df)." } ], "id_raw": 795 }, { "id": "XbJ4BGh2U09ITDHL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_15.png", "conversations": [ { "from": "human", "value": "\nIs the point for the year 2010 higher in {Patent Grants}[0.539, 0.584] or in {Trademark Filings}[0.539, 0.532]?" }, { "from": "gpt", "value": "Patent Grants." } ], "id_raw": 795 }, { "id": "nnhdqMy02bK3gnkv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_15.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the trend which terminates at the higher point for the year 2020, {Patent Applications}[0.836, 0.116] or {Trademark Registrations}[0.836, 0.428]?" }, { "from": "gpt", "value": "#ff028d (Trademark Registrations)." } ], "id_raw": 795 }, { "id": "T8gzaINLCcWQFttR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_15.png", "conversations": [ { "from": "human", "value": "\nWhich trendline has more points above the y-axis tick label '6000'[0.047, 0.523, 0.082, 0.546], {Patent Applications}[0.241, 0.116, 0.836, 0.636] or {Patent Grants}[0.241, 0.427, 0.836, 0.792]?" }, { "from": "gpt", "value": "Patent Grants." } ], "id_raw": 795 }, { "id": "n387KJZ1WMgWsYv6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within the region [0.100, 0.075, 0.319, 0.222]." }, { "from": "gpt", "value": "This region contains the subplot legend with trend labels and their color markers. The labels are: {Patent Applications}[0.144, 0.085, 0.278, 0.108], {Patent Grants}[0.144, 0.120, 0.239, 0.143], {Trademark Filings}[0.144, 0.155, 0.266, 0.178], and {Trademark Registrations}[0.144, 0.189, 0.313, 0.213] with their respective color markers adjacent to them." } ], "id_raw": 795 }, { "id": "cKJ4xsnTDV24CQek", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements between coordinates [0.241, 0.116, 0.836, 0.636]?" }, { "from": "gpt", "value": "This region consists of various points representing the trend for Patent Applications. The values are plotted for the years 2000 to 2020. The trend is represented by a purple line." } ], "id_raw": 795 }, { "id": "OSvQe6zilENffpfn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the coordinates [0.039, 0.107, 0.082, 0.130]." }, { "from": "gpt", "value": "This region contains the y-axis tick label for the value '10000'." } ], "id_raw": 795 }, { "id": "OBpSaNnhNEcllQu1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the text located at [0.116, 0.912, 0.125, 0.936]?" }, { "from": "gpt", "value": "The x-axis tick label '1'." } ], "id_raw": 796 }, { "id": "vYMFoZEHQbXbDoHa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_16.png", "conversations": [ { "from": "human", "value": "\nCan you identify the item found at these coordinates {item from the legend}[0.489, 0.747, 0.614, 0.771]?" }, { "from": "gpt", "value": "It is 'Process Efficiency'." } ], "id_raw": 796 }, { "id": "97KDjm7A6YoEbZcO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_16.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the object at {coordinates}[0.779, 0.306, 0.944, 0.245] in the chart?" }, { "from": "gpt", "value": "It is the 6th point or marker on the Process Efficiency line." } ], "id_raw": 796 }, { "id": "i6DiM7iJ4Tcvirx8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_16.png", "conversations": [ { "from": "human", "value": "\nIs the 3rd point of {Process Efficiency}[0.450, 0.550] above or below the 3rd point of {Process Timing}[0.450, 0.408]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 796 }, { "id": "lwUiBB5xt4hWgOyU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_16.png", "conversations": [ { "from": "human", "value": "\nWhich line marker is higher, the 4th point on the {Process Efficiency} line[0.614, 0.367] or the 4th on the {Process Timing}[0.614, 0.591] line?" }, { "from": "gpt", "value": "The Process Timing line." } ], "id_raw": 796 }, { "id": "HobHKgbbNiJMMVvl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_16.png", "conversations": [ { "from": "human", "value": "\nAt the 6th point, is {Process Efficiency}[0.944, 0.245] having a higher or lower value than {Approval Rate}[0.944, 0.082]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 796 }, { "id": "DPBeYK0QEJH92hdq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_16.png", "conversations": [ { "from": "human", "value": "\nTell me what the area enclosed within [0.445, 0.738, 0.619, 0.885] houses." }, { "from": "gpt", "value": "The area contains the legend detailing the color coding for the lines. The legend includes Process Efficiency, Process Timing, Approval Rate, and Process Cost, each with a unique color and marker." } ], "id_raw": 796 }, { "id": "DqeIJlPvicF2H0k2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region within [0.120, 0.245, 0.944, 0.408]?" }, { "from": "gpt", "value": "The region encompasses the line for Process Efficiency, marked with the color #1f77b4 and 'o' style markers at points representing the six quarters." } ], "id_raw": 796 }, { "id": "Jh4O3XNEdwSYpxA3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_16.png", "conversations": [ { "from": "human", "value": "\nWhat can you find in the region [0.489, 0.782, 0.593, 0.806]?" }, { "from": "gpt", "value": "The label 'Process Timing' from the chart's legend is within this region." } ], "id_raw": 796 }, { "id": "MfjL1xmH0JC1kpDc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at {coordinates}[0.215, 0.911, 0.224, 0.934] on the x-axis?" }, { "from": "gpt", "value": "1." } ], "id_raw": 797 }, { "id": "rcXWl6XFpcCdy9DD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_17.png", "conversations": [ { "from": "human", "value": "\nWhat color does the {legend}[0.075, 0.039, 0.296, 0.223] associate with the \"Medication Group\"?" }, { "from": "gpt", "value": "Blue (#0000ff)." } ], "id_raw": 797 }, { "id": "ACgYbGH4tJJ2XpYy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at {coordinates}[0.040, 0.306, 0.057, 0.329]?" }, { "from": "gpt", "value": "40." } ], "id_raw": 797 }, { "id": "5AmGOq3oDxwLVU4f", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_17.png", "conversations": [ { "from": "human", "value": "\nIs the first point of the {Control Group}[0.220, 0.736, 0.832, 0.842] line above or below the first point of the {Therapy Group}[0.220, 0.447, 0.832, 0.736] line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 797 }, { "id": "1CtIFMW0rd4j5cZP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_17.png", "conversations": [ { "from": "human", "value": "\nBetween the {Therapy Group}[0.220, 0.447, 0.832, 0.736] and the {Combination Group}[0.220, 0.078, 0.832, 0.578], which one reaches higher on the fifth day?" }, { "from": "gpt", "value": "Combination Group." } ], "id_raw": 797 }, { "id": "yWxs4J8wkItVq9kc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_17.png", "conversations": [ { "from": "human", "value": "\nIs the second point in the {Medication Group}[0.220, 0.420, 0.832, 0.789] line above or below the second point in the {Control Group}[0.220, 0.736, 0.832, 0.842] line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 797 }, { "id": "PfsVFgrHgWDB6zHP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the region [0.075, 0.039, 0.296, 0.223]." }, { "from": "gpt", "value": "This region contains the chart's legend. It includes the labels: {Control Group}[0.128, 0.050, 0.244, 0.080], {Therapy Group}[0.128, 0.094, 0.253, 0.124], {Medication Group}[0.128, 0.138, 0.275, 0.168], and {Combination Group}[0.128, 0.182, 0.290, 0.212]. Each label is associated with a unique color represented by a geometric figure to the left of the label." } ], "id_raw": 797 }, { "id": "mbp2hHroRGwIX9C0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.035, 0.290, 0.062, 0.330]." }, { "from": "gpt", "value": "This region contains one y-axis tick label: {40}[0.040, 0.306, 0.057, 0.329]." } ], "id_raw": 797 }, { "id": "KR9cXBpRiHdIPSj2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_17.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.675, 0.911, 0.683, 0.934]?" }, { "from": "gpt", "value": "This region contains the fourth x-axis label, which is the number {4}[0.675, 0.911, 0.683, 0.934]." } ], "id_raw": 797 }, { "id": "jF1BSrqCtBxTx123", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the marker for {Product_B}[0.157, 0.212, 0.227, 0.233]?" }, { "from": "gpt", "value": "#ff4500." } ], "id_raw": 798 }, { "id": "Qh1V4vGarTrjoF2q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the point coordinates of the 8th data for {Product_C}[0.662, 0.642]?" }, { "from": "gpt", "value": "670." } ], "id_raw": 798 }, { "id": "3JySCbyK2iXF5UsN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the text in the label at the location [0.064, 0.133, 0.123, 0.168]?" }, { "from": "gpt", "value": "1700." } ], "id_raw": 798 }, { "id": "OW27Y64c6hsUS9zw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_18.png", "conversations": [ { "from": "human", "value": "\nWhich product's sales trend line from {Product_A}[0.242, 0.136, 0.380, 0.181] and {Product_B}[0.242, 0.202, 0.380, 0.248] is showing an increase in the 11th month?" }, { "from": "gpt", "value": "Product_B." } ], "id_raw": 798 }, { "id": "9cxwNrNNMwFvFnpQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_18.png", "conversations": [ { "from": "human", "value": "\nIs {Product_A}'s 8th data point [0.662, 0.796] higher than {Product_D}'s 8th data point [0.662, 0.531]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 798 }, { "id": "NeWYuS61BgeEDOkV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_18.png", "conversations": [ { "from": "human", "value": "\nCompare the 7th month's sale of {Product_C}[0.587, 0.637] and {Product_D}[0.587, 0.483]. Which is higher?" }, { "from": "gpt", "value": "Product_C." } ], "id_raw": 798 }, { "id": "9YZ9jJQt3bL3owtE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_18.png", "conversations": [ { "from": "human", "value": "\nWhat information is contained in the area [0.153, 0.119, 0.394, 0.398]?" }, { "from": "gpt", "value": "It is a legend providing information about different product lines (Product_A, Product_B, Product_C, Product_D) along with their respective line colors." } ], "id_raw": 798 }, { "id": "Wrmc6JBxbOgO4QEn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_18.png", "conversations": [ { "from": "human", "value": "\nCould you describe the contents within the region [0.064, 0.229, 0.123, 0.264]?" }, { "from": "gpt", "value": "It is a y-axis tick label that reads '1500'." } ], "id_raw": 798 }, { "id": "fvisHNSNPyORl8K8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_18.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.281, 0.869, 0.296, 0.904]?" }, { "from": "gpt", "value": "It's an x-axis label representing the 3rd month." } ], "id_raw": 798 }, { "id": "MIOfeD8UjMXxdsr0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.661, 0.229, 0.941, 0.265] in the chart's legend?" }, { "from": "gpt", "value": "Suburbs Planning Contacts." } ], "id_raw": 799 }, { "id": "6EZGday9eptl9gbT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the x-axis label?" }, { "from": "gpt", "value": "[0.493, 0.934, 0.548, 0.970]." } ], "id_raw": 799 }, { "id": "KkAdmsP8VDka4e5B", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_40_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.059, 0.864, 0.070, 0.892]?" }, { "from": "gpt", "value": "0." } ], "id_raw": 799 }, { "id": "3Gt7s4n1IVjQ9XM4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_19.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'CBD Construction'}[0.162, 0.637, 0.880, 0.852] above or below {the line for 'Suburbs Construction'}[0.162, 0.279, 0.880, 0.685]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 799 }, { "id": "x5ZhIsbTLPD6Tp41", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_19.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at {point 2009}[0.880, 0.852], CBD Construction or Suburbs Construction?" }, { "from": "gpt", "value": "CBD Construction." } ], "id_raw": 799 }, { "id": "nfV4xSm1ZviQMGB4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_40_19.png", "conversations": [ { "from": "human", "value": "\nDoes {CBD Planning Contacts line}[0.162, 0.303, 0.880, 0.518] intersect with {the line for 'Suburbs Planning Contacts'}[0.162, 0.089, 0.880, 0.303]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 799 }, { "id": "bJxL3pV4mlIA5o9A", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.595, 0.058, 0.949, 0.278]." }, { "from": "gpt", "value": "This area contains the chart legend with 4 items: {CBD Construction}[0.661, 0.071, 0.845, 0.107], {Suburbs Construction}[0.661, 0.124, 0.885, 0.160], {CBD Planning Contacts}[0.661, 0.176, 0.900, 0.212], and {Suburbs Planning Contacts}[0.661, 0.229, 0.941, 0.265], each with a marker in a distinct color." } ], "id_raw": 799 }, { "id": "KuDGDlCulyQ9JFty", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.048, 0.030, 0.070, 0.892]." }, { "from": "gpt", "value": "This area contains the y-axis labels from '0' to '35'. Their respective coordinates are '0' at {coordinates}[0.059, 0.864, 0.070, 0.892], '5' at {coordinates}[0.059, 0.745, 0.070, 0.773], '10' at {coordinates}[0.048, 0.626, 0.070, 0.654], '15' at {coordinates}[0.048, 0.507, 0.070, 0.535], '20' at {coordinates}[0.048, 0.388, 0.070, 0.416], '25' at {coordinates}[0.048, 0.268, 0.070, 0.296], '30' at {coordinates}[0.048, 0.149, 0.070, 0.177], and '35' at {coordinates}[0.048, 0.030, 0.070, 0.058]." } ], "id_raw": 799 }, { "id": "RYxTrR0VZ6fLknAs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_40_19.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.162, 0.089, 0.880, 0.852]?" }, { "from": "gpt", "value": "There are 4 objects. They are the line plots for CBD Construction, Suburbs Construction, CBD Planning Contacts, and Suburbs Planning Contacts." } ], "id_raw": 799 }, { "id": "hLB827oP1m7GpTqV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label shown on the chart?" }, { "from": "gpt", "value": "Temperature." } ], "id_raw": 800 }, { "id": "NZCKewBucu3hl6db", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label shown on the chart?" }, { "from": "gpt", "value": "Latitude." } ], "id_raw": 800 }, { "id": "WrIZKjJiIixHQyZx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the line with color code \"#679fce\" represent?" }, { "from": "gpt", "value": "Temperate_Climate." } ], "id_raw": 800 }, { "id": "ENNBMnJd7AUj8eXo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_0.png", "conversations": [ { "from": "human", "value": "\nAt latitude -50, is the temperature for {the line representing 'Temperate_Climate'}[0.310, 0.604] higher or lower than {the line representing 'Tropical_Climate'}[0.310, 0.439]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 800 }, { "id": "BusycQ41daCxShdq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_0.png", "conversations": [ { "from": "human", "value": "\nWhich line, {Temperate_Climate}[0.130, 0.397, 0.941, 0.770] or {Tropical_Climate}[0.130, 0.231, 0.941, 0.604] approaches a higher temperature at latitude 0?" }, { "from": "gpt", "value": "Tropical_Climate." } ], "id_raw": 800 }, { "id": "X4l60yWmjGw01f5y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_0.png", "conversations": [ { "from": "human", "value": "\nDoes the {Temperate_Climate}[0.130, 0.397, 0.941, 0.770] show a higher temperature at latitude 40 or latitude 60?" }, { "from": "gpt", "value": "Latitude 40." } ], "id_raw": 800 }, { "id": "oOgtnPoUrGyIcA3A", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_0.png", "conversations": [ { "from": "human", "value": "\nDescribe what's contained in the coordinates [0.100, 0.079, 0.375, 0.176]." }, { "from": "gpt", "value": "This area contains the legend for the chart, specifying the color and label for each of the two temperature data series: Temperate_Climate and Tropical_Climate." } ], "id_raw": 800 }, { "id": "sbcumg7KjiWbVh0r", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_0.png", "conversations": [ { "from": "human", "value": "\nPlease characterize the content in the area [0.089, 0.066, 0.981, 0.895]." }, { "from": "gpt", "value": "This area comprises the entirety of the chart plot, which includes the plotted lines representing Temperate_Climate and Tropical_Climate, their respective data points, and the grid of the chart." } ], "id_raw": 800 }, { "id": "vzdHpUSqkCzzRqvF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_0.png", "conversations": [ { "from": "human", "value": "\nWhat information is captured within [0.050, 0.056, 0.077, 0.080]?" }, { "from": "gpt", "value": "Within these coordinates, you can find the y-axis tick label '2.0'." } ], "id_raw": 800 }, { "id": "f6reS7esbY7EnVih", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within the coordinates [0.161, 0.091, 0.300, 0.121]?" }, { "from": "gpt", "value": "The label \"Inflation Rate\" in the legend." } ], "id_raw": 801 }, { "id": "zGPsNQBl742ZUpSR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-tick label at [-0.013, 0.911, 0.047, 0.934] represent?" }, { "from": "gpt", "value": "The label \"1997.5\" on the x-axis." } ], "id_raw": 801 }, { "id": "39jToHYtl6P0B1Bj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_1.png", "conversations": [ { "from": "human", "value": "\nWhat is within the coordinates [0.855, 0.669, 0.940, 0.693]?" }, { "from": "gpt", "value": "The last point on the Inflation Rate line for the year 2019." } ], "id_raw": 801 }, { "id": "lxDKVCgDLkFqmCU8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Inflation Rate for the year 2009}[0.511, 0.857] above or below {the point for Unemployment Rate for the year 2009}[0.511, 0.130]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 801 }, { "id": "OxcGsJk4vyYtj2sQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_1.png", "conversations": [ { "from": "human", "value": "\nAt the point [0.726, 0.707], is {Inflation Rate}[0.726, 0.364] above or below {Unemployment Rate}[0.726, 0.706]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 801 }, { "id": "cXmV34G5Gey7MGxB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_1.png", "conversations": [ { "from": "human", "value": "\nWhich rate is higher in the year 2000, {Inflation Rate}[0.124, 0.574] or {Unemployment Rate}[0.124, 0.527]?" }, { "from": "gpt", "value": "Inflation Rate." } ], "id_raw": 801 }, { "id": "E5hsJbn4lOpu6t5l", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the region in [0.095, 0.095, 0.151, 0.157]." }, { "from": "gpt", "value": "It contains a marker for the Inflation Rate in the legend with the color '#af7dfe'." } ], "id_raw": 801 }, { "id": "RW1SL3lMVU3rDCHS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_1.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.507, 0.943, 0.558, 0.975]?" }, { "from": "gpt", "value": "The x-axis label \"Year\" is here, described in black color." } ], "id_raw": 801 }, { "id": "zEUuY68qmaAT1rzD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within [0.202, 0.911, 0.262, 0.934]?" }, { "from": "gpt", "value": "It contains the x-axis tick label '2002.5'." } ], "id_raw": 801 }, { "id": "CC9RKY2dFD8Ve319", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_2.png", "conversations": [ { "from": "human", "value": "\nWhat is demonstrated by {line_1}[0.130, 0.231, 0.941, 0.328]?" }, { "from": "gpt", "value": "Economic Performance." } ], "id_raw": 802 }, { "id": "1cuq4EgduakjZhkg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_2.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.019, 0.377, 0.043, 0.583] refer to?" }, { "from": "gpt", "value": "Performance." } ], "id_raw": 802 }, { "id": "6nlUVqxvFIKhTUDg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of {the subtitle}[0.218, 0.025, 0.852, 0.058]?" }, { "from": "gpt", "value": "Sustainability Measures for CSR Commitment." } ], "id_raw": 802 }, { "id": "rZsnE2TD7X0etStN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_2.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at 0.9 index, {Environmental Performance}[0.130, 0.483, 0.941, 0.508] or {Social Performance}[0.130, 0.591, 0.941, 0.645]?" }, { "from": "gpt", "value": "Social Performance." } ], "id_raw": 802 }, { "id": "xNjLnP0oFWi9EL8L", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_2.png", "conversations": [ { "from": "human", "value": "\nAt index 0.7, is {Economic Performance}[0.130, 0.231, 0.941, 0.328] higher or lower than {Environmental Performance}[0.130, 0.483, 0.941, 0.508]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 802 }, { "id": "qRUsoE3lINosHXem", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_2.png", "conversations": [ { "from": "human", "value": "\nIs {Environmental Performance}[0.130, 0.483, 0.941, 0.508] higher or lower at index 1.0 compared to index 0.1?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 802 }, { "id": "5rxrBBHw5rSEQcLp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within [0.050, 0.056, 0.077, 0.909]?" }, { "from": "gpt", "value": "This region contains the y-axis tick labels. The labels indicate the performance values ranging from 0.2 to 0.8 with an increment of 0.1." } ], "id_raw": 802 }, { "id": "WnwNVwnnfy7Uw0sK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_2.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the coordinates [0.116, 0.911, 0.954, 0.934]?" }, { "from": "gpt", "value": "This area includes the X-axis tick labels that represent the CSR Index ranging from 0.1 to 1.0." } ], "id_raw": 802 }, { "id": "XEgIQd9aJHyhWd1K", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_2.png", "conversations": [ { "from": "human", "value": "\nWhat's in the zone from [0.100, 0.079, 0.464, 0.219]?" }, { "from": "gpt", "value": "The area includes the graph\u2019s legend with the labels: {Environmental Performance}[0.166, 0.091, 0.456, 0.121], {Economic Performance}[0.166, 0.134, 0.405, 0.164], and {Social Performance}[0.166, 0.178, 0.366, 0.208]." } ], "id_raw": 802 }, { "id": "zfsu780AbiTyvHMr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.509, 0.943, 0.561, 0.975] in the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 803 }, { "id": "quwK6SE8mHRG8qzi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of the marker for the second data point in the line graph for the \"Inovation Industries\"?" }, { "from": "gpt", "value": "[0.332, 0.548]" } ], "id_raw": 803 }, { "id": "DK2Km0Hgrpa0zyMX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_3.png", "conversations": [ { "from": "human", "value": "\nWhat are the values represented by {the y-axis tick label} at [0.050, 0.810, 0.077, 0.833]?" }, { "from": "gpt", "value": "0.5" } ], "id_raw": 803 }, { "id": "YfNULr4I9HbZgjzd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_3.png", "conversations": [ { "from": "human", "value": "\nIs {the third data point for \"Knowledge Economy\"}[0.535, 0.292] above or below {the third data point for \"Inovation Industries\"}[0.535, 0.518]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 803 }, { "id": "IS7C7VJaRRlxFgsz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the point for \"Knowledge Economy\" in 2020}[0.941, 0.171] have higher or lower growth compared to {the point for \"Inovation Industries\" in 2020}[0.941, 0.292]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 803 }, { "id": "DVVJgEFKcAFXZYMF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_3.png", "conversations": [ { "from": "human", "value": "\nWhich line on the chart has a higher value at the point labeled {2019}, \"Knowledge Economy\" or \"Inovation Industries\"?" }, { "from": "gpt", "value": "\"Inovation Industries\"." } ], "id_raw": 803 }, { "id": "LL6rvI7mwDR5nWpx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area with coordinates [0.108, 0.911, 0.152, 0.934]." }, { "from": "gpt", "value": "The area contains the x-axis tick label \"2016\"." } ], "id_raw": 803 }, { "id": "5rKCgvUd3sqOBQ4B", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the region from [0.100, 0.079, 0.391, 0.176]?" }, { "from": "gpt", "value": "This area holds the chart's legend, which includes the labels {Knowledge Economy}[0.166, 0.091, 0.382, 0.121] and {Inovation Industries}[0.166, 0.134, 0.372, 0.164], each associated with a specific color." } ], "id_raw": 803 }, { "id": "SCIJ9xT7cQ3ydacO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_3.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the area [0.061, 0.025, 1.009, 0.058]?" }, { "from": "gpt", "value": "The subtitle of the chart, \"Growth Comparison: Knowledge Economy and Innovation Industries,\" is positioned here." } ], "id_raw": 803 }, { "id": "RFRsDVTaodDSt1bc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object represented by the coordinates [0.015, 0.426, 0.034, 0.544]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 804 }, { "id": "ZREtFUrKtTm5YUfs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first legend item in the subplots?" }, { "from": "gpt", "value": "[0.861, 0.068, 0.970, 0.090]." } ], "id_raw": 804 }, { "id": "GksYeveFwXegBhem", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_4.png", "conversations": [ { "from": "human", "value": "\nWhich step does {the x-axis tick label}[0.108, 0.933, 0.117, 0.951] represent?" }, { "from": "gpt", "value": "Step number 1." } ], "id_raw": 804 }, { "id": "2uxGtX2P4rcAnysJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point at step 3 of 'Supply Chain'}[0.200, 0.147] higher or lower than {the point at step 3 of 'Fulfillment'}[0.200, 0.245]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 804 }, { "id": "AUI2Ba1AtVqs7Lrc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_4.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Supply Chain'}[0.113, 0.093, 0.943, 0.507] generally above or below {the line for 'Fulfillment'}[0.113, 0.104, 0.943, 0.834]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 804 }, { "id": "onOt0C6QPOrbgxop", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_4.png", "conversations": [ { "from": "human", "value": "\nWhich process has a higher efficiency at {step 10}[0.506, 0.921], Supply Chain or Fulfillment?" }, { "from": "gpt", "value": "Supply Chain." } ], "id_raw": 804 }, { "id": "FoIv652MzYd8yQ7J", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the region [0.314, 0.019, 0.742, 0.044]." }, { "from": "gpt", "value": "This area contains the subtitle of the chart, \"Value Chain Visualization and Analysis\", styled in black color." } ], "id_raw": 804 }, { "id": "ciCVM1kCNJbj44pY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area with coordinates [0.808, 0.060, 0.977, 0.132]." }, { "from": "gpt", "value": "There are two items in the area. These are the legend entries of the two plotted processes: {Supply Chain}[0.861, 0.068, 0.970, 0.090] with a {color marker}[0.809, 0.071, 0.853, 0.085] and {Fulfillment}[0.861, 0.101, 0.948, 0.123] paired with a different {color marker}[0.809, 0.104, 0.853, 0.118]." } ], "id_raw": 804 }, { "id": "DuQ8zJIXNAWy33d7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_4.png", "conversations": [ { "from": "human", "value": "\nWhat objects are present within the region [0.113, 0.093, 0.943, 0.507]?" }, { "from": "gpt", "value": "Two objects are present within this region. The lines representing the 'Supply Chain' and 'Fulfillment' processes extend within these coordinates, depicting changes in efficiency across different steps." } ], "id_raw": 804 }, { "id": "IVovzIeSkoN8OEZc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_5.png", "conversations": [ { "from": "human", "value": "\nWhat object is placed at the coordinates [0.019, 0.373, 0.043, 0.587]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 805 }, { "id": "mvsgIYcyA3qFbJK0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_5.png", "conversations": [ { "from": "human", "value": "\nCan you let me know the coordinates of the third tick on the x-axis?" }, { "from": "gpt", "value": "[0.270, 0.911, 0.314, 0.934]" } ], "id_raw": 805 }, { "id": "aInvyOX2awGpWzRq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_5.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the text}[0.513, 0.911, 0.557, 0.934] on the x-axis?" }, { "from": "gpt", "value": "The year 2010." } ], "id_raw": 805 }, { "id": "4PmhLVTbNioVyuuS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_5.png", "conversations": [ { "from": "human", "value": "\nAt the year 2002, is the {Electricity Consumption}[0.211, 0.607] higher or lower than the {Gas Consumption}[0.211, 0.740]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 805 }, { "id": "bPTYkj7o8UcGVErU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_5.png", "conversations": [ { "from": "human", "value": "\nAt the year 2008, which consumption is higher, {Electricity Consumption}[0.454, 0.480] or {Gas Consumption}[0.454, 0.834]?" }, { "from": "gpt", "value": "Gas Consumption." } ], "id_raw": 805 }, { "id": "iZeRFNP9QiPrC0mL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_5.png", "conversations": [ { "from": "human", "value": "\nIs the {Gas Consumption}[0.616, 0.872] in the year 2012 greater than the {Electricity Consumption}[0.616, 0.392] in the same year?" }, { "from": "gpt", "value": "No." } ], "id_raw": 805 }, { "id": "p6H7xmcV96Q4bVms", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_5.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content within the region [0.100, 0.079, 0.422, 0.176]?" }, { "from": "gpt", "value": "This area contains the legend for the chart. It includes two items: {Electricity Consumption}[0.166, 0.091, 0.413, 0.121] marked with color '#af7dfe' and {Gas Consumption}[0.166, 0.134, 0.350, 0.164] marked with color '#feac60'." } ], "id_raw": 805 }, { "id": "T1G7Bb79w1P2OzFQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is represented in the location [0.049, 0.222, 0.077, 0.246]?" }, { "from": "gpt", "value": "This area contains the y-axis label '1.2'." } ], "id_raw": 805 }, { "id": "Fep9LrhnZ1M53WLR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the coordinates [0.432, 0.911, 0.476, 0.934]?" }, { "from": "gpt", "value": "There is one object located here, which is an x-axis label for '2008'." } ], "id_raw": 805 }, { "id": "2GZPV5brzJKa4bEF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_6.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.509, 0.943, 0.561, 0.975]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 806 }, { "id": "D1FnvVpI8dVDdoN5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.101, 0.095, 0.156, 0.113]?" }, { "from": "gpt", "value": "Colored marker in the legend." } ], "id_raw": 806 }, { "id": "HYaFlZvgCfN8gLDR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_6.png", "conversations": [ { "from": "human", "value": "\nWhat does {the Y-tick} at the location [0.049, 0.575, 0.077, 0.598] represent?" }, { "from": "gpt", "value": "1.5" } ], "id_raw": 806 }, { "id": "RDDvRi21qc8fxLEc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_6.png", "conversations": [ { "from": "human", "value": "\nDoes the trend of {Biomedicine Trends}[0.170, 0.273, 0.900, 0.729] surpass or fall behind {Medical Tech Biotech Trends}[0.170, 0.086, 0.900, 0.667] in the year indicated by the x-axis tick at [0.414, 0.895]?" }, { "from": "gpt", "value": "Surpass." } ], "id_raw": 806 }, { "id": "qn6mrJDPA6gEjlPH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_6.png", "conversations": [ { "from": "human", "value": "\nIs the point marked at {coordinates}[0.900, 0.273] on the 'Biomedicine Trends' line higher or lower than the corresponding point on the 'Medical Tech Biotech Trends' line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 806 }, { "id": "GFTmU0d6sncJs62Q", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_6.png", "conversations": [ { "from": "human", "value": "\nWhich trend appears to grow faster across the years, {Medical Tech Biotech Trends}[0.170, 0.086, 0.900, 0.667] or {Biomedicine Trends}[0.170, 0.273, 0.900, 0.729]?" }, { "from": "gpt", "value": "Medical Tech Biotech Trends." } ], "id_raw": 806 }, { "id": "h3gRWmrWKm5ZfBiY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_6.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred from the region [0.100, 0.079, 0.466, 0.176]?" }, { "from": "gpt", "value": "This region contains the legend for two data series: {Medical Tech Biotech Trends}[0.166, 0.091, 0.458, 0.121] and {Biomedicine Trends}[0.166, 0.134, 0.368, 0.164], each coupled with its corresponding color marker." } ], "id_raw": 806 }, { "id": "JlcgpoJMFR2workU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region between coordinates [0.089, 0.066, 0.981, 0.895]?" }, { "from": "gpt", "value": "This region primarily comprises the data visualization area. It includes two lines representing the trends of 'Medical Tech Biotech Trends' and 'Biomedicine Trends', along with the x and y axes, their respective tick labels, and grid lines." } ], "id_raw": 806 }, { "id": "6l2hxddFhDFlApEy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_6.png", "conversations": [ { "from": "human", "value": "\nWhat are found within the region [0.170, 0.086, 0.900, 0.667]?" }, { "from": "gpt", "value": "This region contains points and marker lines labelling the trend data for the series: 'Medical Tech Biotech Trends'." } ], "id_raw": 806 }, { "id": "MTbU5navOk7Avhyu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.019, 0.386, 0.043, 0.574] in the chart?" }, { "from": "gpt", "value": "Index Value." } ], "id_raw": 807 }, { "id": "8jAxtqNBhyZKrAuE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the xAxis tick label}[0.297, 0.911, 0.368, 0.934] in the graph?" }, { "from": "gpt", "value": "America." } ], "id_raw": 807 }, { "id": "Eldu7Aia4z3XLNPS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_7.png", "conversations": [ { "from": "human", "value": "\nWhat does {the legend item}[0.733, 0.796, 0.962, 0.826] refer to?" }, { "from": "gpt", "value": "Consumer Confidence." } ], "id_raw": 807 }, { "id": "cjAqDz5RA5Oxbm2X", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Asia' in the 'Consumer Confidence' line}[0.130, 0.713] higher or lower than {the point for 'Asia' in the 'Economy Index' line}[0.130, 0.536]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 807 }, { "id": "QujyqtixS7fySgZH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_7.png", "conversations": [ { "from": "human", "value": "\nCompare the points for 'Europe' in the two lines, which has a higher value, 'Consumer Confidence' {the line for 'Consumer Confidence' at 'Europe'}[0.535, 0.301] or 'Economy Index' {the line for 'Economy Index' at 'Europe'}[0.535, 0.360]?" }, { "from": "gpt", "value": "Economy Index." } ], "id_raw": 807 }, { "id": "VxhrCnrN9Tg6WGy2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'America' in 'Consumer Confidence'}[0.332, 0.418] higher or lower than {the point for 'America' in 'Economy Index'}[0.332, 0.654]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 807 }, { "id": "hFLNcnVscasLFnym", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the box [0.666, 0.785, 0.971, 0.881]?" }, { "from": "gpt", "value": "It contains the chart legend, indicating 'Consumer Confidence' with a dark purple color and 'Economy Index' with an orange color." } ], "id_raw": 807 }, { "id": "Nc5LbLZ7fXJIsbvP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within the box [0.089, 0.066, 0.981, 0.895]." }, { "from": "gpt", "value": "It includes the main plotting area of the line chart depicting 'Consumer Confidence' and 'Economy Index' across different regions." } ], "id_raw": 807 }, { "id": "RN6XwdTNnYQaWEwA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_7.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content within the area [0.493, 0.943, 0.577, 0.975]?" }, { "from": "gpt", "value": "It corresponds to a label with the text 'Region'." } ], "id_raw": 807 }, { "id": "8mFDhRZtQq3Fqn6b", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at these coordinates [0.095, 0.066, 0.981, 0.895] in the chart?" }, { "from": "gpt", "value": "Main plot area." } ], "id_raw": 808 }, { "id": "6AuR3KGTJuOtuaxV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.019, 0.253, 0.043, 0.707]?" }, { "from": "gpt", "value": "Y-axis label representing Production (Millions of Tons)." } ], "id_raw": 808 }, { "id": "hR7AhM0U71JKc2He", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at the coordinates [0.269, 0.025, 0.807, 0.058] refer to?" }, { "from": "gpt", "value": "It refers to the subtitle of the chart, 'Food and Agriculture Industry Analysis'." } ], "id_raw": 808 }, { "id": "0aseFe4kI7GOmPj6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_8.png", "conversations": [ { "from": "human", "value": "\nIn the chart, is the {Grain Production line}[0.135, 0.273, 0.941, 0.584] above or below the {Fruit Production line}[0.135, 0.604, 0.941, 0.739] in the year 2010?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 808 }, { "id": "N24HaXJBfHZZASXT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_8.png", "conversations": [ { "from": "human", "value": "\nBetween the {Grain Production line}[0.135, 0.273, 0.941, 0.584] and the {Fruit Production line}[0.135, 0.604, 0.941, 0.739], which one shows a higher value in year 2016?" }, { "from": "gpt", "value": "Grain Production line." } ], "id_raw": 808 }, { "id": "K4V1TJ435zr1Wn6y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_8.png", "conversations": [ { "from": "human", "value": "\nIn the year 2008, did the {Grain Production line}[0.135, 0.273, 0.941, 0.584] start above or below the {Fruit Production line}[0.135, 0.604, 0.941, 0.739]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 808 }, { "id": "s3mzUpmvRmdCv3mz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content lying within these coordinates: [0.105, 0.079, 0.353, 0.176]?" }, { "from": "gpt", "value": "This region contains the legend for the chart. The legend items specifically here are {Grain Production}[0.172, 0.091, 0.345, 0.121] and {Fruit Production}[0.172, 0.134, 0.335, 0.164], each identified with a different color." } ], "id_raw": 808 }, { "id": "w9hlVieUzPYdLfwj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within the area [0.506, 0.943, 0.570, 0.975]." }, { "from": "gpt", "value": "The X-axis label 'Years' is located within these coordinates." } ], "id_raw": 808 }, { "id": "ZdRBN4w4xPH3iKmQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_8.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within [0.919, 0.911, 0.963, 0.934]?" }, { "from": "gpt", "value": "There is a single object, the x-axis tick label '2017'." } ], "id_raw": 808 }, { "id": "dr7Rw62eLEbwnPmq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.166, 0.091, 0.475, 0.121]?" }, { "from": "gpt", "value": "It's the label for {Eco-Friendly Product Revenue}[0.166, 0.091, 0.475, 0.121]." } ], "id_raw": 809 }, { "id": "Nx4JkuSoFi4t6OXv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.049, 0.375, 0.077, 0.399]?" }, { "from": "gpt", "value": "It's the y-axis tick label {1.0}[0.049, 0.375, 0.077, 0.399]." } ], "id_raw": 809 }, { "id": "tJreduIfXKWkPFz5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.067, 0.911, 0.111, 0.934]?" }, { "from": "gpt", "value": "It's the x-axis tick label {2012}[0.067, 0.911, 0.111, 0.934]." } ], "id_raw": 809 }, { "id": "gTrzXpt6GuskAI8H", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_9.png", "conversations": [ { "from": "human", "value": "\nDoes the {Eco-Friendly Product Revenue} line [0.089, 0.193, 0.959, 0.831] end ahead or behind of the {Social Responsibility Spend} line [0.089, 0.129, 0.959, 0.767] in terms of the Year axis?" }, { "from": "gpt", "value": "Ahead." } ], "id_raw": 809 }, { "id": "PqEZ690d9rRqrrH1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_9.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher Spend/Revenue in 2017, {Eco-Friendly Product Revenue}[0.633, 0.512] or {Social Responsibility Spend}[0.633, 0.639]?" }, { "from": "gpt", "value": "Social Responsibility Spend." } ], "id_raw": 809 }, { "id": "s5EhKwE1F4ARNpRR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_9.png", "conversations": [ { "from": "human", "value": "\nIn 2018, is the {Social Responsibility Spend}[0.742, 0.448] more or less than the {Eco-Friendly Product Revenue}[0.742, 0.384]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 809 }, { "id": "EU3cbpsAnkvoKFMz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.100, 0.079, 0.484, 0.176]." }, { "from": "gpt", "value": "This area contains the legends of two lines: The line {Eco-Friendly Product Revenue}[0.166, 0.091, 0.475, 0.121] and the line {Social Responsibility Spend}[0.166, 0.134, 0.450, 0.164]." } ], "id_raw": 809 }, { "id": "udSKneDoULVkXe2d", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in the bounding box [0.150, 0.025, 0.899, 0.058]?" }, { "from": "gpt", "value": "It's the subtitle of the chart, \"Social Responsibility and Sustainable Business Trends\"." } ], "id_raw": 809 }, { "id": "vMgv0Hpoa83zxOu8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the area [0.502, 0.911, 0.655, 0.934]?" }, { "from": "gpt", "value": "It includes two x-axis tick labels: {2016}[0.502, 0.911, 0.546, 0.934] and {2017}[0.611, 0.911, 0.655, 0.934]." } ], "id_raw": 809 }, { "id": "9ryUwdqbl9yppHsd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.019, 0.377, 0.043, 0.583] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 810 }, { "id": "pE2UA9TbmmmhyZW6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the ninth x-axis tick label?" }, { "from": "gpt", "value": "[0.927, 0.911, 0.954, 0.934]" } ], "id_raw": 810 }, { "id": "ElKJIBTyINppKXRs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_10.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.166, 0.134, 0.358, 0.164] in the legend denote?" }, { "from": "gpt", "value": "IP Protection Level." } ], "id_raw": 810 }, { "id": "P6RmduT0ZSLUEynb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_10.png", "conversations": [ { "from": "human", "value": "\nWhich is in a higher position, {the point on the R&D Performance line at x = 0.4}[0.400, 0.646] or {the point on the IP Protection Level line at x = 0.4}[0.400, 0.701]?" }, { "from": "gpt", "value": "IP Protection Level." } ], "id_raw": 810 }, { "id": "JtPDyuIgryTGGExN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_10.png", "conversations": [ { "from": "human", "value": "\nComparing {the point on the R&D Performance line at x = 0.7}[0.670, 0.265] and {the point on the IP Protection Level line at x = 0.7}[0.670, 0.773], which one has a higher value?" }, { "from": "gpt", "value": "IP Protection Level." } ], "id_raw": 810 }, { "id": "Vehut12J2WOcknN5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_10.png", "conversations": [ { "from": "human", "value": "\nIs {the point on the R&D Performance line}[0.851, 0.154] at x = 0.9 above or below {the point on the IP Protection Level line}[0.851, 0.817] at the same x value?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 810 }, { "id": "wBSDow3jFxPEQGaS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_10.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is in the area [0.125, 0.025, 0.945, 0.058]?" }, { "from": "gpt", "value": "The subtitle of the chart, \"Technological Innovation and Intellectual Property Analysis,\" is located here with parameters such as label, color and rotation." } ], "id_raw": 810 }, { "id": "2ZROJxRss94NI3uW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.100, 0.079, 0.367, 0.176]." }, { "from": "gpt", "value": "This region contains the chart legend, with the labels {R&D Performance}[0.166, 0.091, 0.351, 0.121] and {IP Protection Level}[0.166, 0.134, 0.358, 0.164]. Each label corresponds to a color indicated by its handle." } ], "id_raw": 810 }, { "id": "Wimm7nsFr0CGC7XL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the region [0.927, 0.911, 0.954, 0.934]?" }, { "from": "gpt", "value": "The x-axis tick label '1.0' is situated within these coordinates." } ], "id_raw": 810 }, { "id": "Lahm9YncqaHwifxj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_11.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label at the lower left corner}[0.019, 0.390, 0.043, 0.570] indicate?" }, { "from": "gpt", "value": "Stock Price." } ], "id_raw": 811 }, { "id": "2wNC3Tok69qA0RFR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_11.png", "conversations": [ { "from": "human", "value": "\nWhat year is represented by {the tick mark}[0.255, 0.895]?" }, { "from": "gpt", "value": "2004." } ], "id_raw": 811 }, { "id": "DCRZaDCdxvww0CWo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the label content at the top center of the chart[0.500, 0.943, 0.565, 0.975]?" }, { "from": "gpt", "value": "Years." } ], "id_raw": 811 }, { "id": "CULYCkpVPjfLiKNt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_11.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at {the year 2007}[0.384, 0.307], {WM Stock Price}[0.384, 0.376] or {EM Stock Price}[0.384, 0.307]?" }, { "from": "gpt", "value": "EM Stock Price." } ], "id_raw": 811 }, { "id": "NsHcyICC5rXJHPoA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the error line at 2020}[0.941, 0.193] represent a value greater than the {error line at 2001}[0.126, 0.397]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 811 }, { "id": "R1K7BxPT4FMrsDjX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_11.png", "conversations": [ { "from": "human", "value": "\nIs {the data point for EM Stock in 2010}[0.512, 0.618] above {the data point for WM Stock in the same year}[0.512, 0.411]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 811 }, { "id": "rOmLFhW7KCwjUr9a", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found in the range [0.200, 0.300, 0.400, 0.600]." }, { "from": "gpt", "value": "The region encompasses multiple data points, including {EM and WM Stock Prices}[0.255, 0.411] for the year 2004, with each stock price marked by respective data points." } ], "id_raw": 811 }, { "id": "xSb0gp04oh0W74D9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_11.png", "conversations": [ { "from": "human", "value": "\nWhat is encapsulated in the bounding box [0.540, 0.350, 0.750, 0.450]?" }, { "from": "gpt", "value": "Inside this bounded area lies data points for multiple years around 2014-2016 for both {EM and WM Stock Prices}[0.683, 0.411], [0.683, 0.480], visually charted on the line graphs." } ], "id_raw": 811 }, { "id": "oJdWVKmNyeJ30T9A", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_11.png", "conversations": [ { "from": "human", "value": "\nCharacterize the section [0.820, 0.200, 0.950, 0.350]." }, { "from": "gpt", "value": "This region contains the data points for the year {2020}[0.941, 0.135], including values for both stock types and the error bars reflecting price uncertainties." } ], "id_raw": 811 }, { "id": "v90DC0aPS618vXdA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart title located at the coordinates [0.328, 0.025, 0.742, 0.058]?" }, { "from": "gpt", "value": "\"Time Series Data Comparison\"" } ], "id_raw": 812 }, { "id": "G4OkQTPALzuAchKA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the y-axis label?" }, { "from": "gpt", "value": "[0.019, 0.377, 0.043, 0.583]" } ], "id_raw": 812 }, { "id": "eXAq2w2yiMl5URzd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_12.png", "conversations": [ { "from": "human", "value": "\nWhat numerical value does the x-axis label at coordinates [0.530, 0.911, 0.541, 0.934] represent?" }, { "from": "gpt", "value": "5" } ], "id_raw": 812 }, { "id": "h8Rjqd5jfFO5wyT0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_12.png", "conversations": [ { "from": "human", "value": "\nIs the data point at {coordinates}[0.944, 0.462] from the \"Slow Expansion\" or \"Erratic Growth\" set?" }, { "from": "gpt", "value": "Slow Expansion" } ], "id_raw": 812 }, { "id": "XTpHy11Uj9z7w94m", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_12.png", "conversations": [ { "from": "human", "value": "\nWhich data set between \"Erratic Growth\" and \"Slow Expansion\" has higher values at the time point of 7, considering points at {coordinates}[0.697, 0.448] and {coordinates}[0.697, 0.469]?" }, { "from": "gpt", "value": "Slow Expansion" } ], "id_raw": 812 }, { "id": "EaxAdvxwJ0NlGcXB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_12.png", "conversations": [ { "from": "human", "value": "\nAre the data points for \"Erratic Growth\" at {coordinates}[0.616, 0.433] and \"Slow Expansion\" at {coordinates}[0.616, 0.484] above or below the y = 0.5 line?" }, { "from": "gpt", "value": "Above" } ], "id_raw": 812 }, { "id": "WmW6HYsjxHBQ5O0k", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents within the coordinate area of [0.100, 0.079, 0.337, 0.176]?" }, { "from": "gpt", "value": "This area contains the legend items of the chart: {Erratic Growth}[0.166, 0.091, 0.316, 0.121] and {Slow Expansion}[0.166, 0.134, 0.328, 0.164], each accompanied by a color marker." } ], "id_raw": 812 }, { "id": "saGN9VsKtV6YGdsh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_12.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the region defined by coordinates [0.089, 0.066, 0.981, 0.895]?" }, { "from": "gpt", "value": "The main plot of the chart is located here, showing the time series data for \"Erratic Growth\" and \"Slow Expansion\"." } ], "id_raw": 812 }, { "id": "aFU7F5ajtPXyTU66", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_12.png", "conversations": [ { "from": "human", "value": "\nDescribe what is in the area of [0.019, 0.377, 0.043, 0.583]." }, { "from": "gpt", "value": "This region contains the label for the y-axis, which is \"Performance\"." } ], "id_raw": 812 }, { "id": "Cb1tkBqvPW3Du1oR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at {coordinates}[0.012, 0.452, 0.028, 0.518] on the chart represent?" }, { "from": "gpt", "value": "Value." } ], "id_raw": 814 }, { "id": "ETSu3c338K4etN7S", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_14.png", "conversations": [ { "from": "human", "value": "\nCan you specify what is located at {coordinates}[0.869, 0.068, 0.975, 0.090]?" }, { "from": "gpt", "value": "Speed Increase label." } ], "id_raw": 814 }, { "id": "TsawAn8It4HFBdju", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of {coordinates}[0.869, 0.101, 0.942, 0.123] in the legend?" }, { "from": "gpt", "value": "New Users." } ], "id_raw": 814 }, { "id": "1LVzdJhD4P8DVhNz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_14.png", "conversations": [ { "from": "human", "value": "\nWhich year corresponds to {coordinates}[0.189, 0.876] for the point on the 'Speed Increase' line?" }, { "from": "gpt", "value": "2011." } ], "id_raw": 814 }, { "id": "fXE9VREhtAWuOj8F", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_14.png", "conversations": [ { "from": "human", "value": "\nDoes the year at {coordinates}[0.441, 0.878] in the 'Speed Increase' line precede or follow the year at {coordinates}[0.441, 0.349] in the 'New Users' line?" }, { "from": "gpt", "value": "Precede." } ], "id_raw": 814 }, { "id": "rZWIGxeB2JPDD4rq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_14.png", "conversations": [ { "from": "human", "value": "\nWhich is denser at {coordinates}[0.525, 0.876], labels from 'New Users' or 'Speed Increase'?" }, { "from": "gpt", "value": "Speed Increase." } ], "id_raw": 814 }, { "id": "5l1evvr3wQ9cE4C0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the focal content in the range [0.063, 0.049, 0.988, 0.921] on the chart." }, { "from": "gpt", "value": "It encompasses the main plotting area where multiple lines representing 'Speed Increase' and 'New Users' are displayed with respective points marked, against axes labeled with years and values." } ], "id_raw": 814 }, { "id": "9G8l1sXAj8AqtVEv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.824, 0.060, 0.981, 0.132]?" }, { "from": "gpt", "value": "The legend section indicating the criteria 'Speed Increase' and 'New Users' with their color codes is present here." } ], "id_raw": 814 }, { "id": "jqXQ15hdc76HR5nl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_14.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the coordinates [0.105, 0.862, 0.945, 0.879] on the diagram?" }, { "from": "gpt", "value": "This region includes the line representing the 'Speed Increase', plotted with several defined points corresponding to different years." } ], "id_raw": 814 }, { "id": "N1REN0FXvCtY8KEn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis}[0.019, 0.436, 0.043, 0.525]?" }, { "from": "gpt", "value": "Index." } ], "id_raw": 816 }, { "id": "iHjzTYEzGYOWoIRx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_16.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis tick label}[0.954, 0.911, 0.981, 0.934] represent?" }, { "from": "gpt", "value": "1.1." } ], "id_raw": 816 }, { "id": "DCG65XQZsNKbboEt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the marker at {the last data point}[0.888, 0.694] in the plotted line?" }, { "from": "gpt", "value": "Mustard yellow." } ], "id_raw": 816 }, { "id": "3danxlrS7c3hmaMj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_16.png", "conversations": [ { "from": "human", "value": "\nIs {the first point}[0.169, 0.740] higher or lower than {the fifth point}[0.488, 0.637] on the chart?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 816 }, { "id": "dFM3TBkdbXRfPY1n", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_16.png", "conversations": [ { "from": "human", "value": "\nDoes the line starting at {point one}[0.169, 0.474] end above or below {the final point}[0.888, 0.580]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 816 }, { "id": "iUMRyD5LzdASIxap", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_16.png", "conversations": [ { "from": "human", "value": "\nWhich is greater, the y-value at {beginning point}[0.169, 0.740] or at {midpoint}[0.488, 0.603]?" }, { "from": "gpt", "value": "Beginning point." } ], "id_raw": 816 }, { "id": "VvgbxTUNzqGJlXXH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the region between [0.076, 0.911, 0.249, 0.934] encompass?" }, { "from": "gpt", "value": "It includes the x-axis tick labels for {0.0}[0.076, 0.911, 0.103, 0.934] and {0.1}[0.155, 0.911, 0.183, 0.934]." } ], "id_raw": 816 }, { "id": "RIuukb5G39E92jPz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_16.png", "conversations": [ { "from": "human", "value": "\nDescribe features found within [0.019, 0.436, 0.043, 0.880]." }, { "from": "gpt", "value": "This area includes the y-axis label {Index}[0.019, 0.436, 0.043, 0.525] and several y-axis tick labels, for 0.1 to 0.9." } ], "id_raw": 816 }, { "id": "lc5AkmaXsXnP2pZg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_16.png", "conversations": [ { "from": "human", "value": "\nWhat elements are displayed in [0.169, 0.409, 0.888, 0.934]?" }, { "from": "gpt", "value": "The region contains several {x-axis tick labels}[0.076, 0.911, 0.954, 0.934], {x-axis ticks}[0.089, 0.895, 0.888, 0.895], and multiple data {points}[0.169, 0.409, 0.888, 0.730] plotted on a line." } ], "id_raw": 816 }, { "id": "N961xhH7SoTCSfw4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_17.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis label}[0.503, 0.943, 0.567, 0.975] represent in the chart?" }, { "from": "gpt", "value": "It represents the 'Years'." } ], "id_raw": 817 }, { "id": "5d5Cz9jmvy1P39KA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the line with these coordinates represent [0.130, 0.563, 0.941, 0.770]?" }, { "from": "gpt", "value": "It represents the performance of 'Twitter' over the years." } ], "id_raw": 817 }, { "id": "z8GlwF4rlMRenFdm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_17.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.019, 0.313, 0.043, 0.647] represent in the chart?" }, { "from": "gpt", "value": "It represents 'Performance Metrics'." } ], "id_raw": 817 }, { "id": "8HScznJDF4PorZ74", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_17.png", "conversations": [ { "from": "human", "value": "\nAt {year 2017}[0.454, 0.911, 0.476, 0.934], is {Facebook's performance}[0.454, 0.480] higher than {Twitter's performance}[0.454, 0.646]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 817 }, { "id": "RV4lIAPB5frDW7d5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_17.png", "conversations": [ { "from": "human", "value": "\nIn the chart, is {Facebook's performance point in 2020}[0.941, 0.148] above {Twitter's performance point in 2020}[0.941, 0.770]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 817 }, { "id": "PNUTxbI4dMNUxF97", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_17.png", "conversations": [ { "from": "human", "value": "\nWhich social media had a higher performance in {2017}[0.454, 0.911, 0.476, 0.934], Twitter or Facebook?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 817 }, { "id": "94DNz0d71iuTepQp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in [0.503, 0.943, 0.567, 0.975]." }, { "from": "gpt", "value": "It contains the x-axis label 'Years'." } ], "id_raw": 817 }, { "id": "8uFF7Sc8l75mdbf8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_17.png", "conversations": [ { "from": "human", "value": "\nWhat contents can be found within the area [0.798, 0.079, 0.971, 0.176]?" }, { "from": "gpt", "value": "This area contains the legend indicating the line colors for 'Facebook' and 'Twitter'." } ], "id_raw": 817 }, { "id": "kXxIwDvv2ZYx4uSM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.130, 0.895, 941, 0.770]." }, { "from": "gpt", "value": "It contains the performance over years for 'Facebook' and 'Twitter'. Information of the years is located at the x-axis in the form of x-axis tick labels, 'Facebook' performance is shown in purple line and 'Twitter' performance is shown in orange line." } ], "id_raw": 817 }, { "id": "MW2nA6zhI5gzo02W", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the sixth x-axis tick label represent?" }, { "from": "gpt", "value": "2015." } ], "id_raw": 819 }, { "id": "MNTrA3YinTooqMWy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object in the coordinates [0.050, 0.609, 0.077, 0.632]?" }, { "from": "gpt", "value": "Y-axis tick label." } ], "id_raw": 819 }, { "id": "SeUdaJzHcTNulqXn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_41_19.png", "conversations": [ { "from": "human", "value": "\nWhich company does the line with these coordinates [0.130, 0.204, 0.941, 0.722] represent?" }, { "from": "gpt", "value": "INTEL." } ], "id_raw": 819 }, { "id": "5gm4fV5AfnBCU4Fj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_19.png", "conversations": [ { "from": "human", "value": "\nIs {the INTEL line}[0.130, 0.204, 0.941, 0.722] above or below {the AMD line}[0.130, 0.238, 0.941, 0.584] in 2019?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 819 }, { "id": "UYGqxRQaSYIMPLG2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_19.png", "conversations": [ { "from": "human", "value": "\nIs {the performance of INTEL}[0.130, 0.204, 0.941, 0.722] greater or less than {the performance of AMD}[0.130, 0.238, 0.941, 0.584] in 2010?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 819 }, { "id": "JZwBBDt55oiMKCft", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_41_19.png", "conversations": [ { "from": "human", "value": "\nWhich company had a higher performance in 2014, INTEL or AMD?" }, { "from": "gpt", "value": "AMD." } ], "id_raw": 819 }, { "id": "NMSKZrbWf2M5KDtw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_19.png", "conversations": [ { "from": "human", "value": "\nCan you give me a brief description of the content within [0.100, 0.079, 0.234, 0.176]?" }, { "from": "gpt", "value": "This area contains the legend items for the two plotted lines. `INTEL` is represented by a blue marker located at {coordinates}[inf, inf, -inf, -inf] and `AMD` is represented by a red marker with the {coordinates}[inf, inf, -inf, -inf]." } ], "id_raw": 819 }, { "id": "O0GXwClF6jhXkrtA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects contained within the region [0.198, 0.911, 0.602, 0.934]." }, { "from": "gpt", "value": "This region covers the x-axis labels for the years 2011 through 2015, positioned at {Coordinates}[0.198, 0.911, 0.242, 0.934], [0.288, 0.911, 0.332, 0.934], [0.378, 0.911, 0.422, 0.934], [0.468, 0.911, 0.512, 0.934] and [0.558, 0.911, 0.602, 0.934] respectively." } ], "id_raw": 819 }, { "id": "Xo3Gypp8d1twQXIc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_41_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's within [0.231, 0.025, 0.840, 0.058]?" }, { "from": "gpt", "value": "This region contains the subtitle of the chart: 'Market Competition Analysis: INTEL vs AMD', which is colored black." } ], "id_raw": 819 }, { "id": "IJDsM0SHlzifVrIy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the point at [0.512, 0.342] in the chart?" }, { "from": "gpt", "value": "Houston." } ], "id_raw": 820 }, { "id": "ebe1RvIZBrXs6Wpx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the line with coordinates [0.160, 0.155, 0.865, 0.808] represent?" }, { "from": "gpt", "value": "Average temperature of the year 2020 for various cities." } ], "id_raw": 820 }, { "id": "KIE0eTZa6vyBPAXI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_0.png", "conversations": [ { "from": "human", "value": "\nWhat is found at {the coordinates}[0.852, 0.187, 0.887, 0.215]?" }, { "from": "gpt", "value": "The legend label for \"2021\"." } ], "id_raw": 820 }, { "id": "Lf23fE7QM3nbizzH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_0.png", "conversations": [ { "from": "human", "value": "\nIs the average temperature of {Houston}[0.512, 0.342] in 2021 higher or lower than its temperature in 2020?" }, { "from": "gpt", "value": "The same." } ], "id_raw": 820 }, { "id": "g4jwjv22UD8AkhZT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_0.png", "conversations": [ { "from": "human", "value": "\nBetween {Chicago}[0.395, 0.808] and {Philadelphia}[0.747, 0.668] which city had a higher average temperature in 2020?" }, { "from": "gpt", "value": "Philadelphia." } ], "id_raw": 820 }, { "id": "WuXjkSKH1icGmfUJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_0.png", "conversations": [ { "from": "human", "value": "\nHow do the average temperatures of {New York in 2020}[0.160, 0.762] and {Philadelphia in 2021}[0.747, 0.715] compare?" }, { "from": "gpt", "value": "New York had a lower average temperature." } ], "id_raw": 820 }, { "id": "odk8tygaeCGTLjsU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_0.png", "conversations": [ { "from": "human", "value": "\nWhat is visible within the chart area bounded by the coordinates [0.160, 0.155, 0.865, 0.855]?" }, { "from": "gpt", "value": "It contains lines representing average temperatures for various cities in 2020 and 2021. Individual points on these lines, representing various cities, are connected by the lines." } ], "id_raw": 820 }, { "id": "4IIvGMSgYnYff3qC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the area with coordinates [0.125, 0.890, 0.865, 0.937]." }, { "from": "gpt", "value": "This area includes the x-axis labels representing the cities of {New York}[0.128, 0.909, 0.192, 0.937], {Los Angeles}[0.236, 0.909, 0.319, 0.937], {Chicago}[0.367, 0.909, 0.423, 0.937], {Houston}[0.484, 0.909, 0.541, 0.937], {Phoenix}[0.602, 0.909, 0.657, 0.937], {Philadelphia}[0.705, 0.909, 0.790, 0.937] and {San Antonio}[0.823, 0.909, 0.907, 0.937]." } ], "id_raw": 820 }, { "id": "7mqVbB3QHkexmNf3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_0.png", "conversations": [ { "from": "human", "value": "\nWhat lies in the bounding box with coordinates [0.852, 0.145, 0.887, 0.215]?" }, { "from": "gpt", "value": "This region contains two legend labels: {2020}[0.852, 0.145, 0.887, 0.173] and {2021}[0.852, 0.187, 0.887, 0.215]." } ], "id_raw": 820 }, { "id": "tC5lyiOWar4c5yAI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the main element at the location [0.025, 0.139, 0.057, 0.766] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 821 }, { "id": "SqwCZeay2cUorJ05", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_1.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the fourth element in the title?" }, { "from": "gpt", "value": "[0.500, 1.000, 0.500, 1.000]" } ], "id_raw": 821 }, { "id": "4QVE5oYMo8YkAktY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the fifth {x-axis tick label}[0.731, 0.895, 0.747, 0.923]?" }, { "from": "gpt", "value": "5." } ], "id_raw": 821 }, { "id": "H8h8e12L5EFQ9qMg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_1.png", "conversations": [ { "from": "human", "value": "\nIs the {point labeled 2}[0.260, 0.199] on the 'Knowledge Economy' line lower than the {point labeled 2}[0.380, 0.332] on the 'Innovation Industries' line?" }, { "from": "gpt", "value": "No." } ], "id_raw": 821 }, { "id": "EwzTnPvH3gD09IWt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_1.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Knowledge Economy'}[0.140, 0.130, 0.860, 0.645] generally higher than the {line for 'Innovation Industries'}[0.140, 0.151, 0.860, 0.754]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 821 }, { "id": "n1ea1m69scMgoTok", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_1.png", "conversations": [ { "from": "human", "value": "\nWhich line, 'Knowledge Economy' or 'Innovation Industries', does the point {at 5}[0.740, 0.214] show the highest value for?" }, { "from": "gpt", "value": "Knowledge Economy." } ], "id_raw": 821 }, { "id": "kGcrw2KZLxmMuDoO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_1.png", "conversations": [ { "from": "human", "value": "\nWhat content lies within the region [0.904, 0.257, 0.934, 0.300]?" }, { "from": "gpt", "value": "The area contains the y-axis tick labels for the 'Innovation Industries' chart. The labels '22' and '21' are located at {coordinates}[0.904, 0.137, 0.934, 0.179] and {coordinates}[0.904, 0.257, 0.934, 0.300] respectively." } ], "id_raw": 821 }, { "id": "iXdYkl2iVlvgHGNw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_1.png", "conversations": [ { "from": "human", "value": "\nPlease tell me about the content in the region [0.120, 0.049, 1.491, 0.119]." }, { "from": "gpt", "value": "This area contains the chart legend items: {Avg. Knowledge Economy Growth}[0.224, 0.065, 0.772, 0.103] and {Avg. Innovation Industries Growth}[0.927, 0.065, 1.478, 0.103]." } ], "id_raw": 821 }, { "id": "ibtPtacaa69DHLkw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region between [0.140, 0.130, 0.860, 0.645]?" }, { "from": "gpt", "value": "The highlighted region includes a line plot showing the 'Knowledge Economy' trend over the years. The data points plotted correspond to various years and their corresponding 'Knowledge Economy' growth statistics." } ], "id_raw": 821 }, { "id": "tAJdCMduhdBQhxqA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the Y-axis label with coordinates [0.019, 0.247, 0.036, 0.681] for the top subplot represent?" }, { "from": "gpt", "value": "Agriculture, Manufacturing shares(%)." } ], "id_raw": 822 }, { "id": "eJyzC6WM8EJ2LVVK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label for the bottom subplot located at [0.964, 0.335, 0.981, 0.593]?" }, { "from": "gpt", "value": "Service, ITS shares(%)." } ], "id_raw": 822 }, { "id": "5Hp2JEVs47ikj5AK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the trend line represented by the green circles at coordinates [0.116, 0.397, 0.884, 0.863] in the top subplot?" }, { "from": "gpt", "value": "Agriculture." } ], "id_raw": 822 }, { "id": "ULlNWpbLXAmzKDgd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_2.png", "conversations": [ { "from": "human", "value": "\nIs the data point for {2014 for Agriculture}[0.308, 0.564] higher or lower than the {2013 mark for Manufacturing}[0.212, 0.098]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 822 }, { "id": "7RGt8Fz9bmGY3GT0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_2.png", "conversations": [ { "from": "human", "value": "\nIs the {2015 point for Service}[0.404, 0.254] higher or lower than the {2016 point for ITS}[0.500, 0.836]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 822 }, { "id": "MDmNcFYVv8aIL2fH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_2.png", "conversations": [ { "from": "human", "value": "\nIs the data point at {2019 for Manufacturing}[0.788, 0.331] above or below the {2019 point for Agriculture}[0.788, 0.830]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 822 }, { "id": "3yM3cDHbFaAfeJOD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area with coordinates [0.077, 0.025, 0.923, 0.903] in the top subplot?" }, { "from": "gpt", "value": "It's the plotting area for the top subplot, and it contains two line series \u2013 {Agriculture}[0.116, 0.397, 0.884, 0.863] and {Manufacturing}[0.116, 0.065, 0.884, 0.397], represented by circles and squares, respectively, alongside their corresponding markers in green and red." } ], "id_raw": 822 }, { "id": "KR81bN15AA1I36f1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.077, 0.025, 0.923, 0.903] in the bottom subplot." }, { "from": "gpt", "value": "This is the plotting area for the bottom subplot and it contains two trend lines named {Service}[0.116, 0.065, 0.884, 0.417] and {ITS}[0.116, 0.809, 0.884, 0.863] that are tracked with blue and cyan triangles, respectively, along with their respective colored markers." } ], "id_raw": 822 }, { "id": "Y7UAWMHRrWKZlLMU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.787, 0.037, 0.914, 0.113]." }, { "from": "gpt", "value": "This region contains a legend for the bottom subplot, including {Service}[0.843, 0.046, 0.907, 0.069] with a blue marker and {ITS}[0.843, 0.081, 0.869, 0.104] with a cyan marker." } ], "id_raw": 822 }, { "id": "V7AZN2AacR2UYvn8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the object}[0.943, 0.293, 0.975, 0.612] on the right vertical side of the chart?" }, { "from": "gpt", "value": "Y-axis label of 'Video Error Rate'." } ], "id_raw": 823 }, { "id": "VXATEB2Xn2EGhlVR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_3.png", "conversations": [ { "from": "human", "value": "\nWhat number does the {fifth x-axis tick}[0.491, 0.885, 0.509, 0.921] refer to?" }, { "from": "gpt", "value": "6." } ], "id_raw": 823 }, { "id": "OFbH65cJfDdxWRBV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the seventh x-axis tick}[0.731, 0.885, 0.749, 0.921] in the chart referring to?" }, { "from": "gpt", "value": "8." } ], "id_raw": 823 }, { "id": "p3oeABblffX05TUK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_3.png", "conversations": [ { "from": "human", "value": "\nIs the value of {the second point}[0.260, 0.172] on the 'Video Error' line higher or lower compared to {the third point}[0.380, 0.307]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 823 }, { "id": "mhOBVJsI6cmlHnbD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_3.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at the {last point}[0.860, 0.874], the 'Audio Error' or 'Video Error' line?" }, { "from": "gpt", "value": "Video Error." } ], "id_raw": 823 }, { "id": "2X3mAD2gdMFFPfy8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_3.png", "conversations": [ { "from": "human", "value": "\nIs {Point 1 on the 'Audio Error' line}[0.140, 0.030] to the left or right of {Point 3 on the 'Video Error' line}[0.380, 0.307]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 823 }, { "id": "PFWdnp0paJXKLEIZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.131, 0.885, 0.149, 0.921]." }, { "from": "gpt", "value": "This region contains the number '3', which is a tick label on the x-axis." } ], "id_raw": 823 }, { "id": "JvVymtAIhJR6bkZm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe what's in the area [0.962, 0.885, 0.998, 0.921]." }, { "from": "gpt", "value": "It is the '10' label on the X-axis, representing the digital tools number." } ], "id_raw": 823 }, { "id": "563wJPZNTtr5UNTo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.904, 0.227, 0.934, 0.270]?" }, { "from": "gpt", "value": "This area contains the '18' label from the y-axis tick labels on the 'Video Error Rate' subplot." } ], "id_raw": 823 }, { "id": "7e2MH75hOVKDrEj7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the object located at [0.019, 0.333, 0.036, 0.595] represent?" }, { "from": "gpt", "value": "Y-axis label for Investment Growth(%)." } ], "id_raw": 825 }, { "id": "qcg6lUc0Fsyp6dAf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_5.png", "conversations": [ { "from": "human", "value": "\nWhat is marked by the point at coordinates [0.106, 0.299] on the graph?" }, { "from": "gpt", "value": "The point represents the Investment Growth(%) in the year 2012." } ], "id_raw": 825 }, { "id": "dhG2IegUNdztFVyM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.964, 0.384, 0.981, 0.544] stand for in the chart?" }, { "from": "gpt", "value": "It stands for the Y-axis label for Risk Score(%)." } ], "id_raw": 825 }, { "id": "yqIcR3pndVxGKGhC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_5.png", "conversations": [ { "from": "human", "value": "\nIn 2014, was the {Investment Growth}[0.281, 0.497] higher or lower than the {Risk Score}[0.281, 0.201]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 825 }, { "id": "TAofKmfa9A01iSrj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_5.png", "conversations": [ { "from": "human", "value": "\nIs the {Risk Score}[0.456, 0.815] for 2016 represented above or below the {Investment Growth}[0.456, 0.244] for the same year?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 825 }, { "id": "bhGRR8yaq6oB9yXD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_5.png", "conversations": [ { "from": "human", "value": "\nCompare the {Investment Growth}[0.631, 0.157] and {Risk Score}[0.631, 0.288] in 2018, which one was higher?" }, { "from": "gpt", "value": "Risk Score." } ], "id_raw": 825 }, { "id": "koUwrqCvN76l4iEX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.077, 0.049, 0.297, 0.104]." }, { "from": "gpt", "value": "This region contains the {legend labels}[0.131, 0.046, 0.297, 0.069 and 0.077, 0.084, 0.122, 0.098] for Investment Growth and Risk Score, each associated with a colored marker according to their respective plot line colors." } ], "id_raw": 825 }, { "id": "95Q1AUBHLIA56lPF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects in the area [0.434, 0.919, 0.478, 0.942]." }, { "from": "gpt", "value": "This area features the {X-axis tick label}[0.434, 0.919, 0.478, 0.942] representing the year 2016." } ], "id_raw": 825 }, { "id": "WZGIZFoNus9EjFMy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found within the coordinates [0.259, 0.919, 0.303, 0.942]." }, { "from": "gpt", "value": "Within these coordinates, there exists the {label '2014'}[0.259, 0.919, 0.303, 0.942] on the x-axis of the graph." } ], "id_raw": 825 }, { "id": "HQRLUSLNNC2BMVMf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_6.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the line at coordinates [0.098, 0.060, 0.902, 0.887] with color '#00bfff'?" }, { "from": "gpt", "value": "Economic Growth." } ], "id_raw": 826 }, { "id": "A2kNSPvYCnV6sBze", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the x-axis tick for the year '2016'?" }, { "from": "gpt", "value": "[0.688, 0.928]" } ], "id_raw": 826 }, { "id": "wxKXYcFgDMPeccaT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.015, 0.365, 0.033, 0.582]?" }, { "from": "gpt", "value": "Economic Growth(%)." } ], "id_raw": 826 }, { "id": "KAe0GBJUIi3pATJz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'New Businesses Started' in 2012}[0.473, 0.413] above or below {the point for 'Economic Growth' in 2012}[0.473, 0.279] on the chart?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 826 }, { "id": "eByNkJpVB4ZFRw0t", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_6.png", "conversations": [ { "from": "human", "value": "\nDoes the value of {Economic Growth}[0.795, 0.180] in 2018 exceed the value of {New Businesses Started}[0.795, 0.060]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 826 }, { "id": "KFMtz6v32gexYf3V", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_6.png", "conversations": [ { "from": "human", "value": "\nWhich trend showed a higher percentage in 2007, {Economic Growth}[0.205, 0.074] or {New Businesses Started}[0.205, 0.571]?" }, { "from": "gpt", "value": "New Businesses Started." } ], "id_raw": 826 }, { "id": "yE7ifqDmSEhBgRB1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.134, 0.934, 0.169, 0.958]." }, { "from": "gpt", "value": "The region contains the x-axis label '2006' of the chart." } ], "id_raw": 826 }, { "id": "tCRyLc7Ha78LUNdk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.064, 0.027, 0.281, 0.085]." }, { "from": "gpt", "value": "The area contains the legend items: {Economic Growth}[0.109, 0.034, 0.231, 0.052] marked in deepskyblue, and {New Businesses Started}[0.109, 0.061, 0.276, 0.078] marked in coral." } ], "id_raw": 826 }, { "id": "TMCM4eTNUaKpFhjG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.098, 0.060, 0.902, 0.887]?" }, { "from": "gpt", "value": "There are data points representing each year from 2005 to 2020 for the Economic Growth line." } ], "id_raw": 826 }, { "id": "Dxdcza8eiJtLkkDz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at coordinates at [0.052, 0.379, 0.074, 0.631]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 827 }, { "id": "kHIp4WUxTX3Mt1qm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fourth label text in the legend?" }, { "from": "gpt", "value": "[0.781, 0.835, 0.858, 0.864]" } ], "id_raw": 827 }, { "id": "y0s13RBFsQ3g1jDg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the numerical value of {the y-axis tick label}[0.082, 0.519, 0.110, 0.548]?" }, { "from": "gpt", "value": "20." } ], "id_raw": 827 }, { "id": "wBEPN7eJROH2h4CY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_7.png", "conversations": [ { "from": "human", "value": "\nIn the year 2014, is {the 'Microsoft' line}[0.473, 0.201, 0.769, 0.717] higher or lower than {the 'Google' line}[0.473, 0.662, 0.769, 0.848]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 827 }, { "id": "Vm1P079GADiRqFhY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_7.png", "conversations": [ { "from": "human", "value": "\nBetween the years 2010 and 2012, does {the 'Apple' line}[0.160, 0.855, 0.317, 0.696] show a rising or falling trend compared to {the 'Amazon' line}[0.160, 0.388, 0.317, 0.334]?" }, { "from": "gpt", "value": "Rising." } ], "id_raw": 827 }, { "id": "1lO9XsN2negtDpVt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_7.png", "conversations": [ { "from": "human", "value": "\nIn 2019, does {the 'Microsoft' line}[0.865, 0.275] have a higher or lower value than {the 'Google' line}[0.865, 0.531]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 827 }, { "id": "AOrpeYr2AgHUx1r1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_7.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the area with coordinates [0.781, 0.704, 0.880, 0.733]?" }, { "from": "gpt", "value": "This is the legend item for {Microsoft}[0.781, 0.704, 0.880, 0.733]." } ], "id_raw": 827 }, { "id": "PPErOt66Jd49GBXQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within [0.133, 0.910, 0.188, 0.939]." }, { "from": "gpt", "value": "This area contains {the x-axis tick label '2010'}[0.133, 0.910, 0.188, 0.939]." } ], "id_raw": 827 }, { "id": "Fe8OeZKaxqIBonRd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_7.png", "conversations": [ { "from": "human", "value": "\nWhat is features the region [0.829, 0.815, 0.865, 0.831]?" }, { "from": "gpt", "value": "This region includes the {2018 x-axis label}[0.759, 0.910, 0.814, 0.939] and the {Amazon plot point}[0.786, 0.246]." } ], "id_raw": 827 }, { "id": "zymepUOMcPtr2iTD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_8.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates for the location of the ylabel \"Stock Value\"?" }, { "from": "gpt", "value": "[0.065, 0.436, 0.083, 0.574]" } ], "id_raw": 828 }, { "id": "wjWAb9ocCdkBjFH4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_8.png", "conversations": [ { "from": "human", "value": "\nWhat color is the plot line associated with {Stock C}[0.160, 0.802, 0.865, 0.868]?" }, { "from": "gpt", "value": "#2ca02c" } ], "id_raw": 828 }, { "id": "cGQ8jSyJkEvj2FaN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.495, 0.931, 0.530, 0.956]" }, { "from": "gpt", "value": "Year" } ], "id_raw": 828 }, { "id": "qOGeGsYVyS9JYcMu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_8.png", "conversations": [ { "from": "human", "value": "\nIs {StockA}[0.160, 0.186, 0.865, 0.340] maintaining a higher or lower value trend compared to {StockC}[0.160, 0.802, 0.865, 0.868] over the years?" }, { "from": "gpt", "value": "Higher" } ], "id_raw": 828 }, { "id": "J7OFfcw8xwjDg8L0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_8.png", "conversations": [ { "from": "human", "value": "\nBy the year 2017, did {StockB}[0.160, 0.208, 0.865, 0.364] have a higher or lower stock value than {BondA}[0.160, 0.216, 0.865, 0.409]?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 828 }, { "id": "Pk6vgnJ5xEMiILHn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_8.png", "conversations": [ { "from": "human", "value": "\nWhich stock showed a greater decline from 2018 to 2019, {StockA}[0.583, 0.252, 0.724, 0.296] or {StockB}[0.583, 0.287, 0.724, 0.318]?" }, { "from": "gpt", "value": "StockA" } ], "id_raw": 828 }, { "id": "KgBR4koS66WCef30", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_8.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the region [0.704, 0.136, 0.889, 0.155] on the chart?" }, { "from": "gpt", "value": "It has the text label 'Bond A' with the color #d62728 associated with it, indicating that it represents the data for 'Bond A' on the chart." } ], "id_raw": 828 }, { "id": "iVKNRGuhUMkY4uT7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.171, 0.191, 0.219, 0.210] on the chart?" }, { "from": "gpt", "value": "The region contains the legend text 'Stock C' and is associated with a color marker #2ca02c which represents 'Stock C' on the chart." } ], "id_raw": 828 }, { "id": "nPPATvhU1vPQIGlY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_8.png", "conversations": [ { "from": "human", "value": "\nCould you describe what's located in the region [0.089, 0.220, 0.120, 0.246]?" }, { "from": "gpt", "value": "It is the label for y-tick 300 on the left side of the chart, formatting the y-axis." } ], "id_raw": 828 }, { "id": "8FMDgOPxjrTwyGbA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the main y-axis label}[0.021, 0.340, 0.049, 0.567] on the chart?" }, { "from": "gpt", "value": "Cargo Volume." } ], "id_raw": 829 }, { "id": "LApreGvcdnMjZH0l", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis tick label at [0.428, 0.890, 0.489, 0.920] represent?" }, { "from": "gpt", "value": "Year 2014." } ], "id_raw": 829 }, { "id": "zgXqqCTyX34Gqq8Q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the object represented by coordinates [0.951, 0.343, 0.979, 0.563] refer to?" }, { "from": "gpt", "value": "Trade Volume, which is the y-axis label of the second plot." } ], "id_raw": 829 }, { "id": "PUfc4GwGIayeGzbs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_9.png", "conversations": [ { "from": "human", "value": "\nWhich line trend, between 'Cargo Volume' and 'Trade Volume,' is higher at [0.790, 0.239]?" }, { "from": "gpt", "value": "Cargo Volume." } ], "id_raw": 829 }, { "id": "Ezak5Er7gJttjQzF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_9.png", "conversations": [ { "from": "human", "value": "\nIs {the point on the 'Cargo Volume' line at year 2015}[0.541, 0.646] higher or lower than {the point on the 'Trade Volume' line at the same year}[0.541, 0.368]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 829 }, { "id": "UbizdgBWJoaVZSqT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_9.png", "conversations": [ { "from": "human", "value": "\nCompare the end point of {the 'Cargo Volume' line}[0.873, 0.239] to the end point of {the 'Trade Volume' line}[0.873, 0.196]. Which one is at a higher position?" }, { "from": "gpt", "value": "Cargo Volume." } ], "id_raw": 829 }, { "id": "mLok5UDRxErnZMoj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the region between [0.089, 0.881, 0.987, 0.920]." }, { "from": "gpt", "value": "The area includes the x-axis tick labels representing the years from 2008 to 2020." } ], "id_raw": 829 }, { "id": "3Y6cxF6lCmhrhRmt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_9.png", "conversations": [ { "from": "human", "value": "\nWhat is inside region [0.089, 0.870, 0.943, 0.975]?" }, { "from": "gpt", "value": "The region contains the y-tick labels for the second line plot, representing the 'Trade Volume,' ranging from 100000 to 190000." } ], "id_raw": 829 }, { "id": "25lKnLqioNL9latX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_9.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.127, 0.196, 0.873, 0.881]?" }, { "from": "gpt", "value": "The region includes two lines representing the 'Cargo Volume' and 'Trade Volume' from 2010 to 2019." } ], "id_raw": 829 }, { "id": "k0ymKZGHdRmHBF0w", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the primary color indicated by {the handle for 'Rural'}[0.544, 0.747, 0.632, 0.775]?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 830 }, { "id": "5g9wHZixoJSHnsMv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_10.png", "conversations": [ { "from": "human", "value": "\nWhat does {the vertical axis label}[0.025, 0.266, 0.057, 0.653] indicate?" }, { "from": "gpt", "value": "Urban development." } ], "id_raw": 830 }, { "id": "3Nmf1dJlIuXWOekL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the point at}[0.860, 0.501] for the 'Rural' line graph?" }, { "from": "gpt", "value": "2015." } ], "id_raw": 830 }, { "id": "v9OK2iLM8dDmiSwq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_10.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Average urban' line point at 1995}[0.638, 0.293] higher than {the 'Urban' line point at 1995}[0.638, 0.546]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 830 }, { "id": "Ixq1qbcYjJCr9KE0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_10.png", "conversations": [ { "from": "human", "value": "\nWhich is labeled higher on the plot, {the tick for the year 2000}[0.694, 0.875] or {the tick for the year 2010}[0.805, 0.875]?" }, { "from": "gpt", "value": "The same." } ], "id_raw": 830 }, { "id": "3aOAEGZ4rhdBAelL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Urban' value in 1985}[0.528, 0.418] surpass {the 'Average urban' value in 1985}[0.528, 0.601]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 830 }, { "id": "urcDvo0a9QU0tH2z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_10.png", "conversations": [ { "from": "human", "value": "\nDescribe what is visualized in the region [0.140, 0.148, 0.860, 0.750]." }, { "from": "gpt", "value": "This region exhibits the plot for 'Urban' development from 1950 to 2015 with markers indicating individual data points, represented with a blue line and circle markers." } ], "id_raw": 830 }, { "id": "RPJHzqFhAUaMONnS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found within [0.465, 0.932, 0.534, 0.970]." }, { "from": "gpt", "value": "The x-axis label stating 'Year' is located here in black text." } ], "id_raw": 830 }, { "id": "C5kYolCVCFWtDtHq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_10.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the area [0.104, 0.044, 0.896, 0.875]?" }, { "from": "gpt", "value": "The subplot contains axes, lines representing 'Urban' and 'Average urban' trends, tick labels for years and urban development values, along with the background marked in white." } ], "id_raw": 830 }, { "id": "JBSVjA7f3bGvmLvH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_11.png", "conversations": [ { "from": "human", "value": "\nWhat kind of chart is displayed?" }, { "from": "gpt", "value": "Line." } ], "id_raw": 831 }, { "id": "L5Seb2HiQ4TxTxum", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_11.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.943, 0.224, 0.975, 0.681]?" }, { "from": "gpt", "value": "Y-axis label of the second subplot." } ], "id_raw": 831 }, { "id": "SojPfUPLl0viYfWK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.860, 0.734] in the plot?" }, { "from": "gpt", "value": "Point corresponding to the year 2021 for eCommerce AI data in the second subplot." } ], "id_raw": 831 }, { "id": "rVFODcDrExDge4RA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_11.png", "conversations": [ { "from": "human", "value": "\nIs the {point at '2020'}[0.740, 0.664] on the eCommerce AI line above or below the point of the same year on the Self Driving AI line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 831 }, { "id": "E1Mg1Pu7RYWIqLjz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_11.png", "conversations": [ { "from": "human", "value": "\nWhat line is higher at the {point '2015'}[0.140, 0.805]; is it Self Driving AI or eCommerce AI?" }, { "from": "gpt", "value": "eCommerce AI." } ], "id_raw": 831 }, { "id": "3xeovf7dWDhb8Fyg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_11.png", "conversations": [ { "from": "human", "value": "\nWhich point is higher at {Year '2019'}[0.620, 0.382] on the Self Driving AI line or eCommerce AI line?" }, { "from": "gpt", "value": "Self Driving AI." } ], "id_raw": 831 }, { "id": "sHrYVHwJl8cMTTum", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_11.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area that ranges from [0.500, 0.523] to [0.620, 0.593]?" }, { "from": "gpt", "value": "This region contains two points. The '2018' point for eCommerce AI line is located at [0.500, 0.523] and the '2019' point for the same line is at [0.620, 0.593]." } ], "id_raw": 831 }, { "id": "LcGgAbNikxwuEE6C", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.904, 0.298, 0.934, 0.361] contain?" }, { "from": "gpt", "value": "This area contains the y-axis label '110' for eCommerce AI." } ], "id_raw": 831 }, { "id": "JWb7KxpoO09TxF76", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe what the region [0.105, 0.885, 0.175, 0.921] holds?" }, { "from": "gpt", "value": "It contains '2015' label for x-axis ticks in the Self Driving AI line." } ], "id_raw": 831 }, { "id": "uNZCQFs32Xfk5hN1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_12.png", "conversations": [ { "from": "human", "value": "\nWhat element is at the coordinate [0.023, 0.307, 0.052, 0.595] in the chart figure?" }, { "from": "gpt", "value": "The Y-axis label for 'GDP Change (%)'" } ], "id_raw": 832 }, { "id": "yNNyT9zl425b8BhI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_12.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the object at the coordinates [0.437, 0.031, 0.682, 0.069]?" }, { "from": "gpt", "value": "That represents the legend item 'Market Growth (%)'." } ], "id_raw": 832 }, { "id": "EgjxYHzBohDt5jCW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.107, 0.031, 0.911, 0.870]?" }, { "from": "gpt", "value": "The main plotting area of each subplot." } ], "id_raw": 832 }, { "id": "f4QPLr2HLUEp1oBd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_12.png", "conversations": [ { "from": "human", "value": "\nOn the year 2014, does {GDP Change (%) trend}[0.436, 0.069] has higher or lower value than {Market Growth trend}[0.436, 0.287]?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 832 }, { "id": "ucOQfwNP5KFlV5bU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_12.png", "conversations": [ { "from": "human", "value": "\nIs the point at 2010 for {GDP Change (%) trend}[0.144, 0.352] above or below {Market Growth trend}[0.144, 0.832]?" }, { "from": "gpt", "value": "Above" } ], "id_raw": 832 }, { "id": "Ko1X6T2eGsGYfRzf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_12.png", "conversations": [ { "from": "human", "value": "\nFor the year 2017, is the {Market Growth trend}[0.655, 0.069] above or below the {GDP Change (%) trend}[0.655, 0.188]?" }, { "from": "gpt", "value": "Above" } ], "id_raw": 832 }, { "id": "qmSaYdVW2CZg27H1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_12.png", "conversations": [ { "from": "human", "value": "\nWhat's in the area bounded by [0.013, 0.017, 0.692, 0.083]?" }, { "from": "gpt", "value": "This section contains the legend of the graph, including two items: {GDP Change (%)}[0.096, 0.031, 0.312, 0.069] and {Market Growth (%)}[0.437, 0.031, 0.682, 0.069], each paired with a specific color marker." } ], "id_raw": 832 }, { "id": "ny7UELkmWi8LApru", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_12.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region [0.107, 0.031, 0.911, 0.870]?" }, { "from": "gpt", "value": "This is the main plotting area of the chart, showing the line graphs of both GDP Change (%) and Market Growth (%) over the years from 2010 to 2020." } ], "id_raw": 832 }, { "id": "OXCQoYIrawdhTZtC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_12.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region specified by the coordinates [0.926, -0.063, 0.953, -0.034]?" }, { "from": "gpt", "value": "The tick label '10' for the y-axis of the subplot showing the Market Growth (%) trend." } ], "id_raw": 832 }, { "id": "GdRYBV9JxuZ9JPzi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.966, 0.218, 0.985, 0.715]?" }, { "from": "gpt", "value": "Y-axis label of subplot_1." } ], "id_raw": 833 }, { "id": "Had6aypa8kmLdFBx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {line for \"Farm Civilization\"}[0.102, 0.151, 0.898, 0.724]?" }, { "from": "gpt", "value": "#ff7f00." } ], "id_raw": 833 }, { "id": "4KC4atshJBdjK7lQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the 1973 data point in \"Farm Civilization\" line?" }, { "from": "gpt", "value": "[0.841, 0.701]." } ], "id_raw": 833 }, { "id": "Ge6YvSdTDv3txKzM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_13.png", "conversations": [ { "from": "human", "value": "\nWhich is higher in 1974, {the point \"Industrial Civilization\"}[0.898, 0.037] or {the point \"Farm Civilization\"}[0.898, 0.724]?" }, { "from": "gpt", "value": "Farm Civilization." } ], "id_raw": 833 }, { "id": "cA0uA2CNq8MTo3j5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_13.png", "conversations": [ { "from": "human", "value": "\nIs {the 1972 data point for \"Farm Civilization\"}[0.784, 0.667] below or above {the 1972 data point for \"Industrial Civilization\"}[0.784, 0.123]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 833 }, { "id": "bFeKKjqv7yeaOU5W", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_13.png", "conversations": [ { "from": "human", "value": "\nIs the {1962 data point for \"Farm Civilization\"}[0.216, 0.243] higher or lower than the {1962 data point for \"Industrial Civilization\"}[0.216, 0.552]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 833 }, { "id": "b5z6Qvxl0oSbGmXC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.966, 0.218, 0.985, 0.715]." }, { "from": "gpt", "value": "The area contains the Y-axis label \"Farm Civilization Development\" aligned vertically and styled with the color code '#ff7f00'." } ], "id_raw": 833 }, { "id": "zYzS8ZZSS8SCTaFQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.102, 0.037, 0.159, 0.197]?" }, { "from": "gpt", "value": "There are two objects. The data points for the year 1960 in the lines for {Industrial Civilization}[0.102, 0.638] and {Farm Civilization}[0.102, 0.151] are located within this region." } ], "id_raw": 833 }, { "id": "lAJUbhmH76Oul2Ta", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_13.png", "conversations": [ { "from": "human", "value": "\nWhat is happening in the region [0.425, 0.912, 0.461, 0.936]?" }, { "from": "gpt", "value": "This region contains the x-axis tick label of \"1966\"." } ], "id_raw": 833 }, { "id": "dzYC42wXwXvYor9B", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.023, 0.217, 0.053, 0.684] in the chart?" }, { "from": "gpt", "value": "Y-axis label for subplot 0." } ], "id_raw": 834 }, { "id": "tqm7qPi7155fxCYj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of the legend for 'Cinema goers percentage' line?" }, { "from": "gpt", "value": "There's no dedicated legend for specific lines in the chart." } ], "id_raw": 834 }, { "id": "Q9bNBdXL9UfpqaGO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_14.png", "conversations": [ { "from": "human", "value": "\nWhat value do {the x-axis tick label} at [0.250, 0.880, 0.316, 0.918] refer to?" }, { "from": "gpt", "value": "2012." } ], "id_raw": 834 }, { "id": "lABMTeGK0k8gXUlr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_14.png", "conversations": [ { "from": "human", "value": "\nComparing {the point in 2010} at [0.139, 0.139] in subplot 0, and {the first point in subplot 1}[0.139, 0.803], which one has a higher percentage value?" }, { "from": "gpt", "value": "The point in subplot 1." } ], "id_raw": 834 }, { "id": "jXGLkczDA7zEYe44", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'TV viewers percentage'}[0.139, 0.069, 0.861, 0.832] intersect with {the line for 'Cinema goers percentage'}[0.139, 0.069, 0.861, 0.832]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 834 }, { "id": "Qce431df0P8ThZq2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_14.png", "conversations": [ { "from": "human", "value": "\nIs the {x-axis tick label labelled '2020'}[0.834, 0.890, 0.889, 0.919] to the right or left of {the x-axis tick label labelled '2018'}[0.689, 0.890, 0.744, 0.919]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 834 }, { "id": "1CCaPeLPiN1D2KT3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the region [0.102, 0.870, 0.277, 0.900]." }, { "from": "gpt", "value": "The region contains the topmost portions of the 'TV viewers percentage' and 'Cinema goers percentage' lines for their earliest data points." } ], "id_raw": 834 }, { "id": "YexaOfdHmZepnHaH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_14.png", "conversations": [ { "from": "human", "value": "\nWhat contents does region [0.500, 0.509, 0.570, 0.583] include?" }, { "from": "gpt", "value": "The region contains a point from the 'Cinema goers percentage' line for the year 2016." } ], "id_raw": 834 }, { "id": "jhZhSfn5QCIzIVkx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents around [0.947, 0.188, 0.977, 0.713]." }, { "from": "gpt", "value": "This region holds the y-axis label of subplot 1 marking 'Cinema goers percentage'." } ], "id_raw": 834 }, { "id": "69vePF1F4pE9iA1h", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the label mentioned at the coordinates [0.466, 0.932, 0.534, 0.970]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 835 }, { "id": "lVdg87zDLdA3si78", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.943, 0.273, 0.975, 0.632] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 835 }, { "id": "6M9zeKR2Yv0Aah5c", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the text annotation associated with {the line handle}[0.122, 0.068, 0.210, 0.095] in the legend?" }, { "from": "gpt", "value": "Heart Disease." } ], "id_raw": 835 }, { "id": "zyfo0ItRT3CTzcP6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_15.png", "conversations": [ { "from": "human", "value": "\nWhich of the two lines represented by {the blue line}[0.140, 0.068, 0.860, 0.837] and {the orange line}[0.140, 0.837, 0.860, 0.068] has a descending trend?" }, { "from": "gpt", "value": "The blue line." } ], "id_raw": 835 }, { "id": "wInH0q2YvOS5cWxf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_15.png", "conversations": [ { "from": "human", "value": "\nIs the {Diabetes Mortality line}[0.780, 0.129, 0.860, 0.068] above or below the {Heart Disease Mortality line}[0.780, 0.740, 0.860, 0.837] on the Year 2018?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 835 }, { "id": "nOIhBcABtffrHN37", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_15.png", "conversations": [ { "from": "human", "value": "\nWhich line is represented by the {marker}[0.505, 0.068, 0.593, 0.095] in the legend?" }, { "from": "gpt", "value": "Diabetes." } ], "id_raw": 835 }, { "id": "JkldS8AB6tcntaJv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within the area [0.120, 0.049, 0.765, 0.119]." }, { "from": "gpt", "value": "It contains the legend items: {Heart Disease with a blue handle}[0.224, 0.065, 0.452, 0.103], {Diabetes with an orange handle}[0.608, 0.065, 0.752, 0.103]." } ], "id_raw": 835 }, { "id": "na86IilqYoPBYzNS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the section within [0.271, 0.895, 0.329, 0.923]." }, { "from": "gpt", "value": "This section contains an x-axis label which is {the year '2012'}[0.271, 0.895, 0.329, 0.923]." } ], "id_raw": 835 }, { "id": "9K7PKbsTEPJqPpdU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_15.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.745, 0.895, 0.809, 0.923]?" }, { "from": "gpt", "value": "It contains an x-axis label representing {the year '2018'}[0.745, 0.895, 0.809, 0.923]." } ], "id_raw": 835 }, { "id": "zauw3c9OcYdw62W1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_16.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the \"Unemployment Rate\" in the legend?" }, { "from": "gpt", "value": "#ff7f00[0.075, 0.050, 0.111, 0.062]." } ], "id_raw": 836 }, { "id": "Jmu10LQucr0AdKHo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of {the y-axis label} for \"Unemployment Rate (%)?" }, { "from": "gpt", "value": "[0.015, 0.298, 0.034, 0.641][0.015, 0.298, 0.034, 0.641]." } ], "id_raw": 836 }, { "id": "RLVBoctRpTRaKCLO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_16.png", "conversations": [ { "from": "human", "value": "\nWhich line has {the coordinate}[0.697, 0.264] associated with a data point?" }, { "from": "gpt", "value": "\"Income Inequality\"[0.697, 0.264]." } ], "id_raw": 836 }, { "id": "AK8h6pHSXr3uV2ml", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point at} [0.303, 0.716] higher or lower than {the point at} [0.382, 0.657] on the chart labeled \"Income Inequality\"?" }, { "from": "gpt", "value": "Higher[0.303, 0.716] [0.382, 0.657]." } ], "id_raw": 836 }, { "id": "WfBGLcugoZZcYJ8x", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_16.png", "conversations": [ { "from": "human", "value": "\nDoes the label at {coordinate} [0.185, 0.833] signify a higher or lower value compared to the label at [0.539, 0.387] for the 'Income Inequality' graph?" }, { "from": "gpt", "value": "Higher[0.185, 0.833] [0.539, 0.387]." } ], "id_raw": 836 }, { "id": "RKRh5wLzBtwST0G1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_16.png", "conversations": [ { "from": "human", "value": "\nWhich has a lower point coordinate in 2009, \"Unemployment Rate\" or \"Income Inequality\"?" }, { "from": "gpt", "value": "Unemployment Rate[0.461, 0.080]." } ], "id_raw": 836 }, { "id": "MmrMVubMtSdnOXt0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_16.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is found within the chart area [0.067, 0.029, 0.933, 0.910]." }, { "from": "gpt", "value": "The main chart area contains multiple data points representing both \"Unemployment Rate\" and \"Income Inequality\" with x-axis for 'Years' and y-axes for 'Unemployment Rate (%)' and 'Income Inequality Index'." } ], "id_raw": 836 }, { "id": "gfS0t3uQrTAue0Tw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_16.png", "conversations": [ { "from": "human", "value": "\nWhat is visible in the region [0.500, 0.017, 0.500, 0.017] in the chart?" }, { "from": "gpt", "value": "This region mainly holds the subtitle of the chart but it is empty, indicating no subtitle is set there." } ], "id_raw": 836 }, { "id": "TNWNq1kYbIvayzzV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the content between the coordinates [0.106, 0.054, 0.894, 0.822]." }, { "from": "gpt", "value": "This region includes a line graph plotting the Unemployment Rate in various years, marked with orange circles representing each data point for respective years." } ], "id_raw": 836 }, { "id": "CqSEfiI1AQqfmI3s", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the object at [0.066, 0.439, 0.096, 0.460] in the chart?" }, { "from": "gpt", "value": "The label is 6." } ], "id_raw": 837 }, { "id": "lI6JRBfg5yWpocR4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the {object}[0.066, 0.861, 0.096, 0.882] in the y-axis?" }, { "from": "gpt", "value": "The label is 3." } ], "id_raw": 837 }, { "id": "RwZCXrWMfqIWaUlQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.904, 0.257, 0.934, 0.299]?" }, { "from": "gpt", "value": "It's a y-axis tick label of '80' in the secondary plot." } ], "id_raw": 837 }, { "id": "iVRZnpph75wd5GWq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_17.png", "conversations": [ { "from": "human", "value": "\nIs the {point labeled '2'}[0.220, 0.388] of Experimental group greater than the value of the fifth point in the {Control group}[0.860, 2.046]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 837 }, { "id": "VnOt9QJ6qTD3hjke", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_17.png", "conversations": [ { "from": "human", "value": "\nIs the first point coordinate of {Experimental line}[0.140, 0.262] above or below {Control line point}[0.540, 2.046] in the chart?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 837 }, { "id": "SnnlRn99kn8KXn0H", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_17.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher label in the y-axis, the point at [0.220, 0.388] or the point at [0.540, 2.046]?" }, { "from": "gpt", "value": "The point at [0.540, 2.046]." } ], "id_raw": 837 }, { "id": "dqR3ZYXPqgj2nOaU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the area [0.904, 0.137, 0.934, 0.179]." }, { "from": "gpt", "value": "This area contains a tick label on the y-axis of the subplot. The label reads '85' and it's colored orange indicating the productivity level in the Control group." } ], "id_raw": 837 }, { "id": "9M43rXX8Oa1Yfzdu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_17.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents of the region [0.541, 0.0, 0.860, 2.0]?" }, { "from": "gpt", "value": "This region primarily contains the Control group data in orange, represented by a line plot with upward pointing markers at points with {coordinates}[0.540, 2.046], [0.620, 2.046], [0.700, 2.046], [0.780, 2.046], and [0.860, 2.046]." } ], "id_raw": 837 }, { "id": "qxVcYTKnIz5ttHAE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.120, 0.049, 0.436, 0.119]." }, { "from": "gpt", "value": "This area contains the chart's subtitle with two legend items. One for 'Experimental' group represented in black at {coordinates}[0.133, 0.081, 0.198, 0.081] and the other for 'Control' group also represented in black at {coordinates}[0.309, 0.081, 0.374, 0.081]." } ], "id_raw": 837 }, { "id": "kvmX3lS4Cg9on5kj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_19.png", "conversations": [ { "from": "human", "value": "\nWhat is located at these coordinates [0.146, 0.840] on the chart?" }, { "from": "gpt", "value": "The starting point of the Online Shopping line." } ], "id_raw": 839 }, { "id": "ZBMAdCBWhH6qmisK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.947, 0.278, 0.977, 0.632]?" }, { "from": "gpt", "value": "Y-axis label for subplot_1" } ], "id_raw": 839 }, { "id": "gSa7nbhXqkZAqt1S", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_42_19.png", "conversations": [ { "from": "human", "value": "\nWhat is identified at {these coordinates}[0.062, 0.683, 0.103, 0.712]?" }, { "from": "gpt", "value": "Y-axis tick label '100'." } ], "id_raw": 839 }, { "id": "CT293v5zwdBaJG3l", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_19.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Online Shopping}[0.854, 0.070] greater than the value of {In-store Shopping}[0.854, 0.840] for the year 2020?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 839 }, { "id": "Ec8DuwnWln5YMZH9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_19.png", "conversations": [ { "from": "human", "value": "\nIs the value of {In-store Shopping}[0.358, 0.437] for 2013 less or more than that of {Online Shopping}[0.358, 0.707]?" }, { "from": "gpt", "value": "More." } ], "id_raw": 839 }, { "id": "QwXBzdOefqSLXO2J", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_42_19.png", "conversations": [ { "from": "human", "value": "\nIs the Y-axis tick label '100'[0.062, 0.683, 0.103, 0.712] greater or less than the Y-axis tick label '120'[0.062, 0.537, 0.103, 0.567]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 839 }, { "id": "efJEouIrFpCal92n", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content found from [0.146, 0.070] to [0.854, 0.840]." }, { "from": "gpt", "value": "This region contains the line plot for Online Shopping from 2010 to 2020, indicating the annual values." } ], "id_raw": 839 }, { "id": "8bnTHsRjSQBLYK9F", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_19.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the region [0.146, 0.070, 0.854, 0.840]?" }, { "from": "gpt", "value": "The region portrays the data trends of both Online Shopping and In-store Shopping from 2010 to 2020. The Online Shopping has an increasing trend while In-store Shopping exhibits a slow decrease." } ], "id_raw": 839 }, { "id": "VJw8gCjK8r1hGthk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_42_19.png", "conversations": [ { "from": "human", "value": "\nWhat do you observe in the region [0.111, 0.878, 0.889, 0.031]?" }, { "from": "gpt", "value": "This region presents the data chart for two lines which represent Online Shopping and In-store Shopping. The X-axis is labelled with years from 2008 to 2022, and the Y-axis represents the measurements for Online Shopping and In-store Shopping." } ], "id_raw": 839 }, { "id": "3vHSyKiBoLhWIWfP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_0.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.708, 0.413, 0.962, 0.453] in the chart refer to?" }, { "from": "gpt", "value": "The label \"Coal Consumption\" in the legend." } ], "id_raw": 840 }, { "id": "B6GgF0XLRpH50B8y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_0.png", "conversations": [ { "from": "human", "value": "\nWhat object has the coordinate [0.449, 0.945, 0.488, 0.975]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 840 }, { "id": "DbTyUkuS7BNIwm7q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_0.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis tick label} at [0.027, 0.883, 0.056, 0.906] represent?" }, { "from": "gpt", "value": "The value '750'." } ], "id_raw": 840 }, { "id": "qsRWcEZmZu0MqZz3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Coal Consumption'}[0.103, 0.288] above or below {the point of 'Oil Consumption'}[0.103, 0.808] in the year 2008?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 840 }, { "id": "BixgmYxcSxdsCBur", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_0.png", "conversations": [ { "from": "human", "value": "\nIn the year 2020, is {the 'Coal Consumption'}[0.835, 0.369] above or below {the 'Oil Consumption'}[0.835, 0.482]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 840 }, { "id": "mjdpdl3ky9BCUW6d", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_0.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value at the year 2012, Coal Consumption or Oil Consumption?" }, { "from": "gpt", "value": "Coal Consumption." } ], "id_raw": 840 }, { "id": "w3gKdnWZ8ZG8ftGq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the coordinate [0.620, 0.396, 0.973, 0.530]?" }, { "from": "gpt", "value": "It contains the chart legend with two items: {Coal Consumption}[0.708, 0.413, 0.962, 0.453] represented in red color and {Oil Consumption}[0.708, 0.474, 0.939, 0.514] represented in green color." } ], "id_raw": 840 }, { "id": "cDxe8ngeviVAYoFF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the area within the coordinates [0.103, 0.107, 0.835, 0.369] relate to?" }, { "from": "gpt", "value": "It refers to the line of \"Coal Consumption\" from 2008 to 2020." } ], "id_raw": 840 }, { "id": "8Jyg4CsoaOJxIoud", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within [0.103, 0.482, 0.835, 0.808]?" }, { "from": "gpt", "value": "It contains the line for \"Oil Consumption\" with individual points for the years 2008, 2012, 2016, and 2020." } ], "id_raw": 840 }, { "id": "oVq6Ja5XoxgkK8Gj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at the coordinates [0.161, 0.224, 0.322, 0.256] in the legend refer to?" }, { "from": "gpt", "value": "Buying Intention." } ], "id_raw": 841 }, { "id": "GRbh9KzBTrUCUzlT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_1.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.129, 0.912, 0.156, 0.936] correspond to on the x-axis?" }, { "from": "gpt", "value": "The year 2006." } ], "id_raw": 841 }, { "id": "hgnatrYX8CoKToYS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-axis tick label} at [0.015, 0.256, 0.033, 0.280]?" }, { "from": "gpt", "value": "50." } ], "id_raw": 841 }, { "id": "5PqiLDef0txTR5wQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_1.png", "conversations": [ { "from": "human", "value": "\nIn terms of y-values, does {the point for the year 2013 for 'Average Hours Spent'}[0.514, 0.486] plot above or below {the point for the year 2013 for 'Buying Intention'}[0.514, 0.234]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 841 }, { "id": "0qeT8ZuNH6QMNiBm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_1.png", "conversations": [ { "from": "human", "value": "\nWhich trendline begins higher on the y-axis, the {Average Hours Spent} line at [0.085, 0.739] or the {Buying Intention} line at [0.085, 0.502]?" }, { "from": "gpt", "value": "Average Hours Spent." } ], "id_raw": 841 }, { "id": "buXvuirPpL0kEbeU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_1.png", "conversations": [ { "from": "human", "value": "\nWhich trendline has a greater y-value at {the endpoint}[0.942] on the x-axis, 'Average Hours Spent' or 'Buying Intention'?" }, { "from": "gpt", "value": "Buying Intention." } ], "id_raw": 841 }, { "id": "rkOumrgxxEQmyWBS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the bounding box [0.099, 0.164, 0.375, 0.269]." }, { "from": "gpt", "value": "This area contains the legend of the plot. It includes two trendlines: {Average Hours Spent}[0.161, 0.177, 0.368, 0.208] and {Buying Intention}[0.161, 0.224, 0.322, 0.256], paired with respective markers." } ], "id_raw": 841 }, { "id": "EnkeFpX09PjuDi5f", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_1.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the area defined by coordinates [0.165, 0.025, 0.863, 0.072]?" }, { "from": "gpt", "value": "The subtitle of the plot, \"Entertainment and Gaming Industry Development\", is located in this area." } ], "id_raw": 841 }, { "id": "Hz0OM2IuPBJLxDmP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_1.png", "conversations": [ { "from": "human", "value": "\nWhat information is represented within the region [0.492, 0.945, 0.536, 0.975]?" }, { "from": "gpt", "value": "The region contains the x-axis label, which is \"Years\"." } ], "id_raw": 841 }, { "id": "meh1BPrcUxq6x6dz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_2.png", "conversations": [ { "from": "human", "value": "\nWhat type of object is located at these coordinates [0.124, 0.064, 0.924, 0.492]?" }, { "from": "gpt", "value": "The object is the line representing the 'Knowledge Economy' in the chart." } ], "id_raw": 842 }, { "id": "8RK9UhfBJHz4vGIE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label associated with the figure at the coordinates [0.017, 0.334, 0.039, 0.584]?" }, { "from": "gpt", "value": "The label is 'Value (in USD)' which is the Y-Axis label of the chart." } ], "id_raw": 842 }, { "id": "YM5GsIPvM8IqrcX1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the marker at [0.683, 0.468, 0.723, 0.482] represent?" }, { "from": "gpt", "value": "The marker represents the 'Innovation Economy' trend in the legend." } ], "id_raw": 842 }, { "id": "PQF1wdG62Mm7ar4d", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Software' on the 'Knowledge Economy' line}[0.444, 0.492] higher or lower than {the point for 'Software' on the 'Innovation Economy' line}[0.444, 0.853]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 842 }, { "id": "bd0oEUqngV9dOWKN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_2.png", "conversations": [ { "from": "human", "value": "\nAre the coordinates for {the 'Innovation Economy' line}[0.124, 0.327, 0.924, 0.853] generally above or below {the 'Knowledge Economy' line}[0.124, 0.064, 0.924, 0.492]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 842 }, { "id": "FDwg9JB7DoHIvYDM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_2.png", "conversations": [ { "from": "human", "value": "\nWhere is {the point for 'Biotechnology' in 'Innovation Economy'}[0.924, 0.769] in relation to {the point for 'Biotechnology' in 'Knowledge Economy'}[0.924, 0.480]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 842 }, { "id": "jvD4rEfr2YCeHW3x", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_2.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the area defined by the coordinates [0.681, 0.421, 0.897, 0.497]?" }, { "from": "gpt", "value": "It contains the chart's legend. The 'Knowledge Economy' legend item is located at {coordinates}[0.731, 0.430, 0.891, 0.453] with a color marker at {[0.684, 0.435, 0.721, 0.445]}, and the 'Innovation Economy' legend item is at {coordinates}[0.731, 0.465, 0.888, 0.488], with a marker at {[0.683, 0.468, 0.723, 0.482]}." } ], "id_raw": 842 }, { "id": "bXB2OxTL7lAPNpH6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found in the region [0.045, 0.029, 0.074, 0.053]." }, { "from": "gpt", "value": "The '60k' y-axis tick-label is located within this region at {[0.045, 0.029, 0.074, 0.053]}." } ], "id_raw": 842 }, { "id": "mqoi4w9SUGb4uxK0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_2.png", "conversations": [ { "from": "human", "value": "\nWhat information is concentrated in the region [0.055, 0.547, 0.074, 0.570]?" }, { "from": "gpt", "value": "The '5k' y-axis tick-label is stationed in this area at {coordinates}[0.055, 0.547, 0.074, 0.570]." } ], "id_raw": 842 }, { "id": "Jn7hBzgAC58id5NX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_4.png", "conversations": [ { "from": "human", "value": "\nWhat label is located at the coordinates [0.024, 0.157, 0.061, 0.188] in the y-axis?" }, { "from": "gpt", "value": "$10^{-3}$." } ], "id_raw": 844 }, { "id": "BmZt2sTGOmry6zTc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_4.png", "conversations": [ { "from": "human", "value": "\nCan you specify what {the tick with the label 'Finished Products'} is using its coordinates?" }, { "from": "gpt", "value": "Its coordinates are [0.513, 0.896] on the x-axis." } ], "id_raw": 844 }, { "id": "tEqCm1vJBSilFZFV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_4.png", "conversations": [ { "from": "human", "value": "\nWhat information does {the legend item}[0.766, 0.478, 0.934, 0.518] stand for?" }, { "from": "gpt", "value": "Constrained." } ], "id_raw": 844 }, { "id": "EWj5oke5nEoamApD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_4.png", "conversations": [ { "from": "human", "value": "\nAnalyzing their positions, is {the point representing 'Quality Check' on 'Standard'}[0.112, 0.434] above or below than {the point representing 'Quality Check' on 'Constrained'}[0.112, 0.724]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 844 }, { "id": "531KmXvVfh4QeD1z", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_4.png", "conversations": [ { "from": "human", "value": "\nAt 'Finished Products', which line has a higher y value, {the 'Standard'}[0.513, 0.322] or {the 'Constrained'}[0.513, 0.733]?" }, { "from": "gpt", "value": "Constrained." } ], "id_raw": 844 }, { "id": "MHAZwXK0YJMB9eGd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_4.png", "conversations": [ { "from": "human", "value": "\nWhat is higher in the figure, {the 'Standard'} line at 'Product Lifetime'[0.915, 0.302] or the {the 'Constrained'} line at 'Quality Check'[0.112, 0.724]?" }, { "from": "gpt", "value": "Quality Check on the Constrained line." } ], "id_raw": 844 }, { "id": "S8iDSw96xDupokOR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the box region [0.678, 0.400, 0.945, 0.534]?" }, { "from": "gpt", "value": "It contains the legend of the chart with two items: {Standard}[0.766, 0.417, 0.893, 0.457] and {Constrained}[0.766, 0.478, 0.934, 0.518], each with their respective color indicator." } ], "id_raw": 844 }, { "id": "1wlL0ANjgfFdoNB4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the chart area enclosed by [0.071, 0.038, 0.955, 0.896]." }, { "from": "gpt", "value": "This is the main chart area where the 'Standard' and 'Constrained' lines are plotted representing 'Quality Check', 'Finished Products', and 'Product Lifetime'. The x and y-axis are also part of this area." } ], "id_raw": 844 }, { "id": "I9mx85pRBQaB3la2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_4.png", "conversations": [ { "from": "human", "value": "\nCould you detail what is in the area from [0.446, 0.912, 0.581, 0.936]?" }, { "from": "gpt", "value": "The label 'Finished Products' of the x-axis is located within this area." } ], "id_raw": 844 }, { "id": "9zW09G82GrMuttH5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the text label at these coordinates [0.921, 0.908, 0.956, 0.932]?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 845 }, { "id": "PhzBsRzb7Aur9Wc5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the line with the coordinates [0.933, 0.128, 0.943, 0.428] represent in the chart?" }, { "from": "gpt", "value": "Imports." } ], "id_raw": 845 }, { "id": "aPmwUc7TrX8RGG9H", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_5.png", "conversations": [ { "from": "human", "value": "\nWhich year is represented by the x-axis tick label at [0.915, 0.908, 0.950, 0.932]?" }, { "from": "gpt", "value": "2000." } ], "id_raw": 845 }, { "id": "WbLj0lFv1Tgy3rPT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_5.png", "conversations": [ { "from": "human", "value": "\nDoes the {Imports line}[0.933, 0.128, 0.943, 0.428] have a higher or lower final point than the {Exports line}[0.933, 0.043, 0.943, 0.442]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 845 }, { "id": "vLpJdapv7q6q4xwG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_5.png", "conversations": [ { "from": "human", "value": "\nFor the year represented by the tick label at [0.921, 0.908, 0.956, 0.932], which is greater: Imports or Exports?" }, { "from": "gpt", "value": "Exports." } ], "id_raw": 845 }, { "id": "cV8ZB6uma5Ccl2lx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_5.png", "conversations": [ { "from": "human", "value": "\nIs the {Exports line}[0.933, 0.043, 0.943, 0.442] steadily increasing or decreasing?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 845 }, { "id": "uahZKIhJZH1qlNqx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the region located at [0.080, 0.039, 0.204, 0.135]." }, { "from": "gpt", "value": "This region contains the chart's subplot legend. The two items, {Imports}[0.133, 0.050, 0.197, 0.080] and {Exports}[0.133, 0.094, 0.196, 0.124], are both indicated, each with respective colored markers." } ], "id_raw": 845 }, { "id": "Ve6Hiam612uQ94ps", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_5.png", "conversations": [ { "from": "human", "value": "\nExplain what the region [0.933, 0.128, 0.943, 0.428] on the chart indicates." }, { "from": "gpt", "value": "This region represents the Imports line in the chart. It depicts changes of import values over time, indicated by vertically aligned points on the line." } ], "id_raw": 845 }, { "id": "Ma1KA343zNC7aFwg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_5.png", "conversations": [ { "from": "human", "value": "\nCan you explain what the region [0.015, 0.033, 0.062, 0.057] depicts on the chart?" }, { "from": "gpt", "value": "The region [0.015, 0.033, 0.062, 0.057] includes only the y-axis label \"1e+11\" that represents the highest value on the y-axis." } ], "id_raw": 845 }, { "id": "JCnP5t7ma9kiMG6z", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at the coordinates [0.440, 0.945, 0.479, 0.975] represent in the plot?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 847 }, { "id": "Tw0exjyTlGYWkM1Z", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label contained within the coordinates [0.694, 0.474, 0.972, 0.514] in the sublegend?" }, { "from": "gpt", "value": "Traditional Medicine." } ], "id_raw": 847 }, { "id": "NMZJDbZYHz7Qd4ek", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the category of the line given by {line_1}[0.107, 0.443, 0.812, 0.851]?" }, { "from": "gpt", "value": "Traditional Medicine." } ], "id_raw": 847 }, { "id": "ou4aqsZfTpy8G6Ju", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for the year 2020 under Advanced Medicine}[0.812, 0.592] higher or lower on the chart compared to {the point for the year 2020 under Traditional Medicine}[0.812, 0.851]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 847 }, { "id": "EaDoVlqEZRsS9WdN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_7.png", "conversations": [ { "from": "human", "value": "\nIn 2010, does {Advanced Medicine}[0.343, 0.361] or {Traditional Medicine}[0.343, 0.497] have a higher value?" }, { "from": "gpt", "value": "Advanced Medicine." } ], "id_raw": 847 }, { "id": "Jyr1DfsBxnV1JSJj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the relationship between the points for the year 2015 for {Advanced Medicine}[0.578, 0.484] and {Traditional Medicine}[0.578, 0.592]?" }, { "from": "gpt", "value": "Advanced Medicine is lower." } ], "id_raw": 847 }, { "id": "209Ek9wboGSz5mYt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.534, 0.945, 0.547, 0.975]?" }, { "from": "gpt", "value": "N." } ], "id_raw": 849 }, { "id": "hWkveDbtrmQJpvTW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the text displayed on the {first item in the legend}[0.201, 0.038, 0.388, 0.078]?" }, { "from": "gpt", "value": "Identity Theft." } ], "id_raw": 849 }, { "id": "e65pAP4Of0uf8i7b", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the {y-axis tick label}[0.050, 0.434, 0.087, 0.465]?" }, { "from": "gpt", "value": "$10^{-8}$." } ], "id_raw": 849 }, { "id": "v3U6wu0r79fjdSrI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_9.png", "conversations": [ { "from": "human", "value": "\nWhich curve reaches a higher value, {Identity Theft}[0.138, 0.085, 0.943, 0.437] or {Data Breach}[0.138, 0.514, 0.943, 0.678], at N=4?" }, { "from": "gpt", "value": "Data Breach." } ], "id_raw": 849 }, { "id": "yAKcw5R3oWzw0LvO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_9.png", "conversations": [ { "from": "human", "value": "\nIs the {Identity Theft line}[0.138, 0.085, 0.943, 0.437] above or below the {Data Breach line}[0.138, 0.514, 0.943, 0.678] at N=12?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 849 }, { "id": "5eg19BSn74LVOtcf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_9.png", "conversations": [ { "from": "human", "value": "\nOn the N axis, is the {first plotted point of Identity Theft line}[0.138, 0.437] to the left or right of the {first plotted point of Data Breach line}[0.138, 0.678]?" }, { "from": "gpt", "value": "They are at the same point on the x-axis." } ], "id_raw": 849 }, { "id": "oU9BNdQSwnP3pdkQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_9.png", "conversations": [ { "from": "human", "value": "\nDescribe what is included in the region [0.098, 0.043, 0.983, 0.889]." }, { "from": "gpt", "value": "It includes the graph itself, both Identity theft and Data Breach lines are presented with their markers. It also contains the X and Y-axis with their labels and ticks. The grid lines of Y-axis are visible." } ], "id_raw": 849 }, { "id": "mQkY7246uVrLIfa1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area in [0.112, 0.021, 0.399, 0.155]." }, { "from": "gpt", "value": "The area contains the legend, including two items: {Identity Theft}[0.201, 0.038, 0.388, 0.078] with a blue marker, and {Data Breach}[0.201, 0.099, 0.373, 0.139] with an orange marker." } ], "id_raw": 849 }, { "id": "jmCJbkxSRFYHMD4v", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.138, 0.085, 0.943, 0.437] contain?" }, { "from": "gpt", "value": "It contains the Identity Theft line with its markers on the plot." } ], "id_raw": 849 }, { "id": "95Yzppy2Il13mqNl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_10.png", "conversations": [ { "from": "human", "value": "\nPlease identify the object situated at coordinates [0.501, 0.945, 0.554, 0.975]." }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 850 }, { "id": "hPZi8UIhv26Jgsmt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the co-ordinate of the second label on the y-axis?" }, { "from": "gpt", "value": "[0.043, 0.167, 0.087, 0.191]" } ], "id_raw": 850 }, { "id": "y7Nn3zIgmsRNYCNN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_10.png", "conversations": [ { "from": "human", "value": "\nWhat does {the tick label}[0.904, 0.905, 0.932, 0.936] on the x-axis represent?" }, { "from": "gpt", "value": "10^1" } ], "id_raw": 850 }, { "id": "UCDArfLXHeiEqbVc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_10.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '2'}[0.372, 0.269] on the 'Standard' line higher or lower than {the point labeled '2'}[0.372, 0.412] on the 'Constrained' line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 850 }, { "id": "HEUrEFDldOI40cBO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_10.png", "conversations": [ { "from": "human", "value": "\nWho has a higher value at {point 3}[0.510, 0.105], Standard or Constrained?" }, { "from": "gpt", "value": "Standard." } ], "id_raw": 850 }, { "id": "FkylFqmRhbUPj4Ft", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_10.png", "conversations": [ { "from": "human", "value": "\nWhich line is {the point labeled '5'}[0.683, 0.746] a part of, Standard or Constrained?" }, { "from": "gpt", "value": "Constrained." } ], "id_raw": 850 }, { "id": "Y3JUPJcYLK1CCsrR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content placed in the region [0.688, 0.394, 0.956, 0.528]?" }, { "from": "gpt", "value": "There are two legend items within this region: {Standard}[0.776, 0.411, 0.903, 0.451] and {Constrained}[0.776, 0.472, 0.944, 0.512]. Both items have colored markers associated with them in the scene." } ], "id_raw": 850 }, { "id": "l0MM8jssRhITwIvF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_10.png", "conversations": [ { "from": "human", "value": "\nWhat content can I find in this region [0.098, 0.034, 0.957, 0.889]?" }, { "from": "gpt", "value": "The main plotting area for the graph is contained within this region. It includes the graph lines for both 'Standard' and 'Constrained' conditions. The region also contains the x-axis and y-axis which have multiple labeled tick marks." } ], "id_raw": 850 }, { "id": "n5jePFZZt3Flh1xj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the items located at [0.137, 0.105, 0.683, 0.461]." }, { "from": "gpt", "value": "This region contains points from the 'Standard' line graph. Five points are plotted here, corresponding to entities with values ranging from 1e-08 to 1e-05." } ], "id_raw": 850 }, { "id": "TFpUd6GjTixmtvLQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of the chart with the coordinate of [0.062, 0.064, 0.983, 0.896]?" }, { "from": "gpt", "value": "Line chart." } ], "id_raw": 851 }, { "id": "w3D5e0EsdZKZUEMh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_11.png", "conversations": [ { "from": "human", "value": "\nWhere can I find the label for {the 'Traditional Education'}[0.602, 0.107, 0.830, 0.139]?" }, { "from": "gpt", "value": "In the plot legend." } ], "id_raw": 851 }, { "id": "tMIMJ27Feto8YdC0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the point on the 'Online Learning' line}[0.187, 0.598] represent?" }, { "from": "gpt", "value": "The number of students engaged in Online Learning in 2011." } ], "id_raw": 851 }, { "id": "ty8lDh3NL0X8za2v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_11.png", "conversations": [ { "from": "human", "value": "\nIs the point for {2010 in 'Traditional Education'}[0.104, 0.098] higher than {2010 in 'Online Learning'}[0.104, 0.790]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 851 }, { "id": "yCv0DpjFnVqBWCPZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_11.png", "conversations": [ { "from": "human", "value": "\nDid the number of students in {Traditional Education}[0.941, 0.127] or {Online Learning}[0.941, 0.190] increase more in 2020?" }, { "from": "gpt", "value": "Online Learning." } ], "id_raw": 851 }, { "id": "WkkP2NQDrcJSQNq4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_11.png", "conversations": [ { "from": "human", "value": "\nIs the point for {Year 2018 in 'Traditional Education'}[0.774, 0.125] lower or higher than the point for {Year 2018 in 'Online Learning'}[0.774, 0.293]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 851 }, { "id": "cTfJQR8Jrka6hGHp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.533, 0.094, 0.839, 0.200]." }, { "from": "gpt", "value": "This region contains the legend for the chart. There are two entries, {Traditional Education}[0.602, 0.107, 0.830, 0.139] marked with a blue color, and {Online Learning}[0.602, 0.155, 0.773, 0.187] marked with an orange color." } ], "id_raw": 851 }, { "id": "Dw0YRpQJsb7sJSLI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.062, 0.064, 0.983, 0.896] cover in the chart?" }, { "from": "gpt", "value": "This region covers the entire chart plot including the axes, the lines representing 'Traditional Education' and 'Online Learning' and their respective data points over the years." } ], "id_raw": 851 }, { "id": "hVHmfQXm6TSqEqEl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_11.png", "conversations": [ { "from": "human", "value": "\nWhat is within [0.922, 0.912, 0.961, 0.936]?" }, { "from": "gpt", "value": "The label '2020' of the x-axis is located within these coordinates." } ], "id_raw": 851 }, { "id": "XRWLRZJXXrWGeBft", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the coordinate [0.732, 0.427, 0.955, 0.467]?" }, { "from": "gpt", "value": "\"Existing System\"" } ], "id_raw": 852 }, { "id": "w84loCtfbEJL6s18", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_12.png", "conversations": [ { "from": "human", "value": "\nWhat\u2019s the color of {the line with handle}[0.650, 0.498, 0.714, 0.511] used in sublegend?" }, { "from": "gpt", "value": "#ff7f0e" } ], "id_raw": 852 }, { "id": "bi6VTrn9duxJw7Wy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_12.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what {the y-axis label}[0.017, 0.410, 0.037, 0.544] indicates?" }, { "from": "gpt", "value": "Efficiency" } ], "id_raw": 852 }, { "id": "UjRGUPexkhzjgCmE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_12.png", "conversations": [ { "from": "human", "value": "\nIs the {point for the 'Potential System' at stage 10}[0.413, 0.768] higher or lower on the chart than the {point for the 'Existing System' at stage 10}[0.413, 0.435]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 852 }, { "id": "Rt7NMT64yAd8rJfy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_12.png", "conversations": [ { "from": "human", "value": "\nIs the {last point on the 'Existing System' line}[0.855, 0.103] at the same level as the {last point on the 'Potential System' line}[0.855, 0.602]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 852 }, { "id": "ZHQ4Nu9VJSQIyk7i", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_12.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value at the 15th stage, the 'Existing System' or the 'Potential System'?" }, { "from": "gpt", "value": "Potential System." } ], "id_raw": 852 }, { "id": "8MdUux8kZoES1WYp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the area [0.643, 0.410, 0.977, 0.544]?" }, { "from": "gpt", "value": "This includes the legend for the chart, with two entries: {The Existing System}[0.732, 0.427, 0.955, 0.467] represented with color code #1f77b4, and {The Potential System}[0.732, 0.487, 0.966, 0.527] represented with color code #ff7f0e." } ], "id_raw": 852 }, { "id": "F0klHgKkHGCpmSzU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.229, 0.025, 0.759, 0.058]." }, { "from": "gpt", "value": "This area contains the subtitle of the chart, which is \"Efficiency Improvement Potential Assessment\"." } ], "id_raw": 852 }, { "id": "jFRSQFIjOEXlq6FF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_12.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.396, 0.945, 0.592, 0.975]?" }, { "from": "gpt", "value": "This region contains the label for the x-axis, which is \"Stage of Deployment\"." } ], "id_raw": 852 }, { "id": "kaRYvD4Zi4pfHlte", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the point on the line of Artificial intelligence at [0.777, 0.162]?" }, { "from": "gpt", "value": "The label is 2026." } ], "id_raw": 853 }, { "id": "Fah7KoPwdKYMR8CL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the content located at the coordinates [0.112, 0.080, 0.233, 0.101]?" }, { "from": "gpt", "value": "It's the legend for the \"Artificial intelligence\" line." } ], "id_raw": 853 }, { "id": "3DuARkBc3Y9Qy9jt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the data chart at coordinates of [0.341, 0.025, 0.714, 0.055]?" }, { "from": "gpt", "value": "It's the subtitle, labeled \"Future Technological Development Prospects.\"" } ], "id_raw": 853 }, { "id": "UrTUV8OXOHj6SLJ4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_13.png", "conversations": [ { "from": "human", "value": "\nIs the point for 'Artificial intelligence' at [0.611, 0.213] above or below the point for 'Internet of Things' at [0.611, 0.140]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 853 }, { "id": "rbTGdqOg8fvsdr5T", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_13.png", "conversations": [ { "from": "human", "value": "\nIs the line for 'Artificial intelligence'[0.111, 0.100, 0.943, 0.420] above or below the line for 'Internet of Things'[0.111, 0.119, 0.943, 0.865] in general?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 853 }, { "id": "hvUyN8dHKzhLhicb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_13.png", "conversations": [ { "from": "human", "value": "\nWhich research area has more publications in the year {2024}[0.444, 0.243]?" }, { "from": "gpt", "value": "Artificial intelligence." } ], "id_raw": 853 }, { "id": "St8uh5Pl5tUpcBXx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_13.png", "conversations": [ { "from": "human", "value": "\nWhat is in the regional box [0.075, 0.072, 0.237, 0.140]?" }, { "from": "gpt", "value": "There are the legends of two lines: {Artificial intelligence}[0.112, 0.080, 0.233, 0.101] and {Internet of Things}[0.112, 0.111, 0.216, 0.133]." } ], "id_raw": 853 }, { "id": "Uf3KgIDVjxZXKPV4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area [0.513, 0.952, 0.542, 0.975]." }, { "from": "gpt", "value": "This area contains the x-axis label \"Year\"." } ], "id_raw": 853 }, { "id": "XspJZvqTMYlZPrGR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.611, 0.140, 0.943, 0.865]?" }, { "from": "gpt", "value": "In the region, there are two points on the lines for 'Artificial intelligence' and 'Internet of Things' representing the years {2025}[0.611, 0.213] and {2026}[0.777, 0.162] for 'Artificial intelligence' and {2025}[0.611, 0.140] and {2027}[0.943, 0.749] for 'Internet of Things'." } ], "id_raw": 853 }, { "id": "hmRtnPomkIp7iGf9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at these coordinates [0.578, 0.408, 0.984, 0.448] referred to in the legend of the chart?" }, { "from": "gpt", "value": "EdTech Investment (Billion $)" } ], "id_raw": 854 }, { "id": "pjHioc64xock2lfm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.578, 0.469, 1.021, 0.509] according to the chart's legend?" }, { "from": "gpt", "value": "Number of Innovative Programs" } ], "id_raw": 854 }, { "id": "krduv3gOXt2CqHAm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the data represented at the coordinates [0.616, 0.645]?" }, { "from": "gpt", "value": "It represents data point for the year 2020 in the line 'EdTech Investment (Billion $)'." } ], "id_raw": 854 }, { "id": "Wypu8VJ2oogPoGYB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_14.png", "conversations": [ { "from": "human", "value": "\nWhich line trend has a higher point at the year 2015, is it 'EdTech Investment (Billion $)'[0.464, 0.690] or 'Number of Innovative Programs'[0.464, 0.104]?" }, { "from": "gpt", "value": "EdTech Investment (Billion $)" } ], "id_raw": 854 }, { "id": "cPL51DCNCqqN6lMI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_14.png", "conversations": [ { "from": "human", "value": "\nIn 2011, what chart trend was higher: {EdTech Investment (Billion $)}[0.312, 0.803] or {Number of Innovative Programs}[0.312, 0.169]?" }, { "from": "gpt", "value": "EdTech Investment (Billion $)" } ], "id_raw": 854 }, { "id": "vgXTDCQZIq9yKHix", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_14.png", "conversations": [ { "from": "human", "value": "\nWhich trend is higher on the chart for the year 2016: {EdTech Investment (Billion $)}[0.502, 0.681] or {Number of Innovative Programs}[0.502, 0.095]?" }, { "from": "gpt", "value": "EdTech Investment (Billion $)." } ], "id_raw": 854 }, { "id": "PCbnxJ2kLZGUT4Jq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the coordinates [0.084, 0.625, 0.654, 0.853]." }, { "from": "gpt", "value": "This area contains the line trend for 'EdTech Investment (Billion $)', represented as a blue line with circular markers. The values range from 2005[0.084, 0.853] to 2020[0.654, 0.625]." } ], "id_raw": 854 }, { "id": "xUaZfbzr2aS1uskb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.084, 0.064, 0.654, 0.213]?" }, { "from": "gpt", "value": "This region contains the line trend for 'Number of Innovative Programs' plotted with orange line and \u2018x\u2019 markers. The values range from 2005[0.084, 0.213] to 2020[0.654, 0.064]." } ], "id_raw": 854 }, { "id": "G3EKwi8rIQlS6Min", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_14.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content within the coordinates [0.084, 0.064, 0.654, 0.853]?" }, { "from": "gpt", "value": "This region encompasses both the line trend for 'EdTech Investment (Billion $)' and 'Number of Innovative Programs'. Different years from 2005 to 2020 are along the x-axis and their respective values along the y-axis." } ], "id_raw": 854 }, { "id": "rJ4ATK4WoXajlGd6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_15.png", "conversations": [ { "from": "human", "value": "\nWhat is being indicated by the label at coordinates [0.719, 0.476, 0.933, 0.516] included in the legend area?" }, { "from": "gpt", "value": "It's indicating the 'Order Accuracy' series." } ], "id_raw": 855 }, { "id": "g6MIYJlw9gjroiy0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_15.png", "conversations": [ { "from": "human", "value": "\nWhat object is placed at the coordinates [0.839, 0.091] within the chart?" }, { "from": "gpt", "value": "The last data point of the 'Inventory Velocity' line." } ], "id_raw": 855 }, { "id": "vY78a9oAQZP2MD0J", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of {the text label}[0.463, 0.945, 0.502, 0.975]?" }, { "from": "gpt", "value": "It is the label for the x-axis, representing \"Year\"." } ], "id_raw": 855 }, { "id": "ii7UJjJ4P3IETCdE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_15.png", "conversations": [ { "from": "human", "value": "\nIn 2018, did the 'Inventory Velocity' or 'Order Accuracy' have a higher value according to the points at {coordinates}[0.839, 0.091] and {coordinates}[0.839, 0.439]?" }, { "from": "gpt", "value": "'Order Accuracy'." } ], "id_raw": 855 }, { "id": "UkqFwqnMebX2Pssf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_15.png", "conversations": [ { "from": "human", "value": "\nAre the data points of 'Order Accuracy' line above or below the 'Inventory Velocity' line at the points {2012, Order Accuracy}[0.305, 0.596] and {2012, Inventory Velocity}[0.305, 0.405]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 855 }, { "id": "hNK1kZXY0lsHIB2r", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_15.png", "conversations": [ { "from": "human", "value": "\nIn 2014, how do the locations of 'Inventory Velocity' and 'Order Accuracy' at {points}[0.483, 0.308] and {points}[0.483, 0.552] relative to one another?" }, { "from": "gpt", "value": "The 'Inventory Velocity' point is below the 'Order Accuracy' point." } ], "id_raw": 855 }, { "id": "Fl45bPI7hvYgDz7f", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_15.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the content within the coordinates [0.630, 0.398, 0.980, 0.532]?" }, { "from": "gpt", "value": "This area contains the plot legend. It provides the color coding information for the two lines: 'Inventory Velocity' which is marked in blue color at {coordinates}[0.638, 0.427, 0.700, 0.436] and 'Order Accuracy' marked in orange color at {coordinates}[0.636, 0.486, 0.701, 0.500]." } ], "id_raw": 855 }, { "id": "RCa1S069NBQ6SuXN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_15.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region [0.126, 0.439, 0.839, 0.644]?" }, { "from": "gpt", "value": "This region contains the line for 'Order Accuracy' from the year 2010 to the year 2018. The line has an upward trajectory indicating an increase in order accuracy over these years." } ], "id_raw": 855 }, { "id": "1syLaL3EsVIrHKa8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the area [0.126, 0.091, 0.839, 0.450]?" }, { "from": "gpt", "value": "Unlike the order accuracy line, the 'Inventory Velocity' line is highlighted in this area from the beginning of the year 2010 to the ending of the year 2018." } ], "id_raw": 855 }, { "id": "B8m10XIu7LzsAbE9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {line labeled 'Air Quality Index'}[0.937, 0.246, 0.941, 0.558] in this chart?" }, { "from": "gpt", "value": "The color is #1f77b4, a shade of blue." } ], "id_raw": 856 }, { "id": "fnHvyZKiq1OMDWmp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the X axis represent in this chart according to the coordinate [0.501, 0.945, 0.540, 0.975]?" }, { "from": "gpt", "value": "The X axis represents year." } ], "id_raw": 856 }, { "id": "QYrEGWIJ2vnmBGi2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_16.png", "conversations": [ { "from": "human", "value": "\nWhat type of object is represented at coordinate [0.069, 0.848, 0.115, 0.862]?" }, { "from": "gpt", "value": "It's the color handle for the 'Ocean Acidification Index' in the legend." } ], "id_raw": 856 }, { "id": "6bAJGksotCE8cHXe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_16.png", "conversations": [ { "from": "human", "value": "\nComparing the values in 2020, is the {Air Quality Index}[0.941, 0.558] higher or lower than the {Ocean Acidification Index}[0.941, 0.607]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 856 }, { "id": "KCiSWkCMZ6IgV8RE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_16.png", "conversations": [ { "from": "human", "value": "\nLooking at the coordinates [0.937, 0.363, 0.941, 0.558] and [0.937, 0.363, 0.941, 0.607], does the Air Quality Index or the Ocean Acidification Index start at a higher value in 2011?" }, { "from": "gpt", "value": "Air Quality Index." } ], "id_raw": 856 }, { "id": "8JtUbp2twJN0xIPM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_16.png", "conversations": [ { "from": "human", "value": "\nDoes the {Air Quality Index line}[0.937, 0.246, 0.941, 0.558] intersect with the {Ocean Acidification Index line}[0.937, 0.363, 0.941, 0.607] in the chart?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 856 }, { "id": "MBShWRoHynYQvhId", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_16.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content in area [0.066, 0.786, 0.367, 0.882]?" }, { "from": "gpt", "value": "This area covers the chart's legend, including the labels and color handles for 'Air Quality Index' and 'Ocean Acidification Index'." } ], "id_raw": 856 }, { "id": "90EPHhLdBlqVgHBU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.127, 0.797, 0.279, 0.827] contain in the chart?" }, { "from": "gpt", "value": "This area contains the text for the 'Air Quality Index' item in the legend." } ], "id_raw": 856 }, { "id": "rcxMvQS4cAt9jVpd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_16.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region with coordinates [0.027, 0.402, 0.046, 0.425]?" }, { "from": "gpt", "value": "A Y-axis tick label '80' is located in this region." } ], "id_raw": 856 }, { "id": "hb3IV3nhEORF1AWh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the label shown at these coordinates, [0.500, 0.945, 0.539, 0.975]?" }, { "from": "gpt", "value": "The label at these coordinates is \"Year\", which is the x-axis label." } ], "id_raw": 857 }, { "id": "blcMAKFJys88SM82", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart color for the {Venture Capital line}[0.098, 0.238, 0.941, 0.745]?" }, { "from": "gpt", "value": "The color of the Venture Capital line is '#ff7f0e'." } ], "id_raw": 857 }, { "id": "qMevuAZUgc2MRHKH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the item found at {coordinates}[0.343, 0.472, 0.389, 0.486] in the legend?" }, { "from": "gpt", "value": "It's the color marker for 'Venture Capital (in billions)'." } ], "id_raw": 857 }, { "id": "KPNnBSbrHLvVstUm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_17.png", "conversations": [ { "from": "human", "value": "\nIs the 'Total Investment' higher or lower than the 'Venture Capital' in 2018 judged from the points {2018, Total Investment}[0.848, 0.081] and {2018, Venture Capital}[0.848, 0.258]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 857 }, { "id": "WpABo2FxB3hEkCYz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_17.png", "conversations": [ { "from": "human", "value": "\nDoes {the total investment in 2016}[0.660, 0.133] exceed {the total investment in 2017}[0.754, 0.111]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 857 }, { "id": "HkgCGxG2WY2yqk1m", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_17.png", "conversations": [ { "from": "human", "value": "\nWhat's the relative position of {Venture Capital in 2014}[0.473, 0.405] compared to {Venture Capital in 2015}[0.567, 0.333]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 857 }, { "id": "g5OL2ILjoMmhsoYk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the chart title in the area [0.519, 0.011, 0.519, 0.011]?" }, { "from": "gpt", "value": "The area appears to be empty, with no chart title present." } ], "id_raw": 857 }, { "id": "aKxxlUCSet20oyGm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects located within the region [0.340, 0.411, 0.668, 0.507]." }, { "from": "gpt", "value": "This region contains the legend for the chart. Two items are included: {Total Investment (in billions)}[0.399, 0.422, 0.660, 0.452] and {Venture Capital (in billions)}[0.399, 0.466, 0.649, 0.496]. Each one is associated with a specific color represented by a colored marker." } ], "id_raw": 857 }, { "id": "FtPavurZ5jTHwJ7o", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_17.png", "conversations": [ { "from": "human", "value": "\nWhat's visible inside the bounding box [0.098, 0.081, 0.941, 0.219]?" }, { "from": "gpt", "value": "Located within these coordinates are the plotted points and connecting line for 'Total Investment (in billions)'. Each point represents the investment for a specific year." } ], "id_raw": 857 }, { "id": "PHiQlJSOWrWO8HIh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_18.png", "conversations": [ { "from": "human", "value": "\nWhat is at the point with coordinates [0.532, 0.513] in the chart?" }, { "from": "gpt", "value": "It's a point on the 'Educational Services' line representing the year 2010." } ], "id_raw": 858 }, { "id": "8YbAfhfGuWkNtoCq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_18.png", "conversations": [ { "from": "human", "value": "\nWhat is specified at the coordinates [0.510, 0.945, 0.549, 0.975] in the figure?" }, { "from": "gpt", "value": "That is the place where the x-axis label, 'Year', is located." } ], "id_raw": 858 }, { "id": "g2jcFGACQaaj2Vzr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_18.png", "conversations": [ { "from": "human", "value": "\nWhat is present at [0.017, 0.327, 0.037, 0.590]?" }, { "from": "gpt", "value": "The y-axis label, \"Investment (in $M)\", is situated here." } ], "id_raw": 858 }, { "id": "uzYE0RL5LrWWOAdu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_18.png", "conversations": [ { "from": "human", "value": "\nIn the chart, is {the Educational Services data point for 2000}[0.328, 0.683] higher or lower than {the Public Health Services data point for the same year}[0.328, 0.546]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 858 }, { "id": "sB77Bm3Z0Z7t2KAu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_18.png", "conversations": [ { "from": "human", "value": "\nIs {the Education Services line}[0.122, 0.174, 0.937, 0.853] generally higher or lower in the chart than {the Public Health Services line}[0.122, 0.064, 0.937, 0.628]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 858 }, { "id": "pCkFTYoGSlaSbUJU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_18.png", "conversations": [ { "from": "human", "value": "\nWhich service has a higher Investment in {year 2030}[0.937, 0.174] for Education Services and [0.937, 0.064] for Public Health Services?" }, { "from": "gpt", "value": "Public Health Services." } ], "id_raw": 858 }, { "id": "JsIYFDm5AU3I44PG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the region [0.697, 0.411, 0.969, 0.507]." }, { "from": "gpt", "value": "This area consists of the legend for the chart. It contains two items: 'Educational Services' with a blue circle symbol located at {coordinates}[0.701, 0.431, 0.745, 0.440] and 'Public Health Services' with an orange cross symbol located at {coordinates}[0.700, 0.472, 0.746, 0.486]." } ], "id_raw": 858 }, { "id": "TiOIVOa8hXmqPAyY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_18.png", "conversations": [ { "from": "human", "value": "\nCould you detail the area demarcated by [0.122, 0.064, 0.937, 0.628]?" }, { "from": "gpt", "value": "This range contains the 'Public Health Services' line graph. The line starts at the left edge with a point representing the year 1990 and concludes on the right edge with a point for the year 2030. The line generally moves upwards as time progresses, indicating an increase in investment." } ], "id_raw": 858 }, { "id": "EVkcEnlyjJEUpNWJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the information within [0.082, 0.025, 0.978, 0.892]." }, { "from": "gpt", "value": "This large region encompasses the primary plot of the graph. It holds the two line graphs for 'Educational Services' and 'Public Health Services' along with the x-axis (Year) and y-axis (Investment in $M), as well as the corresponding labels, tick labels, and grid lines." } ], "id_raw": 858 }, { "id": "lOCGXQQRUX0QSyLF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label of the horizontal axis}[0.497, 0.945, 0.536, 0.975] in this line chart?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 859 }, { "id": "IpavEKcHvKPLnZP8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the figure located at the coordinates [0.098, 0.064, 0.936, 0.853]?" }, { "from": "gpt", "value": "It's a line graph representing the term 'Energy'." } ], "id_raw": 859 }, { "id": "fBVzhyYeh4Mhln4l", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_43_19.png", "conversations": [ { "from": "human", "value": "\nWhat do {the legend labels}[0.776, 0.408, 0.874, 0.448] and [0.776, 0.469, 0.919, 0.509] represent in the line chart?" }, { "from": "gpt", "value": "'Energy' and 'Resources', respectively." } ], "id_raw": 859 }, { "id": "yyQR164SaoAHezVb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_19.png", "conversations": [ { "from": "human", "value": "\nLooking at {the 'Energy' line}[0.098, 0.064, 0.936, 0.853], is the point representing the year 2014 above or below the point representing 2015?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 859 }, { "id": "MlPAR6tAOuH7ImLc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_19.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Energy' line}[0.098, 0.064, 0.936, 0.853] above or below {the 'Resources' line}[0.098, 0.324, 0.936, 0.808] in the year of 2018?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 859 }, { "id": "dNlie8qhzGvRcpqY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_43_19.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at the year 2010, {Energy}[0.378, 0.476] or {Resources}[0.378, 0.640]?" }, { "from": "gpt", "value": "Resources." } ], "id_raw": 859 }, { "id": "9yTfyR2PAT0CK7We", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.687, 0.392, 0.930, 0.526]." }, { "from": "gpt", "value": "This region contains the legend of the chart. It includes two items labeled 'Energy' and 'Resources', each accompanied by a corresponding symbol." } ], "id_raw": 859 }, { "id": "0KwkX2WKbIB52auI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_19.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located in the area with coordinates [0.010, -0.528, 0.045, -0.497] and [0.017, -0.170, 0.045, -0.139]?" }, { "from": "gpt", "value": "These areas contain the '10^10' and '10^9' labels for the vertical axis respectively." } ], "id_raw": 859 }, { "id": "GDmwwBS1m8twfhmG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_43_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects enclosed by the coordinates between [0.098, 0.064, 0.936, 0.853]." }, { "from": "gpt", "value": "This area primarily encapsulates a line graph labeled as 'Energy'. The graph plots data from the years 2005 to 2020." } ], "id_raw": 859 }, { "id": "M1ZUGa3fCL9f0smS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_0.png", "conversations": [ { "from": "human", "value": "\nWhat color is the marker for the \"Carbon Sequestration\" data series?" }, { "from": "gpt", "value": "Purple." } ], "id_raw": 860 }, { "id": "lxSAYveuIezh2IdH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_0.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is used for the \"Plastic Reduction\" line on the graph?" }, { "from": "gpt", "value": "Star." } ], "id_raw": 860 }, { "id": "QK6nTWd0jaGCtxBh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line labeled 'Afforestation'}[0.093, 0.139, 0.738, 0.286]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 860 }, { "id": "CbeeHNdb1cQ4dpm8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_0.png", "conversations": [ { "from": "human", "value": "\nIs {the y-coordinate for point 5 on 'Carbon Sequestration'}[0.229, 0.356] higher or lower than {the y-coordinate for point 5 on 'Plastic Reduction'}[0.229, 0.632]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 860 }, { "id": "wh9w9VdXWJjlqx5l", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_0.png", "conversations": [ { "from": "human", "value": "\nWhich line has a lower y-coordinate at year 15, {Regeneration}[0.568, 0.186] or {Afforestation}[0.568, 0.178]?" }, { "from": "gpt", "value": "Afforestation." } ], "id_raw": 860 }, { "id": "7OXfN8uonkukEEDV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_0.png", "conversations": [ { "from": "human", "value": "\nIs the starting point of {Carbon Sequestration}[0.093, 0.433] above or below the starting point of {Plastic Reduction}[0.093, 0.763]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 860 }, { "id": "37YTr0suxrP8uFgB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the coordinate region [0.763, 0.040, 0.985, 0.176]?" }, { "from": "gpt", "value": "The legend with labels for the lines such as Regeneration, Afforestation, Carbon Sequestration, and Plastic Reduction along with their corresponding color markers." } ], "id_raw": 860 }, { "id": "wMqSa7D5DE7aRRtf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region bounded by coordinates [0.076, 0.029, 0.755, 0.910]." }, { "from": "gpt", "value": "The main plot area containing multiple lines representing different data series such as Regeneration, Afforestation, Carbon Sequestration, and Plastic Reduction, marked with various colored markers and styles." } ], "id_raw": 860 }, { "id": "asyv0tCQCXEEGD0L", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are present in the region between [0.093, 0.066, 0.738, 0.763]?" }, { "from": "gpt", "value": "This area includes the data points for the lines representing Carbon Sequestration and Plastic Reduction as they progress over the years." } ], "id_raw": 860 }, { "id": "tAjfKfb3gEpxPw0i", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart object at [0.015, 0.397, 0.029, 0.549]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 861 }, { "id": "KL8FhnGXGKtaoDE6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {Digital Theft's fifth point}[0.474, 0.107] on the chart?" }, { "from": "gpt", "value": "A data point in 2015 for the 'Digital Theft' trend." } ], "id_raw": 861 }, { "id": "wDj97K2OIqhm58aJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second x-axis tick label?" }, { "from": "gpt", "value": "[0.127, 0.931, 0.162, 0.951]." } ], "id_raw": 861 }, { "id": "K1mLzOdfyVNxQwsz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '2019' for 'Data Breach'}[0.803, 0.474] above or below {the point labeled '2019' for 'Privacy Invasion'}[0.803, 0.551]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 861 }, { "id": "IYYHP31t2utYPLvh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_1.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Privacy Invasion' trend line}[0.144, 0.536, 0.885, 0.666] above or below {the 'Cyber Attacks' trend line}[0.144, 0.162, 0.885, 0.350]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 861 }, { "id": "Z0BdJ7XVcWauoHy0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point in 2012 for 'Digital Theft'}[0.227, 0.081] at a higher or lower position than {the point in 2012 for 'Data Breach'}[0.227, 0.323] on the chart?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 861 }, { "id": "ah6hmg9v64vkzWL1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the region at [0.462, 0.891, 0.553, 0.917]." }, { "from": "gpt", "value": "The region contains the x-axis label 'Year', placed centrally within this region, with black color." } ], "id_raw": 861 }, { "id": "8eIsXuPZegxYc6JF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the area within [0.815, 0.038, 0.961, 0.154]." }, { "from": "gpt", "value": "This area contains the legend for the chart with items {Digital Theft}[0.855, 0.045, 0.932, 0.064], {Cyber Attacks}[0.855, 0.073, 0.942, 0.091], {Data Breach}[0.855, 0.100, 0.933, 0.119], and {Privacy Invasion}[0.855, 0.128, 0.956, 0.146]. Each legend item is accompanied by a colored marker." } ], "id_raw": 861 }, { "id": "I0QiiyxvQSnTbp4G", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.538, 0.931, 0.903, 0.951]?" }, { "from": "gpt", "value": "There are 3 objects: the x-axis labels '2016'[0.538, 0.931, 0.573, 0.951], '2017'[0.621, 0.931, 0.656, 0.951] and '2020'[0.868, 0.931, 0.903, 0.951]." } ], "id_raw": 861 }, { "id": "gbrvvmdQRua7YEy9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the primary object located at [0.019, 0.347, 0.044, 0.576] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 862 }, { "id": "bhUQmXtVMaCcWkRI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_2.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the third tick label on the x-axis?" }, { "from": "gpt", "value": "[0.315, 0.902, 0.329, 0.932]." } ], "id_raw": 862 }, { "id": "aQcUiFUlvppEIOa0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} specified by coordinates [0.491, 0.902, 0.504, 0.932]?" }, { "from": "gpt", "value": "5." } ], "id_raw": 862 }, { "id": "AdBp9yeVpQB5R4rz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_2.png", "conversations": [ { "from": "human", "value": "\nIs {the data point from 'Protest Activity'}[0.410, 0.750] of a higher value compared to {the data point from 'Political Stability'}[0.410, 0.275] in year 4?" }, { "from": "gpt", "value": "No." } ], "id_raw": 862 }, { "id": "2I0THsUFFeqee4ji", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the trend line for 'Public Opinion'}[[0.146, 0.079, 0.937, 0.359]] lie above or below {the trend line for 'Protest Activity'}[[0.146, 0.546, 0.937, 0.776]] throughout the years?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 862 }, { "id": "HccBBggZGsacXsZZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_2.png", "conversations": [ { "from": "human", "value": "\nWhich metric shows a higher value during the 5th year, the 'Policy Changes' or 'Public Opinion'?" }, { "from": "gpt", "value": "Public Opinion." } ], "id_raw": 862 }, { "id": "otkIN01Vp1QnoPr6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the region [0.745, 0.049, 0.972, 0.208]?" }, { "from": "gpt", "value": "The area represents the legend of the chart. It contains labels for 'Protest Activity', 'Political Stability', 'Policy Changes', and 'Public Opinion', each associated with a color-coded marker." } ], "id_raw": 862 }, { "id": "WJIUvsOd8MQOKiaq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.077, 0.875, 0.090, 0.905]." }, { "from": "gpt", "value": "This region includes the first tick label '0' on the y-axis." } ], "id_raw": 862 }, { "id": "X1qDxYqGjhNsdzNW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are present within the region [0.322, 0.130, 0.937, 0.359]?" }, { "from": "gpt", "value": "This area includes parts of the trend lines for 'Policy Changes' and 'Public Opinion'. It also includes the respective data points from year 3 up to year 10 for these trends." } ], "id_raw": 862 }, { "id": "VHFt3luGx4r4Ik3g", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the marker for the \"Diet\" line in the legend?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 863 }, { "id": "mJq1Gz7uHdaBlk6e", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the legend item for 'Social_Network'}[0.866, 0.151, 0.973, 0.174]?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 863 }, { "id": "7EVnpw1e2Vk86lLJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the marker shape used for {the Sleep data line}[0.099, 0.226, 0.943, 0.871]?" }, { "from": "gpt", "value": "Diamond." } ], "id_raw": 863 }, { "id": "8WrwUOVlvQJ5jaxw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_3.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at the year 2019, {the line for 'Exercise'}[0.901, 0.656] or {the line for 'Social_Network'}[0.901, 0.763]?" }, { "from": "gpt", "value": "Exercise." } ], "id_raw": 863 }, { "id": "u7fH6yNJzwfZHrwC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_3.png", "conversations": [ { "from": "human", "value": "\nDoes the {point for 'Diet' in 2003}[0.226, 0.387] fall above or below {the point for 'Social_Network' in the same year}[0.226, 0.253]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 863 }, { "id": "unYhlAIPNtQkolGZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_3.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Exercise' at 2010}[0.521, 0.522] closer in value to {the final point for 'Sleep' in 2020}[0.943, 0.871]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 863 }, { "id": "emzOpxM82ADoCsWx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents and visual styles in the region [0.822, 0.037, 0.978, 0.183]." }, { "from": "gpt", "value": "This region contains the chart legend. Items include 'Exercise' with a blue square marker, 'Diet' with an orange triangle-down marker, 'Sleep' with a green diamond marker, and 'Social_Network' with a red circle marker." } ], "id_raw": 863 }, { "id": "L8HHRE53VLFE8Ftl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_3.png", "conversations": [ { "from": "human", "value": "\nWhat types of data and how are they represented in the region [0.099, 0.065, 0.943, 0.871]?" }, { "from": "gpt", "value": "The region contains four line graphs representing different lifestyle categories. Exercise is depicted with blue squares, Diet with orange triangles, Sleep with green diamonds, and Social Network with red circles. Each line graph shows their respective data points across years from 2000 to 2020." } ], "id_raw": 863 }, { "id": "emXkGY5YJNq1rN2y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_3.png", "conversations": [ { "from": "human", "value": "\nWhat information is conveyed in the area [0.057, 0.025, 0.985, 0.911]?" }, { "from": "gpt", "value": "The area spans the main chart area displaying line graphs for Exercise, Diet, Sleep, and Social_Network, ranging from the year 2000 to 2020. Important details like grid lines for both x and y-axis, corresponding tick marks, and data point markers for each category are visible. The y-axis is labeled 'Lifestyle Categories'." } ], "id_raw": 863 }, { "id": "7Ej6Yg36WLR2cTJJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at {coordinates}[0.015, 0.331, 0.035, 0.617] represent in the chart?" }, { "from": "gpt", "value": "Performance Metrics." } ], "id_raw": 864 }, { "id": "xAjYqWh4Oh2Wygw4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_4.png", "conversations": [ { "from": "human", "value": "\nWhat is located at {coordinates}[0.874, 0.885, 0.896, 0.904]?" }, { "from": "gpt", "value": "HR label in the legend." } ], "id_raw": 864 }, { "id": "kC5Y2JVCh8DQJRbD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_4.png", "conversations": [ { "from": "human", "value": "\nPlease identify the object at {coordinates}[0.367, 0.927, 0.377, 0.949]." }, { "from": "gpt", "value": "The x-axis tick labeled '7'." } ], "id_raw": 864 }, { "id": "OCu4rxhk1PN5EMvv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_4.png", "conversations": [ { "from": "human", "value": "\nWhich y-axis tick label value is higher, the one at {coordinates}[0.051, 0.912, 0.072, 0.934] or at {coordinates}[0.051, 0.168, 0.072, 0.190]?" }, { "from": "gpt", "value": "90." } ], "id_raw": 864 }, { "id": "qmbBKzprReyAhQjz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_4.png", "conversations": [ { "from": "human", "value": "\nAre the point coordinates of the 'HR' line at {point 14}[0.690, 0.494] higher or lower in value than those of the 'Optimization' line at {point 14}[0.690, 0.215]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 864 }, { "id": "Ypr9xXZXUBY42M4m", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_4.png", "conversations": [ { "from": "human", "value": "\nWhich has a greater x-coordinate in the fifth point, the line labeled 'Innovation' at {coordinates}[0.281, 0.385] or the line labeled 'HR' at {coordinates}[0.281, 0.683]?" }, { "from": "gpt", "value": "The same." } ], "id_raw": 864 }, { "id": "dMgs0skUrj03hYTi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the components within {region}[0.826, 0.802, 0.954, 0.904]?" }, { "from": "gpt", "value": "This region includes text labels 'Innovation', 'Strategy', 'Optimization', and 'HR' along with their respective color handles in the chart legend." } ], "id_raw": 864 }, { "id": "LihW80E19GW6bNML", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_4.png", "conversations": [ { "from": "human", "value": "\nWhat elements are included in the bounded region {region}[0.374, 0.282, 0.785, 0.770]?" }, { "from": "gpt", "value": "This selected region encompasses the lines representing 'Strategy' and 'Optimization', showing various points marking their respective values." } ], "id_raw": 864 }, { "id": "iSZf7vNszeHB0sAg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_4.png", "conversations": [ { "from": "human", "value": "\nCould you provide details about the contents located within {region}[0.787, 0.317, 0.982, 0.917]?" }, { "from": "gpt", "value": "This region includes parts of the lines and labels corresponding to the last few tasks, specifically from tasks 16 to 20, showing their values for each performance metric." } ], "id_raw": 864 }, { "id": "t7CJzPfkDSqfJqbi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_5.png", "conversations": [ { "from": "human", "value": "\nWhat color does {the legend item for '5G Infrastructure'}[0.108, 0.177, 0.157, 0.191] have on the chart?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 865 }, { "id": "NovfjjMiWvB3MhgB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the marker shape used for {the 2008 point on the 'Fiber Optic Connections'} line[0.434, 0.831]?" }, { "from": "gpt", "value": "Diamond." } ], "id_raw": 865 }, { "id": "aJ5syfGlLV28Q7A3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_5.png", "conversations": [ { "from": "human", "value": "\nWhat data value is indicated by {the y-axis label}[0.019, 0.306, 0.044, 0.625] at the middle position in the chart?" }, { "from": "gpt", "value": "100." } ], "id_raw": 865 }, { "id": "x3Wwnf5naWDb3QuI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_5.png", "conversations": [ { "from": "human", "value": "\nIs {the point for '2001'}[0.139, 0.894] higher or lower than {the point for '2020'}[0.939, 0.122] on the 'Mobile Users' line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 865 }, { "id": "FuDJiDm8PGzE7vgb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_5.png", "conversations": [ { "from": "human", "value": "\nWhich point is further right, {the last point of 'Internet Users'}[0.939, 0.465] or {the last point of 'Fiber Optic Connections'}[0.939, 0.732] on their respective lines?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 865 }, { "id": "QkFBMLk2rVRzqInT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_5.png", "conversations": [ { "from": "human", "value": "\nIs {the first data point for '5G Infrastructure'}[0.139, 0.894] positioned on the same level as {the last data point for '5G Infrastructure'}[0.939, 0.704] on the chart?" }, { "from": "gpt", "value": "No." } ], "id_raw": 865 }, { "id": "n4KGkboSnK9QKVsZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents between the coordinates [0.096, 0.894, 0.948, 0.932]." }, { "from": "gpt", "value": "This area contains x-axis labels for the years ranging from 2001 to 2020, marking the beginning and end of the dataset visualized in the chart, formatted in grey text." } ], "id_raw": 865 }, { "id": "6vsUM4sHekTGGU2C", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_5.png", "conversations": [ { "from": "human", "value": "\nWhat are the components observed within the rectangle specified by [0.112, 0.060, 0.395, 0.198]?" }, { "from": "gpt", "value": "This section contains legend items for 'Mobile Users', 'Internet Users', 'Fiber Optic Connections', and '5G Infrastructure', each paired with their respective color markers." } ], "id_raw": 865 }, { "id": "rt22Us1bPY0R1y4G", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_5.png", "conversations": [ { "from": "human", "value": "\nPlease provide details about the area spanning [0.019, 0.306, 0.090, 0.932]." }, { "from": "gpt", "value": "The region contains y-axis labels marking values from 0 to 450 in intervals, styled in dark grey color, aligning vertically along the left margin of the chart." } ], "id_raw": 865 }, { "id": "eP01iFK0z9u0D1H2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the {ylabel} in the area [0.015, 0.172, 0.035, 0.776]?" }, { "from": "gpt", "value": "Career and Workplace Development Trends." } ], "id_raw": 867 }, { "id": "xTo1HPNw1URSMB2M", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the trend line}[0.105, 0.578, 0.962, 0.771]?" }, { "from": "gpt", "value": "Seniors." } ], "id_raw": 867 }, { "id": "Q2mEDAmXww5rhWEO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label at [0.504, 0.956, 0.563, 0.981]?" }, { "from": "gpt", "value": "Years." } ], "id_raw": 867 }, { "id": "nhb7QcmX2hXLyDmx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Seniors' at year 13}[0.647, 0.771] in higher position compared to {the point for 'Adults' at year 13}[0.647, 0.296]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 867 }, { "id": "d25I5f8IzA4JT6CN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Teenagers' at year 6}[0.331, 0.414] higher or lower than {the point for 'Teenagers' at year 5}[0.285, 0.474]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 867 }, { "id": "H9mSA8HxfCTV6ygY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_7.png", "conversations": [ { "from": "human", "value": "\nWhich group had a higher development trend at {year 16}[0.782, 0.355] on the chart, 'Teenagers' or 'Adults'?" }, { "from": "gpt", "value": "Adults." } ], "id_raw": 867 }, { "id": "uc842YuG4baerLYL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_7.png", "conversations": [ { "from": "human", "value": "\nPlease provide a description of the content in [0.844, 0.819, 0.977, 0.910]." }, { "from": "gpt", "value": "This region presents the legend of the plot. The labels {Teenagers}[0.893, 0.827, 0.971, 0.846], {Adults}[0.893, 0.855, 0.941, 0.874], and {Seniors}[0.893, 0.884, 0.950, 0.902] are included, accompanied by colored markers." } ], "id_raw": 867 }, { "id": "TZSpHPRGJmaU88mu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_7.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the coordinates [0.234, 0.926, 0.247, 0.949]?" }, { "from": "gpt", "value": "The x-axis tick label '4' is located within these coordinates." } ], "id_raw": 867 }, { "id": "qDHaGHrK5nBBiWVT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.105, 0.578, 0.962, 0.771]." }, { "from": "gpt", "value": "This region captures the trend line for 'Seniors'. There are data points for each year, starting from year 1 through year 20. Each data point shows the respective value for 'Seniors' per year." } ], "id_raw": 867 }, { "id": "tSd50Jczq6uV09Iu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.019, 0.321, 0.044, 0.610] in the plot?" }, { "from": "gpt", "value": "It is the y-axis label named \"Usage Rate (%)\"." } ], "id_raw": 870 }, { "id": "0NCxXICNsCLYmReL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the text located at the coordinates [0.670, 0.060, 0.839, 0.085] in the legend?" }, { "from": "gpt", "value": "The text is \"Machine Learning\"." } ], "id_raw": 870 }, { "id": "RvzoQp67YSw0rBoq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the tick label at the coordinates [0.714, 0.902, 0.767, 0.932] on the x-axis indicate?" }, { "from": "gpt", "value": "It indicates the year \"2019\"." } ], "id_raw": 870 }, { "id": "oAUc5aTed6MbVuPT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_10.png", "conversations": [ { "from": "human", "value": "\nDoes the {line marked as 'Machine Learning'}[0.096, 0.379, 0.955, 0.550] line have a higher or lower value at 2016 compared to the {line marked as 'Deep Learning'}[0.096, 0.372, 0.955, 0.585]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 870 }, { "id": "WzYYdskuF2L5qCw4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the relative position of {the 'Robotic Process Automation'}[0.096, 0.240, 0.955, 0.416] line at 2020 compared to the {line labeled 'Machine Learning'}[0.095, 0.379, 0.955, 0.55]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 870 }, { "id": "ePoogApGg3ZqOUGy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_10.png", "conversations": [ { "from": "human", "value": "\nWhich trend, {Machine Learning}[0.096, 0.379, 0.955, 0.550] or {Automation}[0.096, 0.182, 0.955, 0.337], has a higher value at year 2018?" }, { "from": "gpt", "value": "Automation." } ], "id_raw": 870 }, { "id": "S9QtP0BywKDSVLkH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the coordinates [0.096, 0.037, 0.955, 0.894]?" }, { "from": "gpt", "value": "This region mainly contains four line plots representing the trends of different technologies ('Machine Learning', 'Deep Learning', 'Automation', 'Robotic Process Automation') from 2016 to 2020 on a grid, with x-ticks representing years, and y-ticks representing the usage rate in percentage." } ], "id_raw": 870 }, { "id": "Qye6SufPJVDeb96d", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the area from coordinates [0.609, 0.049, 0.945, 0.208]." }, { "from": "gpt", "value": "This is the legend area of the chart that comprises keys for the four different trends. Each trend is denoted by a different color and unique marker symbol." } ], "id_raw": 870 }, { "id": "I6AsAoDYqG8OnmDX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the area within the coordinates [0.096, 0.379, 0.955, 0.550] represent?" }, { "from": "gpt", "value": "This area represents the 'Machine Learning' line trend. It starts from year 2016 with a usage rate approximately at 40% and ends at year 2020 with the usage rate slightly above 60%." } ], "id_raw": 870 }, { "id": "JKyG02Vo54xbRZn2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the primary object at coordinates [0.509, 0.953, 0.555, 0.979]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 872 }, { "id": "J0Ib3iVIdqtfuaYe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_12.png", "conversations": [ { "from": "human", "value": "\nCan you identify {the marker color} at [0.771, 0.077, 0.807, 0.089]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 872 }, { "id": "66MrzHKDpbmW7LKq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the coordinates [0.457, 0.021, 0.607, 0.050]?" }, { "from": "gpt", "value": "Subtitle." } ], "id_raw": 872 }, { "id": "A2JHTXzy7JntuQXl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point for week 3}[0.207, 0.875] positioned higher or lower than {the point for week 5}[0.293, 0.872] on the 'Temperature' graph?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 872 }, { "id": "h2ir0N3XmijnQKVF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the blue marker}[0.771, 0.107, 0.807, 0.119] represent a higher or lower value compared to {the red marker}[0.771, 0.167, 0.807, 0.179] on the 'Public Transport Usage' line chart?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 872 }, { "id": "pjDQ0y3n5VI9H4E0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_12.png", "conversations": [ { "from": "human", "value": "\nIs {the first week point}[0.120, 0.525] on the 'Grocery Purchases' line above or below {the tenth week point}[0.510, 0.485]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 872 }, { "id": "CUoXV1If18ILYnjZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the bounds [0.434, 0.925, 0.604, 0.945]." }, { "from": "gpt", "value": "This area contains the x-axis labels for weeks 9 and 10 at coordinates {week 9}[0.462, 0.925, 0.471, 0.945] and {week 10}[0.501, 0.925, 0.519, 0.945]." } ], "id_raw": 872 }, { "id": "CliCkT33T66EkvK7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_12.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the region [0.100, 0.492, 0.200, 0.860]?" }, { "from": "gpt", "value": "This region includes {the first three points on the 'Hospital Admissions' line} at coordinates [0.120, 0.859], [0.163, 0.858], and [0.207, 0.856]." } ], "id_raw": 872 }, { "id": "lpiLhZYaIAkGctjn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the significant objects found within the area [0.110, 0.100, 0.300, 0.500]." }, { "from": "gpt", "value": "This sector encases two lines; {the 'Grocery Purchases'} from weeks 1 to 3 seen at coordinates [0.120, 0.525], [0.163, 0.552], [0.207, 0.545] and the {beginning of the 'Public Transport Usage'} line from weeks 1 to 3 positioned at coordinates [0.120, 0.195], [0.163, 0.128], [0.207, 0.095]." } ], "id_raw": 872 }, { "id": "drrk3X90Eg2tEW2N", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_13.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the marker at {this position}[0.815, 0.100, 0.865, 0.117]?" }, { "from": "gpt", "value": "Pattern B." } ], "id_raw": 873 }, { "id": "iQojxiBN64Wk3lSp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_13.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is at {the coordinate}[0.340, 0.165, 0.340, 0.165] in the plotted line chart?" }, { "from": "gpt", "value": "Square." } ], "id_raw": 873 }, { "id": "VSoLGwLwTVpzlfCM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_13.png", "conversations": [ { "from": "human", "value": "\nWhat does {this marker}[0.826, 0.654, 0.826, 0.654] represent in the chart?" }, { "from": "gpt", "value": "Pattern B." } ], "id_raw": 873 }, { "id": "qzHUl2c8SMrGS7K6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_13.png", "conversations": [ { "from": "human", "value": "\nIs the value at {point 8}[0.428, 0.337, 0.428, 0.337] for Pattern B above or below the value at {point 8}[0.428, 0.380, 0.428, 0.380] for Pattern D?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 873 }, { "id": "5kZ5ilrNNWAFXSdS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_13.png", "conversations": [ { "from": "human", "value": "\nWhich pattern has a higher value at the 15th point between {Pattern A}[0.738, 0.423, 0.738, 0.423] and {Pattern D}[0.738, 0.551, 0.738, 0.551]?" }, { "from": "gpt", "value": "Pattern A." } ], "id_raw": 873 }, { "id": "rmOrFoERp6nyKGUc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_13.png", "conversations": [ { "from": "human", "value": "\nIs {Pattern C}[0.959, 0.637, 0.959, 0.637] marker at point 20 below {Pattern D}[0.959, 0.680, 0.959, 0.680] on the chart?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 873 }, { "id": "MNrfiwKKRcBoBrMq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found within {this region}[0.112, 0.902, 0.972, 0.932]." }, { "from": "gpt", "value": "This region consists of x-axis labels from '1' to '20', each associated with a single tick along the bottom of the plot." } ], "id_raw": 873 }, { "id": "YQfv3w2scWmvmd7H", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the area {between these coordinates}[0.096, 0.025, 0.874, 0.198]." }, { "from": "gpt", "value": "This area houses multiple elements of interest, including y-axis labels from '0' to '100' scaled every 10 units, and a legend detailing four patterns each identified by different colors and markers." } ], "id_raw": 873 }, { "id": "lw04KcXMfD7uSIOX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_13.png", "conversations": [ { "from": "human", "value": "\nWhat is located within {the space}[0.874, 0.060, 0.962, 0.198]?" }, { "from": "gpt", "value": "The region contains the chart's legend describing Pattern A, Pattern B, Pattern C, and Pattern D, detailed with associated color and marker shape symbols." } ], "id_raw": 873 }, { "id": "qG3gBz4pF5TuGQ5s", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the marker shape of {the line labeled 'Biotechnology'}[0.118, 0.380, 0.959, 0.560]?" }, { "from": "gpt", "value": "v." } ], "id_raw": 874 }, { "id": "RTCUfHAhSpbOaW28", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_14.png", "conversations": [ { "from": "human", "value": "\nWhat color is used for {the line for 'Digital Media'}[0.118, 0.062, 0.959, 0.594]?" }, { "from": "gpt", "value": "#de9ed6." } ], "id_raw": 874 }, { "id": "bmAcWD2AxgX86ayB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the ylabel of the chart?" }, { "from": "gpt", "value": "Average Performance (%)." } ], "id_raw": 874 }, { "id": "M493OnynS7unPxAq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point at step 10 for Nanotechnology}[0.517, 0.354] above or below {the point at step 10 for Digital Media}[0.517, 0.311]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 874 }, { "id": "kXxWVbRpcqNKFC9i", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_14.png", "conversations": [ { "from": "human", "value": "\nWhich category has a higher value at step 20, {Digital Media}[0.959, 0.062] or {Information Technology (IT)}[0.959, 0.037]?" }, { "from": "gpt", "value": "Information Technology (IT)." } ], "id_raw": 874 }, { "id": "h7slTNA4mJuSsdr5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the trend for Nanotechnology} start or end higher than {the trend for Biotechnology}[0.118, 0.380, 0.959, 0.560] at step 1?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 874 }, { "id": "qpIhs0STxzSvDt3A", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content and layout in the area [0.096, 0.025, 0.972, 0.055]." }, { "from": "gpt", "value": "This region contains the y-axis label '100' at {coordinates}[0.051, 0.025, 0.090, 0.055]. It's styled in dark grey font color." } ], "id_raw": 874 }, { "id": "FMivivY7KT0E5Rzu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_14.png", "conversations": [ { "from": "human", "value": "\nWhat visual elements are contained within [0.112, 0.902, 0.130, 0.932]?" }, { "from": "gpt", "value": "This section includes the x-axis_tick label '1' at {coordinates}[0.112, 0.902, 0.125, 0.932]." } ], "id_raw": 874 }, { "id": "jysjWDCiCA4fmdkW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.064, 0.025, 0.090, 0.055] include?" }, { "from": "gpt", "value": "The y-tick label '100' is situated within this space, rendered in dark grey font color." } ], "id_raw": 874 }, { "id": "Etxg154R14hMENvR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_16.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is used for {GradientBoost}[0.670, 0.229, 0.708, 0.229]?" }, { "from": "gpt", "value": "None." } ], "id_raw": 876 }, { "id": "9vJyVh2wW3qMmgie", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_16.png", "conversations": [ { "from": "human", "value": "\nWhat describes {the label 'Task'} at coordinates [0.486, 0.931, 0.539, 0.962]?" }, { "from": "gpt", "value": "x-axis label." } ], "id_raw": 876 }, { "id": "fbBCbtL0IZlbMY06", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the linestyle of {the DeepLearning line}[0.160, 0.212, 0.865, 0.580]?" }, { "from": "gpt", "value": "Dashed." } ], "id_raw": 876 }, { "id": "hK9ODCjwoBBJfIKP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_16.png", "conversations": [ { "from": "human", "value": "\nWhich label has a higher y-coordinate, {DeepLearning}[0.160, 0.212] or {PyTorch}[0.160, 0.426]?" }, { "from": "gpt", "value": "DeepLearning." } ], "id_raw": 876 }, { "id": "KwQGIHUCS90hFdZD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_16.png", "conversations": [ { "from": "human", "value": "\nIs {line for ConvolutionalNN}[0.197, 0.292] above or below {line for GradientBoost}[0.234, 0.557] in the bar chart?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 876 }, { "id": "u27zvW1KT6vLgc88", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_16.png", "conversations": [ { "from": "human", "value": "\nDoes the {20th tick label}[0.854, 0.898, 0.876, 0.921] align with {the highest y-coordinate of line for GradientBoost}[0.865, 0.663]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 876 }, { "id": "czSw9SlwyahjVN5R", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.160, 0.212, 0.865, 0.309]." }, { "from": "gpt", "value": "This region includes points from labels {DeepLearning}[0.160, 0.212, 0.865, 0.580] and {ConvolutionalNN}[0.160, 0.215, 0.865, 0.309]. The points for {ConvolutionalNN} are generally above the points for {DeepLearning} in this area." } ], "id_raw": 876 }, { "id": "L3GpjqQYbWOOEy6E", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_16.png", "conversations": [ { "from": "human", "value": "\nWhat can be identified in the region [0.119, 0.079, 0.906, 0.113]?" }, { "from": "gpt", "value": "The subtitle of the chart {AI & ML Models Task Performance Efficiency Comparison}[0.119, 0.079, 0.906, 0.113] can be found here." } ], "id_raw": 876 }, { "id": "iY55SE9dI81NgX9f", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_16.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the coordinates [0.655, 0.133, 0.890, 0.291]?" }, { "from": "gpt", "value": "The segment includes labels {GradientBoost}[0.723, 0.218, 0.861, 0.243] and {PyTorch}[0.723, 0.256, 0.797, 0.281] including their corresponding color handles, giving details about the legend items." } ], "id_raw": 876 }, { "id": "5dzA4s7lOkQgQDgW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.207, 0.902, 0.214, 0.932] on the x-axis represent?" }, { "from": "gpt", "value": "3." } ], "id_raw": 877 }, { "id": "LPDzR6JpzhmIpH3y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_17.png", "conversations": [ { "from": "human", "value": "\nWhat color corresponds to the legend item at [0.720, 0.179, 0.767, 0.188]?" }, { "from": "gpt", "value": "#de9ed6." } ], "id_raw": 877 }, { "id": "2vIDXna2iW0YcUaC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the engagement percentage at the y-axis tick label located at [0.064, 0.282, 0.090, 0.312]?" }, { "from": "gpt", "value": "70." } ], "id_raw": 877 }, { "id": "EraCSoPRoPyKEw5L", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_17.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, the point at {Healthy Lifestyle}[0.163, 0.320] or {Unhealthy Lifestyle}[0.163, 0.182]?" }, { "from": "gpt", "value": "Unhealthy Lifestyle." } ], "id_raw": 877 }, { "id": "v3IzYIPKDgWSthct", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_17.png", "conversations": [ { "from": "human", "value": "\nIs the label '12' closer to {the label '13'}[0.636, 0.902, 0.663, 0.932] or {the label '11'}[0.548, 0.902, 0.574, 0.932] in terms of x-position?" }, { "from": "gpt", "value": "13." } ], "id_raw": 877 }, { "id": "7AgNhasqcM0gl5pq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_17.png", "conversations": [ { "from": "human", "value": "\nAre the points displayed at {Fitness Training}[0.472, 0.097] and {Healthy Lifestyle}[0.472, 0.414] at the same x-axis value?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 877 }, { "id": "qPFGtHyGmwLGyUTn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region [0.500, 0.871, 0.580, 0.932]." }, { "from": "gpt", "value": "The x-axis label 'Activity' is displayed here, marked in a dark grey color {.15}." } ], "id_raw": 877 }, { "id": "yvNQJ0DBRDBFUoRH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_17.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.064, 0.111, 0.090, 0.141]?" }, { "from": "gpt", "value": "The y-axis tick label '90' is located in this area." } ], "id_raw": 877 }, { "id": "cHQn0Pz6a2RWI678", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_17.png", "conversations": [ { "from": "human", "value": "\nWhat features are present within the region [0.725, 0.085, 0.798, 0.198]?" }, { "from": "gpt", "value": "This area displays the legend item for {Fitness Training}[0.779, 0.173, 0.927, 0.198] with its corresponding color marker at {coordinates}[0.722, 0.179, 0.767, 0.188]." } ], "id_raw": 877 }, { "id": "4a3T6PHXTwdiTV9U", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the information on the y-axis coordinate [0.019, 0.273, 0.044, 0.535]?" }, { "from": "gpt", "value": "Popularity (%)." } ], "id_raw": 878 }, { "id": "QRBGYvGOU4Uid0e3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at coordinate [0.844, 0.741, 0.978, 0.919]?" }, { "from": "gpt", "value": "Private Tutorials." } ], "id_raw": 878 }, { "id": "aON8kBCZsH8HelKI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_18.png", "conversations": [ { "from": "human", "value": "\nWhat do you find at the following coordinates [0.167, 0.534, 0.226, 0.559] in the legend?" }, { "from": "gpt", "value": "The term \"Year 1\"." } ], "id_raw": 878 }, { "id": "qhHYpFNgyU4xkpzN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_18.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {the point \"Private Tutorials\"}[0.911, 0.309], the line for \"Year 3\"[0.135, 0.172, 0.911, 0.237] or the line for \"Year 5\"[0.135, 0.087, 0.911, 0.309]?" }, { "from": "gpt", "value": "Year 5." } ], "id_raw": 878 }, { "id": "GEQQKb52SUmW0wJy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_18.png", "conversations": [ { "from": "human", "value": "\nIs the point for \"Online Courses\" in 'Year 1'[0.652, 0.269] higher or lower than in 'Year 3'[0.652, 0.237]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 878 }, { "id": "uoCh1lITuceUGdVz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_18.png", "conversations": [ { "from": "human", "value": "\nDoes {the point for \"Library Books\"}[0.394, 0.087] on the 'Year 5' line lie above or below {the point at \"Online Courses\"}[0.652, 0.204]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 878 }, { "id": "ExyP0YfO4Hv2M9Pi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area enclosed by the following coordinates [0.096, 0.076, 0.950, 0.733]." }, { "from": "gpt", "value": "This area covers the main plot area of the chart with grid lines. The chart shows trends for different types of educational resources over five years, depicted by different color-coded lines." } ], "id_raw": 878 }, { "id": "ig0VivwZARin3FIu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_18.png", "conversations": [ { "from": "human", "value": "\nWhat is found within [0.106, 0.524, 0.234, 0.720]?" }, { "from": "gpt", "value": "This region contains the chart's legend with labels and color markers for 'Year 1' to 'Year 5'." } ], "id_raw": 878 }, { "id": "JtbMTs3xZ58dyJhq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within these coordinates [0.588, 0.741, 0.716, 0.910]." }, { "from": "gpt", "value": "This is the label for the x-axis tick, specifically \"Online Courses\"." } ], "id_raw": 878 }, { "id": "WIcHKSXoF2OIWaBX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_19.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.051, 0.296, 0.077, 0.326] on the y-axis represent?" }, { "from": "gpt", "value": "14." } ], "id_raw": 879 }, { "id": "r7urzoFhWOOqih1v", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the legend item for 'Government'}[0.823, 0.161, 0.943, 0.186]?" }, { "from": "gpt", "value": "#800080." } ], "id_raw": 879 }, { "id": "3msOQUk3d2OVYmqo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_44_19.png", "conversations": [ { "from": "human", "value": "\nWhat type of tick does {the x-axis tick label} at [0.914, 0.902, 0.967, 0.932] represent?" }, { "from": "gpt", "value": "Year 2020." } ], "id_raw": 879 }, { "id": "l9zSis1TmuvUPmEI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_19.png", "conversations": [ { "from": "human", "value": "\nWhich trend line is closer to {the y-axis label '10'}[0.051, 0.521, 0.077, 0.551], {the 'Residential'}[0.124, 0.296, 0.940, 0.753] trend or {the 'Housing Starts'}[0.124, 0.065, 0.940, 0.516] trend at year 2015?" }, { "from": "gpt", "value": "Residential." } ], "id_raw": 879 }, { "id": "dJQqAJbVgxJCJU1I", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_19.png", "conversations": [ { "from": "human", "value": "\nCompared to {the 'Commercial'}[0.124, 0.680, 0.940, 0.855] trend in year 2012, is {the 'Government'}[0.124, 0.589, 0.940, 0.725] trend higher or lower?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 879 }, { "id": "Zyfp5hH0KopAzES3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_44_19.png", "conversations": [ { "from": "human", "value": "\nAt the point {the x-axis label '2018'}[0.733, 0.902, 0.785, 0.932] which line has a higher value, the {line for 'Residential'}[0.124, 0.296, 0.940, 0.753] or the {line for 'Housing Starts'}[0.124, 0.065, 0.940, 0.516]?" }, { "from": "gpt", "value": "Residential." } ], "id_raw": 879 }, { "id": "qEAkXrf0IHp5fkfX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region located at [0.883, 0.048, 0.989, 0.186]?" }, { "from": "gpt", "value": "The region contains two legend items: {label 'Commercial'}[0.823, 0.123, 0.938, 0.148] with its associated red marker, and {label 'Government'}[0.823, 0.161, 0.943, 0.186] with its associated purple marker." } ], "id_raw": 879 }, { "id": "mKTjAs56LiZkLER2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_19.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.015, 0.902, 0.067, 0.932]?" }, { "from": "gpt", "value": "The x-axis label for the year '2010' is located here." } ], "id_raw": 879 }, { "id": "d6R4hEWTHoOKzNzj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_44_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.124, 0.065, 0.940, 0.853]." }, { "from": "gpt", "value": "The area covers several objects related to the data lines. It includes the {line for 'Residential'}[0.124, 0.296, 0.940, 0.753], the {line for 'Housing Starts'}[0.124, 0.065, 0.940, 0.516], the {line for 'Commercial'}[0.124, 0.680, 0.940, 0.855], and the {line for 'Government'}[0.124, 0.589, 0.940, 0.725]. All these lines represent different categories of building permits trends from the year 2011 to 2020." } ], "id_raw": 879 }, { "id": "7xJ4ARSnKPsJ5DKC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with the coordinates [0.051, 0.319, 0.065, 0.347] in the graph?" }, { "from": "gpt", "value": "Y-axis tick label for the value '70'." } ], "id_raw": 880 }, { "id": "2Zln8reimk0zXl5r", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the label on the x-axis at [0.113, 0.764, 0.185, 0.939] refer to?" }, { "from": "gpt", "value": "'Wind Energy'" } ], "id_raw": 880 }, { "id": "g687rca0kCgfnMrI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_0.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the line with the coordinates [0.582, 0.194, 0.968, 0.500]?" }, { "from": "gpt", "value": "The 2023 projections for development progress in various energy sectors." } ], "id_raw": 880 }, { "id": "Jk80v1loH4K2aV2X", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_0.png", "conversations": [ { "from": "human", "value": "\nWithin the 2022 Observations, is the development progress of {Hydro Energy}[0.282, 0.160] higher than the {Nuclear Energy}[0.378, 0.228]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 880 }, { "id": "LAsYKWIeahtTg8oi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_0.png", "conversations": [ { "from": "human", "value": "\nIn the context of 2023 Projections, is the development progress of {Solar Energy}[0.582, 0.381] lower than {Wind Energy}[0.679, 0.211]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 880 }, { "id": "7GPi1RnzcQ0bw0ri", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_0.png", "conversations": [ { "from": "human", "value": "\nReferring to 2022 Observations, does {Solar Energy}[0.089, 0.500] have a higher development progress than {Fusion Energy}[0.474, 0.670]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 880 }, { "id": "whJdOC3utHdoya9X", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is displayed in the region defined by [0.563, 0.075, 0.988, 0.755]?" }, { "from": "gpt", "value": "This region represents the subplot titled 'Projected Development Progress.' It includes the 2023 Projections of development progress for various energy sectors, such as Solar Energy, Wind Energy, Hydro Energy, Nuclear Energy, and Fusion Energy." } ], "id_raw": 880 }, { "id": "8uh0jVWScXbZEdGD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_0.png", "conversations": [ { "from": "human", "value": "\nWhat content is present in the region from [0.069, 0.075, 0.494, 0.755]?" }, { "from": "gpt", "value": "This region represents the subplot that presents the 'Observed Development Progress' in 2022. It contains development progress data of Solar Energy, Wind Energy, Hydro Energy, Nuclear Energy, and Fusion Energy." } ], "id_raw": 880 }, { "id": "LPMftenhdB40XhYs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area defined by [0.570, 0.091, 0.736, 0.154]." }, { "from": "gpt", "value": "The area contains the legend item {2023 Projections}[0.614, 0.105, 0.730, 0.141], indicating the orange lines in the second subplot signify the 2023 Projections." } ], "id_raw": 880 }, { "id": "eCGoUCMf6g8utE9q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_1.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the line plotted}[0.558, 0.235, 0.967, 0.525] in the right subplot chart?" }, { "from": "gpt", "value": "Social Cultural and Value Change in 2023." } ], "id_raw": 881 }, { "id": "mTze1QPmXXMSJrsb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_1.png", "conversations": [ { "from": "human", "value": "\nWhat coordinate represents the 'Power Distance' in the 2022 value chart?" }, { "from": "gpt", "value": "[0.272, 0.718]." } ], "id_raw": 881 }, { "id": "OSAUdQsqlMqWYh3z", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the 'Masculinity' x-tick label in the 2023 chart?" }, { "from": "gpt", "value": "[0.776, 0.728, 0.831, 0.858]." } ], "id_raw": 881 }, { "id": "JAdqtc4f9qb068bW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_1.png", "conversations": [ { "from": "human", "value": "\nDoes the plotted value of 'Indulgence' increase or decrease from 2022 {coordinate}[0.477, 0.396] to 2023 {coordinate}[0.967, 0.300]?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 881 }, { "id": "dnvSPDIS3lA5BYdy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_1.png", "conversations": [ { "from": "human", "value": "\nWhich of the plotted values is greater in the cultural value change for 2022, 'Power Distance'[0.272, 0.461] or 'Indulgence'[0.477, 0.396]?" }, { "from": "gpt", "value": "'Power Distance'." } ], "id_raw": 881 }, { "id": "7iieF9W22KMxiF4W", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_1.png", "conversations": [ { "from": "human", "value": "\nIs the plotted 'Masculinity' value[0.341, 0.332] in 2022 higher or lower than the '70' y-tick value[0.043, 0.413]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 881 }, { "id": "3voZprJezjEp9umv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within [0.628, 0.030, 0.897, 0.066]." }, { "from": "gpt", "value": "It contains the chart title 'Social Cultural and Value Change 2023' in black color." } ], "id_raw": 881 }, { "id": "8Pk8KxSR1YKtltBf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is located in the region [0.048, 0.075, 0.497, 0.718]?" }, { "from": "gpt", "value": "This region contains the 2022 line chart of Social Cultural and Value Change, with labels 'Tradition', 'Individualism', 'Uncertainty Avoidance', 'Power Distance', 'Masculinity', 'Long-term Orientation', 'Indulgence', and y-axis label 'Values'." } ], "id_raw": 881 }, { "id": "MeKfschcK4IlGiR0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the area from [0.201, 0.728, 0.272, 0.899]." }, { "from": "gpt", "value": "It contains the x-tick label 'Power Distance' for the line chart representing cultural value change in the year 2022." } ], "id_raw": 881 }, { "id": "1R0RAktPLo5estxI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label {the y-axis handle} at [0.013, 0.219, 0.029, 0.600]?" }, { "from": "gpt", "value": "Evaluacion Score" } ], "id_raw": 882 }, { "id": "0MdRrjFIi6ipq6or", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-coordinate of the starting point of {the line in legend[0.059, 0.612, 0.222, 0.728] labeled 'Innovation 2022' with color #3d89be}?" }, { "from": "gpt", "value": "0.062" } ], "id_raw": 882 }, { "id": "R2PybKCKZKTbsYgs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at this set of coordinates [0.178, 0.030, 0.381, 0.066]?" }, { "from": "gpt", "value": "Subtitle of the left-side plot, labeled 'Innovation Project Evaluation'" } ], "id_raw": 882 }, { "id": "FY0L7gZq7nRb6ABV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_2.png", "conversations": [ { "from": "human", "value": "\nWhich line in the right chart rises more steeply, the {line for 'Management 2022'}[0.554, 0.298, 0.967, 0.700] or the {line for 'Management 2023'}[0.554, 0.253, 0.967, 0.610]?" }, { "from": "gpt", "value": "Management 2022" } ], "id_raw": 882 }, { "id": "OwBWUuvzaK1WEW2R", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_2.png", "conversations": [ { "from": "human", "value": "\nIs the position {SEAL's point in 'Management 2023'}[0.898, 0.343] higher or lower than {SEAL's point in 'Innovation 2023'}[0.417, 0.387]?" }, { "from": "gpt", "value": "Lower" } ], "id_raw": 882 }, { "id": "YDyA74GHeZIBI590", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_2.png", "conversations": [ { "from": "human", "value": "\nWhich line in the right chart starts at a higher point, {the line for 'Management 2022'}[0.554, 0.298, 0.967, 0.700] or {the line for 'Management 2023'}[0.554, 0.253, 0.967, 0.610]?" }, { "from": "gpt", "value": "Management 2022" } ], "id_raw": 882 }, { "id": "bUrf2ri85KWElMmY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the features of the objects within this region: [0.258, 0.754, 0.348, 0.970]?" }, { "from": "gpt", "value": "This region contains the x-axis label \"NeoGNN-BUDDY\" for the left chart. The label is inclined at an angle of 45 degrees." } ], "id_raw": 882 }, { "id": "LjMahKQ83Sv3qdNs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_2.png", "conversations": [ { "from": "human", "value": "\nWhat content lies in the rectangular area [0.540, 0.612, 0.721, 0.728]?" }, { "from": "gpt", "value": "The region contains part of the legend of the right-hand chart, specifically the text labels and corresponding colored markers for \"Management 2022\" and \"Management 2023\"." } ], "id_raw": 882 }, { "id": "EVQ4G9WloauCFAky", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.524, 0.754, 0.554, 0.827]." }, { "from": "gpt", "value": "The region contains 'MLP' which is the first x-axis label of the right-side plot." } ], "id_raw": 882 }, { "id": "RSEO3ZeUY1czmfrp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_3.png", "conversations": [ { "from": "human", "value": "\nWhat text is displayed at the y-axis of the chart?" }, { "from": "gpt", "value": "Performance Metrics." } ], "id_raw": 883 }, { "id": "1IzM9kRZhF0QmWyR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the last x-axis tick mark on the left subplot?" }, { "from": "gpt", "value": "PMPS." } ], "id_raw": 883 }, { "id": "6j58i1Y2CaMHOXbf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_3.png", "conversations": [ { "from": "human", "value": "\nWhich company is represented by the point at coordinates [0.190, 0.232]?" }, { "from": "gpt", "value": "LEAP." } ], "id_raw": 883 }, { "id": "3uajAeH1A1Vw93Hw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_3.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'LEAP'}[0.190, 0.232] higher or lower than {the point for 'DHRM'}[0.249, 0.705] in 2022's results?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 883 }, { "id": "mMrdARafJyXqIGY7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the first subplot's Year 2022 label}[0.104, 0.105, 0.173, 0.141] contain more or less data points than {the second subplot's Year 2023 label}[0.588, 0.105, 0.657, 0.141]?" }, { "from": "gpt", "value": "Equal." } ], "id_raw": 883 }, { "id": "StkrrHXVrXwELjxh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_3.png", "conversations": [ { "from": "human", "value": "\nWhich x-axis tick {label}[0.328, 0.872, 0.366, 0.963] is positioned closest to the beginning of the x-axis in the first subplot?" }, { "from": "gpt", "value": "DRSB." } ], "id_raw": 883 }, { "id": "5CCnKqBqo9YQIVQp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content between the y-axis and the first y-axis tick label in subplot 1 for the year 2023." }, { "from": "gpt", "value": "This region contains no visual elements; it serves as padding between the y-axis and the first y-axis tick label." } ], "id_raw": 883 }, { "id": "LWwL3wiTaryY0VGf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_3.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region bounded by [0.013, 0.239, 0.029, 0.698]?" }, { "from": "gpt", "value": "This area covers the y-axis of the chart where {the label 'Performance Metrics'}[0.013, 0.239, 0.029, 0.698] is displayed vertically." } ], "id_raw": 883 }, { "id": "yCiLgk8yf1BMVUiv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the components found within the coordinate bounds [0.520, 0.872, 0.791, 0.960]." }, { "from": "gpt", "value": "This region includes x-axis tick labels for FSRO, ACME, LEAP, DHRM, and BLP in the 2023 subplot, specifically labels for FSRO, ACME, LEAP, DHRM, and BLP are positioned at respective coordinates [0.520, 0.872, 0.557, 0.960], [0.577, 0.872, 0.616, 0.964], [0.639, 0.872, 0.674, 0.956], [0.692, 0.872, 0.733, 0.970], and [0.763, 0.872, 0.791, 0.941]." } ], "id_raw": 883 }, { "id": "hY5MQdo5Dh9NP4O3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_4.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.034, 0.210, 0.048, 0.238] on the y-axis represent?" }, { "from": "gpt", "value": "90." } ], "id_raw": 884 }, { "id": "ZbO0PnfkgF9Vh0MH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_4.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the year 2022 in {the legend}[0.104, 0.689, 0.139, 0.725]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 884 }, { "id": "tDNBkue405TfNNTN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_4.png", "conversations": [ { "from": "human", "value": "\nWhich cuisine type is associated with {the x-axis tick mark}[0.243, 0.808]?" }, { "from": "gpt", "value": "Mexican." } ], "id_raw": 884 }, { "id": "d8IF63aMmLrW5uJB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Moroccan in 2022}[0.470, 0.456] higher than {the point for Indian in 2022}[0.300, 0.368]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 884 }, { "id": "U0unPTufqlnxPwUB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_4.png", "conversations": [ { "from": "human", "value": "\nWhich year shows a higher rating for {the \"Vegan\" cuisine}[0.740, 0.603], 2022 or 2023?" }, { "from": "gpt", "value": "2023." } ], "id_raw": 884 }, { "id": "oIQFnV6vR47zvkPM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_4.png", "conversations": [ { "from": "human", "value": "\nAre the ratings for {the \"Paleo\" cuisine in 2022}[0.968, 0.632] greater or lower compared to {the \"Gluten_free\" cuisine in 2022}[0.911, 0.573]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 884 }, { "id": "ayFuaITN0qqefrtG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within [0.570, 0.544, 0.968, 0.691]." }, { "from": "gpt", "value": "This region includes various points representing the 2022 ratings for Gastronomy cultures such as {Farm-to-table}[0.570, 0.661], {Small_plate}[0.627, 0.691], {Vegetarian}[0.684, 0.632], among others, using a square marker." } ], "id_raw": 884 }, { "id": "Fl3KR0j8DPNrKTNp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.557, 0.676, 0.642, 0.791] contain?" }, { "from": "gpt", "value": "The area contains the legend for Gastronomy Culture Ratings with items for the years {2022}[0.601, 0.689, 0.637, 0.725] and {2023}[0.601, 0.742, 0.637, 0.778], represented in orange and dark orange colors respectively." } ], "id_raw": 884 }, { "id": "khpZrifgTRnbLfSU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_4.png", "conversations": [ { "from": "human", "value": "\nDescribe what is included in the region [0.034, 0.064, 0.048, 0.825]." }, { "from": "gpt", "value": "This section displays the y-axis with tick labels ranging from {95}[0.034, 0.064, 0.048, 0.092] to {70}[0.034, 0.797, 0.048, 0.825], marked in a dark color with no rotation." } ], "id_raw": 884 }, { "id": "3HIHuNmfIYJB9F2p", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart in subplot_0 with coordinates [0.162, 0.030, 0.410, 0.066]?" }, { "from": "gpt", "value": "Baseline Trend in Emerging Markets." } ], "id_raw": 886 }, { "id": "qTSsZ0LEJJhQKATD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_6.png", "conversations": [ { "from": "human", "value": "\nWhat's the content of the object found at coordinates [0.059, 0.064, 0.074, 0.092]?" }, { "from": "gpt", "value": "Y-Axis tick label '80'" } ], "id_raw": 886 }, { "id": "vbfXBrMOqUTUd1Fg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_6.png", "conversations": [ { "from": "human", "value": "\nIn subplot_1, what is the label for the line at coordinates [0.578, 0.091, 0.715, 0.207]?" }, { "from": "gpt", "value": "Subplot Legend." } ], "id_raw": 886 }, { "id": "qRc6RpNLnSHbcpUk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_6.png", "conversations": [ { "from": "human", "value": "\nIn subplot_0, is the {point for 'Emerging-Europe'}[0.191, 0.243] above or below {the point for 'Latin-America'}[0.286, 0.326] in Baseline 2022?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 886 }, { "id": "CopJg0FosN78253v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_6.png", "conversations": [ { "from": "human", "value": "\nIn subplot_1, are the values of {Trend1 2022}[0.590, 0.285, 0.969, 0.431] on average higher or lower than those of {Trend1 2023}[0.590, 0.222, 0.969, 0.326]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 886 }, { "id": "lMGpYHuIgQAIoIOy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_6.png", "conversations": [ { "from": "human", "value": "\nIn subplot_0, does {Emerging-Europe}[0.191, 0.243] have a higher or lower Consumer Index value than {Sub-Saharan Africa}[0.475, 0.368] in Baseline 2022?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 886 }, { "id": "NDAn9CG0YsfJGz1s", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_6.png", "conversations": [ { "from": "human", "value": "\nProvide the information within [0.230, 0.609, 0.380, 0.970]." }, { "from": "gpt", "value": "This is the x-axis tick label 'Middle-East and North Africa'." } ], "id_raw": 886 }, { "id": "F5skigC5FLnV4XwC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_6.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content within region [0.080, 0.467, 0.233, 0.583]?" }, { "from": "gpt", "value": "The region contains the subplot legend for Baseline 2022 and Baseline 2023, each paired with a different colored handle, blue and cyan respectively." } ], "id_raw": 886 }, { "id": "EpOf1W9k43vfpxOe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content of the region [0.572, 0.091, 0.715, 0.207]." }, { "from": "gpt", "value": "This region contains the subplot legend items for Trend1 2022 and Trend1 2023, each paired with a colored handle." } ], "id_raw": 886 }, { "id": "1pHIpTMg06qyc3Yp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.261, 0.954, 0.285, 0.975] for the first subplot?" }, { "from": "gpt", "value": "Years." } ], "id_raw": 887 }, { "id": "hKYEvBTjTloySY2z", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_7.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {the line labeled 'Child Labor'}[0.058, 0.425, 0.487, 0.877] in the chart?" }, { "from": "gpt", "value": "#d62728." } ], "id_raw": 887 }, { "id": "pVYw6hbgljjezsZx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_7.png", "conversations": [ { "from": "human", "value": "\nWhere can the label '2030' for {the x-axis}[0.475, 0.924, 0.499, 0.945] be found in terms of coordinates?" }, { "from": "gpt", "value": "[0.475, 0.924, 0.499, 0.945]." } ], "id_raw": 887 }, { "id": "val8h5f12zKs0jeo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_7.png", "conversations": [ { "from": "human", "value": "\nIs {the value for the year 2023 from the line 'Migration Rights'}[0.635, 0.676] higher than the {value for the year 2022 from the same line}[0.588, 0.726]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 887 }, { "id": "n9TeYV4USUJGC9LU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the handle for 'Global Issues'}[0.043, 0.082, 0.067, 0.096] have the same color as {the handle for 'Child Labor'}[0.043, 0.179, 0.067, 0.193] in the legend?" }, { "from": "gpt", "value": "No." } ], "id_raw": 887 }, { "id": "2U6p5NzlGtdxzLjC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_7.png", "conversations": [ { "from": "human", "value": "\nWhich is further to the right, {the start of the line for 'Health Rights'}[0.540, 0.174] or {the start of the line for 'Free Education'}[0.540, 0.551]?" }, { "from": "gpt", "value": "Health Rights." } ], "id_raw": 887 }, { "id": "PhS6tKGnMSEsKbxq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the features located within the bounded box [0.022, 0.166, 0.034, 0.312]." }, { "from": "gpt", "value": "This region contains y-axis tick labels '90' and '85' at {coordinates}[0.022, 0.166, 0.034, 0.186] and {coordinates}[0.022, 0.291, 0.034, 0.312], respectively." } ], "id_raw": 887 }, { "id": "vKMhB03ttGu8IYyH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the region [0.058, 0.174, 0.154, 0.827]?" }, { "from": "gpt", "value": "This area includes multiple point coordinates for 'Global Issues' from {years 2021 to 2023}[0.058, 0.827, 0.154, 0.626]." } ], "id_raw": 887 }, { "id": "n9fXs52vMJYAiKzA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements found between the coordinates [0.540, 0.174, 0.969, 0.726]?" }, { "from": "gpt", "value": "This region includes the entirety of the 'Health Rights' line from years 2021 to 2030, starting from the highest point at {coordinates}[0.540, 0.726] to the lowest at {coordinates}[0.969, 0.174]." } ], "id_raw": 887 }, { "id": "aSVa9YrwP3Ga3awI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the item located at [0.188, 0.942, 0.261, 0.970] on the chart?" }, { "from": "gpt", "value": "It's the label for {Smart Home}[0.188, 0.942, 0.261, 0.970] in the x-axis." } ], "id_raw": 888 }, { "id": "TweOG1XYcHyHsX4h", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_8.png", "conversations": [ { "from": "human", "value": "\nCan you tell where is the {label for 'Status in 2024'} located?" }, { "from": "gpt", "value": "The label 'Status in 2024' is located at coordinates [0.691, 0.030, 0.792, 0.066]." } ], "id_raw": 888 }, { "id": "WuGaqqBPB9w2HztS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the textual information in the figure at location [0.236, 0.942, 0.329, 0.970]?" }, { "from": "gpt", "value": "The x-axis label for 'Agriculture Tech' is found at these coordinates." } ], "id_raw": 888 }, { "id": "ZgceNo0TlDpyMqS6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {Green Energy} in 2023 located at [0.456, 0.932] compared to its value in 2024 at [0.944, 0.932]?" }, { "from": "gpt", "value": "Increased." } ], "id_raw": 888 }, { "id": "LHhcKWhRTgNK6Lfk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_8.png", "conversations": [ { "from": "human", "value": "\nComparing the year 2023's {City Life} at [0.051, 0.932] and the year 2024's {City Life} at [0.540, 0.932], how did the value change over a year?" }, { "from": "gpt", "value": "Increased." } ], "id_raw": 888 }, { "id": "3t3pOaXNDP3wcfPx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_8.png", "conversations": [ { "from": "human", "value": "\nCan you tell if {Online Work} at [0.109, 0.932] performed better or worse in 2023 than in 2024 at [0.597, 0.932]?" }, { "from": "gpt", "value": "Worse." } ], "id_raw": 888 }, { "id": "PkNjoHOte61Pg1Jn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_8.png", "conversations": [ { "from": "human", "value": "\nWhat details can you provide about the region from [0.354, 0.942, 0.495, 0.970]?" }, { "from": "gpt", "value": "This region contains labels of two x-axis categories from 2023's data: 'Education Tech' and 'Green Energy', positioned respectively at coordinates [0.354, 0.942, 0.441, 0.970] and [0.416, 0.942, 0.495, 0.970]." } ], "id_raw": 888 }, { "id": "thezpfgrx6OaYPS5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the items placed in the space [0.674, 0.942, 0.870, 0.970] please." }, { "from": "gpt", "value": "The region contains two x-axis categories from the data of 2024: 'Smart Home' and 'Agriculture Tech', at the following locations respectively: [0.674, 0.942, 0.749, 0.970] and [0.724, 0.942, 0.817, 0.970]." } ], "id_raw": 888 }, { "id": "du61ZV48KlglTxtY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_8.png", "conversations": [ { "from": "human", "value": "\nCan you tell what is in the area between [0.008, 0.280, 0.031, 0.291]?" }, { "from": "gpt", "value": "This is the location for the y-axis label '85' for the data of 2023." } ], "id_raw": 888 }, { "id": "tNK8oc9yktTheXF1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label in the chart represented at [0.012, 0.327, 0.029, 0.540]?" }, { "from": "gpt", "value": "Profits ($M)." } ], "id_raw": 889 }, { "id": "CPDxYBt3unTtJDyE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the xtick label at [0.095, 0.812, 0.133, 0.889] in the chart?" }, { "from": "gpt", "value": "Retail." } ], "id_raw": 889 }, { "id": "vkhaCXGWu3XRdFYa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the subplot at [0.157, 0.025, 0.406, 0.055]?" }, { "from": "gpt", "value": "Profit Trends in Trade and SCM 2022." } ], "id_raw": 889 }, { "id": "XGRmsqzuDPigb3mc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_9.png", "conversations": [ { "from": "human", "value": "\nIs the {profit of Logistic}[0.371, 0.359] more than the {profit of Consulting}[0.430, 0.270] in 2022?" }, { "from": "gpt", "value": "No." } ], "id_raw": 889 }, { "id": "6mmq6YBFiJlaLwPL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_9.png", "conversations": [ { "from": "human", "value": "\nDoes {Software}[0.490, 0.211] have a higher profit than {Retail}[0.133, 0.448] in 2022?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 889 }, { "id": "PdlTy8R5FdzlweZb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_9.png", "conversations": [ { "from": "human", "value": "\nHow does the {profit of Manufacturing}[0.252, 0.685] compare to the {profit of Wholesale}[0.192, 0.567] in 2022?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 889 }, { "id": "6IlZc7dKs6Ich8T9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in [0.059, 0.738, 0.193, 0.790]?" }, { "from": "gpt", "value": "This region mainly contains the legend for the 2022 profits. It's colored in '#3d89be'." } ], "id_raw": 889 }, { "id": "2woKfZK2nuZFgzK4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.073, 0.211, 0.490, 0.656] denote?" }, { "from": "gpt", "value": "This area represents the line chart displaying the 2022 profits across different sectors from 'All' to 'Software'." } ], "id_raw": 889 }, { "id": "xPAaiyZ8gEiwFkuR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_9.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the region [0.530, 0.062, 0.988, 0.804]?" }, { "from": "gpt", "value": "The region displays the sub-chart for \"Profit Trends in Trade and SCM 2023\". It includes line points for profits of different sectors such as 'All', 'Retail', 'Wholesale', through to 'Software'." } ], "id_raw": 889 }, { "id": "lCClS1WetHsdb2Cy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the object}[0.022, 0.130, 0.039, 0.640] in the chart?" }, { "from": "gpt", "value": "Y-axis label in the first subplot." } ], "id_raw": 890 }, { "id": "Se1rI7grYBJ7hA3L", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second item text in the legend of the first subplot?" }, { "from": "gpt", "value": "[0.125, 0.157, 0.220, 0.193]" } ], "id_raw": 890 }, { "id": "6WTCBqMiG7g0KXwk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the X-axis label} at [0.366, 0.705, 0.475, 0.966] representing?" }, { "from": "gpt", "value": "The \"Relative Vigor Index\" method." } ], "id_raw": 890 }, { "id": "7yuDVwoXYzFrQZtp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_10.png", "conversations": [ { "from": "human", "value": "\nIs {the \"USD-JPY 2022\" line}[0.092, 0.199, 0.475, 0.397] showing a higher or lower result than {the \"USD-JPY 2023\" line}[0.092, 0.099, 0.475, 0.372] at the \"Momentum\" method?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 890 }, { "id": "K21o7BJ98S4W41mI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_10.png", "conversations": [ { "from": "human", "value": "\nDo {the \"USD-JPY 2022\" line}[0.092, 0.199, 0.475, 0.397] and {the \"USD-JPY 2023\" line}[0.092, 0.099, 0.475, 0.372] intersect?" }, { "from": "gpt", "value": "No." } ], "id_raw": 890 }, { "id": "MkyvPJiwU2GWFUAT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_10.png", "conversations": [ { "from": "human", "value": "\nWhich method, \"Buy-and-Hold\" or \"MACD\", has a higher value for \"USD-JPY 2023\" line?" }, { "from": "gpt", "value": "MACD." } ], "id_raw": 890 }, { "id": "JAgSsX0R9I7MaLoC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.366, 0.705, 0.475, 0.966]." }, { "from": "gpt", "value": "It contains the X-axis label for the method \"Relative Vigor Index\"." } ], "id_raw": 890 }, { "id": "ebcT2iwmJtm0Y1mw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.080, 0.091, 0.226, 0.207]." }, { "from": "gpt", "value": "The area in the first subplot represents the legend of two lines: {USD-JPY 2022}[0.125, 0.105, 0.220, 0.141] with color blue and {USD-JPY 2023}[0.125, 0.157, 0.220, 0.193] with color light blue." } ], "id_raw": 890 }, { "id": "cPxEJchyo2xfsO6M", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.509, 0.705, 0.968, 0.695]?" }, { "from": "gpt", "value": "There are 5 objects in the area within the X-axis labels 'MACD', 'Stochastic Oscillator', and 'Relative Vigor Index' at points {coordinate}[0.819, 0.705, 0.859, 0.801], {coordinate}[0.803, 0.705, 0.914, 0.970], and {coordinate}[0.860, 0.705, 0.968, 0.966] respectively." } ], "id_raw": 890 }, { "id": "JiVX9UVB5ToFuJAh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_11.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the line with the highest data values near {the coordinate}[0.967, 0.075]?" }, { "from": "gpt", "value": "Orange." } ], "id_raw": 891 }, { "id": "wcMRWempiwMuUi5J", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the {fourth y-axis tick label}[0.671, 0.609, 0.694, 0.637] from the left in the first subplot?" }, { "from": "gpt", "value": "110." } ], "id_raw": 891 }, { "id": "htL2BHzGYai5JtEJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_11.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is present at {the last visible point}[0.967, 0.105] in the subplot of AI Outliers?" }, { "from": "gpt", "value": "Square." } ], "id_raw": 891 }, { "id": "KDsmZ3aQ8xd0Fhhd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_11.png", "conversations": [ { "from": "human", "value": "\nIs the value of {the point on Case5}[0.311, 0.396] in AI Applications above or below {the point on Case7}[0.427, 0.250]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 891 }, { "id": "azf0GhWTX8eJT3AG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_11.png", "conversations": [ { "from": "human", "value": "\nDoes {Case8}[0.485, 0.135] from the AI Applications line have a higher value than {Case1}[0.563, 0.347] of AI Outliers?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 891 }, { "id": "bZxDOSDVhCP1tO3O", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_11.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {Case3}[0.637, 0.166], AI Applications or AI Outliers?" }, { "from": "gpt", "value": "AI Outliers." } ], "id_raw": 891 }, { "id": "jgV3Z7tOt33F6xg6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.680, 0.345, 0.810, 0.400]." }, { "from": "gpt", "value": "This section contains the point labeled {Case7}[0.909, 0.135] from the AI Outliers Trends plot, highlighted as a higher point with an orange color and square marker." } ], "id_raw": 891 }, { "id": "AcBOVkvdoDLOlg3p", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.030, 0.780, 0.103, 0.960]." }, { "from": "gpt", "value": "This region features part of the y-axis with tick labels '70'[0.056, 0.851] and '80'[0.056, 0.791] and the corresponding gridlines enhancing the visibility of the charted data." } ], "id_raw": 891 }, { "id": "dbf8q5PEcxVs5gpI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.300, 0.320, 0.400, 0.450]?" }, { "from": "gpt", "value": "There are two points. One at {Case6}[0.369, 0.329] from AI Applications Trends, showing a high-value measurement and the second at {Case5}[0.311, 0.420] showing increased trends on a blue line and also being significant." } ], "id_raw": 891 }, { "id": "kXy1J8k7N5JNnkiv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_12.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinate [0.264, 0.030, 0.299, 0.066] in the chart?" }, { "from": "gpt", "value": "Subplot subtitle." } ], "id_raw": 892 }, { "id": "tQAjwwEIxWCYtNJK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the object {ylabel}[0.012, 0.396, 0.029, 0.514] in subplot_0?" }, { "from": "gpt", "value": "'Sales'." } ], "id_raw": 892 }, { "id": "wsKHoRVqOKxZCZo4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {line_0} in [0.564, 0.138, 0.967, 0.582]?" }, { "from": "gpt", "value": "#de3163 (some shade of red)." } ], "id_raw": 892 }, { "id": "jFeDF9IrFfQIEq5I", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_12.png", "conversations": [ { "from": "human", "value": "\nComparing {the 'iPhone' point in 2020}[0.080, 0.328] and {the 'iPhone' point in 2021}[0.564, 0.138], which year had more sales?" }, { "from": "gpt", "value": "2021." } ], "id_raw": 892 }, { "id": "GneNmR2TrQH1InO0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'iPad' in 2021}[0.665,0.455] higher or lower than {the point for 'iPad' in 2020}[0.181,0.582]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 892 }, { "id": "dyyBBXAJF2TzAArH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_12.png", "conversations": [ { "from": "human", "value": "\nWhich product had a higher sales in 2020, {Mac}[0.282,0.455] or {Services}[0.483, 0.709]?" }, { "from": "gpt", "value": "Services." } ], "id_raw": 892 }, { "id": "RKLKxvjXcn0TBV7I", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_12.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the coordinate area [0.060, 0.075, 0.504, 0.835]?" }, { "from": "gpt", "value": "This is the area of subplot_0, including the 2020 sales data for iPhone, iPad, Mac, Watch, and Services. It also contains the xticks and yticks of the plot, as well as three lines indicating the actual sales figures." } ], "id_raw": 892 }, { "id": "2s473tPyg5WyIpEQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the region [0.544, 0.075, 0.988, 0.835]." }, { "from": "gpt", "value": "This region encompasses subplot_1, which contains the 2021 sales data for iPhone, iPad, Mac, Watch, and Services. It also includes the xticks and yticks of the plot, and three separate lines indicating the actual sales figures." } ], "id_raw": 892 }, { "id": "UUd1WlzoYNOswz9i", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_12.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.748, 0.030, 0.783, 0.066]?" }, { "from": "gpt", "value": "The subplot subtitle \"2021\" is in this area." } ], "id_raw": 892 }, { "id": "yWcUq3y7qUfheJFU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.330, 0.030, 0.718, 0.066] in the chart?" }, { "from": "gpt", "value": "Subtitle." } ], "id_raw": 893 }, { "id": "LVd6zdCBNLK34Zss", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel} at [0.015, 0.234, 0.035, 0.516]?" }, { "from": "gpt", "value": "Performance." } ], "id_raw": 893 }, { "id": "mJXq6K0Cjw3VwXtl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.554, 0.685, 0.664, 0.905]?" }, { "from": "gpt", "value": "X-axis tick label for \"Computer Vision\"." } ], "id_raw": 893 }, { "id": "lYIqzOOf7GtQIjAW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_13.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Business AI' in AI Case 1}[0.850, 0.355] above or below {the point for 'Business AI' in AI Case 2}[0.850, 0.297]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 893 }, { "id": "lwC4HJGcYydRrz0E", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_13.png", "conversations": [ { "from": "human", "value": "\nComparing AI Case 1 and AI Case 2, which one has the higher value for \"Cybersecurity AI\": {AI Case 1}[0.943, 0.219] or {AI Case 2}[0.943, 0.102]?" }, { "from": "gpt", "value": "AI Case 2." } ], "id_raw": 893 }, { "id": "wmtnftWyIB8gNUxx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_13.png", "conversations": [ { "from": "human", "value": "\nWhich is plotted higher for \"Computer Vision\", {AI Case 1}[0.664, 0.394] or {AI Case 2}[0.664, 0.316]?" }, { "from": "gpt", "value": "AI Case 2." } ], "id_raw": 893 }, { "id": "QmP1exRDe1NUP8Ur", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_13.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content inside [0.844, 0.091, 0.977, 0.207]?" }, { "from": "gpt", "value": "This region includes the legend for 'AI Case 1' located at {coordinates}[0.897, 0.105, 0.970, 0.141] and 'AI Case 2' located at {coordinates}[0.897, 0.157, 0.970, 0.193]." } ], "id_raw": 893 }, { "id": "jbqJModNbUdt20hy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within [0.360, 0.685, 0.477, 0.920]." }, { "from": "gpt", "value": "The content falls under the x-axis tick label, which refers to the category of AI applications named \"Natural Language.\"" } ], "id_raw": 893 }, { "id": "QJPuzRzzjC1QlZov", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_13.png", "conversations": [ { "from": "human", "value": "\nCould you describe the region [0.041, 0.169, 0.058, 0.197]?" }, { "from": "gpt", "value": "This region features the y-axis tick label '80'." } ], "id_raw": 893 }, { "id": "R3FahzfexEpIHHXV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the {xlabel}[0.282, 0.981, 0.282, 0.981] of subplot_0 on the chart?" }, { "from": "gpt", "value": "There is no xlabel for subplot_0." } ], "id_raw": 894 }, { "id": "N9F8Ewf56xDVMXaM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_14.png", "conversations": [ { "from": "human", "value": "\nCan you identify the { ylabel for subplot_0 }[0.013, 0.321, 0.029, 0.678] on the chart?" }, { "from": "gpt", "value": "Yes, the ylabel for subplot_0 is \"Indicator Values\"." } ], "id_raw": 894 }, { "id": "FJPDrZcWtyRlnKZC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_14.png", "conversations": [ { "from": "human", "value": "\nWhat data is represented in { line_0 }[0.073, 0.147, 0.490, 0.851] in subplot_0?" }, { "from": "gpt", "value": "The line_0 represents the Logistics Efficiency Trend from the year 2022 to 2029." } ], "id_raw": 894 }, { "id": "u7DfXuBprwZ8xWcW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_14.png", "conversations": [ { "from": "human", "value": "\nIn subplot_1, is the {2023 value of 'Transportation Efficiency'}[0.608, 0.536] greater or less than the {2022 value}[0.548, 0.657]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 894 }, { "id": "p0J306kLZJzWB4qk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_14.png", "conversations": [ { "from": "human", "value": "\nIn subplot_0, do the {2025 value for 'Logistics Efficiency'}[0.252, 0.851] and the {2026 value}[0.312, 0.317] show an upward or downward trend?" }, { "from": "gpt", "value": "Downward." } ], "id_raw": 894 }, { "id": "OcIpQx1PFgPJ2EzR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_14.png", "conversations": [ { "from": "human", "value": "\nComparing the {value of 'Logistics Efficiency'}[0.133, 0.609] in 2023 with its {value}[0.252, 0.851] in 2025, is there an increase or decrease?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 894 }, { "id": "N0vjh4RPSlVlo1gL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the section within the coordinates [0.527, 0.075, 0.986, 0.924] on the chart?" }, { "from": "gpt", "value": "This section represents the subplot_1 in the chart which plots the trend of 'Transportation Efficiency' from 2022 to 2029. It includes x-grid, y-grid, title, and data points which are represented as an orange line." } ], "id_raw": 894 }, { "id": "C8fxXEJQsdhov2E1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_14.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the area defined by coordinates [0.323, 0.845, 0.504, 0.908] in subplot_0?" }, { "from": "gpt", "value": "A legend is located in this area, labeled as \"Logistics Efficiency\" with a blue marker." } ], "id_raw": 894 }, { "id": "ST9CrtsotzLHYJrX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region from [0.777, 0.845, 0.979, 0.908] in subplot_1?" }, { "from": "gpt", "value": "This region contains the legend term 'Transportation Impact' associated with an orange marker." } ], "id_raw": 894 }, { "id": "jivUF4NMcfv0A11T", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the secondary object with coordinates [0.527, 0.075, 0.986, 0.924] in the chart?" }, { "from": "gpt", "value": "The plot for 'Lifestyle Trends'." } ], "id_raw": 895 }, { "id": "4urdYpa1Hx9i97pn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the label '2023' in the x-axis?" }, { "from": "gpt", "value": "[0.473, 0.934, 0.508, 0.970]" } ], "id_raw": 895 }, { "id": "zhBK9Va9eQxJr6R3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_15.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.104, 0.105, 0.213, 0.141] indicate in the legend?" }, { "from": "gpt", "value": "Social Activities." } ], "id_raw": 895 }, { "id": "IQKw93XwqjEstJW7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_15.png", "conversations": [ { "from": "human", "value": "\nDoes the value of {Social Activities}[0.073, 0.287, 0.490, 0.712] increase or decrease from 2022 to 2023?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 895 }, { "id": "WOGhDZmI7gcGlnTK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_15.png", "conversations": [ { "from": "human", "value": "\nIs the trend for {Lifestyle}[0.548, 0.754, 0.965, 0.797] increasing or decreasing between 2022 and 2023?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 895 }, { "id": "UnvEdIVzaxmGp7No", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_15.png", "conversations": [ { "from": "human", "value": "\nWhich activity has a higher percentage in 2023: {Social Activities}[0.490, 0.287] or {Lifestyle}[0.965, 0.754]?" }, { "from": "gpt", "value": "Social Activities." } ], "id_raw": 895 }, { "id": "a61hcarPtzrg7Ely", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.522, 0.499, 0.810, 0.666]." }, { "from": "gpt", "value": "This area contains the subplot for 'Lifestyle Trends'. It includes value points for the years 2022 and 2023 with a line joining them indicating the trend." } ], "id_raw": 895 }, { "id": "DPymx6KUsx5vsG7O", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.104, 0.105, 0.213, 0.141]." }, { "from": "gpt", "value": "This area represents a single entry in the legend. It is labeled 'Social Activities' and is associated with a colored handle." } ], "id_raw": 895 }, { "id": "sulCXw42KOoK55kr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_15.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.053, 0.075, 0.511, 0.924]?" }, { "from": "gpt", "value": "This region contains the other subplot, 'Social Activities Trends', which includes x-axis and y-axis labels, tick labels, the plot line, and data points for the years 2022 and 2023." } ], "id_raw": 895 }, { "id": "oJYcr0BffGrzecQt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_16.png", "conversations": [ { "from": "human", "value": "\nWhat is signified by the text at the coordinates [0.061, 0.719, 0.151, 0.933]?" }, { "from": "gpt", "value": "It represents the x-axis label \"Random Forests\"." } ], "id_raw": 896 }, { "id": "x9j9JhSTK3qvffbv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_16.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the coordinates [0.367, 0.233] in the chart?" }, { "from": "gpt", "value": "It represents a point on the line for ANN in task 1 performance." } ], "id_raw": 896 }, { "id": "Hnay3VbcT5nb5hv5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.758, 0.030, 0.801, 0.066]?" }, { "from": "gpt", "value": "It is the sub-title of the second subplot, which is \"Task 2\"." } ], "id_raw": 896 }, { "id": "OscubnXol1CMLII7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'SVM' in Task 1}[0.205, 0.297] above or below {the point for 'KNN' in Task 1}[0.259, 0.455]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 896 }, { "id": "ntYQpf4wE1MxGNXx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_16.png", "conversations": [ { "from": "human", "value": "\nIn subplot for Task 2, is {the point for 'ANN'}[0.860, 0.233] higher or lower than {the point for 'SVM'}[0.698, 0.471]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 896 }, { "id": "9veJcugiYHoHRS4g", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_16.png", "conversations": [ { "from": "human", "value": "\nWhich model has a higher accuracy in Task 2, {Random Forests}[0.644, 0.360] or {RNN}[0.969, 0.287]?" }, { "from": "gpt", "value": "ANN." } ], "id_raw": 896 }, { "id": "4EZtKvbGpfWiUF0X", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_16.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is contained within the coordinates [0.509, 0.719, 0.969, 0.913]?" }, { "from": "gpt", "value": "This region includes all x-axis labels for the second plot, those are Decision Trees, Random Forests, SVM, KNN, Logistic Regression, ANN, CNN and RNN." } ], "id_raw": 896 }, { "id": "khnLqr3rAuqDjuul", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_16.png", "conversations": [ { "from": "human", "value": "\nWhat content does the area defined by [0.571, 0.075, 0.987, 0.709] contain?" }, { "from": "gpt", "value": "This area represents the plotting area of the second subplot which presents the performance of the AI & ML models for 'Task 2'. We have 8 models represented on the x-axis, and the y-axis represents the performance accuracy. Each model has a line plot point representing its accuracy." } ], "id_raw": 896 }, { "id": "VYZNT1VAj5fjYTlS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the coordinates [0.078, 0.075, 0.494, 0.709]." }, { "from": "gpt", "value": "The coordinates encompass the first subplot which displays the performance of AI & ML models for 'Task 1'. It includes the line plots for various models along with their Y-axis 'Accuracy' and X-axis labels for different models 'Decision Trees', 'Random Forests', 'SVM', 'KNN', 'Logistic Regression', 'ANN', 'CNN', and 'RNN'." } ], "id_raw": 896 }, { "id": "WM9sLt1VL8XF7Fbj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the description of the object with coordinates [0.034, 0.792, 0.056, 0.820] in the chart?" }, { "from": "gpt", "value": "Y-axis tick label '450'." } ], "id_raw": 897 }, { "id": "R9RErdMfaekpjZwR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the object labeled as 'Hydro' in the chart?" }, { "from": "gpt", "value": "{X-axis tick label}[0.040, 0.813, 0.080, 0.908]." } ], "id_raw": 897 }, { "id": "FDj5YCZXYNVIAhAc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_17.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what's the color of {the legend marker}[0.551, 0.745, 0.586, 0.762]?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 897 }, { "id": "hTicqv0ShpVjkJ3D", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_17.png", "conversations": [ { "from": "human", "value": "\nLooking at the chart, is {the Coal energy production point 2030}[0.967, 0.667] higher or lower than {the NaturalGas energy production point 2030}[0.832, 0.384]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 897 }, { "id": "kNuUbUUHN6OT9njI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_17.png", "conversations": [ { "from": "human", "value": "\nIs {the Solar energy production point in 2025}[0.148, 0.306] positioned lower or higher in the chart than {the Solar energy production point in 2030}[0.630, 0.242]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 897 }, { "id": "lAbGOOi69TcVXL2x", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_17.png", "conversations": [ { "from": "human", "value": "\nWhich energy source has the lowest production in 2030, {Nuclear}[0.765, 0.175] or {Wind}[0.697, 0.769]?" }, { "from": "gpt", "value": "Nuclear." } ], "id_raw": 897 }, { "id": "wnn0xG4LorIBmUIC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects located within the bounding box [0.111, 0.737, 0.251, 0.773]?" }, { "from": "gpt", "value": "It is the legend label 'Energy Source 2025' for the first subplot." } ], "id_raw": 897 }, { "id": "fbJJ596X9Q9XwDWU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the region at [0.215, 0.794, 0.485, 0.690]." }, { "from": "gpt", "value": "This area marks the Energy Production points on the 2025 chart for Wind, Nuclear, NaturalGas, Oil, and Coal." } ], "id_raw": 897 }, { "id": "ZX6EZKOCAjVp6qlN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_17.png", "conversations": [ { "from": "human", "value": "\nWhat does [0.682, 0.030, 0.848, 0.066] region contain?" }, { "from": "gpt", "value": "It contains the subplot title 'Energy Production 2030'." } ], "id_raw": 897 }, { "id": "m5nHT2uAfLeAOdMX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_18.png", "conversations": [ { "from": "human", "value": "\nWhat does the object with coordinates [0.030, 0.272, 0.046, 0.557] represent?" }, { "from": "gpt", "value": "Y-axis label of the subplot on the left." } ], "id_raw": 898 }, { "id": "7E7dxKp3GXjrAQPt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.121, 0.157, 0.708, 0.193]?" }, { "from": "gpt", "value": "The label for \"Policy B 2021\" in the legend of the second subplot." } ], "id_raw": 898 }, { "id": "1LRTNecprXiJmw3v", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label}[0.051, 0.249, 0.066, 0.277]?" }, { "from": "gpt", "value": "80." } ], "id_raw": 898 }, { "id": "A3wvt77cXa5yt2Ez", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_18.png", "conversations": [ { "from": "human", "value": "\nIs the score of {Policy A 2021 for 'Taxation'}[0.474, 0.383] lower or higher than {Policy B 2020 for 'Taxation'}[0.968, 0.569]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 898 }, { "id": "sND78zqfKvOMr6SS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_18.png", "conversations": [ { "from": "human", "value": "\nIs the data point {Policy B 2020 for 'Education'}[0.748, 0.383] located higher or lower than {Policy B 2021 for 'Education'}[0.748, 0.322]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 898 }, { "id": "PiwmkCltmeyMm2Mn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_18.png", "conversations": [ { "from": "human", "value": "\nIn the Year 2020, did {Policy B for 'Environment'}[0.583, 0.445] have a higher impact level than {Policy A for Transportation'}[0.364, 0.322]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 898 }, { "id": "ugW8QIzQG2qAYTbf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found in the region [0.058, 0.764, 0.144, 0.970]." }, { "from": "gpt", "value": "It contains the {x-axis label 'Unemployment'}[0.058, 0.764, 0.144, 0.970] for the subplot on the left." } ], "id_raw": 898 }, { "id": "shKiByjJjNgMlxHt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents found in the area defined by [0.570, 0.091, 0.714, 0.207]." }, { "from": "gpt", "value": "It contains the legend for the subplot on the right with labels {Policy B 2020}[0.615, 0.105, 0.708, 0.141] and {Policy B 2021}[0.615, 0.157, 0.708, 0.193]." } ], "id_raw": 898 }, { "id": "ISuzUrAC443XmP6m", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_18.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region defined by the coordinates [0.089, 0.136, 0.474, 0.445]?" }, { "from": "gpt", "value": "It encompasses the line for {Policy A 2020}[0.089, 0.136, 0.474, 0.445] in the left subplot. Specifically, it includes the points from \"Environment\" to \"Taxation\". Each point corresponds to a value of the 'Policy A 2020'." } ], "id_raw": 898 }, { "id": "c6lgWzbCgPXGSqSI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the title for the first subplot within the chart?" }, { "from": "gpt", "value": "New Media methods Results in 2022." } ], "id_raw": 899 }, { "id": "UOhBdb2EfUXDWoHz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_19.png", "conversations": [ { "from": "human", "value": "\nWhat color is the line representing 'New Media methods 2022' in subplot 1?" }, { "from": "gpt", "value": "#3d89be." } ], "id_raw": 899 }, { "id": "WCPzJv3of53UHJxk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_45_19.png", "conversations": [ { "from": "human", "value": "\nWhat element lies at the coordinates [0.093, 0.488, 0.107, 0.516] in the first subplot?" }, { "from": "gpt", "value": "It is the \"20\" label in y-axis tick labels." } ], "id_raw": 899 }, { "id": "wxyDiTDGV9yBL2lt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_19.png", "conversations": [ { "from": "human", "value": "\nIn the first subplot, is the data point 'SEO Optimization'[0.245, 0.312] above or below 'Influencer Marketing'[0.418, 0.363]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 899 }, { "id": "mBkRvd0agt0ggX1v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_19.png", "conversations": [ { "from": "human", "value": "\nIn the second subplot, is the datapoint for 'AI Content Generator'[0.623, 0.533] higher or lower than 'Influencer Marketing'[0.912, 0.312]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 899 }, { "id": "Fm5lwax18DvK7Lzg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_45_19.png", "conversations": [ { "from": "human", "value": "\nIn the second subplot, how does the value of 'SEO Optimization'[0.738, 0.448] compare to 'Email Marketing'[0.796, 0.550]?" }, { "from": "gpt", "value": "It is lower." } ], "id_raw": 899 }, { "id": "bovfI4udL8X3D5hY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is within the coordinates [0.611, 0.090, 0.844, 0.155] in subplot 2?" }, { "from": "gpt", "value": "This region contains the subplot legend labeling the 'New Media methods 2023' line in a particular color." } ], "id_raw": 899 }, { "id": "ZMyq5MjNoW6jtO9B", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_19.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within coordinates [0.508, 0.667, 0.971, 0.959] in the second subplot?" }, { "from": "gpt", "value": "This area contains the x-axis labels for the categories in New Media methods Results for 2023, such as 'AI Content Generator', 'Social Media Platforms', 'SEO Optimization', 'Email Marketing', 'VR/AR Content', 'Influencer Marketing', and 'Video Marketing'." } ], "id_raw": 899 }, { "id": "yiSD6C9NK7HC2RFE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_45_19.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the coordinates [0.603, 0.074, 0.989, 0.670]?" }, { "from": "gpt", "value": "The region contains the entire second subplot, which represents the 'New Media methods Results in 2023' with a line and marked points for 'AI Content Generator', 'Social Media Platforms', 'SEO Optimization', 'Email Marketing', 'VR/AR Content', 'Influencer Marketing', and 'Video Marketing'." } ], "id_raw": 899 }, { "id": "N0UuHFoFYc8wZfC9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label associated with {the y-axis tick mark}[0.083, 0.184]?" }, { "from": "gpt", "value": "62000." } ], "id_raw": 900 }, { "id": "GuUn8GPxC9sq8JR2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the purpose of the graphical object located at}[0.906, 0.184]?" }, { "from": "gpt", "value": "To display the point value for '24 hours' on the 'User Engagement' line chart." } ], "id_raw": 900 }, { "id": "FTXxUW8z3MElxMht", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_0.png", "conversations": [ { "from": "human", "value": "\nWhat does {the last point of the 'User Engagement' line path}[0.941, 0.152] represent?" }, { "from": "gpt", "value": "62500." } ], "id_raw": 900 }, { "id": "Bh3RpKoHCg54xqU3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '10:00'}[0.465, 0.279] positioned higher or lower than {the point labeled '12:00'}[0.533, 0.120] in the 'User Engagement' chart?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 900 }, { "id": "9TnEU8LiRBHp5sPX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_0.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher count at {point 14}[0.601, 0.630], the 'Ad Views' or 'User Engagement' lines?" }, { "from": "gpt", "value": "Ad Views." } ], "id_raw": 900 }, { "id": "SK0mtWaRuYGk9SYK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_0.png", "conversations": [ { "from": "human", "value": "\nIs the marker at {location}[0.669, 0.152] on the 'User Engagement' higher or lower relative to the marker at {coordinate}[0.669, 0.632] on the 'Ad Views'?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 900 }, { "id": "MlmhKpjwfejJCdxy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.065, 0.302, 0.131, 0.477]." }, { "from": "gpt", "value": "This section contains a part of the {y-axis}[0.015, 0.222, 0.029, 0.270] for 'User Engagement' and part of the {grid line}[0.083, 0.037, 0.982, 0.454], which appears along the y-axis scale." } ], "id_raw": 900 }, { "id": "WCe5lQhBYg4B1vYm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_0.png", "conversations": [ { "from": "human", "value": "\nDescribe what is found in the range [0.800, 0.250, 0.980, 0.400]." }, { "from": "gpt", "value": "This region includes the {last section of the 'User Engagement' line}[0.941, 0.152] and the {x-tick near label \"20\"}[0.805, 0.454]." } ], "id_raw": 900 }, { "id": "ubdSKO8Q3TGoggyd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within [0.450, 0.890, 0.550, 0.970]?" }, { "from": "gpt", "value": "This region includes the lower half of the {x-axis labels}[0.488, 0.971, 0.578, 0.985] on the 'Ad Views' chart." } ], "id_raw": 900 }, { "id": "clZL0evazMxs86Zy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label}[0.464, 0.946, 0.613, 0.960] corresponding to the x-axis in the lower subplot?" }, { "from": "gpt", "value": "Time (Hours)." } ], "id_raw": 902 }, { "id": "ZiHFt3GVpX01Q9W1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_2.png", "conversations": [ { "from": "human", "value": "\nCan you identify {the marker color}[0.408, 0.980, 0.454, 0.980] for the Online Education line?" }, { "from": "gpt", "value": "#dc143c." } ], "id_raw": 902 }, { "id": "zhGzbQH2yni1XZv7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_2.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.025, 0.679, 0.048, 0.772] in the lower subplot represent?" }, { "from": "gpt", "value": "Activity Level." } ], "id_raw": 902 }, { "id": "pUiPXXdM32odDqjm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the x-axis tick labeled '10'}[0.466, 0.922] align horizontally with {the point at time 10}[0.466, 0.763] in Online Education subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 902 }, { "id": "o4IWk2a1ZT1CzSF5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point with coordinates }[0.321, 0.345] above or below {the point with coordinates }[0.576, 0.382] in the Traditional Education plot?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 902 }, { "id": "PVGEWJeIa5nBMn9S", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_2.png", "conversations": [ { "from": "human", "value": "\nIs {the y-axis tick labeled '3.0'}[0.102, 0.586] located higher than {the y-axis tick labeled '2.5'}[0.102, 0.642] in the Online Education chart?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 902 }, { "id": "60FZ4f4P8prcahcr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.398, 0.968, 0.679, 0.993]." }, { "from": "gpt", "value": "It contains the legend for Online Education, with the text \"Online Education\" and a corresponding line styled in red." } ], "id_raw": 902 }, { "id": "KXexAQqztweWsvUo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_2.png", "conversations": [ { "from": "human", "value": "\nCan you detail what is located in [0.058, 0.200, 0.094, 0.438]?" }, { "from": "gpt", "value": "This longitudinal slice includes all the y-axis tick labels ranging from '0.0' to '2.0' in the Online Education subplot." } ], "id_raw": 902 }, { "id": "AF6tjOGQ2YL9uNbY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_2.png", "conversations": [ { "from": "human", "value": "\nExplain what can be observed in [0.095, 0.434, 0.481, 0.448]." }, { "from": "gpt", "value": "This region captures the x-axis tick labels '0', '5', and '10' along the bottom of the Traditional Education chart." } ], "id_raw": 902 }, { "id": "ZvJGeDDVFbVJmZq0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the plot located at these coordinates [0.106, 0.037, 0.954, 0.390]?" }, { "from": "gpt", "value": "This plot is a line chart representing the number of 'Patents Applied Over the Years'." } ], "id_raw": 904 }, { "id": "BqL6MoOcdWEhHJXT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at these coordinates [0.068, 0.613, 0.100, 0.627] signify?" }, { "from": "gpt", "value": "It is a y-axis tick label for the 'Research Grants Received Over the Years' plot with a value of '2.0'." } ], "id_raw": 904 }, { "id": "pwE0qqYrbabS6Oyz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the text located at the position [0.050, 0.091, 0.100, 0.105]?" }, { "from": "gpt", "value": "It is a label on the y-axis of the 'Patents Applied Over the Years' line chart with a value of '4000'." } ], "id_raw": 904 }, { "id": "MAOwY3HLneBHjwLp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_4.png", "conversations": [ { "from": "human", "value": "\nIs the {point in 2012}[0.276, 0.285] for 'Patents Applied' higher or lower than the {point in 2013}[0.361, 0.302]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 904 }, { "id": "3ltX8OQSRQUlLokQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_4.png", "conversations": [ { "from": "human", "value": "\nCompared to the {value in 2011}[0.191, 0.773] for 'Research Grants Received', is the {value in 2020}[0.954, 0.839] for 'Research Grants Received' higher or lower?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 904 }, { "id": "oktSk8ybsQbLyuFF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_4.png", "conversations": [ { "from": "human", "value": "\nDoes the {value for 'Patents Applied'} at [0.445, 0.255] in 2014 surpass the {value for 'Research Grants Received'} at [0.445, 0.817] in the same year?" }, { "from": "gpt", "value": "No." } ], "id_raw": 904 }, { "id": "txSVQ3rGgHIeOPFF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_4.png", "conversations": [ { "from": "human", "value": "\nCan you give me a summary of the information contained within the area defined by these coordinates [0.106, 0.530, 0.954, 0.883]?" }, { "from": "gpt", "value": "This area plots the line chart for 'Research Grants Received Over the Years'. It has an x-axis labeled with years ranging from '2010' to '2020' and a y-axis labeled with amounts from '1.4' to '2.0'." } ], "id_raw": 904 }, { "id": "NK8OWxGzVVobiatE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the area [0.201, 0.468, 0.428, 0.493]." }, { "from": "gpt", "value": "This area is a legend specifying that the '--' line in the color '#ff7f50' represents the 'Patents Applied' data series." } ], "id_raw": 904 }, { "id": "vRMvXyb0fX4ZNcFJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_4.png", "conversations": [ { "from": "human", "value": "\nWhat visual elements are included in the area [0.284, 0.508, 0.777, 0.526]?" }, { "from": "gpt", "value": "The subtitle of the 'Research Grants Received Over the Years' line chart is located in this area. The subtitle is styled in color '.15' and has no rotation." } ], "id_raw": 904 }, { "id": "MlBM9Xga4BICyN5E", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_5.png", "conversations": [ { "from": "human", "value": "\nWhat is shown at {the coordinates}[0.351, 0.479, 0.400, 0.493] in the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 905 }, { "id": "nFYneb6DRSE3259X", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label placed at {this specific point}[0.642, 0.122]?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 905 }, { "id": "mktqNH7ZnoHIbnNI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the attribute of {the color handle}[0.663, 0.236, 0.709, 0.236]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 905 }, { "id": "ssTiRKLdBJFsr4Ez", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_5.png", "conversations": [ { "from": "human", "value": "\nDoes {the label '2000 at point}'[0.287, 0.454] come before or after {the label '2010 at point}'[0.464, 0.454] chronologically on the x-axis?" }, { "from": "gpt", "value": "Before." } ], "id_raw": 905 }, { "id": "BhPuKijld5OfEUEi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_5.png", "conversations": [ { "from": "human", "value": "\nIs {the y-tick '2'}[0.072, 0.340, 0.102, 0.354] above or below {the y-tick '4'}[0.072, 0.232, 0.102, 0.246] on the chart?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 905 }, { "id": "IED6pEfCKNfF2UPa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_5.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {the point at '2007'}[r][0.411, 0.153] or {the point at '2010'}[r][0.464, 0.151] on the Trade Volume line?" }, { "from": "gpt", "value": "2007." } ], "id_raw": 905 }, { "id": "t1m3xieZE3QRE1eF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the region marked by [0.087, 0.015, 0.102, 0.029]." }, { "from": "gpt", "value": "The bottom y-axis tick with a label of '8' showing the lowest part of the scale for the Globalization Index in a rotated font." } ], "id_raw": 905 }, { "id": "es6nZQYysu1j9sod", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_5.png", "conversations": [ { "from": "human", "value": "\nOutline what is contained between [0.080, 0.459, 0.139, 0.473]." }, { "from": "gpt", "value": "The x-axis tick label '1990' initiating the timeline in black colored text in a horizontal orientation." } ], "id_raw": 905 }, { "id": "4xdFguXdFNWWDr5J", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_5.png", "conversations": [ { "from": "human", "value": "\nDescribe what is represented in the bounding box [0.663, 0.225, 0.975, 0.250]." }, { "from": "gpt", "value": "The area contains the chart legend indicating {Global Trade Volume}[0.727, 0.230, 0.966, 0.244] with a corresponding green line indicator {color handle}[0.663, 0.236, 0.709, 0.236] beside the label text." } ], "id_raw": 905 }, { "id": "C1rAo6DXXBE8VX2Z", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the location of the x-axis label for the second subplot?" }, { "from": "gpt", "value": "{The x-axis label for the second subplot}[0.499, 0.971, 0.528, 0.985]" } ], "id_raw": 906 }, { "id": "uBsc2eJtOdWh74Fl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the set of points in {line_0}[0.066, 0.555, 0.961, 0.913] represent in the chart?" }, { "from": "gpt", "value": "They represent the growth of the Game Industry." } ], "id_raw": 906 }, { "id": "0wBHo5yuXFF3fC2d", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_6.png", "conversations": [ { "from": "human", "value": "\nWhat can be found with coordinates [0.846, 0.878, 0.948, 0.892] in the sublegend of the second subplot?" }, { "from": "gpt", "value": "The label 'Game Industry'." } ], "id_raw": 906 }, { "id": "wO7J2vzp70bsMzOM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_6.png", "conversations": [ { "from": "human", "value": "\nCompare the revenue points in 2001 for the Movie and Game industry. Which one had higher revenue?" }, { "from": "gpt", "value": "The Game Industry." } ], "id_raw": 906 }, { "id": "YF99FyFsgNQkuPT9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_6.png", "conversations": [ { "from": "human", "value": "\nDoes the Movie Industry point at year 2002 {coordinates}[0.155, 0.412] have higher or lower revenue than the Point at the year 2001 {coordinates}[0.111, 0.437]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 906 }, { "id": "aMv3IE92tLhvi7XF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_6.png", "conversations": [ { "from": "human", "value": "\nIs the revenue of the Game Industry at year 2007 [0.379, 0.742] more than the revenue in 2006 [0.334, 0.755]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 906 }, { "id": "gTINPuuxPI0zu5Ux", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_6.png", "conversations": [ { "from": "human", "value": "\nWhat aspect of the chart is described in [0.419, 0.508, 0.608, 0.526]?" }, { "from": "gpt", "value": "This region includes a subtitle which is specified as 'Game Industry Growth'." } ], "id_raw": 906 }, { "id": "bZNk1zJVbB3tGLa8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_6.png", "conversations": [ { "from": "human", "value": "\nWhat's the content within the region [0.801, 0.873, 0.954, 0.898] of the second subplot?" }, { "from": "gpt", "value": "This area includes two items: the color handle of Game Industry and the corresponding text label." } ], "id_raw": 906 }, { "id": "Er1xEGhGJ52sy9nx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_6.png", "conversations": [ { "from": "human", "value": "\nCould you describe the region between [0.737, 0.647] and [0.871, 0.580] in the second subplot?" }, { "from": "gpt", "value": "The Game Industry's line between 2015 and 2018 is found within these coordinates. It indicates growth from 460 to 540 Million in Revenue." } ], "id_raw": 906 }, { "id": "4kzugPg3944QczBX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the x-axis label}[0.460, 0.479, 0.610, 0.493] in the first subplot?" }, { "from": "gpt", "value": "Grey." } ], "id_raw": 907 }, { "id": "Pubo1kcPLurACXs3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the linestyle of {Investment 2 Return Rate line}[0.110, 0.572, 0.926, 0.947]?" }, { "from": "gpt", "value": "Dashed." } ], "id_raw": 907 }, { "id": "NP3ti1ufq8GTr41b", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the shape of {the marker for the second subplot's legend}[0.131, 0.548, 0.177, 0.548]?" }, { "from": "gpt", "value": "None." } ], "id_raw": 907 }, { "id": "nJygCZ8G0HoKXegM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point at 5 hours}[0.280, 0.454] above or below {the point at 10 hours}[0.450, 0.412] for Investment 1 Return Rate?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 907 }, { "id": "IjAwBSt8PfZqnvxW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the 90% ylabel}[0.058, 0.365, 0.102, 0.379] come before or after {the 130% ylabel}[0.058, 0.240, 0.102, 0.254] in order of their numeric value?" }, { "from": "gpt", "value": "Before." } ], "id_raw": 907 }, { "id": "vcwtybnyKQ3pD5If", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_7.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher end value, {Investment 1 line at 19 hours}[0.756, 0.454] or {Investment 2 line at 19 hours}[0.756, 0.676]?" }, { "from": "gpt", "value": "Investment 2." } ], "id_raw": 907 }, { "id": "Q9cq0YC6zAapbOTo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.103, 0.459, 0.975, 0.473]." }, { "from": "gpt", "value": "The area contains x-axis tick labels for both subplots, ranging from '0' to '25' hours in increments of 5 hours for each tick, labeled in grey color." } ], "id_raw": 907 }, { "id": "xOckRXB09RKf6g0f", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_7.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in [0.110, 0.037, 0.960, 0.454]?" }, { "from": "gpt", "value": "This region holds the subplot displaying Investment 1 Return Rate, inclusive of {line graph}[0.110, 0.079, 0.926, 0.454], {y-axis labels}[0.058, 0.074, 0.102, 0.463], {x-axis and its labels}[0.103, 0.459, 0.975, 0.473], and a {legend}[0.121, 0.044, 0.502, 0.069], all rendered with a light grey grid background." } ], "id_raw": 907 }, { "id": "x7HmPLLcpH6GyxsT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_7.png", "conversations": [ { "from": "human", "value": "\nDescribe what is shown in [0.025, 0.683, 0.048, 0.794]." }, { "from": "gpt", "value": "This area includes y-axis labels for the second subplot, with labels for return rates ranging from 110% to 270%, displayed vertically oriented in grey color." } ], "id_raw": 907 }, { "id": "AgfD1tvzBmu5eAc0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis}[0.032, 0.174, 0.056, 0.293]?" }, { "from": "gpt", "value": "Emissions (Units)." } ], "id_raw": 908 }, { "id": "5QYcaYVswXK8LxOB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_8.png", "conversations": [ { "from": "human", "value": "\nWhich color corresponds to {the line indicating average greenhouse gas emissions per hour}[0.440, 0.481, 0.954, 0.495]?" }, { "from": "gpt", "value": "#228b22." } ], "id_raw": 908 }, { "id": "o6Gub8PjIXWhjH9d", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the value on the x-axis}[0.999, 0.434, 1.026, 0.448]?" }, { "from": "gpt", "value": "25." } ], "id_raw": 908 }, { "id": "DOVTPkZXXyAj28hj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_8.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {the value at point 0}[0.102, 0.299] on the emissions line or {the value at point 24}[0.975, 0.549] on the recycling line?" }, { "from": "gpt", "value": "Emissions." } ], "id_raw": 908 }, { "id": "0UOwknqmB28Rf3JN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_8.png", "conversations": [ { "from": "human", "value": "\nIs {the point shown at 0 hours}[0.102, 0.922] in recycling rates above or below {the point at 12 hours}[0.539, 0.640]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 908 }, { "id": "hBTYalSkNjhWISpf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_8.png", "conversations": [ { "from": "human", "value": "\nWhich label corresponds to {the higher y-tick}[0.102, 0.037]?" }, { "from": "gpt", "value": "30." } ], "id_raw": 908 }, { "id": "OSimmaXEnezdVdQy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements contained in [0.375, 0.488, 0.954, 0.495]." }, { "from": "gpt", "value": "This is the legend area for 'Average greenhouse gas emissions per hour' with a green handle at {points}[0.375, 0.488, 0.421, 0.488] and text in {points}[0.440, 0.481, 0.954, 0.495]." } ], "id_raw": 908 }, { "id": "nhyx6XcIhmDl5q4q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_8.png", "conversations": [ { "from": "human", "value": "\nDescribe what is located in [0.275, 0.508, 0.725, 0.526]." }, { "from": "gpt", "value": "The subtitle 'Recycling Rates Over Time' is located in this region, and it describes the second subplot of the chart." } ], "id_raw": 908 }, { "id": "SNgmCcEmyjgwStEH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.080, 0.163, 0.094, 0.373]." }, { "from": "gpt", "value": "The y-axis tick labels '20', '15', '10', and '5' for the emissions unit are included in this region." } ], "id_raw": 908 }, { "id": "tcqpy7PaLOrEqNud", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_9.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the coordinates [0.242, 0.361] in the subplot_0 of the figure?" }, { "from": "gpt", "value": "It is a data point on the \"Cultural Development\" line at the time 2000 years." } ], "id_raw": 909 }, { "id": "LW1rI8mD91fG4TrN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_9.png", "conversations": [ { "from": "human", "value": "\nWhat color does {scientific development line}[0.061, 0.576, 0.963, 0.947] in subplot_1 of the chart represent?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 909 }, { "id": "6FFi4hbheYE0ydCB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the element found at {coordinates} [0.242, 0.872] in subplot_1?" }, { "from": "gpt", "value": "This is a point on the \"Scientific Development\" line at the time 2000 years." } ], "id_raw": 909 }, { "id": "4eKrhMeoXTaRELWO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_9.png", "conversations": [ { "from": "human", "value": "\nIs the {Cultural development line}[0.061, 0.454, 0.963, 0.195] growth faster or slower than the {Scientific Development line}[0.061, 0.576, 0.963, 0.947] over the same time period in the fig?" }, { "from": "gpt", "value": "Slower." } ], "id_raw": 909 }, { "id": "xLeiRAcrIoYxbiEv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_9.png", "conversations": [ { "from": "human", "value": "\nAt time point '2000', is the development level of {Cultural Development line's point}[0.242, 0.361] higher or lower than that of {Scientific Development line's point}[0.242, 0.872]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 909 }, { "id": "dSyyMCjJr8MeECn4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_9.png", "conversations": [ { "from": "human", "value": "\nDoes the {Cultural Development line}[0.061, 0.195, 0.963, 0.454] have a higher or lower starting point than the {Scientific Development line}[0.061, 0.576, 0.963, 0.947]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 909 }, { "id": "mbLbsB9MQbWzuDE3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the region [0.330, 0.508, 0.695, 0.526]." }, { "from": "gpt", "value": "The subtitle \"Scientific Development over Human History\" for subplot_1 is located in this region." } ], "id_raw": 909 }, { "id": "Drl8Gf7znssynvl4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_9.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell me about the region at [0.745, 0.915, 0.956, 0.940] in subplot_1?" }, { "from": "gpt", "value": "The area contains the legend for the \"Scientific Development\" line, which is associated with the color green." } ], "id_raw": 909 }, { "id": "uBxlwKIkYEen9siY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.753, 0.422, 0.956, 0.447] in subplot_0 contain?" }, { "from": "gpt", "value": "The area contains the legend for subplot_0, specifically for the \"Cultural Development\" line which is associated with the color dark blue." } ], "id_raw": 909 }, { "id": "JlCid88NkUHPNgUq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object represented by the coordinates [0.025, 0.167, 0.048, 0.260]?" }, { "from": "gpt", "value": "Y-axis label of the subplot_0." } ], "id_raw": 910 }, { "id": "jI6z937CvMng8tVC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first icon in the legend of the subplot_1?" }, { "from": "gpt", "value": "[0.549, 0.972, 0.595, 0.972]" } ], "id_raw": 910 }, { "id": "PTB1Ib9L9RhOWNqc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label present at the coordinates [0.776, 0.966, 0.969, 0.980]?" }, { "from": "gpt", "value": "Remote Work." } ], "id_raw": 910 }, { "id": "YCwFBSBGYAYNu7YS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_10.png", "conversations": [ { "from": "human", "value": "\nIs the {point at the 10th hour under Remote Work}[0.441, 0.685] higher or lower than {the tenth point under In-Office Work}[0.441, 0.316]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 910 }, { "id": "IGzIajyvMG1RPsAQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_10.png", "conversations": [ { "from": "human", "value": "\nHow does the {activity level at the end of the day for Remote Work}[0.926, 0.544] compare to {the starting activity level for In-Office Work}[0.095, 0.390]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 910 }, { "id": "YXJuJVi89H1qwE5n", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_10.png", "conversations": [ { "from": "human", "value": "\nAre the {label coordinates for \"5\" on the X-axis}[0.261, 0.395, 0.275, 0.409] of the subplot_0 above or below the {label coordinates for \"25\" on the X-axis}[0.946, 0.888, 0.975, 0.902] of subplot_1?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 910 }, { "id": "2dY7yLc4XfUokzno", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the region [0.053, 0.244, 0.102, 0.409]." }, { "from": "gpt", "value": "Inside this region, we have the tick labels for Y-axis from '10' to '30' for subplot_0." } ], "id_raw": 910 }, { "id": "nDCreonbtE8rmu37", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_10.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents within the region represented by coordinates [0.054, 0.670, 0.200, 0.760]?" }, { "from": "gpt", "value": "The section includes a part of y-axis label of subplot_1." } ], "id_raw": 910 }, { "id": "jnk8HRfJ6zWR7wsd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region marked by coordinates [0.095, 0.390, 0.926, 0.390]?" }, { "from": "gpt", "value": "This region represents the complete line graph of the 'In-Office Work' trend from the beginning till the 24th hour." } ], "id_raw": 910 }, { "id": "hb6eNPy02hG9nZD8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the xlabel}[0.466, 0.946, 0.604, 0.960] of the second sub plot?" }, { "from": "gpt", "value": "Hour of Day." } ], "id_raw": 911 }, { "id": "SEmr0mcaXRTUwmYW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the text located at {coordinates}[0.476, 0.481, 0.658, 0.495] in the legend of subplot_0?" }, { "from": "gpt", "value": "Standard Living." } ], "id_raw": 911 }, { "id": "85V8KNsxuAZmSiFd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the top y-axis label in the first subplot?" }, { "from": "gpt", "value": "90." } ], "id_raw": 911 }, { "id": "XQhz6hLubIymzHW4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_11.png", "conversations": [ { "from": "human", "value": "\nIs {the point at hour 10}[0.462, 0.681] of Innovative Living above or below {the point at hour 10}[0.462, 0.230] of Standard Living?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 911 }, { "id": "rTVdBGaZyuObmCFL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Standard Living'}[0.095, 0.116, 0.975, 0.369] have a higher or lower end value than the start for the first subplot?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 911 }, { "id": "LESiqur8UWo558yy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_11.png", "conversations": [ { "from": "human", "value": "\nIs the data point for hour 15 in Standard Living above or below the equivalent point for Innovative Living?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 911 }, { "id": "ebUgpj1LluC1KpYK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.025, 0.676, 0.048, 0.776]." }, { "from": "gpt", "value": "This region contains the y-axis label for the second subplot, which is \"Level of Living\"." } ], "id_raw": 911 }, { "id": "LHf2icC7zPjYvUyf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within the region [0.466, 0.946, 0.604, 0.960]?" }, { "from": "gpt", "value": "This specific area contains the x-label of the second subplot which is \"Hour of Day\"." } ], "id_raw": 911 }, { "id": "AQxxfB2aUPVWRLSD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_11.png", "conversations": [ { "from": "human", "value": "\nWhat objects are in the region [0.394, 0.968, 0.676, 0.993]?" }, { "from": "gpt", "value": "The region contains the legend title for subplot_1 but the title is just an empty string, and the label {Innovative Living}[0.468, 0.974, 0.666, 0.988]. The color corresponding to {Innovative Living}[0.404, 0.980, 0.450, 0.980] in the legend is in a purplish (#800080) color." } ], "id_raw": 911 }, { "id": "Uwt5XzhBpQF74UfI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_12.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the Rural Migration line in the chart?" }, { "from": "gpt", "value": "#1e90ff." } ], "id_raw": 912 }, { "id": "p1EQXF8x7rjkdwGt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_12.png", "conversations": [ { "from": "human", "value": "\nWhich label belongs to the y-axis tick at coordinates [0.058, 0.358, 0.114, 0.372]?" }, { "from": "gpt", "value": "-0.3." } ], "id_raw": 912 }, { "id": "NxWQuSA92YVRRax6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label of the x-axes tick}[0.443, 0.395, 0.472, 0.409] in the Rural Migration graph?" }, { "from": "gpt", "value": "10." } ], "id_raw": 912 }, { "id": "Zv40W16VVg30PLeD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_12.png", "conversations": [ { "from": "human", "value": "\nIs {the last point on the Rural Migration line}[0.960, 0.305] higher or lower than {the last point on the Urban Migration line}[0.960, 0.539]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 912 }, { "id": "VeQx4pltcgBsE6DX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_12.png", "conversations": [ { "from": "human", "value": "\nCompare the value of {point 10 on Urban Migration line}[0.457, 0.640] to {point 10 on Rural Migration line}[0.457, 0.356]." }, { "from": "gpt", "value": "Higher." } ], "id_raw": 912 }, { "id": "By5FH2r6D2acaBki", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the subtitle for Rural Migration}[0.393, 0.015, 0.689, 0.033] have a higher veritical position than {the subtitle for Urban Migration}[0.387, 0.508, 0.695, 0.526]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 912 }, { "id": "LYEZDLQjbhbci3ev", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found within [0.025, 0.037, 0.114, 0.429]." }, { "from": "gpt", "value": "It contains {the y-axis label 'Migration Trend'}<[0.025, 0.160, 0.048, 0.268]> and several {y-axis tick labels}<[0.058, 0.385, 0.114, 0.399]>." } ], "id_raw": 912 }, { "id": "gBzoLgNAOOfhXO1f", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the chart elements within the region [0.114, 0.385, 0.175, 0.425]?" }, { "from": "gpt", "value": "Several {x-axis tick values} lie here, including '0' and '5' at {coordinates}[0.114, 0.395, 0.129, 0.409] and [0.282, 0.395, 0.297, 0.409] respectively." } ], "id_raw": 912 }, { "id": "cNwss9aJzTDvqBdJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_12.png", "conversations": [ { "from": "human", "value": "\nWhat can be described in the area [0.122, 0.301, 0.167, 0.409]?" }, { "from": "gpt", "value": "This area includes the start of two migration lines {Rural Migration}[0.122, 0.305, 0.167, 0.382] and {Urban Migration}[0.122, 0.539, 0.167, 0.617] starting points, with the x-axes just below them." } ], "id_raw": 912 }, { "id": "gpIzM4RG47Z1qLGC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart found within the coordinates [0.345, 0.508, 0.696, 0.526]?" }, { "from": "gpt", "value": "\"Progression of Innovation\"" } ], "id_raw": 913 }, { "id": "e9jre7YbayxN8wuO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_13.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at [0.688, 0.422, 0.949, 0.447]?" }, { "from": "gpt", "value": "A legend with the label \"Self-confidence\"" } ], "id_raw": 913 }, { "id": "P1jHEzPQ7N8fPyrH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the axis label at [0.025, 0.172, 0.048, 0.319] represent?" }, { "from": "gpt", "value": "Self-confidence Level" } ], "id_raw": 913 }, { "id": "LcOGe95KPVb10UTP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_13.png", "conversations": [ { "from": "human", "value": "\nIs {the point corresponding to time 10.0}[0.468, 0.401] above or below {the point corresponding to time 20.0}[0.784, 0.329] on the Self-confidence line?" }, { "from": "gpt", "value": "Above" } ], "id_raw": 913 }, { "id": "z0fVXbrRI7eq4vnZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the label for '2.0' on the y-axis}[0.058, 0.330, 0.072, 0.344] appear above or below {the label for '4.0' on the y-axis}[0.058, 0.210, 0.072, 0.224] for the Innovation chart?" }, { "from": "gpt", "value": "Above" } ], "id_raw": 913 }, { "id": "pBz9YVmGfpGOwa69", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_13.png", "conversations": [ { "from": "human", "value": "\nIs the {line trend labeled 'Self-confidence'}[0.080, 0.299, 0.925, 0.436] positioned higher than {the line trend labeled 'Innovation'}[0.080, 0.542, 0.925, 0.798] at the point labeled '0.0' (coordinate check)?" }, { "from": "gpt", "value": "Below" } ], "id_raw": 913 }, { "id": "8t3t9tPUyvVeXQcc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_13.png", "conversations": [ { "from": "human", "value": "\nCould you describe the bottom left section of the chart area coordinated in [0.080, 0.037, 0.230, 0.500]?" }, { "from": "gpt", "value": "This area features the y-axis of the Self-confidence chart with labels ranging from '0' to '7', each represented individually, starting from {label '0'}[0.058, 0.449, 0.072, 0.463] to {label '7'}[0.058, 0.032, 0.072, 0.046]." } ], "id_raw": 913 }, { "id": "MfyhJxJ2yOjWKqF2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_13.png", "conversations": [ { "from": "human", "value": "\nWhat insights can we gather from the middle region [0.200, 0.400, 0.600, 0.800]?" }, { "from": "gpt", "value": "This area encompasses multiple trends shown by lines marking the growth of Self-confidence and Innovation. Points labeled '5.0' and '10.0' for Self-confidence are distinctly visible at {coordinates}[0.256, 0.433] and {coordinates}[0.432, 0.406], respectively." } ], "id_raw": 913 }, { "id": "F1iRvMUC0NDlsUwM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the components within the coordinates [0.446, 0.479, 0.595, 0.493]." }, { "from": "gpt", "value": "This narrow region holds the x-axis label for the upper chart about the Time (Hours) scale, indicating progression in hours from start to end." } ], "id_raw": 913 }, { "id": "pGzOweRY59Kmcu3g", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the {subplot title}[0.212, 0.508, 0.858, 0.526] in the chart?" }, { "from": "gpt", "value": "Clean Technology Development Over 24 Hours" } ], "id_raw": 914 }, { "id": "QCzVLsAbtEZTodQo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_14.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the tick mark corresponding to '100' on the y-axis of the first subplot?" }, { "from": "gpt", "value": "[0.110, 0.335]" } ], "id_raw": 914 }, { "id": "a1Apo3qOcKqpWWby", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing 'New Energy Development'}[0.110, 0.317, 0.926, 0.365] in the first subplot?" }, { "from": "gpt", "value": "Green - #008000" } ], "id_raw": 914 }, { "id": "e4OGdAjvXawlvPBF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_14.png", "conversations": [ { "from": "human", "value": "\nIn the 'New Energy Development' data, is the level at {hour 10}[0.450, 0.335] higher or lower than the level at {hour 0}[0.110, 0.335]?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 914 }, { "id": "oEHWCVSWDHWymBWG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_14.png", "conversations": [ { "from": "human", "value": "\nIn 'Clean Technology Development', is the development level at {hour 24}[0.926, 0.554] higher or lower than the level at {hour 0}[0.110, 0.708]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 914 }, { "id": "x9xzQIP9jEbX8sRq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_14.png", "conversations": [ { "from": "human", "value": "\nAt {hour 12}[0.518, 0.333] in 'New Energy Development', is the development level higher or lower than that at {hour 12}[0.518, 0.637] in 'Clean Technology Development'?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 914 }, { "id": "Cyr0n4YvhIdCnvtU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the bounding box [0.110, 0.530, 0.960, 0.947]." }, { "from": "gpt", "value": "This region contains the second subplot of the line chart, titled \"Clean Technology Development Over 24 Hours\". It depicts changes in clean technology development over 24 hours using a dashed line colored blue. The x-axis represents time in hours, ranging from 0 to 25 hours, while the y-axis represents the development level, ranging from 0 to 350. Both axes are labeled and have tick marks indicating varying levels and times." } ], "id_raw": 914 }, { "id": "x9FSwZtuZXQwICwX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within this bounding box [0.110, 0.037, 0.960, 0.454]." }, { "from": "gpt", "value": "This area contains the first subplot of the line chart titled \"New Energy Development Over 24 Hours\". The plot displays the variation in new energy development over a period of 24 hours using a solid green line. The x-axis and y-axis denote time (Hours) and Development Level respectively. Both the axes are suitably labeled and contain appropriate tick marks signifying different levels of time and development." } ], "id_raw": 914 }, { "id": "AdWJ1Svq1TXqugSM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_14.png", "conversations": [ { "from": "human", "value": "\nWhat contents can be found inside the bounding box [0.058, 0.032, 0.102, 0.046]?" }, { "from": "gpt", "value": "Within this area, there is a y-axis tick label indicating '350' for the second subplot." } ], "id_raw": 914 }, { "id": "q3GUTijudvinhMQO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_15.png", "conversations": [ { "from": "human", "value": "\nWhat's the label associated with the y-axis at coordinates [0.025, 0.653, 0.048, 0.798]?" }, { "from": "gpt", "value": "Investment Value ($)." } ], "id_raw": 915 }, { "id": "8a6XrIyljlAOuBTN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_15.png", "conversations": [ { "from": "human", "value": "\nIn the subplot titled \"Standard Investment Strategy\", what coordinates are associated with the line representing the Standard Strategy?" }, { "from": "gpt", "value": "[0.124, 0.326, 0.927, 0.429]." } ], "id_raw": 915 }, { "id": "oCxSKPhw4StvXYFo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label located at [0.468, 0.946, 0.617, 0.960] in 'Innovative Investment Strategy' sub-plot?" }, { "from": "gpt", "value": "Time (Hours)." } ], "id_raw": 915 }, { "id": "MxqP3MXDtmvuwihT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_15.png", "conversations": [ { "from": "human", "value": "\nComparing {the point 4}[0.258, 0.828] and {the point 5}[0.291, 0.796] for 'Innovative Strategy', is the investment value increasing or decreasing over time?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 915 }, { "id": "ix1gUFXxYTEp66aN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_15.png", "conversations": [ { "from": "human", "value": "\nIs {the first point}[0.124, 0.922] of 'Innovative Strategy' line higher or lower than {the last point}[0.927, 0.655]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 915 }, { "id": "7hQz41Iub9qj5REB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_15.png", "conversations": [ { "from": "human", "value": "\nIs the {15th point of 'Innovative Strategy'}[0.626, 0.702] higher or lower than the {17th point of 'Innovative Strategy'}[0.693, 0.640]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 915 }, { "id": "UULiFZJiWOXqDH1S", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_15.png", "conversations": [ { "from": "human", "value": "\nCan you elaborate on the contents within the region [0.124, 0.530, 0.960, 0.922]?" }, { "from": "gpt", "value": "This region contains the line graph for the \"Innovative Strategy\", the x and y axis, as well as their labels and tick labels. Also, the subtitle, 'Innovative Investment Strategy', is located in this region." } ], "id_raw": 915 }, { "id": "G3DabjPtLo9ia208", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within the coordinates [0.323, 0.508, 0.762, 0.526]." }, { "from": "gpt", "value": "This area contains the subtitle for the 'Innovative Investment Strategy' subplot." } ], "id_raw": 915 }, { "id": "zb8rlapDtFehE6Ba", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_15.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within the region [0.394, 0.968, 0.698, 0.993]?" }, { "from": "gpt", "value": "It contains the subplot legend, specifically, the color handle and text for the 'Innovative Strategy'." } ], "id_raw": 915 }, { "id": "PkGgAKCcQXHs0I53", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the information represented by the line plotted within {coordinates}[0.124, 0.121, 0.927, 0.454] in the first subplot?" }, { "from": "gpt", "value": "This line represents the Global Trade Volume over time." } ], "id_raw": 916 }, { "id": "NqdopSWcfpxcPEYW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_16.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted by the line within {coordinates}[0.124, 0.613, 0.927, 0.947] in the second subplot?" }, { "from": "gpt", "value": "This line illustrates the Supply Chain Efficiency over time." } ], "id_raw": 916 }, { "id": "yjDGl5T54CXMvMo2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_16.png", "conversations": [ { "from": "human", "value": "\nWhat text can be found at the location {coordinates}[0.324, 0.015, 0.761, 0.033]?" }, { "from": "gpt", "value": "It's the subtitle of the first subplot: \"Global Trade Volume Over Time\"." } ], "id_raw": 916 }, { "id": "4mHRv8Onmqg3WYwx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_16.png", "conversations": [ { "from": "human", "value": "\nIs the value of Supply Chain Efficiency at {coordinates}[0.726, 0.613] higher or lower than its value at {coordinates}[0.659, 0.634]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 916 }, { "id": "mXjIEhEZGrEWWW64", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_16.png", "conversations": [ { "from": "human", "value": "\nComparing the two points on the Global Trade Volume line at {coordinates}[0.425, 0.121] and {coordinates}[0.927, 0.121], which point reflects a higher trade volume?" }, { "from": "gpt", "value": "The point at [0.425, 0.121]." } ], "id_raw": 916 }, { "id": "aRJphUnaZSkHmobI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_16.png", "conversations": [ { "from": "human", "value": "\nIs the Supply Chain Efficiency at the first point {coordinates}[0.124, 0.947] higher or lower than its value at {coordinates}[0.927, 0.655]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 916 }, { "id": "zH1RToiy5556su2y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the region from {coordinates}[0.854, 0.469, 0.999, 0.492]?" }, { "from": "gpt", "value": "This region contains the x-axis label \"Time (Months)\" for the first subplot." } ], "id_raw": 916 }, { "id": "0OFNdVxMoPHm82g8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_16.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region from {coordinates}[0.594, 0.537, 0.949, 0.562]?" }, { "from": "gpt", "value": "This area includes the legend for the second subplot. It describes the line color for \"Supply Chain Efficiency\"." } ], "id_raw": 916 }, { "id": "UlGXSZv1165TUgDN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_16.png", "conversations": [ { "from": "human", "value": "\nCan you detail what is shown in the region {coordinates}[0.304, 0.508, 0.781, 0.526]?" }, { "from": "gpt", "value": "The region contains the subtitle of the second subplot: \"Supply Chain Efficiency Over Time\"." } ], "id_raw": 916 }, { "id": "eSjvGgToSInmz2lP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the object in [0.025, 0.194, 0.048, 0.273] represent?" }, { "from": "gpt", "value": "Y-axis label for the chart 'Standard Energy Use'." } ], "id_raw": 917 }, { "id": "oGgXEgCRJbLaJZUa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_17.png", "conversations": [ { "from": "human", "value": "\nCan you tell me about the object within these coordinates [0.122, 0.037, 0.960, 0.429]?" }, { "from": "gpt", "value": "This is the chart area for 'Standard Energy Use'." } ], "id_raw": 917 }, { "id": "KlW7OzDBaVeyX65J", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_17.png", "conversations": [ { "from": "human", "value": "\nWhat does {the line}[0.122, 0.549, 0.927, 0.791] in subplot_1 represent?" }, { "from": "gpt", "value": "The 'Innovative Energy Use' data line." } ], "id_raw": 917 }, { "id": "tKVAuqgu5btlB9V5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_17.png", "conversations": [ { "from": "human", "value": "\nBetween {point '2' at [0.189, 0.364]} and {point '9' at [0.424, 0.201]}, which point from the 'Standard Energy Use' line has a lower value?" }, { "from": "gpt", "value": "Point '9'." } ], "id_raw": 917 }, { "id": "tnZxE3mvv175umoL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_17.png", "conversations": [ { "from": "human", "value": "\n{The point at [0.726, 0.634]} and {the point at [0.793, 0.608]}, which one on the 'Innovative Energy Use' line has the higher energy use?" }, { "from": "gpt", "value": "The point at [0.726, 0.634]." } ], "id_raw": 917 }, { "id": "z8yjW0ELdIaTX1nk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_17.png", "conversations": [ { "from": "human", "value": "\nIs {the label 'Standard Energy Use'}[0.451, 0.481, 0.696, 0.495] in subplot_0 above or below {the label 'Innovative Energy Use'}[0.443, 0.974, 0.704, 0.988]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 917 }, { "id": "tXv42l1mK4brT35j", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_17.png", "conversations": [ { "from": "human", "value": "\nWhat can you describe about the layout within the coordinates [0.369, 0.968, 0.713, 0.993]?" }, { "from": "gpt", "value": "This is the legend area for the 'Innovative Energy Use' chart, containing a colored marker and the corresponding label for 'Innovative Energy Use'." } ], "id_raw": 917 }, { "id": "W8G6H0VfetR6mpIH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the region of [0.122, 0.549, 0.927, 0.791]?" }, { "from": "gpt", "value": "This area contains the data line for 'Innovative Energy Use', with several data points representing various energy use values at different times." } ], "id_raw": 917 }, { "id": "FJWJs6oTwQy2uK85", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the area bound by [0.025, 0.687, 0.048, 0.765] contain?" }, { "from": "gpt", "value": "This region contains a Y-axis label, denoting 'Energy Use' for the 'Innovative Energy Use' chart." } ], "id_raw": 917 }, { "id": "jmKNn1iWBFP3djjs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_18.png", "conversations": [ { "from": "human", "value": "\nWhat kind of label is situated at these coordinates [0.025, 0.678, 0.048, 0.799]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 918 }, { "id": "1BIqAAuh5Q5NQaDn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_18.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the line representing 'Market Share Traditional'?" }, { "from": "gpt", "value": "[0.095, 0.271, 0.946, 0.379]." } ], "id_raw": 918 }, { "id": "7khC5BkDczt2lpD7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the x-ticks located at [0.633, 0.951, 0.692, 0.965]?" }, { "from": "gpt", "value": "2016." } ], "id_raw": 918 }, { "id": "IHmX6dmV9E7wD4YT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_18.png", "conversations": [ { "from": "human", "value": "\nWhich line, {Market Share Traditional}[0.095, 0.271, 0.946, 0.379] or {Market Share Innovative}[0.095, 0.572, 0.946, 0.863], shows a more significant decline in the period represented in the graph?" }, { "from": "gpt", "value": "Market Share Traditional." } ], "id_raw": 918 }, { "id": "bf6R4M5QQuAxXTfA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_18.png", "conversations": [ { "from": "human", "value": "\nWhich type of Market Share, Traditional or Innovative, marks more than 30% at the end of the period as per the data represented by the line {Market Share Innovative}[0.095, 0.572, 0.946, 0.863]?" }, { "from": "gpt", "value": "Market Share Innovative." } ], "id_raw": 918 }, { "id": "zag9SGTQOmOkmoxH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_18.png", "conversations": [ { "from": "human", "value": "\nIs the {Market Share Traditional}[0.095, 0.271, 0.946, 0.379] point at 2010 closer to 20% or 25%?" }, { "from": "gpt", "value": "20%." } ], "id_raw": 918 }, { "id": "6XPHrHMOr3Wilo7d", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within this area [0.568, 0.537, 0.934, 0.562]?" }, { "from": "gpt", "value": "It contains the legend of the graph which includes the item {Market Share Innovative}[0.642, 0.542, 0.925, 0.556]. The legend box has its color #eaeaf2 and arranges items in one column. The color of the Market Share Innovative is #228b22." } ], "id_raw": 918 }, { "id": "um4yKKUe6DJRp2eB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.566, 0.951, 0.975, 0.965]." }, { "from": "gpt", "value": "It contains the label of the last x-axis tick which represents the year '2020'." } ], "id_raw": 918 }, { "id": "4gqfVUfcT5t1zxPi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_18.png", "conversations": [ { "from": "human", "value": "\nWhich objects exist in the region from [0.490, 0.971, 0.551, 0.985]?" }, { "from": "gpt", "value": "It contains the x-axis label \"Years\"." } ], "id_raw": 918 }, { "id": "NfJaG5vvr19q7GDW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {line plotted} for 'Standard Sales'[0.066, 0.294, 0.940, 0.385]?" }, { "from": "gpt", "value": "#1e90ff." } ], "id_raw": 919 }, { "id": "AmRi58xZBpKl399g", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the upper subplot?" }, { "from": "gpt", "value": "Sales (units)." } ], "id_raw": 919 }, { "id": "q2TW3yDOf3Y6xEai", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_46_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the {x-axis tick label} at [0.785, 0.951, 0.803, 0.965] in the lower subplot?" }, { "from": "gpt", "value": "20." } ], "id_raw": 919 }, { "id": "dCwiqrjLKIssxrDB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_19.png", "conversations": [ { "from": "human", "value": "\nIs the {point for Standard Sales}[0.139, 0.374] above or below the {point for Standard Sales}[0.175, 0.367] in the time series?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 919 }, { "id": "mUlgdBQgIM6Yop5e", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_19.png", "conversations": [ { "from": "human", "value": "\nAre the 'Standard Sales' above or below 300 units at {the 12th time point}[0.503, 0.336]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 919 }, { "id": "z4YGnZtd6Vlhq4QT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_46_19.png", "conversations": [ { "from": "human", "value": "\nAre 'Innovative Sales' increasing or decreasing between {the 20th and 21st time points}[0.794, 0.637, 0.831, 0.627]?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 919 }, { "id": "CQEVZ2n3xwtHWSos", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.065, 0.294, 0.940, 0.385] in the first subplot." }, { "from": "gpt", "value": "This area includes the line plot for 'Standard Sales'. The line represents sales over time, ranging from 200 to 330 units. It is colored in '#1e90ff', indicating the sales of standard vehicles." } ], "id_raw": 919 }, { "id": "lRe7uht1jQshEUde", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.316, 0.508, 0.725, 0.526]." }, { "from": "gpt", "value": "This area contains the subtitle \"Automotive Market Research - Innovative Vehicle\". The text is presented in black ('.15' color), and is not rotated." } ], "id_raw": 919 }, { "id": "p3IROfJYUJyVwqEy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_46_19.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.785, 0.951, 0.976, 0.965]?" }, { "from": "gpt", "value": "There is 1 object. The x-axis tick label '20' is located at {coordinates}[0.785, 0.951, 0.803, 0.965]." } ], "id_raw": 919 }, { "id": "n6SJRQoK10qKA2lF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.043, 0.766, 0.054, 0.789] in the chart?" }, { "from": "gpt", "value": "It's a y-axis tick label in subplot_0." } ], "id_raw": 920 }, { "id": "ptmw6u65ASufUo05", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_0.png", "conversations": [ { "from": "human", "value": "\nWhat text belongs to the x-axis label located at [0.473, 0.952, 0.510, 0.975]?" }, { "from": "gpt", "value": "The text is \"Year\"." } ], "id_raw": 920 }, { "id": "vr3O8E9gGRg1AzRz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_0.png", "conversations": [ { "from": "human", "value": "\nWhat is present at the position [0.756, 0.0.919, 0.816, 0.942] in this chart?" }, { "from": "gpt", "value": "It is an x-axis tick label representing the year \"2017.5\"." } ], "id_raw": 920 }, { "id": "bv8a3zQfXScbowC0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_0.png", "conversations": [ { "from": "human", "value": "\nLooking at the points for the years 2000, is the point for 'Rural Development' higher or lower than that of 'Urban Development'?" }, { "from": "gpt", "value": "'Urban Development' has a higher value. The coordinates of the 'Rural Development' point are [0.100, 0.871] and the coordinates for the 'Urban Development' point are [0.100, 0.815]." } ], "id_raw": 920 }, { "id": "xldDiLRNfYwYxQ9M", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_0.png", "conversations": [ { "from": "human", "value": "\nIn the year 2002, did 'Rural Development' increase or decrease compared to the previous year?" }, { "from": "gpt", "value": "It increased. The coordinates of the 'Rural Development' points for 2001 and 2002 are [0.139, 0.852] and [0.178, 0.813] respectively." } ], "id_raw": 920 }, { "id": "x4Wi5y2oqgjS80d5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_0.png", "conversations": [ { "from": "human", "value": "\nBy comparing the 'Urban Development' values, which is greater between the years 2008 and 2009?" }, { "from": "gpt", "value": "The year 2008 is greater. It's coordinate is [0.413, 0.537], while the coordinates of 2009 is [0.453, 0.565]." } ], "id_raw": 920 }, { "id": "M4MSbTwoO0qcG2xy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the region with coordinates [0.756, 0.919, 0.816, 0.942]?" }, { "from": "gpt", "value": "This area contains an x-axis tick label that represents the year \"2017.5\"." } ], "id_raw": 920 }, { "id": "caUMEMVkAdWbaCdU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the significant features found within the region of [0.964, 0.351, 0.981, 0.585]." }, { "from": "gpt", "value": "The region contains the y-axis label for the subplot_1 section of the chart, denoted as 'Urban Development'." } ], "id_raw": 920 }, { "id": "iXf7p3RXRe2x9MgY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area defined by [0.492, 0.919, 0.522, 0.942]?" }, { "from": "gpt", "value": "A label that indicates the year \"2010.0\" on the x-axis is located in this area." } ], "id_raw": 920 }, { "id": "vrgMJAQwIdchuYdY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.157, 0.086, 0.233, 0.110] in the chart?" }, { "from": "gpt", "value": "Revenue legend label." } ], "id_raw": 921 }, { "id": "H0fAQQ3bA7mMtDiD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_1.png", "conversations": [ { "from": "human", "value": "\nWhat does {the object}[0.283, 0.862, 0.385, 0.862] represent?" }, { "from": "gpt", "value": "Cost marker." } ], "id_raw": 921 }, { "id": "Y6RLf4BkFI8CAfXw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label}[0.087, 0.714, 0.087, 0.725] indicating?" }, { "from": "gpt", "value": "5000." } ], "id_raw": 921 }, { "id": "3VXcxlmLexebSII6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point}[0.870, 0.911] for the Revenue line higher or lower than {the point}[0.870, 0.911] for the Cost line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 921 }, { "id": "kJAgHWrIn45WLCuw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_1.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher at {point 20}[0.615, 0.287], Revenue or Cost?" }, { "from": "gpt", "value": "Revenue." } ], "id_raw": 921 }, { "id": "CgODCzYUmQZ0yBks", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_1.png", "conversations": [ { "from": "human", "value": "\nIs {the revenue point at 15}[0.487, 0.330] higher than {the cost point at 15}[0.487, 0.875] on the chart?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 921 }, { "id": "yDjeQDYvzCUOBVQR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.100, 0.911, 0.870, 0.939]." }, { "from": "gpt", "value": "The x-axis with labels ranging from -5 to 35 at intervals of 5, styled in dark text." } ], "id_raw": 921 }, { "id": "tW217Azs5wOqPnYu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.093, 0.066, 0.907, 0.266]." }, { "from": "gpt", "value": "This part of the chart includes the lower values of the revenue line starting from approximately 4000 and includes points 1 to 10, displayed as blue circles." } ], "id_raw": 921 }, { "id": "lmLpKAHnZgVUeFSl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.040, 0.790, 0.200, 0.925] contain?" }, { "from": "gpt", "value": "It contains the y-axis labels for Revenue from 4500 to 8000, positioned vertically." } ], "id_raw": 921 }, { "id": "V7emN5d1UCmtKGBA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_2.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinate [0.229, 0.647] in the chart?" }, { "from": "gpt", "value": "A point on the Cloud Computing line." } ], "id_raw": 922 }, { "id": "RX1pxPi6o2Z7Os43", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.071, 0.957] represent in the graph?" }, { "from": "gpt", "value": "Y-axis tick label '0.9'." } ], "id_raw": 922 }, { "id": "ezHo2IDydtPWJazi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_2.png", "conversations": [ { "from": "human", "value": "\nWhat feature is at [0.774, 0.875] in the data visualization?" }, { "from": "gpt", "value": "It is empty space." } ], "id_raw": 922 }, { "id": "qFlMUfiIYyNQIPNe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point}[0.115,0.873] higher or lower than {the point}[0.144,0.801] on the Artificial Intelligence line?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 922 }, { "id": "0AsJthYGWufe7qlT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the point at [0.457,0.209]} belong to the Artificial Intelligence trend or the Cloud Computing trend?" }, { "from": "gpt", "value": "Artificial Intelligence." } ], "id_raw": 922 }, { "id": "O3p6Zex99tJLM7jm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point at [0.799, 0.698]} situated above or below {the point at [0.856, 0.739]} in the trend line for Cloud Computing?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 922 }, { "id": "1PBfQRsZMYwnhduB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the area bounded by [0.000, 0.850, 0.500, 0.950]." }, { "from": "gpt", "value": "The region contains the chart's x-axis, labeled as 'Weeks' in a light gray color, and the right section is primarily empty except for grid lines." } ], "id_raw": 922 }, { "id": "sAq1irP768sIzbGv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe what's within the region [0.350, 0.000, 0.650, 1.000]." }, { "from": "gpt", "value": "The central area of the chart contains many intersecting lines and points from the data trends Artificial Intelligence and Cloud Computing, marked by circles and x-markers respectively, with varying y-values." } ], "id_raw": 922 }, { "id": "RdfNlXnKKzLhYINT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_2.png", "conversations": [ { "from": "human", "value": "\nCould you describe the visual elements found between the coordinates [0.010, 0.300, 0.980, 0.320]?" }, { "from": "gpt", "value": "This narrow horizontal band mainly features lines from both Artificial Intelligence and Cloud Computing trends, crossing this region at varied elevations along the chart." } ], "id_raw": 922 }, { "id": "BeL7D7ESZKqClPam", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the line for 'Artificial Intelligence'}[0.110, 0.226, 0.890, 0.810] in the chart?" }, { "from": "gpt", "value": "It's a line plot showing the trend of Artificial Intelligence." } ], "id_raw": 923 }, { "id": "HMG1cq5RhOVX8AiX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third point in the line for 'Virtual Reality'?" }, { "from": "gpt", "value": "[0.283, 0.632]" } ], "id_raw": 923 }, { "id": "8giB66wKdNKSXz60", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the title of the chart}[0.200, 0.025, 0.800, 0.058]?" }, { "from": "gpt", "value": "Emerging Technologies and Trends Outlook." } ], "id_raw": 923 }, { "id": "uShx1oANm0DDRUpf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_3.png", "conversations": [ { "from": "human", "value": "\nBy the end of 2020, was Artificial Intelligence more or less popular than Virtual Reality based on {their points at 2020}[0.890, 0.226], [0.890, 0.810]?" }, { "from": "gpt", "value": "More Popular." } ], "id_raw": 923 }, { "id": "ecsOErYE3DXMyR21", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_3.png", "conversations": [ { "from": "human", "value": "\nWas {the popularity of Artificial Intelligence}[0.110, 0.226, 0.890, 0.810] higher or lower in 2015 than {the popularity of Virtual Reality}[0.110, 0.615, 0.890, 0.810]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 923 }, { "id": "sxDHjAboE87Jg6fo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_3.png", "conversations": [ { "from": "human", "value": "\nCompare {the second point in Artificial Intelligence line}[0.197, 0.784] with {the first point in Virtual Reality line}[0.110, 0.717], which was higher?" }, { "from": "gpt", "value": "Artificial Intelligence." } ], "id_raw": 923 }, { "id": "q7GGqS3npgLUNx14", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_3.png", "conversations": [ { "from": "human", "value": "\nDescribe what all elements are in the region [0.080, 0.077, 0.321, 0.119]?" }, { "from": "gpt", "value": "This area contains the legend for Artificial Intelligence which is represented by a blue color and a circle marker." } ], "id_raw": 923 }, { "id": "KlyJIHVu6kbFF5r1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_3.png", "conversations": [ { "from": "human", "value": "\nGive a brief detail about the items present in the region [0.735, 0.077, 0.920, 0.119]?" }, { "from": "gpt", "value": "These coordinates contain the legend for Virtual Reality which is denoted by a red color and a cross marker." } ], "id_raw": 923 }, { "id": "XHBbYJBRzHXGHm6N", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_3.png", "conversations": [ { "from": "human", "value": "\nWhat's present in the region [0.370, 0.604, 0.457, 0.615] and [0.543, 0.497, 0.630, 0.421]?" }, { "from": "gpt", "value": "The region contains 2 line points for Artificial Intelligence for the years 2014 and 2016 respectively." } ], "id_raw": 923 }, { "id": "6yxeR6O7nIwcML3O", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object in the chart at the coordinates [0.964, 0.330, 0.981, 0.646]?" }, { "from": "gpt", "value": "It's the Y-axis label for the second subplot, named 'Data Science Development'." } ], "id_raw": 924 }, { "id": "Zp2jhe3hWnZOaS6I", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_4.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the 4th point on the Cloud Computing line?" }, { "from": "gpt", "value": "[0.494, 0.386]" } ], "id_raw": 924 }, { "id": "YpfjazIXY78JmouV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.346, 0.919, 0.390, 0.942]?" }, { "from": "gpt", "value": "2017." } ], "id_raw": 924 }, { "id": "dagDGJ2s8cbXZNzL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_4.png", "conversations": [ { "from": "human", "value": "\nComparing the 3rd point for Data Science [0.368, 0.392] with the 3rd point for Cloud Computing [0.368, 0.590], which point is higher?" }, { "from": "gpt", "value": "The Cloud Computing point." } ], "id_raw": 924 }, { "id": "FJEQekljQcohIUm3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_4.png", "conversations": [ { "from": "human", "value": "\nIs the first Data Science development point [0.115, 0.873] higher or lower than the first Cloud Computing development point [0.115, 0.104]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 924 }, { "id": "Nj5ZwAC8bB7YoHcy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_4.png", "conversations": [ { "from": "human", "value": "\nLooking at the 5th points for both the Cloud Computing line [0.621, 0.347] and the Data Science line [0.621, 0.200], which line has a higher value?" }, { "from": "gpt", "value": "Cloud Computing." } ], "id_raw": 924 }, { "id": "vmTFoWj3WLMeTrf1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.726, 0.077, 0.903, 0.119] on the chart?" }, { "from": "gpt", "value": "This area contains the legend for the plot \"Data Science\" with the handle colored in red and associated text. The legend item is specifically located at {coordinates}[0.782, 0.086, 0.896, 0.110]." } ], "id_raw": 924 }, { "id": "XPrEWlcoTBYOUkoD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.043, 0.959, 0.956, 0.983]." }, { "from": "gpt", "value": "It contains the label '1.00' on the y-axis for the 'Data Science Development' line, specifically located at {coordinates}[0.918, 0.959, 0.956, 0.983]." } ], "id_raw": 924 }, { "id": "oNRZHywiQyeeKlwR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.077, 0.066, 0.912, 0.911] in the chart." }, { "from": "gpt", "value": "This region contains the main plot area for the chart. It envelops both the 'Cloud Computing' and 'Data Science' development trend lines along with their corresponding points, the x and y axes, and gridlines." } ], "id_raw": 924 }, { "id": "ZMvTGGYCHUfHMPyn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_5.png", "conversations": [ { "from": "human", "value": "\nCan you identify what the object {the line for Classification 'Recurrent Neural Network'}[0.720, 0.100] represents?" }, { "from": "gpt", "value": "Precision for Recurrent Neural Network." } ], "id_raw": 925 }, { "id": "3qRiEpj78B4CMyAn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the set of coordinates [0.964, 0.433, 0.981, 0.503] correspond to in the chart figure?" }, { "from": "gpt", "value": "The y-axis label for Recall." } ], "id_raw": 925 }, { "id": "1WnhV6BSA3EfkYsa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the {xlabel}[0.503, 1.000, 0.503, 1.000] for the subplot_1?" }, { "from": "gpt", "value": "It's blank, the xlabel doesn't contain any text." } ], "id_raw": 925 }, { "id": "8mH1y3MKnWT5H23A", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_5.png", "conversations": [ { "from": "human", "value": "\nIs {the Precision for 'Recurrent Neural Network'}[0.720, 0.100] higher or lower than {the recall for the same 'Recurrent Neural Network'}[0.720, 0.830]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 925 }, { "id": "EuUsB60mXCIxEb7P", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_5.png", "conversations": [ { "from": "human", "value": "\nCompared to {the point representing Precision for 'Support Vector Machine'}[0.865, 0.871], where does {the point representing the Recall for 'Support Vector Machine'}[0.865, 0.871] lie?" }, { "from": "gpt", "value": "At the same place." } ], "id_raw": 925 }, { "id": "bnl4XoT6xeJ1iK30", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_5.png", "conversations": [ { "from": "human", "value": "\nAre the Precision values for {the 'Decision Tree'}[0.431, 0.556] greater or less than its Recall values {in subplot_1}[0.431, 0.186]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 925 }, { "id": "PB77C8zNQSVYDmtz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_5.png", "conversations": [ { "from": "human", "value": "\nWhat can you describe about the area with coordinates [0.020, 0.919, 0.969, 0.942]?" }, { "from": "gpt", "value": "The area contains the x-axis labels: {Robotic Process Automation}[0.020, 0.919, 0.262, 0.942], {Neural Network}[0.218, 0.919, 0.354, 0.942], {Decision Tree}[0.373, 0.919, 0.488, 0.942], {Convolutional Neural Network}[0.445, 0.919, 0.706, 0.942], {Recurrent Neural Network}[0.608, 0.919, 0.833, 0.942] and {Support Vector Machine}[0.761, 0.919, 0.969, 0.942]." } ], "id_raw": 925 }, { "id": "w1MmhvjinqE0p1XQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_5.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region marked by the coordinates [0.907, -0.024, 0.957, -0.001]?" }, { "from": "gpt", "value": "It contains the y-axis tick label '0.900' for the factor Recall." } ], "id_raw": 925 }, { "id": "P4oaGrFlHh4YvKPV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_5.png", "conversations": [ { "from": "human", "value": "\nWhat is available in the region defined by [0.105, 0.025, 0.901, 0.911]?" }, { "from": "gpt", "value": "This region primarily contains the chart or plot area with all the data points, gridlines and labels for both Precision and Recall measurements." } ], "id_raw": 925 }, { "id": "DHfQ7A0N1BetnjJE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_6.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label} at [0.396, 0.296] on the 'Enrollment Rate' line represent?" }, { "from": "gpt", "value": "The 5th quarter." } ], "id_raw": 926 }, { "id": "dnEOkWoCvDxQDghE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_6.png", "conversations": [ { "from": "human", "value": "\nWhat's the annotation for {the point} at [0.258, 0.450] on the \"Enrollment Rate\" graph?" }, { "from": "gpt", "value": "60." } ], "id_raw": 926 }, { "id": "9ilYilNRN1k1YW3z", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the Y-axis label with coordinates [0.019, 0.395, 0.036, 0.581] represent?" }, { "from": "gpt", "value": "Enrollment Rate." } ], "id_raw": 926 }, { "id": "qUykI8gSeJPTYlDu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_6.png", "conversations": [ { "from": "human", "value": "\nDoes the {point on the Dropout Rate line} at [0.465, 0.826] occur earlier or later in the same year as the {point on the Enrollment Rate line} at [0.465, 0.281]?" }, { "from": "gpt", "value": "Same time." } ], "id_raw": 926 }, { "id": "xd0TsCIPksycdiGp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_6.png", "conversations": [ { "from": "human", "value": "\nIs the {3rd point on the Dropout Rate line}[0.258, 0.865] lower or higher than the {third point on the Enrollment Rate line}[0.258, 0.450]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 926 }, { "id": "ta1o59OACo63m9KT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_6.png", "conversations": [ { "from": "human", "value": "\nDoes the {Dropout Rate} at point [0.880, 0.788] surpass the {Enrollment Rate} at point [0.880, 0.173]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 926 }, { "id": "1jNdOYHRa5YwoRCs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_6.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region with coordinates [0.082, 0.066, 0.918, 0.911]?" }, { "from": "gpt", "value": "This region contains the axes for the plot, along with the line plot for the enrollment rate, sequentially dated from the first quarter to the twelfth quarter." } ], "id_raw": 926 }, { "id": "rJErZKAQ2yKIL5iC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the coordinate [0.120, 0.788, 0.880, 0.873]." }, { "from": "gpt", "value": "This region contains the Dropout Rate line chart, marked by points from the first quarter to the twelfth quarter." } ], "id_raw": 926 }, { "id": "txyLMSG5nRfuXWvU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the region with coordinates [0.731, 0.077, 0.909, 0.119] contain?" }, { "from": "gpt", "value": "This region contains a legend labeling 'Dropout Rate', represented by a red dashed line with 'x' markers." } ], "id_raw": 926 }, { "id": "N3uvXD38wqklFQs8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_7.png", "conversations": [ { "from": "human", "value": "\nWhere is {the y-axis label for 'Twitter'} located in the chart?" }, { "from": "gpt", "value": "[0.971, 0.462, 0.985, 0.520]" } ], "id_raw": 927 }, { "id": "ItVd4vZW380mVOTv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_7.png", "conversations": [ { "from": "human", "value": "\nWhat value does {the x-axis tick label}[0.798, 0.939, 0.816, 0.957] represent?" }, { "from": "gpt", "value": "25" } ], "id_raw": 927 }, { "id": "8Y9XRdIsunvL7A2q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of {the label '15' on the x-axis for Facebook}[0.506, 0.939, 0.523, 0.957]?" }, { "from": "gpt", "value": "[0.515, 0.933]" } ], "id_raw": 927 }, { "id": "8YrLzogmN3r2aUEe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_7.png", "conversations": [ { "from": "human", "value": "\nDoes {the point at week 10 for Twitter}[0.368, 0.288] have a higher value than {the point at week 10 for Facebook}[0.368, 0.602]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 927 }, { "id": "BpgRZNgVehDDU36C", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_7.png", "conversations": [ { "from": "human", "value": "\nIs {the first y-axis tick label for Twitter}[0.939, 0.926, 0.957, 0.944] positioned above {the first y-axis tick label for Facebook}[0.043, 0.926, 0.061, 0.944]?" }, { "from": "gpt", "value": "No" } ], "id_raw": 927 }, { "id": "HKTCltAoSO63CCIa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_7.png", "conversations": [ { "from": "human", "value": "\nWhich dataset, 'Facebook' or 'Twitter', starts higher at {week 1}[0.105, 0.756]?" }, { "from": "gpt", "value": "Twitter" } ], "id_raw": 927 }, { "id": "pNWtpg6ujnfH3LP6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.035, 0.816, 0.061, 0.833] contain in the chart?" }, { "from": "gpt", "value": "It includes the y-axis tick label '100' for Facebook, colored in blue, located at {coordinate}[0.035, 0.816, 0.061, 0.833]." } ], "id_raw": 927 }, { "id": "Y0XyjhOBM22DC5Pp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found within the bounding box [0.836, 0.070, 0.945, 0.140]." }, { "from": "gpt", "value": "This area contains several points from Twitter's data plot: Point 26 at {coordinates}[0.836, 0.076], Point 27 at {coordinates}[0.865, 0.071], and Point 28 at {coordinates}[0.895, 0.067]." } ], "id_raw": 927 }, { "id": "5j5vrgXxyOaVsgJ2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_7.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents of the rectangular region defined by [0.470, 0.949, 0.962, 0.984]?" }, { "from": "gpt", "value": "It spans several x-axis tick labels for both Facebook and Twitter. These labels are '15', '20', '25', '30' located at their respective {coordinates}[0.506, 0.939, 0.523, 0.957], [0.652, 0.939, 0.670, 0.957], [0.798, 0.939, 0.816, 0.957], and [0.944, 0.939, 0.962, 0.957]." } ], "id_raw": 927 }, { "id": "mHlP0OVXiGiXDf8C", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_8.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker does {the data point at week 17}[0.576, 0.363, 0.576, 0.363] use in the Social Media Engagement line?" }, { "from": "gpt", "value": "x." } ], "id_raw": 928 }, { "id": "L8ou64SFMlAlZhhU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the y-axis label}[0.019, 0.359, 0.036, 0.564]?" }, { "from": "gpt", "value": "tab:blue." } ], "id_raw": 928 }, { "id": "6XjlI7nUrw7UjvOI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_8.png", "conversations": [ { "from": "human", "value": "\nWhat style is {the line for website traffic}[0.141, 0.069, 0.875, 0.854] drawn with in the chart?" }, { "from": "gpt", "value": "Solid." } ], "id_raw": 928 }, { "id": "Yplpf7mfB2P2wc9k", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_8.png", "conversations": [ { "from": "human", "value": "\nIs {the point at week 5}[0.250, 0.407] higher than {the point at week 25}[0.794, 0.575] on the 'Website Traffic' line?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 928 }, { "id": "m33zj6jfaUjSHcMr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_8.png", "conversations": [ { "from": "human", "value": "\nBetween {the point at week 12}[0.440, 0.154] and {the point at week 16}[0.549, 0.491], which point is lower in terms of 'Social Media Engagement'?" }, { "from": "gpt", "value": "Week 16." } ], "id_raw": 928 }, { "id": "Sd6kj6w3ALR0klC6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_8.png", "conversations": [ { "from": "human", "value": "\nWho has a steeper increase between week 6 to week 7, Website Traffic or Social Media Engagement, when comparing {point 6}[0.277, 0.491] to {point 7}[0.304, 0.069]?" }, { "from": "gpt", "value": "Website Traffic." } ], "id_raw": 928 }, { "id": "n9FYoGsLzVlcYd7I", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the cluster [0.043, 0.143, 0.098, 0.508]." }, { "from": "gpt", "value": "This region encompasses multiple y-axis labels including '11000', '12000', '13000', '14000', '15000', and '16000' for the Website Traffic data, all displayed in blue." } ], "id_raw": 928 }, { "id": "MaSlFf5KUncVt9OZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_8.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.108, 0.903, 0.941, 0.931]?" }, { "from": "gpt", "value": "The x-axis has labels ranging from '0' to '30' marking different weeks, positioned across this horizontal span." } ], "id_raw": 928 }, { "id": "orHpyTcn2SiQuRqq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_8.png", "conversations": [ { "from": "human", "value": "\nCan you provide details on the area marked from [0.918, 0.058, 0.957, 0.969]?" }, { "from": "gpt", "value": "This region includes a range of y-axis labels for Social Media Engagement values from '12.5' to '17.0', presented in red." } ], "id_raw": 928 }, { "id": "ZJClpaz7ZO6mMaBA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_9.png", "conversations": [ { "from": "human", "value": "\nWhat is being represented by the plot line with coordinates [0.113, 0.244, 0.887, 0.870] in the chart?" }, { "from": "gpt", "value": "Export Values." } ], "id_raw": 929 }, { "id": "Q3xUBE1l876kAZFH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_9.png", "conversations": [ { "from": "human", "value": "\nWhich year corresponds to the x-axis tick label at [0.358, 0.333]?" }, { "from": "gpt", "value": "2006." } ], "id_raw": 929 }, { "id": "oRr6WPxwRozVNoN3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label at coordinates [0.015, 0.408, 0.029, 0.545] indicate?" }, { "from": "gpt", "value": "Import Values." } ], "id_raw": 929 }, { "id": "4eOhfWuMqHZBkYQt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_9.png", "conversations": [ { "from": "human", "value": "\nIs the the Import Values at year {2005}[0.317, 0.298] greater than Export Values at the same year {2005}[0.317, 0.346]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 929 }, { "id": "Ck8obrvzwj8Io0As", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_9.png", "conversations": [ { "from": "human", "value": "\nAt {2019}[0.887, 0.870], is the Export Value more than the Import Value at {2019}[0.887, 0.816]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 929 }, { "id": "NitI127bzbRTLJ1C", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_9.png", "conversations": [ { "from": "human", "value": "\nIs there a trend of Import Values decreasing over time from the start {2000}[0.113, 0.208] to the end {2019}[0.887, 0.816]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 929 }, { "id": "nu1FzB2G42BJVzq3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content found between coordinates [0.075, 0.029, 0.925, 0.924]?" }, { "from": "gpt", "value": "This area consists of a line chart detailing Import and Export Values over time. It includes x-axis and y-axis labels, tick labels for specific years and values, and two lines each representing import and export values over the years from 2000 to 2019." } ], "id_raw": 929 }, { "id": "24qmnsLyZNRUn7ML", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_9.png", "conversations": [ { "from": "human", "value": "\nWhat's within the region marked by [0.823, 0.039, 0.918, 0.075]?" }, { "from": "gpt", "value": "It is the legend for the line representing Export with color indication." } ], "id_raw": 929 }, { "id": "R8biAgiTV0zFmGFQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located in the region with coordinates [0.035, 0.916, 0.070, 0.936]?" }, { "from": "gpt", "value": "The y-tick label with value '600' is located in this area." } ], "id_raw": 929 }, { "id": "wrdx51MmES3rPppA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_10.png", "conversations": [ { "from": "human", "value": "\nWhat is shown by {the glyphs located at}[0.065, 0.090, 0.101, 0.104]?" }, { "from": "gpt", "value": "Internet of Things data series marker." } ], "id_raw": 930 }, { "id": "CRyCRdvHwvNz8Dex", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_10.png", "conversations": [ { "from": "human", "value": "\nWhat does {the object at}[0.015, 0.471, 0.029, 0.506] represent on the graph?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 930 }, { "id": "ZlTyBpOAVR5aagnK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_10.png", "conversations": [ { "from": "human", "value": "\nWhat details {the area}[0.073, 0.919, 0.121, 0.942] provides about the chart?" }, { "from": "gpt", "value": "X-axis label for the year 2020." } ], "id_raw": 930 }, { "id": "ECHAEsRRHOUT7Gnv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_10.png", "conversations": [ { "from": "human", "value": "\nIs {the point at}[0.255, 0.630] higher or lower than {the point at}[0.412, 0.375] on the graph?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 930 }, { "id": "IeN9tFCJdfrr64To", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the point for the year 2025}[0.295, 0.589] have a greater value in IoT or AR?" }, { "from": "gpt", "value": "IoT." } ], "id_raw": 930 }, { "id": "4IsA1LNTpeqeLBwb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_10.png", "conversations": [ { "from": "human", "value": "\nWhich is earlier, {the x-coordinate}[0.354, 0.911] or {the x-coordinate}[0.493, 0.911]?" }, { "from": "gpt", "value": "The x-coordinate at [0.354, 0.911] is earlier." } ], "id_raw": 930 }, { "id": "7Ng1hvGTIYAjkEfx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_10.png", "conversations": [ { "from": "human", "value": "\nDescribe what's contained in the area spanning from the left side of the graph to the mid-point." }, { "from": "gpt", "value": "The area includes half of the x-axis with labels from the year 2017.5 to 2025, corresponding tick marks and portions of IoT and AR data lines, with IoT data trending higher in this section." } ], "id_raw": 930 }, { "id": "H3TGMPjEWExDoMTi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_10.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.057, 0.066, 0.493, 0.911] of the line chart?" }, { "from": "gpt", "value": "This region includes the y-axis labels for IoT, multiple data points for IoT and AR from 2020 up to approximately 2025, including their associated markers." } ], "id_raw": 930 }, { "id": "oOgcIe1Dfxcf8Eaf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the lower-right quadrant of the graph." }, { "from": "gpt", "value": "This quadrant contains the years from around 2027.5 to 2040 on the x-axis, including their associated x-tick labels and portions of IoT and AR data lines showing an upward trend for both IoT and AR but with IoT having generally higher values." } ], "id_raw": 930 }, { "id": "UbdiU9hGGXrzucdl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_11.png", "conversations": [ { "from": "human", "value": "\nWhat is at the location represented by the coordinates [0.964, 0.378, 0.981, 0.598] in the given plot?" }, { "from": "gpt", "value": "Second Y-axis label." } ], "id_raw": 931 }, { "id": "zC5iiMR3EJKl30Bs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the tick label at the coordinates [0.270, 0.919, 0.330, 0.942] represent?" }, { "from": "gpt", "value": "Year 2005." } ], "id_raw": 931 }, { "id": "3AfsyEpn5kmQmahS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at {coordinates}[0.759, 0.086, 0.924, 0.110] in the subplot 1's legend?" }, { "from": "gpt", "value": "Label for \"Advanced Robotics\"" } ], "id_raw": 931 }, { "id": "5nTTyEh0yWqDoNCw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_11.png", "conversations": [ { "from": "human", "value": "\nIs the {point marked for the year 2000}[0.100, 0.860] on the 'AI Development' line with color blue higher or lower than the {point for the same year on 'Advanced Robotics'}[0.100, 0.843] line with red color?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 931 }, { "id": "o0pEsKEgqAqBt0oh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_11.png", "conversations": [ { "from": "human", "value": "\nLooking at the last points on the lines for both \"AI Development\" with the coordinates [0.900, 0.150] and \"Advanced Robotics\" at [0.900, 0.133], which has a higher value in 2020?" }, { "from": "gpt", "value": "AI Development." } ], "id_raw": 931 }, { "id": "i824SfMtkkr5pw5X", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_11.png", "conversations": [ { "from": "human", "value": "\nConsidering the point for \"Advanced Robotics\" at [0.540, 0.505] and \"AI Development\" at [0.540, 0.522], which has a higher value in 2011?" }, { "from": "gpt", "value": "AI Development." } ], "id_raw": 931 }, { "id": "DK1XCsUgSo7nuIOo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_11.png", "conversations": [ { "from": "human", "value": "\nCan you provide details of the region defined by [0.260, 0.691, 0.540, 0.505]?" }, { "from": "gpt", "value": "In this region, you can see part of the lines for \"AI Development\" and \"Advanced Robotics\" from 2004 to 2011. The line for \"AI Development\" is blue with circles as markers, while the line for \"Advanced Robotics\" is drawn in red with x-shaped markers." } ], "id_raw": 931 }, { "id": "2rdWA7OSSfRjOaLl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area at [0.703, 0.077, 0.931, 0.119]?" }, { "from": "gpt", "value": "This area holds the legend for subplot 1, with the label \"Advanced Robotics\", colored red, at the coordinates [0.759, 0.086, 0.924, 0.110]." } ], "id_raw": 931 }, { "id": "APqJ0oPVKvII9H09", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_11.png", "conversations": [ { "from": "human", "value": "\nWhat content is enclosed within the region [0.060, 0.066, 0.500, 0.911]?" }, { "from": "gpt", "value": "This region covers the left portion of the plot including the first half of both \"AI Development\" and \"Advanced Robotics\" lines from the year 2000 to 2010, along with relevant Y-axis labels and ticks." } ], "id_raw": 931 }, { "id": "5nrSZDNM49ZN61Rw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_12.png", "conversations": [ { "from": "human", "value": "\nWhat information is described by the label at this specific position [0.019, 0.270, 0.036, 0.706]?" }, { "from": "gpt", "value": "Facebook Active User Count (Millions)." } ], "id_raw": 932 }, { "id": "aYKnc7rxuxhXeWnS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_12.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the marker at}[0.801, 0.090, 0.846, 0.104] in the subplot?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 932 }, { "id": "dIrWTsRYYfmtMbNW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_12.png", "conversations": [ { "from": "human", "value": "\nCan you specify the content at [0.543, 0.694, 0.543, 0.694] in the data plot?" }, { "from": "gpt", "value": "Point 16 on the Facebook line." } ], "id_raw": 932 }, { "id": "J31bwzMg1Wv932oi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_12.png", "conversations": [ { "from": "human", "value": "\nIs the user count for {Facebook} at point 7 at [0.283, 0.766] higher or lower than point 10 at [0.370, 0.766]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 932 }, { "id": "dW5CLUv4TvOAXQVc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_12.png", "conversations": [ { "from": "human", "value": "\nCompare the gradient, is {point 15}[0.514, 0.742] for Facebook trending upwards or downwards from {point 14}[0.486, 0.766]?" }, { "from": "gpt", "value": "Upwards." } ], "id_raw": 932 }, { "id": "MN6jLpz0ixHz32ZC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_12.png", "conversations": [ { "from": "human", "value": "\nWhich point has a higher value on the Twitter activity graph, {point 20}[0.659, 0.331] or {point 25}[0.803, 0.307]?" }, { "from": "gpt", "value": "Point 20." } ], "id_raw": 932 }, { "id": "w0ZpwU9RYjMzSbf2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the visualization within the area [0.071, 0.066, 0.929, 0.911]?" }, { "from": "gpt", "value": "The area comprises of both Facebook and Twitter active users trend lines with points marked for various weeks. The background is shaded in a light gray color, aiding in emphasizing the plotted data." } ], "id_raw": 932 }, { "id": "wcCdkTrxcHUPK2dJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.082, 0.090, 0.779, 0.525] contain in terms of data visualization?" }, { "from": "gpt", "value": "This region contains the upwards trending Facebook user data from week 1 to week 24 along with corresponding line and markers for certain values." } ], "id_raw": 932 }, { "id": "051WqYXavEXXi17N", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content and features located at [0.643, 0.428, 0.987, 0.559]." }, { "from": "gpt", "value": "This specific part of the chart shows a decrease and then stabilization in the curve for Twitter User Count as the weeks progress from around week 23 to 24, marked by specific point markers." } ], "id_raw": 932 }, { "id": "ylcJTK0hKHDA9bDn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_13.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label} at [0.019, 0.376, 0.036, 0.600] represent?" }, { "from": "gpt", "value": "Donations received." } ], "id_raw": 933 }, { "id": "FOnQEQLmFFBrsSkX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the line representing the donation trend} located at [0.140, 0.143, 0.860, 0.459]?" }, { "from": "gpt", "value": "Donations." } ], "id_raw": 933 }, { "id": "iYRyrHybKGd7gsYo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_13.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the {text} at [0.248, 0.025, 0.752, 0.058]?" }, { "from": "gpt", "value": "The subtitle of the chart, \"Social Welfare and Charity Activities\"." } ], "id_raw": 933 }, { "id": "ORaSnxEhQMELVuw6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_13.png", "conversations": [ { "from": "human", "value": "\nIs the {donation value for the second month} at [0.206, 0.173] higher than the {donation value for the third month} at [0.271, 0.189]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 933 }, { "id": "CuqRuYCW3ZUgIdw0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_13.png", "conversations": [ { "from": "human", "value": "\nAt what position is the {volunteer hours at the last} month at [0.860, 0.909] regarding the y-axis label for volunteer hours at [0.964, 0.397, 0.981, 0.580]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 933 }, { "id": "RdDTpwhvT9X0hIek", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the data point for the third month's donation}[0.271, 0.189] fall right of the {data point for the second month's donation}[0.206, 0.173] on the x-axis coordinate?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 933 }, { "id": "J1H3E1vPh9yKFod4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content located within [0.113, 0.077, 0.263, 0.119]." }, { "from": "gpt", "value": "This area includes a legend indicating that the blue line styled with circles represents \"Donations\"." } ], "id_raw": 933 }, { "id": "xVsNlqzTqpG4HvXH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is in the region [0.688, 0.077, 0.887, 0.119]." }, { "from": "gpt", "value": "The region contains a legend, demonstrating that the red line styled with 'x' symbols represents \"Volunteer hours\"." } ], "id_raw": 933 }, { "id": "mXniRpzDquA6uTNv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents in the region [0.140, 0.143, 0.860, 0.459]?" }, { "from": "gpt", "value": "There is a blue line representing donations over the months, with twelve points on the line marking the donation amount for each month." } ], "id_raw": 933 }, { "id": "GjkPX5xun1y9euqy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object found at the coordinates [0.971, 0.448, 0.985, 0.534]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 934 }, { "id": "XKWYbU9NlgZpIyTt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_14.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.097, 0.173, 0.903, 0.898]?" }, { "from": "gpt", "value": "The line for 'Death Toll'." } ], "id_raw": 934 }, { "id": "TJw7DvH7lWslmIxy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object marked by these coordinates [0.108, 0.065, 0.208, 0.082] in the chart?" }, { "from": "gpt", "value": "A legend label, specifically 'Disease Cases'." } ], "id_raw": 934 }, { "id": "PYL1ztIj29LHXjMP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_14.png", "conversations": [ { "from": "human", "value": "\nWhich data label, 'Disease Cases' or 'Death Toll', has a higher value at {week 10}[0.479, 0.639] and [0.479, 0.703]?" }, { "from": "gpt", "value": "Death Toll." } ], "id_raw": 934 }, { "id": "cvhoHfM792IlllE3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_14.png", "conversations": [ { "from": "human", "value": "\nIs the {y value for 'Disease Cases'}[0.732, 0.456] at week 16 less than the {y value for 'Death Toll'}[0.733, 0.462] at the same week?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 934 }, { "id": "KVPIKjNcrdVtb0MX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_14.png", "conversations": [ { "from": "human", "value": "\nWhich point is higher at the 7th week, {Disease Cases} [0.352, 0.727] or {Death Toll} [0.352, 0.805]?" }, { "from": "gpt", "value": "Death Toll." } ], "id_raw": 934 }, { "id": "V1nJUmrk9OAgULx5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_14.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region at [0.064, 0.058, 0.214, 0.089]?" }, { "from": "gpt", "value": "This area hosts the legend for 'Disease Cases', along with a colored marker." } ], "id_raw": 934 }, { "id": "sB1aDMzJfWWgAh88", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_14.png", "conversations": [ { "from": "human", "value": "\nPlease provide details for the region specified by coordinates [0.817, 0.058, 0.936, 0.089]." }, { "from": "gpt", "value": "This region contains the legend for 'Death Toll', and a corresponding colored marker." } ], "id_raw": 934 }, { "id": "G11AEcVSlCUk7DTz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_14.png", "conversations": [ { "from": "human", "value": "\nWhat content is contained within the region specified as [0.256, 0.939, 0.278, 0.957]?" }, { "from": "gpt", "value": "This is where the x-axis label '5.0' is located." } ], "id_raw": 934 }, { "id": "09jL38DVPFl0TSMy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the object present at these coordinates [0.964, 0.364, 0.981, 0.613]?" }, { "from": "gpt", "value": "The Y-axis label of the second subplot, which is \"Raw Materials Supply\"." } ], "id_raw": 935 }, { "id": "BGiJI6njBvqmb1Tf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_15.png", "conversations": [ { "from": "human", "value": "\nWhat does {the point} at [0.668, 0.235] in the subplot_1 represent?" }, { "from": "gpt", "value": "It represents the Raw Materials Supply value at the 9th month." } ], "id_raw": 935 }, { "id": "j0ov5P2RIYgIE3Ao", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-axis tick label} at [0.043, 0.395, 0.087, 0.418] in the subplot_0?" }, { "from": "gpt", "value": "1500." } ], "id_raw": 935 }, { "id": "O6ezdqMJzUDXPvfu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_15.png", "conversations": [ { "from": "human", "value": "\nIs {the Vehicle Sales value at 10th month}[0.735, 0.627] less than or greater than {the Raw Materials Supply value at 10th month}[0.735, 0.252] in subplot_1?" }, { "from": "gpt", "value": "Less than." } ], "id_raw": 935 }, { "id": "YnjYGMjDz0xnM2W2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_15.png", "conversations": [ { "from": "human", "value": "\nDoes {Vehicle Sales}[0.130, 0.573, 0.870, 0.776] increase or decrease from 1st month to 12th month in subplot_0?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 935 }, { "id": "qp4uqbvKUsjoTu2Q", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_15.png", "conversations": [ { "from": "human", "value": "\nAre {Vehicle Sales values} of subplot_0 and {Raw Materials Supply values} of subplot_1 plotted against the same x-axis?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 935 }, { "id": "4loqDRNadrzCmdXQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.093, 0.066, 0.907, 0.911] in both subplots." }, { "from": "gpt", "value": "This region indicates the plot area for both subplots. It is where the lines for Vehicle Sales and Raw Materials Supply, along with the grid, x-axis and y-axis labels are plotted." } ], "id_raw": 935 }, { "id": "DxyH63J1DH0uwk3k", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_15.png", "conversations": [ { "from": "human", "value": "\nWhat's inside the region specified by these coordinates [0.649, 0.077, 0.898, 0.119] in subplot_1?" }, { "from": "gpt", "value": "The legend of the subplot_1 is located here. It includes an item named {Raw Materials Supply}[0.704, 0.086, 0.891, 0.110] with a corresponding handle marked with an 'x'." } ], "id_raw": 935 }, { "id": "lKEORD5KMox9ZyW3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the region [0.043, 0.395, 0.087, 0.418] and [0.913, 0.395, 0.957, 0.418]?" }, { "from": "gpt", "value": "These regions contain y-axis tick labels '1500' for subplot_0 and subplot_1, respectively." } ], "id_raw": 935 }, { "id": "bZjN1g2wgmflmQSE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_16.png", "conversations": [ { "from": "human", "value": "\nWhich text represents {the highest point on the booking trendline}[0.570, 0.404]?" }, { "from": "gpt", "value": "440." } ], "id_raw": 936 }, { "id": "dgS0bagBIRwLnIIc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_16.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label in the region}[0.019, 0.379, 0.036, 0.597] represent?" }, { "from": "gpt", "value": "Number of Tourists." } ], "id_raw": 936 }, { "id": "Rwa7RLdBq9UyFtor", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_16.png", "conversations": [ { "from": "human", "value": "\nCan you identify {the xlabel near}[0.472, 0.952, 0.528, 0.975]?" }, { "from": "gpt", "value": "Weeks." } ], "id_raw": 936 }, { "id": "SOqvrVTuyYdJpuwS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_16.png", "conversations": [ { "from": "human", "value": "\nIs {the week 10 booking point}[0.373, 0.531] above or below {the week 15 booking point}[0.514, 0.467]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 936 }, { "id": "wr2Bq3MMipg22sbG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_16.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at {week 20}[0.655, 0.277], the tourists or bookings?" }, { "from": "gpt", "value": "Tourists." } ], "id_raw": 936 }, { "id": "ls52yi8JPuqJzJjW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_16.png", "conversations": [ { "from": "human", "value": "\nAre {the bookings in week 1}[0.120, 0.805] more or less than {the bookings in week 12}[0.430, 0.594]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 936 }, { "id": "XUsKmIDiknzYvzhm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_16.png", "conversations": [ { "from": "human", "value": "\nWhat is noticeable about the area [0.082, 0.066, 0.918, 0.911]?" }, { "from": "gpt", "value": "This area contains two primary axes, with fluctuating line trends representing tourists and bookings data, plotted from week 1 to week 28." } ], "id_raw": 936 }, { "id": "VD6aGHvlorPe9FeI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.043, 0.056, 0.957, 0.925]." }, { "from": "gpt", "value": "This region holds all the y-ticks ranging from 200 to 600 marked in blue and red for number of tourists and bookings respectively, alongside their corresponding labels and markers." } ], "id_raw": 936 }, { "id": "p5TQR9wRop8x1wXG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe what occupies the rectangle [0.120, 0.171, 0.880, 0.805]?" }, { "from": "gpt", "value": "It encompasses the trendlines of bookings and tourists with various data points marked from week 1 through week 28, displaying an overall pattern." } ], "id_raw": 936 }, { "id": "q9Bx1iZtL8EPkuVa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the color associated with the line representing \"TV Ratings\" in the chart?" }, { "from": "gpt", "value": "#1f77b4[0.085, 0.090, 0.130, 0.104]." } ], "id_raw": 937 }, { "id": "pWdVoeAABGGW24X1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_17.png", "conversations": [ { "from": "human", "value": "\nWhat is marking style used for the \"Music Downloads\" data points?" }, { "from": "gpt", "value": "x[0.706, 0.090, 0.751, 0.104]." } ], "id_raw": 937 }, { "id": "OXXdvLyfnlABZarw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_17.png", "conversations": [ { "from": "human", "value": "\nWhat label corresponds to the x-coordinate [0.801, 0.911]?" }, { "from": "gpt", "value": "25[0.790, 0.919, 0.812, 0.942]." } ], "id_raw": 937 }, { "id": "f88nC29wC4IHAIUp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_17.png", "conversations": [ { "from": "human", "value": "\nDoes the point {labeled '20'}[0.657, 0.308] for \"TV Ratings\" lie above or below the point {labeled '20'}[0.657, 0.404] for \"Music Downloads\"?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 937 }, { "id": "Lpy7Xpo2a5ruXxm2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_17.png", "conversations": [ { "from": "human", "value": "\nWhich line's point labeled '1' has a lower y-coordinate, \"TV Ratings\" or \"Music Downloads\"?" }, { "from": "gpt", "value": "Music Downloads[0.114, 0.426]." } ], "id_raw": 937 }, { "id": "LXALQXAz2PjEtYDm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_17.png", "conversations": [ { "from": "human", "value": "\nIs the point at {coordinate}[0.343, 0.562] from the same dataset as the point at {coordinate}[0.114, 0.426]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 937 }, { "id": "HcfXXkubas8eM6pU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.075, 0.066, 0.925, 0.911]." }, { "from": "gpt", "value": "The region contains two line charts representing \"TV Ratings\" and \"Music Downloads\", each with a respective subplot, axes labeled for \"Weeks\", and y-axes labeled for \"TV Ratings\" and \"Music Downloads\" with respective color coordination." } ], "id_raw": 937 }, { "id": "4VDCYBzu49xv1uPf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found between [0.075, 0.935, 0.955, 1.000]." }, { "from": "gpt", "value": "This section of the chart includes x-axis labels for weeks from '\u22125' to '30', using a light gray color for text and line marks located below the main plotting area." } ], "id_raw": 937 }, { "id": "Gd2nmlAR4mIfXuLn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_17.png", "conversations": [ { "from": "human", "value": "\nWhat can you find in the region [0.075, 0.077, 0.916, 0.119]?" }, { "from": "gpt", "value": "The region contains legends for \"TV Ratings\" and \"Music Downloads\", respectively using a light blue line and a red dotted line with an 'x' marker style." } ], "id_raw": 937 }, { "id": "V0MHtM9HK4COviDc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_18.png", "conversations": [ { "from": "human", "value": "\nWhat object do the coordinates [0.964, 0.407, 0.981, 0.569] refer to in the chart?" }, { "from": "gpt", "value": "Y-axis label of subplot 1." } ], "id_raw": 938 }, { "id": "A9L8jwWW8TVsZQer", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_18.png", "conversations": [ { "from": "human", "value": "\nCan you tell what specific data do the label coordinates [0.946, 0.902, 0.957, 0.925] represent in the chart?" }, { "from": "gpt", "value": "Y-axis tick label '400' of subplot 1." } ], "id_raw": 938 }, { "id": "Xk84YME7WOXZH8R7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_18.png", "conversations": [ { "from": "human", "value": "\nWhat is denoted by the object {Population Migration line point 2007}[0.386, 0.206] in the chart?" }, { "from": "gpt", "value": "The point represents the data for the year 2007 in the Population Migration trend, with the value of 650." } ], "id_raw": 938 }, { "id": "bpEUkZbKDFsj1g9a", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_18.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at {the year 2010 point}[0.500, 0.629], {Population migration}[0.500, 0.404] or {Mobility Trend}[0.500, 0.629]?" }, { "from": "gpt", "value": "Mobility Trend." } ], "id_raw": 938 }, { "id": "vmIjQxpVRYF7zmVu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_18.png", "conversations": [ { "from": "human", "value": "\nIs the data point of {Population Migration}[0.348, 0.347] in 2006 higher or below the data point of {Mobility Trend}[0.348, 0.460] in the same year?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 938 }, { "id": "5Wbw5j6RMOQuRJoe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_18.png", "conversations": [ { "from": "human", "value": "\nBetween the trendlines {Population Migration}[0.120, 0.108, 0.880, 0.911] and {Mobility Trend}[0.120, 0.235, 0.880, 0.629], which one has a higher value at {Year 2020}[0.880]?" }, { "from": "gpt", "value": "Population Migration." } ], "id_raw": 938 }, { "id": "L530XBqhMCtewhx3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_18.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in this area of the chart [0.500, 0.235, 0.880, 0.629]?" }, { "from": "gpt", "value": "This region of the chart depicts the part of the Mobility Trend line from the year 2010 to 2020. It indicates the changes in Mobility Trend values over this period." } ], "id_raw": 938 }, { "id": "SdoTEVhMlEcKU2Dg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the area bounded by the coordinates [0.725, 0.077, 0.909, 0.119]." }, { "from": "gpt", "value": "This is the area displaying the legend of subplot 1. It contains the name {Mobility Trend}[0.781, 0.086, 0.902, 0.110] with its associated marker represented with a red color." } ], "id_raw": 938 }, { "id": "Qq3xj6CjZAk6v2dh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_18.png", "conversations": [ { "from": "human", "value": "\nWhat features are notable in the region [0.082, 0.066, 0.918, 0.911]?" }, { "from": "gpt", "value": "This region consists of subplot 1 of the chart. It contains two line plots: {Population Migration}[0.120, 0.108, 0.880, 0.911] represented with blue color and solid line and {Mobility Trend}[0.120, 0.235, 0.880, 0.629] depicted with red color and dashed line. Both these plots represent data from the year 2000 to 2020. The region also includes the x-axis and y- axis labels and ticks for the corresponding subplots." } ], "id_raw": 938 }, { "id": "2epPwoEEks69f3WV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_19.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.105, 0.540] in the chart?" }, { "from": "gpt", "value": "The point for the year 2000 of the Crop Production line." } ], "id_raw": 939 }, { "id": "ezuAneXrClwwVci8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the coordinates [0.263, 0.939, 0.327, 0.957]?" }, { "from": "gpt", "value": "2007.5." } ], "id_raw": 939 }, { "id": "NQT9Fnvae9cnJKxD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_47_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the subplot located at [0.285, 0.019, 0.715, 0.044]?" }, { "from": "gpt", "value": "Food and Agriculture Industry Analysis." } ], "id_raw": 939 }, { "id": "89kQJGBC99QbChkS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_19.png", "conversations": [ { "from": "human", "value": "\nIs the first point marked [0.105, 0.540] from the Crop Production line higher or lower than the point [0.303, 0.481] from the Livestock Production line?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 939 }, { "id": "NW9GRhLPRgnhq2BY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_19.png", "conversations": [ { "from": "human", "value": "\nCompared to the line for Livestock Production at the point [0.855, 0.422], is the point from the Crop Production line at [0.855, 0.363] above or below?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 939 }, { "id": "ZuZLXYQq3SrEKiQn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_47_19.png", "conversations": [ { "from": "human", "value": "\nWhich line corresponds to the lower point at {the coordinates}[0.697, 0.540], the Crop Production or Livestock Production?" }, { "from": "gpt", "value": "Both." } ], "id_raw": 939 }, { "id": "J18zEoZc3fO2lJfi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the region [0.277, 0.911, 0.631, 0.979]?" }, { "from": "gpt", "value": "The area includes the x-axis labeled as \"Year\" in light grey color, all x-axis tick labels and their corresponding tick marks for the years 1997.5 to 2022.5 starting from {the coordinate}[0.081, 0.939, 0.129, 0.957] through {the coordinate}[0.969, 0.939, 1.017, 0.957]." } ], "id_raw": 939 }, { "id": "NiWsN8esuXbxaAIV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_19.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the area [0.000, 0.400, 0.050, 0.700]?" }, { "from": "gpt", "value": "The y-axis label for Crop Production in tab blue color and the y-axis tick labels ranging from '95' to '115' starting at {the coordinate}[0.043, 0.926, 0.061, 0.944] ending at {the coordinate}[0.035, 0.337, 0.061, 0.354]." } ], "id_raw": 939 }, { "id": "XSBqgsF639BD5Z1I", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_47_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the region that spans [0.030, 0.030, 0.970, 0.970]." }, { "from": "gpt", "value": "This wide area includes the chart's entire plotting area for both Crop Production and Livestock Production data lines, encompassing all axis labels, tick marks, and data points from the initial year 2000 point at {the coordinate}[0.105, 0.835] to the closing year 2020 point at {the coordinate}[0.895, 0.344]." } ], "id_raw": 939 }, { "id": "ezsqgUbhQ0RECR4q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the {y-axis label}[0.025, 0.354, 0.048, 0.550] represent?" }, { "from": "gpt", "value": "Share Price." } ], "id_raw": 941 }, { "id": "0EFc7syJWRcJ7dSj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the value represented by {this point}[0.416, 0.176] on Share Price2?" }, { "from": "gpt", "value": "245." } ], "id_raw": 941 }, { "id": "55OJ7Jb8AnNk6hcG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_1.png", "conversations": [ { "from": "human", "value": "\nWhat month does {this x-axis tick label}[0.392, 0.879, 0.440, 0.914] represent?" }, { "from": "gpt", "value": "May." } ], "id_raw": 941 }, { "id": "Z5q7wwv2cMvWgEgD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_1.png", "conversations": [ { "from": "human", "value": "\nFor the month of Septemeber, is {Share Price1}[0.723, 0.673] higher or lower than {Share Price2}[0.723, 0.120]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 941 }, { "id": "nrR4o5TKczJoQfPF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_1.png", "conversations": [ { "from": "human", "value": "\nDoes {Share Price1}[0.340, 0.469] show an increase or decrease compared to its previous month's value {Share Price1}[0.263, 0.452]?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 941 }, { "id": "OANekeP7BuLFG1QH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_1.png", "conversations": [ { "from": "human", "value": "\nAt the start of the year, was {Share Price1}[0.110, 0.369] higher or lower than {Share Price2}[0.110, 0.314]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 941 }, { "id": "jreP3qJ8xG8I1dgo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area bounded by [0.121, 0.055, 0.350, 0.170]." }, { "from": "gpt", "value": "This area contains the legend of the graph. It has two entries: {Share Price1}[0.196, 0.069, 0.341, 0.104] and {Share Price2}[0.196, 0.121, 0.341, 0.156], each paired with a distinct color marker." } ], "id_raw": 941 }, { "id": "Wx5IasXQlT1wBu45", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.489, 0.927, 0.573, 0.963]." }, { "from": "gpt", "value": "This area contains the label for the x-axis, which is \"Months\"." } ], "id_raw": 941 }, { "id": "D8P4EmPlUnGOT1K2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_1.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.110, 0.093, 0.953, 0.314]?" }, { "from": "gpt", "value": "This area contains the data line representing \"Share Price2\" for each month from January to December." } ], "id_raw": 941 }, { "id": "sGyKdMsSdnpdIsyi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object of the plot with coordinates [0.066, 0.030, 0.985, 0.893]?" }, { "from": "gpt", "value": "It is the main subplot for the line chart." } ], "id_raw": 942 }, { "id": "4HSGEk6UxmmLjYL3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_2.png", "conversations": [ { "from": "human", "value": "\nWhich part of the chart is located at the coordinates [0.043, 0.898, 0.061, 0.926]?" }, { "from": "gpt", "value": "Y-axis tick label '80' is displayed at these coordinates." } ], "id_raw": 942 }, { "id": "r71WK4blRUi2ZX7q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_2.png", "conversations": [ { "from": "human", "value": "\nWhat object does the coordinate [0.108, 0.699] refer to in the line chart?" }, { "from": "gpt", "value": "It refers to the point where the 'Contemporary Art Market' curve registered in year 2005." } ], "id_raw": 942 }, { "id": "YBzic22hqLRwknnr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_2.png", "conversations": [ { "from": "human", "value": "\nIn the year 2010, is the point on the {Contemporary Art Market curve}[0.386, 0.439] higher or lower than the point on the {Heritage Art Market curve}[0.386, 0.799]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 942 }, { "id": "mF2Rbj0bH90vWdPd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_2.png", "conversations": [ { "from": "human", "value": "\nWhich Market Index Value had a greater value in the year 2008, {Contemporary Art Market}[0.275, 0.605] or {Heritage Art Market}[0.275, 0.799]?" }, { "from": "gpt", "value": "Contemporary Art Market." } ], "id_raw": 942 }, { "id": "a16wo6OtleBHWCPB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_2.png", "conversations": [ { "from": "human", "value": "\nWhere is the {Heritage Art Market point in 2015}[0.665, 0.633] relative to the {Contemporary Art Market point in 2015}[0.665, 0.218]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 942 }, { "id": "7WZvdn36A9bn5Php", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region at [0.966, 0.879, 1.012, 0.907]" }, { "from": "gpt", "value": "This region consists of the point on the 'Heritage Art Market' trend line for the year 2022." } ], "id_raw": 942 }, { "id": "dqEvWeWXPOy0AYEg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.073, 0.044, 0.301, 0.136]." }, { "from": "gpt", "value": "This region contains the legend for the line chart which includes two items: {Contemporary Art Market}[0.117, 0.055, 0.295, 0.083] depicted in color '#8280cd' and {Heritage Art Market}[0.117, 0.097, 0.255, 0.125] represented in color '#ff0000'." } ], "id_raw": 942 }, { "id": "qnyugdgpOVm0mLKP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_2.png", "conversations": [ { "from": "human", "value": "\nWhat elements are included in [0.507, 0.942, 0.544, 0.970] region?" }, { "from": "gpt", "value": "This region contains the X axis label which is 'Years'." } ], "id_raw": 942 }, { "id": "2xuY6JtcMM8z7ejV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the {xlabel}[0.497, 0.927, 0.539, 0.963] in the chart?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 943 }, { "id": "gozdWk9VMMq6NFT4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third Y-axis tick label?" }, { "from": "gpt", "value": "[0.050, 0.645, 0.075, 0.680]." } ], "id_raw": 943 }, { "id": "w2tykKQrOTUUjngC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the {legend item with the color '#008000'}[0.099, 0.136, 0.139, 0.136]?" }, { "from": "gpt", "value": "Co2 Emissions." } ], "id_raw": 943 }, { "id": "t4dQlKY4k0VrZDLE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_3.png", "conversations": [ { "from": "human", "value": "\nIs {the point for year 2003 of Global Average Temperature}[0.212, 0.825] above or below {the point for year 2003 of Co2 Emissions}[0.212, 0.351] in the chart?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 943 }, { "id": "47gDefYUYRn03dWP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the data line for 'Global Average Temperature'}[0.081, 0.808, 0.910, 0.829] remain above, equal to, or below {the data line for 'Co2 Emissions'}[0.081, 0.075, 0.910, 0.432] throughout the years 2000 to 2019 in the chart?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 943 }, { "id": "PMz8dmmSUfDG9kPU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_3.png", "conversations": [ { "from": "human", "value": "\nAt {Year 2018}[0.866, 0.879, 0.892, 0.914], was the Co2 Emissions higher, the same, or lower than the global average temperature values?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 943 }, { "id": "JOcgaor1T2qUC7iT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_3.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area [0.091, 0.055, 0.447, 0.170] in the chart?" }, { "from": "gpt", "value": "This area contains the legend of the chart. It includes two labels: {Global Average Temperature}[0.155, 0.069, 0.439, 0.104] with a corresponding blue marker, and {Co2 Emissions}[0.155, 0.121, 0.298, 0.156] with a corresponding green marker." } ], "id_raw": 943 }, { "id": "HBsB7tNLrozptnb2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_3.png", "conversations": [ { "from": "human", "value": "\nCan you explain the region [0.081, 0.075, 0.910, 0.432]?" }, { "from": "gpt", "value": "This area marks the position of the data line for Co2 Emissions in the chart. The line starts near the midpoint of the y-axis and ends near the x-axis, indicating the rise of Co2 Emissions over time." } ], "id_raw": 943 }, { "id": "KCp59atFpIGqipia", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.300, 0.822, 0.343, 0.870] of the chart?" }, { "from": "gpt", "value": "There are 3 objects. The {x-axis tick label '2006'}[0.318, 0.879, 0.368, 0.914], a point at [0.343, 0.821] for Global Average Temperature in 2006, and a point at [0.343, 0.270] for Co2 Emissions in 2006." } ], "id_raw": 943 }, { "id": "kp07p3YNh399bWcX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the data point at [0.118, 0.464] on the 'GDP Growth Rate' line?" }, { "from": "gpt", "value": "2001." } ], "id_raw": 945 }, { "id": "5jlee0kD9Y39yvbU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the data point at [0.938, 0.871] on the 'GDP Growth Rate' line?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 945 }, { "id": "IAvTTtdGjAiOLXoV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line for 'Interest Rate'}[0.118, 0.065, 0.938, 0.531]?" }, { "from": "gpt", "value": "#2baf2b." } ], "id_raw": 945 }, { "id": "Xm6dft3rjZM1R8VL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_5.png", "conversations": [ { "from": "human", "value": "\nIs {the value of 'GDP Growth Rate'}[0.118, 0.464] higher or lower than {the value of 'Interest Rate'}[0.118, 0.139] in 2001?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 945 }, { "id": "ZLsHlvN9w6nCUo13", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_5.png", "conversations": [ { "from": "human", "value": "\nIs {the value of 'GDP Growth Rate'}[0.938, 0.871] higher or lower than {the value of 'Interest Rate'}[0.938, 0.531] in 2020?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 945 }, { "id": "4517KakpJd4medAf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_5.png", "conversations": [ { "from": "human", "value": "\nWhich one drops more dramatically from 2007 to 2008, the 'GDP Growth Rate' or the 'Interest Rate'?" }, { "from": "gpt", "value": "GDP Growth Rate." } ], "id_raw": 945 }, { "id": "R08b3DjtlYfUAUyZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.759, 0.037, 0.973, 0.113]." }, { "from": "gpt", "value": "This area contains the legend items for the chart. These include the GDP Growth Rate{labeled with color #189ab4}[0.814, 0.046, 0.966, 0.069] and Interest Rate{labeled with color #2baf2b}[0.814, 0.081, 0.927, 0.104], each paired with a colored marker." } ], "id_raw": 945 }, { "id": "SQxM3V7FIModmFWd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.019, 0.418, 0.036, 0.518]." }, { "from": "gpt", "value": "This area features the y-axis label 'Rate (%)' placed vertically to denote the proportion of values in percentage." } ], "id_raw": 945 }, { "id": "bSlRdftvmad7rMQU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.043, 0.720, 0.069, 0.743]?" }, { "from": "gpt", "value": "A single object is present, the y-axis tick label '-2'." } ], "id_raw": 945 }, { "id": "FzhqRelkYt5qZc1O", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.025, 0.351, 0.048, 0.553]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 946 }, { "id": "AGAlWigjiCPXvQJD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second x-axis tick label?" }, { "from": "gpt", "value": "[0.193, 0.879, 0.230, 0.914]" } ], "id_raw": 946 }, { "id": "nJF7LI1hdcVMrefG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend item represent at [0.196, 0.121, 0.283, 0.156]?" }, { "from": "gpt", "value": "Stock B." } ], "id_raw": 946 }, { "id": "S9UEBncEesvqqCr4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Stock A}[0.191, 0.783] above or below {the point for Stock B}[0.191, 0.719] at time 2.5?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 946 }, { "id": "QBreOGcXoMfS3G00", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_6.png", "conversations": [ { "from": "human", "value": "\nDoes {Stock A}[0.149, 0.161, 0.936, 0.829] or {Stock B}[0.149, 0.075, 0.936, 0.760] have a higher value at time 20?" }, { "from": "gpt", "value": "Stock A." } ], "id_raw": 946 }, { "id": "mKSzfMnoROuYk5xh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_6.png", "conversations": [ { "from": "human", "value": "\nIs the second point of {Stock A}[0.191, 0.783] higher or lower than its first point [0.149, 0.760]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 946 }, { "id": "dMAqrHVdbeh4wiIR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.193, 0.879, 0.958, 0.914]." }, { "from": "gpt", "value": "This region contains the x-axis tick labels, which represents time periods from 2.5 to 22.5." } ], "id_raw": 946 }, { "id": "n8VOAcJ1aqx760iR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_6.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area [0.121, 0.055, 0.292, 0.170]?" }, { "from": "gpt", "value": "This area contains the legend of the chart. It labels {Stock A}[0.196, 0.069, 0.283, 0.104] with a handle in color '#12a0d7' and {Stock B}[0.196, 0.121, 0.283, 0.156] with a handle in color '#de8812'." } ], "id_raw": 946 }, { "id": "xXoJTC6dzl0Ii6eJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is within the coordinates [0.149, 0.161, 0.936, 0.829]." }, { "from": "gpt", "value": "Within these coordinates are the points and line for Stock A, indicating its value change over time from time 1 to time 20." } ], "id_raw": 946 }, { "id": "2ZEhlVlmaZRXMZNZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with the coordinates [0.019, 0.364, 0.036, 0.576]?" }, { "from": "gpt", "value": "It's the y-axis label." } ], "id_raw": 947 }, { "id": "xokMownD4jN2QCzO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the x-axis label?" }, { "from": "gpt", "value": "[0.468, 0.952, 0.595, 0.975]." } ], "id_raw": 947 }, { "id": "TXIbnNT92r1KLtOI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.054, 0.695, 0.076, 0.718]?" }, { "from": "gpt", "value": "60." } ], "id_raw": 947 }, { "id": "YOLiBFvphcPmX9sv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point at time 10}[0.510, 0.185] higher or lower than {the point at time 16}[0.767, 0.135] on the 'Media Influence' line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 947 }, { "id": "LnsAOOojfClh3CcD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_7.png", "conversations": [ { "from": "human", "value": "\nIs {the start point of the 'Media Influence' line}[0.082, 0.871] on the left side or the right side of {the end point of the line}[0.938, 0.871]?" }, { "from": "gpt", "value": "Left side." } ], "id_raw": 947 }, { "id": "wBTqYEisohnj4kxU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point at time 5}[0.296, 0.570] higher or lower than {the point at time 7}[0.382, 0.453] on the 'Media Influence' line?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 947 }, { "id": "Qgkd2xK19UMXwqI4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the area [0.773, 0.040, 0.973, 0.082]." }, { "from": "gpt", "value": "This area consists of a legend titled 'Media Influence' with {its corresponding color marker}[0.774, 0.052, 0.819, 0.066] and {the legend text itself}[0.828, 0.049, 0.966, 0.072]." } ], "id_raw": 947 }, { "id": "xmobdCbxgK5xl6f5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.082, 0.028, 0.981, 0.911]." }, { "from": "gpt", "value": "The region includes grid lines, both x and y-axes with their labels and tick marks, and a line plot representing 'Media Influence' over 'Time (months)' on the x-axis and 'Public Opinion (%)' on the y-axis." } ], "id_raw": 947 }, { "id": "hWOjEOQ2MEE6cjXr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are contained in the region [0.082, 0.871, 0.938, 0.871]?" }, { "from": "gpt", "value": "This includes an array of points plotted for the 'Media Influence' line graph." } ], "id_raw": 947 }, { "id": "mPyBuq0PfEFjNh6f", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the 'x-axis tick label' represent at the coordinates [0.111, 0.879, 0.192, 0.914]?" }, { "from": "gpt", "value": "The x-axis tick label represents the year 2000.0" } ], "id_raw": 948 }, { "id": "speGgMOqgzKKCNxA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the area represented by the coordinates [0.025, 0.286, 0.048, 0.618]?" }, { "from": "gpt", "value": "The label \"Amount Consumed\" on the y-axis." } ], "id_raw": 948 }, { "id": "WPt8VrjUSoXwqd0v", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_8.png", "conversations": [ { "from": "human", "value": "\nWhat does {Coal line}[0.191, 0.075, 0.933, 0.528] stand for?" }, { "from": "gpt", "value": "The line represents the amount of Coal consumed for different years." } ], "id_raw": 948 }, { "id": "fgzaPlTkqaIzzwjD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_8.png", "conversations": [ { "from": "human", "value": "\nAre the data points represented by the line of {Hydroelectric Power}[0.191, 0.644, 0.933, 0.829] generally higher or lower than that of {Coal line}[0.191, 0.075, 0.933, 0.528]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 948 }, { "id": "CVxUPeLmxRyFFQ75", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_8.png", "conversations": [ { "from": "human", "value": "\nDoes the energy consumed by {Coal}[0.191, 0.528] in the year 2001 greater than the amount consumed by {Hydroelectric Power}[0.191, 0.727]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 948 }, { "id": "wotfDKoVDmBtxOKj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_8.png", "conversations": [ { "from": "human", "value": "\nDid the consumption of {Coal}[0.933, 0.075] increase or decrease from 2001 to 2020?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 948 }, { "id": "JsRkkQulPBx7lzDd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_8.png", "conversations": [ { "from": "human", "value": "\nWhat observations can be made for the region [0.191, 0.075, 0.933, 0.528]?" }, { "from": "gpt", "value": "This area encompasses the energy consumption of Coal over the years 2001 to 2020. Through line plot, it shows an upward trajectory indicating the increase in Coal consumption." } ], "id_raw": 948 }, { "id": "Wfm1xe6U7qTlZchf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the specifics in the area [0.165, 0.055, 0.480, 0.170]." }, { "from": "gpt", "value": "This is the legend area, containing 2 legend items. One for {Coal}[0.239, 0.069, 0.290, 0.104] and another for {Hydroelectric Power}[0.239, 0.121, 0.471, 0.156], each paired with a colored marker." } ], "id_raw": 948 }, { "id": "6VOmO15rPW6twn4v", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects can be spotted within the coordinates [0.502, 0.879, 0.582, 0.914]?" }, { "from": "gpt", "value": "The x-axis tick label for the year '2010.0' can be spotted here." } ], "id_raw": 948 }, { "id": "Y5s8iP3RwKoNNcMQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.375, 0.029, 0.570]?" }, { "from": "gpt", "value": "Development Index." } ], "id_raw": 949 }, { "id": "aO5QEjrmFtrIhpV5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the text right above {the y-axis mark at 90}[0.066, 0.226]?" }, { "from": "gpt", "value": "80." } ], "id_raw": 949 }, { "id": "SxDmoe3scSxAEwAR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the legend item} with text coordinates [0.815, 0.478, 0.955, 0.498]?" }, { "from": "gpt", "value": "Urban Development." } ], "id_raw": 949 }, { "id": "tR5NUv14dKCgzypz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_9.png", "conversations": [ { "from": "human", "value": "\nIs {the point corresponding to the year 1995 for Rural Development}[0.404, 0.555] above or below {the point corresponding to the year 1995 for Urban Development}[0.404, 0.391]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 949 }, { "id": "tXbmpvAwUvf7XZnD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_9.png", "conversations": [ { "from": "human", "value": "\nAt {the year 2010}[0.742, 0.924], which had a higher development index, Rural Development at [0.742, 0.719] or Urban Development at [0.742, 0.079]?" }, { "from": "gpt", "value": "Urban Development." } ], "id_raw": 949 }, { "id": "cryLbuauAxgfuxmE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_9.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Urban Development at the year 2020}[0.968, 0.062] higher or lower than {the point for Urban Development at the year 2015}[0.855, 0.062]?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 949 }, { "id": "bvIMkEMCWqPOdCUj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area bounded by the coordinates [0.771, 0.440, 0.961, 0.505]." }, { "from": "gpt", "value": "This region includes the sublegend of the chart, with {the label for Rural Development}[0.815, 0.448, 0.949, 0.468] marked in a color denoted by {coordinates}[0.772, 0.450, 0.808, 0.462] and {the label for Urban Development}[0.815, 0.478, 0.955, 0.498] marked using a color represented by {coordinates}[0.776, 0.486, 0.804, 0.486]." } ], "id_raw": 949 }, { "id": "7J3IA6fmhhrJe3yw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.066, 0.062, 0.968, 0.883]?" }, { "from": "gpt", "value": "Within this region are the two plotted lines representing {Rural Development}[0.066, 0.062, 0.968, 0.883] and {Urban Development}[0.066, 0.062, 0.968, 0.883], which are the main data elements of the line chart." } ], "id_raw": 949 }, { "id": "4rQVDfhAV9gaFzOp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.048, 0.931, 0.985, 0.951]." }, { "from": "gpt", "value": "This region contains the x-axis labels representing years from {1980}[0.048, 0.931, 0.083, 0.951] to {2020}[0.950, 0.931, 0.985, 0.951]." } ], "id_raw": 949 }, { "id": "qZoSlTa3ZZHJaeEq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart?" }, { "from": "gpt", "value": "{Study-related Anxiety Levels among Students}[0.333, 0.025, 0.713, 0.055]." } ], "id_raw": 950 }, { "id": "skRVPjmIFQSh45Sk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_10.png", "conversations": [ { "from": "human", "value": "\nWhat specific category does the x-axis label at [0.658, 0.756, 0.770, 0.942] refer to?" }, { "from": "gpt", "value": "{Professor interaction}[0.658, 0.756, 0.770, 0.942]" } ], "id_raw": 950 }, { "id": "fh4B69ZCLAsYkNfC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis tick in the position indicated by these coordinates, [0.035, 0.666, 0.057, 0.690]?" }, { "from": "gpt", "value": "{0.1}[0.035, 0.666, 0.057, 0.690]" } ], "id_raw": 950 }, { "id": "EJqj5adqFWCDzNC0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_10.png", "conversations": [ { "from": "human", "value": "\nIs the {anxiety level for 'Exams'} [0.103, 0.091] rating higher or lower than the {anxiety level for 'Transcripts'} [0.867, 0.566]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 950 }, { "id": "hjRRH8dcevafagMF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_10.png", "conversations": [ { "from": "human", "value": "\nDoes the anxiety level decrease or increase from {Homework}[0.180, 0.164] to {Presentations}[0.256, 0.237]?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 950 }, { "id": "jHB9x7eFFF8vStI8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_10.png", "conversations": [ { "from": "human", "value": "\nIs the {anxiety level for 'Group work'}[0.409, 0.281] higher than that of {Internet research}[0.561, 0.347]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 950 }, { "id": "ZVCXcDeEdYKFMxGk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the region [0.035, 0.082, 0.057, 0.357]?" }, { "from": "gpt", "value": "This region holds the y-axis labels for the chart, ranging from {0.0}[0.035, 0.739, 0.057, 0.763] to {0.9}[0.035, 0.082, 0.057, 0.106] representing the described Anxiety Levels." } ], "id_raw": 950 }, { "id": "GFDLD9NtTD3HYtkk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the area indicated by the coordinates [0.790, 0.756, 0.819, 0.853]." }, { "from": "gpt", "value": "The region [0.790, 0.756, 0.819, 0.853] contains the x-axis tick label {Class size}[0.790, 0.756, 0.819, 0.853]." } ], "id_raw": 950 }, { "id": "UaIV6fXyoA5cfYYY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_10.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.061, 0.091, 0.943, 0.675]?" }, { "from": "gpt", "value": "{A line with several points (Exams to Certificates)}[0.103, 0.091, 0.943, 0.675] is plotted in this region. This line represents the study-related anxiety levels among students for the specified categories." } ], "id_raw": 950 }, { "id": "ovestHUiHREm8AZy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_12.png", "conversations": [ { "from": "human", "value": "\nWhat is located at these coordinates [0.181, 0.069, 0.339, 0.104]?" }, { "from": "gpt", "value": "The label for Investment_A in the legend." } ], "id_raw": 952 }, { "id": "BWFUQiKMuF8VypC1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_12.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.807, 0.102, 0.807, 0.102]?" }, { "from": "gpt", "value": "Point 17 on the Investment_A line." } ], "id_raw": 952 }, { "id": "rLmCBaaSAtlEEr57", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the xlabel}[0.474, 0.927, 0.596, 0.963] on this chart?" }, { "from": "gpt", "value": "'# of Steps'." } ], "id_raw": 952 }, { "id": "2kxszwGmCFEM4LIt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_12.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at step 11, {Investment_A}[0.556, 0.239] or {Investment_B}[0.556, 0.729]?" }, { "from": "gpt", "value": "Investment_B" } ], "id_raw": 952 }, { "id": "rqKagzDqXRXKkWgH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_12.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Investment_A}[0.682, 0.148] at step 14 more than the value of {Investment_B}[0.682, 0.620] at the same step?" }, { "from": "gpt", "value": "No." } ], "id_raw": 952 }, { "id": "D5MrdfCnnEzf3JdX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_12.png", "conversations": [ { "from": "human", "value": "\nIs {Investment_A}[0.514, 0.284] at step 10 above or below {Investment_B}[0.514, 0.747]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 952 }, { "id": "pZB7M47TBdhg8WhY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_12.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region defined by [0.537, 0.017, 0.537, 0.017]?" }, { "from": "gpt", "value": "The subplot\u2019s subtitle is located here, which is currently empty." } ], "id_raw": 952 }, { "id": "CK3tCfvvWx6hH1iS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements located within [0.107, 0.055, 0.348, 0.171]." }, { "from": "gpt", "value": "This region contains the legend for the chart, including Investment_A[0.181, 0.069, 0.339, 0.104] and Investment_B[0.181, 0.122, 0.339, 0.157]." } ], "id_raw": 952 }, { "id": "NuNtVeSNY2FOAKdi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_12.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the region [0.095, 0.866, 0.933, 0.075]?" }, { "from": "gpt", "value": "The graphed data for both Investment_A and Investment_B are plotted within this region. Each line is composed of a series of points representing investment returns at various steps." } ], "id_raw": 952 }, { "id": "HGOWWf4J7ZPpPbnP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at these coordinates [0.019, 0.397, 0.036, 0.548] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 953 }, { "id": "WCn2h1JxFH3u0U9P", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second icon on the legend?" }, { "from": "gpt", "value": "[0.759, 0.093, 0.804, 0.107]." } ], "id_raw": 953 }, { "id": "NgBuBXLEObBzwDCk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_13.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the x-axis tick label}[0.291, 0.919, 0.302, 0.942]?" }, { "from": "gpt", "value": "5." } ], "id_raw": 953 }, { "id": "W0SRRe0N2hYffqTx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_13.png", "conversations": [ { "from": "human", "value": "\nIs {the point for year 10 in Modern Values}[0.510, 0.421] above or below {the point for year 10 in Traditional Values}[0.510, 0.601]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 953 }, { "id": "KJaUAszEBcesISol", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_13.png", "conversations": [ { "from": "human", "value": "\nIs {the line representing Traditional Values}[0.125, 0.420, 0.938, 0.719] overall above or below {the line representing Modern Values}[0.125, 0.310, 0.938, 0.587]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 953 }, { "id": "wmxILbq3Z4efmcLz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_13.png", "conversations": [ { "from": "human", "value": "\nWhich line is at a higher value at {year 20}[0.938, 0.919, 0.949, 0.942], Traditional Values or Modern Values?" }, { "from": "gpt", "value": "Modern Values." } ], "id_raw": 953 }, { "id": "Hs3DWHrbXNvnx8OJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in [0.757, 0.046, 0.973, 0.123]?" }, { "from": "gpt", "value": "This region contains the legend of the chart. It includes two items: {Traditional Values}[0.813, 0.055, 0.966, 0.078] with a color marker of '#003f5c', and {Modern Values}[0.813, 0.090, 0.940, 0.113] with a color marker of '#ff6361'." } ], "id_raw": 953 }, { "id": "S3ztuGFNovKW6MxG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.510, 0.952, 0.553, 0.975]." }, { "from": "gpt", "value": "This area contains the x-axis label which is labeled as 'Time'." } ], "id_raw": 953 }, { "id": "FpMG6r0wGo7NBVs3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the coordinates [0.125, 0.310, 0.938, 0.587]?" }, { "from": "gpt", "value": "The line representing the Modern Values can be found within these coordinates, including the points for each year from 1 to 20." } ], "id_raw": 953 }, { "id": "DrtsgqvVc9L2QIsi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object positioned at [0.025, 0.351, 0.048, 0.572] on the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 954 }, { "id": "Jmy97x07ODVBqjdt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_14.png", "conversations": [ { "from": "human", "value": "\nCan you mention the coordinate of the last element found in the legend?" }, { "from": "gpt", "value": "[0.124, 0.101, 0.184, 0.118]" } ], "id_raw": 954 }, { "id": "rcr9HQUERVZePz5S", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.518, 0.942, 0.567, 0.970]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 954 }, { "id": "VwOpcOlcweVZ1AgS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_14.png", "conversations": [ { "from": "human", "value": "\nIs {Physical Media sales point in 2008}[0.296, 0.527] higher or lower than {Digital Media sales point in 2008}[0.296, 0.772]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 954 }, { "id": "PpOPIxw1rOFEE4qz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_14.png", "conversations": [ { "from": "human", "value": "\nHow does {Physical Media sales point in 2021}[0.936, 0.829] compare to {Digital Media sales point in 2021}[0.936, 0.069]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 954 }, { "id": "eGutrbZHwv32TiyS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_14.png", "conversations": [ { "from": "human", "value": "\nAt {point on x-axis corresponding to 2010}[0.395, 0.893], which is greater - Physical Media sales or Digital Media sales?" }, { "from": "gpt", "value": "Physical Media sales." } ], "id_raw": 954 }, { "id": "9HUfGmHBalemH4Yw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.110, 0.030, 0.975, 0.893]." }, { "from": "gpt", "value": "This region contains the main body of the line plot, where the data for 'Physical Media' and 'Digital Media' sales from 2005 to 2021 are plotted over the Y-axis range of 0 to 600 (in millions) and the X-axis representing the years from 2004 to 2022." } ], "id_raw": 954 }, { "id": "gcWNNS9JqDrc1uGA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.121, 0.044, 0.376, 0.136]." }, { "from": "gpt", "value": "This area constitutes the legend of the chart, containing markers and labels for {Physical Media}[0.195, 0.055, 0.367, 0.083] and {Digital Media}[0.195, 0.097, 0.348, 0.125], correlated with color markers at respective positions." } ], "id_raw": 954 }, { "id": "O3YGpcBTHn68X0eT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within [0.464, 0.903, 0.522, 0.931]?" }, { "from": "gpt", "value": "There is one object. The X-axis tick label for the year '2012' is located at these coordinates." } ], "id_raw": 954 }, { "id": "QL2ONWcIn1DhUitI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_15.png", "conversations": [ { "from": "human", "value": "\nWhat kind of data is present at the coordinates [0.150, 0.810]?" }, { "from": "gpt", "value": "Data point for \"Slow Growth\" in year 2011." } ], "id_raw": 955 }, { "id": "Ctilc7JZx1JVsmdY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_15.png", "conversations": [ { "from": "human", "value": "\nWhat chart element is represented by the coordinates [0.125, 0.106, 0.306, 0.129]?" }, { "from": "gpt", "value": "The legend item 'Pronounced Increase'." } ], "id_raw": 955 }, { "id": "Msg8WLUaMeHeXuvA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.060, 0.872]?" }, { "from": "gpt", "value": "The data point for \"Pronounced Increase\" in 2010." } ], "id_raw": 955 }, { "id": "oCOWW1acZJnuqrmp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_15.png", "conversations": [ { "from": "human", "value": "\nAt the year 2015, is the 'Slow growth' rate greater or lesser compared to the 'Pronounced increase' rate referring to {coordinates for Slow growth}[0.510, 0.788] and {coordinates for Pronounced increase}[0.510, 0.743]?" }, { "from": "gpt", "value": "Lesser." } ], "id_raw": 955 }, { "id": "JM5uOIYnWT0tA3ne", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_15.png", "conversations": [ { "from": "human", "value": "\nCompared to the investment in 2013, was there an increase or decrease in investment for 'Slow growth' between the points at the {coordinates}[0.330, 0.799] and {coordinates}[0.420, 0.794]?" }, { "from": "gpt", "value": "Increase." } ], "id_raw": 955 }, { "id": "BkUkW3RFif77bFHa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_15.png", "conversations": [ { "from": "human", "value": "\nIs the 'Pronounced increase' line slopeed steeper than the slope of the 'Slow growth' line when compared {line coordinates for Pronounced increase}[0.060, 0.089, 0.959, 0.872] and {line coordinates for Slow growth}[0.060, 0.760, 0.959, 0.816]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 955 }, { "id": "cvRkDaDLHBxy7xUQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_15.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is in the area [0.069, 0.062, 0.312, 0.139]?" }, { "from": "gpt", "value": "This area encompasses the legend for the chart, indicating both the 'Slow growth' and 'Pronounced increase' categories." } ], "id_raw": 955 }, { "id": "uMarF83Bg7oPZsIO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_15.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content identified within the coordinates [0.060, 0.760, 0.959, 0.816]?" }, { "from": "gpt", "value": "This region covers the line representing 'Slow growth'. The line goes from 2010 with a slight increase each year up until 2020." } ], "id_raw": 955 }, { "id": "dpA7F15cv8XX8vJp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_15.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.060, 0.089, 0.959, 0.872]?" }, { "from": "gpt", "value": "This region contains the line that represents 'Pronounced increase', starting in 2010 and showing a significant increase each year till 2020." } ], "id_raw": 955 }, { "id": "KEfmxsvJfrC43exB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at [0.019, 0.360, 0.036, 0.576] referring to in the chart?" }, { "from": "gpt", "value": "It is the y-axis label, indicating 'Number of Patents'." } ], "id_raw": 956 }, { "id": "vXC8yjEFR2Oe4Rsq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_16.png", "conversations": [ { "from": "human", "value": "\nCan you identify the coordinates of {the marker for 'Patent Grants'} in the legend?" }, { "from": "gpt", "value": "It's located at [0.109, 0.091, 0.144, 0.091]." } ], "id_raw": 956 }, { "id": "ySWYou5VkZkCAeFa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_16.png", "conversations": [ { "from": "human", "value": "\nCould you tell me what {the x-axis tick label} at [0.721, 0.919, 0.765, 0.942] denotes?" }, { "from": "gpt", "value": "It represents the year '2015'." } ], "id_raw": 956 }, { "id": "oGRMCLfOxzTbhBrz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_16.png", "conversations": [ { "from": "human", "value": "\nDoes the line of 'Patent Applications'[0.093, 0.065, 0.916, 0.679] cross or meet with the line of 'Patent Grants'[0.093, 0.506, 0.916, 0.871] at any point?" }, { "from": "gpt", "value": "No." } ], "id_raw": 956 }, { "id": "1MDdDWKH3qvX1DxJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Patent Applications'} at year '2010'[0.526, 0.391] above or below {the point for 'Patent Grants'} at the same year?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 956 }, { "id": "OWchTzg7HlVXQTvt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_16.png", "conversations": [ { "from": "human", "value": "\nWhich patent category had a higher number in 2019, 'Patent Applications' or 'Patent Grants'?" }, { "from": "gpt", "value": "'Patent Applications'." } ], "id_raw": 956 }, { "id": "laQgJa9Td0YLLeEb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_16.png", "conversations": [ { "from": "human", "value": "\nWhat's contained within the [_0.043, 1.053, 0.087, 0.118] area of the chart?" }, { "from": "gpt", "value": "This area contains y-axis tick labels, specifically the labels for '1000', '1500', '2000', '2500', '3000' and '3500'." } ], "id_raw": 956 }, { "id": "q1D4WIls5cNVknX3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_16.png", "conversations": [ { "from": "human", "value": "\nMay you describe the region [0.102, 0.037, 0.332, 0.113] for me?" }, { "from": "gpt", "value": "This region holds the legend of the chart. It includes identifiers for 'Patent Applications' and 'Patent Grants'. Each category comes with a color marker." } ], "id_raw": 956 }, { "id": "BQRNBSdRyFOjo8n0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_16.png", "conversations": [ { "from": "human", "value": "\nWhat is shown in the region from [_0.043, -0.097, 0.087, -0.074]?" }, { "from": "gpt", "value": "In this region, the y-axis label '4000' is depicted." } ], "id_raw": 956 }, { "id": "iYA9nMDimaRCrex5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the significance of the coordinate [0.025, 0.025, 0.048, 0.826]?" }, { "from": "gpt", "value": "It is the location of the chart's y-axis label." } ], "id_raw": 957 }, { "id": "wut98M3egtMqhRHe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_17.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what {the x-axis label}[0.532, 0.927, 0.581, 0.963] is?" }, { "from": "gpt", "value": "The x-axis label is 'Year'." } ], "id_raw": 957 }, { "id": "PpuvXcUXN7XymJtr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_17.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about {the point}[0.937, 0.777] on the line?" }, { "from": "gpt", "value": "This point represents the year 2020 with an immunization percentage of about 80." } ], "id_raw": 957 }, { "id": "VZh03xJyf4I06wZd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_17.png", "conversations": [ { "from": "human", "value": "\nIs {the immunization point for 2000}[0.430, 0.778] higher or lower than {the disease cases point for the same year}[0.430, 0.309]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 957 }, { "id": "dcHgoQ90vYY6KeLF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_17.png", "conversations": [ { "from": "human", "value": "\nCompare the {line for 'Immunizations'}[0.177, 0.777, 0.937, 0.779] and the {line for 'Disease Cases'}[0.177, 0.073, 0.937, 0.686]. Which shows a steeper decline?" }, { "from": "gpt", "value": "Disease Cases." } ], "id_raw": 957 }, { "id": "O2jWWZ07mPTZgwXL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_17.png", "conversations": [ { "from": "human", "value": "\nDoes {the immunization point for 2010}[0.684, 0.777] exceed or fall short of {the immunization point for 2015}[0.810, 0.777]?" }, { "from": "gpt", "value": "Fall short." } ], "id_raw": 957 }, { "id": "bVkfhbPlROzPwUdX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the area [0.710, 0.055, 0.963, 0.170]?" }, { "from": "gpt", "value": "The area contains the chart's legend and categorizes two items: {Immunizations}[0.784, 0.069, 0.954, 0.104] and {Disease Cases}[0.784, 0.121, 0.951, 0.156]. Each is associated with a color marker." } ], "id_raw": 957 }, { "id": "yA2TbRM05sAqu399", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_17.png", "conversations": [ { "from": "human", "value": "\nTell me about the region with coordinates [0.401, 0.826, 0.459, 0.913]." }, { "from": "gpt", "value": "This region represents the x-axis tick label '2000'." } ], "id_raw": 957 }, { "id": "e3s83uo68u7SUqv9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_17.png", "conversations": [ { "from": "human", "value": "\nWhat are the objects within the coordinates [0.139, 0.503, 0.975, 0.779]?" }, { "from": "gpt", "value": "Within these coordinates, there are two lines. The first is 'Immunizations', {colors and patterns marked in blue with circles}[0.177, 0.777, 0.937, 0.779]. The second line is for 'Disease Cases', {marked in red}[0.177, 0.073, 0.937, 0.686]." } ], "id_raw": 957 }, { "id": "eShAMFMuiS2O5wen", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the representation of the coordinates [0.082, 0.084, 0.127, 0.098] in the chart?" }, { "from": "gpt", "value": "It is the color marker for education expenditure in the legend." } ], "id_raw": 958 }, { "id": "pKyz0o08t7SoveDJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis?" }, { "from": "gpt", "value": "\"Expenditure (% of GDP)\"." } ], "id_raw": 958 }, { "id": "70cyPGZWLwHhNYwu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_18.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the point at coordinates [0.515, 0.111]?" }, { "from": "gpt", "value": "It is the data point for healthcare expenditure in the year 2019." } ], "id_raw": 958 }, { "id": "y9r3s78LzyLEBYYa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_18.png", "conversations": [ { "from": "human", "value": "\nAs per the chart, is the healthcare expenditure for the year 2019 at coordinates [0.915, 0.111] higher or lower than education expenditure for the same year at coordinates [0.915, 0.802]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 958 }, { "id": "K1kXj1CdQtIfQxd6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the trend shown by line_0 within the coordinates of [0.071, 0.065, 0.915, 0.364] in comparison with line_1, within [0.071, 0.698, 0.915, 0.871]?" }, { "from": "gpt", "value": "Line_0, representing Healthcare Expenditure, tends to have higher values than line_1, which represents Education Expenditure." } ], "id_raw": 958 }, { "id": "wSTmn1nolWfrwEwT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_18.png", "conversations": [ { "from": "human", "value": "\nAre the expenditures for healthcare and education in the year 2000 at coordinates [0.071, 0.364] and [0.071, 0.871] respectively, closer to each other or further apart compared to year 2019 [0.915, 0.111] and [0.915, 0.802]?" }, { "from": "gpt", "value": "Closer." } ], "id_raw": 958 }, { "id": "3YUn5LKJ5y9Alj65", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area covered by the coordinates [0.080, 0.037, 0.348, 0.113]?" }, { "from": "gpt", "value": "This area hosts the legend of the chart, which includes two entries: Healthcare Expenditure with a color indicator of '#31977e' and Education Expenditure with a color indicator of '#f96b39'." } ], "id_raw": 958 }, { "id": "oJ5mrT8YpkOK6bxS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is contained within the region [0.071, 0.065, 0.915, 0.871]." }, { "from": "gpt", "value": "This area contains the plots for both Healthcare and Education Expenditure. The data points for each year from 2000 to 2019 are plotted and connected by lines to show the trend of expenditure over the years. The specific values of each year are represented by the coordinates of the points on the lines." } ], "id_raw": 958 }, { "id": "cZkdiutbncWAHecn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_18.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is located in the region [0.043, -0.070, 0.981, 0.988]?" }, { "from": "gpt", "value": "This area contains all elements of the chart, including the x-axis and y-axis with their respective labels, the legend, and the plotted lines for the Education and Healthcare Expenditures over the years 2000 to 2019." } ], "id_raw": 958 }, { "id": "VdsUCUM54KQcmely", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_19.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.019, 0.359, 0.036, 0.586]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 959 }, { "id": "eqO2MJEIGDjY0sWP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the {y-axis tick label} at [0.043, 0.171, 0.065, 0.194]?" }, { "from": "gpt", "value": "10." } ], "id_raw": 959 }, { "id": "YFrMRLqhMYdFhCzt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_48_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the {legend item} at [0.136, 0.090, 0.311, 0.113] represent?" }, { "from": "gpt", "value": "Urban Development." } ], "id_raw": 959 }, { "id": "659olFyJ8CduoS7f", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_19.png", "conversations": [ { "from": "human", "value": "\nCompare the values of {Rural Development Index} at [0.790, 0.340] and {Urban Development Index} at [0.800, 0.135]. Which one is higher?" }, { "from": "gpt", "value": "Urban Development Index." } ], "id_raw": 959 }, { "id": "zBJxNK8mPNNdRb7T", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_19.png", "conversations": [ { "from": "human", "value": "\nIs the last point of {Rural Development} [0.936, 0.319] on the chart higher or lower than the first point of {Urban Development} [0.071, 0.400]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 959 }, { "id": "lyL9WXrrSf8LRYdV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_48_19.png", "conversations": [ { "from": "human", "value": "\nFor year 2012, which index is higher: {Urban Development} at [0.572, 0.210] or {Rural Development} at [0.572, 0.367]?" }, { "from": "gpt", "value": "Urban Development" } ], "id_raw": 959 }, { "id": "hldDZATtdf6KxHgh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.080, 0.046, 0.317, 0.123]." }, { "from": "gpt", "value": "The area contains the legend for the line chart. Two items are included: {Rural Development}[0.136, 0.055, 0.303, 0.078] and {Urban Development}[0.136, 0.090, 0.311, 0.113], each paired with a color marker." } ], "id_raw": 959 }, { "id": "rhcCyALWXe0MRpVM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_19.png", "conversations": [ { "from": "human", "value": "\nWhat do the labels from [0.140, 0.919, 0.184, 0.942] to [0.868, 0.919, 0.912, 0.942] in the x-axis represent?" }, { "from": "gpt", "value": "They represent the years 2003 to 2019 in the x-axis of the chart." } ], "id_raw": 959 }, { "id": "YxsvSJGSi5E2JTKJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_48_19.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the region [0.071, 0.115, 0.936, 0.400]?" }, { "from": "gpt", "value": "This region contains the line representing the {Urban Development} trend from 2001 to 2020 in the chart." } ], "id_raw": 959 }, { "id": "DIY3xXaLeb6YZrm8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the first point of the Domestic Trade line}[0.066, 0.693]?" }, { "from": "gpt", "value": "The coordinate represents the point where the Domestic Trade line starts, with x-axis label 1 and y value of 3.0." } ], "id_raw": 960 }, { "id": "hCgrVh8kyc6wR4cB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the content at {coordinates}[0.805, 0.087, 0.934, 0.117]?" }, { "from": "gpt", "value": "It's the label for the Domestic Trade line in the legend." } ], "id_raw": 960 }, { "id": "JZEADhXf2Fs0vMal", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-value of the {seventh point on the International Trade line}[0.563, 0.524]?" }, { "from": "gpt", "value": "The y-value at this point is 3.8." } ], "id_raw": 960 }, { "id": "MXuRNIriQTMykomb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_0.png", "conversations": [ { "from": "human", "value": "\nIs {Quarter 7 point of Domestic Trade}[0.563, 0.335] above or below {Quarter 7 point of International Trade}[0.563, 0.524]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 960 }, { "id": "zSeN0iiResgjt0mm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_0.png", "conversations": [ { "from": "human", "value": "\nAt {quarter 12}[0.976, 0.903], which line's y-value is higher, Domestic Trade or International Trade?" }, { "from": "gpt", "value": "Domestic Trade." } ], "id_raw": 960 }, { "id": "mYIUwAQFLQkZOs6P", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_0.png", "conversations": [ { "from": "human", "value": "\nIs the value of {the International Trade at Quarter 6}[0.480, 0.398] higher or lower than that at {Quarter 5}[0.397, 0.461]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 960 }, { "id": "QNOmrENyGoJjEzrU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area within [0.751, 0.076, 0.968, 0.172]." }, { "from": "gpt", "value": "This is the area of the chart legend. It includes two entries, one for Domestic Trade, color-coded in blue, and another for International Trade, color-coded in red." } ], "id_raw": 960 }, { "id": "eEytdHWBFyM8yWHn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.885, 0.911, 0.985, 0.934]?" }, { "from": "gpt", "value": "The x-axis label '12' is located within these coordinates." } ], "id_raw": 960 }, { "id": "BBXQ9YXkxIcWp9XS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.286, 0.025, 0.756, 0.055]." }, { "from": "gpt", "value": "The area contains the subtitle of the chart: \"Domestic VS International Trade Performance Over Time.\"" } ], "id_raw": 960 }, { "id": "uVBfxfB47X8xwzXB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.263, 0.025, 0.788, 0.058] in the chart?" }, { "from": "gpt", "value": "Subtitle of the chart." } ], "id_raw": 962 }, { "id": "iNybaCIFFArcARFd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.456, 0.943, 0.596, 0.975]?" }, { "from": "gpt", "value": "Time (Months)." } ], "id_raw": 962 }, { "id": "RFKrv4j0hOEdpwAc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the line}[0.108, 0.294, 0.943, 0.613]?" }, { "from": "gpt", "value": "Physical Resources." } ], "id_raw": 962 }, { "id": "EC3uhV02BISiGK3M", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point at 'Time' 2 for 'Physical Resources'}[0.247, 0.355] above or below {the point at 'Time' 2 for 'Online Resources'}[0.247, 0.575] in the chart?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 962 }, { "id": "0sHdNjZVdWTnpDBV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Physical Resources' data point at 'Time' 0}[0.108, 0.294] have a higher or lower value than {the 'Online Resources' data point at 'Time' 0}[0.108, 0.674]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 962 }, { "id": "D5B95UNfdlqZ2XOC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_2.png", "conversations": [ { "from": "human", "value": "\nBetween {the 'Physical Resources' data point at 'Time' 12}[0.943, 0.613] and {the 'Online Resources' data point at 'Time' 12}[0.943, 0.180], which line has a lower value?" }, { "from": "gpt", "value": "Online Resources." } ], "id_raw": 962 }, { "id": "ktZXiUYmrwx4Cfip", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content located in [0.759, 0.079, 0.977, 0.176]." }, { "from": "gpt", "value": "It's a legend section containing two items. One is 'Physical Resources' with a blue marker located at {the coordinates}[0.765, 0.104, 0.799, 0.104], and the other one is 'Online Resources' with a red marker located at {the coordinates}[0.765, 0.148, 0.799, 0.148]." } ], "id_raw": 962 }, { "id": "FNLfYvZzDeMeHZAW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_2.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.066, 0.066, 0.985, 0.903]?" }, { "from": "gpt", "value": "This area contains the entirety of the main chart. It includes two line plots representing 'Physical Resources' and 'Online Resources', their respective x-y coordinates, and grid lines." } ], "id_raw": 962 }, { "id": "qaUW6DftupfC81Br", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are presented within the region of [0.104, 0.911, 0.113, 0.934]?" }, { "from": "gpt", "value": "There is an x-axis tick label '0' within this region." } ], "id_raw": 962 }, { "id": "bZpZuzYNJiHCcTdj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at coordinates [0.098, 0.109, 0.120, 0.132] on the vertical axis?" }, { "from": "gpt", "value": "1.0." } ], "id_raw": 963 }, { "id": "Ws6IYyhcDfC9E6zq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_3.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what {the point}[0.673, 0.622] belongs to on the chart?" }, { "from": "gpt", "value": "Innovation Industries." } ], "id_raw": 963 }, { "id": "FMalaJegU646BUfR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the 'Innovation Industries'} at [0.663, 0.203, 0.697, 0.203] in the legend?" }, { "from": "gpt", "value": "It is 'red'." } ], "id_raw": 963 }, { "id": "Lfwjw9HMF0MmtdJ6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_3.png", "conversations": [ { "from": "human", "value": "\nComparing the starting values, is the performance of Knowledge Economy at {point 0}[0.160, 0.794] better or worse than {Innovation Industries}[0.160, 0.732]?" }, { "from": "gpt", "value": "Better." } ], "id_raw": 963 }, { "id": "4tRqvJSLnmbB1y9s", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_3.png", "conversations": [ { "from": "human", "value": "\nAt the {coordinate}[0.865, 0.229], does {Knowledge Economy}([0.865, 0.229]) have a better performance or {Innovation Industries}([0.865, 0.855])?" }, { "from": "gpt", "value": "Knowledge Economy." } ], "id_raw": 963 }, { "id": "MtdFG6SvYMv7mw1d", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_3.png", "conversations": [ { "from": "human", "value": "\nAt {point 7}[0.609, 0.155] of {Knowledge Economy}([0.609, 0.155]), is the index greater or lesser than {Innovation Industries}([0.609, 0.585]) at the same time?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 963 }, { "id": "dVE9xvBn4Uuc9TN6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area [0.657, 0.134, 0.892, 0.230]." }, { "from": "gpt", "value": "This region contains the legend of the chart displaying the categories Knowledge Economy and Innovation Industries, each assigned with their respective colors: blue for {Knowledge Economy}[0.710, 0.145, 0.883, 0.175] and red for {Innovation Industries}[0.710, 0.189, 0.885, 0.219]." } ], "id_raw": 963 }, { "id": "Te2CZslTbwrBGYKm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.252, 0.083, 0.773, 0.113]." }, { "from": "gpt", "value": "The given area accommodates the chart's subtitle which is \"Performance of Knowledge Economy and Innovation Industries\"." } ], "id_raw": 963 }, { "id": "s1SYKRhSOqqSTkv7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_3.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.160, 0.192, 0.865, 0.855]?" }, { "from": "gpt", "value": "The plot area contains two lines representing the performance of Knowledge Economy and Innovation Industries over time. The lines consist of connected points representing different time units." } ], "id_raw": 963 }, { "id": "rgxcunp6dBgQL6X3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.015, 0.388, 0.034, 0.577]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 964 }, { "id": "NTEg6cqbuAi98a6t", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the sixth label for the x-tick marks?" }, { "from": "gpt", "value": "Gradual improvement." } ], "id_raw": 964 }, { "id": "DO1izF7NHxFM65g9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-coordinate value for the x-label 'Slow increase'?" }, { "from": "gpt", "value": "0.5." } ], "id_raw": 964 }, { "id": "5Q22pKNjSFx3wNbO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_4.png", "conversations": [ { "from": "human", "value": "\nIs the trend value for 'Slight decrease' higher or lower than 'Downward trend'?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 964 }, { "id": "vZ7ub8uwJCLylY0X", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_4.png", "conversations": [ { "from": "human", "value": "\nDoes 'Pronounced increase' have a higher trend value than 'Subdued expansion'?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 964 }, { "id": "fmB1KoCrQEF6fPm4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_4.png", "conversations": [ { "from": "human", "value": "\nIs the trend value for 'Gradual improvement' higher than 'Slow increase'?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 964 }, { "id": "fiHL9J60RVJfYi7H", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.040, 0.994, 0.073, 1.018]." }, { "from": "gpt", "value": "The y-axis tick label '-0.4' is located in this region." } ], "id_raw": 964 }, { "id": "0eSSHuT37rJORUNU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within [0.065, 0.911, 0.172, 0.934]?" }, { "from": "gpt", "value": "The x-axis label 'Slight decrease' is in this area." } ], "id_raw": 964 }, { "id": "XKCvkI8Y7ud4vEYk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_4.png", "conversations": [ { "from": "human", "value": "\nWhat is found within the region [0.741, 0.087, 0.958, 0.117]?" }, { "from": "gpt", "value": "The label 'International Trade Trends' of the legend is located here." } ], "id_raw": 964 }, { "id": "sDGrMwVnQ8Ywcleo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the y-coordinate tick}[0.039, -0.085, 0.062, -0.062] in the subplot?" }, { "from": "gpt", "value": "There is no label because it's outside the chart boundary of y-coordinates." } ], "id_raw": 965 }, { "id": "OdmGEDQgcsnM8Vjj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_5.png", "conversations": [ { "from": "human", "value": "\nWhat type of object is at the coordinates [0.128, 0.087, 0.232, 0.117] within the chart?" }, { "from": "gpt", "value": "This is the label of a legend item for 'Active Users'." } ], "id_raw": 965 }, { "id": "MYMrW6cWrdrtFu7R", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for an x-axis tick located at [0.104, 0.911, 0.113, 0.934]?" }, { "from": "gpt", "value": "The label is '0'." } ], "id_raw": 965 }, { "id": "Lwq9wVfSecRG9Yp0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_5.png", "conversations": [ { "from": "human", "value": "\nLooking at the 'Active Users' line and the point at [0.178, 0.206], is the Active Users count increasing or decreasing compared to the previous point?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 965 }, { "id": "wFLDEMR5lzlEEY0V", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_5.png", "conversations": [ { "from": "human", "value": "\nComparing the 'Active Users' and 'New Users' lines, which one has a higher value at [0.943, 0.347]?" }, { "from": "gpt", "value": "'Active Users'" } ], "id_raw": 965 }, { "id": "ae9lIl7w1BHcg8H4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_5.png", "conversations": [ { "from": "human", "value": "\nOn the 'New Users' line, is the point at [0.595, 0.465] at a higher or lower y-value than the point at [0.734, 0.524]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 965 }, { "id": "M6BW1WqpHaYsZ52n", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe what can be seen in the area marked by [0.075, 0.076, 0.238, 0.172]?" }, { "from": "gpt", "value": "This area contains the chart's legend. We can spot two items, one labeled 'Active Users' with a blue color at [0.128, 0.087, 0.232, 0.117], and the other one called 'New Users' with a red color at [0.128, 0.131, 0.217, 0.161]." } ], "id_raw": 965 }, { "id": "MCs39kcSjMjrEaYt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents in the region [0.231, 0.025, 0.821, 0.055] ?" }, { "from": "gpt", "value": "The area contains the chart's subtitle, which reads \"Monthly Trends of Active and New Users on Social Networking Platform\"." } ], "id_raw": 965 }, { "id": "HEVd99mKLGrOBOmI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the bounds [0.066, 0.062, 0.985, 0.903]." }, { "from": "gpt", "value": "This is a large area, covering most of the chart. It contains the plot of 'Active Users' and 'New Users' lines with various x-ticks representing months and y-ticks representing the number of users. There is also a legend in the lower-left corner with two entries, 'Active Users' and 'New Users'." } ], "id_raw": 965 }, { "id": "v0hutc82qsa6FCCj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the xlabel}[0.449, 0.943, 0.589, 0.975] in the chart?" }, { "from": "gpt", "value": "Time (Months)." } ], "id_raw": 966 }, { "id": "1okwgXRZUVlpk5sW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_6.png", "conversations": [ { "from": "human", "value": "\nWhat is present at coordinates [0.068, 0.101, 0.102, 0.101]?" }, { "from": "gpt", "value": "A blue handle marker corresponding to \"Inflation\" in the legend." } ], "id_raw": 966 }, { "id": "22cBq6GIqs9Pf0F3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_6.png", "conversations": [ { "from": "human", "value": "\nWhat information does {ylabel}[0.015, 0.277, 0.034, 0.688] convey?" }, { "from": "gpt", "value": "Economic Indicator Value." } ], "id_raw": 966 }, { "id": "VTqPMlKZ4fTcioVG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_6.png", "conversations": [ { "from": "human", "value": "\nAt the fifth point, is the {Inflation}[0.404, 0.725] value higher or lower than {Unemployment}[0.404, 0.427]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 966 }, { "id": "CZGhbsHxgQzboFIF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_6.png", "conversations": [ { "from": "human", "value": "\nAt the 8th point, is {Unemployment}[0.635, 0.287] higher or lower than {Inflation}[0.635, 0.776]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 966 }, { "id": "9sK6OMIujjrgiD19", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_6.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher at the first point, {Unemployment}[0.096, 0.491] or {Inflation}[0.096, 0.835]?" }, { "from": "gpt", "value": "Inflation." } ], "id_raw": 966 }, { "id": "YktT4t8GAMprb1L9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area [0.062, 0.076, 0.250, 0.172]." }, { "from": "gpt", "value": "This area contains the legend for the chart. It includes {Inflation}[0.115, 0.087, 0.183, 0.117] with a blue marker at {coordinates}[0.068, 0.101, 0.102, 0.101], and {Unemployment}[0.115, 0.131, 0.243, 0.161] with a red marker at {coordinates}[0.068, 0.145, 0.102, 0.145]." } ], "id_raw": 966 }, { "id": "0GKj0CAoUZaobMDz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.096, 0.124, 0.943, 0.504]." }, { "from": "gpt", "value": "This area contains the line representing the 'Unemployment' data. It is coded with a red line." } ], "id_raw": 966 }, { "id": "Bc2AKYBCOhjIhpDH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_6.png", "conversations": [ { "from": "human", "value": "\nWhat content is inside [0.414, 0.025, 0.624, 0.055]?" }, { "from": "gpt", "value": "It contains the subtitle of the chart, \"Statistical Data Overview.\"" } ], "id_raw": 966 }, { "id": "6hARLGT9KB5KT7e1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart object located at [0.015, 0.330, 0.034, 0.635]?" }, { "from": "gpt", "value": "The y-axis label." } ], "id_raw": 967 }, { "id": "MsOY5CdHmlmxCreV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the location of the label for the second x-axis tick?" }, { "from": "gpt", "value": "[0.243, 0.911, 0.252, 0.934]." } ], "id_raw": 967 }, { "id": "Hj4dMi9TrgTI6SfE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the index at time point 5 along the 'Financial Management' line} at coordinate [0.456, 0.776]?" }, { "from": "gpt", "value": "0.52." } ], "id_raw": 967 }, { "id": "nTpdeoeFbQueTBXr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_7.png", "conversations": [ { "from": "human", "value": "\nIs {the index at the 7th time point for 'Financial Management'}[0.595, 0.633] higher or lower than {the index at the same time point for 'Risk Control'}[0.595, 0.491]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 967 }, { "id": "CtVxFStMxabp8QRa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_7.png", "conversations": [ { "from": "human", "value": "\nIs {the third point on the 'Financial Management' line}[0.247, 0.793] greater or less than {the third point on the 'Risk Control' line}[0.247, 0.314]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 967 }, { "id": "uJL3l8vlGlottAS9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_7.png", "conversations": [ { "from": "human", "value": "\nWhich index is higher at {the 12th time point}, 'Financial Management' or 'Risk Control'?" }, { "from": "gpt", "value": "Risk Control." } ], "id_raw": 967 }, { "id": "rMgDmufagPredzms", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the plot area [0.108, 0.100, 0.943, 0.864]." }, { "from": "gpt", "value": "This region contains the plotted lines for 'Financial Management' and 'Risk Control' indices over time. Each line consists of multiple points representing the index value at specific time points, with the blue line representing 'Financial Management' and the red line representing 'Risk Control'." } ], "id_raw": 967 }, { "id": "IoWmH4ymystRw3J0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the coordinate [0.075, 0.076, 0.322, 0.172]?" }, { "from": "gpt", "value": "This area contains a legend indicating the color codes for the two plotted lines, 'Financial Management' labeled with blue and 'Risk Control' labeled with red." } ], "id_raw": 967 }, { "id": "RZk0qIV1E9vVaCG2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.456, 0.598, 0.595, 0.633]?" }, { "from": "gpt", "value": "There are 2 objects. The points on the 'Financial Management' and 'Risk Control' lines representing the index at the 5th time point are included within these coordinates." } ], "id_raw": 967 }, { "id": "pAAnd9c6EDTV45cp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the line tagged as 'line_0' located at [0.067, 0.365, 0.976, 0.714]?" }, { "from": "gpt", "value": "AI Automation trend line." } ], "id_raw": 968 }, { "id": "Tjj27wTL9DLV35ya", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of the label {at coordinate}[0.781, 0.131, 0.961, 0.161] in the legend?" }, { "from": "gpt", "value": "Industrial Automation." } ], "id_raw": 968 }, { "id": "h8rWskfFrx6JMzFd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the value represented by {the y-axis tick label}[0.040, 0.641, 0.062, 0.665]?" }, { "from": "gpt", "value": "0.6." } ], "id_raw": 968 }, { "id": "WxOGwevTfCLpGEbb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_8.png", "conversations": [ { "from": "human", "value": "\nBetween {the AI Automation trend line}[0.067, 0.365, 0.976, 0.714] and {the Industrial Automation trend line}[0.067, 0.449, 0.976, 0.646], which line is higher at time 8 (Month)?" }, { "from": "gpt", "value": "AI Automation." } ], "id_raw": 968 }, { "id": "jQZWAJU61b3EZNw8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_8.png", "conversations": [ { "from": "human", "value": "\nIs {the point on the AI Automation trend line at time '1'}[0.142, 0.651] above or below {the point on the Industrial Automation trend line at time '1'}[0.142, 0.575]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 968 }, { "id": "0oA5g3qfSBeHmfWk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_8.png", "conversations": [ { "from": "human", "value": "\nIs the endpoint of {the 'Industrial Automation'}[0.967, 0.911, 0.985, 0.934] line higher or lower than that of {the 'AI Automation'}[0.967, 0.885, 0.985, 0.908] line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 968 }, { "id": "9PSgJ4W2LEEUlNMM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the coordinates [0.728, 0.076, 0.968, 0.172]." }, { "from": "gpt", "value": "This region contains the chart legend. The legend has two items: {AI Automation}[0.781, 0.087, 0.900, 0.117] marked with a blue line, and {Industrial Automation}[0.781, 0.131, 0.961, 0.161] marked with a red line." } ], "id_raw": 968 }, { "id": "CcvKqtV7lwUxdnv1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_8.png", "conversations": [ { "from": "human", "value": "\nCan you provide a description of the area delimited by the coordinates [0.040, 0.557, 0.062, 0.581]?" }, { "from": "gpt", "value": "This region houses the y-axis tick label '0.8'." } ], "id_raw": 968 }, { "id": "IorHK5ujnHftRkwJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.451, 0.943, 0.591, 0.975]." }, { "from": "gpt", "value": "This region contains the x-axis label: {Time (Months)}[0.451, 0.943, 0.591, 0.975]." } ], "id_raw": 968 }, { "id": "XMetAHARQjvrxmJl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis represent in the chart?" }, { "from": "gpt", "value": "{The y-axis}<[0.015, 0.367, 0.034, 0.597]> represents the 'Adoption Rate'." } ], "id_raw": 969 }, { "id": "y7gvq36HMTFL1DeD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the nature of the line with the legend 'AR Technology'?" }, { "from": "gpt", "value": "{The line plotted}[0.108, 0.100, 0.943, 0.755] for 'AR Technology' shows a fluctuating trend over time." } ], "id_raw": 969 }, { "id": "wdArmtF2FZW89Dza", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis depict in the graph?" }, { "from": "gpt", "value": "{The x-axis}<[0.456, 0.943, 0.596, 0.975]> represents 'Time (Months)'." } ], "id_raw": 969 }, { "id": "AsEU6OY9CKBXKPK6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_9.png", "conversations": [ { "from": "human", "value": "\nBetween 'AR Technology' and 'VR Technology', which line begins at a higher point?" }, { "from": "gpt", "value": "The line representing {AR Technology}[0.108, 0.100, 0.943, 0.755] begins at a higher point on the graph than the line for {VR Technology}[0.108, 0.624, 0.943, 0.864]." } ], "id_raw": 969 }, { "id": "arJVnMxDdl82Mapn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_9.png", "conversations": [ { "from": "human", "value": "\nWhere does the line for 'AR Technology' end relative to 'VR Technology'?" }, { "from": "gpt", "value": "The line representing 'AR Technology' ends higher on the graph as compared to the line representing {VR Technology}[0.108, 0.624, 0.943, 0.864]." } ], "id_raw": 969 }, { "id": "B84BYf8yPt6vtQhH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_9.png", "conversations": [ { "from": "human", "value": "\nWhich technology has a higher adoption rate at the 5th month?" }, { "from": "gpt", "value": "{AR Technology}[0.412, 0.210] has a higher adoption rate at the 5th month compared to VR Technology." } ], "id_raw": 969 }, { "id": "3ywpriRwbqZ0KNQg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_9.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the coordinates [0.849, 0.087, 0.970, 0.117]?" }, { "from": "gpt", "value": "Within this area, {the legend}[0.849, 0.087, 0.970, 0.117] for 'AR Technology' is located." } ], "id_raw": 969 }, { "id": "Jd0LEHli5ENhVnxc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_9.png", "conversations": [ { "from": "human", "value": "\nWhat is specified in the coordinates [0.795, 0.076, 0.977, 0.172]?" }, { "from": "gpt", "value": "This area contains the legend section, including descriptions for {AR Technology}[0.849, 0.087, 0.970, 0.117] and {VR Technology}[0.849, 0.131, 0.970, 0.161] with their respective color labels." } ], "id_raw": 969 }, { "id": "BuzXqKTuHBuwA7vT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_9.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.108, 0.100, 0.943, 0.755]?" }, { "from": "gpt", "value": "The region contains the plotted line for AR Technology from points 1 to 12 representing the varying adoption rate for the technology over time." } ], "id_raw": 969 }, { "id": "90qZaB9L7LOQnAhl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third text in the x-axis tick labels?" }, { "from": "gpt", "value": "[0.157, 0.912, 0.166, 0.936]" } ], "id_raw": 970 }, { "id": "v0NP6JoMbrPHRjIJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_10.png", "conversations": [ { "from": "human", "value": "\nWhat kind of object lies at [0.117, 0.118, 0.246, 0.141] in the chart?" }, { "from": "gpt", "value": "Sublegend labeled 'Automation Speed'." } ], "id_raw": 970 }, { "id": "fAovpUdszBJxv9fE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.039, 0.589, 0.061, 0.612]?" }, { "from": "gpt", "value": "0.6" } ], "id_raw": 970 }, { "id": "I7FkWhF16Yy8Z8yf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_10.png", "conversations": [ { "from": "human", "value": "\nIs the {point at time '7'}[0.353, 0.445] on 'AI Efficiency' line above or below the {point at time '7'}[0.353, 0.445] on 'Automation Speed' line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 970 }, { "id": "NEGZf5BGMiLpXHzc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_10.png", "conversations": [ { "from": "human", "value": "\nDoes the {AI Efficiency line}[0.065, 0.430, 0.976, 0.828] intersect with the {Automation Speed line}[0.065, 0.139, 0.976, 0.751] from starting time to ending time?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 970 }, { "id": "EOBkQJrdsaaQrxwN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_10.png", "conversations": [ { "from": "human", "value": "\nWhich trend is higher at {point at time '10'}[0.497, 0.445], AI Efficiency or Automation Speed?" }, { "from": "gpt", "value": "Automation Speed." } ], "id_raw": 970 }, { "id": "Q6HLn60tHVIQK9bj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_10.png", "conversations": [ { "from": "human", "value": "\nWhat can you find in [0.291, 0.025, 0.751, 0.055]?" }, { "from": "gpt", "value": "The subtitle of the chart, \"Artificial Intelligence and Automation Trends Over Time,\" can be found in this region." } ], "id_raw": 970 }, { "id": "l0uFpVPBj5VyuWTP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.461, 0.945, 0.581, 0.975] contain?" }, { "from": "gpt", "value": "This region displays the x-axis label \"Time (Months).\"" } ], "id_raw": 970 }, { "id": "YPSKp6mGyjSYWhyJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_10.png", "conversations": [ { "from": "human", "value": "\nCan you provide details in the coordinates [0.253, 0.912, 0.262, 0.936]?" }, { "from": "gpt", "value": "The x-axis tick label, '5', locates within these coordinates." } ], "id_raw": 970 }, { "id": "NbF3EMsX0OBp6hyB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_11.png", "conversations": [ { "from": "human", "value": "\nWhat type of label corresponds to {the label at the vertical coordinates}[0.015, 0.330, 0.034, 0.635]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 971 }, { "id": "Uio14TirbI9QSgUd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of {the text box}[0.273, 0.025, 0.778, 0.055]?" }, { "from": "gpt", "value": "The subtitle \"Trends in New Media and Digital Content Creation Over Time\"." } ], "id_raw": 971 }, { "id": "vVWXIwx1rYT1ItEm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_11.png", "conversations": [ { "from": "human", "value": "\nWhat element is located at {coordinates}[0.456, 0.943, 0.596, 0.975] on the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 971 }, { "id": "wXvDaJI64OgxdgZq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_11.png", "conversations": [ { "from": "human", "value": "\nWhich is higher on the graph, {the point with coordinates '0.108, 0.145'} or {the point with coordinates '0.943, 0.145'}?" }, { "from": "gpt", "value": "Equal." } ], "id_raw": 971 }, { "id": "k4t7a2bBjkvA4UwN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_11.png", "conversations": [ { "from": "human", "value": "\nComparing the values at {the first point on the 'Interface' line}[0.108, 0.323] and {the first point on the 'Algorithm' line}[0.108, 0.500], which one is higher?" }, { "from": "gpt", "value": "Interface." } ], "id_raw": 971 }, { "id": "WUJBgp5zDAAeuMGE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the first marked point on the 'Algorithm' trend}[0.108, 0.500] appear before or after the {last marked point on the 'Design Pattern' trend}[0.943, 0.145]?" }, { "from": "gpt", "value": "Before." } ], "id_raw": 971 }, { "id": "ffmMmOKlx6axnJSQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region between [0.066, 0.062, 0.985, 0.903]." }, { "from": "gpt", "value": "This region encloses all the axes and plotted data for the chart including grid lines, y-axis labels ranging from '-0.2' to '0.8', x-axis labels from '\u22122' to '14', and three primary lines labeled 'Design Pattern', 'Interface', and 'Algorithm'." } ], "id_raw": 971 }, { "id": "MgYdp2sWE6oZZiRi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the area bounded by [0.794, 0.076, 0.977, 0.216] contain?" }, { "from": "gpt", "value": "This area houses the legend of the chart with the labels {Design Pattern}[0.847, 0.087, 0.970, 0.117], {Interface}[0.847, 0.131, 0.922, 0.161], and {Algorithm}[0.847, 0.175, 0.929, 0.205] associated with their respective colors." } ], "id_raw": 971 }, { "id": "OasSh3GxzViuOYuT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the area [0.108, 0.145, 0.943, 0.820]?" }, { "from": "gpt", "value": "This region includes all three plotted lines for 'Design Pattern', 'Interface', and 'Algorithm' showing their trends over time along the x-axis from '\u22122' to '14' months and y-axis from '\u22120.2' to '0.8' in performance index." } ], "id_raw": 971 }, { "id": "kwcmm1Lm7oEmlcuG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_12.png", "conversations": [ { "from": "human", "value": "\nWhat denotes the location [0.015, 0.312, 0.034, 0.653] in this chart?" }, { "from": "gpt", "value": "It is the Y-axis label or 'Production Efficiency'." } ], "id_raw": 972 }, { "id": "nw5xagRrqR7wL4qR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the bounding box of the X-axis Label?" }, { "from": "gpt", "value": "The bounding box is [0.445, 0.943, 0.585, 0.975]" } ], "id_raw": 972 }, { "id": "HHuIFb5PjerNwbMQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_12.png", "conversations": [ { "from": "human", "value": "\nWhat is {the point}[0.669, 0.331] on the 'Standard Production' line?" }, { "from": "gpt", "value": "It is point 8." } ], "id_raw": 972 }, { "id": "pNmywMBhn7jpU80b", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_12.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value at {point 4}[0.361, 0.466], Standard Production or High-tech Production?" }, { "from": "gpt", "value": "High-tech Production." } ], "id_raw": 972 }, { "id": "KbK2yXsQraW59Hor", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point 2}[0.207, 0.415] on 'Standard Production' line located above or below the point 2 on 'High-tech Production'[0.207, 0.365] line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 972 }, { "id": "OxPAaNFvZ85vCF5c", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the point at the 12th interval}[0.976, 0.482] on the 'Standard Production' line have a higher or lower production efficiency than the corresponding point on the 'High-tech Production'[0.976, 0.499] line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 972 }, { "id": "dkl96GflxFXnClG8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements at location [0.062, 0.076, 0.296, 0.172]?" }, { "from": "gpt", "value": "Yes, this area describes two legend items: {Standard Production}[0.115, 0.087, 0.284, 0.117] with a color indicator at {coordinates}[0.068, 0.101, 0.102, 0.101], and {High-tech Production}[0.115, 0.131, 0.289, 0.161] with a color indicator at {coordinates}[0.068, 0.145, 0.102, 0.145]." } ], "id_raw": 972 }, { "id": "9tCj3Wiziu6djv0n", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.053, 0.264, 0.976, 0.701] contain?" }, { "from": "gpt", "value": "This region contains the line for {High-tech Production} with a number of points that represent different time intervals and their corresponding production efficiencies." } ], "id_raw": 972 }, { "id": "EwF5vTGSVlm9BZRY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.053, 0.264, 0.976, 0.735]." }, { "from": "gpt", "value": "This area includes the line for {Standard Production} with multiple data points for different time intervals representing their production efficiencies." } ], "id_raw": 972 }, { "id": "7QdfpoeK3KVkfGIw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the element located at coordinates [0.015, 0.343, 0.034, 0.622] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 973 }, { "id": "jhtzY6VZtgNSJqjo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_13.png", "conversations": [ { "from": "human", "value": "\nWhere is the seventh element (associated with label '12') positioned on the x-axis?" }, { "from": "gpt", "value": "[0.935, 0.911, 0.952, 0.934]" } ], "id_raw": 973 }, { "id": "kYCCF8yQxLuGZ4j2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_13.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what the x-axis label at coordinates [0.497, 0.943, 0.558, 0.975] is?" }, { "from": "gpt", "value": "Month." } ], "id_raw": 973 }, { "id": "cn25bscSOniL2EHV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_13.png", "conversations": [ { "from": "human", "value": "\nAre the sales values represented by the Regular Purchase line above or below Panic Buying at point 7?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 973 }, { "id": "ymLeWNzRbVJ84Nn9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_13.png", "conversations": [ { "from": "human", "value": "\nBy observing the coordinates of {Regular Purchase's 5th point}[0.415, 0.287] and {Panic Buying's 5th point}[0.415, 0.211], which line is higher on the graph?" }, { "from": "gpt", "value": "Panic Buying." } ], "id_raw": 973 }, { "id": "f7nmqNRwXn21jF71", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_13.png", "conversations": [ { "from": "human", "value": "\nComparing the last points of both {Regular Purchase}[0.943, 0.143] and {Panic Buying}[0.943, 0.100], which of them represents a higher purchasing volume?" }, { "from": "gpt", "value": "Panic Buying." } ], "id_raw": 973 }, { "id": "Hh23TpNLNMSeMWc8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content and elements found between [0.079, 0.076, 0.284, 0.172]." }, { "from": "gpt", "value": "This region contains the chart legend. It includes two elements: {Regular Purchase}[0.132, 0.087, 0.277, 0.117] labeled with the blue color and {Panic Buying}[0.132, 0.131, 0.239, 0.161] labeled with the red color." } ], "id_raw": 973 }, { "id": "2sJZdyliZiDfznW3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within the area from [0.497, 0.943, 0.558, 0.975]?" }, { "from": "gpt", "value": "The x-axis label \"Month\" is situated within these coordinates." } ], "id_raw": 973 }, { "id": "xB4aSrDzdzdlzK56", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements present within the region [0.074, 0.870, 0.204, 0.917]." }, { "from": "gpt", "value": "The region contains two quantities from the y-axis marking values of 0 and 100, located at {coordinates}[0.057, 0.855, 0.066, 0.879] and {coordinates}[0.040, 0.685, 0.066, 0.709] respectively." } ], "id_raw": 973 }, { "id": "0tpfc9apD02Zv0gC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the plotted data denoted by {the line with blue color}[0.108, 0.278, 0.943, 0.863]?" }, { "from": "gpt", "value": "AI Implementation." } ], "id_raw": 974 }, { "id": "EJkrJ6CnGtKaZ1R1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at {coordinates}[0.456, 0.943, 0.596, 0.975]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 974 }, { "id": "Cha0aNdo358EslII", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at {coordinates}[0.809, 0.050, 0.963, 0.080] in the legend?" }, { "from": "gpt", "value": "AI Implementation." } ], "id_raw": 974 }, { "id": "vlDmWrCtLenXJ6O3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_14.png", "conversations": [ { "from": "human", "value": "\nWhich line, {AI Implementation}[0.108, 0.278, 0.943, 0.863] or {ML Implementation}[0.108, 0.065, 0.943, 0.224], has a higher value at time=6 months, according to the chart?" }, { "from": "gpt", "value": "ML Implementation." } ], "id_raw": 974 }, { "id": "sEKRWZVG8PlbZm6D", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point representing the 8th month of AI Implementation}[0.665, 0.650] above or below {the point representing the 7th month of ML Implementation}[0.595, 0.086]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 974 }, { "id": "81fWrenbWwRWeoHB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_14.png", "conversations": [ { "from": "human", "value": "\nAre the values of {AI Implementation}[0.108, 0.278, 0.943, 0.863] generally decreasing or increasing over time?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 974 }, { "id": "l2OFeTqogEgDZoso", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_14.png", "conversations": [ { "from": "human", "value": "\nCould you describe the objects within the region [0.040, 0.162, 0.062, 0.185]?" }, { "from": "gpt", "value": "The region contains the y-axis tick label '0.9'." } ], "id_raw": 974 }, { "id": "BA9Pac4TAtj1djGy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_14.png", "conversations": [ { "from": "human", "value": "\nWhat is within the area defined by [0.756, 0.039, 0.977, 0.135]?" }, { "from": "gpt", "value": "It contains the legend of the chart with labels for {AI Implementation}[0.809, 0.050, 0.963, 0.080] and {ML Implementation}[0.809, 0.094, 0.970, 0.124], and colored markers corresponding to each label." } ], "id_raw": 974 }, { "id": "XqFTDsf6HsJctJWN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in region [0.040, 0.694, 0.062, 0.717]." }, { "from": "gpt", "value": "The y-axis tick label '0.4' is enclosed within these coordinates." } ], "id_raw": 974 }, { "id": "0TSjxPyBVJmFJgKp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_16.png", "conversations": [ { "from": "human", "value": "\nWhat object is at these coordinates [0.015, 0.379, 0.034, 0.586]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 976 }, { "id": "OnLPdCl0zPrFWCdc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the 7th text in the x-axis?" }, { "from": "gpt", "value": "[0.593, 0.911, 0.602, 0.934]." } ], "id_raw": 976 }, { "id": "vd6nOoPQBatbURqU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_16.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label}[0.040, 0.359, 0.062, 0.382]?" }, { "from": "gpt", "value": "0.7." } ], "id_raw": 976 }, { "id": "t40e7Pxec8igXNxN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Facebook User Activity at month 1}[0.142, 0.444] above or below {the point for Instagram User Activity at month 1}[0.142, 0.368]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 976 }, { "id": "TefCJwjsqdO0Flnr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_16.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Facebook User Activity'}[0.067, 0.215, 0.976, 0.521] generally above or below {the line for 'Instagram User Activity'}[0.067, 0.139, 0.976, 0.444]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 976 }, { "id": "0ZJ3QOCKnqcSIhqt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_16.png", "conversations": [ { "from": "human", "value": "\nWhich social network has a higher user activity at {month 3}[0.294, 0.368], Facebook or Instagram?" }, { "from": "gpt", "value": "Instagram." } ], "id_raw": 976 }, { "id": "BJ0UxUGup8HdeO3k", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.321, 0.025, 0.722, 0.055]." }, { "from": "gpt", "value": "The region includes the subtitle of the chart, \"Social Networks and Interpersonal Relationships.\"" } ], "id_raw": 976 }, { "id": "zP1oT509bxPVVQOn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_16.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.712, 0.076, 0.968, 0.172]." }, { "from": "gpt", "value": "The area holds the chart legend, containing legend items for {Facebook User Activity}[0.766, 0.087, 0.955, 0.117] and {Instagram User Activity}[0.766, 0.131, 0.961, 0.161], each depicted with a specific color marker, blue for Facebook and red for Instagram." } ], "id_raw": 976 }, { "id": "zoT4gQ8Wi6qjKHPd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_16.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.594, 0.911, 0.826, 0.934]?" }, { "from": "gpt", "value": "There are 3 objects. The x-axis tick labels '7', '8', and '9' are located in this area." } ], "id_raw": 976 }, { "id": "QgqfvpGkLP69isV8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the {subtitle} at [0.295, 0.025, 0.757, 0.055] in the chart?" }, { "from": "gpt", "value": "Private Investment Vs Public Venture Capital Over Time." } ], "id_raw": 977 }, { "id": "TrJn0pgLtJb0jGGr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of the tick labeled as '10' on the x-axis?" }, { "from": "gpt", "value": "[0.859, 0.911, 0.876, 0.934]" } ], "id_raw": 977 }, { "id": "8AaWwoPqIhiVJkgC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the {ylabel} at [0.015, 0.282, 0.034, 0.683]?" }, { "from": "gpt", "value": "Market Value (in billions)." } ], "id_raw": 977 }, { "id": "mbLTOJKMCRAr5Pvv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_17.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at the {point with x=5}[0.488], {Private Investment line}[0.488, 0.521] or {Public Venture Capital line}[0.488, 0.304]?" }, { "from": "gpt", "value": "Public Venture Capital." } ], "id_raw": 977 }, { "id": "ixVmgYuYtPxFGvz8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_17.png", "conversations": [ { "from": "human", "value": "\nIs the {Private Investment line}[0.943, 0.100] above or below {Public Venture Capital line}[0.943, 0.190] at {x=11}[0.943]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 977 }, { "id": "BUAgkpOuk8UAhc4O", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_17.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at the {point with x=2}[0.260], {Private Investment line}[0.260, 0.673] or {Public Venture Capital line}[0.260, 0.521]?" }, { "from": "gpt", "value": "Private Investment." } ], "id_raw": 977 }, { "id": "AAmpiPgW4JgEhHNY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.788, 0.131, 0.970, 0.161]." }, { "from": "gpt", "value": "This area contains the legend item: {Public Venture Capital}[0.788, 0.131, 0.970, 0.161], represented with a blue color marker." } ], "id_raw": 977 }, { "id": "ZkEIcsXcq6znmH1M", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_17.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.891, 0.216, 1.019, 0.237]?" }, { "from": "gpt", "value": "There is one object present in this area which is the {12th x-tick label}[1.010, 0.911, 1.028, 0.934]." } ], "id_raw": 977 }, { "id": "BWZeLBiLqq7IxBRR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.495, 0.943, 0.556, 0.975]." }, { "from": "gpt", "value": "The x-axis label, \"Month\", is located in this area, styled with a dark color." } ], "id_raw": 977 }, { "id": "6ljWXNI8cwuUAfua", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.824, 0.131, 0.946, 0.161] in the chart?" }, { "from": "gpt", "value": "The label for the \"Modern Values\" line in the legend." } ], "id_raw": 978 }, { "id": "12eHzZV0TnGZBlI7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the sixth point on the \"Traditional Values\" line?" }, { "from": "gpt", "value": "[0.275, 0.616]" } ], "id_raw": 978 }, { "id": "6hprwTIkUgGoMsXT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-axis tick label} at [0.040, 0.855, 0.062, 0.879]?" }, { "from": "gpt", "value": "0.5" } ], "id_raw": 978 }, { "id": "2NKsr16WzRPa0uhj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_18.png", "conversations": [ { "from": "human", "value": "\nAre the points of {the line for 'Traditional Values'}[0.108, 0.482, ... , 0.943, 0.864] generally above or below {points of the line for 'Modern Values'}[0.108, 0.100, ..., 0.943, 0.864]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 978 }, { "id": "4LirTxrkJgN7eZoN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_18.png", "conversations": [ { "from": "human", "value": "\nIs {the point at year 2000}[0.387, 0.578] on the 'Modern Values' line higher or lower than {the point at year 2000}[0.387, 0.721] on the 'Traditional Values' line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 978 }, { "id": "p48VqPg9Hy7IehhI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_18.png", "conversations": [ { "from": "human", "value": "\nWhich line in the chart has a higher value at {year 2010}[0.665, 0.330, 0.665, 0.788]: Traditional Values or Modern Values?" }, { "from": "gpt", "value": "Modern Values." } ], "id_raw": 978 }, { "id": "PA2Geua1a2Unw2Wq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the coordinates [0.771, 0.076, 0.977, 0.172]." }, { "from": "gpt", "value": "This region contains the chart's legend. It includes two items: {Traditional Values with a blue handle}[0.824, 0.087, 0.970, 0.117] and {Modern Values with an orange handle}[0.824, 0.131, 0.946, 0.161]." } ], "id_raw": 978 }, { "id": "7gRTbn7L2UoGXvca", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.543, 0.911, 0.682, 0.934]?" }, { "from": "gpt", "value": "The area contains one text element, an {x-axis tick label}[0.647, 0.911, 0.682, 0.934] reading \"2010\"." } ], "id_raw": 978 }, { "id": "Z7rTL9S37NCvXsq9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_18.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the coordinates [0.824, 0.087, 0.946, 0.161]?" }, { "from": "gpt", "value": "There are 2 objects, {the label for Modern Values which reads \"Modern Values\"}[0.824, 0.131, 0.946, 0.161] and {the label for the line representing Traditional Values reading \"Traditional Values\"}[0.824, 0.087, 0.970, 0.117]. These are both located in the chart's legend." } ], "id_raw": 978 }, { "id": "bv8XS1LhBuThwcFV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the title as per the chart at location [0.338, 0.025, 0.731, 0.055]?" }, { "from": "gpt", "value": "Innovation Project Evaluation and Management." } ], "id_raw": 979 }, { "id": "gNGvm0N7WKGnP9HN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart's x-axis represent, indicated by the label present at coordinates [0.465, 0.943, 0.605, 0.975]?" }, { "from": "gpt", "value": "Time (Months)." } ], "id_raw": 979 }, { "id": "CV4EJJnl2uem3pXO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_49_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis depict as mentioned at the coordinates [0.015, 0.330, 0.034, 0.635]?" }, { "from": "gpt", "value": "Performance Index." } ], "id_raw": 979 }, { "id": "eKof8gAjZZt7XSas", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_19.png", "conversations": [ { "from": "human", "value": "\nIs the {Standard Performance line}[0.125, 0.062, 0.944, 0.482] above or below the {Innovative Performance line}[0.125, 0.194, 0.944, 0.509] at point {2}[0.274, 0.325]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 979 }, { "id": "YUOrYqGJ43Yn7lD5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_19.png", "conversations": [ { "from": "human", "value": "\nWhich performance line is higher at {point 11}[0.944, 0.062] - Standard Performance or Innovative Performance?" }, { "from": "gpt", "value": "Standard Performance." } ], "id_raw": 979 }, { "id": "qXPYGNXwP27pmbUN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_49_19.png", "conversations": [ { "from": "human", "value": "\nAt which point on the x-axis does the {Standard Performance line}[0.125, 0.062, 0.944, 0.482] intersect with the {Innovative Performance line}[0.125, 0.194, 0.944, 0.509]?" }, { "from": "gpt", "value": "Point 8." } ], "id_raw": 979 }, { "id": "zpUwhLmF8zOQBN2Q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_19.png", "conversations": [ { "from": "human", "value": "\nWhat is positioned within the coordinates [0.719, 0.076, 0.977, 0.172]?" }, { "from": "gpt", "value": "This region contains the chart legend. Two items are included: {Standard Performance}[0.772, 0.087, 0.958, 0.117] indicated by a blue color and {Innovative Performance}[0.772, 0.131, 0.970, 0.161] represented in red." } ], "id_raw": 979 }, { "id": "IOF6Iq6p4OI7LVD3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the items found within the area [0.125, 0.062, 0.944, 0.482]." }, { "from": "gpt", "value": "This region is occupied by the {Standard Performance line}[0.125, 0.062, 0.944, 0.482], represented with a blue line chart starting from 0.8 performance index at point 0 to 1.6 at point 11." } ], "id_raw": 979 }, { "id": "7MyYJztaBdWushur", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_49_19.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.125, 0.194, 0.944, 0.509]?" }, { "from": "gpt", "value": "This area is filled by the {Innovative Performance line}[0.125, 0.194, 0.944, 0.509]. It's a red line chart starting from 1.0 performance index at point 0 and reaching 1.35 at point 11." } ], "id_raw": 979 }, { "id": "Jl66IoISNwD5giz1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.030, 0.370, 0.047, 0.575]?" }, { "from": "gpt", "value": "Application Cases." } ], "id_raw": 986 }, { "id": "TGf9jpYXAQjxk2Fq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.492, 0.952, 0.541, 0.975]?" }, { "from": "gpt", "value": "Fields." } ], "id_raw": 986 }, { "id": "kdkTID0B1MEhYzyL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the 'Application Case 2' line} in the chart?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 986 }, { "id": "lQo1TlnJERAfphng", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_6.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Application Case 1'}[[0.104, 0.765], [0.854, 0.911]] higher or lower than {the 'Application Case 2'}[[0.104, 0.180], [0.854, 0.911]] in 'Prediction Accuracy'?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 986 }, { "id": "T6iXhJzbxebLA9MR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_6.png", "conversations": [ { "from": "human", "value": "\nIn the comparison between {the 'Application Case 1'}[[0.104, 0.765], [0.854, 0.911]] and {the 'Application Case 2'}[[0.104, 0.180], [0.854, 0.911]], which has the higher 'Number of Training Samples'?" }, { "from": "gpt", "value": "Application Case 2." } ], "id_raw": 986 }, { "id": "XqBj76k9hTi4ryZF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_6.png", "conversations": [ { "from": "human", "value": "\nWhich application case has a higher point in {the 'Prediction Accuracy'} point[0.104, 0.911]?" }, { "from": "gpt", "value": "Application Case 2." } ], "id_raw": 986 }, { "id": "RLrShhYCXo7nsMkH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_6.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is within the area [0.696, 0.046, 0.920, 0.123]?" }, { "from": "gpt", "value": "This is the legend of the chart, which indicates the color associations of each application case. Specifically, Application Case 1 is associated with the color blue, as indicated by the marker at {coordinate}[inf, inf, -inf, -inf]. Similarly, Application Case 2 is associated with the color red, as denoted by the marker at {coordinate}[inf, inf, -inf, -inf]." } ], "id_raw": 986 }, { "id": "71GFrRF5qidP8nNL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the section of the chart from [0.019, 0.919, 0.190, 0.942]." }, { "from": "gpt", "value": "This area contains the first x-axis tick label 'Prediction Accuracy' which is located at {coordinates}[0.019, 0.919, 0.190, 0.942]." } ], "id_raw": 986 }, { "id": "UCAmRicds5kXc9A5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.104, 0.180, 0.854, 0.911]?" }, { "from": "gpt", "value": "There are two application case line objects within these coordinates. Both the line for Application Case 1 at {coordinates}[0.104, 0.765, 0.854, 0.911] and the line for Application Case 2 at {coordinates}[0.104, 0.180, 0.854, 0.911] are located here, represented with blue and red colors respectively." } ], "id_raw": 986 }, { "id": "LPqzZGzdCbsL3y9y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the line with coordinates [0.123, 0.468, 0.940, 0.662] in the chart represent?" }, { "from": "gpt", "value": "Services." } ], "id_raw": 988 }, { "id": "uEZDNo5wgtDtIon9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label associated with {the tick on the x-axis}[0.287, 0.911]?" }, { "from": "gpt", "value": "2012." } ], "id_raw": 988 }, { "id": "I2oX6td3jL8klqEA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_8.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by {the leftmost y-axis tick label}[0.082, 0.911] in the chart?" }, { "from": "gpt", "value": "0." } ], "id_raw": 988 }, { "id": "rPNK7HLHdvrrEy1A", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_8.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Manufacturing'}[0.205, 0.405] on the line lower or higher than {the point for 'Logistics'}[0.205, 0.615] in 2011?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 988 }, { "id": "gCWwTwm0JwfXnfdf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_8.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value in 2018, the {line representing 'Marketing'}[0.777, 0.409] or the {line representing 'Services'}[0.777, 0.505]?" }, { "from": "gpt", "value": "Services." } ], "id_raw": 988 }, { "id": "48xdYiut2fltbdZK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_8.png", "conversations": [ { "from": "human", "value": "\nAre the values for 'Product Development' increasing or decreasing between the points {2016}[0.614, 0.210] and {2017}[0.695, 0.208]?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 988 }, { "id": "t0zzLDvK1Iq5mXzp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the information contained in the region [0.091, 0.718, 0.342, 0.899]?" }, { "from": "gpt", "value": "The area contains the legend for the line labels: {Manufacturing}[0.147, 0.727, 0.273, 0.751], {Logistics}[0.147, 0.762, 0.222, 0.785], {Marketing}[0.147, 0.797, 0.234, 0.820], {Services}[0.147, 0.832, 0.220, 0.855], and {Product Development}[0.147, 0.867, 0.335, 0.890], each paired with a unique color marker." } ], "id_raw": 988 }, { "id": "QWfav05RNxE29HI4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.546, 0.919, 0.591, 0.942]." }, { "from": "gpt", "value": "This area contains the 2015 label of the x-axis." } ], "id_raw": 988 }, { "id": "hG8kzwRlNzsEZB2H", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_8.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the area [0.819, 0.919, 0.863, 0.945]?" }, { "from": "gpt", "value": "The x-axis label for the year 2019 is located here." } ], "id_raw": 988 }, { "id": "N41s8f59cbvZxSai", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the seventh x-axis tick label}[0.590, 0.939, 0.625, 0.957] in the graph?" }, { "from": "gpt", "value": "2016." } ], "id_raw": 991 }, { "id": "oWSqPfw9QVBKljWE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.015, 0.394, 0.029, 0.565] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 991 }, { "id": "iRqo9tlTT63ciLTE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis label at {coordinates}[0.509, 0.964, 0.538, 0.981]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 991 }, { "id": "hZB1KMipVj2hXJY6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_11.png", "conversations": [ { "from": "human", "value": "\nIs {Policy 3}[0.104, 0.069, 0.943, 0.121] above or below {Policy 2}[0.104, 0.631, 0.943, 0.890] on the graph?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 991 }, { "id": "HUqBy3dMEu6QhvvH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_11.png", "conversations": [ { "from": "human", "value": "\nIs {Policy 4}[0.104, 0.328, 0.943, 0.717] above or below {Policy 1}[0.104, 0.415, 0.943, 0.631] throughout its trend?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 991 }, { "id": "I2tcfgVsEWm2gP1Y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_11.png", "conversations": [ { "from": "human", "value": "\nWhich policy has a higher effectiveness score in 2020, Policy 1 or Policy 4?" }, { "from": "gpt", "value": "Policy 4." } ], "id_raw": 991 }, { "id": "aj4R3mpuyYMni1EW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the area {coordinates}[0.035, 0.926, 0.057, 0.944]." }, { "from": "gpt", "value": "The y-axis tick label '0.8' is located at these coordinates." } ], "id_raw": 991 }, { "id": "A4UQKuwSXfSEJ0Zq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the entire {x-axis section}[0.506, 0.939, 0.541, 0.957]." }, { "from": "gpt", "value": "The x-axis tick label '2015' is located here." } ], "id_raw": 991 }, { "id": "eaKVdCrO6CvVUb6e", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_11.png", "conversations": [ { "from": "human", "value": "\nTell me about the details that are presented from {coordinates}[0.338, 0.939, 0.373, 0.957] on the plot?" }, { "from": "gpt", "value": "An x-axis tick label '2013' is positioned at this location." } ], "id_raw": 991 }, { "id": "d0opSs0W4t1BrrP4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the x-axis label represent in the chart? Its coordinates are [0.498, 0.931, 0.527, 0.954]." }, { "from": "gpt", "value": "Year." } ], "id_raw": 992 }, { "id": "WobksUTS3zZgc6MS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_12.png", "conversations": [ { "from": "human", "value": "\nWhich technological field does the legend coordinate [0.176, 0.176, 0.322, 0.199] represent?" }, { "from": "gpt", "value": "Quantum Computing." } ], "id_raw": 992 }, { "id": "qpGawGBaNDRlJgfr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate for the y-axis tick label \"10\"?" }, { "from": "gpt", "value": "[0.103, 0.573, 0.120, 0.596]." } ], "id_raw": 992 }, { "id": "SpZ5ImBdaWHQDiJm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_12.png", "conversations": [ { "from": "human", "value": "\nIs the point for 'Artificial Intelligence' in 2014 [[0.317, 0.649]] higher or lower than the point for 'Blockchain' in the same year [[0.317, 0.744]]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 992 }, { "id": "vH1BQIaKNwesV6E2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_12.png", "conversations": [ { "from": "human", "value": "\nAre the values for 'Biotechnology' in 2020 [[0.786, 0.751]] higher or lower than the values for 'Quantum Computing' in the same year [[0.786, 0.622]]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 992 }, { "id": "j93GmkC8YHE2tzoF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_12.png", "conversations": [ { "from": "human", "value": "\nWhich technology has a higher position at {the year 2016}[0.473], 'Artificial Intelligence' [0.473, 0.563] or 'Blockchain' [0.473, 0.726] ?" }, { "from": "gpt", "value": "Artificial Intelligence." } ], "id_raw": 992 }, { "id": "DHiOFmyhZFpXT7Et", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the area bounded by the coordinates [0.160, 0.563, 0.865, 0.855] contain?" }, { "from": "gpt", "value": "it contains the trend line for Quantum Computing over the years, represented by a line with square markers." } ], "id_raw": 992 }, { "id": "yFloY5b2xVdT5uXN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.160, 0.711, 0.865, 0.155]?" }, { "from": "gpt", "value": "This region contains the trend line for 'Artificial Intelligence' over different years. It is marked with circular dots along the line." } ], "id_raw": 992 }, { "id": "fQSOYvs4nibTXNhu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.299, 0.912, 0.669, 0.965]?" }, { "from": "gpt", "value": "The primary chart title, \"Future Technological Development Prospects\". It's located centrally above the chart." } ], "id_raw": 992 }, { "id": "TcOGsCyuDeJeffhC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the label related to the coordinates [0.810, 0.423, 0.959, 0.446] in the chart's legend section?" }, { "from": "gpt", "value": "Living Conditions." } ], "id_raw": 999 }, { "id": "b8JC0ZfHGYMFhM7h", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_19.png", "conversations": [ { "from": "human", "value": "\nWhat coordinate does the x-axis label '2015.0' lie in?" }, { "from": "gpt", "value": "[0.703, 0.919, 0.763, 0.942]" } ], "id_raw": 999 }, { "id": "kG5NHSqU7WFjjXYj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_50_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at {coordinates}[0.019, 0.399, 0.036, 0.574]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 999 }, { "id": "4DbEAnKmQIktKz0y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_19.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Living Conditions' at '2000'}[0.113, 0.872] plotted higher or lower than {the point for 'Housing Cost' at '2000'}[0.113, 0.871]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 999 }, { "id": "Q67XqJZSpfg5VczU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_19.png", "conversations": [ { "from": "human", "value": "\nComparing {line for 'Life Expectancy'}[0.113, 0.101, 0.940, 0.174] and {line for 'Housing Cost'}[0.113, 0.868, 0.940, 0.871], which line is consistently higher throughout 2000 to 2020?" }, { "from": "gpt", "value": "Housing Cost." } ], "id_raw": 999 }, { "id": "CaqqGyzZcS4hlOSn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_50_19.png", "conversations": [ { "from": "human", "value": "\nIs the end point of {the line for 'Living Conditions'}[0.113, 0.871, 0.940, 0.872] at 2020 higher or lower than its start point at 2000?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 999 }, { "id": "rpaFzlIbFaTeJVDV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the coordinate [0.755, 0.413, 0.973, 0.560]." }, { "from": "gpt", "value": "It is the chart's legend section. It contains labels for 'Living Conditions', 'Life Expectancy', 'Housing Cost', and 'Employment Rate', each color-coded and tagged with a different marker symbol." } ], "id_raw": 999 }, { "id": "0cBQi6dqXNKOyZqt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_19.png", "conversations": [ { "from": "human", "value": "\nWhat information is shown in the region [0.248, 0.025, 0.804, 0.055]?" }, { "from": "gpt", "value": "This region shows the chart's subtitle 'Human Life & Living Environment Trends 2000 - 2020'." } ], "id_raw": 999 }, { "id": "el6XmrCCfOs0lbiC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_50_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe the data in the region [0.113, 0.868, 0.940, 0.872]?" }, { "from": "gpt", "value": "This is the 'Living Conditions' line in the chart, spanning from the year 2000 to 2020, with an increasing trend depicted by the upward rising line." } ], "id_raw": 999 }, { "id": "gGRRzugNZ7DuQyQs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the primary color of {the line representing 'Avg Class Size'}[0.155, 0.896, 0.942, 0.896]?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 1000 }, { "id": "Wz5OOipfTixJKTbL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_0.png", "conversations": [ { "from": "human", "value": "\nHow is {the marker shape} for 'Active Students' represented in the data line[0.155, 0.054, 0.942, 0.141]?" }, { "from": "gpt", "value": "Triangle." } ], "id_raw": 1000 }, { "id": "jFYQFZad2pQLdPVJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the marker style used to represent {the line for 'Active Teachers'}[0.155, 0.891, 0.942, 0.892]?" }, { "from": "gpt", "value": "Circle." } ], "id_raw": 1000 }, { "id": "fRK6vG6geaDU5P4p", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_0.png", "conversations": [ { "from": "human", "value": "\nIs {2014 data point for 'Active Students'}[0.504, 0.103] above or below {2014 data point for 'Avg Class Size'}[0.504, 0.896]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1000 }, { "id": "iAla88VWndq4m6j5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_0.png", "conversations": [ { "from": "human", "value": "\nWhich {year's point}[0.242, 0.132] is higher for 'Active Students', {2011}[0.242, 0.132] or {2012}[0.330, 0.123]?" }, { "from": "gpt", "value": "2012." } ], "id_raw": 1000 }, { "id": "JIkkDA5lBluLm31R", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the first point for 'Active Teachers'}[0.155, 0.891] have a higher y-coordinate than {the first point for 'Active Students'}[0.155, 0.141]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1000 }, { "id": "A4o3unkPPZFIBBAq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the coordinates [0.126, 0.062, 0.316, 0.164]." }, { "from": "gpt", "value": "This area displays a legend for the graph, including items for 'Active Teachers', 'Active Students', and 'Avg Class Size' with their respective color-coded markers." } ], "id_raw": 1000 }, { "id": "eZRoiv464swNakW5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_0.png", "conversations": [ { "from": "human", "value": "\nWhat is located in the area [0.050, 0.445, 0.110, 0.825]?" }, { "from": "gpt", "value": "The y-axis is contained in this region, showing a vertical label 'Count / Class Size' and multiple y-axis tick labels ranging from 0.411 to 0.821." } ], "id_raw": 1000 }, { "id": "3KtN9yeQZOMRMOD5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the zone [0.155, 0.054, 0.942, 0.896]?" }, { "from": "gpt", "value": "This broad section encapsulates the entire primary chart area, displaying a series of data points with line representations for 'Active Teachers', 'Active Students', and 'Avg Class Size' on an increasing timeline." } ], "id_raw": 1000 }, { "id": "OV1S2phIjRSrnitX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.413, 0.943, 0.664, 0.975]?" }, { "from": "gpt", "value": "Fuel Economy (mpg)." } ], "id_raw": 1001 }, { "id": "U9Hyoj5IffDjIVdd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the legend item}[0.864, 0.782, 0.966, 0.805]?" }, { "from": "gpt", "value": "Lambda-S+." } ], "id_raw": 1001 }, { "id": "liZkx3PHGKrAecO6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_1.png", "conversations": [ { "from": "human", "value": "\nWhich model is associated with {the marker}[0.810, 0.855, 0.855, 0.869] shown in the legend?" }, { "from": "gpt", "value": "Our Model." } ], "id_raw": 1001 }, { "id": "4fnCyDbQHW3ouy73", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_1.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {the fourth point}[0.740, 0.181], Lambda-S or DeepTraffic?" }, { "from": "gpt", "value": "DeepTraffic." } ], "id_raw": 1001 }, { "id": "x7mKGACeDQ9ZOBfl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_1.png", "conversations": [ { "from": "human", "value": "\nIs the line for {Our Model}[0.135, 0.357, 0.941, 0.552] above or below the line for {DeepTraffic}[0.135, 0.070, 0.941, 0.181]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1001 }, { "id": "aLkqs7a5k0oIQbue", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_1.png", "conversations": [ { "from": "human", "value": "\nComparing {Lambda-S+}[0.135, 0.568, 0.941, 0.728] and {DeepTraffic}[0.135, 0.070, 0.941, 0.181], which model has higher value at {20.0 mpg}[0.135, 0.728]?" }, { "from": "gpt", "value": "DeepTraffic." } ], "id_raw": 1001 }, { "id": "0yUSDjmedBlv48YI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within the region [0.135, 0.070, 0.941, 0.728]." }, { "from": "gpt", "value": "The region encompasses four lines, each representing a model's classification accuracy at different Fuel Economy (mpg) values. The models are Lambda-S, Lambda-S+, DeepTraffic, and Our Model. The lines are colored differently and marked with different markers for distinction." } ], "id_raw": 1001 }, { "id": "pgJSt9KWMWqGukKj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.808, 0.738, 0.973, 0.884]." }, { "from": "gpt", "value": "This region contains the chart legend which indicates four lines Lambda-S, Lambda-S+, DeepTraffic, and Our Model. Each label is associated with a colored marker matching the line of the corresponding model in the chart." } ], "id_raw": 1001 }, { "id": "hQFI1yVWpEc8tja6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_1.png", "conversations": [ { "from": "human", "value": "\nWhat are in the region [0.013, 0.884, 0.095, 0.896]?" }, { "from": "gpt", "value": "Two objects are here. The first is an x-axis tick {at 17.5}[0.012, 0.904, 0.058, 0.934], and the second is a y-axis tick {at 0}[0.076, 0.884, 0.089, 0.914]." } ], "id_raw": 1001 }, { "id": "20TB5hh4w1YV1mRS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.297, 0.034, 0.679] in the line chart?" }, { "from": "gpt", "value": "Influence on Public Opinion (%)." } ], "id_raw": 1002 }, { "id": "0XQ2CNZDFIF26iD0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_2.png", "conversations": [ { "from": "human", "value": "\nWhich media source does the tick label have at {coordinates}[0.102, 0.933, 0.133, 0.951]?" }, { "from": "gpt", "value": "CNN." } ], "id_raw": 1002 }, { "id": "1BWlb9mU491gqWUh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label}[0.050, 0.500, 0.071, 0.523] on the chart?" }, { "from": "gpt", "value": "50." } ], "id_raw": 1002 }, { "id": "OwIXOeTXIgXCsXKB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Elections' line}[0.117, 0.233, 0.942, 0.593] start at a higher or lower value than {the 'Immigration' line}[0.117, 0.300, 0.942, 0.693]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1002 }, { "id": "frezxOs3OIYKYgzQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_2.png", "conversations": [ { "from": "human", "value": "\nAt the point for media source 'BBC', is the {value for 'Elections'}[0.282, 0.317] higher or lower than the {value for 'Immigration'}[0.282, 0.425]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1002 }, { "id": "oVzQj7USMLqepnx1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_2.png", "conversations": [ { "from": "human", "value": "\nWhich category has a higher value for the 'FOX' media source, {'Elections'}[0.200, 0.258] or {'Immigration'}[0.200, 0.300]?" }, { "from": "gpt", "value": "Elections." } ], "id_raw": 1002 }, { "id": "Lythudx2yOPxBtGo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.333, 0.933, 0.397, 0.951] in the line chart." }, { "from": "gpt", "value": "This region contains the text for the media source 'REUTERS'." } ], "id_raw": 1002 }, { "id": "UnLGPA6hs6tBg6Yw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.118, 0.233, 0.945, 0.593]." }, { "from": "gpt", "value": "This area captures the line representing 'Elections', showing a sequence of individual points and a line connecting them in order from left to right." } ], "id_raw": 1002 }, { "id": "e3sL7Jd8vvyEX2AJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_2.png", "conversations": [ { "from": "human", "value": "\nWhat content is enclosed in the region [0.127, 0.065, 0.190, 0.108]?" }, { "from": "gpt", "value": "This area contains the legend entries for 'Elections' and 'Immigration'. Each is represented textually and also with a color that matches the lines on the chart: blue for 'Elections' and orange for 'Immigration'." } ], "id_raw": 1002 }, { "id": "cPLVpBVqa57gjw5W", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at the coordinates [0.063, 0.703, 0.206, 0.894]?" }, { "from": "gpt", "value": "It is the x-axis label 'Moderate decline'." } ], "id_raw": 1003 }, { "id": "IgEwDakEdQIUy7NO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-coordinate of the point in the Innovation line whose x is 'Abrupt drop'?" }, { "from": "gpt", "value": "{point_coordinates}[0.447, 0.440]" } ], "id_raw": 1003 }, { "id": "GbOKqlahSYG51vII", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the nature of {the object}[0.848, 0.081, 0.933, 0.104] in the legend?" }, { "from": "gpt", "value": "Its label is 'Creativity'." } ], "id_raw": 1003 }, { "id": "DABLXXtV4xlpGqKr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_3.png", "conversations": [ { "from": "human", "value": "\nWhich is more significant, {the point indicating 'Innovation' on 'Steep decline'}[0.291, 0.344] or {the point indicating 'Creativity' on 'Steep decline'}[0.291, 0.471]?" }, { "from": "gpt", "value": "'Innovation'." } ], "id_raw": 1003 }, { "id": "tjZbem6nYZGi9kNJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_3.png", "conversations": [ { "from": "human", "value": "\nIs {the point 'Innovation' on 'Consistent expansion'}[0.604, 0.121] higher or lower than {the point 'Innovation' on 'Moderate decline'}[0.134, 0.153]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1003 }, { "id": "NYh52bayhhoRj14d", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_3.png", "conversations": [ { "from": "human", "value": "\nIs {the point indicating 'Creativity' for 'Abrupt drop'}[0.447, 0.567] higher or lower than {the point indicating 'Creativity' for 'Erratic decline'}[0.917, 0.248]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1003 }, { "id": "6jRgYwuDN9GscczU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content inside the region [0.134, 0.089, 0.917, 0.567]." }, { "from": "gpt", "value": "The area contains two line graphs, one for 'Innovation' and another for 'Creativity', with specific data points plotted for 'Moderate decline', 'Steep decline', 'Abrupt drop', 'Consistent expansion', 'Subdued decline', and 'Erratic decline'." } ], "id_raw": 1003 }, { "id": "pK0aSBYzXGJH1WV6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within the region [0.793, 0.037, 0.947, 0.113]." }, { "from": "gpt", "value": "This region contains the legend for the chart, with 'Innovation' and 'Creativity' listed as labels, each paired with their respective colored markers represented by {coordinates for Innovation}[0.794, 0.049, 0.840, 0.063] and {coordinates for Creativity}[0.794, 0.084, 0.840, 0.098], respectively." } ], "id_raw": 1003 }, { "id": "qOC1UTxVZ5UsnHZ2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_3.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents found within the bounding box [0.233, 0.703, 0.349, 0.857]?" }, { "from": "gpt", "value": "The bounding box contains the label 'Steep decline' on the x-axis." } ], "id_raw": 1003 }, { "id": "NvO4ILeQFTAqnija", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the description within the coordinates [0.104, 0.928, 0.130, 0.951]?" }, { "from": "gpt", "value": "It is an x-axis tick label with text \"5.0\"." } ], "id_raw": 1004 }, { "id": "jBY8gvL3pDTSYJL7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.781, 0.839, 0.818, 0.849]?" }, { "from": "gpt", "value": "It is the color marker for the legend item \"Research Centers\"." } ], "id_raw": 1004 }, { "id": "LaXsdTp8vblpeRuI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_4.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.825, 0.810, 0.957, 0.828] refer to in the legend?" }, { "from": "gpt", "value": "It refers to the \"Startups Incubated\"." } ], "id_raw": 1004 }, { "id": "Qeg48uXP46B1Orv7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_4.png", "conversations": [ { "from": "human", "value": "\nDoes the {Startups Incubated line}[0.117, 0.475, 0.944, 0.613] have a higher or lower point than the {Ecosystem Partners line}[0.117, 0.427, 0.944, 0.517] at SNR 10?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1004 }, { "id": "63o8n3WdAK3koJjz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_4.png", "conversations": [ { "from": "human", "value": "\nIs the {Research Centers line}[0.117, 0.607, 0.944, 0.724] above or below the {Policies Implemented line}[0.117, 0.222, 0.944, 0.415] at SNR 20?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1004 }, { "id": "B9i88Wr84TdPjqJL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_4.png", "conversations": [ { "from": "human", "value": "\nWhich trend, {Ecosystem Partners}[0.117, 0.427, 0.944, 0.517] or {Startups Incubated}[0.117, 0.475, 0.944, 0.613], has a lower value at SNR 10?" }, { "from": "gpt", "value": "Ecosystem Partners." } ], "id_raw": 1004 }, { "id": "CDaNpLy1qROyh6Ms", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content inside coordinates [0.780, 0.803, 0.978, 0.913]." }, { "from": "gpt", "value": "This region contains a chart legend. Items present are \"Startups Incubated\", \"Research Centers\", \"Ecosystem Partners\", and \"Policies Implemented\", each paired with a colored marker." } ], "id_raw": 1004 }, { "id": "7xHmsI8GqOVPWm76", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within the coordinates [0.061, 0.913, 0.071, 0.936]?" }, { "from": "gpt", "value": "It is a y-axis label marked as '0'." } ], "id_raw": 1004 }, { "id": "U92vJ7GPG8TR5PSo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.117, 0.427, 0.944, 0.613]." }, { "from": "gpt", "value": "The region contains part of two lines, the \"Startups Incubated\" line and the \"Ecosystem Partners\" line, showing the trend in percentage values for different SNR points." } ], "id_raw": 1004 }, { "id": "wpevIezgrgsOVTEz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label represent at these coordinates [0.019, 0.222, 0.043, 0.711]?" }, { "from": "gpt", "value": "Technology Adaption Rate (%)." } ], "id_raw": 1005 }, { "id": "BeSqrTRw9j7NSsIf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the label '2020' on the X-Axis?" }, { "from": "gpt", "value": "[0.914, 0.904, 0.967, 0.934]." } ], "id_raw": 1005 }, { "id": "QxpBnaKR0KE1ll3q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the line item} in the subplot legend at [0.146, 0.162, 0.240, 0.186]?" }, { "from": "gpt", "value": "Blockchain." } ], "id_raw": 1005 }, { "id": "jrtPUWUhSaSlHx8X", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_5.png", "conversations": [ { "from": "human", "value": "\nBy looking at {the 'Multimedia Technology' data points}[[0.123, 0.282], [0.286, 0.258], [0.450, 0.245], [0.613, 0.196], [0.777, 0.159], [0.940, 0.123]], are the values increasing or decreasing over the years?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 1005 }, { "id": "E9XAGS4cLtqUp33v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_5.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Blockchain' line chart}[[0.123, 0.405], [0.286, 0.712], [0.450, 0.650], [0.613, 0.589], [0.777, 0.528], [0.940, 0.405]] have more or fewer points than the {2015 X-axis tick label}[0.096, 0.904, 0.149, 0.934]?" }, { "from": "gpt", "value": "More." } ], "id_raw": 1005 }, { "id": "rDXUaaOdZ187AJqh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_5.png", "conversations": [ { "from": "human", "value": "\nIs the {2016 y-coordinate data point}'s value for 'Artificial Intelligence' at [0.286, 0.626] more or less than its 2015 value at [0.123, 0.650]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 1005 }, { "id": "52585dmoZTdIK9xY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region at [0.082, 0.037, 0.981, 0.896]?" }, { "from": "gpt", "value": "This region encompasses the main body of the chart, including the x-axis and y-axis labels, all the data points and lines representing the four technologies (Multimedia Technology, Virtual Reality, Artificial Intelligence, and Blockchain), and the grid background." } ], "id_raw": 1005 }, { "id": "dM3t6lD58XDXGFOT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_5.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area at [0.096, 0.904, 0.967, 0.934]?" }, { "from": "gpt", "value": "This area contains all the tick labels for the x-axis, representing different years from 2015 to 2020." } ], "id_raw": 1005 }, { "id": "ixd7Po9F9GcSju4Z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.091, 0.048, 0.352, 0.195] comprise of?" }, { "from": "gpt", "value": "This region includes the subplot legend that lists the four technologies: {Multimedia Technology}[0.146, 0.057, 0.346, 0.081], {Virtual Reality}[0.146, 0.092, 0.268, 0.116], {Artificial Intelligence}[0.146, 0.127, 0.324, 0.151], and {Blockchain}[0.146, 0.162, 0.240, 0.186]. Each technology label is paired with a colored marker." } ], "id_raw": 1005 }, { "id": "UcR61VKmgImvnmBv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_6.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis label}[0.509, 0.956, 0.553, 0.981] indicate?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 1006 }, { "id": "8x0FNVdzeY0zuGOK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_6.png", "conversations": [ { "from": "human", "value": "\nWhich energy resource is represented by {the line with the colored marker '[0.087, 0.096, 0.126, 0.106]'}?" }, { "from": "gpt", "value": "Natural Gas Consumption." } ], "id_raw": 1006 }, { "id": "4Gvv8x4DCMNTOpm1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_6.png", "conversations": [ { "from": "human", "value": "\nWhich year corresponds to {the x-axis tick label}[0.221, 0.921, 0.250, 0.949]?" }, { "from": "gpt", "value": "2002.5." } ], "id_raw": 1006 }, { "id": "6jAXpK2LJstCXESl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_6.png", "conversations": [ { "from": "human", "value": "\nIs {the trend of 'Coal Consumption'}[0.118, 0.103, 0.944, 0.447] located above or below {the trend of 'Oil Consumption'}[0.118, 0.357, 0.944, 0.639] at the starting point?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1006 }, { "id": "KA1zU2OQXwfqTcv8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_6.png", "conversations": [ { "from": "human", "value": "\nWhich trend, {the 'Renewable Energy'}[0.118, 0.075, 0.944, 0.864] or {the 'Natural Gas Consumption'}[0.118, 0.041, 0.944, 0.385], ends higher in the last data year marked?" }, { "from": "gpt", "value": "Renewable Energy." } ], "id_raw": 1006 }, { "id": "seRVxPaIUPiJnZ1E", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_6.png", "conversations": [ { "from": "human", "value": "\nIs {the 2010 point of 'Oil Consumption'}[0.531, 0.470] higher than {the point for the same year of 'Natural Gas'}[0.531, 0.216]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1006 }, { "id": "EdQVWacjIvlnmLzO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the region framed within [0.015, 0.860, 0.072, 0.878]." }, { "from": "gpt", "value": "This region contains the y-axis tick label '300' marked at a coordinate position indicative of the lower values for energy usage." } ], "id_raw": 1006 }, { "id": "3sAoNfqI0dWVmi4S", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_6.png", "conversations": [ { "from": "human", "value": "\nWhat can be identified in the area enclosed by [0.041, 0.292, 0.072, 0.314]?" }, { "from": "gpt", "value": "This part of the chart features the y-axis tick label '500', indicating a value towards the middle range for energy usage and resource consumption as per the plot scale." } ], "id_raw": 1006 }, { "id": "KEr7ALjAS15fmCKJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_6.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the rectangle from [0.077, 0.019, 0.985, 0.921]?" }, { "from": "gpt", "value": "The main area of the plot displaying multiple lines representing different energy sources such as Coal, Oil, Natural Gas, and Renewables, tracking their consumption over years." } ], "id_raw": 1006 }, { "id": "ecOFU6OQN5pQfZIq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the content located at the coordinates [0.502, 0.956, 0.549, 0.981]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 1007 }, { "id": "Kii8s4joJhtfm3UT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_7.png", "conversations": [ { "from": "human", "value": "\nWhat object is represented at the coordinates [0.118, 0.139, 0.229, 0.148]?" }, { "from": "gpt", "value": "Text label for 'Immunotherapy' in the legend." } ], "id_raw": 1007 }, { "id": "lAPlHnaNISVTNs6H", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the Y-axis label placed at [0.015, 0.347, 0.035, 0.601]?" }, { "from": "gpt", "value": "Adoption Rate (%)." } ], "id_raw": 1007 }, { "id": "Q2zZQVbjeTwOIXgb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_7.png", "conversations": [ { "from": "human", "value": "\nIs the plotted line for {Immunotherapy}[0.108, 0.356, 0.943, 0.644] above or below the plotted line for {Telemedicine}[0.108, 0.101, 0.943, 0.797]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1007 }, { "id": "a9G0RQKf3VBNX4wY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the point on the plotted line for {AI Diagnostics}[0.108, 0.238, 0.943, 0.885] at the year {2005}[0.317, 0.839]?" }, { "from": "gpt", "value": "35.972." } ], "id_raw": 1007 }, { "id": "oAun1TSLCXNLt7os", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_7.png", "conversations": [ { "from": "human", "value": "\nBetween the lines for {Radiology}[0.108, 0.147, 0.943, 0.251] and {DNA Sequencing}[0.108, 0.246, 0.943, 0.502], which one is located higher in the chart for the year {2015}[0.735, 0.188] and {2015}[0.735, 0.309], respectively?" }, { "from": "gpt", "value": "Radiology." } ], "id_raw": 1007 }, { "id": "oCc6cJ5Izv55Js3k", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects found within [0.108, 0.356, 0.943, 0.644]?" }, { "from": "gpt", "value": "This region contains the plotted line for Immunotherapy, with points representing data from different years." } ], "id_raw": 1007 }, { "id": "D8STa72e3OAVKs2w", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the area marked by [0.073, 0.036, 0.240, 0.172] contain?" }, { "from": "gpt", "value": "This region includes the legend describing the different trends represented in the chart: Immunotherapy, Radiology, AI Diagnostics, DNA Sequencing, and Telemedicine, each labeled with a unique color and marker symbol." } ], "id_raw": 1007 }, { "id": "bsgGuVfLgoL2I4Rn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_7.png", "conversations": [ { "from": "human", "value": "\nDescribe everything from coordinates [0.041, 0.294, 0.062, 0.316]." }, { "from": "gpt", "value": "At this location, it has the y-axis label '75'." } ], "id_raw": 1007 }, { "id": "Yv6Or1dQHn6GmG2S", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.551, 0.011, 0.551, 0.011]?" }, { "from": "gpt", "value": "The subplot subtitle." } ], "id_raw": 1008 }, { "id": "tK1pD7bzmfkP3mnR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-coordinate for the first item in the legend?" }, { "from": "gpt", "value": "[0.132, 0.049, 0.177, 0.063]." } ], "id_raw": 1008 }, { "id": "i1aIKq90JBWluJdL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the label refer to at {coordinates} [0.525, 0.943, 0.577, 0.975]?" }, { "from": "gpt", "value": "The x-axis label which is \"Year\"." } ], "id_raw": 1008 }, { "id": "HzPG9LWexX3hCAki", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_8.png", "conversations": [ { "from": "human", "value": "\nBased on the points of {the 'Modern Art' line} [0.160, 0.083, 0.942, 0.614] and {the 'Ethnic Art' line} [0.160, 0.315, 0.942, 0.702], which art type had a higher revenue in 2020?" }, { "from": "gpt", "value": "Modern Art." } ], "id_raw": 1008 }, { "id": "Rma8QdMjZJ0Xc5uA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_8.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Performing Art' revenue point in 2015} [0.551, 0.600] higher or lower than {the 'Artisan Crafts' revenue point in 2015} [0.551, 0.590]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1008 }, { "id": "Y8fIYkjml0zm7ktP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_8.png", "conversations": [ { "from": "human", "value": "\nHow does the {2011 revenue of 'Modern Art'}[0.239, 0.586] compare to the {2011 revenue of 'Performing Art'}[0.239, 0.602]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1008 }, { "id": "Nm0AvJLoohjwMkh4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area [0.121, 0.025, 0.981, 0.896]." }, { "from": "gpt", "value": "This area contains the primary chart, including all the line trends for \"Modern Art\", \"Ethnic Art\", \"Performing Art\", and \"Artisan Crafts\". It also includes x-axis with label \"Year\" and y-axis with label \"Revenue ('000 USD)\"" } ], "id_raw": 1008 }, { "id": "ZT3atlXb5cf5DCCp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_8.png", "conversations": [ { "from": "human", "value": "\nWhat resides in the region [0.130, 0.037, 0.319, 0.183]?" }, { "from": "gpt", "value": "It's a legend with four items: {Modern Art}[0.186, 0.046, 0.282, 0.069], {Ethnic Art}[0.186, 0.081, 0.271, 0.104], {Performing Art}[0.186, 0.116, 0.312, 0.139] and {Artisan Crafts}[0.186, 0.151, 0.304, 0.174], each with a corresponding color marker." } ], "id_raw": 1008 }, { "id": "bR5TQBKA64gTRwlB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the area [0.595, 0.312, 0.755, 0.645]." }, { "from": "gpt", "value": "This area primarily includes points and line segments from the trends 'Modern Art', 'Ethnic Art', 'Performing Art', and 'Artisan Crafts'. It also contains x-axis tick labels '2016' and '2018'." } ], "id_raw": 1008 }, { "id": "wiropLv987DWp30X", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.019, 0.216, 0.043, 0.705]?" }, { "from": "gpt", "value": "Expenditure (in billion dollars)." } ], "id_raw": 1009 }, { "id": "IaIt65jGmM1RpNMP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_9.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the line with coordinates [0.135, 0.555, 0.941, 0.642]?" }, { "from": "gpt", "value": "Healthcare expenses over the years." } ], "id_raw": 1009 }, { "id": "FTw3yQKu8z6hX9ax", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing 'Transportation expenses'}[0.135, 0.170, 0.941, 0.308]?" }, { "from": "gpt", "value": "#d62728." } ], "id_raw": 1009 }, { "id": "BKHhrcpRTW7WEKhp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_9.png", "conversations": [ { "from": "human", "value": "\nDoes the {value for 'Public Housing'}[0.135, 0.736, 0.941, 0.809] increase or decrease over the years?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 1009 }, { "id": "LTTu6Bll7ZZwI6Tz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_9.png", "conversations": [ { "from": "human", "value": "\nWhich sector has higher expenditure in year 3, {Education}[0.538, 0.286] or {Transportation}[0.538, 0.272]?" }, { "from": "gpt", "value": "Education." } ], "id_raw": 1009 }, { "id": "67s0SbUWnch4ihjt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_9.png", "conversations": [ { "from": "human", "value": "\nIs the {point for 'Healthcare'}[0.941, 0.555] in year 5 higher or lower than the {point for 'Education'}[0.941, 0.221] in the same year?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1009 }, { "id": "6I57jeymj5qggj9x", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_9.png", "conversations": [ { "from": "human", "value": "\nWhat lies within the region [0.783, 0.738, 0.973, 0.884]?" }, { "from": "gpt", "value": "This region contains the legend for the chart with labels for 'Healthcare', 'Education', 'Public Housing', and 'Transportation. Each label is paired with a color-key as a line mark." } ], "id_raw": 1009 }, { "id": "7mNXkmk3Fypneilq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the left-side region [0.019, 0.216, 0.043, 0.705]." }, { "from": "gpt", "value": "This region contains the y-axis label 'Expenditure (in billion dollars)' for the chart." } ], "id_raw": 1009 }, { "id": "N4W6no1wLV6puek7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_9.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the region [0.129, 0.904, 0.948, 0.934]?" }, { "from": "gpt", "value": "This region consists of the x-axis tick labels corresponding to the years: '1', '2', '3', '4', and '5'." } ], "id_raw": 1009 }, { "id": "YkYGfxERhqtRPGow", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_10.png", "conversations": [ { "from": "human", "value": "\nIn the legend, what is the second entry's label?" }, { "from": "gpt", "value": "Biotechnology R&D." } ], "id_raw": 1010 }, { "id": "RiW7FKyo08CRqRIu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label on y-axis}[0.019, 0.326, 0.043, 0.595]?" }, { "from": "gpt", "value": "R&D activity (%)." } ], "id_raw": 1010 }, { "id": "5PnnjykBzhJ0w1qJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the last point represent for the 'Pharmaceutical R&D' line?" }, { "from": "gpt", "value": "The value for 2030." } ], "id_raw": 1010 }, { "id": "MmSVs8T2RltQFCFS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_10.png", "conversations": [ { "from": "human", "value": "\nIn the trends in 2030, which has a higher value, {Pharmaceutical R&D}[0.940, 0.087] or {Healthcare Services R&D}[0.940, 0.174]?" }, { "from": "gpt", "value": "Pharmaceutical R&D." } ], "id_raw": 1010 }, { "id": "LX8K3pYRdMP7XzLP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_10.png", "conversations": [ { "from": "human", "value": "\nDoes {the line representing 'Biotechnology R&D'}[0.123, 0.398, 0.940, 0.772] show a general increasing or decreasing trend over years?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 1010 }, { "id": "l4Zb7BAjjJeVpSPM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_10.png", "conversations": [ { "from": "human", "value": "\nIn 2020, is {the value for 'Medical Equipment R&D'} [0.532, 0.299] above or below {the value for 'Biotechnology R&D'}[0.532, 0.585]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1010 }, { "id": "64cXCuw5P8csr9jj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents within the region [0.748, 0.747, 0.927, 0.770]." }, { "from": "gpt", "value": "This region contains the label for the legend item 'Pharmaceutical R&D'." } ], "id_raw": 1010 }, { "id": "r4FEmFKCMElWFATN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what the region [0.096, 0.904, 0.149, 0.934] represents?" }, { "from": "gpt", "value": "This region contains the x-axis tick label '2010'." } ], "id_raw": 1010 }, { "id": "nTJAVeskUTfWJjte", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_10.png", "conversations": [ { "from": "human", "value": "\nCould you describe the contents within the area bounded by [0.301, 0.904, 0.354, 0.934]?" }, { "from": "gpt", "value": "This region contains the x-axis tick label for the year '2015'." } ], "id_raw": 1010 }, { "id": "pUpnyRNaIsHiXx8C", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label represent?" }, { "from": "gpt", "value": "The y-axis label {coordinates}[0.015, 0.353, 0.034, 0.588] represents the 'Popularity Rate (%)'." } ], "id_raw": 1011 }, { "id": "MaLIbFfzSBZbrvH9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_11.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the coordinates [0.943, 0.470] on the 'Gym Membership' line?" }, { "from": "gpt", "value": "This indicates the 'Gym Membership' Popularity Rate at the year 2020." } ], "id_raw": 1011 }, { "id": "MOph2AHkLHoBdGeR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the item in the legend located in this area {[0.850, 0.889, 0.973, 0.906]}?" }, { "from": "gpt", "value": "The item is 'Gym Membership'." } ], "id_raw": 1011 }, { "id": "91mAtE0H7r3X2DGd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_11.png", "conversations": [ { "from": "human", "value": "\nIs the Popularity Rate of {the 'Bootcamp'}[0.943, 0.771] in 2020 higher or lower than {that of 'Outdoor Fitness'}[0.943, 0.847]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1011 }, { "id": "tpXVgicdsaARfQt3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_11.png", "conversations": [ { "from": "human", "value": "\nFor the year 2017, is {the line for 'Smart Bracelets'}[0.442, 0.169] above or below {the line for 'Outdoor Fitness'}[0.442, 0.591] in terms of Popularity Rate?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1011 }, { "id": "pYrUi5pGljt5byma", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_11.png", "conversations": [ { "from": "human", "value": "\nIn 2019, which has a lower Popularity Rate, {line 'Gym Membership'}[0.776, 0.425] or {line 'Smart Bracelets'}[0.776, 0.245]?" }, { "from": "gpt", "value": "Smart Bracelets." } ], "id_raw": 1011 }, { "id": "zDiJyNz4PWCjhF0A", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the [0.805, 0.803, 0.978, 0.913] region." }, { "from": "gpt", "value": "This region contains the legend of the line chart. Four items are included: {Bootcamp}[0.850, 0.810, 0.921, 0.828], {Outdoor Fitness}[0.850, 0.836, 0.959, 0.854], {Smart Bracelets}[0.850, 0.863, 0.962, 0.880], and {Gym Membership}[0.850, 0.889, 0.973, 0.906]. Each is paired with a colored marker." } ], "id_raw": 1011 }, { "id": "gsvsnS8Wvi5YLQ5R", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.065, 0.019, 0.985, 0.922]." }, { "from": "gpt", "value": "This is the main region of the line chart which includes x-axis and y-axis labels, line trend for four categories 'Bootcamp', 'Outdoor Fitness', 'Smart Bracelets' and 'Gym Membership' across years 2015 to 2020, and corresponding legend." } ], "id_raw": 1011 }, { "id": "vZLNkCFFhOanQgpY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_11.png", "conversations": [ { "from": "human", "value": "\nCan you provide details regarding the area [0.015, 0.353, 0.034, 0.588]?" }, { "from": "gpt", "value": "This region includes the y-axis label \"Popularity Rate (%)\" that indicates the metric used for the line trends." } ], "id_raw": 1011 }, { "id": "NKrGEevJCDuesCD9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_12.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the entity at these coordinates [0.019, 0.267, 0.043, 0.654] in our chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1012 }, { "id": "cNqyo6kXKaiNokEl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_12.png", "conversations": [ { "from": "human", "value": "\nCould you provide me with the coordinates of the caption stating 'Online Programs' in the legend?" }, { "from": "gpt", "value": "[0.146, 0.151, 0.290, 0.174]." } ], "id_raw": 1012 }, { "id": "hJtJjW2JqENUJduS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_12.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis tick label} positioned at [0.505, 0.904, 0.558, 0.934] refer to?" }, { "from": "gpt", "value": "2018." } ], "id_raw": 1012 }, { "id": "0CpNQljIIib6D78v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the line representing 'Public Institutions'} [0.123, 0.324, 0.940, 0.460] have a placement that is above or below {the line representing 'Private Institutions'} [0.123, 0.280, 0.940, 0.367]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1012 }, { "id": "kn8TSARYlFGSaKA5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_12.png", "conversations": [ { "from": "human", "value": "\nAt {point marked as '2019'} [0.736, 0.896], does 'Home School' dip above or below 'Online Programs' in our resource allocation?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1012 }, { "id": "WVEdMv7S2HT49VG7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_12.png", "conversations": [ { "from": "human", "value": "\nWhich line shows a greater percentage of resource allocation at {point 2018} [0.532, 0.896]: Home School or Public Institutions?" }, { "from": "gpt", "value": "Public Institutions." } ], "id_raw": 1012 }, { "id": "TDWjIzTAnJDzWY7h", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_12.png", "conversations": [ { "from": "human", "value": "\nCould you please describe the area demarcated by the following coordinates: [0.091, 0.037, 0.316, 0.183]?" }, { "from": "gpt", "value": "This area includes a graphical legend containg the items: {Public Institutions}[0.146, 0.046, 0.300, 0.069], {Private Institutions}[0.146, 0.081, 0.309, 0.104], {Home School}[0.146, 0.116, 0.260, 0.139] and {Online Programs}[0.146, 0.151, 0.290, 0.174], with each having a distinct marker." } ], "id_raw": 1012 }, { "id": "RyaniY3KqEuYQ5bY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe what can be found within the region [0.056, 0.904, 0.965, 0.934]?" }, { "from": "gpt", "value": "This area encompasses the x-axis labels displaying the years from 2016 to 2020." } ], "id_raw": 1012 }, { "id": "pRc0bi5vd95557Vn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the nature of the content observed in the region [0.123, 0.087, 0.940, 0.274]?" }, { "from": "gpt", "value": "This region holds the line graph that represents the trend of 'Online Programs', together with its associated data points across the years from 2016 to 2020." } ], "id_raw": 1012 }, { "id": "btZ3S8G6bCvFAubG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the content at the coordinates [0.128, 0.039, 0.244, 0.059]?" }, { "from": "gpt", "value": "The legend label \"EM Stock Market\"." } ], "id_raw": 1013 }, { "id": "uHnVc3CfIU0Tvqbc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the co-ordinate of the label '2011' on the x-axis?" }, { "from": "gpt", "value": "[0.179, 0.883, 0.221, 0.943]." } ], "id_raw": 1013 }, { "id": "p0dvoNq6FEk4eRPH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.327, 0.034, 0.570]?" }, { "from": "gpt", "value": "Normalized Value." } ], "id_raw": 1013 }, { "id": "RyVHjn4oUblktSpW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_13.png", "conversations": [ { "from": "human", "value": "\nBased on the lines' final points, did {EM Stock Market}[0.944, 0.225] perform better or worse than {Disposable Income}[0.944, 0.187] in the final year?" }, { "from": "gpt", "value": "Worse." } ], "id_raw": 1013 }, { "id": "dNpWNaSKdYGzn7Y6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_13.png", "conversations": [ { "from": "human", "value": "\nAt the point '2016' in the x-axis, is the {point for 'Consumer Confidence'}[0.613, 0.269] higher or lower than the {point for 'Private Investment'}[0.613, 0.269]?" }, { "from": "gpt", "value": "Same." } ], "id_raw": 1013 }, { "id": "l6MeOAVrufGy44ql", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_13.png", "conversations": [ { "from": "human", "value": "\nIn year 2011, which has a higher value, {EM Stock Market}[0.200, 0.352] or {Disposable Income}[0.200, 0.396]?" }, { "from": "gpt", "value": "Disposable Income." } ], "id_raw": 1013 }, { "id": "NuXwcPPUj7hIMguT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents in the region [0.083, 0.031, 0.286, 0.157]?" }, { "from": "gpt", "value": "This area contains the legend items for the line chart which includes {EM Stock Market}[0.128, 0.039, 0.244, 0.059], {Consumer Confidence}[0.128, 0.069, 0.281, 0.089], {Private Investment}[0.128, 0.099, 0.259, 0.119], {Disposable Income}[0.128, 0.129, 0.259, 0.149], all paired with a color distinct marker." } ], "id_raw": 1013 }, { "id": "ZvRA9GHZliXfAgXA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.096, 0.883, 0.965, 0.943]." }, { "from": "gpt", "value": "This area contains the x-axis labels for the chart, ranging from the year 2010 to 2020." } ], "id_raw": 1013 }, { "id": "xUN8EufVbIlGOp4c", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.117, 0.159, 0.944, 0.435]?" }, { "from": "gpt", "value": "The region includes the line data points for Consumer Confidence and Private Investment." } ], "id_raw": 1013 }, { "id": "Rt0woRrsMZ6X3hN9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_14.png", "conversations": [ { "from": "human", "value": "\nWhat label is associated with the coordinates [0.040, 0.608, 0.061, 0.633] on the chart?" }, { "from": "gpt", "value": "The Y-axis label '45'." } ], "id_raw": 1014 }, { "id": "PntDhbyMO3XSTSYG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third text in the legend?" }, { "from": "gpt", "value": "[0.917, 0.843, 0.972, 0.863]." } ], "id_raw": 1014 }, { "id": "JKShpiHA4JPrWBJR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the legend label}[0.917, 0.873, 0.973, 0.893]?" }, { "from": "gpt", "value": "\"Policy D\"." } ], "id_raw": 1014 }, { "id": "U4ykZ8foMQZaYd4M", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Year_2' on 'Policy B'}[0.316, 0.559] higher or lower than {the point for 'Year_5' on 'Policy B'}[0.943, 0.422]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1014 }, { "id": "Ue0EsQWW8DE3VQHh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_14.png", "conversations": [ { "from": "human", "value": "\nIn year 4, is the effectiveness of {Policy A}[0.734, 0.676] greater than or less than {Policy C}[0.734, 0.618]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 1014 }, { "id": "bcGytjUNfbuOVOx5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_14.png", "conversations": [ { "from": "human", "value": "\nWhich policy has a higher effectiveness at {Year_5}[0.943, 0.911], Policy C or Policy D?" }, { "from": "gpt", "value": "Policy D." } ], "id_raw": 1014 }, { "id": "Nkk7ZCERMuXl51kl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.873, 0.775, 0.978, 0.901]." }, { "from": "gpt", "value": "This region contains the legend for the chart with labels for Policy A, B, C, and D each paired with a unique color and marker symbol." } ], "id_raw": 1014 }, { "id": "CaGuiveCcf2wIlVR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.107, 0.422, 0.943, 0.618]." }, { "from": "gpt", "value": "Within this area are the line graphs for Policy A and Policy B, they span from Year 1 to Year 5. Each line has different patterns or point markers." } ], "id_raw": 1014 }, { "id": "55i5EwE07BExfoY7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_14.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.730, 0.918, 0.948, 0.943]?" }, { "from": "gpt", "value": "One object, the x-axis label '5', is located at these coordinates." } ], "id_raw": 1014 }, { "id": "k0UdTRH8I9iOjKgn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the 'IT Training' line}[0.135, 0.092, 0.941, 0.360] in the chart?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 1016 }, { "id": "Xjdk6ZsYFq9tPlq8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the 2018 marker in the Soft Skills Training line?" }, { "from": "gpt", "value": "[0.780, 0.253]" } ], "id_raw": 1016 }, { "id": "7EeDfw2ZFFkDviHi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_16.png", "conversations": [ { "from": "human", "value": "\nWhat are the text and color details of {the label on the y-axis}[0.019, 0.273, 0.043, 0.648]?" }, { "from": "gpt", "value": "The label is \"Performance Score (%)\" and the color is '.15'." } ], "id_raw": 1016 }, { "id": "HgJqzis1o0VJaglm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_16.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Leadership Training' line}[0.135, 0.414, 0.941, 0.628] above or below {the 'Process Improvement Training' line}[0.135, 0.454, 0.941, 0.762] in 2012?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1016 }, { "id": "x4eQO9Fljw6L2VSg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_16.png", "conversations": [ { "from": "human", "value": "\nWhich training type had a higher score in 2018, {IT Training}[0.780, 0.159] or {Soft Skills Training}[0.780, 0.253]?" }, { "from": "gpt", "value": "IT Training." } ], "id_raw": 1016 }, { "id": "zLfPDQeMgiuTcJhb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_16.png", "conversations": [ { "from": "human", "value": "\nComparing {the 'Soft Skills Training' line}[0.135, 0.186, 0.941, 0.427] and {the 'Leadership Training' line}[0.135, 0.414, 0.941, 0.628], which one showed an overall upward trend?" }, { "from": "gpt", "value": "Soft Skills Training." } ], "id_raw": 1016 }, { "id": "8kCDSMcHrXq8MoeS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_16.png", "conversations": [ { "from": "human", "value": "\nWhat is in the display area [0.109, 0.904, 0.162, 0.934]?" }, { "from": "gpt", "value": "It is the x-axis label that corresponds to the year 2010." } ], "id_raw": 1016 }, { "id": "ctjimY4CnXQIYG0t", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.704, 0.852, 0.966, 0.875]." }, { "from": "gpt", "value": "In this area, the text \"Process Improvement Training\" is located. It is linked to the color '#d62728', which represents the Process Improvement Training line on the chart." } ], "id_raw": 1016 }, { "id": "YWGCvPBTR73oiEtd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_16.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.646, 0.735, 0.974, 0.881]?" }, { "from": "gpt", "value": "This area contains the subplot legend with its items 'IT Training', 'Soft Skills Training', 'Leadership Training', and 'Process Improvement Training', each associated with a specific color." } ], "id_raw": 1016 }, { "id": "Fmda0rDF9ONKrfbd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_17.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.361, 0.035, 0.556] in the chart figure?" }, { "from": "gpt", "value": "Indicator Value." } ], "id_raw": 1017 }, { "id": "awL7goyPVZUgk3Qr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_17.png", "conversations": [ { "from": "human", "value": "\nCan you identify what {the line in the color '#2ca02c'}[0.118, 0.145, 0.944, 0.263] stands for?" }, { "from": "gpt", "value": "Global Credit Supply." } ], "id_raw": 1017 }, { "id": "FoSxfNCgnvAeKfqM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the text label for {the first tick on the x-axis}[0.097, 0.896, 0.140, 0.949]?" }, { "from": "gpt", "value": "2007." } ], "id_raw": 1017 }, { "id": "u9JuFgjpPJUvmboj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_17.png", "conversations": [ { "from": "human", "value": "\nWhich of the plotted lines has the highest value at {the year '2015'}[0.647, 0.896, 0.690, 0.949] in the chart?" }, { "from": "gpt", "value": "Global Credit Supply." } ], "id_raw": 1017 }, { "id": "XpRG3gEX3SWQyfkv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_17.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Inflation Rate'}[0.256, 0.877] greater than or less than {the point for 'Financial Stability Index'}[0.256, 0.341] in 2009?" }, { "from": "gpt", "value": "Less than." } ], "id_raw": 1017 }, { "id": "obs5KO4YTwene8vv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_17.png", "conversations": [ { "from": "human", "value": "\nBy looking at the chart, during which year did the {Consumer Confidence Index} line first cross the value of 90?" }, { "from": "gpt", "value": "2014." } ], "id_raw": 1017 }, { "id": "Gkh6FSRmL5HI70uR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents at this position [0.732, 0.771, 0.978, 0.881]?" }, { "from": "gpt", "value": "This region represents the legend of the chart. It lists labels and their corresponding markers for {Consumer Confidence Index}[0.777, 0.778, 0.973, 0.796], {Financial Stability Index}[0.777, 0.804, 0.942, 0.822], {Global Credit Supply}[0.777, 0.831, 0.919, 0.848], and {Inflation Rate}[0.777, 0.857, 0.870, 0.874]." } ], "id_raw": 1017 }, { "id": "ZWwK2LITadGk8jz8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.077, 0.028, 0.985, 0.890]." }, { "from": "gpt", "value": "This region covers the entire plot area of the line chart. It consists of four lines representing Consumer Confidence Index, Financial Stability Index, Global Credit Supply and Inflation Rate with their respective color codes and markers over the years from 2007 to 2019." } ], "id_raw": 1017 }, { "id": "3cFNHsLNou1tB5y7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_17.png", "conversations": [ { "from": "human", "value": "\nWhat information does the region [0.041, 0.019, 0.072, 0.041] represent?" }, { "from": "gpt", "value": "This region corresponds to the lowest y-axis tick label which denotes the numerical value '110'." } ], "id_raw": 1017 }, { "id": "eSsB8JbiSu6STXUW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the chart top right at [0.015, 0.375, 0.034, 0.566]?" }, { "from": "gpt", "value": "Value (Billion $)." } ], "id_raw": 1018 }, { "id": "bjzUe3CDcUeVhK4e", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_18.png", "conversations": [ { "from": "human", "value": "\nWhat is hinted by the marker at the third item of the sub-legend coordinates [0.074, 0.089, 0.110, 0.100]?" }, { "from": "gpt", "value": "Average Home Prices (Billion $)." } ], "id_raw": 1018 }, { "id": "qIYhqt2XnOPavEYd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-tick label}[0.922, 0.928, 0.964, 0.951]?" }, { "from": "gpt", "value": "2019." } ], "id_raw": 1018 }, { "id": "uHqWXmpJ2bghYcOs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_18.png", "conversations": [ { "from": "human", "value": "\nIs the {point of 2019 in 'Public Infrastructure Investment (Billion $)'}[0.943, 0.394] higher or lower than the {point of 2019 in 'Local Government Revenue (Billion $)'}[0.943, 0.554]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1018 }, { "id": "fbpEZ67SkS2m9hp7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_18.png", "conversations": [ { "from": "human", "value": "\nDoes the 'Average Home Prices (Billion $)' series represented by cross marker given by {this series}[0.107, 0.918, 0.943, 0.920] increase or decrease over the years?" }, { "from": "gpt", "value": "Increases." } ], "id_raw": 1018 }, { "id": "CSL3th4HBNDjVQUK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_18.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher in the year 2017, {Local Government Revenue (Billion $)}[0.525, 0.284] or {Public Infrastructure Investment (Billion $)}[0.525, 0.486]?" }, { "from": "gpt", "value": "Public Infrastructure Investment (Billion $)." } ], "id_raw": 1018 }, { "id": "UXtbDCDr32Od2tAE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the coordinates [0.072, 0.027, 0.441, 0.137]." }, { "from": "gpt", "value": "This area contains the legend of the line chart. The legend includes indicators such as Local Government Revenue (Billion $), Community Development Spending (Billion $), Average Home Prices (Billion $), and Public Infrastructure Investment (Billion $). Each indicator has a unique line style and marker." } ], "id_raw": 1018 }, { "id": "c8NmZlSci0QSJ0EF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_18.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the region [0.066, 0.019, 0.985, 0.922]?" }, { "from": "gpt", "value": "This is the main chart area. It contains 4 line series each representing 'Local Government Revenue (Billion $)', 'Community Development Spending (Billion $)', 'Average Home Prices (Billion $),', and 'Public Infrastructure Investment (Billion $)', plotted against the year on the x-axis." } ], "id_raw": 1018 }, { "id": "uyVN3Sc3Y8qYSQPi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_18.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the region [0.066, 0.561, 0.066, 0.561]?" }, { "from": "gpt", "value": "This region includes the y-axis tick label '40'." } ], "id_raw": 1018 }, { "id": "jIiu9PWxdZAjY5vm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.036, 0.723, 0.071, 0.746] represent on the y-axis?" }, { "from": "gpt", "value": "1500." } ], "id_raw": 1019 }, { "id": "HwtgyW4AGOayooNE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the marker at {the point for the year 2017 on the 'US-China Balance of Trade' line}[0.937, 0.910]?" }, { "from": "gpt", "value": "#1f77b4." } ], "id_raw": 1019 }, { "id": "sw1vTmeIsMDNnboC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_51_19.png", "conversations": [ { "from": "human", "value": "\nWhat year corresponds to the x-axis label located at [0.575, 0.928, 0.623, 0.951]?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 1019 }, { "id": "Jm1abAQENETRZXsx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_19.png", "conversations": [ { "from": "human", "value": "\nWhich line trend at {point for the year 2009}[0.550, 0.829] is higher, 'Global Trade Partners Sudden Change' or 'EU-China Balance of Trade'?" }, { "from": "gpt", "value": "Global Trade Partners Sudden Change." } ], "id_raw": 1019 }, { "id": "pVOLaYQ8TpLICCv5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_19.png", "conversations": [ { "from": "human", "value": "\nIs the line for 'World Total Trade' at {point for the year 2017}[0.937, 0.033] above or below the line for 'US-China Balance of Trade' at {the same point}[0.937, 0.910]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1019 }, { "id": "1fFPF8UKZawMbAaH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_51_19.png", "conversations": [ { "from": "human", "value": "\nWhich trend decreases more sharply between 2010 and 2012, {the 'US-China Balance of Trade'}[0.599, 0.900] or {the 'EU-China Balance of Trade'}[0.599, 0.891]?" }, { "from": "gpt", "value": "US-China Balance of Trade." } ], "id_raw": 1019 }, { "id": "UXoP1i8jMJYEauBw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the area occupied by x-grid visible on the chart with the range [0.075, 0.922, 0.978, 0.922]." }, { "from": "gpt", "value": "The x-grid area contains horizontal white dashed lines, indicating each year's position scaling from 1997.5 to 2020.0." } ], "id_raw": 1019 }, { "id": "NUJ6q3fNy9SIFDUk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the section from [0.081, 0.027, 0.342, 0.137] contain?" }, { "from": "gpt", "value": "This region includes the legend, displaying labels and markers for 'US-China Balance of Trade', 'EU-China Balance of Trade', 'World Total Trade', and 'Global Trade Partners Sudden Change', with corresponding colored markers at {locations}[0.082, 0.037, 0.112, 0.047], [0.082, 0.063, 0.112, 0.073], [0.082, 0.089, 0.112, 0.100] and [0.082, 0.115, 0.112, 0.126]." } ], "id_raw": 1019 }, { "id": "7vfuokakzkMQwVaU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_51_19.png", "conversations": [ { "from": "human", "value": "\nDetail the objects found at [0.425, 0.869, 0.620, 0.888]." }, { "from": "gpt", "value": "The space contains y-axis tick labels '2000.0', '2002.5', '2005.0' and their corresponding ticks, signaling the specific years on the x-axis." } ], "id_raw": 1019 }, { "id": "EnaG92QONebXgVbQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the text located at [0.643, 0.647, 0.728, 0.694] represent?" }, { "from": "gpt", "value": "Ethereum." } ], "id_raw": 1020 }, { "id": "lfORXnqtrN5g143m", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fifth task on the x-axis in the second subplot?" }, { "from": "gpt", "value": "[0.754, 0.821, 0.765, 0.868]." } ], "id_raw": 1020 }, { "id": "YSdj64t8ZI3g12GK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel}[0.019, 0.153, 0.043, 0.777] for the first subplot?" }, { "from": "gpt", "value": "Average Usage (%)." } ], "id_raw": 1020 }, { "id": "FUlMsqLHPBvu19pG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_0.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Ethereum'}[0.597, 0.164, 0.963, 0.284] above or below {the line for 'Neobanks'}[0.597, 0.264, 0.963, 0.540]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1020 }, { "id": "fv0UoZVyXn1wr7Df", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_0.png", "conversations": [ { "from": "human", "value": "\nIn the first subplot, is the {Bitcoin} point at Task 3[0.188, 0.420] higher or lower than the {Blockchain} point at Task 3[0.188, 0.493]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1020 }, { "id": "UQHgKIFAMORDEyhe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_0.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at Task 6 in subplot 1, Bitcoin or Blockchain?" }, { "from": "gpt", "value": "Blockchain." } ], "id_raw": 1020 }, { "id": "UhXyQ0A16j5thiQj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the area within the range [0.188, 264, 0.963, 540]." }, { "from": "gpt", "value": "This area contains the Ethereum and Neobanks lines in the second subplot. The Ethereum line starts at Task 1 and extends to Task 10, with coordinates ranging from [0.597, 0.164] to [0.963, 0.284]. The Neobanks line also starts at Task 1 and extends to Task 10, its coordinates ranging from [0.597, 0.264] to [0.963, 0.540]." } ], "id_raw": 1020 }, { "id": "fb57QOW4AE8PBS1I", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_0.png", "conversations": [ { "from": "human", "value": "\nWhat are the components in the region [0.325, 0.148, 0.482, 0.301]?" }, { "from": "gpt", "value": "The region contains the legend for the first subplot, with two items: {Bitcoin}[0.381, 0.166, 0.440, 0.213] and {Blockchain}[0.381, 0.236, 0.475, 0.283]." } ], "id_raw": 1020 }, { "id": "Zk742QPALFQ98WiZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_0.png", "conversations": [ { "from": "human", "value": "\nDescribe what is in the region [0.519, 0.050, 1.039, 0.110]." }, { "from": "gpt", "value": "This region contains the subtitle for the second subplot which reads 'Performance Comparison: Ethereum vs Neobanks'." } ], "id_raw": 1020 }, { "id": "KJMll0T9YQICRP4g", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the {axes label}[0.505, 0.370, 0.514, 0.598] in this chart?" }, { "from": "gpt", "value": "Percentages (%)" } ], "id_raw": 1021 }, { "id": "UEQRSGTQ1tHEsPE3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of {year '2024'} label on the x-axis?" }, { "from": "gpt", "value": "[0.727, 0.903, 0.750, 0.931]" } ], "id_raw": 1021 }, { "id": "UAAryECWZXvkqBDd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel}[0.010, 0.370, 0.019, 0.598] in the first subplot of the chart?" }, { "from": "gpt", "value": "Percentages (%)" } ], "id_raw": 1021 }, { "id": "3S9SYgtaGGEfGqXJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_1.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at {year 2022}[0.139, 0.903, 0.163, 0.931], {Training Spending}[0.151, 0.492] or {Promotion Rate}[0.151, 0.825]?" }, { "from": "gpt", "value": "Promotion Rate" } ], "id_raw": 1021 }, { "id": "Jzqo15L9GBlh4Tjn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_1.png", "conversations": [ { "from": "human", "value": "\nIs {the line labeled 'Work-From-Home Rate'}[0.554, 0.112, 0.969, 0.391] in general above or below {the line labeled 'Attrition Rate'}[0.554, 0.650, 0.969, 0.856]?" }, { "from": "gpt", "value": "Above" } ], "id_raw": 1021 }, { "id": "3c6iD73hq72zoSSj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_1.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Training Spending}[0.336, 0.247] in 2026 higher or lower than in 2024[0.243, 0.332]?" }, { "from": "gpt", "value": "Higher" } ], "id_raw": 1021 }, { "id": "ZFcFzflVMppSepKt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the information}[0.581, 0.030, 0.942, 0.066] in the uppermost region of the second subplot?" }, { "from": "gpt", "value": "The region houses the subplot title, \"Workplace Development: Work-From-Home Rate vs Attrition Rate.\"" } ], "id_raw": 1021 }, { "id": "aFSVOVo68mdmgyFR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is contained in {the legend}[0.538, 0.089, 0.676, 0.181]?" }, { "from": "gpt", "value": "The area contains two legend items: {Work-From-Home Rate}[0.567, 0.100, 0.672, 0.128], with a color marker {color marker}[0.539, 0.104, 0.563, 0.120], and {Attrition Rate}[0.567, 0.142, 0.629, 0.170], with a {color marker}[0.539, 0.146, 0.563, 0.162]." } ], "id_raw": 1021 }, { "id": "jahGEHQ0smxGUYkv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_1.png", "conversations": [ { "from": "human", "value": "\nDescribe region [0.727, 0.903, 0.750, 0.931]." }, { "from": "gpt", "value": "The region hosts the label '2024' on the x-axis of the second subplot." } ], "id_raw": 1021 }, { "id": "LbVJUzQEXzk8mkKH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.453, 0.911, 0.481, 0.934] in the chart?" }, { "from": "gpt", "value": "X-axis label for year 2020." } ], "id_raw": 1022 }, { "id": "dOLNuXRKbPlhRVG7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_2.png", "conversations": [ { "from": "human", "value": "\nWhat object is represented by the coordinates [0.425, 0.083, 0.478, 0.106] in the sub-legend section?" }, { "from": "gpt", "value": "The text label 'Microsoft'." } ], "id_raw": 1022 }, { "id": "sjNamyzNJ5uAwCsx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the area enclosed by [0.270, 0.817, 0.270, 0.817] represent?" }, { "from": "gpt", "value": "Data point for Apple's market share in 2018." } ], "id_raw": 1022 }, { "id": "0teAmWnIF0jsKrKG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_2.png", "conversations": [ { "from": "human", "value": "\nIs {the market share of Apple in 2017}[0.171, 0.852] greater than or less than {the market share of Microsoft in 2017}[0.171, 0.137]?" }, { "from": "gpt", "value": "Less than." } ], "id_raw": 1022 }, { "id": "jAwBlToFhFaU2TeP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_2.png", "conversations": [ { "from": "human", "value": "\nAt {year 2020}[0.468, 0.750] and [0.468, 0.332], did Microsoft or Apple have a higher market share?" }, { "from": "gpt", "value": "Microsoft." } ], "id_raw": 1022 }, { "id": "QT8bzjciVDNN5arO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_2.png", "conversations": [ { "from": "human", "value": "\nDoes {Market share of Microsoft in 2016}[0.072, 0.100] have a higher value than {Market share of Amazon in 2020}[0.968, 0.100]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1022 }, { "id": "6Ov3rrKbNi8M9YVX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the content in the area represented by [0.597, 0.025, 0.938, 0.055]?" }, { "from": "gpt", "value": "It is the subtitle of second subplot, with the text \"Market Share Comparison: Amazon vs Facebook\"." } ], "id_raw": 1022 }, { "id": "2hORSoR1GweHHR96", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is in the area [0.552, 0.074, 0.655, 0.151]." }, { "from": "gpt", "value": "This area contains the legend for subplot_1. It includes two items: {Amazon}[0.594, 0.083, 0.641, 0.106] and {Facebook}[0.594, 0.118, 0.648, 0.141], each paired with its respective marker." } ], "id_raw": 1022 }, { "id": "hHScrLpK6sUf0jIl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be found inside the coordinates [0.400, 0.062, 0.859, 0.709]?" }, { "from": "gpt", "value": "This region contains part of subplot_1 including the y-grid lines, a portion of the Amazon and Facebook data lines, and x-tick labels for 2016 to 2019." } ], "id_raw": 1022 }, { "id": "SIPUregNTtDvstuG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the role of the object located at [0.259, 0.943, 0.294, 0.975] in the chart?" }, { "from": "gpt", "value": "It is the x-axis label in the left subplot which is indicating the 'Task'." } ], "id_raw": 1023 }, { "id": "3hlg9oXexN1qCSS8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label } at [0.033, 0.221, 0.055, 0.244]?" }, { "from": "gpt", "value": "120." } ], "id_raw": 1023 }, { "id": "IRsLdNs4Ex9sMgnt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the handled object found at [0.269, 0.085, 0.301, 0.101]?" }, { "from": "gpt", "value": "It is the color marker for the line representing 'Global Trade Volume (billion $)'." } ], "id_raw": 1023 }, { "id": "rj0CYSMjD09gQFf6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_3.png", "conversations": [ { "from": "human", "value": "\nWhich is lower, {the point at Task 1 in the left subplot}[0.079, 0.342] or {the point at Task 1 in the right subplot}[0.573, 0.454]?" }, { "from": "gpt", "value": "The point at Task 1 in the right subplot is lower." } ], "id_raw": 1023 }, { "id": "G7POxlSD6QNtOfRv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_3.png", "conversations": [ { "from": "human", "value": "\nComparing {the point at Task 6 in the right subplot}[0.792, 0.460] and {the point at Task 7 in the right subplot}[0.836, 0.454], which one is higher?" }, { "from": "gpt", "value": "The point at Task 7 in the right subplot is higher." } ], "id_raw": 1023 }, { "id": "6aNl6P2BZrAfEB3q", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_3.png", "conversations": [ { "from": "human", "value": "\nIs the {value at Task 4 in the left subplot}[0.210, 0.242] greater or lesser than {the value at Task 4 in the right subplot}[0.704, 0.421]?" }, { "from": "gpt", "value": "Lesser." } ], "id_raw": 1023 }, { "id": "gqnSvUB34C9Phz8i", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region between [0.269, 0.074, 0.488, 0.116]?" }, { "from": "gpt", "value": "It is the legend area of the left subplot, providing the label 'Global Trade Volume (billion $)' and the associated color marker is located here." } ], "id_raw": 1023 }, { "id": "xzm4vcsUwMC7Etpr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the area defined by [0.376, 0.025, 0.913, 0.055]?" }, { "from": "gpt", "value": "In this area, the titles of the subplots are located. They say 'Trend in Global Trade Volume' and 'Trend in Supply Chain Performance Index' for the left and right subplots respectively." } ], "id_raw": 1023 }, { "id": "a6dO9B0RO6oI9ylo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_3.png", "conversations": [ { "from": "human", "value": "\nWhat details are present in [0.506, 0.438, 0.522, 0.527]?" }, { "from": "gpt", "value": "It contains the y-axis label of the right subplot 'Value', displayed vertically." } ], "id_raw": 1023 }, { "id": "f7SXh3hcl2xbWBpL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.562, 0.050, 0.922, 0.110]?" }, { "from": "gpt", "value": "Subtitle of the second chart." } ], "id_raw": 1024 }, { "id": "qLDZFhc2cJen8Elc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.537, 0.170, 0.584, 0.203] in the legend?" }, { "from": "gpt", "value": "Marker for the \"Total Malware Cases\"." } ], "id_raw": 1024 }, { "id": "AobqkyxTzOmm2Q53", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the xlabel} at [0.686, 0.887, 0.798, 0.950]?" }, { "from": "gpt", "value": "Category." } ], "id_raw": 1024 }, { "id": "4aJsfQdZFNHXD3RV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Total Cases'}[0.604, 0.397] higher or lower than {the point of 'Cases with Data Breach'}[0.880, 0.451] in 'Total Malware Cases' line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1024 }, { "id": "AiwFrzl4S4tpyJUx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_4.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Total Phishing Cases'}[[0.113, 0.261, 0.389, 0.315]] generally above or below {the line for 'Phishing Cases with Data Breach'}[[0.113, 0.315, 0.389, 0.364]]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1024 }, { "id": "iDQ3jkqdZSII5eZr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_4.png", "conversations": [ { "from": "human", "value": "\nWhich category has a higher value at {point 'Cases with Data Breach'}[0.880, 0.451], Total Malware Cases or Malware Cases with Data Breach?" }, { "from": "gpt", "value": "Malware Cases with Data Breach." } ], "id_raw": 1024 }, { "id": "2B1hVIqnlFzyQpAs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_4.png", "conversations": [ { "from": "human", "value": "\nPlease, describe the content in the area with coordinates [0.536, 0.148, 0.885, 0.301]." }, { "from": "gpt", "value": "This region contains the legend of the second chart. It includes {the label 'Total Malware Cases'}[0.592, 0.166, 0.768, 0.213] and {the label 'Malware Cases with Data Breach'}[0.592, 0.236, 0.878, 0.283], both paired with colored markers." } ], "id_raw": 1024 }, { "id": "syfhD42kxZdBzXjd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.099, 0.124, 0.403, 0.805]." }, { "from": "gpt", "value": "This region is the first subplot, illustrating phishing cases versus data breaches. It displays two lines, named 'Total Phishing Cases' and 'Phishing Cases with Data Breach', with different markers." } ], "id_raw": 1024 }, { "id": "JMilfuyk4ePb2Dsf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_4.png", "conversations": [ { "from": "human", "value": "\nCan you provide a description for the region [0.590, 0.124, 0.894, 0.805]?" }, { "from": "gpt", "value": "This area is the second subplot showing malware cases compared to the instances of data breaches. Two plotted lines represent 'Total Malware Cases' and 'Malware Cases with Data Breach'. Also, both lines are distinguished by different types of markers." } ], "id_raw": 1024 }, { "id": "GGBebCvAnYLc0k1k", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line representing {Investment A}[0.077, 0.363, 0.466, 0.831] in the chart?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 1025 }, { "id": "NLhXGlF4N14CYJ7I", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_5.png", "conversations": [ { "from": "human", "value": "\nWhich label corresponds to the x-axis tick at {position}[0.427, 0.866]?" }, { "from": "gpt", "value": "10." } ], "id_raw": 1025 }, { "id": "I3CFNo8LY7oWXJSD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_5.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is used for {Investment D}[0.576, 0.128, 0.966, 0.535] on its line plot?" }, { "from": "gpt", "value": "Circle." } ], "id_raw": 1025 }, { "id": "6tuW0KGfnU5ACMoo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_5.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher starting investment value, {Investment C}[0.576, 0.461, 0.966, 0.831] or {Investment D}[0.576, 0.128, 0.966, 0.535]?" }, { "from": "gpt", "value": "Investment D." } ], "id_raw": 1025 }, { "id": "r9hHA3o5WmwA0O9h", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_5.png", "conversations": [ { "from": "human", "value": "\nAre the points at {year 5 for Investment B}[0.232, 0.207] above or below {year 5 for Investment A}[0.232, 0.675]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1025 }, { "id": "miwfR8zMMuVJ39eo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_5.png", "conversations": [ { "from": "human", "value": "\nIn year 9, which investment shows a greater value, {Investment A}[0.388, 0.441] or {Investment C}[0.888, 0.609]?" }, { "from": "gpt", "value": "Investment A." } ], "id_raw": 1025 }, { "id": "dkb8hIasTFnOjNMr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the features present in the region [0.056, 0.093, 0.486, 0.866]." }, { "from": "gpt", "value": "This region contains the plot for {Investment A}[0.077, 0.363, 0.466, 0.831] and {Investment B}[0.077, 0.128, 0.466, 0.467], displaying their investment values over the years with respective color-coded lines, blue for A and red for B. Each point on the line is marked by a circle." } ], "id_raw": 1025 }, { "id": "WIBlFFZ3nejNpka0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_5.png", "conversations": [ { "from": "human", "value": "\nHow do the elements within [0.556, 0.093, 0.985, 0.866] represent data?" }, { "from": "gpt", "value": "The area has two line plots for {Investment C}[0.576, 0.461, 0.966, 0.831] and {Investment D}[0.576, 0.128, 0.966, 0.535], each indicated with a different color, green and magenta, respectively. These plots illustrate investment amounts over 11 years using circular markers." } ], "id_raw": 1025 }, { "id": "bVLPC3cwND03D3u0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_5.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the space [0.337, 0.111, 0.479, 0.226]?" }, { "from": "gpt", "value": "This area includes the legend with text labels for {Investment A}[0.381, 0.125, 0.473, 0.160] and {Investment B}[0.381, 0.177, 0.473, 0.212], each next to a colored rectangle representing the line color on the charts." } ], "id_raw": 1025 }, { "id": "amCa9LR7pUjfelMu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_6.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted by {the handle of 'Startup Companies'}[0.377, 0.102, 0.400, 0.122] in the color coding of the subplot?" }, { "from": "gpt", "value": "A blue square." } ], "id_raw": 1026 }, { "id": "gb4AOuWUSNYN1jCR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_6.png", "conversations": [ { "from": "human", "value": "\nWhat does {the first x-tick label in the second subplot}[0.560, 0.903, 0.565, 0.931] represent?" }, { "from": "gpt", "value": "1." } ], "id_raw": 1026 }, { "id": "AS5m1C7rou8ISbfA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_6.png", "conversations": [ { "from": "human", "value": "\nWhat kind of marker represents 'Government Support' as indicated in {its legend handle}[0.866, 0.148, 0.886, 0.159]?" }, { "from": "gpt", "value": "Circle." } ], "id_raw": 1026 }, { "id": "RcI94ELK35QqJaUQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_6.png", "conversations": [ { "from": "human", "value": "\nIs {the data point at month 3 for 'Startup Companies'}[0.158, 0.605] higher than {the point at month 9}[0.430, 0.599] on the graph?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1026 }, { "id": "esjxWbFOBgsIFpGC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_6.png", "conversations": [ { "from": "human", "value": "\nWhich is positioned higher, {the y-tick labeled '1000'}[0.022, 0.473, 0.044, 0.501] or {the y-tick labeled '1500'}[0.022, 0.268, 0.044, 0.296]?" }, { "from": "gpt", "value": "1500." } ], "id_raw": 1026 }, { "id": "q5hnCgvKpz6jhnLA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the seventh x-tick label in the first subplot}[0.336, 0.903, 0.342, 0.931] have a lower value compared to {the ninth x-tick in the same subplot}[0.427, 0.903, 0.432, 0.931]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1026 }, { "id": "Bd4V8348tmyrhPcP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.542, 0.075, 0.991, 0.182] contain in terms of chart elements?" }, { "from": "gpt", "value": "This region contains {the axes for the second subplot}[0.542, 0.075, 0.991, 0.893] which includes the {legend}[0.864, 0.089, 0.986, 0.181] with items for 'Innovation Index' and 'Government Support'." } ], "id_raw": 1026 }, { "id": "liJMEIMO40r37nbg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.047, 0.075, 0.495, 0.181] and its function or classic PH factors." }, { "from": "gpt", "value": "This area includes {the first subplot's axes}[0.047, 0.075, 0.495, 0.893] and the {mini-legend}[0.376, 0.089, 0.491, 0.181] containing labels and color handles for 'Startup Companies' and 'R&D Spend'." } ], "id_raw": 1026 }, { "id": "Nootu3gy4I574tqG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_6.png", "conversations": [ { "from": "human", "value": "\nCould you describe the information enclosed within the region [0.022, 0.268, 0.044, 0.501]?" }, { "from": "gpt", "value": "The region hosts {y-tick labels}[0.022, 0.268, 0.044, 0.501] which range from '750' to '2000', indicating varying data values on the y-axis." } ], "id_raw": 1026 }, { "id": "1fNgscR6HqZMaN4W", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the {ylabel} at [0.010, 0.425, 0.023, 0.532] in the subplot_0?" }, { "from": "gpt", "value": "Value." } ], "id_raw": 1027 }, { "id": "48WkQSDIBdBoik8q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the {xlabel} at [0.727, 0.932, 0.762, 0.970] in the subplot_1?" }, { "from": "gpt", "value": "Trend." } ], "id_raw": 1027 }, { "id": "pKX2nkYrpdgX0cKq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the second {xtick label} at [0.638, 0.893, 0.730, 0.921] in subplot_1?" }, { "from": "gpt", "value": "Accelerated Decline." } ], "id_raw": 1027 }, { "id": "1toodnipN4BUUESf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_7.png", "conversations": [ { "from": "human", "value": "\nIs the point with coordinates [0.310, 0.337] higher or lower than the point with coordinates at [0.431, 0.386] in subplot_0 of the Data Science trend line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1027 }, { "id": "93lMfkWDzlD9dfUB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_7.png", "conversations": [ { "from": "human", "value": "\nComparing the line points in subplot_0 and subplot_1, is the value at the 'Inexorable Rise' in the 'Data Science' trend line ([0.310, 0.337]) or the 'Robotics' trend line ([0.805, 0.228]) higher?" }, { "from": "gpt", "value": "Data Science." } ], "id_raw": 1027 }, { "id": "fE2iStucxor4S1nI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_7.png", "conversations": [ { "from": "human", "value": "\nIn subplot_0, is the value at 'Subdued Expansion' point [0.068, 0.540] or 'Unpredictable Fluctuations' point [0.431, 0.386] higher?" }, { "from": "gpt", "value": "Subdued Expansion." } ], "id_raw": 1027 }, { "id": "3ENHkIyiuxbaf64Q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects within the region [0.274, 0.893, 0.493, 0.921] in subplot_0?" }, { "from": "gpt", "value": "This region contains two x-axis labels for trends in Data Science. These are {Subdued Expansion}[0.022, 0.893, 0.114, 0.921] and {Unpredictable Fluctuations}[0.368, 0.893, 0.493, 0.921]." } ], "id_raw": 1027 }, { "id": "4LovStiskwt5jdwq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.196, 0.030, 0.303, 0.066] in subplot_0." }, { "from": "gpt", "value": "This area contains {the subtitle of the subplot}[0.196, 0.030, 0.303, 0.066]. The text reads \"Data Science Trend\"." } ], "id_raw": 1027 }, { "id": "e3xcWsGInwGotAFD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_7.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the coordinates [0.815, 0.089, 0.939, 0.139] in subplot_1?" }, { "from": "gpt", "value": "The {legend entry}[0.815, 0.089, 0.939, 0.139] for 'Robotics Innovation' with a marker in color code '#e7969c' is located here." } ], "id_raw": 1027 }, { "id": "sIjh7M4Ua5JVneR3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_8.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at the given coordinates [0.508, 0.403, 0.522, 0.565]?" }, { "from": "gpt", "value": "Y-axis label for the subplot_1." } ], "id_raw": 1028 }, { "id": "cVH0kDigmGC11wcu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the label say at the coordinates of [0.081, 0.030, 0.462, 0.066]?" }, { "from": "gpt", "value": "\"Emerging Markets Index in different countries\"." } ], "id_raw": 1028 }, { "id": "z5b2kao9UQLoCiz5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_8.png", "conversations": [ { "from": "human", "value": "\nWhat object can be found at coordinates [0.035, 0.498, 0.052, 0.526]?" }, { "from": "gpt", "value": "One of the y-axis tick labels on subplot_0." } ], "id_raw": 1028 }, { "id": "P86XjTnpk5T1dipe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_8.png", "conversations": [ { "from": "human", "value": "\nBased on the line graph, is {Brazil}[0.077, 0.459] higher or lower in the index than {China}[0.174, 0.112] in subplot_0?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1028 }, { "id": "zXbOW8suBAyml0qb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_8.png", "conversations": [ { "from": "human", "value": "\nAre the market values for {India}[0.272, 0.509] and {Russia}[0.467, 0.161] in subplot_0 higher or lower than {China}[0.174, 0.112]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1028 }, { "id": "lj7nDSMfUhGQD0VB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_8.png", "conversations": [ { "from": "human", "value": "\nDoes {China}[0.667, 0.112] in subplot_1 have a higher or lower Consumer Behavior Index value compared to {Russia}[0.959, 0.177]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1028 }, { "id": "DyiDkjguiIhBlXLA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_8.png", "conversations": [ { "from": "human", "value": "\nWhat information does the area [0.567, 0.030, 0.961, 0.066] contain?" }, { "from": "gpt", "value": "The subtitle for subplot_1, which says \"Consumer Behavior Index in different countries\"." } ], "id_raw": 1028 }, { "id": "AS0aoY1BpfRZBM15", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region at [0.057, 0.075, 0.486, 0.893]?" }, { "from": "gpt", "value": "This region includes the plot for subplot_0, which shows the Emerging Markets Index for different countries, represented as a blue line graph. Indices for different countries like Brazil, China, India, Mexico, and Russia are plotted." } ], "id_raw": 1028 }, { "id": "4YPk9lKGDFVc62vu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_8.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen within the region [0.550, 0.075, 0.979, 0.893]?" }, { "from": "gpt", "value": "This area contains the plot for subplot_1, detailing a pink line graph of Consumer Behavior Index values across different countries, including Brazil, China, India, Mexico, and Russia." } ], "id_raw": 1028 }, { "id": "GssCJSK1343WoeZP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the label described at the coordinates [0.009, 0.368, 0.021, 0.597]?" }, { "from": "gpt", "value": "Popularity (%)." } ], "id_raw": 1029 }, { "id": "8ca8E1hVcnO9OSXk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_9.png", "conversations": [ { "from": "human", "value": "\nWhat can be found at {[0.469, 0.911, 0.480, 0.934]}?" }, { "from": "gpt", "value": "x-axis tick label '10'." } ], "id_raw": 1029 }, { "id": "1DNV4xZPTpUHAXUy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_9.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the object at}[0.893, 0.074, 0.986, 0.151] in the second subplot?" }, { "from": "gpt", "value": "The legend for the trends 'Thai Desserts' and 'Russian Vodka'." } ], "id_raw": 1029 }, { "id": "438FSLTXENX2bqKJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_9.png", "conversations": [ { "from": "human", "value": "\nAre the values for {Italian French Fusion}[0.065, 0.139, 0.475, 0.253] in general ascending, descending or fluctuating over the months?" }, { "from": "gpt", "value": "Ascending." } ], "id_raw": 1029 }, { "id": "FpuYk7wQLKp87bgQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_9.png", "conversations": [ { "from": "human", "value": "\nBetween {Thai Desserts}[0.560, 0.482, 0.970, 0.658] and {Russian Vodka}[0.560, 0.635, 0.970, 0.765], which trend has a higher popularity in the 10th month?" }, { "from": "gpt", "value": "Russian Vodka." } ], "id_raw": 1029 }, { "id": "72bS2JkvTWU7vLs9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_9.png", "conversations": [ { "from": "human", "value": "\nDoes the {Cuban Sandwiches} trend line [0.065, 0.398, 0.475, 0.475] show an increase or decrease in popularity over time?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 1029 }, { "id": "TtLbwzJYYsALU7hS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_9.png", "conversations": [ { "from": "human", "value": "\nPlease detail the area given by the coordinates [0.540, 0.062, 0.991, 0.903]." }, { "from": "gpt", "value": "This area corresponds to the second subplot of the chart which displays the trends of Thai Desserts and Russian Vodka over time. It includes the plot lines, axis labels, tick marks, and the trend legend." } ], "id_raw": 1029 }, { "id": "JPdmGLBlr28f71iR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the region [0.046, 0.074, 0.168, 0.151]." }, { "from": "gpt", "value": "This area includes the chart legend for the first subplot, containing the labels for {Italian French Fusion}[0.076, 0.083, 0.165, 0.106] and {Cuban Sandwiches}[0.076, 0.118, 0.158, 0.141], alongside their respective color indicators." } ], "id_raw": 1029 }, { "id": "0d5IsUpDKQMgN61W", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_9.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.642, 0.025, 0.888, 0.055]?" }, { "from": "gpt", "value": "The region includes the subtitle for the second subplot, which is \"Trend Analysis: Thai Desserts vs Russian Vodka\"." } ], "id_raw": 1029 }, { "id": "UpDlUXsDM99OVyck", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the marker for 'Agriculture'}[0.406, 0.085, 0.431, 0.101] in the chart?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 1030 }, { "id": "QcWkJfoBaZgVrqg3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_10.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is used for representing {the line for 'Services'}[0.565, 0.558, 0.970, 0.672] data points?" }, { "from": "gpt", "value": "Circle." } ], "id_raw": 1030 }, { "id": "6ZNnySK25L6sjXD6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label color of {the y-axis}[0.010, 0.341, 0.023, 0.624]?" }, { "from": "gpt", "value": "Dark gray." } ], "id_raw": 1030 }, { "id": "zkDJDRcr09KA8yQ1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_10.png", "conversations": [ { "from": "human", "value": "\nIs {the trend for '2010'}[0.070, 0.461] in {Retail data}[0.070, 0.335, 0.475, 0.461] above or below {the trend for '2010'}[0.565, 0.672] in {Services data}[0.565, 0.558, 0.970, 0.672]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1030 }, { "id": "KwnB3GHFrejBTytn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_10.png", "conversations": [ { "from": "human", "value": "\nWhich data point is higher, {the '2016' marker for Agriculture}[0.340, 0.268] or {the '2016' marker for Retail}[0.340, 0.382]?" }, { "from": "gpt", "value": "Retail." } ], "id_raw": 1030 }, { "id": "Oy3OyyQjhE0PaHeG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_10.png", "conversations": [ { "from": "human", "value": "\nCompare {the starting point for Manufacturing in '2010'}[0.565, 0.575] with {the ending point for the same in '2019'}[0.970, 0.386]; did it increase or decrease?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 1030 }, { "id": "2eNn5KBBFiADe3kU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the area bounded by [0.406, 0.074, 0.490, 0.151]." }, { "from": "gpt", "value": "This section contains {the color handles}[0.406, 0.085, 0.431, 0.101] and {the text labels}[0.436, 0.083, 0.487, 0.106] for the 'Agriculture' data line, indicating its color in the chart." } ], "id_raw": 1030 }, { "id": "SntyBKsbyzBvOTj4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_10.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the enclosed space [0.259, 0.943, 0.286, 0.975]?" }, { "from": "gpt", "value": "The chart houses {the x-axis label 'Year'}[0.259, 0.943, 0.286, 0.975], positioned centrally below the x-axis ticks." } ], "id_raw": 1030 }, { "id": "c1SFfpHi2nVedYfZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_10.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region spanning [0.527, 0.053, 0.542, 0.076]?" }, { "from": "gpt", "value": "This area consists of {the y-axis tick label '20.0'}[0.521, 0.053, 0.542, 0.076] and {its corresponding tick mark}[0.545, 0.062] on the chart." } ], "id_raw": 1030 }, { "id": "AU15Hd5bnqrKZnQt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the line with coordinates from [0.107, 0.164] to [0.472, 0.459] represent?" }, { "from": "gpt", "value": "Performance of Asian Recipes." } ], "id_raw": 1031 }, { "id": "TiZRpL3WDP2CZsSA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the content encased by the coordinates [0.507, 0.192, 0.533, 0.737]?" }, { "from": "gpt", "value": "Y-axis label for the second subplot." } ], "id_raw": 1031 }, { "id": "7hdehplrqPHmHQz5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_11.png", "conversations": [ { "from": "human", "value": "\nWhat is positioned at [0.107, 0.164] in the chart?" }, { "from": "gpt", "value": "The first point of the 'Asian Recipes' trend line." } ], "id_raw": 1031 }, { "id": "ummtIQPHDyrkiTNU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_11.png", "conversations": [ { "from": "human", "value": "\nIs the performance of {Asian Recipes}[0.107, 0.164] higher or lower than {Western Recipes}[0.597, 0.287] at Task 1?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1031 }, { "id": "PBaqRRgtrb0MyCkj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_11.png", "conversations": [ { "from": "human", "value": "\nWhich recipe shows an increasing trend at {Task 8}[0.391, 0.392, 0.882, 0.451], Asian or Western?" }, { "from": "gpt", "value": "Western Recipes." } ], "id_raw": 1031 }, { "id": "uszNkDrnfZDkcUvi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_11.png", "conversations": [ { "from": "human", "value": "\nDoes the {Asian Recipes}[0.188, 0.247] performance increase or decrease from Task 2 to Task 3?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 1031 }, { "id": "Gj7PSQMlwVI3ZYaP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the information spanned by the coordinates [0.300, 0.148, 0.482, 0.231]?" }, { "from": "gpt", "value": "This region contains the legend for the first subplot indicating the performance of {Asian Recipes}[0.355, 0.166, 0.475, 0.213], symbolized with a blue square marker handled at [0.301, 0.170, 0.347, 0.203]." } ], "id_raw": 1031 }, { "id": "WKqhoaABIZdB2itT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_11.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region from [0.592, 0.821] to [0.974, 0.868]?" }, { "from": "gpt", "value": "This region contains the x-axis labels '1' to '10' for the second subplot." } ], "id_raw": 1031 }, { "id": "Fpee38jkpdoyl5d5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the coordinates [0.579, 0.124, 0.981, 0.805]?" }, { "from": "gpt", "value": "This region contains the second subplot that deals with the 'Performance of Western Recipes across 10 Tasks', it also includes x-axis and y-axis labels, grid lines, tick labels, and a line chart." } ], "id_raw": 1031 }, { "id": "kpwYbTnD2KseW5nx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_12.png", "conversations": [ { "from": "human", "value": "\nWhat type of plot is the main object represented by the coordinates [0.075, 0.075, 0.493, 0.893]?" }, { "from": "gpt", "value": "It is a subplot of a line plot." } ], "id_raw": 1032 }, { "id": "9IxUUbcPCY6Skbjt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the data point located at coordinates [0.178, 0.491]?" }, { "from": "gpt", "value": "It is a data point for 'Twitter' on Week 3 showing 540 platform usage." } ], "id_raw": 1032 }, { "id": "3lgQPKMMYgprCNZX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at coordinates [0.551, 0.030, 1.001, 0.066] refer to?" }, { "from": "gpt", "value": "It refers to the subplot title: 'Usage Comparison: Instagram vs Online Communities'." } ], "id_raw": 1032 }, { "id": "6hIkzQBYdApb6rdG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_12.png", "conversations": [ { "from": "human", "value": "\nDoes the {usage of 'Twitter'}[0.094, 0.387, 0.474, 0.521] show an increasing or decreasing trend from Week 2 to Week 6?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 1032 }, { "id": "AiZi6GMJuloGi3fF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_12.png", "conversations": [ { "from": "human", "value": "\nWhat is higher on Week 5, the {usage of 'Facebook'}[0.262, 0.298] or the {usage of 'Twitter'}[0.262, 0.462]?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 1032 }, { "id": "H7bhigFTxXQMQ9ud", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_12.png", "conversations": [ { "from": "human", "value": "\nWhich platform had a higher usage at Week 10, {Instagram}[0.966, 0.674] or {Online Communities}[0.966, 0.759]?" }, { "from": "gpt", "value": "Online Communities." } ], "id_raw": 1032 }, { "id": "FULytYmui2s6CAdv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region between [0.370, 0.089, 0.486, 0.181] in the chart." }, { "from": "gpt", "value": "This region contains the legend for the first subplot. It includes details for the 'Facebook' line which is represented in blue and the 'Twitter' line represented in orange." } ], "id_raw": 1032 }, { "id": "rjT5f1rAkKGQNKmK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of the region [0.574, 0.089, 0.765, 0.181]?" }, { "from": "gpt", "value": "This region contains the legend for the second subplot. It includes 'Instagram' labeled in green and 'Online Communities' labeled in red." } ], "id_raw": 1032 }, { "id": "NiO8TpSv85HTyktT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents of the region [0.043, 0.733, 0.070, 0.761] to [0.075, 0.819]?" }, { "from": "gpt", "value": "This region covers the y-axis labels between 200 and 300 for Platform Usage." } ], "id_raw": 1032 }, { "id": "1uBHiik44DYbtGsL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_13.png", "conversations": [ { "from": "human", "value": "\nWhat is positioned at {coordinates} [0.112, 0.821, 0.123, 0.868] in the chart?" }, { "from": "gpt", "value": "Quarter 1 x-axis tick label." } ], "id_raw": 1033 }, { "id": "S8FLA9KPsbC8z7HP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_13.png", "conversations": [ { "from": "human", "value": "\nWhat line does the point [0.963,0.252] belong to?" }, { "from": "gpt", "value": "It belongs to the Mobile Sales line." } ], "id_raw": 1033 }, { "id": "xPI0IojWYsT9WxNU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the line with color '#e74c3c' and marker 'v'?" }, { "from": "gpt", "value": "Mobile Sales." } ], "id_raw": 1033 }, { "id": "bTm5MrGRSKwqRsD9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_13.png", "conversations": [ { "from": "human", "value": "\nOn the 6th quarter, do Mobile or In-Store Sales have a higher value?" }, { "from": "gpt", "value": "Mobile Sales." } ], "id_raw": 1033 }, { "id": "AGI8HIWl1Bck7iy0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_13.png", "conversations": [ { "from": "human", "value": "\nIs the {first data point of the In-Store Sales line}[0.608, 0.741] higher or lower than the {first data point of the Online Sales line}[0.117, 0.550]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1033 }, { "id": "nQ4CWE4REWLOcsHm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_13.png", "conversations": [ { "from": "human", "value": "\nIs {the segment line of 'Online Sales'}[0.117, 0.124, 0.473, 0.550] above or below {the segment line of 'Offline Sales'}[0.117, 0.507, 0.473, 0.635]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1033 }, { "id": "cWy6AKWhHAiywi9p", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_13.png", "conversations": [ { "from": "human", "value": "\nWhat is represented in the region [0.108, 0.148, 0.280, 0.301]?" }, { "from": "gpt", "value": "This region contains the sub-legend for the first subplot, including labels for 'Online Sales' and 'Offline Sales', each with their own color markers." } ], "id_raw": 1033 }, { "id": "e0v1aNsAskZpobTk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.599, 0.148, 0.781, 0.301]?" }, { "from": "gpt", "value": "This area contains the sub-legend for the second subplot, which includes labels for 'Mobile Sales' and 'In-Store Sales', each paired with a colored marker." } ], "id_raw": 1033 }, { "id": "J96A4brvEKFf7oGm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.058, 0.747, 0.094, 0.839]." }, { "from": "gpt", "value": "The y-axis tick label '4000' is located here." } ], "id_raw": 1033 }, { "id": "S5DxLgvuBOtOH3FN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.011, 0.378, 0.024, 0.584] of the left subplot?" }, { "from": "gpt", "value": "\"Sales (units)\"" } ], "id_raw": 1034 }, { "id": "pHplxu1Bhz4mOlgW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_14.png", "conversations": [ { "from": "human", "value": "\nWhat object is at position [0.582, 0.113, 0.966, 0.440] in the right subplot?" }, { "from": "gpt", "value": "The line representing \"Gasoline Sales\"." } ], "id_raw": 1034 }, { "id": "O55iNVDwtWhAVbqc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the significance of {the object}[0.873, 0.148, 0.899, 0.164] in the right subplot legend?" }, { "from": "gpt", "value": "It is the marker symbol for \"Diesel Sales\"." } ], "id_raw": 1034 }, { "id": "NY2h68GMFx0a0WTM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_14.png", "conversations": [ { "from": "human", "value": "\nIn the left subplot, does the series represented by {the line }[0.088, 0.113, 0.471, 0.603] have higher or lower sales in 'Q4_next' than action that series represented by {the line }[0.088, 0.309, 0.471, 0.848]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1034 }, { "id": "sQOllYsRjX5y82LG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_14.png", "conversations": [ { "from": "human", "value": "\nIn the right subplot, which series sees an increase in sales from 'Q2' to 'Q3' - the series represented by {the line }[0.582, 0.113, 0.966, 0.440] or the series represented by {the line }[0.582, 0.701, 0.966, 0.848]?" }, { "from": "gpt", "value": "The line representing \"Diesel Sales\"." } ], "id_raw": 1034 }, { "id": "9IFLtFomtDTjsZ47", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_14.png", "conversations": [ { "from": "human", "value": "\nIn the left subplot, which series reaches the lower sales point at 'Q3_next': the series represented by {the line}[0.088, 0.113, 0.471, 0.603] or the series represented by {the line}[0.088, 0.309, 0.471, 0.848]?" }, { "from": "gpt", "value": "The line representing \"Electric Sales\"." } ], "id_raw": 1034 }, { "id": "ixl0CUlfgED1CRjr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_14.png", "conversations": [ { "from": "human", "value": "\nCould you describe the features of the area [0.069, 0.077, 0.490, 0.885] of the chart?" }, { "from": "gpt", "value": "This region is a subplot of the chart, displaying the trend in \"Hybrid Sales\" and \"Electric Sales\" over time. The x-axis represents different 'Quarters', while the y-axis signifies the 'Sales (units)'. Two distinct lines, one for each sales type, show the fluctuation in unit sales across the quarters." } ], "id_raw": 1034 }, { "id": "XJaT48plqBrlKeST", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region [0.872, 0.091, 0.980, 0.183]" }, { "from": "gpt", "value": "This region contains the legend for the right subplot of the chart. It specifically includes indicators for 'Gasoline Sales' and 'Diesel Sales', each represented by a different color and marker symbol." } ], "id_raw": 1034 }, { "id": "jXQdAdf23iN8vYTR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.563, 0.077, 0.985, 0.885]." }, { "from": "gpt", "value": "This region is a subplot of the chart, graphically representing \"Gasoline Sales\" and \"Diesel Sales\" over time. It contains two lines, each denoting one type of sales. The x-axis defines the 'Quarters' and the y-axis represents the 'Sales (units)'." } ], "id_raw": 1034 }, { "id": "DhMtllDiKi1wZwWC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_15.png", "conversations": [ { "from": "human", "value": "\nWhat object is at the coordinates [0.153, 0.647, 0.199, 0.694]?" }, { "from": "gpt", "value": "Sushi label." } ], "id_raw": 1035 }, { "id": "TclVgwbSG7McUGNL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_15.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object located at [0.907, 0.442, 0.954, 0.488]?" }, { "from": "gpt", "value": "Pasta label." } ], "id_raw": 1035 }, { "id": "fHGupDMRxpWrFReT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_15.png", "conversations": [ { "from": "human", "value": "\nWhat is at the provided coordinate [0.431, 0.281, 0.437, 0.299]?" }, { "from": "gpt", "value": "Marker." } ], "id_raw": 1035 }, { "id": "av7Pa4LSo1aPcCBZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_15.png", "conversations": [ { "from": "human", "value": "\nIs {the Pizza point at time period 5}[0.760, 0.145] above or below {the Pasta point at time period 5}[0.760, 0.670]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1035 }, { "id": "yP0OX20s21kzUBDR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_15.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at time period 3, {Sushi point}[0.188, 0.205] or {Dim Sum point}[0.188, 0.226]?" }, { "from": "gpt", "value": "Dim Sum." } ], "id_raw": 1035 }, { "id": "59RoC4YtRIeidTpn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_15.png", "conversations": [ { "from": "human", "value": "\nDoes {the Burger point at time period 8}[0.882, 0.315] have a higher value compared to {the Pizza point at the same time period}[0.882, 0.217]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1035 }, { "id": "RgdUpMKWtz4dojqR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.550, 0.233, 0.990, 0.805]." }, { "from": "gpt", "value": "This area contains multiple plots showing popularity percentages of Pizza, Pasta, and Burger. {Pizza's data points}[0.597, 0.128, 0.963, 0.220] and {Pasta's data points}[0.597, 0.583, 0.963, 0.736], along with {Burger's data points}[0.597, 0.315, 0.963, 0.356] are spread throughout this space." } ], "id_raw": 1035 }, { "id": "rfwIsPwGt7b4XA7l", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_15.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed between the coordinates [0.085, 0.100, 0.490, 0.900]?" }, { "from": "gpt", "value": "The left subplot showcases {Sushi's plot}[0.107, 0.175, 0.472, 0.290] and {Dim Sum's plot}[0.107, 0.147, 0.472, 0.259] tracking popularity over various time periods." } ], "id_raw": 1035 }, { "id": "KTJR534uOZIoKa1f", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the section from [0.400, 0.230, 0.592, 0.900]." }, { "from": "gpt", "value": "This region overlaps with two separate line charts comparing the progress of distinct cuisines. The latter halves of {Sushi's and Dim Sum's plots}[0.107, 0.147, 0.472, 0.290] are visible, including some later {time period points}[0.426, 0.821, 0.463, 0.868]." } ], "id_raw": 1035 }, { "id": "WlolnQyShbYNblPo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_16.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label} at [0.013, 0.312, 0.028, 0.652]?" }, { "from": "gpt", "value": "Count (in thousands)." } ], "id_raw": 1036 }, { "id": "CHpUGrWEGsEf51uR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.375, 0.083, 0.483, 0.106] in the chart?" }, { "from": "gpt", "value": "Gym Memberships." } ], "id_raw": 1036 }, { "id": "JbgvSsfb3Z16xUMn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_16.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label} at [0.256, 0.943, 0.290, 0.975]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 1036 }, { "id": "s9y5mCe9ttVm651k", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_16.png", "conversations": [ { "from": "human", "value": "\nAccording to the graph, is the data point for Gym Memberships in year 7 higher or lower than year 8 found at {coordinates}[0.340, 0.470] and [0.384, 0.464] respectively?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1036 }, { "id": "drMyKsW38apZo6qr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_16.png", "conversations": [ { "from": "human", "value": "\nIs the first data point for Cycling Participation at {coordinates}[0.566, 0.477] higher or lower than the 5th data point at {coordinates}[0.744, 0.314]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1036 }, { "id": "MWqk9eqJkFA2n8Oe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the trend of {Gym Memberships} from year 5 to year 10 based on the provided chart?" }, { "from": "gpt", "value": "Increasing trend." } ], "id_raw": 1036 }, { "id": "5whV5YvPcPyl1D9s", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the area defined by [0.745, 0.940, 0.780, 0.975] contain?" }, { "from": "gpt", "value": "This area contains the x-axis label for the second subplot, 'Year'." } ], "id_raw": 1036 }, { "id": "ENK5OEqqavCcxReV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_16.png", "conversations": [ { "from": "human", "value": "\nExplain the content inside [0.338, 0.074, 0.488, 0.116]." }, { "from": "gpt", "value": "This area contains the legend for the subplot 'Gym Memberships', colored in a particular shade and marked with a square symbol." } ], "id_raw": 1036 }, { "id": "jus3EfSyWqdvl26r", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_16.png", "conversations": [ { "from": "human", "value": "\nDescribe what can be found within the area [0.066, 0.913, 0.077, 0.935]." }, { "from": "gpt", "value": "In this area, there is an x-axis label '1' for the first subplot." } ], "id_raw": 1036 }, { "id": "MOY9JbZE8SVD1AIW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at coordinates [0.509, 0.106, 0.533, 0.824]?" }, { "from": "gpt", "value": "Y-axis label of the second sub-plot." } ], "id_raw": 1037 }, { "id": "T6XtZ25D9dIZjgFF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the first label on the x-axis in subplot_0?" }, { "from": "gpt", "value": "[0.101, 0.821, 0.112, 0.868]" } ], "id_raw": 1037 }, { "id": "OQI7OgaKz5ba3AUu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the x-axis tick} at [0.592, 0.821, 0.603, 0.868]?" }, { "from": "gpt", "value": "1." } ], "id_raw": 1037 }, { "id": "rlOraO3mW8vfsUiF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_17.png", "conversations": [ { "from": "human", "value": "\nDoes {the line representing 'Optimized Quality'}[0.597, 0.479, 0.963, 0.775] finish above or below {the line for 'DBMS Consolidation'}[0.597, 0.649, 0.963, 0.701]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1037 }, { "id": "Lg3pfLUo1aOc6itW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_17.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at {task 3}[0.290, 0.262], the line for 'Early Integration' or 'Advanced Streaming'?" }, { "from": "gpt", "value": "Early Integration." } ], "id_raw": 1037 }, { "id": "N63g3WYCZWLMO9TP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_17.png", "conversations": [ { "from": "human", "value": "\nDid the 'Optimized Quality' line rise or decline between {task 1}[0.597, 0.479] and {task 2}[0.689, 0.775]?" }, { "from": "gpt", "value": "Decline." } ], "id_raw": 1037 }, { "id": "7hLUuBzdqHHXrTsD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.097, 0.629, 0.341, 0.782]." }, { "from": "gpt", "value": "The area contains two items of the legend, {Early Integration}[0.153, 0.647, 0.298, 0.694] and {Advanced Streaming}[0.153, 0.717, 0.334, 0.764], represented with the colors '#1f77b4' and '#17becf' respectively." } ], "id_raw": 1037 }, { "id": "VGUmoBOqhx1DoPAW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.375, 0.821, 0.478, 0.868]?" }, { "from": "gpt", "value": "The labeled tick '5' of x-axis on the subplot_0 is located here." } ], "id_raw": 1037 }, { "id": "i6CUWRwanxVtfVkv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_17.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.101, 0.821, 0.204, 0.868]?" }, { "from": "gpt", "value": "There are two x-axis labels - '1' at {coordinates}[0.101, 0.821, 0.112, 0.868] and '2' at {coordinates}[0.193, 0.821, 0.204, 0.868] in the subplot_0." } ], "id_raw": 1037 }, { "id": "oVUmsSYKJHlchijL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_18.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at the coordinates [0.073, 0.821, 0.116, 0.868]?" }, { "from": "gpt", "value": "The x-axis tick label 'Drag'." } ], "id_raw": 1038 }, { "id": "dvJ8pKNueAXNYDnm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_18.png", "conversations": [ { "from": "human", "value": "\nWhat is plotted at {the point}[0.585, 0.278]?" }, { "from": "gpt", "value": "This is the point where 'Drag' is intersected in the Logistics department." } ], "id_raw": 1038 }, { "id": "4eWP0G8POGJYgrRc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_18.png", "conversations": [ { "from": "human", "value": "\nHow is {the line}[0.585, 0.278, 0.920, 0.346] styled in the chart?" }, { "from": "gpt", "value": "It is styled as a solid line with marker 'o' and color '#e7969c'." } ], "id_raw": 1038 }, { "id": "fKCroHCLIerC0Rv6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_18.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Drag'}[0.585, 0.278] in the Logistics department higher or lower than {the point of 'Delivery Time'}[0.920, 0.346] in the same department?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1038 }, { "id": "4j1LcwPlNib1PGTm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_18.png", "conversations": [ { "from": "human", "value": "\nIs {the label 'Quality Control'}[0.300, 0.166, 0.431, 0.213] located to the left or right of {the label 'Logistics'}[0.847, 0.166, 0.922, 0.213]?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 1038 }, { "id": "ZFqbh0tmLoDDpyM6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_18.png", "conversations": [ { "from": "human", "value": "\nWhich axis label is closer to {the point 'Drag'}[0.585, 0.278], the x-axis or the y-axis label?" }, { "from": "gpt", "value": "X-axis." } ], "id_raw": 1038 }, { "id": "6L5oZFkkq33BmNM3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the region [0.245, 0.148, 0.438, 0.231]?" }, { "from": "gpt", "value": "This region contains the legend of the chart sub-section 'Quality Control Trend Analysis', with the label 'Quality Control' colored in '#2171b5'." } ], "id_raw": 1038 }, { "id": "xlCiqfhGB4MN0pi8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_18.png", "conversations": [ { "from": "human", "value": "\nDescribe what can be seen in the box [0.568, 0.124, 0.937, 0.805]." }, { "from": "gpt", "value": "This is the subplot region of 'Logistics Trend Analysis'. It contains lines representing the trends, x and y axes with labels 'Factors' and 'Quality Scores', grid lines, subtitle and legend for 'Logistics' in color '#e7969c'." } ], "id_raw": 1038 }, { "id": "Rx8m1AW5Sp6ajnuo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_18.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the region [0.791, 0.148, 0.929, 0.231]?" }, { "from": "gpt", "value": "It's a sublegend for the 'Logistics Trend Analysis' subtitled chart. It contains the legend item 'Logistics', color coded with '#e7969c'." } ], "id_raw": 1038 }, { "id": "DM1Q0nLuJzSPxdJg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the x-axis label in the second subplot?" }, { "from": "gpt", "value": "[0.739, 0.932, 0.800, 0.970]" } ], "id_raw": 1039 }, { "id": "P3L8qghOKQN4qxBt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.048, 0.710, 0.057, 0.738]?" }, { "from": "gpt", "value": "2." } ], "id_raw": 1039 }, { "id": "eqsBuSqAVMnaDsIR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_52_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the text with coordinates [0.885, 0.142, 0.947, 0.170] represent?" }, { "from": "gpt", "value": "It represents the legend label 'Studying'." } ], "id_raw": 1039 }, { "id": "zGCJbd062ORCI1iP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_19.png", "conversations": [ { "from": "human", "value": "\nWhich trend has a higher value at point 8, {Sleeping}[0.386, 0.204] or {Running}[0.386, 0.730]?" }, { "from": "gpt", "value": "Running." } ], "id_raw": 1039 }, { "id": "5psKOapvI2B571oz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_19.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Social Media'}[0.574, 0.536, 0.965, 0.673] above or below {the line for 'Studying'}[0.574, 0.641, 0.965, 0.713]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1039 }, { "id": "8Zrx7Ini7tWVuUCC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_52_19.png", "conversations": [ { "from": "human", "value": "\nWhich activity had a higher average time spent in Month 1, {Sleeping}[0.082, 0.293] or {Studying}[0.574, 0.713]?" }, { "from": "gpt", "value": "Sleeping." } ], "id_raw": 1039 }, { "id": "cde9gl1nkUPKO7wM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.841, 0.089, 0.978, 0.181]." }, { "from": "gpt", "value": "This area contains the legend for the second subplot. It has two items, {Social Media}[0.841, 0.102, 0.973, 0.122] and {Studying}[0.841, 0.144, 0.947, 0.164], each represented with different colors and markers." } ], "id_raw": 1039 }, { "id": "u2CrpzST12Yx8XYk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.069, 0.089, 0.179, 0.181]." }, { "from": "gpt", "value": "This area houses the legend for the first subplot. It consists of two elements: {Sleeping}[0.070, 0.102, 0.173, 0.122] and {Running}[0.070, 0.144, 0.107, 0.164], each associated with a distinct color and symbol." } ], "id_raw": 1039 }, { "id": "LxXOEA24fSKhGtKp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_52_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within [0.474, 0.087, 0.999, 0.920]." }, { "from": "gpt", "value": "This section encompasses the right subplot of the chart. It includes the line graph of {Social Media}[0.574, 0.536, 0.965, 0.673] and {Studying}[0.574, 0.641, 0.965, 0.713] trends along with their corresponding legend [0.841, 0.089, 0.978, 0.181], axis labels and tick labels." } ], "id_raw": 1039 }, { "id": "VRmFGgfYJviMYIiI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.369, 0.033, 0.560] in the chart?" }, { "from": "gpt", "value": "Sales Amount." } ], "id_raw": 1040 }, { "id": "5pmotkaEMKbjymfg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third line in the chart?" }, { "from": "gpt", "value": "[0.187, 0.065, 0.885, 0.385]" } ], "id_raw": 1040 }, { "id": "l6Wls0agRjXStDY0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_0.png", "conversations": [ { "from": "human", "value": "\nCan you tell what {the seventh x-axis tick label}[0.755, 0.912, 0.816, 0.936] is?" }, { "from": "gpt", "value": "Q3_2019." } ], "id_raw": 1040 }, { "id": "FpFHrp7LRJwWyyfP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Product A'}[0.486, 0.774] at Q4_2018 higher or lower than {the point for 'Product B'}[0.486, 0.811] at the same time period?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1040 }, { "id": "6mq9SxsRlgwK0FnL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Product C'}[0.885, 0.065] at Q4_2019 located above or below {the point for 'Product A'}[0.885, 0.838]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1040 }, { "id": "jaBoJKiTAEwpcZ4z", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_0.png", "conversations": [ { "from": "human", "value": "\nWhich product has a higher sales amount at Q1_2019, {Product A}[0.586, 0.784] or {Product B}[0.586, 0.795]?" }, { "from": "gpt", "value": "Product B." } ], "id_raw": 1040 }, { "id": "y0ps2Z5Jw660nC3C", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the area [0.094, 0.037, 0.211, 0.148]?" }, { "from": "gpt", "value": "It's the legend of the chart. Inside this area, we have the legends for three different products: {Product A}[0.139, 0.046, 0.206, 0.069], {Product B}[0.139, 0.081, 0.206, 0.104], and {Product C}[0.139, 0.116, 0.206, 0.139], each has a marker in different colors." } ], "id_raw": 1040 }, { "id": "Kr9wgx6ESEnlUT7X", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_0.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the area [0.500, 0.945, 0.573, 0.975]?" }, { "from": "gpt", "value": "The x-axis label \"Quarters\" is located in this area." } ], "id_raw": 1040 }, { "id": "IW0i9fDYz9bvCTQx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_0.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.187, 0.638, 0.287, 0.764]?" }, { "from": "gpt", "value": "Within this region, two lines intercepted. These lines represent the sales amount of {Product A}[0.187, 0.704, 0.287, 0.731] and {Product B}[0.187, 0.864, 0.287, 0.838] for the periods of Q1_2018 and Q2_2018." } ], "id_raw": 1040 }, { "id": "fM3LbTl9Gj81kqiD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the line} at [0.217, 0.057, 0.831, 0.272] representing?" }, { "from": "gpt", "value": "TV Influence." } ], "id_raw": 1041 }, { "id": "NiyPwsAuZehtgtCK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x coordinate} for the year 2010?" }, { "from": "gpt", "value": "[0.524, 0.941]." } ], "id_raw": 1041 }, { "id": "ej422pvzoE0I027d", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the term {the legend} at [0.114, 0.069, 0.192, 0.083] referring to?" }, { "from": "gpt", "value": "Newspaper." } ], "id_raw": 1041 }, { "id": "v5WPI9774zSyqLBi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_1.png", "conversations": [ { "from": "human", "value": "\nIn 2020, is the {Influence of TV}[0.831, 0.272] more than {Influence of Social Media}[0.831, 0.272]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1041 }, { "id": "PMtnWg1D2UbctQ3U", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_1.png", "conversations": [ { "from": "human", "value": "\nWhich influence saw a greater decline between 2000 and 2005, {TV}[0.217, 0.057, 0.370, 0.110] or {Newspaper}[0.217, 0.281, 0.370, 0.327]?" }, { "from": "gpt", "value": "Newspaper." } ], "id_raw": 1041 }, { "id": "yUEMgEZ7nQHZyEUw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_1.png", "conversations": [ { "from": "human", "value": "\nIn 2010, was {Social Media Influence}[0.524, 0.667] less or more than {Newspaper Influence}[0.524, 0.447]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 1041 }, { "id": "nxz6dXjoeRE6jeyV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.217, 0.057, 0.831, 0.272] in the chart?" }, { "from": "gpt", "value": "This region of the chart represents the line graph showing the decline in TV influence from 80.0% in the year 2000 [0.217, 0.057] to 60.8% in the year 2020 [0.831, 0.272]." } ], "id_raw": 1041 }, { "id": "iHHyA959bG46M0EN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents of [0.218, 0.272, 0.831, 0.899]?" }, { "from": "gpt", "value": "This region marks the ascent of Social Media Influence from 5.0% in 2000 [0.218, 0.899] to 60.8% in 2020 [0.831, 0.272]." } ], "id_raw": 1041 }, { "id": "pqiGrAHqZoaoBxRR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.217, 0.281, 0.831, 0.679] on the figure illustrate?" }, { "from": "gpt", "value": "This region represents the line graph highlighting the decline in Newspaper influence from 60.0% in the year 2000 [0.217, 0.281] to 24.6% in the year 2020 [0.831, 0.679]." } ], "id_raw": 1041 }, { "id": "9pwr23DRLzL7EKni", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_2.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.229, 0.601]?" }, { "from": "gpt", "value": "Point of Heart Rate Monitor line." } ], "id_raw": 1042 }, { "id": "q02dOgof731G3FLi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the vertical grid line} at [0.530, 0.025, 0.530, 0.901]?" }, { "from": "gpt", "value": "Grey." } ], "id_raw": 1042 }, { "id": "QM9dnoRZZtDWrW1q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_2.png", "conversations": [ { "from": "human", "value": "\nWhat object corresponds to {the coordinates}[0.091, 0.051, 0.133, 0.061]?" }, { "from": "gpt", "value": "Handle of Heart Rate Monitor." } ], "id_raw": 1042 }, { "id": "PbbK13PKUEsgFRh0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point at year 2015 for Heart Rate Monitor}[0.680, 0.775] above or below {the point at year 2015 for Blood Pressure Monitor}[0.680, 0.307]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1042 }, { "id": "pfUEZOENPVr0zJrQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_2.png", "conversations": [ { "from": "human", "value": "\nWhich label holds a higher value at {coordinate}[0.830, 0.899], 2020 or 2015?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 1042 }, { "id": "OJFfPtexxAYL8a8h", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_2.png", "conversations": [ { "from": "human", "value": "\nIs {the legend text for 'Glucose Monitor'}[0.143, 0.081, 0.284, 0.104] above or below {the legend text for 'Blood Pressure Monitor'}[0.143, 0.116, 0.343, 0.139]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1042 }, { "id": "CG9UaGhP1pNDAdYV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.130, 0.046, 0.343, 0.139]." }, { "from": "gpt", "value": "This area contains labels and handles for {Heart Rate Monitor}[0.143, 0.046, 0.309, 0.069], {Glucose Monitor}[0.143, 0.081, 0.284, 0.104], and {Blood Pressure Monitor}[0.143, 0.116, 0.343, 0.139] as legend items." } ], "id_raw": 1042 }, { "id": "VhdDz3V4LxZ6wONt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_2.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the coordinates [0.019, 0.331, 0.073, 0.595]?" }, { "from": "gpt", "value": "The y-axis label \"Error Rate (%)\" is styled in gray color." } ], "id_raw": 1042 }, { "id": "U6S2Iqxo83GuoPWm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in [0.200, 0.580, 0.900, 0.930]?" }, { "from": "gpt", "value": "This region includes x-tick labels for the years 2000 to 2020 along with their corresponding tick marks." } ], "id_raw": 1042 }, { "id": "q7hKqKfeuNCf6kGl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_3.png", "conversations": [ { "from": "human", "value": "\nWhat is referred to by the object with coordinates [0.373, 0.325, 0.712, 0.650] on the chart?" }, { "from": "gpt", "value": "This refers to the Facebook data line in the line chart." } ], "id_raw": 1043 }, { "id": "qaAhSOFV1kzKV7EQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_3.png", "conversations": [ { "from": "human", "value": "\nWhich y-axis tick label has its coordinates at [0.051, 0.642, 0.084, 0.660]?" }, { "from": "gpt", "value": "The tick label '100'." } ], "id_raw": 1043 }, { "id": "bBl2d8hwYOJQxMJj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the object with these coordinates [0.098, 0.087, 0.147, 0.101]?" }, { "from": "gpt", "value": "It corresponds to the LinkedIn data line color marker in the legend." } ], "id_raw": 1043 }, { "id": "81b0DizTFS0TrG2c", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_3.png", "conversations": [ { "from": "human", "value": "\nAt point marked {20}[0.277, 0.932, 0.299, 0.949], is {the Facebook data point}[0.277, 0.354] higher or lower than {the Twitter data point}[0.277, 0.761]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1043 }, { "id": "OOHxbADZfVAeC4a9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_3.png", "conversations": [ { "from": "human", "value": "\nIs the {LinkedIn data line}[0.164, 0.708, 0.907, 0.885] generally above or below the {Facebook data line}[0.164, 0.060, 0.907, 0.649]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1043 }, { "id": "1ttywnsKHuGTWof4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_3.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at the age mark {45}[0.896, 0.932, 0.918, 0.949], {Twitter data line}[0.164, 0.354, 0.907, 0.796] or {LinkedIn data line}[0.164, 0.708, 0.907, 0.885]?" }, { "from": "gpt", "value": "Twitter data line." } ], "id_raw": 1043 }, { "id": "2mFpjoWC2AgKyEuO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects in the region [0.098, 0.027, 0.243, 0.111]." }, { "from": "gpt", "value": "This region includes the legend of the line chart; it contains three color markers corresponding to the three data lines: {Facebook}[0.098, 0.035, 0.147, 0.049], {Twitter}[0.098, 0.061, 0.147, 0.075], and {LinkedIn}[0.098, 0.087, 0.147, 0.101], along with their respective color-coded labels." } ], "id_raw": 1043 }, { "id": "WPzMLW3AWFmiBqbM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects can be found in the region with these coordinates [0.535, 0.226, 0.907, 0.708]?" }, { "from": "gpt", "value": "In these coordinates, we have the data points of {Twitter popularity}[0.535, 0.661, 0.907, 0.708] and {LinkedIn popularity}[0.535, 0.781, 0.907, 0.708]" } ], "id_raw": 1043 }, { "id": "5Ejl9l2B7Mzzm5TT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_3.png", "conversations": [ { "from": "human", "value": "\nWhat content can be found within the region [0.401, 0.932, 0.423, 0.949]?" }, { "from": "gpt", "value": "There is an x-axis label, representing the age mark '25'." } ], "id_raw": 1043 }, { "id": "r3k92NRPaaPtvEyP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis represent in this line chart, based on the label provided at coordinates [0.025, 0.331, 0.058, 0.595]?" }, { "from": "gpt", "value": "Error Rate (%)." } ], "id_raw": 1045 }, { "id": "iYYDcTmiRNF67M0y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at coordinates [0.627, 0.416], and which line does it belong to?" }, { "from": "gpt", "value": "It's a point on the 'Engineering' line." } ], "id_raw": 1045 }, { "id": "n3X5lPHDO0ARmL0m", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_5.png", "conversations": [ { "from": "human", "value": "\nWhat's the content of the x-axis label located at [0.471, 0.942, 0.609, 0.975]?" }, { "from": "gpt", "value": "Sectors." } ], "id_raw": 1045 }, { "id": "dp9nGxAzp711FReC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_5.png", "conversations": [ { "from": "human", "value": "\nComparing the 'Cruncher' line {point for 'Manufacturing'}[0.627, 0.555] and the 'Scientific' line {point for 'Manufacturing'}[0.627, 0.149], which error rate is higher in the Manufacturing sector?" }, { "from": "gpt", "value": "Scientific." } ], "id_raw": 1045 }, { "id": "tQJjoArBqJ4aYw3k", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_5.png", "conversations": [ { "from": "human", "value": "\nIn the 'Pharmaceutical' sector, is the error rate of {Cruncher}[0.279, 0.861] higher or lower than {Engineering}[0.279, 0.697]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1045 }, { "id": "tIrY6Dz0Z7ViP4C2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_5.png", "conversations": [ { "from": "human", "value": "\nWhich line represents the highest error rate in the 'ICT' sector, {Cruncher}[0.801, 0.065], {Engineering}[0.801, 0.390] or {Scientific}[0.801, 0.332]?" }, { "from": "gpt", "value": "Cruncher." } ], "id_raw": 1045 }, { "id": "0syqFTwAPggTWrGN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_5.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area with coordinates [0.116, 0.037, 0.339, 0.148]?" }, { "from": "gpt", "value": "This area contains the legend items: {Cruncher}[0.191, 0.046, 0.297, 0.069], {Engineering}[0.191, 0.081, 0.329, 0.104], and {Scientific}[0.191, 0.116, 0.296, 0.139], each paired with a different colored marker." } ], "id_raw": 1045 }, { "id": "4iBLeu3XHxPE3ujo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.279, 0.065, 0.801, 0.861]." }, { "from": "gpt", "value": "The region includes the data points for the 'Cruncher' error rates across all sectors, namely {Pharmaceutical}[0.279, 0.861], {Construction}[0.453, 0.694], {Manufacturing}[0.627, 0.555], and {ICT}[0.801, 0.065]." } ], "id_raw": 1045 }, { "id": "dM1SYCz2hp8X0jNz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_5.png", "conversations": [ { "from": "human", "value": "\nWhat content is present in the region defined by coordinates [0.782, 0.909, 0.820, 0.932]?" }, { "from": "gpt", "value": "This region contains the label 'ICT' part of the x-axis tick labels." } ], "id_raw": 1045 }, { "id": "w7d7BiDTkBfsDJUX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_6.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.191, 0.081, 0.424, 0.104] in the legend refer to?" }, { "from": "gpt", "value": "Urban Development." } ], "id_raw": 1046 }, { "id": "Zcn19nCg6E7YHv7B", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at coordinates [0.540, 0.011, 0.540, 0.011]?" }, { "from": "gpt", "value": "The subtitle of the chart." } ], "id_raw": 1046 }, { "id": "lJf2BwVlZzkAhke4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-coordinate}[0.685, 0.103] of the Urban Development line for the year 2015?" }, { "from": "gpt", "value": "80." } ], "id_raw": 1046 }, { "id": "HEdnbhywmQhF5EJi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_6.png", "conversations": [ { "from": "human", "value": "\nIs {the Rural Development point}[0.830, 0.292] in 2020 higher or lower than {the Urban Development point}[0.830, 0.065] in the same year?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1046 }, { "id": "J0tVmpvHkYZQ5ECM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Rural Development'}[0.250, 0.292, 0.830, 0.861] exhibit an upward or downward trend through the years?" }, { "from": "gpt", "value": "Upward." } ], "id_raw": 1046 }, { "id": "LyCw2XH1QAwrv52E", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_6.png", "conversations": [ { "from": "human", "value": "\nIn the year 2010, was the {Rural Development index}[0.540, 0.634] higher or lower than the {Urban Development index}[0.540, 0.160]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1046 }, { "id": "S3Gc9FRB0IGYpf9x", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents in the area [0.220, 0.909, 0.860, 0.932]?" }, { "from": "gpt", "value": "The area corresponds to the x-axis labels for the years 2000, 2005, 2010, 2015, and 2020." } ], "id_raw": 1046 }, { "id": "eJ1Wb582jZ84b5uy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects in the region [0.068, -0.096, 0.097, 1.065]?" }, { "from": "gpt", "value": "This region contains y-axis labels representing the development index from 30 to 90." } ], "id_raw": 1046 }, { "id": "MQOu4VX9NuhY5FNo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.191, 0.046, 0.424, 0.104] contain?" }, { "from": "gpt", "value": "The area comprises of texts from the legend representing \"Rural Development\" and \"Urban Development\", along with their respective colored handles." } ], "id_raw": 1046 }, { "id": "IkbNtXnNikpcY2f9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.025, 0.323, 0.058, 0.603] in the chart?" }, { "from": "gpt", "value": "The y-axis label." } ], "id_raw": 1047 }, { "id": "kw6OjTeEnMrIQ06W", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_7.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the second item in the legend?" }, { "from": "gpt", "value": "[0.206, 0.081, 0.280, 0.104]" } ], "id_raw": 1047 }, { "id": "h797A6JH4rQcFj9Q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_7.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.454, 0.909, 0.470, 0.932]?" }, { "from": "gpt", "value": "2." } ], "id_raw": 1047 }, { "id": "j106UuSo4jKhBiPt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Television'} at [0.462, 0.622] higher or lower than {the point of 'Online'} at [0.462, 0.782] for the second week?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1047 }, { "id": "SD41ZRvSTKyrGmA4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_7.png", "conversations": [ { "from": "human", "value": "\nDo {the points of 'Online'} [0.291, 0.861], [0.462, 0.782], [0.633, 0.383], [0.804, 0.144] show an increasing or decreasing trend?" }, { "from": "gpt", "value": "Decreasing." } ], "id_raw": 1047 }, { "id": "kHXffGGPUsqB2cfY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_7.png", "conversations": [ { "from": "human", "value": "\nWhich type has a higher influence level in the last week, {point from 'Newspaper'}[0.804, 0.383] or {point from 'Online'}[0.804, 0.144]?" }, { "from": "gpt", "value": "Newspaper." } ], "id_raw": 1047 }, { "id": "e149EVKvMUzBBKvn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.131, 0.037, 0.345, 0.148]." }, { "from": "gpt", "value": "This area contains the chart's legend, including {Television} with a handle color of #e8d2cc [0.136, 0.051, 0.192, 0.061], {Online} with a handle color of #9f6a8d [0.136, 0.086, 0.192, 0.096] and {Newspaper} with a handle color of #5c5048 [0.136, 0.121, 0.192, 0.131]." } ], "id_raw": 1047 }, { "id": "Vvx1mC68eft6RkHz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.416, 0.942, 0.678, 0.975]." }, { "from": "gpt", "value": "This space contains the chart's title. The title is \"Week Number\"." } ], "id_raw": 1047 }, { "id": "GfksQ1Da1ezbfCSL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.275, 0.900, 0.815, 0.935]?" }, { "from": "gpt", "value": "There are 4 objects. The x-axis tick labels '$1$', '$2$', '$3$', and '$4$' at {coordinates}[0.283, 0.909, 0.299, 0.932], {coordinates}[0.454, 0.909, 0.470, 0.932], {coordinates}[0.625, 0.909, 0.641, 0.932] and {coordinates}[0.796, 0.909, 0.812, 0.932], respectively." } ], "id_raw": 1047 }, { "id": "VNy3yKOt0O0hibxE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_9.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinates [0.504, 0.942, 0.576, 0.975]?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 1049 }, { "id": "jaPY1WA9o3HntGcF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_9.png", "conversations": [ { "from": "human", "value": "\nWhere can I find the third x-tick label?" }, { "from": "gpt", "value": "[0.417, 0.909, 0.446, 0.932]" } ], "id_raw": 1049 }, { "id": "9jbfv4Cs78Dh9bzd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the second point of the line 'Books Read: Low Stress'}[0.323, 0.116]?" }, { "from": "gpt", "value": "38" } ], "id_raw": 1049 }, { "id": "vlA2vNapiPxak6Gv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_9.png", "conversations": [ { "from": "human", "value": "\nIs {the fifth data point from 'Books Read: High Stress'}[0.649, 0.784] above or below the fifth data point from {the line 'Books Read: Low Stress'}[0.649, 0.142]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1049 }, { "id": "EDobIyWSeFE35Mor", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_9.png", "conversations": [ { "from": "human", "value": "\nIs {the first point from 'Books Read: Low Stress'}[0.214, 0.065] greater or smaller than {the first point from 'Books Read: High Stress'}[0.214, 0.450] in terms of y-value?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 1049 }, { "id": "yW4pPWVvUgoVElsx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_9.png", "conversations": [ { "from": "human", "value": "\nAt the x-axis tick of 30, does the line for 'Books Read: Low Stress' or 'Books Read: High Stress' have a higher y-coordinate?" }, { "from": "gpt", "value": "Books Read: High Stress." } ], "id_raw": 1049 }, { "id": "gCvuWOC1LFAprJ5p", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe what the visualization displays in the area [0.598, 0.037, 0.963, 0.113]." }, { "from": "gpt", "value": "The area contains the legend items: {Books Read: High Stress}[0.672, 0.046, 0.954, 0.069] and {Books Read: Low Stress}[0.672, 0.081, 0.947, 0.104] each paired with a colored marker. The color specified for 'Books Read: High Stress' is '#f08080' and for 'Books Read: Low Stress' is '#808080'." } ], "id_raw": 1049 }, { "id": "DiQ1UK5r3ORqLcI5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe what's in the region [0.199, 0.909, 0.881, 0.932]?" }, { "from": "gpt", "value": "The area includes all the x-axis tick labels: {10}[0.199, 0.909, 0.228, 0.932], {15}[0.308, 0.909, 0.337, 0.932], {20}[0.417, 0.909, 0.446, 0.932], {25}[0.525, 0.909, 0.555, 0.932], {30}[0.634, 0.909, 0.663, 0.932], {35}[0.743, 0.909, 0.772, 0.932], and {40}[0.852, 0.909, 0.881, 0.932]." } ], "id_raw": 1049 }, { "id": "3tVosZk7Wes1Gc2j", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects do we have within the coordinates [0.025, 0.355, 0.058, 0.571]?" }, { "from": "gpt", "value": "On checking, we see the Y-axis label 'Books Read' is within these coordinates." } ], "id_raw": 1049 }, { "id": "Ve03iuclrw6QZ5SY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_10.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the data point [0.253, 0.494] in the E-Learning line?" }, { "from": "gpt", "value": "The adoption rate for E-Learning in the year 2012." } ], "id_raw": 1050 }, { "id": "Yxok21TJST45PbNr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the graph's y-axis label}[0.019, 0.379, 0.041, 0.568]?" }, { "from": "gpt", "value": "Adoption Rate (%)." } ], "id_raw": 1050 }, { "id": "XJgK1A7vtpz4W8Kr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend item {at} [0.141, 0.034, 0.233, 0.052] represent?" }, { "from": "gpt", "value": "E-Learning." } ], "id_raw": 1050 }, { "id": "OLslwDIEsUBv6lY0", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_10.png", "conversations": [ { "from": "human", "value": "\nIs the data point {E-Learning at 2013} [0.341, 0.406] higher or lower than the data point {Mobile Learning at 2013} [0.341, 0.471]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1050 }, { "id": "YqUc9wvBA6Dm36UL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_10.png", "conversations": [ { "from": "human", "value": "\nDoes {E-Learning} [0.606, 0.216] in 2016 have a higher or lower adoption rate than {Gamified Learning} [0.606, 0.564] in the same year?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1050 }, { "id": "n6sOCjHZgPemVRDt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_10.png", "conversations": [ { "from": "human", "value": "\nComparing the data points for {Mobile Learning at 2018} [0.783, 0.203] and {E-Learning at 2018} [0.783, 0.172], which one has higher adoption rate?" }, { "from": "gpt", "value": "Mobile Learning." } ], "id_raw": 1050 }, { "id": "6rNfneIRHoac7sop", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region within the bounding box [0.076, 0.060, 0.959, 0.592]?" }, { "from": "gpt", "value": "This region represents the line chart for E-Learning adoption rates from 2010 to 2020." } ], "id_raw": 1050 }, { "id": "oA3qsoMRYHHMDz6U", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_10.png", "conversations": [ { "from": "human", "value": "\nDescribe what's inside the region [0.085, 0.027, 0.307, 0.111]." }, { "from": "gpt", "value": "The legend for the graph is within this region, illustrating the colors representing E-Learning, Mobile Learning, and Gamified Learning." } ], "id_raw": 1050 }, { "id": "Px2y88KRtjDQHP20", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.076, 0.129, 0.959, 0.754] represent?" }, { "from": "gpt", "value": "This region outlines the line graph for Mobile Learning adoption rate from 2010 to 2020." } ], "id_raw": 1050 }, { "id": "QwN3GuGeK1d1FNWz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the graph caption located at [0.505, 0.950, 0.552, 0.979]?" }, { "from": "gpt", "value": "Year" } ], "id_raw": 1051 }, { "id": "WhHzTbuMKluTpkMs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the y-axis tick label} [0.041, 0.812, 0.067, 0.832]?" }, { "from": "gpt", "value": "100" } ], "id_raw": 1051 }, { "id": "nD2g8xJVRUkn6nsl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_11.png", "conversations": [ { "from": "human", "value": "\nWhat symbol does the legend at [0.082, 0.104, 0.115, 0.112] represent?" }, { "from": "gpt", "value": "Solar Consumption" } ], "id_raw": 1051 }, { "id": "BBacB3cUVuBiQFZo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_11.png", "conversations": [ { "from": "human", "value": "\nDoes {point 2012 on the Coal Consumption line} [0.279, 0.095] have higher Consumption value than {point 2012 on the Solar Consumption line} [0.279, 0.853]?" }, { "from": "gpt", "value": "Yes" } ], "id_raw": 1051 }, { "id": "g5GvWzY2c2PUKCho", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_11.png", "conversations": [ { "from": "human", "value": "\nBetween {the first point on 'Hydro Consumption' line} [0.113, 0.712] and {the last point on 'Solar Consumption' line} [0.943, 0.669], which has higher consumption?" }, { "from": "gpt", "value": "The first point on the 'Hydro Consumption' line." } ], "id_raw": 1051 }, { "id": "96bfKRdpfiHhAaOx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_11.png", "conversations": [ { "from": "human", "value": "\nWhich type of consumption decreases more from {point 2010}[0.113] to {point 2020}[0.943], Coal Consumption or Hydro Consumption?" }, { "from": "gpt", "value": "Coal Consumption." } ], "id_raw": 1051 }, { "id": "WA25UTewX28GUayH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_11.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is contained within the coordinates [0.079, 0.031, 0.266, 0.127]?" }, { "from": "gpt", "value": "This area contains three entries in the legend note: {Coal Consumption} [0.123, 0.039, 0.250, 0.059], {Hydro Consumption} [0.123, 0.069, 0.261, 0.089], and {Solar Consumption} [0.123, 0.099, 0.255, 0.119]." } ], "id_raw": 1051 }, { "id": "gRG4O45ygOC5XUXL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_11.png", "conversations": [ { "from": "human", "value": "\nPlease provide detail on the objects between [0.446, 0.916, 0.466, 0.936]?" }, { "from": "gpt", "value": "This area shows the x-axis tick label '2014'." } ], "id_raw": 1051 }, { "id": "HqwNH7ria0EzXGUZ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe what the region [0.072, 0.021, 0.985, 0.915] contains?" }, { "from": "gpt", "value": "This region contains the entire plotted chart. This includes three lines depicting Coal Consumption, Hydro Consumption, Solar Consumption over the years 2010 through 2020." } ], "id_raw": 1051 }, { "id": "CKZD2Zwyudc4xANE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_12.png", "conversations": [ { "from": "human", "value": "\nWhat is centered at the coordinates [0.529, 0.008, 0.529, 0.008] in the chart?" }, { "from": "gpt", "value": "The subtitle of the chart." } ], "id_raw": 1052 }, { "id": "HgKfuMrXtrw7fMGZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_12.png", "conversations": [ { "from": "human", "value": "\nWith respect to the coordinates, [0.050, 0.672, 0.071, 0.690], what is represented?" }, { "from": "gpt", "value": "The y-axis tick label '20'." } ], "id_raw": 1052 }, { "id": "iW0H9gWsmSTEYxuV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_12.png", "conversations": [ { "from": "human", "value": "\nWhat is defined at the coordinates, [0.723, 0.163, 0.712, 0.141, 0.734, 0.158]?" }, { "from": "gpt", "value": "The point for the year 2019 on the VR Training line, indicating 45% adoption rate." } ], "id_raw": 1052 }, { "id": "MoYqWZxZvJzKH2mv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point for the year 2015 on the Online Learning line}[0.207, 0.679] above or below the {point for the year 2015 on the Classroom Tech line}[0.207, 0.473]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1052 }, { "id": "plnqIqacfLAPHxV3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_12.png", "conversations": [ { "from": "human", "value": "\nIs the {VR Training line}[0.207, 0.060, 0.852, 0.886] generally above or below the {Classroom Tech line}[0.207, 0.473, 0.852, 0.576]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1052 }, { "id": "pStiT7Nxrp0k8NpE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_12.png", "conversations": [ { "from": "human", "value": "\nBetween {Online Learning}[0.207, 0.163, 0.852, 0.679] and {VR Training}[0.207, 0.060, 0.852, 0.886], which line has a higher point at 2018?" }, { "from": "gpt", "value": "Online Learning." } ], "id_raw": 1052 }, { "id": "edvCGLMPudm6naIz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_12.png", "conversations": [ { "from": "human", "value": "\nDescribe what is in the coordinates [0.086, 0.027, 0.285, 0.111]." }, { "from": "gpt", "value": "It is the legend of the chart which includes references for Online Learning, Classroom Tech, and VR Training, each associated with different markers and colors:\n {Online Learning}[0.142, 0.034, 0.279, 0.052];\n {Classroom Tech}[0.142, 0.061, 0.277, 0.078] and;\n {VR Training}[0.142, 0.087, 0.240, 0.104]." } ], "id_raw": 1052 }, { "id": "5eNDYFJPFiMxHItD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the area enclosed by [0.207, 0.163, 0.852, 0.886]" }, { "from": "gpt", "value": "This area contains three overlapping lines for the respective trends of Online Learning, Classroom Tech, and VR Training. Each line includes a series of points and annotations representing adoption rates for each year from 2015-2020." } ], "id_raw": 1052 }, { "id": "C29kLBMKEMiuUj98", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_12.png", "conversations": [ { "from": "human", "value": "\nWhat lies in the region [0.185, 0.933, 0.874, 0.951]?" }, { "from": "gpt", "value": "The x-axis tick labels representing the years 2015, 2016, 2017, 2018, 2019, and 2020 are located in this region. Each label is associated with a specific year." } ], "id_raw": 1052 }, { "id": "oFyliTnbQqT33MSD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.041, 0.231, 0.063, 0.254] in the chart?" }, { "from": "gpt", "value": "Y-axis tick label \"4.0\"." } ], "id_raw": 1053 }, { "id": "VkfXHeZPQAyY1RvE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the second text in the legend?" }, { "from": "gpt", "value": "[0.119, 0.081, 0.220, 0.104]" } ], "id_raw": 1053 }, { "id": "l4OQE4qpM8iMiLoZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_13.png", "conversations": [ { "from": "human", "value": "\nWhat is {an x-axis tick label} at [0.215, 0.822, 0.281, 0.932]?" }, { "from": "gpt", "value": "01/02/2021." } ], "id_raw": 1053 }, { "id": "5bkVdDHOhQb1yboF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_13.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Exercise hours'}[[0.109, 0.419, 0.943, 0.778]] above or below {the line for 'Watched TV hours'}[[0.109, 0.061, 0.943, 0.688]]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1053 }, { "id": "HlB674y3VGYpG0Qq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_13.png", "conversations": [ { "from": "human", "value": "\nAre the {reading hours}[0.665, 0.151] on the fifth date higher or lower than the {exercise hours}[0.665, 0.509]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1053 }, { "id": "BtwCGsM8nkeAB26j", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_13.png", "conversations": [ { "from": "human", "value": "\nWhich activity has a higher hours spent at {01/01/2021}[0.109], Watched TV, Exercise or Reading?" }, { "from": "gpt", "value": "Watched TV." } ], "id_raw": 1053 }, { "id": "Lsi3wdPxXyVOOfMo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.074, 0.037, 0.251, 0.148]." }, { "from": "gpt", "value": "This area shows the legend for the chart. It includes {Watched TV hours}[0.119, 0.046, 0.245, 0.069], {Exercise hours}[0.119, 0.081, 0.220, 0.104] and {Reading hours}[0.119, 0.116, 0.219, 0.139], each paired with a symbol and color." } ], "id_raw": 1053 }, { "id": "uFDu0SKdzUqW4oJ9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.215, 0.822, 0.281, 0.932]." }, { "from": "gpt", "value": "The area contains the x-axis tick label for the second date: {01/02/2021}[0.215, 0.822, 0.281, 0.932]." } ], "id_raw": 1053 }, { "id": "3U14LvfjvC4UEw0h", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_13.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.109, 0.061, 0.943, 0.688]?" }, { "from": "gpt", "value": "This region contains three lines, {Watched TV hours}[0.109, 0.061, 0.943, 0.688], {Exercise hours}[0.109, 0.419, 0.943, 0.778], and {Reading hours}[0.109, 0.061, 0.943, 0.688], each representing daily hours spent on each activity from 01/01/2021 to 01/07/2021." } ], "id_raw": 1053 }, { "id": "i9pduZ23NlLLlcUm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_14.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at the coordinates [0.396, 0.942, 0.685, 0.975]?" }, { "from": "gpt", "value": "The X-axis label." } ], "id_raw": 1054 }, { "id": "Sp0UkA34msY6T3rY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_14.png", "conversations": [ { "from": "human", "value": "\nWhich object is at the location [0.191, 0.081, 0.403, 0.104] in the legend?" }, { "from": "gpt", "value": "Medium Efficiency label." } ], "id_raw": 1054 }, { "id": "OVvoIof6VywwG9PJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at the coordinate [0.115, 0.030, 0.150, 0.060] represent?" }, { "from": "gpt", "value": "The point value '70' from the \"Low Efficiency\" line." } ], "id_raw": 1054 }, { "id": "XZL9smbNyLoDRMpT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '6'}[0.268, 0.762] for 'High Efficiency' greater or lesser than {the point labeled '6'}[0.268, 0.563] for 'Medium Efficiency'?" }, { "from": "gpt", "value": "Lesser." } ], "id_raw": 1054 }, { "id": "ifLOPUOooup3hBok", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_14.png", "conversations": [ { "from": "human", "value": "\nHow does the value of {High Efficiency at point 12}[0.431, 0.861] compare to that of {Medium Efficiency at point 12}[0.431, 0.662]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1054 }, { "id": "JgdQ9oQQbyfMpEV2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_14.png", "conversations": [ { "from": "human", "value": "\nComparing the values at point 18, does {High Efficiency}[0.594, 0.702] have a greater or lower value than {Low Efficiency}[0.594, 0.204]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 1054 }, { "id": "5olWwv8bt5sLo2oG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_14.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is within the area [0.117, 0.037, 0.412, 0.148]?" }, { "from": "gpt", "value": "This region contains the legend of the chart with three labels and corresponding markers: {High Efficiency}[0.191, 0.046, 0.363, 0.069], {Medium Efficiency}[0.191, 0.081, 0.403, 0.104] and {Low Efficiency}[0.191, 0.116, 0.356, 0.139]." } ], "id_raw": 1054 }, { "id": "lGb3ufsmtQJ3YXpp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.132, 0.662, 0.921, 0.861]." }, { "from": "gpt", "value": "It is the plot line for 'High Efficiency' with six points labeled with different values." } ], "id_raw": 1054 }, { "id": "2xl9ecs6JaI87UGi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_14.png", "conversations": [ { "from": "human", "value": "\nCould you tell me what's situated in the region [0.132, 0.065, 0.921, 0.363]?" }, { "from": "gpt", "value": "It is the plot line for 'Low Efficiency' which has six points marked with respective values." } ], "id_raw": 1054 }, { "id": "zX30kZQPVqKjRCUK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_15.png", "conversations": [ { "from": "human", "value": "\nWhat does {the legend item }[0.681, 0.811, 0.884, 0.834] refer to in the chart?" }, { "from": "gpt", "value": "Large Scale Framework." } ], "id_raw": 1055 }, { "id": "C6FFpwvQiXzzYlTo", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of {the y-axis label}[0.056, 0.412, 0.079, 0.597]?" }, { "from": "gpt", "value": "Effectiveness." } ], "id_raw": 1055 }, { "id": "i6CfLRztrDGjGDtL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the x-axis tick label}[0.781, 0.898, 0.792, 0.921]?" }, { "from": "gpt", "value": "8." } ], "id_raw": 1055 }, { "id": "mIprOFAgrunFeVxl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_15.png", "conversations": [ { "from": "human", "value": "\nIs {point 7 of the Large Scale Framework}[0.708, 0.155] higher or lower than {point 7 of the Small Scale Framework}[0.708, 0.330]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1055 }, { "id": "7sGZFcPCFeA40Evn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_15.png", "conversations": [ { "from": "human", "value": "\nAt {point 0}[0.160, 0.155], does Large Scale Framework have a greater or lesser value than the Small Scale Framework at {point 0}[0.160, 0.855]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 1055 }, { "id": "4f8RPzgdddPX1YBC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_15.png", "conversations": [ { "from": "human", "value": "\nAt {point 9}[0.865, 0.330], does Large Scale Framework have a higher or lower Effectiveness than the Small Scale Framework at {point 9}[0.865, 0.447]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1055 }, { "id": "Fpo1G69Z8kkmQBBG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects located in this region: [0.160, 0.272, 0.865, 0.855]." }, { "from": "gpt", "value": "The region contains the Small Scale Framework data line, with points at different stages on the x-axis ranging from 0 to 9, plotted against Effectiveness on the y-axis. Each point is marked by an 'x'." } ], "id_raw": 1055 }, { "id": "WymITFUasKBIXe2W", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.626, 0.802, 0.891, 0.878]." }, { "from": "gpt", "value": "The area contains the legend items: {Large Scale Framework}[0.681, 0.811, 0.884, 0.834] and {Small Scale Framework}[0.681, 0.846, 0.884, 0.869], each paired with a marker." } ], "id_raw": 1055 }, { "id": "qERcFHSxAfFRmYwD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_15.png", "conversations": [ { "from": "human", "value": "\nWhat objects can you find within [0.160, 0.155, 0.865, 0.505]?" }, { "from": "gpt", "value": "The region contains the Large Scale Framework data line, with points at different stages on the x-axis ranging from 0 to 9, plotted against the Effectiveness on the y-axis. Each point is marked by an 'o'." } ], "id_raw": 1055 }, { "id": "1Jz7s9Y1KwhMuLVb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_16.png", "conversations": [ { "from": "human", "value": "\nWhat label corresponds with the object at [0.189, 0.211, 0.279, 0.234] in the legend?" }, { "from": "gpt", "value": "ESG Score." } ], "id_raw": 1056 }, { "id": "lAXjVaNcCeiEsK7c", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_16.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker corresponds with the CSR Index in the legend?" }, { "from": "gpt", "value": "Circle {CSR Index Marker}[0.137, 0.146, 0.179, 0.156]." } ], "id_raw": 1056 }, { "id": "bvht1vNHGoLrxP41", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label turn out to be?" }, { "from": "gpt", "value": "Score {y-axis label}[0.076, 0.453, 0.101, 0.557]." } ], "id_raw": 1056 }, { "id": "903jS6g6jeDfW9rL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_16.png", "conversations": [ { "from": "human", "value": "\nAt the point in 2012, is the CSR Index higher or lower than the SDG Achievement index?" }, { "from": "gpt", "value": "Lower {CSR Index}[0.489, 0.683] {SDG Achievement}[0.489, 0.437]." } ], "id_raw": 1056 }, { "id": "j5qHYuPWNB3aFeiO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_16.png", "conversations": [ { "from": "human", "value": "\nIn 2005, does the ESG Score or SDG Achievement index have a higher score?" }, { "from": "gpt", "value": "ESG Score {ESG Score}[0.160, 0.585] {SDG Achievement}[0.160, 0.708]." } ], "id_raw": 1056 }, { "id": "rv2rTzzVIpYiLlG4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_16.png", "conversations": [ { "from": "human", "value": "\nWhich line starts higher on the plot, ESG Score or CSR Index?" }, { "from": "gpt", "value": "CSR Index {CSR Index}[0.160, 0.830] {ESG Score}[0.160, 0.585]." } ], "id_raw": 1056 }, { "id": "GKZ3cdP0jgsdjED4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_16.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.134, 0.132, 0.353, 0.243]?" }, { "from": "gpt", "value": "This area contains the legend. The legend includes three entries: CSR Index with an orange circle as a symbol, SDG Achievement with a green cross symbol, and ESG Score with a blue square symbol." } ], "id_raw": 1056 }, { "id": "A3yb3TzUYWDEjELc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the region [0.467, 0.898, 0.511, 0.921]." }, { "from": "gpt", "value": "It contains the X-axis tick label for the year 2012." } ], "id_raw": 1056 }, { "id": "gwRt1ROLlwWkBwuf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_16.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the chart region [0.160, 0.302, 0.865, 0.855]?" }, { "from": "gpt", "value": "This region represents the three plotted lines for CSR Index, SDG Achievement, and ESG Score over the years from 2005 to 2020." } ], "id_raw": 1056 }, { "id": "ITcSi0zXNGUCivhk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_17.png", "conversations": [ { "from": "human", "value": "\nWhat color does the line representing the 'China Unemployment Rate' in the chart have?" }, { "from": "gpt", "value": "#d62728." } ], "id_raw": 1057 }, { "id": "NTLMisd6jIEJKS82", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_17.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is the object located at [0.117, 0.048, 0.246, 0.062] in the sublegend?" }, { "from": "gpt", "value": "The label 'China GDP Growth'." } ], "id_raw": 1057 }, { "id": "TaGmJYbUwiq8kK9w", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_17.png", "conversations": [ { "from": "human", "value": "\nWhat is identified by the coordinates [0.522, 0.719] on the graph?" }, { "from": "gpt", "value": "A mark for the Q2 value of China GDP Growth." } ], "id_raw": 1057 }, { "id": "ljc2LWiJlFXq7YSb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_17.png", "conversations": [ { "from": "human", "value": "\nBased on the graph, has the 'USA Unemployment Rate' increased or decreased between Q1 and Q2?" }, { "from": "gpt", "value": "Increased." } ], "id_raw": 1057 }, { "id": "6WEeK4433o6g3J85", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_17.png", "conversations": [ { "from": "human", "value": "\nBetween 'China GDP Growth' and 'USA GDP Growth', which one has a higher value at the end of Q4?" }, { "from": "gpt", "value": "China GDP Growth." } ], "id_raw": 1057 }, { "id": "xGmgIKpThONEmFtp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_17.png", "conversations": [ { "from": "human", "value": "\nWhich had a higher value in Q4, 'China Inflation Rate' or 'USA Inflation Rate'?" }, { "from": "gpt", "value": "USA Inflation Rate." } ], "id_raw": 1057 }, { "id": "nEFLmRfConV8XvIr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_17.png", "conversations": [ { "from": "human", "value": "\nCan you explain what the content within [0.117, 0.027, 0.235, 0.041] is?" }, { "from": "gpt", "value": "This area contains the label of the 'USA GDP Growth' line in the chart legend, which is paired with a colored marker." } ], "id_raw": 1057 }, { "id": "OFOWZfRIt6rRt26b", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_17.png", "conversations": [ { "from": "human", "value": "\nDescribe what is located at [0.066, 0.015, 0.985, 0.941]." }, { "from": "gpt", "value": "This region contains the entire graphical area of the chart, including the plotted lines for GDP Growth, Unemployment Rate, and Inflation Rate for the countries USA and China across Q1 to Q4. It also contains the chart's x-axis and y-axis, tick labels, and gridlines." } ], "id_raw": 1057 }, { "id": "vnjMgxeRgNXfLYGB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the text represent at [0.278, 0.236, 0.061, 0.250]?" }, { "from": "gpt", "value": "It represents a y-axis tick label of '25'." } ], "id_raw": 1057 }, { "id": "pEi5sZsC5ZG8Ypbj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object located at these coordinates [0.019, 0.391, 0.044, 0.553]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1058 }, { "id": "1EsxbWqr796sAfDK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_18.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates of the fifth indexed year in the x-axis?" }, { "from": "gpt", "value": "[0.497, 0.932, 0.541, 0.949]" } ], "id_raw": 1058 }, { "id": "BkqC7t05WQl63Qkl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the Index Value} at [0.584, 0.570, 0.629, 0.592]?" }, { "from": "gpt", "value": "79.7." } ], "id_raw": 1058 }, { "id": "n3NYe8B35puKwB0s", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_18.png", "conversations": [ { "from": "human", "value": "\nIs {the point of the year 2016 on the Eco-Index Chart}[0.607, 0.289] above or below {the point of the year 2016 on the Ethical Conduct Index Chart}[0.607, 0.575]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1058 }, { "id": "ciGiFrb63F6rkEJG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_18.png", "conversations": [ { "from": "human", "value": "\nIs {the Eco-Index line}[start: [0.079, 0.060], end: [0.958, 0.885]] generally above or below {the Ethical Conduct Index line}[start: [0.079, 0.174], end: [0.958, 0.747]]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1058 }, { "id": "cxlz8UTM0wqINlJh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_18.png", "conversations": [ { "from": "human", "value": "\nWhich Index has a higher value at {year 2017}[0.694, 0.232], Eco-Index or Community Involvement Index?" }, { "from": "gpt", "value": "Eco-Index." } ], "id_raw": 1058 }, { "id": "IWn2RGmI6LwHE2xS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.088, 0.027, 0.417, 0.111]." }, { "from": "gpt", "value": "This region contains the legend, where the labels {Eco-Index}[0.143, 0.034, 0.228, 0.052], {Ethical Conduct Index}[0.143, 0.061, 0.333, 0.078], and {Community Involvement Index}[0.143, 0.087, 0.410, 0.104] are present, each paired with a unique colored marker." } ], "id_raw": 1058 }, { "id": "rWCXOGtv3l2pjvPb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area [0.436, 0.932, 0.485, 0.949]?" }, { "from": "gpt", "value": "The area contains one of the x-axis tick labels, specifically the label {2015}[0.497, 0.932, 0.541, 0.949], formatted in dark color." } ], "id_raw": 1058 }, { "id": "pueNEgRGG6YNaZqn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_18.png", "conversations": [ { "from": "human", "value": "\nWhich elements are present within the region [0.079, 0.976, 0.958, 0.885]?" }, { "from": "gpt", "value": "The region contains the Eco-Index line chart with multiple data points, ranging from years 2010 to 2020, each point is annotated with its respective index value." } ], "id_raw": 1058 }, { "id": "OUXNyvsEBalylP60", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the object {[0.021, 0.359, 0.047, 0.581]} represent in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1059 }, { "id": "RCL0qpXPq5LIRueH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third tick label on the x-axis?" }, { "from": "gpt", "value": "[0.508, 0.925, 0.558, 0.945]" } ], "id_raw": 1059 }, { "id": "nJ2wbZDMDRfv6JFE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_53_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the point on PlayStation 5's line} at [0.533, 0.429]?" }, { "from": "gpt", "value": "22.1." } ], "id_raw": 1059 }, { "id": "QXcblppJWU5MxzOG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_19.png", "conversations": [ { "from": "human", "value": "\nIs {the PlayStation 5 data point}[0.236, 0.774] higher or lower than {Xbox Series X data point}[0.236, 0.877] in the year 2020?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1059 }, { "id": "5wVLt59crSi7IDrB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_19.png", "conversations": [ { "from": "human", "value": "\nIn 2024, is {Nintendo Switch's sales figure}[0.808, 0.208, 0.852, 0.228] higher or lower than {PlayStation 5's sales figure}[0.808, 0.037, 0.852, 0.057]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1059 }, { "id": "MwcYqwWhVrw5camO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_53_19.png", "conversations": [ { "from": "human", "value": "\nWhich console's sales increased more from 2020 to 2021, PlayStation 5 or Xbox Series X?" }, { "from": "gpt", "value": "PlayStation 5." } ], "id_raw": 1059 }, { "id": "kTdl3C4BBbmxGvFE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.097, 0.031, 0.331, 0.127]." }, { "from": "gpt", "value": "This region contains the chart's legend. It includes labels for PlayStation 5, Nintendo Switch, and Xbox Series X, each paired with a respective colored marker: {PlayStation 5}[0.161, 0.039, 0.292, 0.059] is blue, {Nintendo Switch}[0.161, 0.069, 0.323, 0.089] is red, and {Xbox Series X}[0.161, 0.099, 0.297, 0.119] is green." } ], "id_raw": 1059 }, { "id": "HQaAXHMCKUeVw5s9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.236, 0.062, 0.830, 0.774]." }, { "from": "gpt", "value": "This region represents the trend line of PlayStation 5 sales from 2020 to 2024 with data points corresponding to each year's units sold in millions." } ], "id_raw": 1059 }, { "id": "dFRp35epipLSd55Q", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_53_19.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.236, 0.062, 0.830, 0.774]?" }, { "from": "gpt", "value": "There are 5 objects. These are the data points for PlayStation 5 sales from 2020 to 2024, located at {[0.236, 0.774]}, {[0.384, 0.602]}, {[0.533, 0.429]}, {[0.681, 0.272]}, and {[0.830, 0.062]} respectively." } ], "id_raw": 1059 }, { "id": "VdxqW6PDTqd3ZC48", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_0.png", "conversations": [ { "from": "human", "value": "\nWhat type of object is located at [0.149, 0.796, 0.936, 0.948] in the chart?" }, { "from": "gpt", "value": "Subplot for Travel Expenses." } ], "id_raw": 1060 }, { "id": "Ax4WrYSpffyK5aR7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_0.png", "conversations": [ { "from": "human", "value": "\nIdentify the object at [0.058, 0.048, 0.102, 0.170] on the chart." }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1060 }, { "id": "4MyXazlyAxiut5fB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_0.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at [0.431, 0.794, 0.654, 0.815]?" }, { "from": "gpt", "value": "Sublegend." } ], "id_raw": 1060 }, { "id": "PI3CJ5g6VP7Czq2Q", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point at [0.149, 0.207]} above {the point at [0.936, 0.207]} in the Grocery Consumption subplot?" }, { "from": "gpt", "value": "Equal." } ], "id_raw": 1060 }, { "id": "p1pzEd5kPUlxLiHT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the x-tick labeled '0'} at [0.149, 0.215] precede {the x-tick labeled '10'} at [0.936, 0.215] on the x-axis?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1060 }, { "id": "lDtlGU8YD2sVe7Fq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_0.png", "conversations": [ { "from": "human", "value": "\nWhich has a larger expense value, {the third point on the Electronics consumption graph}[0.228, 0.672] or {the last point on the Travel expenses graph}[0.936, 0.796]?" }, { "from": "gpt", "value": "Travel expenses." } ], "id_raw": 1060 }, { "id": "vDqDXo4QuS7alK4K", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the information found in the region spanning [0.456, 0.713, 0.936, 0.771]." }, { "from": "gpt", "value": "This area covers the lower portions of the Electronics and Travel expenses subplots, including parts of the x-axis, labels for '4', '6', '8', '10', and the corresponding grid lines." } ], "id_raw": 1060 }, { "id": "2UoDu9WUQyFqkmIi", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements present between [0.025, 0.093, 0.048, 0.247]." }, { "from": "gpt", "value": "This vertical space contains the Y-axis labels for multiple subplots, indicating various metrics like consumption and expenses across sections such as Grocery and Travel." } ], "id_raw": 1060 }, { "id": "3ChtVqDMApWz48uc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.054, 0.305, 0.690, 0.321]?" }, { "from": "gpt", "value": "This region captures the sublegends of both Clothing and Electronics consumption trends, indicating their respective colors and trend identifiers nearby the lower part of their graphs." } ], "id_raw": 1060 }, { "id": "Wvenf4mMPCP4lQSz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label for the graph in the bounding box [0.153, 0.295, 0.975, 0.462]?" }, { "from": "gpt", "value": "Students Enrolled." } ], "id_raw": 1061 }, { "id": "7YKmIaK8NXWm308D", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_1.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is associated with {the legend item 'Online Courses Developed'}[0.239, 0.552, 0.545, 0.564]?" }, { "from": "gpt", "value": "None." } ], "id_raw": 1061 }, { "id": "WBoMBolLoI0oE55t", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_1.png", "conversations": [ { "from": "human", "value": "\nWhich year's x-axis label is found at {coordinates}[0.490, 0.960, 0.519, 0.971] in the 'Growth of Online Learning Platforms' graph?" }, { "from": "gpt", "value": "2014." } ], "id_raw": 1061 }, { "id": "BMoORxGLdoTGp7tm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point at year 2016}[0.639, 0.385] higher or lower than {the point at year 2018}[0.788, 0.366] in the 'Growth of Students Enrolled in Online Learning' line chart?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1061 }, { "id": "ELFJ0LH0oNgnAA4H", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_1.png", "conversations": [ { "from": "human", "value": "\nIs the growth rate of {point for 2016}[0.415, 0.923] or {point for 2020}[0.938, 0.796] steeper in the 'Online Learning Platforms' chart?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 1061 }, { "id": "k9uMkiG39Ovn9C5X", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_1.png", "conversations": [ { "from": "human", "value": "\nWhich line segment is steeper, from {year 2014 to 2016}[0.490, 0.157, 0.639, 0.136] or from {year 2016 to 2018}[0.639, 0.136, 0.788, 0.091] in the 'Online Learning Resources' subplot?" }, { "from": "gpt", "value": "2016 to 2018." } ], "id_raw": 1061 }, { "id": "3Pr9p6GywWLybj3S", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the region between [0.153, 0.048, 0.975, 0.215]." }, { "from": "gpt", "value": "This region presents the chart titled 'Growth of Online Learning Resources' with x-axis labeled 'Years' and y-axis labeled 'Online Learning Resources'. Markers are absent, and the graph shows an upward trend in data points." } ], "id_raw": 1061 }, { "id": "gFAdzj54GlnxEw0j", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_1.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the contents of the region [0.153, 0.295, 0.975, 0.462]?" }, { "from": "gpt", "value": "The area includes the 'Growth of Students Enrolled in Online Learning' chart, displaying a line graph plotted against the years on the x-axis and the number of students on the y-axis. The line trend is upward." } ], "id_raw": 1061 }, { "id": "XqDOh7O2f3VW5nDu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_1.png", "conversations": [ { "from": "human", "value": "\nExplain the information presented in the space [0.153, 0.542, 0.975, 0.709]." }, { "from": "gpt", "value": "This section of the chart is dedicated to 'Growth of Online Courses Developed'. It includes an upward trending line graph depicting the increase in online courses developed over the years, with y-axis representing the number of courses and x-axis representing years." } ], "id_raw": 1061 }, { "id": "ePGRdMGgLjLFe91Y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the x-axis in the first subplot?" }, { "from": "gpt", "value": "Years." } ], "id_raw": 1062 }, { "id": "AFU10Fta5f20qR7G", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_2.png", "conversations": [ { "from": "human", "value": "\nWhich color represents the 'Trade Balance' line, according to the legend in the first subplot?" }, { "from": "gpt", "value": "#ff00ff." } ], "id_raw": 1062 }, { "id": "uqmJ9fhPJkxjqfiN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_2.png", "conversations": [ { "from": "human", "value": "\nHow many y-ticks are present in the subplot corresponding to \"Globalization Trends\"?" }, { "from": "gpt", "value": "6." } ], "id_raw": 1062 }, { "id": "wSY5AIc9gGiqsAb9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_2.png", "conversations": [ { "from": "human", "value": "\nWhich year's FDI data point is higher, {1983}[0.201, 0.294] or {1993}[0.610, 0.321]?" }, { "from": "gpt", "value": "1993." } ], "id_raw": 1062 }, { "id": "u7EJTmWP4YIa7KVN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_2.png", "conversations": [ { "from": "human", "value": "\nIs the lowest GDP point on the 'Real GDP' graph from {1996}[0.732, 0.602] or from {1982}[0.161, 0.701]?" }, { "from": "gpt", "value": "1982." } ], "id_raw": 1062 }, { "id": "lXEPHhW4rmNZDeIi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_2.png", "conversations": [ { "from": "human", "value": "\nDoes the 'Trade Balance' value for the year {1986}[0.324, 0.198] exhibit a greater magnitude than the year {1989}[0.446, 0.090]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1062 }, { "id": "TjFoqvIwzVYpwvtq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the layout and content found within the area [0.122, 0.533, 0.975, 0.709]." }, { "from": "gpt", "value": "This subplot displays 'Real Gross Domestic Product' over years, with black colored x-axis labeled 'Years' at the bottom and 'Real GDP' label in black on the y-axis; the plot line in blue." } ], "id_raw": 1062 }, { "id": "kvc28fKF18BrBgcm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_2.png", "conversations": [ { "from": "human", "value": "\nCan you provide details on what is within the region [0.077, 0.477, 0.975, 0.688]?" }, { "from": "gpt", "value": "This area includes portions of subplots for Foreign Direct Investment and Real GDP, showing y-ticks and plot lines within each subplot, each with distinct color coding, green for FDI and blue for GDP." } ], "id_raw": 1062 }, { "id": "7nizw96n5Ufp75GJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_2.png", "conversations": [ { "from": "human", "value": "\nWhat are the elements contained within the coordinates [0.038, 0.466, 0.423, 0.724]?" }, { "from": "gpt", "value": "This includes x-axis labels for the subplots of Foreign Direct Investment and Real GDP, spanning several years, marked specifically from '1980.0' to '1990.0'." } ], "id_raw": 1062 }, { "id": "a3SY9fnDbiFEu8ix", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_3.png", "conversations": [ { "from": "human", "value": "\nWhat does {the color of the point on line 0 of the first subplot}[0.110, 0.198] represent?" }, { "from": "gpt", "value": "Traditional Shopping." } ], "id_raw": 1063 }, { "id": "qgImyxXiBhVqNEji", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_3.png", "conversations": [ { "from": "human", "value": "\nWhat is {the value of the x-axis tick label} located at [0.617, 0.219, 0.632, 0.231]?" }, { "from": "gpt", "value": "3." } ], "id_raw": 1063 }, { "id": "2iWqP38IuWkYY2Cu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_3.png", "conversations": [ { "from": "human", "value": "\nWhat object is positioned at {coordinates}[0.282, 0.466, 0.289, 0.477]?" }, { "from": "gpt", "value": "Online Shopping x-axis tick label '1'." } ], "id_raw": 1063 }, { "id": "EFkdGQiv9kbGKZEX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_3.png", "conversations": [ { "from": "human", "value": "\nIs {the peak value of the line in subplot 1}[0.968, 0.303] higher or lower than {the peak value of the line in subplot 3}[0.968, 0.822]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1063 }, { "id": "bdDDiKqEQhCqijOh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_3.png", "conversations": [ { "from": "human", "value": "\nWhat is higher at {x=4.5}{{x-coordinate}[0.882, 0.181] for Traditional Shopping or {x-coordinate}[0.882, 0.835] for Environmental Shopping?" }, { "from": "gpt", "value": "Traditional Shopping." } ], "id_raw": 1063 }, { "id": "YnqEarZRwpPQjwRy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the point at x=2 from subplot 1}[0.453, 0.395] have higher or lower participation than {the corresponding point at x=2 from subplot 3}[0.453, 0.909]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1063 }, { "id": "iLsja66jRZ3rlDRD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_3.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the area framed between coordinates [0.567, 0.482, 0.953, 0.709]?" }, { "from": "gpt", "value": "This region contains the ylabel 'Population Participation (%)' for the Online Shopping subplot, segments of the x-axis, and a part of the line representing Online Shopping." } ], "id_raw": 1063 }, { "id": "UdZf4SDJHBHowwvE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the region bounded by [0.102, 0.219, 0.367, 0.231]." }, { "from": "gpt", "value": "This region contains two x-axis tick labels '0' and '1' for the first subplot dealing with Traditional Shopping." } ], "id_raw": 1063 }, { "id": "aEP0Yu0W4pFO9iF1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_3.png", "conversations": [ { "from": "human", "value": "\nWhat is located within the coordinates [0.389, 0.506, 0.688, 0.521]?" }, { "from": "gpt", "value": "The subtitle \"Impulse Buying Trend\" is located within this region." } ], "id_raw": 1063 }, { "id": "rjT68twOqsKuQBFs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label for 'Product D'}[0.511, 0.774, 0.624, 0.786]?" }, { "from": "gpt", "value": "Product D." } ], "id_raw": 1064 }, { "id": "iE0HVpzr7VRiosTY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_4.png", "conversations": [ { "from": "human", "value": "\nWhat color is associated with {the line for 'Product C'}[0.110, 0.554, 0.960, 0.696] in the legend?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 1064 }, { "id": "Si0lqlZIY1WpfhuQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_4.png", "conversations": [ { "from": "human", "value": "\nWhere is the label '4' located on the x-axis for {Product A's graph}[0.110, 0.048, 0.960, 0.215]?" }, { "from": "gpt", "value": "[0.386, 0.219, 0.401, 0.231]." } ], "id_raw": 1064 }, { "id": "TUjjV9NSnHbwlDGY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_4.png", "conversations": [ { "from": "human", "value": "\nDoes the {first point of 'Product D'}[0.110, 0.955] have a higher y-value than the {second point of 'Product B'}[0.181, 0.319]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1064 }, { "id": "X65fKvNe363SDc8u", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_4.png", "conversations": [ { "from": "human", "value": "\nIs the last x-axis label on {Product A's plot}[0.946, 0.219, 0.975, 0.231] closer to the left or right edge of {the subplot frame for Product C}[0.110, 0.542, 0.960, 0.709]?" }, { "from": "gpt", "value": "Close to the right edge." } ], "id_raw": 1064 }, { "id": "DKz16IRM33APJUWW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_4.png", "conversations": [ { "from": "human", "value": "\nWhich sales figure is higher at {6 months on the x-axis}[0.535, 0.215], Product A or Product B?" }, { "from": "gpt", "value": "Product A." } ], "id_raw": 1064 }, { "id": "JjmA6J5X3TQiAKst", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the area enclosed within [0.025, 0.048, 0.048, 0.147]." }, { "from": "gpt", "value": "This region contains the y-axis label 'Sales' for Product A, rendered in a .15 color tone and rotated at an angle of 90 degrees." } ], "id_raw": 1064 }, { "id": "JoH6Y3zgaZv12anz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_4.png", "conversations": [ { "from": "human", "value": "\nCan you give a summary of the contents within [0.058, 0.043, 0.102, 0.222]?" }, { "from": "gpt", "value": "Within this area, y-axis labels for Product A are located, displaying numbers ranging from 90 to 130, oriented vertically with a .15 color tone." } ], "id_raw": 1064 }, { "id": "4x8y1MyTRRAbDLLd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_4.png", "conversations": [ { "from": "human", "value": "\nWhat elements are present within the subsection [0.110, 0.215, 0.960, 0.462]?" }, { "from": "gpt", "value": "The section includes both the line graph of Product B, x-axis and y-axis grids marked with white color, sales data points across several months, and x-axis and y-axis labels indicating time in months and sales, respectively." } ], "id_raw": 1064 }, { "id": "TaoxsE8G4IbtcF7f", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_5.png", "conversations": [ { "from": "human", "value": "\nWhat is marked by the coordinates [0.388, 0.038, 0.434, 0.038] in the chart?" }, { "from": "gpt", "value": "The handle for the Consumer Spending legend." } ], "id_raw": 1065 }, { "id": "H3OAEjQpxP2MwT88", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_5.png", "conversations": [ { "from": "human", "value": "\nIdentify the content at coordinates [0.069, 0.760, 0.072, 0.795] on the y-axis in the fourth subplot." }, { "from": "gpt", "value": "The value 4 (in %)." } ], "id_raw": 1065 }, { "id": "MzdnmVr8SF4tCqli", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_5.png", "conversations": [ { "from": "human", "value": "\nWhat is located at [0.025, 0.432, 0.048, 0.463] next to the second subplot?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1065 }, { "id": "7vGociEPgirp1BCf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_5.png", "conversations": [ { "from": "human", "value": "\nWhich is greater at [0.855, 0.653], the GDP or GDP per Capita?" }, { "from": "gpt", "value": "GDP." } ], "id_raw": 1065 }, { "id": "v2rjM8E82YXhvn89", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_5.png", "conversations": [ { "from": "human", "value": "\nIs {the tick mark for 2015.0}[0.736, 0.462] in the second subplot above or below {the tick mark for 2015.0}[0.736, 0.955] in the fourth subplot?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1065 }, { "id": "9aCC6xcJBTyfmedb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_5.png", "conversations": [ { "from": "human", "value": "\nDoes {Consumer Spending at point [0.338, 0.194]} exceed {GDP at point [0.338, 0.380]}?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1065 }, { "id": "mOpUnK9vWpulbwn9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents confined within [0.078, 0.225, 0.131, 0.250]." }, { "from": "gpt", "value": "The y-axis label \"Consumer Spending (Billion $)\" is situated within this area." } ], "id_raw": 1065 }, { "id": "kePe8esE6VZdpyIN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.025, 0.043, 0.054, 0.216] include?" }, { "from": "gpt", "value": "The area includes the y-axis label 'Consumer Spending (Billion $)'{y-axis label}[0.025, 0.043, 0.048, 0.219]." } ], "id_raw": 1065 }, { "id": "C8V751mViuheJMKW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_5.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the area [0.055, 0.956, 0.130, 0.974]?" }, { "from": "gpt", "value": "The y-axis label 'Inflation Rate (%)' is contained in this region." } ], "id_raw": 1065 }, { "id": "D0WvlpZKVjYpU5TY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_6.png", "conversations": [ { "from": "human", "value": "\nWhat type of object is located at [0.466, 0.033, 0.676, 0.045]?" }, { "from": "gpt", "value": "Legend label." } ], "id_raw": 1066 }, { "id": "k1oe31mfkDQ7qMSs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_6.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.117, 0.215]?" }, { "from": "gpt", "value": "{the starting point of the line for 'EdTech Innovation'}[0.117, 0.215]." } ], "id_raw": 1066 }, { "id": "0BP62sdakygxMVpq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the type of object} at [0.094, 0.071, 0.109, 0.083]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1066 }, { "id": "7eu39ke8JHqcaJAv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_6.png", "conversations": [ { "from": "human", "value": "\nIs {the 3 year mark}[0.370, 0.198] above or below {the 4 year mark}[0.412, 0.195] in the 'EdTech Innovation' graph?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1066 }, { "id": "VqfN9CCDe7hWoSJF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_6.png", "conversations": [ { "from": "human", "value": "\nWhich label has a higher vertical position, {label '1'}[0.117, 0.187] or {label '5'}[0.117, 0.076] on the Y-axis?" }, { "from": "gpt", "value": "'5'." } ], "id_raw": 1066 }, { "id": "bc8slhURnFV1Td4E", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the first marker of the 'Educational Apps' line}[0.117, 0.939] appear before or after {the first marker of the 'Online Learning' line}[0.117, 0.667] in the chart?" }, { "from": "gpt", "value": "After." } ], "id_raw": 1066 }, { "id": "u5L3HoUhIK4824Zz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements present from [0.058, 0.521, 0.117, 0.716]." }, { "from": "gpt", "value": "Y-axis labels and tick marks for 'Online Learning' ranging from '17.5' to '0.0', each paired with a corresponding tick along the axis." } ], "id_raw": 1066 }, { "id": "T18KDoamEXQbr1rx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_6.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.094, 0.043, 0.109, 0.222]?" }, { "from": "gpt", "value": "The y-axis labels '0' to '6' and corresponding tick marks for the 'EdTech Innovation' graph, written in black with a vertical orientation." } ], "id_raw": 1066 }, { "id": "VBC0bbkqmuU765kH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the interval [0.509, 0.219, 0.975, 0.477]." }, { "from": "gpt", "value": "X-axis labels and tick marks from '0' to '10' are positioned along this interval for the 'Software Tools' chart, with labels indicating the year mark." } ], "id_raw": 1066 }, { "id": "JXMMCUF8CZHo0tHr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the role of the object located at [0.489, 0.235, 0.538, 0.247] on the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 1067 }, { "id": "s33w03PsoZa3t9yj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_7.png", "conversations": [ { "from": "human", "value": "\nWhat is situated at [0.058, 0.127, 0.072, 0.138]?" }, { "from": "gpt", "value": "Y-axis tick label." } ], "id_raw": 1067 }, { "id": "BbLZOI3V0lUSU7mj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_7.png", "conversations": [ { "from": "human", "value": "\nIdentify the object at the coordinates [0.489, 0.976, 0.538, 0.988]." }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 1067 }, { "id": "5SSi6zCIqHeKVouA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_7.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at the point [0.686, 0.094], {the line labeled 'Sustainable Development'} or {the y-axis tick} at [0.080, 0.090]?" }, { "from": "gpt", "value": "Line." } ], "id_raw": 1067 }, { "id": "dvp3u38HCW7HMO9B", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_7.png", "conversations": [ { "from": "human", "value": "\nIs the first marker in {the 'Sustainable Development'} line at [0.080, 0.181] above or below the y-axis tick for '4' found at [0.080, 0.215]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1067 }, { "id": "AJcLA6wCVfRgfzll", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_7.png", "conversations": [ { "from": "human", "value": "\nWhich is closer to the x-axis label at [0.489, 0.235, 0.538, 0.247], the x-tick label '2020' at [0.051, 0.219, 0.110, 0.231] or the x-tick label '2030' at [0.917, 0.219, 0.975, 0.231]?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 1067 }, { "id": "5RlUzCgIL5fp60Ea", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the region observed within coordinates [0.080, 0.048, 0.946, 0.215]." }, { "from": "gpt", "value": "This is the {axes}[0.080, 0.048, 0.946, 0.215] region entitled 'Sustainable Development' with grid lines visible. It displays a line plot tracking the metrics of Sustainable Development from 2020 to 2030." } ], "id_raw": 1067 }, { "id": "NCYSdyUqKThTQedX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the area [0.080, 0.295, 0.946, 0.462]?" }, { "from": "gpt", "value": "The charts {ylabel}[0.025, 0.362, 0.048, 0.394] and {xlabel}[0.489, 0.482, 0.538, 0.494] indicate this area captures 'Democratic Consolidation' values over years plotted on a line graph with visible grid lines." } ], "id_raw": 1067 }, { "id": "WMKnuT5dRSiGZoJS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_7.png", "conversations": [ { "from": "human", "value": "\nWhat elements are contained within the coordinates [0.080, 0.788, 0.946, 0.955]?" }, { "from": "gpt", "value": "This region comprises the line graph for 'Market Orientation' marked with the {subplot title}[0.383, 0.753, 0.643, 0.768] and axis labels found at {ylabel}[0.025, 0.856, 0.048, 0.888] and {xlabel}[0.489, 0.976, 0.538, 0.988]. It also includes the accompanying grid lines." } ], "id_raw": 1067 }, { "id": "KWkOMq0AV1eWwKGi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object with coordinates [0.080, 0.713, 0.139, 0.724] in the chart?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 1068 }, { "id": "spdpuyr15OoujGqn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.359, 0.960, 0.418, 0.971]?" }, { "from": "gpt", "value": "2014." } ], "id_raw": 1068 }, { "id": "0gsod1caWqLlytJi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fifth text in the legend for the Literature art subplot?" }, { "from": "gpt", "value": "[0.481, 0.713, 0.511, 0.724]." } ], "id_raw": 1068 }, { "id": "6CTeJEU2cfNtDNXT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_8.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Film Industry'}[[0.110, 0.955], [0.946, 0.914]] generally above or below {the line for 'Performing Arts'}[[0.110, 0.462], [0.946, 0.445]]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1068 }, { "id": "4sjrK1xqVLPppoVJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_8.png", "conversations": [ { "from": "human", "value": "\nWhich trend has a higher value at {point 2020}[0.946, 0.445], Visual Arts or Film Industry?" }, { "from": "gpt", "value": "Film Industry." } ], "id_raw": 1068 }, { "id": "x8FPkO00i16GPSpX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_8.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '2020'}[0.946, 0.822] in the Film Industry section above or below {the point labeled '2016'}[0.574, 0.830] in the same section?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1068 }, { "id": "ztJd0R7qn44Wdj56", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the label shown at {the coordinate}[0.508, 0.235, 0.557, 0.247]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 1070 }, { "id": "h0lDNoW5CKhl481C", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_10.png", "conversations": [ { "from": "human", "value": "\nWhat content is indicated by {this particular point}[0.731, 0.082] in the chart?" }, { "from": "gpt", "value": "Sales value for the year 2015." } ], "id_raw": 1070 }, { "id": "vfOBrzArfLDgSO3s", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_10.png", "conversations": [ { "from": "human", "value": "\nCan you identify the label at {this location}[0.058, 0.061, 0.087, 0.073]?" }, { "from": "gpt", "value": "80." } ], "id_raw": 1070 }, { "id": "Rjr5YtSpoJ5VeqG2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_10.png", "conversations": [ { "from": "human", "value": "\nIs the value represented by {the point}[0.135, 0.207] higher or lower than the one at {the point}[0.175, 0.203]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1070 }, { "id": "BwB9IwxnDbHKsjV3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_10.png", "conversations": [ { "from": "human", "value": "\nDoes the data trend represented by {the point}[0.214, 0.440] occur before or after the data at {the point}[0.572, 0.399]?" }, { "from": "gpt", "value": "Before." } ], "id_raw": 1070 }, { "id": "KvbzYrVRq2w0wbst", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_10.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, the starting point at {coordinates}[0.135, 0.701] or the ending point at {coordinates}[0.930, 0.549]?" }, { "from": "gpt", "value": "Ending point." } ], "id_raw": 1070 }, { "id": "RBVbimO9xt0SxPZ3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the function of the space between [0.025, 0.082, 0.048, 0.180]." }, { "from": "gpt", "value": "This area houses the y-axis label \"Sales (in billions)\" and various corresponding y-tick labels such as '0', '20', '40', '60', '80', '100'." } ], "id_raw": 1070 }, { "id": "TPM1xCmDHnlYyywQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_10.png", "conversations": [ { "from": "human", "value": "\nWhat significant details are present in the region [0.095, 0.187, 0.095, 0.228]?" }, { "from": "gpt", "value": "It contains the first two y-tick labels and points for the Game Sales subplot which indicates fluctuations in sales across the years starting from 2000." } ], "id_raw": 1070 }, { "id": "ZaKbKgRA98xMUUmo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_10.png", "conversations": [ { "from": "human", "value": "\nCould you describe what occupies the coordinates [0.719, 0.919, 0.741, 0.930]?" }, { "from": "gpt", "value": "The y-axis label \"Streams (in billions)\" for the Online Streams subplot spans this area." } ], "id_raw": 1070 }, { "id": "9o7LHobUbd3nD8bm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_11.png", "conversations": [ { "from": "human", "value": "\nWhat object corresponds to {the point marking the lowest value in 'AI Patent Application' on the x-axis at}[0.936, 0.215]?" }, { "from": "gpt", "value": "2018." } ], "id_raw": 1071 }, { "id": "HErgZRoqLCECgdqJ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_11.png", "conversations": [ { "from": "human", "value": "\nIdentify {the y-axis tick label} located at [0.102, 0.214, 0.116, 0.226]." }, { "from": "gpt", "value": "0." } ], "id_raw": 1071 }, { "id": "1x6Hv2FC1V0VKjZs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_11.png", "conversations": [ { "from": "human", "value": "\nWhich {axis label} can be found at [0.025, 0.558, 0.048, 0.692]?" }, { "from": "gpt", "value": "Number of Publications." } ], "id_raw": 1071 }, { "id": "plcpYLISQIzVAY5f", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_11.png", "conversations": [ { "from": "human", "value": "\nIs {the point marked '2016'}[0.782, 0.709] above or below {the point marked '2010'}[0.318, 0.709] in regard to AI Paper Publication?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1071 }, { "id": "Lc0uwl77yvoeB4rp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_11.png", "conversations": [ { "from": "human", "value": "\nWhich value is greater, {label for the year 2012 at}[0.472, 0.462] or {label for the year 2016 at}[0.782, 0.462] in the AI Startups chart?" }, { "from": "gpt", "value": "2016." } ], "id_raw": 1071 }, { "id": "i7R6FDSvHiEcuyHj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'AI VC Investment' at}[0.859, 0.841] conclude at a higher or lower value compared to {its point at}[0.163, 0.948]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1071 }, { "id": "BCbB1WCg6DkxQlJL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the region between [0.124, 0.048, 0.975, 0.215]." }, { "from": "gpt", "value": "This region contains the subplot titled 'AI Patent Application over the years' with a graph showing a declining trend, labeled x-axis for years and y-axis for number of applications in shades of purple." } ], "id_raw": 1071 }, { "id": "hPZranyne1huHfTn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_11.png", "conversations": [ { "from": "human", "value": "\nWhat features are contained in the area [0.124, 0.295, 0.975, 0.462]?" }, { "from": "gpt", "value": "This section includes the subplot for 'AI Startups over the years' showcasing ascending green-colored line graph along with incrementally labeled x-axis for years and y-axis for number of startups." } ], "id_raw": 1071 }, { "id": "x9rjvibbUvre39DW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_11.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the section with coordinates ranging from [0.124, 0.542, 0.975, 0.709]?" }, { "from": "gpt", "value": "It's a graph representing 'AI Paper Publication over the years' with a line graph fluctuating over years, detailed with its corresponding x-axis for years and y-axis labeled for number of publications, predominantly in blue." } ], "id_raw": 1071 }, { "id": "vJaarWJNXmTcR6Nx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate for {the last x-axis tick label of the Net Trade chart}[0.992, 0.713, 1.072, 0.724]?" }, { "from": "gpt", "value": "[0.992, 0.713, 1.072, 0.724]" } ], "id_raw": 1072 }, { "id": "2CNoPVV8wuEM4k4G", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_12.png", "conversations": [ { "from": "human", "value": "\nWhat type of plot is represented at [0.156, 0.055, 0.935, 0.207]?" }, { "from": "gpt", "value": "line chart" } ], "id_raw": 1072 }, { "id": "26u2bEaZkC9FTXYh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_12.png", "conversations": [ { "from": "human", "value": "\nWhat does {the vertical axis label}[0.046, 0.597, 0.070, 0.653] on the third subplot represent?" }, { "from": "gpt", "value": "Net Trade" } ], "id_raw": 1072 }, { "id": "8QJo3QkB7M0k1xA5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_12.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at {xtick '1980.0'}[0.545, 0.462] in the Industrial Production Index chart, the beginning or the end of the line?" }, { "from": "gpt", "value": "end" } ], "id_raw": 1072 }, { "id": "0MpUZOo5ucBAiwsZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the last point on the line}[0.935, 0.796] in the GDP Growth chart exceed {the first point}[0.156, 0.948]?" }, { "from": "gpt", "value": "yes" } ], "id_raw": 1072 }, { "id": "nNnFOmmACSpRsJvv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_12.png", "conversations": [ { "from": "human", "value": "\nIs {the x-axis label}[0.521, 0.729, 0.570, 0.741] higher in position than {the subtitle}[0.373, 0.506, 0.717, 0.521] in the third subplot?" }, { "from": "gpt", "value": "no" } ], "id_raw": 1072 }, { "id": "wnQV81CetOy5tJMN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects contained in [0.521, 0.013, 0.804, 0.247]." }, { "from": "gpt", "value": "This region includes {the subtitle 'Food Production Index over the years'}[0.287, 0.013, 0.804, 0.028] and {the x-axis label 'Year'}[0.521, 0.235, 0.570, 0.247]." } ], "id_raw": 1072 }, { "id": "YUESdTgZFgV67eyN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the features of the space [0.058, 0.095, 0.177, 0.254]?" }, { "from": "gpt", "value": "This area includes {the x-axis label text for '30.0'}[0.058, 0.095, 0.109, 0.106] and part of {the main line chart representation for the Food Production Index}[0.156, 0.055, 0.935, 0.207]." } ], "id_raw": 1072 }, { "id": "ASB1aGlfTV9QOwxe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_12.png", "conversations": [ { "from": "human", "value": "\nCan you provide details about the section [0.273, 0.506, 0.717, 0.741]?" }, { "from": "gpt", "value": "The highlighted section contains {the subtitle 'Net Trade over the years'}[0.373, 0.506, 0.717, 0.521] and {the x-axis label 'Year'}[0.521, 0.729, 0.570, 0.741]." } ], "id_raw": 1072 }, { "id": "IMfAxL87zSGENb1L", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line representing 'Improved Standard'?" }, { "from": "gpt", "value": "#800080." } ], "id_raw": 1073 }, { "id": "JuPDDhh8HRRFC50H", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_13.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.117, 0.215, 0.960, 0.215] in the chart?" }, { "from": "gpt", "value": "X-axis." } ], "id_raw": 1073 }, { "id": "pAm4ringg4RRApWU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_13.png", "conversations": [ { "from": "human", "value": "\nCan you identify the label for the tick at {coordinates}[0.792, 0.215]?" }, { "from": "gpt", "value": "8." } ], "id_raw": 1073 }, { "id": "skxkjRF2dZkwYeEf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_13.png", "conversations": [ { "from": "human", "value": "\nWhich label corresponds to the point at {coordinates}[0.201, 0.438] on the 'Improved Preferred' line?" }, { "from": "gpt", "value": "1.0." } ], "id_raw": 1073 }, { "id": "CA4pMMdZMr89QL7T", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_13.png", "conversations": [ { "from": "human", "value": "\nIs the color of the point at {coordinates}[0.370, 0.680] on the 'Improved Premium' line the same as its line's color?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1073 }, { "id": "hTJRfZA5zyFVWwRk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_13.png", "conversations": [ { "from": "human", "value": "\nWhich y-axis tick label is closer to the point at {coordinates}[0.454, 0.462]?" }, { "from": "gpt", "value": "4." } ], "id_raw": 1073 }, { "id": "jbuu7f1TldtjmLAJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the area between [0.117, 0.219, 0.960, 0.231]." }, { "from": "gpt", "value": "This region contains the x-axis, including tick labels ranging from '0' to '10' placed at regular intervals, styled in dark gray color." } ], "id_raw": 1073 }, { "id": "RNukFxQKo31KlyHT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.117, 0.955, 0.960, 0.976] depict in the chart?" }, { "from": "gpt", "value": "It includes the x-axis for the 'Improved Exclusive' subplot, with tick labels for values from '0' to '10', displayed in a dark gray font at fixed points." } ], "id_raw": 1073 }, { "id": "rDj7G8rtS1WR6IR2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.110, 0.466, 0.975, 0.477]." }, { "from": "gpt", "value": "This region showcases the x-axis of the 'Improved Preferred' subplot with labels ranging from '0' to '10', formatted in dark gray, placed beneath the corresponding tick marks." } ], "id_raw": 1073 }, { "id": "pEnJ7VvIdPH1eCyU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis for the first subplot in the chart?" }, { "from": "gpt", "value": "Production (GWh)." } ], "id_raw": 1074 }, { "id": "FGvNOqHfCP5qtyhx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_14.png", "conversations": [ { "from": "human", "value": "\nWhat does {the last tick on the x-axis}[1.011, 0.960, 1.070, 0.971] represent?" }, { "from": "gpt", "value": "2034." } ], "id_raw": 1074 }, { "id": "FOo8yJ1h3S6gUX9W", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_14.png", "conversations": [ { "from": "human", "value": "\nCan you tell the color of {the line representing 'Coal Production'}[0.124, 0.137, 0.975, 0.215]?" }, { "from": "gpt", "value": "Black." } ], "id_raw": 1074 }, { "id": "Ce8CHd0UqamtDUuj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_14.png", "conversations": [ { "from": "human", "value": "\nWhich has higher production in 2033, {the point representing Oil Production}[0.975, 0.295] or {the point representing Coal Production}[0.975, 0.215]?" }, { "from": "gpt", "value": "Oil Production." } ], "id_raw": 1074 }, { "id": "6JA0qjz22rsj3dzK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_14.png", "conversations": [ { "from": "human", "value": "\nIn the year 2023, is the production of {Natural Gas}[0.320, 0.619] higher or lower than {Renewables}[0.320, 0.917]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1074 }, { "id": "ySbF0npgCjeFcpjX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_14.png", "conversations": [ { "from": "human", "value": "\nIs the first tick mark for {Oil Production}[0.124, 0.462] higher on the chart than {Natural Gas Production}[0.124, 0.709]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1074 }, { "id": "LNiAEjNI5XsBNqwr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the components within the region [0.025, 0.080, 0.975, 0.462]." }, { "from": "gpt", "value": "This region contains the plots for Coal and Oil production. Each plot has a y-axis labeled \"Production (GWh)\", x-axis labeled \"Year\", and various data points are plotted along the lines representing productions from 2020 to 2034. The Coal plot is located in the upper part of this region, while the Oil plot appears just below it." } ], "id_raw": 1074 }, { "id": "SOCIOaBeywgrFqQ0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_14.png", "conversations": [ { "from": "human", "value": "\nExplain the content in the bottom part of the chart within [0.025, 0.542, 0.975, 0.955]." }, { "from": "gpt", "value": "The bottom part showcases the line plots for Natural Gas and Renewables production. Both subplots are structured similarly with a y-axis labeled \"Production (GWh)\", x-axis labeled \"Year\", and data points representing annual production from 2020 to 2034. Natural Gas production details appear in the upper half while Renewables production is displayed in the lower half." } ], "id_raw": 1074 }, { "id": "nICD2KbB8t7KrBAy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_14.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the area [0.025, 0.013, 0.575, 0.247]?" }, { "from": "gpt", "value": "This segment includes the y-axis for Coal production marked \"Production (GWh)\", x-axis labeled \"Year\", and the title \"Coal Production\" near the top left. The plot also includes vertical grid lines for clarity, extending throughout the upper subplot area." } ], "id_raw": 1074 }, { "id": "MG8nSMWCk80TG1mm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_15.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the 'Anxiety' trend on the chart?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 1075 }, { "id": "iRBMoXbEsYVOH5tk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis for the first subplot?" }, { "from": "gpt", "value": "Value." } ], "id_raw": 1075 }, { "id": "II6dxifIWffm4Kqp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_15.png", "conversations": [ { "from": "human", "value": "\nWhere does the 'Resilience' line start on the graph?" }, { "from": "gpt", "value": "[0.095, 0.908]" } ], "id_raw": 1075 }, { "id": "8hgVSG5BBTon44eW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_15.png", "conversations": [ { "from": "human", "value": "\nIs the starting point of {the 'Procrastination' line}[0.095, 0.191] higher than {the 'Resilience' line}[0.095, 0.908] on the graph?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1075 }, { "id": "T6xsrTFBtSDTcO2S", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_15.png", "conversations": [ { "from": "human", "value": "\nDoes the point labeled '0' on the x-axis for 'Anxiety' fall before or after {the point labeled '2'}[0.268, 0.709]?" }, { "from": "gpt", "value": "Before." } ], "id_raw": 1075 }, { "id": "N9c5HQOVrdlKL3X4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_15.png", "conversations": [ { "from": "human", "value": "\nIs the end value of the {line for 'Anxiety'}[0.960, 0.544] greater than {the end value of the line for 'Procrastination'}[0.960, 0.191] on the y-axis?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1075 }, { "id": "sn9bSoyiSZbI9JKL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the bottom subplot including its x-axis, y-axis, and the line attributes." }, { "from": "gpt", "value": "The subplot has an x-axis labeled as 'Time' with tick marks at intervals labeled from '0' to '10'. The y-axis is labeled as 'Value' with labels from '\u22122' to '14'. The line representing 'Resilience' is green, spanning the length of the plot with tick points at half unit intervals on the x-axis." } ], "id_raw": 1075 }, { "id": "Mq80VYtFpGLyOWeO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the region encompassing [0.095, 0.295, 0.960, 0.462]." }, { "from": "gpt", "value": "This area hosts the subplot for 'Motivation' detailed with a blue line graph. The x-axis is labeled 'Time', y-axis labeled 'Value', and points plotted at regular intervals. It also includes 'Motivation' label." } ], "id_raw": 1075 }, { "id": "MfrOaffUN6tVYvBB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_15.png", "conversations": [ { "from": "human", "value": "\nProvide the content breakdown of the area [0.095, 0.048, 0.960, 0.215]." }, { "from": "gpt", "value": "This section contains the 'Procrastination Overtime' subplot with a pale orange line representing the trend of 'Procrastination'. Key axis labels include 'Time' on the x-axis, 'Value' on the y-axis, and several distinct tick labels on both axes." } ], "id_raw": 1075 }, { "id": "4YSovHDVAZ8lNu8m", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_16.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label} of the y-axis in the first subplot[0.025, 0.057, 0.048, 0.190]?" }, { "from": "gpt", "value": "Progress Measurement." } ], "id_raw": 1076 }, { "id": "f7EP5V1mMQM72IuT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the style of {the fourth x-tick label} in the second subplot[0.447, 0.466, 0.462, 0.477]?" }, { "from": "gpt", "value": "Bold." } ], "id_raw": 1076 }, { "id": "OmMBQMoWIn1o7Ajl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_16.png", "conversations": [ { "from": "human", "value": "\nWhat is {the title} located at[0.406, 0.260, 0.671, 0.275] in the second subplot?" }, { "from": "gpt", "value": "Substantial Growth." } ], "id_raw": 1076 }, { "id": "hxePEz2mvEo2jY9k", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_16.png", "conversations": [ { "from": "human", "value": "\nIs {the end point of the line for 'Application A'}[0.960, 0.056] higher than {the start point of the line for 'Application B'}[0.117, 0.436] on the chart?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1076 }, { "id": "DCsGMSlXFPtYQUsj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_16.png", "conversations": [ { "from": "human", "value": "\nWhich has a darker color, {the point representing '10.0' on Application A}[0.960, 0.056] or {the point representing '0.0' on Application B}[0.117, 0.436]?" }, { "from": "gpt", "value": "Application B." } ], "id_raw": 1076 }, { "id": "8nNi5l8YgAqmc0sl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_16.png", "conversations": [ { "from": "human", "value": "\nWhich label is at a higher position, {the label of '55'}[0.080, 0.300, 0.109, 0.312] from the third subplot or {the label of '25'}[0.080, 0.594, 0.109, 0.606] from the third subplot?" }, { "from": "gpt", "value": "25." } ], "id_raw": 1076 }, { "id": "h6q18ctSzVdNB1AC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found in the region[0.000, 0.260, 1.000, 0.482]?" }, { "from": "gpt", "value": "This region contains the entire second subplot titled \"Substantial Growth\" with various elements like x-axis labeled 'Time (in terms of an arbitrary unit)', y-axis labeled 'Progress Measurement', {x-tick labels}[0.110, 0.466, 0.124, 0.477], and {y-tick labels}[0.080, 0.457, 0.109, 0.469], as well as {the Application B line}[0.117, 0.284, 0.960, 0.446]." } ], "id_raw": 1076 }, { "id": "taiLJCB7S0qXG6xN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_16.png", "conversations": [ { "from": "human", "value": "\nProvide details for the area located at[0.100, 0.013, 0.900, 0.235]." }, { "from": "gpt", "value": "This is the first subplot area displaying a line chart for 'Application A'. The chart includes the x-axis titled 'Time (in terms of an arbitrary unit)', y-axis titled 'Progress Measurement', {y-tick labels}[0.058, 0.210, 0.109, 0.222], and {line points}[0.117, 0.154]." } ], "id_raw": 1076 }, { "id": "haujW6BiZGllnYJK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_16.png", "conversations": [ { "from": "human", "value": "\nExplain the features within[0.000, 0.713, 1.000, 0.955]." }, { "from": "gpt", "value": "This region encompasses the fourth subplot which is labeled 'Periodic Spikes'. Key features include 'Time (in terms of an arbitrary unit)' on the x-axis, 'Progress Measurement' on the y-axis, and {line intervals for Application D}[0.117, 0.955, 0.960, 0.800]." } ], "id_raw": 1076 }, { "id": "HAOL6fhC6tCzz7jN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object within the coordinates [0.088, 0.012, 0.970, 0.224]?" }, { "from": "gpt", "value": "Plot area." } ], "id_raw": 1077 }, { "id": "rYUKjjtDkxnMlrE7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at [0.030, 0.089, 0.047, 0.147] refer to?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1077 }, { "id": "g5Iu0X44EXWVySV7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the role of the object located at [0.030, 0.333, 0.047, 0.391]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1077 }, { "id": "BmaU86G6h2rUIQTL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_17.png", "conversations": [ { "from": "human", "value": "\nWhat number appears on {the x-axis tick}{r}[0.082, 0.228, 0.093, 0.239]{r}?" }, { "from": "gpt", "value": "0." } ], "id_raw": 1077 }, { "id": "bOdJFkzj7sKbJcJm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_17.png", "conversations": [ { "from": "human", "value": "\nIs the numerical value at {the y-axis tick}[0.054, 0.034, 0.082, 0.046] higher or lower than that at {another y-axis tick}[0.054, 0.219, 0.082, 0.231]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1077 }, { "id": "8MTDRB7dY3GKHJrq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_17.png", "conversations": [ { "from": "human", "value": "\nWhich y-axis tick represents a larger number, the one at {coordinates}{r}[0.054, 0.140, 0.082, 0.152]{r} or {another point}{r}[0.054, 0.219, 0.082, 0.231]{r}?" }, { "from": "gpt", "value": "The first." } ], "id_raw": 1077 }, { "id": "25nOECnjuLvPoFMX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.088, 0.012, 0.970, 0.224]." }, { "from": "gpt", "value": "This region displays a line chart representing the popularity of 'Topic Modelling' over time, highlighted in cyan color. Time progresses along the x-axis from 0 to 10, and popularity values are recorded on the y-axis." } ], "id_raw": 1077 }, { "id": "pF8vu16LUjQre0V8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_17.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the area bounded by the coordinates [0.088, 0.256, 0.970, 0.468]?" }, { "from": "gpt", "value": "The area includes a line chart tracking the popularity of 'Neural Networks' colored in purple across a time sequence on the x-axis." } ], "id_raw": 1077 }, { "id": "tztmfHZtItBFrlem", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements present in the section [0.088, 0.744, 0.970, 0.955]." }, { "from": "gpt", "value": "In this section, there is a line chart. It illustrates the tracking of 'Reinforcement Learning', color-coded in green, across various time points, with x-axis titled 'Time'. Popularity values are marked on the y-axis." } ], "id_raw": 1077 }, { "id": "mxFTnb0PdchWlQ7x", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the point on the Consumer Confidence line for the year 2015}[0.584, 0.071]?" }, { "from": "gpt", "value": "#ff00ff." } ], "id_raw": 1078 }, { "id": "48EaKPG6wu5zcsyS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_18.png", "conversations": [ { "from": "human", "value": "\nWhat object does the text \"2020\" refer to in the area [0.907, 0.960, 0.965, 0.971]?" }, { "from": "gpt", "value": "x-axis tick label." } ], "id_raw": 1078 }, { "id": "9gjv8tln9PHqD4Xd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_18.png", "conversations": [ { "from": "human", "value": "\nWhat is {the highest point on the Stock Market line}[0.936, 0.549] corresponding to?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 1078 }, { "id": "AvHVmVCRgicHiytv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_18.png", "conversations": [ { "from": "human", "value": "\nIs the value of {Consumer Confidence in 2012}[0.372, 0.177] higher than the value of {Inflation Rate in the same year}[0.372, 0.416]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1078 }, { "id": "u6KFzx7bFVcyvz6u", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_18.png", "conversations": [ { "from": "human", "value": "\nWhich is lower, {the GDP Growth rate in 2010}[0.231, 0.936] or {the Stock Market index in 2010}[0.231, 0.661]?" }, { "from": "gpt", "value": "GDP Growth." } ], "id_raw": 1078 }, { "id": "RaYxUambrbsSJVMs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_18.png", "conversations": [ { "from": "human", "value": "\nDoes the {Stock Market index for 2017}[0.725, 0.581] exceed the {GDP Growth for 2017}[0.725, 0.827]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1078 }, { "id": "APObiuX9Jg8tH1g0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the region around [0.292, 0.013, 0.804, 0.028]." }, { "from": "gpt", "value": "This region contains the subtitle 'Consumer Confidence over the Years' with text colored at .15." } ], "id_raw": 1078 }, { "id": "7f9Dcva6AYaLhVv7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_18.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the area spanning [0.352, 0.259, 0.744, 0.274]?" }, { "from": "gpt", "value": "The region includes the subtitle 'Inflation Rate over the Years', presented in a dark grey color." } ], "id_raw": 1078 }, { "id": "9wAuhYgL2IRLBt2j", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_18.png", "conversations": [ { "from": "human", "value": "\nGive details about the area [0.355, 0.506, 0.742, 0.521]." }, { "from": "gpt", "value": "This area holds the subtitle 'Stock Market over the Years', shown in a dark grey font specifically positioned in this section." } ], "id_raw": 1078 }, { "id": "pZoJshRsbnBvXwHy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of {the ylabel}[0.025, 0.051, 0.048, 0.181] in the chart?" }, { "from": "gpt", "value": "Energy allocation in %." } ], "id_raw": 1079 }, { "id": "NE8WrOt8Cx0HhrpI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the subtitle}[0.528, 0.010, 0.528, 0.010] in the chart?" }, { "from": "gpt", "value": ".15 (gray)." } ], "id_raw": 1079 }, { "id": "L9k7mZgk0gIHXVJu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_54_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the fifth {x-axis tick label}[0.749, 0.219, 0.808, 0.231]?" }, { "from": "gpt", "value": "2028." } ], "id_raw": 1079 }, { "id": "72pjqV2c2VKTagFm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_19.png", "conversations": [ { "from": "human", "value": "\nIs {the point at '2020'}[0.110, 0.195] higher or lower in value compared to {the point at '2021'}[0.193, 0.191]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1079 }, { "id": "Ig0o7FmwgedDnvDZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_19.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Efficient Energy'}[0.110, 0.067, 0.946, 0.195] end higher or lower than {the line for 'Nuclear Energy'}[0.110, 0.622, 0.946, 0.659]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1079 }, { "id": "QayHeZTG67QOCrto", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_54_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the label color of {the text for 'Efficient Energy'}[0.742, 0.027, 0.925, 0.039] in the legend?" }, { "from": "gpt", "value": "#ff00ff." } ], "id_raw": 1079 }, { "id": "Vj4zmcf9GAbDA203", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the content inside the region [0.110, 0.017, 0.946, 0.215]." }, { "from": "gpt", "value": "This region contains the x-axis marked 'Year' with labels ranging from 2020 to 2030 and the y-axis labeled 'Energy allocation in %'. The background color is pale blue, and multiple horizontal grid lines are visible." } ], "id_raw": 1079 }, { "id": "DNZGyC9lb1R0wfdm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_19.png", "conversations": [ { "from": "human", "value": "\nWhat can be represented in the section [0.110, 0.266, 0.946, 0.462]?" }, { "from": "gpt", "value": "This area represents the subplot for Non-renewable Energy with corresponding axes labeled for 'Year' and 'Energy allocation in %'. The plot displays a declining trend in data points from year 2020 to 2030." } ], "id_raw": 1079 }, { "id": "TrNtfxZgg5jJknDI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_54_19.png", "conversations": [ { "from": "human", "value": "\nCan you provide details in the space [0.110, 0.758, 0.946, 0.955]?" }, { "from": "gpt", "value": "This portion outlines the subplot for Renewable Energy, featuring an upward trend line starting from the lowest value in 2020 to the highest in 2030. The axes are labeled corresponding to the years and percentage of energy allocation." } ], "id_raw": 1079 }, { "id": "hSfzp3ZDpcGVOTao", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_0.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label}[0.017, 0.200, 0.039, 0.228] on the y-axis represent?" }, { "from": "gpt", "value": "75000." } ], "id_raw": 1080 }, { "id": "xIp1WPRMnOLaFO4H", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the color for the {line representing 'Social Welfare'}[0.720, 0.094, 0.975, 0.796]?" }, { "from": "gpt", "value": "#8cf2a5." } ], "id_raw": 1080 }, { "id": "ZQQjfJ7SnoaxSzj3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_0.png", "conversations": [ { "from": "human", "value": "\nWhich year corresponds to {the tick}[0.212, 0.893]?" }, { "from": "gpt", "value": "2009." } ], "id_raw": 1080 }, { "id": "dOS8HkGN40b1nL97", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_0.png", "conversations": [ { "from": "human", "value": "\nIs {the point for the year 2002}[0.754, 0.466] above or below {the point for the year 2009}[0.873, 0.406] in the chart for Charity?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1080 }, { "id": "hfAdEeM8lD7dTTgA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_0.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {the x-tick for the year 2007}[0.839, 0.893], Social Welfare or Charity?" }, { "from": "gpt", "value": "Charity." } ], "id_raw": 1080 }, { "id": "5cZSq2TnfJcwYb9s", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the handle on the legend for 'Charity'}[0.046, 0.146, 0.064, 0.162] use a brighter or darker shade compared to {the handle on the legend for 'Social Welfare'}[0.046, 0.104, 0.064, 0.120]?" }, { "from": "gpt", "value": "Darker." } ], "id_raw": 1080 }, { "id": "mZeAhCTsIbcEciPv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in [0.203, 0.903, 0.251, 0.931]." }, { "from": "gpt", "value": "The x-axis labels for the years 2007 through 2009 are located here." } ], "id_raw": 1080 }, { "id": "s4BXnd0zdtcJ2pJ4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.034, 0.414, 0.051, 0.515]?" }, { "from": "gpt", "value": "This area shows a portion of the y-axis with labels for 65000 and 60000." } ], "id_raw": 1080 }, { "id": "lzJGlyk8voBF8XhU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the section [0.720, 0.485, 0.806, 0.625]." }, { "from": "gpt", "value": "The lines for Social Welfare and Charity intersect in this region, marking dramatic changes in donation amounts around the years 2005 to 2007." } ], "id_raw": 1080 }, { "id": "6C97NoIMSDkW2fmR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object at the coordinate [0.024, 0.445, 0.036, 0.467] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1081 }, { "id": "JlWTswTVtWKfd83b", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at [0.455, 0.798, 0.511, 0.826] represent?" }, { "from": "gpt", "value": "Meditation label." } ], "id_raw": 1081 }, { "id": "Ky38Dz0JufSufp5H", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_1.png", "conversations": [ { "from": "human", "value": "\nIdentify the object located at [0.172, 0.903, 0.175, 0.931]." }, { "from": "gpt", "value": "X-axis tick label '5'." } ], "id_raw": 1081 }, { "id": "qW7VJVPDWUIotsHi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Exercise' at month 1}[0.067, 0.715] above or below {the point for 'Meditation' at month 1}[0.067, 0.819]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1081 }, { "id": "JtMwnPHxnKTxQ53B", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_1.png", "conversations": [ { "from": "human", "value": "\nWhich {y-axis tick label}[0.683, 0.733, 0.698, 0.761] has a lower range value, 0.3 or 0.4?" }, { "from": "gpt", "value": "0.3." } ], "id_raw": 1081 }, { "id": "230ThW8nZCk7iRJg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_1.png", "conversations": [ { "from": "human", "value": "\nDoes {the last point in the \"Meditation\" series}[0.964, 0.462] have a higher or lower value than {the last point in the \"Exercise\" series}[0.964, 0.149]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1081 }, { "id": "6QJ6Qzn32hnA4xoq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.900, 0.780, 0.990, 0.880]." }, { "from": "gpt", "value": "This region contains the legend for the 'Advanced' level sub-plot, indicating two line types, one for Meditation colored in purple, and one for Exercise colored in green." } ], "id_raw": 1081 }, { "id": "u81LDVuPkC9KzDug", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.040, 0.075, 0.330, 0.930]." }, { "from": "gpt", "value": "This region encompasses the entire first subplot titled 'Beginner', including primary axes for the plot, both x and y axes labeled respectively, grid lines, and plotted data points for Meditation and Exercise over time periods." } ], "id_raw": 1081 }, { "id": "J7juJygxDzgMuEwd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_1.png", "conversations": [ { "from": "human", "value": "\nDescribe what is included within [0.350, 0.040, 0.660, 0.950]." }, { "from": "gpt", "value": "This portion of the chart holds the intermediate level subplot, showing axes labeled with well-being index and time period, gridlines, markers indicating data points, and legend details for the categories being represented: Meditation and Exercise." } ], "id_raw": 1081 }, { "id": "s50qfKnWbVo4BQG1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label of the y-axis} in the chart?" }, { "from": "gpt", "value": "Performance." } ], "id_raw": 1082 }, { "id": "rvIHuqQ63zSBpPi9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_2.png", "conversations": [ { "from": "human", "value": "\nCan you identify the coordinate of the second x-axis tick label?" }, { "from": "gpt", "value": "[0.075, 0.903, 0.081, 0.931]" } ], "id_raw": 1082 }, { "id": "u9SkD0MvStoNkvf0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_2.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at coordinates [0.500, 0.496, 0.500, 0.496]?" }, { "from": "gpt", "value": "Data point for Period 5 on the 'Manager Performance' line." } ], "id_raw": 1082 }, { "id": "93Xrd55nv0WoPIrQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_2.png", "conversations": [ { "from": "human", "value": "\nIs the point at {Period 3}[0.108, 0.332] on {the line for 'Lead Performance'} lower than the point at {Period 6}[0.201, 0.192]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1082 }, { "id": "J3TvPGZHEI3cuvvB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the line 'Lead Performance'} cover {lower values} at Period 3 compared to Period 7?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1082 }, { "id": "LK2BpppaGqeBQ3CS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_2.png", "conversations": [ { "from": "human", "value": "\nWhich of the performances, {Lead Performance} or {Manager Performance}, has higher value at {Period 9}?" }, { "from": "gpt", "value": "Lead Performance." } ], "id_raw": 1082 }, { "id": "NWaQxS37c2Ol8Vol", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_2.png", "conversations": [ { "from": "human", "value": "\nDescribe what is within the region [0.044, 0.903, 0.330, 0.931]." }, { "from": "gpt", "value": "This area contains x-axis tick labels ranging from '1' to '10' across three subplots, annotated sequentially." } ], "id_raw": 1082 }, { "id": "FW0b1nfwvsy5AJmA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_2.png", "conversations": [ { "from": "human", "value": "\nWhat can be described in the region [0.340, 0.396, 0.530, 0.972] in the chart?" }, { "from": "gpt", "value": "This section encompasses the y-axis labeled 'Performance', along with detailed tick labels displaying performance numeric values such as 0.40, 0.45, up to 0.70." } ], "id_raw": 1082 }, { "id": "KgvSvUISUumIpW4y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_2.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the space [0.340, 0.089, 0.702, 0.181]?" }, { "from": "gpt", "value": "Within these confines, there are legends for 'Lead Performance' and 'Manager Performance', labeled with designated color markers for each performance type on graph." } ], "id_raw": 1082 }, { "id": "XiF3tH0LnS0ig8OY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_3.png", "conversations": [ { "from": "human", "value": "\nWhat does {the 2nd y-tick label}[0.353, 0.678, 0.368, 0.706] represent?" }, { "from": "gpt", "value": "0.5." } ], "id_raw": 1083 }, { "id": "Ivt5lFN3C5HmUvvh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing 'Theatre Art'}[0.055, 0.143, 0.316, 0.443]?" }, { "from": "gpt", "value": "#0072b2." } ], "id_raw": 1083 }, { "id": "ZjWwDlm35SUAZ83k", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at {the x-tick label position}[0.378, 0.903, 0.393, 0.931]?" }, { "from": "gpt", "value": "0.1." } ], "id_raw": 1083 }, { "id": "8FjSukLEuf6fzWVU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_3.png", "conversations": [ { "from": "human", "value": "\nIs {the 0.6 labelled point}[0.200, 0.334] higher or lower than {the 0.2 labelled point}[0.084, 0.197] on the 'Theatre Art' graph?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1083 }, { "id": "nbDsjPuPDKWFDYhX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_3.png", "conversations": [ { "from": "human", "value": "\nIs {the starting point}[0.055, 0.143] of 'Theatre Art' more to the left or right of {the starting point}[0.385, 0.143] of 'Theatre Art' in the second subplot?" }, { "from": "gpt", "value": "Left." } ], "id_raw": 1083 }, { "id": "1rcFxCqAJTt8TdUA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_3.png", "conversations": [ { "from": "human", "value": "\nIs {the end point of 'Modern Art'}[0.976, 0.511] at a higher position compared to {the end point of 'Theatre Art'}[0.976, 0.443]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1083 }, { "id": "ou94HZ81juEhICW7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_3.png", "conversations": [ { "from": "human", "value": "\nWhat features are visible in the area designated as [0.010, 0.303, 0.330, 0.893]?" }, { "from": "gpt", "value": "The axes, x-grid and y-grid are visible here, which are components of the subplot hosting the 'Initial Stage' graph with x-label 'Time Progression' and y-label 'Industry Development', displaying lines for 'Theatre Art' and 'Modern Art'." } ], "id_raw": 1083 }, { "id": "CjbiIfaEv5PsWgLq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.371, 0.075, 0.660, 0.893]." }, { "from": "gpt", "value": "This area includes the subplot for 'Middle Stage', with visual elements such as axes, x-grid, y-grid, titled 'Time Progression' for x-axis and 'Industry Development' for y-axis. It displays lines representing 'Theatre Art' and 'Modern Art' with their respective data points." } ], "id_raw": 1083 }, { "id": "OlmOG967QS48GOqz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_3.png", "conversations": [ { "from": "human", "value": "\nCan you elaborate on what is within [0.701, 0.075, 0.990, 0.893]?" }, { "from": "gpt", "value": "This region comprises the subplot titled 'Final Stage', including axes, a visible x-grid and y-grid, labels 'Time Progression' for the x-axis, and 'Industry Development' for the y-axis. It features lines for 'Theatre Art' and 'Modern Art', marked with their progression points." } ], "id_raw": 1083 }, { "id": "lRqmJU1YEuP6rT3m", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.351, 0.062, 0.369, 0.090] in the subplot_2 part of the chart?" }, { "from": "gpt", "value": "A y-axis tick label: '100'." } ], "id_raw": 1086 }, { "id": "PsY1NZjV7QS6fKPs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the point marker for the line representing 'Running' in subplot_1 of the chart?" }, { "from": "gpt", "value": "The color is '#ff7f0e'." } ], "id_raw": 1086 }, { "id": "j2OWv8IumqbcHryC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_6.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the object with bounding box coordinates of [0.561, 0.085, 0.799, 0.865]?" }, { "from": "gpt", "value": "This is the second subplot of the chart, depicting performance data from 'April - August'." } ], "id_raw": 1086 }, { "id": "H6NMqN9xByEflxxM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the weightlifting line in subplot_2}[0.712, 0.112, 0.961, 0.138] depict an increasing or decreasing trend?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 1086 }, { "id": "2kZetvZhkd4PAqHh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_6.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Weightlifting' data point for '01-03-2020'}[0.222, 0.193] higher or lower than {the 'Running' data point for '01-03-2020'}[0.222, 0.848] in subplot_0?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1086 }, { "id": "ySmRNOc5xhCZ0dFE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_6.png", "conversations": [ { "from": "human", "value": "\nIn subplot_0, does {the 'Weightlifting' trend}[0.056, 0.128, 0.305, 0.209] have a higher or lower value than {the 'Running' trend}[0.056, 0.848, 0.305, 0.856] in March (3rd data point)?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1086 }, { "id": "XkByd9tMupfkbxhw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_6.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area defined by [0.044, 0.075, 0.317, 0.893]?" }, { "from": "gpt", "value": "This area depicts the first subplot of the chart. It displays the performance data trends for 'Weightlifting' and 'Running' for the months 'January - March' with corresponding x-axis and y-axis labels and a legend." } ], "id_raw": 1086 }, { "id": "s21QKJkvZmjZNZud", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_6.png", "conversations": [ { "from": "human", "value": "\nDescribe what's in the area [0.686, 0.903, 0.987, 0.931]." }, { "from": "gpt", "value": "In this area, there are two x-axis labels, '01-11-2020' and '01-12-2020', for the third subplot of the chart." } ], "id_raw": 1086 }, { "id": "toYUpvDlUPm4k4iA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_6.png", "conversations": [ { "from": "human", "value": "\nPlease detail what things are inside the box [0.951, 1.032, 0.985, 1.122]." }, { "from": "gpt", "value": "There are two things: the x-axis label '01-09-2020' and the last point from the 'Running' line for '09-01-2020' in the subplot_2." } ], "id_raw": 1086 }, { "id": "9P8OF8WkK0K7MySS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in the chart?" }, { "from": "gpt", "value": "Score." } ], "id_raw": 1087 }, { "id": "3iRyB4eEWw1EnY4W", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_7.png", "conversations": [ { "from": "human", "value": "\nPlease provide the coordinates of the tick labeled 'Times' in subplot 0." }, { "from": "gpt", "value": "[0.260, 0.735]." } ], "id_raw": 1087 }, { "id": "1ooMsi7Hguktsoyx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the handle representing 'Public Opinion Score'}[0.058, 0.101, 0.088, 0.118] in subplot 0?" }, { "from": "gpt", "value": "#ff7f0e." } ], "id_raw": 1087 }, { "id": "j04LWrataPnK04iQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_7.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher score at point 4, {Media Influence Score}[0.176, 0.143] or {Public Opinion Score}[0.176, 0.420] in subplot 0?" }, { "from": "gpt", "value": "Media Influence Score." } ], "id_raw": 1087 }, { "id": "91bJ6fvCqtoqGxTB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_7.png", "conversations": [ { "from": "human", "value": "\nAt the tick located at {coordinates}[0.393, 0.735] in subplot 1, which label is displayed?" }, { "from": "gpt", "value": "CNN." } ], "id_raw": 1087 }, { "id": "VxJz6tfvAKUatKhR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_7.png", "conversations": [ { "from": "human", "value": "\nIs {the data point for CBS}[0.148, 0.231] higher or lower than {the point for FOX}[0.176, 0.143] in subplot 0?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1087 }, { "id": "cMr6c0hS57SfHiS9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.380, 0.030, 0.658, 0.735] within subplot 1." }, { "from": "gpt", "value": "It includes axes for plotting labeled 'Media Channel' along the x-axis and 'Score' on the y-axis, with data points and labels for media outlets like CNN, NBC, and CBS, where lines and markers indicate scores." } ], "id_raw": 1087 }, { "id": "A06hrLW31eGO4bVx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_7.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the region [0.710, 0.030, 0.988, 0.735] in subplot 2?" }, { "from": "gpt", "value": "This region contains a graph with x-axis labeled 'Media Channel' and y-axis 'Score'. Markers represent various media channels such as ABC and HuffPost, with data points plotted to represent scores." } ], "id_raw": 1087 }, { "id": "bfC5XM65F1wYJiPa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_7.png", "conversations": [ { "from": "human", "value": "\nWhat is featured in [0.051, 0.030, 0.329, 0.735] in subplot 0?" }, { "from": "gpt", "value": "This area hosts a chart detailing media channels against scores with elements like grid lines, axis labels, and color-coded lines representing different data series, specifically 'Media Influence Score' and 'Public Opinion Score'." } ], "id_raw": 1087 }, { "id": "wB0zMRGUQqZEu8yi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the marker type of the {line for 'CO2 Emissions'}[0.194, 0.805, 0.515, 0.864] at the point {2016}[0.600, 0.775]?" }, { "from": "gpt", "value": "v." } ], "id_raw": 1088 }, { "id": "XqHzpXCT1Ny7YkjF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the y-axis in {subplot 0}[0.038, 0.075, 0.336, 0.893]?" }, { "from": "gpt", "value": "Average Life Expectancy vs CO2 Emissions." } ], "id_raw": 1088 }, { "id": "m8uF6qBuQmadHUpM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_8.png", "conversations": [ { "from": "human", "value": "\nWhat color is used for the {Average Life Expectancy line}[0.052, 0.080, 0.322, 0.179] in the first subplot?" }, { "from": "gpt", "value": "#9467bd." } ], "id_raw": 1088 }, { "id": "OyrLSproOWMGKtxR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_8.png", "conversations": [ { "from": "human", "value": "\nIs the {data point for 'CO2 Emissions' in 2019}[0.964, 0.759] above or below the {data point for 'Average Life Expectancy' in 2016}[0.922, 0.092]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1088 }, { "id": "JbnhYciveI1hj06n", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_8.png", "conversations": [ { "from": "human", "value": "\nAre the {CO2 Emissions markers}[0.373, 0.759, 0.643, 0.864] closer to the {bottom or top}[0.0, 0.0, 1.0, 1.0] of the chart in the second subplot?" }, { "from": "gpt", "value": "Bottom." } ], "id_raw": 1088 }, { "id": "sLTDOpeW3J7Db2wp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_8.png", "conversations": [ { "from": "human", "value": "\nDoes the {tick label at 2012}[0.544, 0.893] align more accurately with the {beginning or end}[0.359, 0.075, 0.657, 0.893] of the CO2 Emissions line in the second subplot?" }, { "from": "gpt", "value": "Beginning." } ], "id_raw": 1088 }, { "id": "yobp2l2m5HRkgvMY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found at coordinates [0.681, 0.903, 0.933, 0.931]." }, { "from": "gpt", "value": "The area contains x-axis labels for the years 2016, 2018, and 2020 arranged along with their corresponding tick points." } ], "id_raw": 1088 }, { "id": "30YEgXZ3nql9Wn2a", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_8.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the coordinate range [0.351, 0.186, 0.362, 0.782]?" }, { "from": "gpt", "value": "This narrow strip represents the y-axis of the second subplot, including y-tick points, devoid of any labels." } ], "id_raw": 1088 }, { "id": "CnUJoDXOZJLRauOr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_8.png", "conversations": [ { "from": "human", "value": "\nWhat are the visual elements located in the region [0.0, 0.075, 1.0, 0.893]?" }, { "from": "gpt", "value": "The entire chart background spanning all three subplots, consisting of grid lines, lines representing 'Average Life Expectancy' and 'CO2 Emissions', as well as data point markers and x-axis and y-axis labels at various points." } ], "id_raw": 1088 }, { "id": "mOzfAP6MntycypvK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the marked object at coordinates [0.511, 0.717, 0.525, 0.745]?" }, { "from": "gpt", "value": "Y-axis tick label." } ], "id_raw": 1089 }, { "id": "UfKBbgCrvlrYb4mM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_9.png", "conversations": [ { "from": "human", "value": "\nWhat is {the legend title} at [0.287, 0.089, 0.413, 0.181]?" }, { "from": "gpt", "value": "There is no title." } ], "id_raw": 1089 }, { "id": "CpJG4BBHIm5SvAJg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_9.png", "conversations": [ { "from": "human", "value": "\nWhat content is related to {the object} at [0.511, 0.080, 0.525, 0.108]?" }, { "from": "gpt", "value": "Y-axis tick label." } ], "id_raw": 1089 }, { "id": "bCnCNbXjb85noGf6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_9.png", "conversations": [ { "from": "human", "value": "\nIs {the point marked '1' in Q1}[0.038, 0.784] higher on the grid than the point marked '4' in Q1[0.254, 0.129]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1089 }, { "id": "9lmdeefy3jU3OaTn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_9.png", "conversations": [ { "from": "human", "value": "\nDoes {the last y-axis tick} at [0.770, 0.001, 0.784, 0.016] represent a higher or lower value compared to the y-axis tick at[0.770, 0.186, 0.784, 0.214]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1089 }, { "id": "lbp1kPPtjGUOhqJi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_9.png", "conversations": [ { "from": "human", "value": "\nWhich has a greater y-coordinate on the plotting grid, {the point marked '1' on the line for 'Tech Company 1' in Q3}[0.528, 0.835] or {the point marked '4' on the line for 'Innovative Business 1' in Q3}[0.743, 0.250]?" }, { "from": "gpt", "value": "Point 1." } ], "id_raw": 1089 }, { "id": "Ri60rXzZd1UMCHwg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the area [0.750, 0.880, 0.999, 0.940]." }, { "from": "gpt", "value": "The region contains the xlabel \"Quarters\" styled in a pale gray font." } ], "id_raw": 1089 }, { "id": "wM5PgXOCwwF4xS2J", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are included in the region [0.075, 0.905, 0.999, 0.999]?" }, { "from": "gpt", "value": "This region contains x-axis labels '1', '2', '3', '4' across quarters distributed along the bottom of the chart with gradually increasing x-coordinates." } ], "id_raw": 1089 }, { "id": "OUYZdtgZRSG85Olm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.200, 0.100, 0.400, 0.400]." }, { "from": "gpt", "value": "This region includes parts of the background grid for the chart, rendered in a faint gray color, aiding in visual alignment of plotted data points." } ], "id_raw": 1089 }, { "id": "ma7Ng7ShlhXrMUxK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the value at {the last point on the Instagram line in the subplot titled 'Rapid recovery'}[0.976, 0.934]?" }, { "from": "gpt", "value": "114." } ], "id_raw": 1090 }, { "id": "HNcF9TBWcqQrl7pw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_10.png", "conversations": [ { "from": "human", "value": "\nWhich social media does the blue marker represent in the first subplot?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 1090 }, { "id": "bd2CyODmQjQjZVrH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the color used for marking {the Twitter line in the subplot labeled 'Slow expansion'}[0.576, 0.844, 0.600, 0.861]?" }, { "from": "gpt", "value": "Dark Blue." } ], "id_raw": 1090 }, { "id": "IywvdKAdrLU495mE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_10.png", "conversations": [ { "from": "human", "value": "\nWhich line starts at a higher point at x=0.1, the Instagram or Twitter line in the 'Volatile movement' chart?" }, { "from": "gpt", "value": "Twitter." } ], "id_raw": 1090 }, { "id": "boVudsOyEDA9h57F", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_10.png", "conversations": [ { "from": "human", "value": "\nIn {the subplot labeled 'Rapid recovery'}, is the ending y-value for {the Instagram line}[0.976, 0.934] higher than the ending y-value for {the Twitter line}[0.976, 0.443]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1090 }, { "id": "qFwLOfKMueSH2vAb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_10.png", "conversations": [ { "from": "human", "value": "\nAre the markers for the Instagram line in {the second subplot}[0.576, 0.802, 0.600, 0.819] squares or circles?" }, { "from": "gpt", "value": "Squares." } ], "id_raw": 1090 }, { "id": "2askCnAhzmU2vATc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the section in [0.340, 0.371, 0.349, 0.597]." }, { "from": "gpt", "value": "This is the y-axis area of the subplot titled 'Slow expansion'. It contains labels for follower growth values such as 120, 125, 130, and is formatted in dark color text." } ], "id_raw": 1090 }, { "id": "q6OrAanScfSvdcPs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_10.png", "conversations": [ { "from": "human", "value": "\nWhat essential elements are within [0.0, 0.075, 0.660, 0.893]?" }, { "from": "gpt", "value": "The area includes two subplots, 'Slow expansion' and 'Rapid recovery', displaying line graphs for Instagram and Twitter follower growth with respective titles and axes labels." } ], "id_raw": 1090 }, { "id": "drBM5ZwA8vAtAhzk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe the chart content and format in the area [0.044, 0.371, 0.330, 0.893]?" }, { "from": "gpt", "value": "The outlined region features the main plotting area of the first subplot 'Volatile movement'. It includes the gridlines and two lines representing the follower growth of Instagram and Twitter, marked by red and blue colors respectively." } ], "id_raw": 1090 }, { "id": "qALFDEsqgRjWK1Be", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_11.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted at {coordinates}[0.042, 0.903, 0.066, 0.931] on the chart?" }, { "from": "gpt", "value": "Year label '2010'." } ], "id_raw": 1091 }, { "id": "ZdXyVh722HvMQcwC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_11.png", "conversations": [ { "from": "human", "value": "\nIdentify the marker color for {Citizenship Performance}[0.046, 0.104, 0.071, 0.120]." }, { "from": "gpt", "value": "Blue." } ], "id_raw": 1091 }, { "id": "Tm4Pl1Cb8dnFcHNR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_11.png", "conversations": [ { "from": "human", "value": "\nWhat type of plot is located at [0.702, 0.075, 0.988, 0.893]?" }, { "from": "gpt", "value": "Line plot." } ], "id_raw": 1091 }, { "id": "kM3Vz9VpONW9Xa7T", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_11.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {year 2050 location}[0.975, 0.893], Citizenship or Amendment Performance?" }, { "from": "gpt", "value": "Citizenship Performance." } ], "id_raw": 1091 }, { "id": "68IojeWaGYPvheDD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_11.png", "conversations": [ { "from": "human", "value": "\nIs {Amendment Performance point in 2020}[0.449, 0.312] above or below {Citizenship Performance point at the same year}[0.449, 0.602]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1091 }, { "id": "lXqUESCx2oRSuYjp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the first y-label '0.5'}[0.023, 0.954, 0.038, 0.982] appear above or below {the last y-label '1.0'}[0.023, 0.046, 0.038, 0.074]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1091 }, { "id": "Ga6yQrUwuu7dRWAq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the information in the region [0.046, 0.089, 0.197, 0.181]." }, { "from": "gpt", "value": "This area encompasses the sublegend with labels {Citizenship Performance}[0.075, 0.100, 0.188, 0.128] and {Amendment Performance}[0.075, 0.142, 0.194, 0.170], both depicted alongside their respective marker colors." } ], "id_raw": 1091 }, { "id": "1vzXZwMHkCcwDdjn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_11.png", "conversations": [ { "from": "human", "value": "\nWhat content is present between the area [0.372, 0.903, 0.657, 0.931]?" }, { "from": "gpt", "value": "This region contains year labels '2010' to '2050' on the x-axis, specifically ranging from {2010}[0.372, 0.903, 0.396, 0.931] to {2050}[0.633, 0.903, 0.657, 0.931]." } ], "id_raw": 1091 }, { "id": "R05At9YhY65bP63H", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_11.png", "conversations": [ { "from": "human", "value": "\nWhat elements are contained within the graphical coordinates [0.714, 0.075, 0.975, 0.893]?" }, { "from": "gpt", "value": "It includes a line plot for Overall Performance Change, featuring {Citizenship and Amendment performance lines}[0.714, 0.330, 0.975, 0.783], as well as associated x and y-axis labels and year labels set across the bottom." } ], "id_raw": 1091 }, { "id": "lso4Wsw8lwpn7VCj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the function of the object at [0.154, 0.030, 0.217, 0.066] in the chart?" }, { "from": "gpt", "value": "Subtitle." } ], "id_raw": 1093 }, { "id": "xU8NainWkT1txdjL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the legend handle for 'KEI'}[0.046, 0.104, 0.071, 0.120]?" }, { "from": "gpt", "value": "#1f77b4." } ], "id_raw": 1093 }, { "id": "KbsakMmgT2FFtCn7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_13.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at {the ylabel coordinates}[0.010, 0.396, 0.019, 0.572]?" }, { "from": "gpt", "value": "Performance." } ], "id_raw": 1093 }, { "id": "yQDvdwsS1OKhNIsC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_13.png", "conversations": [ { "from": "human", "value": "\nIs {the first point of KEI}[0.055, 0.447] above {the first point of Innovation Industries}[0.055, 0.715]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1093 }, { "id": "JNyNk4heUYn3X95L", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_13.png", "conversations": [ { "from": "human", "value": "\nWhich label is at a higher position, {0.2 Economy Ratio x-tick label}[0.077, 0.903, 0.092, 0.931] or {0.9 Economy Ratio x-tick label}[0.939, 0.903, 0.954, 0.931]?" }, { "from": "gpt", "value": "Equal." } ], "id_raw": 1093 }, { "id": "aBHSHs91AXJ4wMjj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_13.png", "conversations": [ { "from": "human", "value": "\nAt {0.6 Economy Ratio x-tick}[0.200, 0.893], which line point is higher, KEI or Innovation Industries?" }, { "from": "gpt", "value": "KEI." } ], "id_raw": 1093 }, { "id": "z8Y4FMcJfsBB0hF4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the features within the region [0.300, 0.075, 0.650, 0.300]." }, { "from": "gpt", "value": "This region includes part of the chart's plot area with grid lines and points from both KEI and Innovation Industries lines, showing increasing trends." } ], "id_raw": 1093 }, { "id": "piuRmTyViJQsMixh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the area from [0.710, 0.870, 0.990, 0.930] include?" }, { "from": "gpt", "value": "The area contains x-axis labels for the 'Economy Ratio r' from 0.6 to 1.0 with respective tick marks and labels positioned below the lines." } ], "id_raw": 1093 }, { "id": "akxxY9uExIqf4NGO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_13.png", "conversations": [ { "from": "human", "value": "\nOutline the contents found within [0.020, 0.180, 0.300, 0.950]" }, { "from": "gpt", "value": "This contains the y-axis with labels of performance metrics from 0.4 to 0.9, y-axis grid lines, and part of the plot area, including some of the Innovation Industries line." } ], "id_raw": 1093 }, { "id": "q59NjJynZ4JtGxZW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the object marked with a handle at [0.058, 0.067, 0.077, 0.067]?" }, { "from": "gpt", "value": "The handle represents the 'End Price of Stock'." } ], "id_raw": 1094 }, { "id": "mujOkjtJomz3DcnE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_14.png", "conversations": [ { "from": "human", "value": "\nWhat is plotted at {the point}[0.802, 0.095] in the chart?" }, { "from": "gpt", "value": "Financial Market Index." } ], "id_raw": 1094 }, { "id": "r3f2EKP5eR4szt5c", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the coordinate [0.392, 0.849] represent?" }, { "from": "gpt", "value": "The start of the 'End Price of Stock' line." } ], "id_raw": 1094 }, { "id": "JKS4mRYbze64N1BX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_14.png", "conversations": [ { "from": "human", "value": "\nIs {the handle labeled 'End Price of Stock'}[0.058, 0.067] above or below {the handle labeled 'Financial Market Index'}[0.058, 0.109]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1094 }, { "id": "g05vmoSRlU0GVKpZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_14.png", "conversations": [ { "from": "human", "value": "\nIs the value plotted at {the point}[0.301, 0.854] higher or lower than the value at {the point}[0.301, 0.069]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1094 }, { "id": "OieNg3yasIzPycEb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the mark at point}[0.631, 0.069] correspond to a higher or lower value than {the mark at point}[0.631, 0.854]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1094 }, { "id": "PBKwb8XE26NCyGhE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the visual elements found within [0.355, 0.031, 0.643, 0.092]." }, { "from": "gpt", "value": "This area encompasses x-axis labels '2018-05-01' {at}[0.365, 0.903, 0.418, 0.931], '2018-06-01' {at}[0.445, 0.903, 0.498, 0.931], '2018-07-01' {at}[0.525, 0.903, 0.578, 0.931], '2018-08-01' {at}[0.604, 0.903, 0.658, 0.931] and corresponding x-grid lines." } ], "id_raw": 1094 }, { "id": "sUtmRFKRXCoZ2313", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_14.png", "conversations": [ { "from": "human", "value": "\nWhat elements are inside the coordinates [0.380, 0.030, 0.643, 0.893]?" }, { "from": "gpt", "value": "The subplot includes axes, x-grid lines, y-grid lines, x-axis and y-axis labels, x-ticks, y-ticks, a sublegend consisting of labels and handles for 'End Price of Stock' and 'Financial Market Index', and the two colored lines depicting the data trends for these labels." } ], "id_raw": 1094 }, { "id": "v0v47NUgaUOjKDeY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be described about the region [0.050, 0.030, 0.313, 0.893]?" }, { "from": "gpt", "value": "This region contains axes, x-grid lines, y-grid lines, labeled x-ticks and y-ticks, x-axis and y-axis labels where 'Value' and 'Time Period in Months' are written, a sublegend for 'End Price of Stock' and 'Financial Market Index', with their respective colored handles grouped together." } ], "id_raw": 1094 }, { "id": "pThKj8iF1NNEHiIl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the function of the area marked with coordinates [0.371, 0.075, 0.660, 0.893]?" }, { "from": "gpt", "value": "This region represents the subplot for the line chart with the title {Inverse Trend of HEPHAR_G}[0.439, 0.030, 0.592, 0.067]." } ], "id_raw": 1095 }, { "id": "4v3dix9gmZKyyYsI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.340, 0.396, 0.349, 0.572] for the subplot displayed in the middle segment of the chart?" }, { "from": "gpt", "value": "Performance." } ], "id_raw": 1095 }, { "id": "KYtoejckSqACIU5t", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_15.png", "conversations": [ { "from": "human", "value": "\nWhat does the point at {coordinates}[0.142, 0.149] on {HEPHAR_S line}[0.385, 0.149, 0.646, 0.372] represent?" }, { "from": "gpt", "value": "It represents a value of 0.85 at the 0.4 ratio on the x-axis." } ], "id_raw": 1095 }, { "id": "EPt2CO8kuGkTiA60", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_15.png", "conversations": [ { "from": "human", "value": "\nDoes the value at {x = 0.8 point}[0.918, 0.283] on the {HEPHAR_S line}[0.715, 0.149, 0.976, 0.372] in the third subplot have a higher or lower performance compared to {x = 0.8 point}[0.588, 0.283] on the same line in the second subplot?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1095 }, { "id": "1hq2HU2zdFsxeVzY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_15.png", "conversations": [ { "from": "human", "value": "\nIs the 0.6 ratio value of {HEPHAR_S}[0.316, 0.372, 0.646, 0.372] greater than {HEPHAR_G}[0.316, 0.789, 0.646, 0.789] in the second subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1095 }, { "id": "sxvohsQSoYdkpFGB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_15.png", "conversations": [ { "from": "human", "value": "\nWhich series has a value above 0.7 at {x = 1.0 point}[0.316, 0.283] in the first subplot?" }, { "from": "gpt", "value": "HEPHAR_G." } ], "id_raw": 1095 }, { "id": "rkJtYrhqI8zDiytR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region framed by [0.371, 0.396, 0.660, 0.572]." }, { "from": "gpt", "value": "This area contains {the y-axis}[0.340, 0.396, 0.349, 0.572] with tick labels from 0.4 to 0.8 indicating performance levels for the {Inverse Trend of HEPHAR_G}[0.439, 0.030, 0.592, 0.067] subplot." } ], "id_raw": 1095 }, { "id": "rid0sMQuJRKo4llG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_15.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed within the boxed area [0.041, 0.089, 0.330, 0.181]?" }, { "from": "gpt", "value": "This region encloses {the legend}[0.046, 0.089, 0.129, 0.181] identifying colors and labels such as HEPHAR_S and HEPHAR_G." } ], "id_raw": 1095 }, { "id": "mK7kfwBJ8nZnpA8f", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the space bounded by [0.371, 0.01, 0.660, 0.07]." }, { "from": "gpt", "value": "This space showcases the subplot title {Inverse Trend of HEPHAR_G}[0.439, 0.030, 0.592, 0.067], stylized in a moderate font weight and dark color." } ], "id_raw": 1095 }, { "id": "surcby2PP2diLOat", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_16.png", "conversations": [ { "from": "human", "value": "\nWhat color represents {the line labeled 'Slow Recovery'}[0.749, 0.126, 0.968, 0.740]?" }, { "from": "gpt", "value": "#9467bd." } ], "id_raw": 1096 }, { "id": "z453K3bQZ1LpdZkv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_16.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is used for {the line \"Fluctuating Decline\"}[0.705, 0.126, 0.968, 0.637]?" }, { "from": "gpt", "value": "square." } ], "id_raw": 1096 }, { "id": "6JsiHX1XVpe4HS5X", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_16.png", "conversations": [ { "from": "human", "value": "\nWhat represents the point with coordinates [0.924, 0.330] on the chart?" }, { "from": "gpt", "value": "{the data point}[0.924, 0.330]." } ], "id_raw": 1096 }, { "id": "bEz55KPQTnHb9WxW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point at [0.749, 0.126]} higher or lower than {the point at [0.968, 0.279]} on the 'Slow Recovery' line?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1096 }, { "id": "G02Te33Z6qBpgbvh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_16.png", "conversations": [ { "from": "human", "value": "\nWhich line is {the point at [0.749, 0.126]} on, Slow Recovery or Fluctuating Decline?" }, { "from": "gpt", "value": "Slow Recovery." } ], "id_raw": 1096 }, { "id": "G0x4ipQl53ZHeVvs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_16.png", "conversations": [ { "from": "human", "value": "\nAt {the position [0.968, 0.463]}, which line is lower, 'Slow Recovery' or 'Fluctuating Decline'?" }, { "from": "gpt", "value": "Fluctuating Decline." } ], "id_raw": 1096 }, { "id": "B0nalYhEcFUDpgAV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the area bounded by [0.373, 0.075, 0.658, 0.893]." }, { "from": "gpt", "value": "This region includes an axis plot for the 'World Peace Initiative to Environmental Conservation' with the y-label as 'Fluctuating Decline' and the {x-label}[0.482, 0.942, 0.549, 0.970] as 'Slow Recovery'. Axes are visible and marked with {yticks}[0.349, 0.282, 0.373, 0.856]." } ], "id_raw": 1096 }, { "id": "G3w1IPsXki6VpKtm", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_16.png", "conversations": [ { "from": "human", "value": "\nWhat elements are present in the region [0.373, 0.787, 0.653, 0.879]?" }, { "from": "gpt", "value": "This region mainly contains the {legend}[0.531, 0.787, 0.653, 0.879] for the plot, with entries for 'Slow Recovery' and 'Fluctuating Decline'." } ], "id_raw": 1096 }, { "id": "IYn2EieVR25ctTP6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_16.png", "conversations": [ { "from": "human", "value": "\nExplain what is depicted in the rectangle [0.705, 0.075, 0.990, 0.893]." }, { "from": "gpt", "value": "The region encompasses the subplot titled 'Education Expansion to Human Rights' featuring tense data movement depicted by two lines for 'Slow Recovery' and 'Fluctuating Decline'. The area displays active grid visibility with appropriately marked {yticks}[0.687, 0.217, 0.705, 0.859] and {xticks}[0.738, 0.903, 0.978, 0.931]." } ], "id_raw": 1096 }, { "id": "kBESWcjIoZSN82N4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_17.png", "conversations": [ { "from": "human", "value": "\nWhat type of line represents {the Box Office Revenue} at [0.474, 0.706, 0.554, 0.726]?" }, { "from": "gpt", "value": "Dashed line." } ], "id_raw": 1097 }, { "id": "1mXueF9zf7wkNYfT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the marker for {Subscription Sales} at [0.374, 0.104, 0.398, 0.120]?" }, { "from": "gpt", "value": "Purple." } ], "id_raw": 1097 }, { "id": "7WoIaKrih1h7W2Bb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_17.png", "conversations": [ { "from": "human", "value": "\nHow many {xtick labels} can be found in the region [0.383, 0.903, 0.645, 0.931]?" }, { "from": "gpt", "value": "Seven." } ], "id_raw": 1097 }, { "id": "Ah8OUJCCJMC40CQ3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_17.png", "conversations": [ { "from": "human", "value": "\nIs the start of the {Subscription Sales line} at [0.065, 0.640, 0.144, 0.767] higher than the start of the {Box Office Revenue line} at [0.065, 0.697, 0.144, 0.707] in 1990?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1097 }, { "id": "O3OSGy3stWtlcumh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_17.png", "conversations": [ { "from": "human", "value": "\nWhich revenue source had a higher revenue increase from 1996 to 1997, {'Subscription Sales'}[0.884, 0.450] or {'Box Office Revenue'}[0.884, 0.726]?" }, { "from": "gpt", "value": "Subscription Sales." } ], "id_raw": 1097 }, { "id": "MZ6BOdiSgI0RPAax", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_17.png", "conversations": [ { "from": "human", "value": "\nDoes the {point at '1996'}[0.554, 0.450] represent a higher value for Subscription Sales than the {point at '1994'}[0.501, 0.591]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1097 }, { "id": "tawZklNSVCbPumsT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_17.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted in the section [0.668, 0.075, 0.990, 0.893]?" }, { "from": "gpt", "value": "It shows a graph for the years 1997 to 1999 detailing Subscription Sales and Box Office Revenue trends. Subscription Sales have {markers color purple}[0.704, 0.104, 0.728, 0.120], while Box Office Revenue has {markers color orange}[0.704, 0.146, 0.728, 0.162]. The lines show an upward trend for Subscription Sales and a downward trend for Box Office Revenue." } ], "id_raw": 1097 }, { "id": "KGiP2c41ME39Imt9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the coordinates [0.340, 0.343, 0.660, 0.625]." }, { "from": "gpt", "value": "The area displays a y-axis labeled 'Revenue (in billions)' with y-axis tick labels from '0' to '50' at regular intervals and colored in dark gray. Additionally, graphs for Subscription Sales and Box Office Revenue from 1994 to 1996 are shown, reflecting decreasing trends for Box Office and increasing for Subscription Sales." } ], "id_raw": 1097 }, { "id": "oZoiCYIdEGxFs1Ow", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_17.png", "conversations": [ { "from": "human", "value": "\nExplain the visual elements presented in the range [0.038, 0.075, 0.660, 0.893]." }, { "from": "gpt", "value": "This region covers two line charts corresponding to different time periods, '1990-1993' and '1994-1996', showing Subscription Sales and Box Office Revenue. Each chart has its own set of x and y axes, grid lines, and subtitles. The graphs indicate various trends in revenues, with distinct markers for each revenue type." } ], "id_raw": 1097 }, { "id": "FF7RaMmiLlmPPMpw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_18.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the 'Steady Increase Trend' in the legend?" }, { "from": "gpt", "value": "{color #9467bd}[0.044, 0.104, 0.068, 0.120]." } ], "id_raw": 1098 }, { "id": "7z5vojXisMl6UbSN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label for the chart?" }, { "from": "gpt", "value": "Trend Value." } ], "id_raw": 1098 }, { "id": "qXyxj1E1o70yxBfB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_18.png", "conversations": [ { "from": "human", "value": "\nWhat does {the tick mark}[0.065, 0.893] on the x-axis represent?" }, { "from": "gpt", "value": "The year 2011." } ], "id_raw": 1098 }, { "id": "BRvQpS0jFnOR2YAj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_18.png", "conversations": [ { "from": "human", "value": "\nDoes {the point for 2012 in the 'Steady Increase Trend'}[0.091, 0.702] have a higher value than {the point for 2012 in the 'Fluctuating Decline Trend'}[0.091, 0.266]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1098 }, { "id": "FvWiK0dfHvHG5qcE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_18.png", "conversations": [ { "from": "human", "value": "\nIs the starting point of {the 'Steady Increase Trend'}[0.065, 0.757] lower in value than the ending point[0.303, 0.157]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1098 }, { "id": "xLl0RrAEDllkHLJX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_18.png", "conversations": [ { "from": "human", "value": "\nWhich point is at a higher position, {2017 in 'Fluctuating Decline Trend'}[0.224, 0.566] or {2017 in 'Steady Increase Trend'}[0.224, 0.348]?" }, { "from": "gpt", "value": "2017 in 'Steady Increase Trend'." } ], "id_raw": 1098 }, { "id": "2D7nkGp9jZtb2616", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found within[0.292, 0.903, 0.315, 0.931]." }, { "from": "gpt", "value": "This area contains the x-axis label for the year 2020, displayed in dark grey text." } ], "id_raw": 1098 }, { "id": "PS6wcPWGr6AbaiXe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_18.png", "conversations": [ { "from": "human", "value": "\nCan you detail the components located in [0.053, 0.903, 0.103, 0.931]?" }, { "from": "gpt", "value": "This section includes the x-axis labels for the years 2011 and 2012, both in a dark grey font." } ], "id_raw": 1098 }, { "id": "nvDeVKK8UolvjXTG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_18.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region[0.368, 0.757, 0.633, 0.839]?" }, { "from": "gpt", "value": "This area includes multiple data points of the 'Fluctuating Decline Trend' from years 2016 to 2020, indicated by square markers." } ], "id_raw": 1098 }, { "id": "baTzCY4GCcG5sKFw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_19.png", "conversations": [ { "from": "human", "value": "\nHow is the line chart titled on this visualization?" }, { "from": "gpt", "value": "Since the JSON includes no data about subtitles or main titles being displayed, there is no title on the line chart visible within the current viewing frame." } ], "id_raw": 1099 }, { "id": "FPJYY1TCESB35XEr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line that represents {the Price Index}[0.075, 0.353, 0.945, 0.387]?" }, { "from": "gpt", "value": "#ff7f0e." } ], "id_raw": 1099 }, { "id": "dUu58u5Ky7EpBtfz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_55_19.png", "conversations": [ { "from": "human", "value": "\nWhat label is assigned to {the point}[0.558, 0.619] on the Yield Return chart in the subplot_1?" }, { "from": "gpt", "value": "2021-06-01." } ], "id_raw": 1099 }, { "id": "FzS2teBffDdCtmg7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_19.png", "conversations": [ { "from": "human", "value": "\nWhich subplot has a higher initial point, subplot_0 or subplot_1 using the starting point coordinates of {Yield Return}[0.075, 0.289] and {Yield Return}[0.075, 0.619] respectively?" }, { "from": "gpt", "value": "subplot_1." } ], "id_raw": 1099 }, { "id": "BOVl4rwt4v2xUdCJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_19.png", "conversations": [ { "from": "human", "value": "\nIs the trend of the {Price Index line}[0.075, 0.353, 0.945, 0.387] increasing or decreasing between {the points labeled '2021-02-01'}[0.171, 0.378] and {the points labeled '2021-10-01'}[0.945, 0.683]?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 1099 }, { "id": "egrwRNM3qqYtlm2p", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_55_19.png", "conversations": [ { "from": "human", "value": "\nWhich subplot's first point of the Yield Return line is higher, comparing {Yield Return at '2021-01-01'}[0.075, 0.289] from subplot_0 to {Yield Return at '2021-01-01'}[0.075, 0.619] from subplot_1?" }, { "from": "gpt", "value": "subplot_1." } ], "id_raw": 1099 }, { "id": "LiZUJ9NZqvU6rosa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_19.png", "conversations": [ { "from": "human", "value": "\nWhat can be found within the coordinates [0.015, 0.129, 0.044, 0.185]?" }, { "from": "gpt", "value": "The y-axis label indicating \"Yield Return\" is located in this region." } ], "id_raw": 1099 }, { "id": "xKKLRTD2L6YRHqNY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_19.png", "conversations": [ { "from": "human", "value": "\nProvide details found in the region from [0.075, 0.308, 0.985, 0.317]." }, { "from": "gpt", "value": "This region contains x-axis tick labels for all the dates from '2021-01-01' to '2021-10-01' spaced evenly across the chart." } ], "id_raw": 1099 }, { "id": "Rg1aDeP3zaPDKfJ6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_55_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the area defined by coordinates [0.805, 0.345, 0.938, 0.375]." }, { "from": "gpt", "value": "This region contains the sub-legend for subplot_1, including labels and colored handles indicating \"Yield Return\" and \"Price Index\"." } ], "id_raw": 1099 }, { "id": "RU2ycUagns49Xl84", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the chart?" }, { "from": "gpt", "value": "The chart does not have a title." } ], "id_raw": 1100 }, { "id": "IeSZp0aY6DSlCY3B", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_0.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the handle for 'Innovative'}[0.084, 0.764, 0.167, 0.785] in the legend?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 1100 }, { "id": "rPpFHquDAyBgwSTQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the figure size?" }, { "from": "gpt", "value": "The figure size is [5.0, 4.0]." } ], "id_raw": 1100 }, { "id": "YVBueS78l8pjKaVd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_0.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value for the education level of Master's, {the 'Analytical' line}[0.185, 0.317, 0.924, 0.637] or {the 'Conceptual' line}[0.185, 0.137, 0.924, 0.797]?" }, { "from": "gpt", "value": "The Analytical line." } ], "id_raw": 1100 }, { "id": "oaVZmMcUDnr2ML3C", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_0.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Conceptual' point} for Bachelor's[0.185, 0.797] above or below {the 'Innovative' point} for Bachelor's[0.185, 0.717]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1100 }, { "id": "Y9SORWI5jk0Zwhvf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_0.png", "conversations": [ { "from": "human", "value": "\nWhich education level has a higher score for 'Innovative' skills, Bachelor's or PhD?" }, { "from": "gpt", "value": "PhD." } ], "id_raw": 1100 }, { "id": "ku9jb8iOCG8zTWVw", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.079, 0.737, 0.944, 0.816]." }, { "from": "gpt", "value": "This area contains the legend, which lists 'Innovative', 'Analytical', and 'Conceptual'. Each category is accompanied by a color marker." } ], "id_raw": 1100 }, { "id": "5dYs8CBtL3B95DrM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_0.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the coordinates [0.148, 0.037, 0.961, 0.836]?" }, { "from": "gpt", "value": "This area encompasses the plot of the chart, including the lines representing 'Innovative', 'Analytical', and 'Conceptual' performance ratings over different education levels, as well as the labels for those education levels on the x-axis, and the performance ratings on the y-axis." } ], "id_raw": 1100 }, { "id": "gTjmzoM9waXHrZd1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is found from [0.081, 0.816, 0.138, 0.866]?" }, { "from": "gpt", "value": "The y-axis tick label '60' is located within these coordinates." } ], "id_raw": 1100 }, { "id": "qhF692pN93NIJfDM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis}[0.052, 0.356, 0.077, 0.654] in the chart?" }, { "from": "gpt", "value": "Review Score." } ], "id_raw": 1102 }, { "id": "9Vqn3oL4lA9AbYS2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the {line associated with 'Italian'}[0.160, 0.197, 0.865, 0.601]?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 1102 }, { "id": "WrGcU61aibHEKKzn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_2.png", "conversations": [ { "from": "human", "value": "\nWhat does {the point}[0.552, 0.409] on the 'Mexican' line refer to?" }, { "from": "gpt", "value": "The review score of Mexican cuisine on 2022-02-19." } ], "id_raw": 1102 }, { "id": "Ph4lSgeQGC486xSW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_2.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Chinese' review score line}[0.160, 0.312, 0.865, 0.736] generally rising or falling over time?" }, { "from": "gpt", "value": "Rising." } ], "id_raw": 1102 }, { "id": "NEJvtqlZt6fZCkOb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_2.png", "conversations": [ { "from": "human", "value": "\nAt the {point}[0.786, 0.467], does the 'Mexican' review line score higher or lower than the 'Italian' review line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1102 }, { "id": "QS94XsO832FTCyKC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_2.png", "conversations": [ { "from": "human", "value": "\nWhich cuisine has a higher score at {the point}[0.708, 0.447], Italian or Mexican?" }, { "from": "gpt", "value": "Italian." } ], "id_raw": 1102 }, { "id": "zXo7ELlCq5m9uEUg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the region [0.135, 0.137, 0.296, 0.305]." }, { "from": "gpt", "value": "This region contains a legend with three items: {Chinese}[0.139, 0.158, 0.191, 0.175] with a green marker, {Italian}[0.139, 0.211, 0.191, 0.227] with a red marker, and {Mexican}[0.139, 0.263, 0.191, 0.280] with a blue marker." } ], "id_raw": 1102 }, { "id": "XW7C6PW1kH7xBe9z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_2.png", "conversations": [ { "from": "human", "value": "\nWhat is placed in the region [0.083, 0.874, 0.119, 0.914]?" }, { "from": "gpt", "value": "The y-axis tick label '60' is located in this area." } ], "id_raw": 1102 }, { "id": "WPK2VCyFmjRRw7q7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content of the region [0.814, 0.900, 0.915, 1.061]." }, { "from": "gpt", "value": "This area contains the x-axis tick label for the date '2022-03-30'." } ], "id_raw": 1102 }, { "id": "k1ubDMcLYV52WBOM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the object represented by the coordinates [0.747, 0.758, 0.916, 0.803] refer to?" }, { "from": "gpt", "value": "Hospital C." } ], "id_raw": 1103 }, { "id": "zaPRC0mjy0M21YpT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line for Hospital B}[0.197, 0.339, 0.911, 0.680]?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 1103 }, { "id": "cBnVWkmqJoGas5vL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_3.png", "conversations": [ { "from": "human", "value": "\nWhat point is represented by the coordinates {the point}[0.911, 0.539] in the chart?" }, { "from": "gpt", "value": "An equal recovery rate of 0.75 for Hospital C for two health indicators." } ], "id_raw": 1103 }, { "id": "QWwLoWpMgkcIAf31", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_3.png", "conversations": [ { "from": "human", "value": "\nBetween {the line for 'Hospital A'}[0.197, 0.238, 0.911, 0.740] and {the line for 'Hospital B'}[0.197, 0.339, 0.911, 0.680], which line ends at a higher point in the chart's 'Rate (%)' indicator?" }, { "from": "gpt", "value": "Hospital B." } ], "id_raw": 1103 }, { "id": "4LGtKCrcevQXF1bM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_3.png", "conversations": [ { "from": "human", "value": "\nIs the {marker for 'Hospital C'}[0.646, 0.768, 0.729, 0.789] the same color as {the line for 'Hospital C'}[0.197, 0.539, 0.911, 0.539]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1103 }, { "id": "buKAQKhDWNnCjPRz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_3.png", "conversations": [ { "from": "human", "value": "\nWhich hospital shows equal rate for both health indicators?" }, { "from": "gpt", "value": "Hospital C." } ], "id_raw": 1103 }, { "id": "Tee3jg96KHgQWiZ2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the content within the coordinates [0.083, 0.741, 0.930, 0.819]?" }, { "from": "gpt", "value": "The legend of the graph is located here, it contains items for Hospital A at {coordinates}[0.190, 0.758, 0.358, 0.803], Hospital B at {coordinates}[0.468, 0.758, 0.637, 0.803], and Hospital C at {coordinates}[0.747, 0.758, 0.916, 0.803]. Each item is labeled with the respective hospital name and an accompanying color marker." } ], "id_raw": 1103 }, { "id": "TMQo4rmPKYcxW26M", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_3.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area encompassed by [0.197, 0.238, 0.911, 0.740]." }, { "from": "gpt", "value": "This region contains {the line for 'Hospital A'}[0.197, 0.238, 0.911, 0.740], starting from 0.65 patient recovery rate up to 0.9 patient admission rate." } ], "id_raw": 1103 }, { "id": "Aw65RqZGulmLShbk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_3.png", "conversations": [ { "from": "human", "value": "\nWhat is situated in the area defined by coordinates [0.241, 0.912, 0.867, 0.963]?" }, { "from": "gpt", "value": "The x-label of the subplot, \"Health Indicators (log scale)\" is found in this section." } ], "id_raw": 1103 }, { "id": "M5YMhjFfJrCzddLE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_4.png", "conversations": [ { "from": "human", "value": "\nWhat label is indicated by the text_coordinates [0.112, 0.078, 0.257, 0.101] in the legend?" }, { "from": "gpt", "value": "Energy Consumption" } ], "id_raw": 1104 }, { "id": "lVor7MMyLNjsR6l3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_4.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the region specified at [0.103, 0.198, 0.943, 0.879] in the line objects of the subplot?" }, { "from": "gpt", "value": "This area plots the line representing Energy Consumption over the years 2000 to 2010 in the graph." } ], "id_raw": 1104 }, { "id": "5neJpVgpPzzscWD4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_4.png", "conversations": [ { "from": "human", "value": "\nWhat element occupies the specific coordinates [0.508, 0.952, 0.537, 0.975] on the subplot?" }, { "from": "gpt", "value": "The X-axis label 'Year' is located here." } ], "id_raw": 1104 }, { "id": "6OWKWws1toWPupE7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_4.png", "conversations": [ { "from": "human", "value": "\nDoes {the line graph for Resource Consumption}[0.103, 0.266, 0.943, 0.538] tend to go up or down over the X-axis from the coordinates spanned in the chart figure?" }, { "from": "gpt", "value": "Up." } ], "id_raw": 1104 }, { "id": "HXkBqkyAqDYJDXfa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_4.png", "conversations": [ { "from": "human", "value": "\nWhich line object reaches the highest point, the {line graph for Energy Consumption}[0.103, 0.198, 0.943, 0.879] or the {line graph for Carbon Output}[0.103, 0.096, 0.943, 0.708]?" }, { "from": "gpt", "value": "The line graph for Energy Consumption." } ], "id_raw": 1104 }, { "id": "0q2OxihBq1qirONc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_4.png", "conversations": [ { "from": "human", "value": "\nAt the year {2004}[0.439, 0.918] on the X-axis, which line object is higher, {Carbon Output}[0.439, 0.640] or {Resource Consumption}[0.439, 0.396]?" }, { "from": "gpt", "value": "Carbon Output." } ], "id_raw": 1104 }, { "id": "LcsHccVgv1wVKQL8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_4.png", "conversations": [ { "from": "human", "value": "\nWhat details can you give about the region specified at [0.067, 0.069, 0.277, 0.180]?" }, { "from": "gpt", "value": "This section contains the chart's sub-legend, which includes three items: Energy Consumption colored green, Resource Consumption in red, and Carbon Output in blue, each paired with a colored marker." } ], "id_raw": 1104 }, { "id": "PzUTpwwxcvyB4DDP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the area at [0.061, 0.057, 0.985, 0.918]" }, { "from": "gpt", "value": "This is a large area that contains the primary plot or graph, which is composed of several line objects. The grid is visible, and the plot represents the Energy Consumption, Resource Consumption, and Carbon Output from the year 2000 to 2010." } ], "id_raw": 1104 }, { "id": "ZFroqmP1p5qECRU9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_4.png", "conversations": [ { "from": "human", "value": "\nCould you identify the objects located within the coordinates range [0.355, 0.811, 0.439, 0.777]?" }, { "from": "gpt", "value": "This area includes two points on the Energy Consumption line graph, these points represent the values of Energy Consumption for the years 2003 and 2004." } ], "id_raw": 1104 }, { "id": "6k9z6kRAG9PUMzoR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the title of the graph say which has coordinates [0.213, 0.025, 0.839, 0.058]?" }, { "from": "gpt", "value": "The title says \"Innovation and Technology Entrepreneurship Ecosystem\"." } ], "id_raw": 1105 }, { "id": "aHkVaDxekTHzVKuI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_5.png", "conversations": [ { "from": "human", "value": "\nWhat information does the legend item at coordinates [0.763, 0.134, 0.956, 0.164] contain?" }, { "from": "gpt", "value": "It represents the 'University Partnerships'." } ], "id_raw": 1105 }, { "id": "5K0mdxn5q5xScD7e", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_5.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.015, 0.005, 0.035, 0.955] represent?" }, { "from": "gpt", "value": "It represents 'Investment/University Partnerships/Startup Growth'." } ], "id_raw": 1105 }, { "id": "7Z25sEPH2nT46PsB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_5.png", "conversations": [ { "from": "human", "value": "\nIs {the tech investment for 'Boston'}[0.526, 0.241] higher or lower than {that for 'Seattle'}[0.735, 0.354]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1105 }, { "id": "4AkoMEJWLHuzyipx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_5.png", "conversations": [ { "from": "human", "value": "\nDoes {startup growth for 'New York'}[0.317, 0.777] have more or less growth compared to {startup growth for 'Chicago'}[0.943, 0.816]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 1105 }, { "id": "4eWTJXleqQMTzOZG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, does {the 'San Francisco'}[0.108, 0.821] have more or fewer university partnerships than {the 'Chicago'}[0.943, 0.832]?" }, { "from": "gpt", "value": "Fewer." } ], "id_raw": 1105 }, { "id": "f3p20vINFld8D2zT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_5.png", "conversations": [ { "from": "human", "value": "\nWhat comprises the region with coordinates [0.710, 0.079, 0.977, 0.219]?" }, { "from": "gpt", "value": "This region contains the legend, which consists of three labels each represented by a different color: Green for 'Investment in Tech (bn$)', Red for 'University Partnerships', and Blue for 'Startup Growth (%)." } ], "id_raw": 1105 }, { "id": "DS3nfuYTF5lM8dE2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the content of the area within [0.041, 0.220, 0.062, 0.250]." }, { "from": "gpt", "value": "This area holds the label '80' of the y-axis, which likely represents an indicator value or measurement scale." } ], "id_raw": 1105 }, { "id": "jwmMuhqO12iPdFda", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the region with coordinates [0.279, 0.902, 0.355, 0.932] contain?" }, { "from": "gpt", "value": "This area includes one of the x-axis tick labels, specifically 'New York'." } ], "id_raw": 1105 }, { "id": "tSSFfdezEJqhlajk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the y-axis label and coordinate {y-axis label}[0.019, 0.250, 0.044, 0.663] stand for in the chart?" }, { "from": "gpt", "value": "Population growth (%)." } ], "id_raw": 1106 }, { "id": "25VEUsToufR2hTaT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label}[0.100, 0.896, 0.157, 0.932]?" }, { "from": "gpt", "value": "10^-2." } ], "id_raw": 1106 }, { "id": "h9xBRGlO4mnCsDra", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend in the area {legend}[0.309, 0.734, 0.971, 0.874] represent?" }, { "from": "gpt", "value": "It represents the different population categories: Current age population, Baby boomer population, Youth population, Senior population and Virtual worker population." } ], "id_raw": 1106 }, { "id": "W8hSoHLousTteQL7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_6.png", "conversations": [ { "from": "human", "value": "\nIs {the point representing Youth population}[0.941, 0.456] above or below {the point representing Senior population}[0.941, 0.629]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1106 }, { "id": "fz2t43jiI0bSzOMi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_6.png", "conversations": [ { "from": "human", "value": "\nAre most points of {Current age population}[0.128, 0.543, 0.941, 0.543] above or below those of {Baby boomer population}[0.128, 0.111, 0.941, 0.629]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1106 }, { "id": "QpGdvTcfweIwzF6L", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_6.png", "conversations": [ { "from": "human", "value": "\nWhich population category has a higher growth at 10^-2, {Current age population}[0.534, 0.543] or {Virtual worker population}[0.534, 0.370]?" }, { "from": "gpt", "value": "Current age population." } ], "id_raw": 1106 }, { "id": "IUyimE6YJtWsjMil", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the region [0.309, 0.734, 0.971, 0.874]." }, { "from": "gpt", "value": "It contains the legend for the chart labels with their colors: {Current age population}[0.376, 0.745, 0.616, 0.775] with color '#1f77b4', {Baby boomer population}[0.376, 0.789, 0.632, 0.819] with color '#ff7f0e', {Youth population}[0.376, 0.833, 0.549, 0.863] with color '#2ca02c', {Senior population}[0.700, 0.745, 0.883, 0.775] with color '#d62728' and {Virtual worker population}[0.700, 0.789, 0.963, 0.819] with color '#9467bd'." } ], "id_raw": 1106 }, { "id": "IQz2TzxzHILOaEwz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area [0.318, 0.942, 0.750, 0.975]." }, { "from": "gpt", "value": "The area contains the x-axis label of the chart, which is 'Population category (log scale)'." } ], "id_raw": 1106 }, { "id": "4rltZzgsnUtlLdpF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_6.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.534, 0.370, 0.941, 0.543]?" }, { "from": "gpt", "value": "It includes the data points for three population categories: {Current age population}[0.941, 0.543], {Youth population}[0.941, 0.456], and {Virtual worker population}[0.941, 0.543]." } ], "id_raw": 1106 }, { "id": "gzoMab591O4eSsvP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the primary axis represented by the coordinates [0.021, 0.106, 0.050, 0.797]?" }, { "from": "gpt", "value": "Number of people (Thousands)." } ], "id_raw": 1107 }, { "id": "9nGPpILJWxFXxYvg", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_7.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what {the point on the 'Immigration' line}[0.924, 0.484] represents?" }, { "from": "gpt", "value": "The point represents the year 2020 with an immigration number of 80,000." } ], "id_raw": 1107 }, { "id": "6d0VA0SYMgZIrGKM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the point having coordinates [0.418, 0.224] on {the line for 'Immigration'}[0.164, 0.095, 0.924, 0.484] denote?" }, { "from": "gpt", "value": "It denotes the year 2010 with an Immigration number of 120,000." } ], "id_raw": 1107 }, { "id": "TS6E2ghfe2FwUOyl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_7.png", "conversations": [ { "from": "human", "value": "\nAre the values of {Emigration}[0.164, 0.419, 0.924, 0.807] less than the values of {Immigration}[0.164, 0.095, 0.924, 0.484] in 2020?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1107 }, { "id": "nAyfscBRv0yDjjPL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_7.png", "conversations": [ { "from": "human", "value": "\nIn the 2015 data, is the {Emigration}[0.671, 0.548] higher or lower than the {Internal Migration}[0.671, 0.419]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1107 }, { "id": "oapC7BEwSmmCvbRr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_7.png", "conversations": [ { "from": "human", "value": "\nWhich migration category has seen a decrease from 2015 to 2020, {Immigration}[0.671, 0.095, 0.924, 0.484] or {Internal Migration}[0.671, 0.419, 0.924, 0.224]?" }, { "from": "gpt", "value": "Immigration." } ], "id_raw": 1107 }, { "id": "gHS25fQbhhNP9EW3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe the section with coordinates [0.222, 0.793, 0.950, 0.856]?" }, { "from": "gpt", "value": "The area consists of the legend which has three items: {Immigration}[0.298, 0.806, 0.443, 0.842] with a green color marker, {Emigration}[0.522, 0.806, 0.651, 0.842] with a red color marker, and {Internal Migration}[0.729, 0.806, 0.940, 0.842] with a blue color marker." } ], "id_raw": 1107 }, { "id": "sR3nw0B9QAsYSqJ4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_7.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region from [0.058, 0.208, 0.119, 0.248]?" }, { "from": "gpt", "value": "The count of 120 on the y-axis from the y-ticks is within the provided region." } ], "id_raw": 1107 }, { "id": "DD9Rnac6hWZs8195", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_7.png", "conversations": [ { "from": "human", "value": "\nWhat is outlined in the area specified by [0.164, 0.224, 0.924, 0.613]?" }, { "from": "gpt", "value": "This is the observation region for the 'Internal Migration' line, which demonstrates the change in numbers for internal migration over the years." } ], "id_raw": 1107 }, { "id": "0yxL6QYavBVZ2Nqc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the text with these coordinates [0.110, 0.120, 0.233, 0.144] refer to in the chart?" }, { "from": "gpt", "value": "Sluggish progress." } ], "id_raw": 1108 }, { "id": "BBL1Z2JL8ZsPejeW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_8.png", "conversations": [ { "from": "human", "value": "\nWhat label can be found at [0.036, 0.406, 0.053, 0.429] on the Y-axis?" }, { "from": "gpt", "value": "20." } ], "id_raw": 1108 }, { "id": "zYPsFc2QGh5VxfsL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_8.png", "conversations": [ { "from": "human", "value": "\nWhat is indicated by the text {this} located at [0.110, 0.155, 0.221, 0.178] in the legend?" }, { "from": "gpt", "value": "Erratic behavior." } ], "id_raw": 1108 }, { "id": "COpn3zet8yNkrq0u", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_8.png", "conversations": [ { "from": "human", "value": "\nIs {the point for the 'Sluggish progress'}[0.263, 0.440] above or below {the point for the 'Declining pattern'}[0.263, 0.300] on the 'Product Design' x-coordinate point?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1108 }, { "id": "QCRq0jjY1I4K4l3T", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Erratic behavior'}[0.099, 0.351, 0.918, 0.605] end above or below {the line for 'Upward trajectory'}[0.099, 0.059, 0.918, 0.631] on 'After-sales Service' x-coordinate point?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1108 }, { "id": "veNoYLwKRNBOZSzN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_8.png", "conversations": [ { "from": "human", "value": "\nWhich trend line has a higher value at {the 'Sales and Distribution' point}[0.755, 0.415], Erratic behavior or Sluggish progress?" }, { "from": "gpt", "value": "Sluggish progress." } ], "id_raw": 1108 }, { "id": "ZSZQlWT3CztnYAn0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_8.png", "conversations": [ { "from": "human", "value": "\nCould you describe the content in [0.065, 0.041, 0.241, 0.223]?" }, { "from": "gpt", "value": "This region represents the legend of the chart containing the names of five different patterns: {Slight increase}[0.110, 0.050, 0.212, 0.074], {Declining pattern}[0.110, 0.085, 0.230, 0.109], {Sluggish progress}[0.110, 0.120, 0.233, 0.144], {Erratic behavior}[0.110, 0.155, 0.221, 0.178] and {Upward trajectory}[0.110, 0.190, 0.235, 0.213] each accompanied with a corresponding colored marker." } ], "id_raw": 1108 }, { "id": "J6sPKhm14FTatVGp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_8.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region with coordinates [0.016, 0.278, 0.030, 0.425]?" }, { "from": "gpt", "value": "This area contains the y-axis label: {Performance}[0.016, 0.278, 0.030, 0.425]." } ], "id_raw": 1108 }, { "id": "KK41ojxq7yKn72Oo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects located in [0.378, 0.681, 0.476, 0.844]." }, { "from": "gpt", "value": "This region includes an x-axis label, which refers to the 'Manufacturing' factor of the Value Chain." } ], "id_raw": 1108 }, { "id": "KONILTTTwsWpd2oi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_9.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at coordinates [0.019, 0.389, 0.041, 0.532] in the chart represent?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1109 }, { "id": "b8pwJQKxaFfl1sAC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fourth text in the legend?" }, { "from": "gpt", "value": "[0.436, 0.073, 0.535, 0.089]." } ], "id_raw": 1109 }, { "id": "cuNnMLGcfJpzWL1v", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the point in the 'GDP Growth Rate' line}[0.717, 0.468]?" }, { "from": "gpt", "value": "2.8." } ], "id_raw": 1109 }, { "id": "nqAXEKWL1h5rs7Fg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_9.png", "conversations": [ { "from": "human", "value": "\nWhich is higher at {point at year 2012}, the 'GDP Growth Rate'[0.345, 0.505] or the 'Inflation Rate'[0.345, 0.468]?" }, { "from": "gpt", "value": "Inflation Rate." } ], "id_raw": 1109 }, { "id": "6toBXtbo8DExWoRu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_9.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Unemployment Rate' at 2012}[0.345, 0.245] higher or lower than {the 'Unemployment Rate' at 2020}[0.940, 0.156]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1109 }, { "id": "1moDCUXxRtdLYQ3A", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_9.png", "conversations": [ { "from": "human", "value": "\nAt {the year 2019}, is the 'Inflation Rate'[0.865, 0.322] more or less than the 'Unemployment Rate'[0.866, 0.416]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 1109 }, { "id": "CzBvDSTOMwsZgVAN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_9.png", "conversations": [ { "from": "human", "value": "\nPlease detail the objects in the region with the coordinates [0.089, 0.058, 0.713, 0.108]." }, { "from": "gpt", "value": "This region comprises three legend items, i.e., 'GDP Growth Rate', 'Unemployment Rate', and 'Inflation Rate'. Each of these items is paired with a different colored marker that is consistent with the color of the respective line in the plot." } ], "id_raw": 1109 }, { "id": "Q630KHr5n0P27kFj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_9.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the region described by [0.081, 0.044, 0.981, 0.877]?" }, { "from": "gpt", "value": "The area hosts the entire line plot, including the x and y axes labels, the x and y ticks, and the plotted lines for 'GDP Growth Rate', 'Unemployment Rate', and 'Inflation Rate'." } ], "id_raw": 1109 }, { "id": "8eiivzmcoOE7ayTE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the content enclosed within the region [0.089, 0.058, 0.713, 0.108]." }, { "from": "gpt", "value": "This area is an inclusive representation of the legend. It exhibits the labels for 'GDP Growth Rate'[0.145, 0.069, 0.296, 0.097], 'Unemployment Rate'[0.353, 0.069, 0.532, 0.097], and 'Inflation Rate'[0.589, 0.069, 0.706, 0.097], each coupled with a respective color handle." } ], "id_raw": 1109 }, { "id": "hHIgxABldauQPMz9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.176, 0.057, 0.966, 0.823] in the chart?" }, { "from": "gpt", "value": "Graph plotting area." } ], "id_raw": 1110 }, { "id": "apCa6ATJIL1DfOVz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_10.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.212, 0.181, 0.930, 0.737]?" }, { "from": "gpt", "value": "Aggressive investment strategy line." } ], "id_raw": 1110 }, { "id": "7uJI6Y5oqXrMu8P5", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.030, 0.277, 0.070, 0.604]?" }, { "from": "gpt", "value": "Returns (%)." } ], "id_raw": 1110 }, { "id": "hmTjULin7iC2Wk8Y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_10.png", "conversations": [ { "from": "human", "value": "\nAt {point x = 0.01}, is the {Aggressive strategy}[0.212, 0.453] placed above or below the {Conservative strategy}[0.212, 0.527]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1110 }, { "id": "ctmBgusL27NMy95D", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_10.png", "conversations": [ { "from": "human", "value": "\nDoes the {Aggressive strategy line}[0.212, 0.181, 0.930, 0.737] deliver higher or lower returns than the {Conservative strategy line}[0.212, 0.218, 0.930, 0.700] at the {point x=1.0}[0.930, 0.700]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1110 }, { "id": "mr9puEUw2XhW0DNy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_10.png", "conversations": [ { "from": "human", "value": "\nWhich line is closer to the bottom of the graph at the coordinate of {x = 0.1} -- {Aggressive strategy}[0.571, 0.181] or {Conservative strategy}[0.571, 0.218]?" }, { "from": "gpt", "value": "Aggressive." } ], "id_raw": 1110 }, { "id": "gMo72nLWgeuIUYO4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_10.png", "conversations": [ { "from": "human", "value": "\nWhat is included in the region [0.318, 0.724, 0.949, 0.803]?" }, { "from": "gpt", "value": "This region contains the plot legend with Aggressive and Conservative investment strategies. Aggressive being labelled with Green color and Conservative with Red color." } ], "id_raw": 1110 }, { "id": "OdHGe5FxDWDZlERQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_10.png", "conversations": [ { "from": "human", "value": "\nCould you describe what is inside the coordinates [0.212, 0.181, 0.930, 0.737]?" }, { "from": "gpt", "value": "This area encompasses the line graph representing the Aggressive investment strategy, including its plotted data points." } ], "id_raw": 1110 }, { "id": "OJinUZGeWrBFHhC2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the region [0.081, 0.037, 0.166, 0.088]." }, { "from": "gpt", "value": "This area includes the y-axis label '100'." } ], "id_raw": 1110 }, { "id": "oeAHz2LvwlUKYwVa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_11.png", "conversations": [ { "from": "human", "value": "\nWhat is at the coordinates {lebel}[0.154, 0.049, 0.194, 0.812]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1111 }, { "id": "AN87rjKhIXN680gK", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_11.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at the {coordinate}[0.896, 0.836, 0.975, 0.899]?" }, { "from": "gpt", "value": "X-axis label for '$\\\\mathdefault{10^{0}}$'." } ], "id_raw": 1111 }, { "id": "lIgW9eE6fWefqT2x", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the {line}[0.330, 0.299, 0.936, 0.649] represent?" }, { "from": "gpt", "value": "It represents the trend for 'Early Stage Growth'." } ], "id_raw": 1111 }, { "id": "6v2jPEqgwUeSP4WT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_11.png", "conversations": [ { "from": "human", "value": "\nHow does the {point}[0.330, 0.299] on the 'Early Stage Growth' line compare to the {point}[0.330, 0.474] on the 'Seed Stage' line?" }, { "from": "gpt", "value": "It is below." } ], "id_raw": 1111 }, { "id": "lSAmeDjrMHZOuqpo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_11.png", "conversations": [ { "from": "human", "value": "\nWhich has a higher value at {x=1.0}, the 'Early Stage Growth' or the 'Late Stage'?" }, { "from": "gpt", "value": "'Late Stage'." } ], "id_raw": 1111 }, { "id": "oo0GB4uSU3zoUee7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_11.png", "conversations": [ { "from": "human", "value": "\nIn relation to the {point}[0.936, 0.771] on the 'Seed Stage', where is the {point}[0.936, 0.474] on the 'Late Stage' line?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1111 }, { "id": "SsCYm0LqyIVCdh0Y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_11.png", "conversations": [ { "from": "human", "value": "\nWhat is in the area [0.327, 0.741, 0.647, 0.786]?" }, { "from": "gpt", "value": "The legend item labeled as 'Early Stage Growth' with a handle color of '#ff0000'." } ], "id_raw": 1111 }, { "id": "zQSXQeDh5FDq3GxW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region from [0.234, 0.803, 0.290, 0.853]?" }, { "from": "gpt", "value": "This is the location of a Y-axis label marked as '80'." } ], "id_raw": 1111 }, { "id": "oLSW4nvZhj0KIWgs", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within [0.330, 0.474, 0.936, 0.771]." }, { "from": "gpt", "value": "A line representation of 'Seed Stage' data located in this area with three points corresponding to different x values." } ], "id_raw": 1111 }, { "id": "q5ad0L23ceiVbhEu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the main object at the location [0.025, 0.237, 0.057, 0.657]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1112 }, { "id": "dU8eYU7Lz2EmtF2V", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_12.png", "conversations": [ { "from": "human", "value": "\nWhat color corresponds to the legend {item}[0.598, 0.772, 0.664, 0.807]?" }, { "from": "gpt", "value": "Green." } ], "id_raw": 1112 }, { "id": "o9UMCOJ4nPBfR1TE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_12.png", "conversations": [ { "from": "human", "value": "\nWhat is labeled at the x-point [0.148, 0.839]?" }, { "from": "gpt", "value": "10." } ], "id_raw": 1112 }, { "id": "ZR8VFWKGIZxBNOoM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Europe'}[0.929, 0.682] above or below {the point for 'Africa'}[0.929, 0.447]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1112 }, { "id": "eKYHaYwwLWlozgM4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_12.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Asia'}[-1636.981, 0.329, 0.929, 0.604] higher than {line for 'Europe'}[-1636.981, 0.447, 0.929, 0.682] at the -10 latitude point?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1112 }, { "id": "HGO2o7zgGm6v5JxU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_12.png", "conversations": [ { "from": "human", "value": "\nWhich region experiences a higher temperature at the {30th latitude}[0.929, 0.447]? Africa, Asia, or Europe?" }, { "from": "gpt", "value": "Africa." } ], "id_raw": 1112 }, { "id": "3jSOqgM2KiEzAJJI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the chart content within [0.524, 0.759, 0.956, 0.821]." }, { "from": "gpt", "value": "This region includes the chart's legend, listing the three regions, Africa, Asia, and Europe, each with a designated color for their data lines: green for Africa, red for Asia, and blue for Europe." } ], "id_raw": 1112 }, { "id": "jtu6uWsbg4FN7GQ1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the element region defined by [0.109, 0.839, 3.419, 0.901]." }, { "from": "gpt", "value": "This is the X-axis scale section of the chart. It starts with 10^-1 and ends at 10^3, marking logarithmic scale values at the base of 10." } ], "id_raw": 1112 }, { "id": "F6UQRkLvNH1s9s6U", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the content inside the area [-1636.981, 0.212, 0.148, 0.447]?" }, { "from": "gpt", "value": "This area contains the first half of the 'Africa' data line depicting temperature trends at latitudes from -30 to 0 degrees. The line starts at a temperature of 30\u00b0C at latitude -30 and increases to 36\u00b0C at latitude 0." } ], "id_raw": 1112 }, { "id": "RXJSeEpT2tGI9xEM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for {the x-axis tick mark} at [0.726, 0.893]?" }, { "from": "gpt", "value": "2014." } ], "id_raw": 1114 }, { "id": "ch7dgtD6MtHoYHQ9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the name of {the line series} that has the color marker of coordinates [0.726, 0.112, 0.777, 0.129] in the legend?" }, { "from": "gpt", "value": "Clinical Trials." } ], "id_raw": 1114 }, { "id": "8xLvWHkgMXgAqrl4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_14.png", "conversations": [ { "from": "human", "value": "\nWhat is {the label for the y-axis} at coordinates [0.021, 0.426, 0.041, 0.508]?" }, { "from": "gpt", "value": "Count." } ], "id_raw": 1114 }, { "id": "UTfWqLsbbiW4uEvt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 2014 in the 'Doctor Visits'}[0.726, 0.158] above or below {the point for 2014 in the 'Clinical Trials'}[0.726, 0.723]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1114 }, { "id": "43hrce7HI7ExNE9I", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_14.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher in the year 2011, {the 'Medical Research'}[0.347, 0.797] or {the 'Clinical Trials'}[0.347, 0.707]?" }, { "from": "gpt", "value": "Medical Research." } ], "id_raw": 1114 }, { "id": "mCn7OiilinWenAEQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_14.png", "conversations": [ { "from": "human", "value": "\nAre {the 'Doctor Visits'}[0.852, 0.201] values increasing or decreasing from 2013[0.600, 0.137] to 2015?" }, { "from": "gpt", "value": "Increasing." } ], "id_raw": 1114 }, { "id": "Qbhg9005tsXU0vmE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within this region [0.724, 0.055, 0.969, 0.189]." }, { "from": "gpt", "value": "This region contains the legend of the plot, with entries for 'Doctor Visits', 'Clinical Trials', and 'Medical Research'. Each entry has associated color markers at {coordinates}[0.726, 0.070, 0.777, 0.087], {coordinates}[0.726, 0.112, 0.777, 0.129], and {coordinates}[0.726, 0.154, 0.777, 0.170] respectively." } ], "id_raw": 1114 }, { "id": "BenL0h4rv0heWzZv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_14.png", "conversations": [ { "from": "human", "value": "\nDescribe what is within the region [0.194, 0.893, 0.852, 0.933]." }, { "from": "gpt", "value": "This region includes all tick labels along the x-axis, which are the years from '2010' to '2015', each on coordinates [{coordinates}[0.221, 0.893], {coordinates}[0.347, 0.893], {coordinates}[0.473, 0.893], {coordinates}[0.600, 0.893], {coordinates}[0.726, 0.893], {coordinates}[0.852, 0.893]] respectively." } ], "id_raw": 1114 }, { "id": "6mbUQDZEeKQCp5ej", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_14.png", "conversations": [ { "from": "human", "value": "\nWhat is inside the area with coordinates [0.221, 0.680, 0.852, 0.733]?" }, { "from": "gpt", "value": "This area includes the 'Clinical Trials' line with points at {coordinates}[0.221, 0.680], {coordinates}[0.347, 0.707], {coordinates}[0.473, 0.733], {coordinates}[0.600, 0.717], {coordinates}[0.726, 0.723], and {coordinates}[0.852, 0.733]." } ], "id_raw": 1114 }, { "id": "RCtjtqJwWw9mVLku", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_16.png", "conversations": [ { "from": "human", "value": "\nWhat is described by the object at coordinates [0.315, 0.304, 0.362, 0.344]?" }, { "from": "gpt", "value": "Y-axis tick label '80'." } ], "id_raw": 1116 }, { "id": "aIpDIbkkMLcl6VW7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the title in the given figure?" }, { "from": "gpt", "value": "There is no title in the given figure." } ], "id_raw": 1116 }, { "id": "DepbHIMunYFGKXCL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_16.png", "conversations": [ { "from": "human", "value": "\nWhat type of object is represented by the bounding box at [0.633, 0.803, 0.938, 0.839] within the figure?" }, { "from": "gpt", "value": "It is the legend item \"Operational Efficiency\"." } ], "id_raw": 1116 }, { "id": "ojylOdSITFvzKQu2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_16.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Customer Satisfaction' line}[0.397, 0.101, 0.936, 0.375] above or below the {the 'Product Quality' line}[0.397, 0.320, 0.936, 0.732] in 2020?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1116 }, { "id": "z9Wa8D60MV4mRBoc", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_16.png", "conversations": [ { "from": "human", "value": "\nDid {the 'Product Quality' metrics}[0.397, 0.320, 0.936, 0.732] improve or decline from 2018 to 2020?" }, { "from": "gpt", "value": "Decline." } ], "id_raw": 1116 }, { "id": "Ent802EMrNnSgu1p", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_16.png", "conversations": [ { "from": "human", "value": "\nWhich metric had better performance in 2020, {Customer Satisfaction}[0.828, 0.266] or {Operational Efficiency}[0.828, 0.375]?" }, { "from": "gpt", "value": "Operational Efficiency." } ], "id_raw": 1116 }, { "id": "eTipMrbl7rWv39X4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_16.png", "conversations": [ { "from": "human", "value": "\nPlease provide a description for the region with the coordinates [0.350, 0.879, 0.444, 0.919]." }, { "from": "gpt", "value": "This region contains the x-coordinate label for the year 2016." } ], "id_raw": 1116 }, { "id": "eDKPm2L47qIO6CWY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the region of [0.315, 0.030, 0.362, 0.070]?" }, { "from": "gpt", "value": "This region comprises the y-coordinate label indicating the '90%' mark on the axis." } ], "id_raw": 1116 }, { "id": "ed2DsK4QLlEd2SYY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_16.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.768, 0.879, 1.091, 0.919]?" }, { "from": "gpt", "value": "The x-axis labels for the years 2021 and 2022 are located within this area." } ], "id_raw": 1116 }, { "id": "dt0Wm56FoOHcoIiG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the text object located within the coordinate [0.670, 0.109, 0.725, 0.126]?" }, { "from": "gpt", "value": "Patent Rejection's color marker in the legend." } ], "id_raw": 1117 }, { "id": "jLvJJQiIBIGJ8vco", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_17.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the line}[0.177, 0.783, 0.930, 0.875] in the chart?" }, { "from": "gpt", "value": "The trend of Patent Rejections." } ], "id_raw": 1117 }, { "id": "NDUNMIy4xReHJjqE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at the 5th x-axis tick whose coordinates are [0.523, 0.885, 0.584, 0.921]?" }, { "from": "gpt", "value": "2010." } ], "id_raw": 1117 }, { "id": "QeNN3sdXJc2dfdD9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_17.png", "conversations": [ { "from": "human", "value": "\nOn the line representing 'Patent Application', is 2008 data point higher or lower than the data point for 2007 at {coordinates}[0.327, 0.322]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1117 }, { "id": "QQdOgqenw7jg2wiH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_17.png", "conversations": [ { "from": "human", "value": "\nWhich line is positioned higher at 2012 - {the 'Patent Application' line}[0.704, 0.184] or {the 'Patent Approval' line}[0.704, 0.507]?" }, { "from": "gpt", "value": "Patent Approval line." } ], "id_raw": 1117 }, { "id": "L0m013p3KwT1JZOF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_17.png", "conversations": [ { "from": "human", "value": "\nIn 2005, was the rate of 'Patent Approval' higher or lower than that of 'Patent Rejection' at {the point}[0.177, 0.875]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1117 }, { "id": "37RP4oD1FtkP8y8M", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the coordinate box [0.150, 0.092, 0.924, 0.146]?" }, { "from": "gpt", "value": "This region contains the legend of the chart. The labels are as follows: {Patent Application}[0.220, 0.104, 0.419, 0.134], {Patent Approval}[0.491, 0.104, 0.665, 0.134], and {Patent Rejection}[0.737, 0.104, 0.916, 0.134]. Each label is associated with a colored marker." } ], "id_raw": 1117 }, { "id": "kG89sHTVLRIuViGq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_17.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the box [0.523, 0.885, 0.584, 0.921]?" }, { "from": "gpt", "value": "The year '2010' x-axis label is located in these coordinates." } ], "id_raw": 1117 }, { "id": "MeqtFf6hxbrKNg99", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects within the coordinate region [0.177, 0.783, 0.930, 0.875]." }, { "from": "gpt", "value": "This area contains the data points of the 'Patent Rejection' line from the years 2005 to 2015. The trend of the data points shows a general increase over this period." } ], "id_raw": 1117 }, { "id": "3XoSKVErCEYTjldn", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.183, 0.359]?" }, { "from": "gpt", "value": "It's a point on the line for Amazon in 2019." } ], "id_raw": 1118 }, { "id": "SeJlPMIUHP7VxeY0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the Walmart handle in the legend?" }, { "from": "gpt", "value": "[0.169, 0.151, 0.253, 0.172]" } ], "id_raw": 1118 }, { "id": "rdgXxyWUnCR2bGUE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the {y-axis tick label}[0.109, 0.820, 0.138, 0.870]?" }, { "from": "gpt", "value": "5." } ], "id_raw": 1118 }, { "id": "Ua7nfv86CUFQ5CAV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_18.png", "conversations": [ { "from": "human", "value": "\nIs {the point for Amazon} [0.536, 0.278] above or below {the point for Walmart} [0.536, 0.519] for 2020?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1118 }, { "id": "xQTLVqew1AhZJOD3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_18.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for Alibaba} [0.183, 0.492, 0.888, 0.546] cross or not cross with {the line for Walmart} [0.183, 0.439, 0.888, 0.573]?" }, { "from": "gpt", "value": "No, doesn't cross." } ], "id_raw": 1118 }, { "id": "vWOSRSMnShmofMhL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_18.png", "conversations": [ { "from": "human", "value": "\nIs {the Amazon line} [0.183, 0.145, 0.888, 0.359] trending upwards or downwards between 2019 and 2021?" }, { "from": "gpt", "value": "Upwards." } ], "id_raw": 1118 }, { "id": "MYGebubnDUlKRKJL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.164, 0.058, 0.424, 0.400]." }, { "from": "gpt", "value": "The area contains labels and color markers for different companies: {Amazon}[0.271, 0.075, 0.405, 0.120], {Walmart}[0.271, 0.141, 0.411, 0.186], {Alibaba}[0.271, 0.207, 0.395, 0.252], {Ebay}[0.271, 0.272, 0.353, 0.317], {BestBuy}[0.271, 0.338, 0.408, 0.383], with markers in colors of green, blue, red, purple, and orange respectively." } ], "id_raw": 1118 }, { "id": "MIYuM6kQOWAEv5Vv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.183, 0.145, 0.888, 0.760]." }, { "from": "gpt", "value": "This area contains multiple data lines representing market share of different companies from 2019 to 2021. It includes lines of {Amazon}[0.183, 0.145, 0.888, 0.359], {Walmart}[0.183, 0.439, 0.888, 0.573], {Alibaba}[0.183, 0.492, 0.888, 0.546], {Ebay}[0.183, 0.573, 0.888, 0.680], and {BestBuy}[0.183, 0.680, 0.888, 0.760]." } ], "id_raw": 1118 }, { "id": "IVScJmdrCNs8oaKq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_18.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.169, 0.151, 0.253, 0.172]?" }, { "from": "gpt", "value": "The area contains the color marker for {Walmart}[0.169, 0.151, 0.253, 0.172] in the legend." } ], "id_raw": 1118 }, { "id": "UN2N9ywZiu9aUNJB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the attribute of {the subplot_0 axes}[0.148, 0.037, 0.940, 0.836] in this chart?" }, { "from": "gpt", "value": "The background color of the subplot is given by the attribute, which is specified as #eaeaf2." } ], "id_raw": 1119 }, { "id": "rVaLeFDujR29jDIS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_19.png", "conversations": [ { "from": "human", "value": "\nHow can the {line_2}[0.184, 0.437, 0.904, 0.677] on the chart be recognized?" }, { "from": "gpt", "value": "It is the line labeled as \"Twitter\" that is represented in a red color (#ff0000), using a hyphen as linestyle and 'o' as the marker." } ], "id_raw": 1119 }, { "id": "Vj6aKCRKP9ZtyHDu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_56_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the content associated with the {coordinates}[0.741, 0.688, 0.910, 0.733] in the sublegend?" }, { "from": "gpt", "value": "The content is labeled as 'Instagram'." } ], "id_raw": 1119 }, { "id": "sqU5Hr64F4YpIuuN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_19.png", "conversations": [ { "from": "human", "value": "\nOn the x-axis value '15-35', which social media has more active users, {Facebook}[0.904, 0.277] or {Instagram}[0.904, 0.117]?" }, { "from": "gpt", "value": "Instagram." } ], "id_raw": 1119 }, { "id": "NdeBTtpHGjh51v1o", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_19.png", "conversations": [ { "from": "human", "value": "\nIn relation to the y-axis label {Active Users (millions)}[0.030, 0.126, 0.070, 0.748], where is the legend located?" }, { "from": "gpt", "value": "To the right." } ], "id_raw": 1119 }, { "id": "dSJHvXhOy9UsvxBG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_56_19.png", "conversations": [ { "from": "human", "value": "\nComparing the data points of '35+' and '15-35' for {Twitter}[0.184, 0.677, 0.904, 0.437], does the number of active users increase or decrease?" }, { "from": "gpt", "value": "Decrease." } ], "id_raw": 1119 }, { "id": "Qt2pX6eC84YjEPaO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area encompassed by the coordinates [0.635, 0.606, 0.924, 0.816]?" }, { "from": "gpt", "value": "This area includes the legend for the chart. There are three entries in the legend: {Facebook}[0.741, 0.622, 0.897, 0.667] with a green marker, {Instagram}[0.741, 0.688, 0.910, 0.733] with a blue marker, and {Twitter}[0.741, 0.754, 0.853, 0.799] with a red marker." } ], "id_raw": 1119 }, { "id": "0GZlpQj3HuFCoTgy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_19.png", "conversations": [ { "from": "human", "value": "\nWhat information is available within the coordinates [0.081, 0.177, 0.138, 0.227] on the y-axis?" }, { "from": "gpt", "value": "In this area on the y-axis, the label '90' is displayed." } ], "id_raw": 1119 }, { "id": "cEhgcB8Q4Frug5Tr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_56_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the features displayed within the {x-axis}[0.137, 0.849, 0.231, 0.899, 0.839, 0.849, 0.969, 0.899]?" }, { "from": "gpt", "value": "This area contains two labels, '35+' and '15-35', which represent the categories of user ages." } ], "id_raw": 1119 }, { "id": "FSmRAKunoVytJEPS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the location of the Y-axis label in the chart?" }, { "from": "gpt", "value": "It is at {coordinates}[0.015, 0.250, 0.035, 0.765]." } ], "id_raw": 1120 }, { "id": "b8iLdcgfqMlc6FR2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_0.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates for the last x-axis tick label in the chart?" }, { "from": "gpt", "value": "It is at {coordinates}[0.923, 0.934, 0.965, 0.970]." } ], "id_raw": 1120 }, { "id": "FPvdepT2VHh4DHYy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_0.png", "conversations": [ { "from": "human", "value": "\nWhat is {the line labeled 'Residential'} with coordinates starting at [0.129, 0.410] and ending at [0.944, 0.160]?" }, { "from": "gpt", "value": "This is one of the lines representing the trend in community interactions for different types of neighborhoods." } ], "id_raw": 1120 }, { "id": "22aLUHfV2EHiFbMD", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_0.png", "conversations": [ { "from": "human", "value": "\nHow do the trends of {the line labeled 'Low-Income'}[0.129, 0.619, 0.944, 0.896] and {the line labeled 'High-Income'}[0.129, 0.758, 0.944, 0.855] compare over the years?" }, { "from": "gpt", "value": "The High-Income line starts higher but ends at a lower point than the Low-Income line, indicating that Neighborhood Interactions decreased more steeply for the High-Income neighborhoods compared to Low-Income neighborhoods." } ], "id_raw": 1120 }, { "id": "rBc5QGvsjTBS74TZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_0.png", "conversations": [ { "from": "human", "value": "\nBetween {the point for '2021' in 'Commercial'}[0.944, 0.507] and {the point for '2021' in 'Residential'}[0.944, 0.160], which one is higher on the graph?" }, { "from": "gpt", "value": "The point for '2021' in 'Residential'." } ], "id_raw": 1120 }, { "id": "AJUE3rcjDtNQyOkw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_0.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Residential' line}[0.129, 0.160, 0.944, 0.410] end higher or lower than {the 'Commercial' line}[0.129, 0.507, 0.944, 0.841]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1120 }, { "id": "wj46Qjo49BunC2DM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in coordinates [0.099, 0.113, 0.320, 0.375]." }, { "from": "gpt", "value": "This region contains the legend of the line graph and distinguishes different lines with their respective colors. Each label is paired with a colored line symbol. It includes labels 'Low-Income', 'High-Income', 'Commercial', and 'Residential'." } ], "id_raw": 1120 }, { "id": "HUvI8WQNp6Jfka9N", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_0.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.108, 0.934, 0.965, 0.970]." }, { "from": "gpt", "value": "This region contains six x-axis tick labels, distributed evenly from left to right. The labels represent the years from 2016 to 2021." } ], "id_raw": 1120 }, { "id": "WJ3Cx6J0nA28Ej1f", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_0.png", "conversations": [ { "from": "human", "value": "\nWhat is present within coordinates [0.055, 0.214, 0.083, 0.254]?" }, { "from": "gpt", "value": "The y-axis tick label indicating a count of '90' interactions is within these coordinates." } ], "id_raw": 1120 }, { "id": "f6h9m2lPMMnTaYD3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at {coordinates}[0.015, 0.380, 0.035, 0.525]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1121 }, { "id": "vczhfCJ5JGMviC72", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_1.png", "conversations": [ { "from": "human", "value": "\nWhere is the text of the first item in the legend situated?" }, { "from": "gpt", "value": "[0.156, 0.070, 0.274, 0.110]" } ], "id_raw": 1121 }, { "id": "DHOzd253iKKhuGhU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label}[0.094, 0.885, 0.137, 0.970]?" }, { "from": "gpt", "value": "2015." } ], "id_raw": 1121 }, { "id": "kXjfZpCEYi7HQ4nA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_1.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher on the plot at year 2020, {the 'Innovation' line}[0.806, 0.278] or {the 'Investments' line}[0.806, 0.189]?" }, { "from": "gpt", "value": "Innovation." } ], "id_raw": 1121 }, { "id": "tIuSD9aLJsLwMfOF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_1.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 2016 in 'Investments'}[0.253, 0.229] above or below {the point for 2016 in 'Innovation'}[0.253, 0.328]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1121 }, { "id": "Hg60C8QPg87PLWhu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_1.png", "conversations": [ { "from": "human", "value": "\nFor {the point for 2018}[0.529, 0.199] on Investments, is it higher or lower compared to {the point for 2018 on Innovation}[0.529, 0.298]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1121 }, { "id": "OYcFHYoqVZuiq4cb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the region [0.085, 0.052, 0.303, 0.190]." }, { "from": "gpt", "value": "This region contains the legend of the plot. It shows two items: 'Innovation' and 'Investments' both paired with a colored marker, where 'Innovation' is located at [0.156, 0.070, 0.274, 0.110] and 'Investments' at [0.156, 0.132, 0.294, 0.172]." } ], "id_raw": 1121 }, { "id": "OcAu06TXkZEitf0W", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the range [0.115, 0.149, 0.944, 0.249]." }, { "from": "gpt", "value": "This range includes the line for 'Investments' from year 2015 to 2021. The line marker is square and its color is orange. It includes upper and lower bounds for each point." } ], "id_raw": 1121 }, { "id": "FbXXh8TPT9UY4vEW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_1.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is in the region with coordinates [0.115, 0.259, 0.944, 0.378]." }, { "from": "gpt", "value": "This region contains the 'Innovation' line from year 2015 to 2021. It's marked with a blue line and circular points. It also contains the maximum and minimum values at each point." } ], "id_raw": 1121 }, { "id": "aclaWFLfwdh1lVs4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the top right corner with coordinates [0.650, 0.095, 0.977, 0.263]?" }, { "from": "gpt", "value": "A legend showing five different trends of subculture and their colors." } ], "id_raw": 1122 }, { "id": "IJtKq14oAvi7YN6n", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_2.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the point}[0.105, 0.358] on the line chart?" }, { "from": "gpt", "value": "It represents the popularity of the \"Streetwear\" trend in the 2000s." } ], "id_raw": 1122 }, { "id": "dC2cDyeBRDwUz75p", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_2.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.015, 0.300, 0.035, 0.660] stand for?" }, { "from": "gpt", "value": "Trend Popularity." } ], "id_raw": 1122 }, { "id": "AfLgNRBjm14l3Z4V", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_2.png", "conversations": [ { "from": "human", "value": "\nComparing the trends in 2020s, is the popularity of {the 'Streetwear'}[0.943, 0.115] trend higher or lower than {the 'Punk'}[0.943, 0.456] trend?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1122 }, { "id": "Mn4DzGZL6hnQawCB", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_2.png", "conversations": [ { "from": "human", "value": "\nComparing the trends in 2000s, is the popularity of {the 'Boho'}[0.105, 0.456] trend higher or lower than {the 'Sporty Chic'}[0.105, 0.358] trend?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1122 }, { "id": "nuCfSLQwON5lBykV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_2.png", "conversations": [ { "from": "human", "value": "\nComparing the trends in the 2010s, did {the 'Vintage'}[0.524, 0.115] trend have a higher or lower popularity than {the 'Sporty Chic'}[0.524, 0.602] trend?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1122 }, { "id": "9fDwshdPNrqVPzc4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.703, 0.109, 0.795, 0.145]." }, { "from": "gpt", "value": "The content is a legend entry labeled 'Streetwear', represented with the color #ff00ff." } ], "id_raw": 1122 }, { "id": "qeZizqQLCqDiiFF7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_2.png", "conversations": [ { "from": "human", "value": "\nDescribe what is contained in this area [0.875, 0.109, 0.915, 0.145]." }, { "from": "gpt", "value": "It contains a legend entry labeled 'Punk' with the associated color of #0000ff." } ], "id_raw": 1122 }, { "id": "J39VN01xlCqbNi33", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_2.png", "conversations": [ { "from": "human", "value": "\nWhat is placed in the region [0.650, 0.095, 0.977, 0.263]?" }, { "from": "gpt", "value": "This area contains the chart's legend. It includes labels for five different clothing trends - 'Streetwear', 'Vintage', 'Boho', 'Punk', and 'Sporty Chic', each with a color representing the trend on the chart." } ], "id_raw": 1122 }, { "id": "4Z140IIUciVynjXC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.015, 0.251, 0.034, 0.603]?" }, { "from": "gpt", "value": "It is the y-axis label, which reads \"Economic Indicator Index\"." } ], "id_raw": 1123 }, { "id": "BQamMfbfO6cllepk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.087, 0.059, 0.129, 0.070]?" }, { "from": "gpt", "value": "It's a colored marker for \"Manufacturing\" in the legend." } ], "id_raw": 1123 }, { "id": "aCAcSgBHulA3ugFQ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_3.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the marker at [0.117, 0.189, 0.930, 0.348]?" }, { "from": "gpt", "value": "It is the line representing the \"Manufacturing\" sector in the line chart." } ], "id_raw": 1123 }, { "id": "GEPYlLyYIE1bjSGN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_3.png", "conversations": [ { "from": "human", "value": "\nIs the value for the {Services line}[0.117, 0.348, 0.930, 0.506] higher or lower than the value for the {Manufacturing line}[0.117, 0.189, 0.930, 0.348] in the Fourth Quarter?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1123 }, { "id": "m4iUKXKgx57YF1VF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_3.png", "conversations": [ { "from": "human", "value": "\nDoes the {Retail line}[0.117, 0.490, 0.930, 0.664] go above or below the {Services line}[0.117, 0.348, 0.930, 0.506] in the line chart?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1123 }, { "id": "WiuXArtLeHVQ3MdA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_3.png", "conversations": [ { "from": "human", "value": "\nWhich sector line ends at a higher value in the Fourth Quarter, the {Real Estate line}[0.117, 0.348, 0.930, 0.585] or the {Technology line}[0.117, 0.221, 0.930, 0.379]?" }, { "from": "gpt", "value": "Real Estate." } ], "id_raw": 1123 }, { "id": "m0q0XP5CAn4X3FAe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_3.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents within the region [0.084, 0.043, 0.265, 0.276]?" }, { "from": "gpt", "value": "This region contains the chart's legend with entries like {Manufacturing}[0.138, 0.053, 0.258, 0.079], {Services}[0.138, 0.090, 0.208, 0.116], and {Retail}[0.138, 0.128, 0.185, 0.154], etc. Each entry is paired with a colored marker." } ], "id_raw": 1123 }, { "id": "UQportqWZgzAHWnY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_3.png", "conversations": [ { "from": "human", "value": "\nCould you describe the items in the coordinates [0.076, 0.031, 0.970, 0.822] on the chart?" }, { "from": "gpt", "value": "This is the main area for the chart, containing the x-grid, y-grid, title, x and y labels, tick labels, and the line paths for different economic sectors." } ], "id_raw": 1123 }, { "id": "UDFeve3DcbyD5fAX", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region [0.050, 0.812, 0.071, 0.837] of the chart?" }, { "from": "gpt", "value": "It is the y-axis tick label '50'." } ], "id_raw": 1123 }, { "id": "YYrNgqpgmswW09hP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the object at the coordinates [0.033, 0.255, 0.054, 0.575] represent in the chart?" }, { "from": "gpt", "value": "Y-axis label, referring to \"Energy/Resource Value\"." } ], "id_raw": 1124 }, { "id": "VrGjIhU61S2NdMwq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the data point at coordinates [0.812, 0.351] represent?" }, { "from": "gpt", "value": "It represents the 'Energy Use' value for 'Wind Power'." } ], "id_raw": 1124 }, { "id": "o5d2dwBu2ORckm8d", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line with the points located at coordinates [0.337, 0.313] and [0.931, 0.287]?" }, { "from": "gpt", "value": "The line color is '#ff7f0e', indicating the 'Resource Consumption' values in the chart." } ], "id_raw": 1124 }, { "id": "IC7U2fBJBq12X7Qo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_4.png", "conversations": [ { "from": "human", "value": "\nIs the {Energy Use}[0.099, 0.134, 0.931, 0.543] line of 'Solar Power' higher or lower than the {Resource Consumption}[0.099, 0.262, 0.931, 0.670] line of 'Solar Power'?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1124 }, { "id": "S15gtJlADdqWjSGo", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_4.png", "conversations": [ { "from": "human", "value": "\nDoes the {Energy Use}[0.099, 0.134, 0.931, 0.543] value of 'Biomass Power' coincide with the {Resource Consumption}[0.099, 0.262, 0.931, 0.670] value?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1124 }, { "id": "Vs0Ln7P6AipUAfDK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_4.png", "conversations": [ { "from": "human", "value": "\nIs the {Resource Consumption}[0.099, 0.262, 0.931, 0.670] of 'Wind Power' higher or lower than its {Energy Use}[0.099, 0.134, 0.931, 0.543]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1124 }, { "id": "gSsYhLCozfIiQUSL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_4.png", "conversations": [ { "from": "human", "value": "\nDescribe what is in the coordinates [0.442, 0.046, 0.639, 0.136]" }, { "from": "gpt", "value": "The area contains the legend items: {Energy Use}[0.444, 0.057, 0.536, 0.084] and {Resource Consumption}[0.444, 0.098, 0.631, 0.125], each paired with a respective marker." } ], "id_raw": 1124 }, { "id": "oxh1FvuP128DledR", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_4.png", "conversations": [ { "from": "human", "value": "\nWhat is found in the area with the coordinates [0.455, 0.211, 0.931, 0.236]?" }, { "from": "gpt", "value": "It includes a portion of the 'Resource Consumption' configurations for 'Nuclear Power' and 'Natural Gas Power'." } ], "id_raw": 1124 }, { "id": "SJvT1wG5KX69PKLD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region from [0.053, 0.787, 0.055, 0.814]?" }, { "from": "gpt", "value": "The region contains the y-axis tick label '30'." } ], "id_raw": 1124 }, { "id": "Xbc7juGFSsIsYzYa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of chart represented in the data?" }, { "from": "gpt", "value": "Line." } ], "id_raw": 1125 }, { "id": "zH0zIyU6WcUKJHLY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_5.png", "conversations": [ { "from": "human", "value": "\nWhat object can we find at [0.926, 0.701, 0.961, 0.771]?" }, { "from": "gpt", "value": "The last x-axis label, \"Lives\"." } ], "id_raw": 1125 }, { "id": "MzmkZvDfwPBmr40U", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_5.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at {the point with coordinates}[0.592, 0.342]?" }, { "from": "gpt", "value": "The data point representing the \"Virtual Reality Contents\" in \"Instagram\"." } ], "id_raw": 1125 }, { "id": "0cL9EeCWuNpDXo3Q", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_5.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Podcasts' in 'Instagram'}[0.709, 0.305] positioned higher than {the point for 'Podcasts' in 'Facebook'}[0.709, 0.302]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1125 }, { "id": "PGqtuUcOpUYablf1", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_5.png", "conversations": [ { "from": "human", "value": "\nIs {the line representing 'Instagram'}[0.122, 0.214, 0.944, 0.618] positioned below or above {the line representing 'Facebook'}[0.122, 0.085, 0.944, 0.361]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1125 }, { "id": "nQAvyT4xgui3g0Jl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_5.png", "conversations": [ { "from": "human", "value": "\nWhich line reaches a higher point at {the point for 'Lives'}[0.944, -0.025, 0.944, 0.214], the line for Instagram or Facebook?" }, { "from": "gpt", "value": "Facebook." } ], "id_raw": 1125 }, { "id": "x634eOcxW3bOlQen", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the coordinates [0.081, 0.691, 0.944, 0.771]." }, { "from": "gpt", "value": "This area contains the x-axis labels and corresponding ticks. The labels are: \"All\"[0.111, 0.701, 0.133, 0.745], \"Blog Post\"[0.212, 0.701, 0.267, 0.811], \"Video\"[0.338, 0.701, 0.375, 0.776], \"Infographics\"[0.439, 0.701, 0.509, 0.811], \"Virtual Reality Contents\"[0.528, 0.701, 0.655, 0.953], \"Podcasts\"[0.682, 0.701, 0.736, 0.807], \"User Generated Contents\"[0.759, 0.701, 0.894, 0.953] and \"Lives\"[0.926, 0.701, 0.961, 0.771]." } ], "id_raw": 1125 }, { "id": "HkNQ0FwBZr4bI1xQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_5.png", "conversations": [ { "from": "human", "value": "\nPlease describe the contents found between the coordinates [0.530, 0.052, 0.934, 0.128]." }, { "from": "gpt", "value": "Within this region, there is a legend containing two items. They are {Instagram}[0.601, 0.070, 0.714, 0.110] and {Facebook}[0.821, 0.070, 0.925, 0.110]. Each of them is paired with a different colored marker." } ], "id_raw": 1125 }, { "id": "Du3H6tvZY0dmq7KJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the coordinates [0.081, -0.015, 0.076, 0.120]?" }, { "from": "gpt", "value": "The region hosts the y-axis labels. They are '500', '700', '900', '1100', and '1300' at different heights on the y-axis." } ], "id_raw": 1125 }, { "id": "bqkLfZ3uYg7861X9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_7.png", "conversations": [ { "from": "human", "value": "\nWhat label is associated with the coordinate [0.041, 0.309, 0.069, 0.349]?" }, { "from": "gpt", "value": "60." } ], "id_raw": 1127 }, { "id": "g0YreFPLcnppLsVR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line for Sound Design} whose points are at [0.114, 0.325, 0.920, 0.424]?" }, { "from": "gpt", "value": "#ef5350." } ], "id_raw": 1127 }, { "id": "HZxIlMutRTReQm5x", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the handle with coordinates [inf, inf, -inf, -inf] represent in the legend?" }, { "from": "gpt", "value": "Colored marker for each trend." } ], "id_raw": 1127 }, { "id": "8BI8uCmLSG17LHUl", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_7.png", "conversations": [ { "from": "human", "value": "\nIs {Visual Design output at Interaction Design}[0.920, 0.128] greater or less than {Sound Design output at Interaction Design}[0.920, 0.424]?" }, { "from": "gpt", "value": "Less." } ], "id_raw": 1127 }, { "id": "Y6wGjC7K1FVg7Lre", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_7.png", "conversations": [ { "from": "human", "value": "\nAt {Interaction Design}[0.920, 0.719], which has a greater value: {Sound Design}[0.920, 0.424] or {Interaction Design}[0.920, 0.522]?" }, { "from": "gpt", "value": "Sound Design." } ], "id_raw": 1127 }, { "id": "DxCn5DCjdy3p9WUK", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_7.png", "conversations": [ { "from": "human", "value": "\nDoes {Sound Design output at Visual Design}[0.114, 0.325] have a higher or lower value than {Interaction Design output at Visual Design}[0.114, 0.128]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1127 }, { "id": "0UUPmyo9fjFvY05Y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.664, 0.052, 0.949, 0.252] in the chart figure." }, { "from": "gpt", "value": "This area contains the legend of the chart. It includes Visual Design with color '#42a5f5', Sound Design with color '#ef5350', and Interaction Design with color '#ab47bc'. The handles for each legend item are not represented by specific coordinates." } ], "id_raw": 1127 }, { "id": "AtcHhSgSquANdFZQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_7.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents within the coordinates [0.074, 0.030, 0.960, 0.719]?" }, { "from": "gpt", "value": "These coordinates contain the entire plot area of the subplot including the lines representing Visual Design, Sound Design, and Interaction Design, along with the x-axis and y-axis and their respective labels and ticks." } ], "id_raw": 1127 }, { "id": "jwXB9OWAOVn6UbES", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.068, 0.729, 0.980, 0.970] in the chart?" }, { "from": "gpt", "value": "The x-axis labels for 'Visual Design', 'Sound Design', and 'Interaction Design' are located within this region. Their respective coordinates are {Visual Design}[0.068, 0.729, 0.160, 0.913], {Sound Design}[0.470, 0.729, 0.564, 0.916], and {Interaction Design}[0.859, 0.729, 0.980, 0.970]." } ], "id_raw": 1127 }, { "id": "QTKhrAWWpByVQmAb", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third text in the legend?" }, { "from": "gpt", "value": "[0.896, 0.139, 0.927, 0.167]." } ], "id_raw": 1128 }, { "id": "WRGXalEBxudrtcXW", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.115, 0.523]?" }, { "from": "gpt", "value": "2000's Hippie point." } ], "id_raw": 1128 }, { "id": "M9ch2UoFPWwfPbfI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis label}[0.922, 0.885, 0.965, 0.970]?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 1128 }, { "id": "S7e9wjJsqhAzp0Xx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_8.png", "conversations": [ { "from": "human", "value": "\nIs {the Emo point in 2020} [0.944, 0.424] higher or lower than {the Streetwear point in 2020} [0.944, 0.171]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1128 }, { "id": "TXIb6EAVF2hdHv6q", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_8.png", "conversations": [ { "from": "human", "value": "\nIs {the interpreting grid of Hippie point}[0.115, 0.734] above or below {interpreting grid of Punk point}[0.115, 0.523]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1128 }, { "id": "GJjDuWKsV6zE9JMZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_8.png", "conversations": [ { "from": "human", "value": "\nBetween {the Emo point in 2000} [0.115, 0.199] and {the Streetwear point in 2000} [0.115, 0.312], which point is higher?" }, { "from": "gpt", "value": "Emo point in 2000." } ], "id_raw": 1128 }, { "id": "Sd6Pkz2PpcSIeaS9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found in coordinates [0.852, 0.044, 0.978, 0.304]." }, { "from": "gpt", "value": "The chart's legend resides in that area. Fashion trends such as Hippie, Goth, Emo, Punk, Vintage, and Streetwear are listed here with corresponding colors. The respective coordinates are {Hippie}[0.853, 0.059, 0.889, 0.076], {Goth}[0.853, 0.101, 0.889, 0.118], {Emo}[0.853, 0.143, 0.889, 0.159], {Punk}[0.853, 0.185, 0.889, 0.201], {Vintage}[0.853, 0.227, 0.889, 0.243], and {Streetwear}[0.853, 0.268, 0.889, 0.285]." } ], "id_raw": 1128 }, { "id": "ePSQojE8Qm3QYRXu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of the region [0.041, 0.155, 0.069, 0.195]?" }, { "from": "gpt", "value": "The region contains the Y-axis label for '80'." } ], "id_raw": 1128 }, { "id": "neYaVJlqQgV9BZT3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects found within the coordinates [0.074, 0.030, 0.985, 0.875]?" }, { "from": "gpt", "value": "This area contains the main chart figure, including the grids and plotted lines representing different fashion trends across the years 2000, 2010, and 2020. There are lines with markers denoting points for the trends Hippie, Goth, Emo, Punk, Vintage, and Streetwear, with corresponding Y-axis labels from 30 to 80 indicating their prevalence." } ], "id_raw": 1128 }, { "id": "grSBhx7vyg7zZEQp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of {the label}[0.041, 0.252, 0.069, 0.292] from the y-axis?" }, { "from": "gpt", "value": "It represents the 77% return rate." } ], "id_raw": 1129 }, { "id": "OiOGkgf9UwGFwQ20", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the identifier at {this location}[0.695, 0.741] on the x-axis?" }, { "from": "gpt", "value": "Citi Group." } ], "id_raw": 1129 }, { "id": "vwUMF9mbKjpJHffD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the significance of {the line}[0.114, 0.185, 0.927, 0.602] in the chart?" }, { "from": "gpt", "value": "It reflects the Investment Return Rate for each bank." } ], "id_raw": 1129 }, { "id": "ezATkqpd3IolOSnv", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_9.png", "conversations": [ { "from": "human", "value": "\nDoes {the marker for 'HSBC'}[0.579, 0.408] denotes a better return rate than {the marker for 'Citi Group'}[0.695, 0.491]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1129 }, { "id": "A47DMG8DyAZrOqg5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_9.png", "conversations": [ { "from": "human", "value": "\nDoes {the marker for 'JP Morgan'}[0.231, 0.185] shows a higher or lower return rate than {the marker for 'Goldman Sachs'}[0.114, 0.268]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1129 }, { "id": "p8QOqEuzplBsC2jN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_9.png", "conversations": [ { "from": "human", "value": "\nIs {the return rate for 'Barclays'}[0.347, 0.324] higher or lower than {the return rate for 'Deutsche Bank'}[0.463, 0.602]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1129 }, { "id": "SsrzS7ytO9Yu2Sr6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_9.png", "conversations": [ { "from": "human", "value": "\nCan you specify the content in the region [0.041, 0.447, 0.069, 0.487]?" }, { "from": "gpt", "value": "In this region, there is a y-axis label '70', indicating a return rate of 70%." } ], "id_raw": 1129 }, { "id": "kFfcUlrllqjjGdfc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_9.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the area [0.756, 0.751, 0.866, 0.970]?" }, { "from": "gpt", "value": "In this area, there is an x-axis label 'Bank of America'." } ], "id_raw": 1129 }, { "id": "aoniPv20caRs3aBt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the area [0.114, 0.324, 0.927, 0.435]?" }, { "from": "gpt", "value": "This region contains information about the Investment Return Rate for each bank. The identified banks are: {'Goldman Sachs'}[0.114, 0.268], {'JP Morgan'}[0.231, 0.185], {'Barclays'}[0.347, 0.324], {'HSBC'}[0.579, 0.408], {'Citi Group'}[0.695, 0.491], {'Bank of America'}[0.811, 0.463] and {'Morgan Stanley'}[0.927, 0.380]." } ], "id_raw": 1129 }, { "id": "JW1aQnEsbgoDu2oh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_10.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at the coordinate [0.736, 0.070, 0.965, 0.110] represent?" }, { "from": "gpt", "value": "The label 'Unemployment Rate' for the line chart." } ], "id_raw": 1130 }, { "id": "l4fLGTkgUKOAh7Hv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_10.png", "conversations": [ { "from": "human", "value": "\nWhich month does the x-axis tick label at [0.776, 0.845, 0.809, 0.911] refer to?" }, { "from": "gpt", "value": "October." } ], "id_raw": 1130 }, { "id": "nqnNnp6tzymFIgLi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_10.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the y-axis tick label}[0.041, 0.098, 0.067, 0.133]?" }, { "from": "gpt", "value": "4.5." } ], "id_raw": 1130 }, { "id": "hZ65eLdI3PQE6nBb", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_10.png", "conversations": [ { "from": "human", "value": "\nWhich is higher in April, {the Unemployment Rate}[0.340, 0.223] or {the GDP Growth Rate}[0.340, 0.706]?" }, { "from": "gpt", "value": "GDP Growth Rate." } ], "id_raw": 1130 }, { "id": "hwznbNHN2qhfMqlL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_10.png", "conversations": [ { "from": "human", "value": "\nIs {Unemployment Rate in July}[0.566, 0.191] more than or less than {GDP Growth Rate in July}[0.566, 0.626]?" }, { "from": "gpt", "value": "Less than." } ], "id_raw": 1130 }, { "id": "heO0vqP3p6wXdZQW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_10.png", "conversations": [ { "from": "human", "value": "\nAt which rate is the decrease from {November to December}[0.868, 0.207, 0.943, 0.207] steeper, Unemployment Rate or GDP Growth Rate?" }, { "from": "gpt", "value": "GDP Growth Rate." } ], "id_raw": 1130 }, { "id": "5qcKyb2YWVoCPLOB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.736, 0.070, 0.965, 0.172]." }, { "from": "gpt", "value": "Within this area, there are two chart line labels: {Unemployment Rate}[0.736, 0.070, 0.965, 0.110] and {GDP Growth Rate}[0.736, 0.132, 0.930, 0.172]. Each label is color-coded respectively to match the line it represents on the chart." } ], "id_raw": 1130 }, { "id": "WukdGoESz0WzjACT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.323, 0.845, 0.434, 0.919]." }, { "from": "gpt", "value": "In this area, we have the x-axis label {May}[0.397, 0.845, 0.434, 0.919]." } ], "id_raw": 1130 }, { "id": "EfyHef86XXgA62i2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects can be found within the region [0.793, 0.223, 0.943, 0.706]?" }, { "from": "gpt", "value": "Within this region, you can see the Unemployment Rate and GDP Growth Rate data points for the {month of October}[0.793, 0.223, 0.793, 0.626] and {December}[0.943, 0.207, 0.943, 0.674]." } ], "id_raw": 1130 }, { "id": "sL1SVuQLLBcWRzw8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_11.png", "conversations": [ { "from": "human", "value": "\nWhat is {the object}[0.719, 0.692, 0.781, 0.732] appearing in orange color in the chart?" }, { "from": "gpt", "value": "Line representing \"NewTown\"." } ], "id_raw": 1131 }, { "id": "q0QRFLAyLujgsszD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_11.png", "conversations": [ { "from": "human", "value": "\nCan you tell the position of {the Bike Lanes label} that is shown on the x-axis of the chart?" }, { "from": "gpt", "value": "[0.432, 0.698, 0.508, 0.850]" } ], "id_raw": 1131 }, { "id": "z9h02uDO1frOvORM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_11.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the coordinates of {the chart element}[0.825, 0.371]?" }, { "from": "gpt", "value": "It is a point on the line representing development index of Industries in GlobalImpact." } ], "id_raw": 1131 }, { "id": "KNPlzKn4b3llXZVI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_11.png", "conversations": [ { "from": "human", "value": "\nBased on the graph, is the development index of {Residential Areas in 'GlobalImpact'}[0.707, 0.181] higher or lower than {Residential Areas in 'NewTown'}[0.707, 0.340]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1131 }, { "id": "UhNkAaZAmeTfOM7r", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_11.png", "conversations": [ { "from": "human", "value": "\nWhich entity shows a higher development index for {Bike Lanes}[0.470], is it 'GlobalImpact' or 'NewTown'?" }, { "from": "gpt", "value": "NewTown." } ], "id_raw": 1131 }, { "id": "jhyCtACsiJfTOcTW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_11.png", "conversations": [ { "from": "human", "value": "\nComparing the development index at {point representing Commercial Areas for 'NewTown'}[0.589, 0.419], is the index for Industries higher or lower for 'NewTown'?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1131 }, { "id": "a0eU4Jy4LmUAXqRz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region [0.074, 0.055, 0.985, 0.688]." }, { "from": "gpt", "value": "This region contains the main graph showing the development index of different areas like 'All', 'Greenspace', 'Public Transportation', etc., for two entities, 'GlobalImpact' and 'NewTown'. The graph includes a series of points connected by lines for both 'GlobalImpact'{GlobalImpact line coordinates}[0.115, 0.181, 0.944, 0.371] and 'NewTown'{NewTown line coordinates}[0.115, 0.340, 0.944, 0.593]." } ], "id_raw": 1131 }, { "id": "lDbGi8pyVkfk0RkL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the elements in the range [0.392, 0.037, 0.667, 0.087]?" }, { "from": "gpt", "value": "Within these coordinates, there are two labels named {'GlobalImpact'}[0.437, 0.048, 0.529, 0.076] and {'NewTown'}[0.596, 0.048, 0.661, 0.076], appearing in the graph's legend." } ], "id_raw": 1131 }, { "id": "37TlREOo70cAjZse", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_11.png", "conversations": [ { "from": "human", "value": "\nWhat can you see within the coordinates [0.192, 0.698, 0.275, 0.864]?" }, { "from": "gpt", "value": "These coordinates contain the x-axis label for 'Greenspace'." } ], "id_raw": 1131 }, { "id": "fHprgWy842qOkrz2", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.070, 0.321, 0.083, 0.506]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1132 }, { "id": "eTBrPF5KbherXKqH", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_12.png", "conversations": [ { "from": "human", "value": "\nWhat are the coordinates for the tick of {GreenEnergy}?" }, { "from": "gpt", "value": "[0.548, 0.808]." } ], "id_raw": 1132 }, { "id": "tCNQfSjv5oHzXkgz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the chart element at coordinates [0.224, 0.044, 0.367, 0.069]?" }, { "from": "gpt", "value": "The legend label 'Living Environment'." } ], "id_raw": 1132 }, { "id": "G56YKb1LFoMn7jCd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_12.png", "conversations": [ { "from": "human", "value": "\nIs {the 'Human Life' line point at 'Mindfulness'}[0.349, 0.366] above or below {the 'Living Environment' line point at 'Mindfulness'}[0.349, 0.492]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1132 }, { "id": "wQhDu1WhpfuGutdT", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Human Life' line point at 'SimpleLiving'}[0.149, 0.571] have a higher or lower value than {the 'Living Environment' line point at 'SimpleLiving'}[0.149, 0.650]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1132 }, { "id": "XipBZKYpv1nbFjwn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_12.png", "conversations": [ { "from": "human", "value": "\nWhich has a greater value at OutdoorActivities, the {Human Life line}[0.947, 0.508] or the {Living Environment line}[0.947, 0.603]?" }, { "from": "gpt", "value": "Living Environment." } ], "id_raw": 1132 }, { "id": "eenFV0W9t8NnxXq1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content in the region [0.020, 0.033, 0.373, 0.080]?" }, { "from": "gpt", "value": "This region contains the legend for two lines in the chart. The label for 'Human Life' is located at {coordinates} [0.068, 0.044, 0.153, 0.069] and the label for 'Living Environment' is located at {coordinates} [0.224, 0.044, 0.367, 0.069]." } ], "id_raw": 1132 }, { "id": "WAHOb48kT1qrYidP", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_12.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.621, 0.814, 0.676, 0.913]?" }, { "from": "gpt", "value": "It contains the x-axis label 'Minimalism'." } ], "id_raw": 1132 }, { "id": "xoFlQStUHQA2Dbpd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_12.png", "conversations": [ { "from": "human", "value": "\nWhat objects does the region [0.421, 0.814, 0.475, 0.915] include?" }, { "from": "gpt", "value": "The x-axis label 'Sustainable' is located within this region." } ], "id_raw": 1132 }, { "id": "NGlmcwbgjOYK0f1G", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_13.png", "conversations": [ { "from": "human", "value": "\nWhat is shown by the label at this coordinate {the axis label}[0.015, 0.288, 0.035, 0.677]?" }, { "from": "gpt", "value": "Normalized Index." } ], "id_raw": 1133 }, { "id": "iyKEpsAds9gWn2LE", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_13.png", "conversations": [ { "from": "human", "value": "\nAt what coordinate does {the legend item for Grain Production}[0.128, 0.110, 0.245, 0.138] appear?" }, { "from": "gpt", "value": "[0.128, 0.110, 0.245, 0.138]." } ], "id_raw": 1133 }, { "id": "oRi7apApgeRqN9ZO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_13.png", "conversations": [ { "from": "human", "value": "\nCan you specify the year corresponding to {this tick}[0.944, 0.881] on the x-axis?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 1133 }, { "id": "wFondob5XQPONBC5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_13.png", "conversations": [ { "from": "human", "value": "\nDoes the graph point for {Grain Production in 2016}[0.118, 0.350] have a higher value than {Agriculture GDP in 2020}[0.944, 0.395]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1133 }, { "id": "V2RiZZL2lxmkaBV2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_13.png", "conversations": [ { "from": "human", "value": "\nWhich line appears higher on the chart at the coordinate {Grain Production in 2020}[0.944, 0.168], Grain Production or Meat Production?" }, { "from": "gpt", "value": "Grain Production." } ], "id_raw": 1133 }, { "id": "sX1s606BfstnCnyO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_13.png", "conversations": [ { "from": "human", "value": "\nIs the marker for {Fisheries in 2018}[0.531, 0.456] located above or below {Fisheries in 2019}[0.737, 0.380]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1133 }, { "id": "XH7pxqNpwfuFh9sH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_13.png", "conversations": [ { "from": "human", "value": "\nDescribe the significant chart elements located in the area between [0.051, 0.260, 0.072, 0.903]." }, { "from": "gpt", "value": "This region contains y-axis tick labels for the values: {50}[0.051, 0.867, 0.072, 0.903], {60}[0.051, 0.715, 0.072, 0.751], {70}[0.051, 0.564, 0.072, 0.600], {80}[0.051, 0.412, 0.072, 0.448], {90}[0.051, 0.260, 0.072, 0.296], and {100}[0.041, 0.108, 0.072, 0.144]." } ], "id_raw": 1133 }, { "id": "putwzfQnYikzga4J", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be observed in the region ranging from [0.101, 0.881, 0.961, 0.919]?" }, { "from": "gpt", "value": "This is the area for x-axis tick labels and coordinates corresponding to the years from {2016}[0.101, 0.891, 0.136, 0.919] to {2020}[0.926, 0.891, 0.961, 0.919]." } ], "id_raw": 1133 }, { "id": "2nKEcUZFmw5TEz0L", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_13.png", "conversations": [ { "from": "human", "value": "\nWhat type of elements are located in the subsection [0.128, 0.110, 0.245, 0.305]?" }, { "from": "gpt", "value": "This area includes {legend items for production types}[0.128, 0.110, 0.245, 0.138], specifically Grain Production, and continues downward to include {Meat Production}[0.128, 0.152, 0.241, 0.180] and {Dairy Production}[0.128, 0.193, 0.244, 0.221]." } ], "id_raw": 1133 }, { "id": "ZosF4OQ7Y7vNvy9w", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the object with coordinates [0.034, 0.108, 0.058, 0.141] represent?" }, { "from": "gpt", "value": "It represents a 'y' axis tick label, with the value 85." } ], "id_raw": 1134 }, { "id": "RYLz4T7MZHXyo6E7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_14.png", "conversations": [ { "from": "human", "value": "\nWhat is described by the label {coordinates}[0.633, 0.705, 0.730, 0.898]?" }, { "from": "gpt", "value": "It's the x-axis label for 'Music_and_Songs'." } ], "id_raw": 1134 }, { "id": "ETFE59Lt8mxegF5p", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line that represents \"Heritage Site Preservation\"}[0.099, 0.179, 0.848, 0.332]?" }, { "from": "gpt", "value": "The color is #3d89be." } ], "id_raw": 1134 }, { "id": "BrBI8GIYziFpCY09", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_14.png", "conversations": [ { "from": "human", "value": "\nDoes the {point \"Traditional Clothing\"}[0.099, 0.313] have a higher or lower rating compared to the {point \"Traditional Dance\"}[0.182, 0.217] according to the \"Heritage Site Preservation\" scores?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1134 }, { "id": "TSpOTZDmCRkyPCol", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_14.png", "conversations": [ { "from": "human", "value": "\nIn the \"Ethnic Groups Integration\" chart, is the score for {Art_and_Sculpture}[0.266, 0.428] more or less than the score for {Traditional_Dance}[0.182, 0.351]?" }, { "from": "gpt", "value": "More." } ], "id_raw": 1134 }, { "id": "eey1ZvswYOIrQZvS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the closer x-axis tick value to {the marker for Ethnic Groups Integration at Traditional Tools}[0.848, 0.639]?" }, { "from": "gpt", "value": "Native_Sports." } ], "id_raw": 1134 }, { "id": "cPE9vWRjFJ0WCzyS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found within the region [0.045, 0.705, 0.231, 0.919]." }, { "from": "gpt", "value": "This region contains two x-axis labels: {Traditional_Clothing}[0.045, 0.705, 0.153, 0.919] and {Traditional_Dance}[0.133, 0.705, 0.231, 0.900]." } ], "id_raw": 1134 }, { "id": "QiEZHMvlOttnOihF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_14.png", "conversations": [ { "from": "human", "value": "\nWhat features can be seen within the area [0.336, 0.044, 0.974, 0.106]?" }, { "from": "gpt", "value": "This region contains two legend items related to {Heritage Site Preservation}[0.396, 0.058, 0.639, 0.092] and {Ethnic Groups Integration}[0.728, 0.058, 0.966, 0.092], each with its corresponding color marker." } ], "id_raw": 1134 }, { "id": "rZI2fNFCQeaK13hY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_14.png", "conversations": [ { "from": "human", "value": "\nTell me what's contained within the region [0.099, 0.255, 0.848, 0.409]." }, { "from": "gpt", "value": "This region includes a series of markers denoting the \"Heritage Site Preservation\" scores for each category of heritage. It displays scores from {Traditional_Clothing} to {Traditional_Tools}." } ], "id_raw": 1134 }, { "id": "iDW01PfnCzHOZHvB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_16.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at the coordinates [0.831, 0.193, 0.964, 0.233]?" }, { "from": "gpt", "value": "The label for \"Food Export\" in the legend." } ], "id_raw": 1136 }, { "id": "k88LOTzvXnLJMUVX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_16.png", "conversations": [ { "from": "human", "value": "\nWhich object can be found at the coordinate [0.129, 0.213]?" }, { "from": "gpt", "value": "The data point representing Food Export for the year 2020." } ], "id_raw": 1136 }, { "id": "FQRXzbJt3vhZsYJt", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the label at [0.515, 0.930, 0.560, 0.970]?" }, { "from": "gpt", "value": "X-axis label - \"Year\"." } ], "id_raw": 1136 }, { "id": "PNBgqDEdn2C96A6E", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point representing 'Food Import' for 2020}[0.129, 0.335] higher or lower than {the point representing 'Food Waste' for 2021}[0.944, 0.653] on the chart?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1136 }, { "id": "kOBy2Cllw9iaj2jR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Food Export' in 2021}[0.944, 0.164] to the left or right of {the point for 'Food Import' in 2021}[0.944, 0.384]?" }, { "from": "gpt", "value": "They are at the same position horizontally on the x-axis, so neither left nor right." } ], "id_raw": 1136 }, { "id": "mUBX5mo3NqJ58N71", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_16.png", "conversations": [ { "from": "human", "value": "\nWhich is greater at {the year '2020' point}[0.129, 0.213], 'Food Import' or 'Food Export'?" }, { "from": "gpt", "value": "'Food Export'." } ], "id_raw": 1136 }, { "id": "Yk17JprBcbM0WYd7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_16.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content within the area [0.764, 0.131, 0.965, 0.295]?" }, { "from": "gpt", "value": "This area contains the labels of the chart legend: {Food Import}[0.831, 0.131, 0.965, 0.171], {Food Export}[0.831, 0.193, 0.964, 0.233], and {Food Waste}[0.831, 0.255, 0.959, 0.295] with their corresponding color markers." } ], "id_raw": 1136 }, { "id": "bntEq6nptZEkaXcy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_16.png", "conversations": [ { "from": "human", "value": "\nWhat components are found within [0.108, 0.834, 0.150, 0.919]?" }, { "from": "gpt", "value": "The area contains the x-axis tick label '2020'." } ], "id_raw": 1136 }, { "id": "xmmT5hGotAZINvNI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_16.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects in the area delimited by [0.129, 0.164, 0.944, 0.384]." }, { "from": "gpt", "value": "This region contains three data points. These are: the 'Food Import' data points for years 2020 and 2021 at {coordinates}[0.129, 0.335] and [0.944, 0.384] respectively; and the 'Food Export' datapoint for the year 2021 located at {coordinates}[0.944, 0.164]." } ], "id_raw": 1136 }, { "id": "vqpLAZyFwsjWgi1L", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the x-tick item}[0.063, 0.760, 0.167, 0.966]?" }, { "from": "gpt", "value": "Slight decrease." } ], "id_raw": 1137 }, { "id": "wrKyhT9VEWnVqtXr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the legend item located at [0.583, 0.086, 0.673, 0.126] represent?" }, { "from": "gpt", "value": "Reading." } ], "id_raw": 1137 }, { "id": "LCQ7HoRvMmK8f8j6", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_17.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.283, 0.035, 0.513]?" }, { "from": "gpt", "value": "Frequency." } ], "id_raw": 1137 }, { "id": "IZrXesoCH3IbL52s", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_17.png", "conversations": [ { "from": "human", "value": "\nAt the category of 'Persistent rise', is the point for {Reading}[0.526, 0.257] above or below the point for {Exercising}[0.526, 0.234]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1137 }, { "id": "qls6ffOXmKP7dqU2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_17.png", "conversations": [ { "from": "human", "value": "\nIs {the second point of line 'Reading'}[0.321, 0.445] located at a higher value than {the second point of line 'Exercising'}[0.321, 0.633]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1137 }, { "id": "lj3PbUIBKN0xq2CC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_17.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at 'Abrupt drop', {Reading}[0.938, 0.304] or {Exercising}[0.938, 0.468]?" }, { "from": "gpt", "value": "Exercising." } ], "id_raw": 1137 }, { "id": "eAj1X5Neh8ov7J41", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_17.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the area with coordinates [0.512, 0.068, 0.903, 0.144]?" }, { "from": "gpt", "value": "The area contains a legend for two categories 'Reading' and 'Exercising'." } ], "id_raw": 1137 }, { "id": "D6kSxMVrroivrVZ5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.896, 0.760, 0.979, 0.925] of the chart include?" }, { "from": "gpt", "value": "The area contains the x-axis label for the category 'Abrupt drop'." } ], "id_raw": 1137 }, { "id": "gYi51MM60sd1arax", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found at coordinates [0.115, 0.093, 0.938, 0.374]." }, { "from": "gpt", "value": "Points and markers related to the values of the categories 'Slight decrease', 'Gradual decline', 'Persistent rise', 'Rapid recovery', and 'Abrupt drop' representing the line 'Reading' are located in this region." } ], "id_raw": 1137 }, { "id": "SaJYqGftA9j1XJUe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the text represented by the coordinates [0.767, 0.070, 0.954, 0.110]?" }, { "from": "gpt", "value": "Investment Fund." } ], "id_raw": 1139 }, { "id": "Le7nwtL4DnJHoa2m", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_19.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.213, 0.035, 0.521]" }, { "from": "gpt", "value": "Funding Level" } ], "id_raw": 1139 }, { "id": "xEtekIQGeHS49nsI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_57_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the {x-axis tick}[0.887, 0.714, 0.980, 0.899]?" }, { "from": "gpt", "value": "Steep decline." } ], "id_raw": 1139 }, { "id": "AgQS2fcARYscgfAa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_19.png", "conversations": [ { "from": "human", "value": "\nAt the 'Pronounced decline' stage, is the {Investment Fund point} [0.128, 0.569] lower or higher than the {Startup point}[0.128, 0.637]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1139 }, { "id": "ZKGSk54jTXr0Ttnq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_19.png", "conversations": [ { "from": "human", "value": "\nComparing {Investment Fund point}[0.934,0.637] and {Startup point}[0.934,0.704] at the 'Steep decline' stage, whether the former is higher or lower?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1139 }, { "id": "rYLfvUT8vJIEkcIQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_57_19.png", "conversations": [ { "from": "human", "value": "\nIn the 'Abrupt increase' stage, which one is higher, {Investment Fund point}[0.665, 0.165] or {Startup point} [0.665, 0.232]?" }, { "from": "gpt", "value": "Investment Fund." } ], "id_raw": 1139 }, { "id": "WmSblenNwghZRrl7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_19.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area with the coordinates [0.696, 0.052, 0.963, 0.190]." }, { "from": "gpt", "value": "This area holds the legend for the chart showing 'Investment Fund' [0.767, 0.070, 0.954, 0.110] and 'Startup' [0.767, 0.132, 0.850, 0.172], with color markers." } ], "id_raw": 1139 }, { "id": "GQ9OMJ3TT2ohTNTa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_19.png", "conversations": [ { "from": "human", "value": "\nWhat content is represented by the coordinates [0.063, 0.714, 0.192, 0.970]?" }, { "from": "gpt", "value": "The area represents the x-axis label 'Pronounced decline'." } ], "id_raw": 1139 }, { "id": "1O0mkSB65CjAgMdg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_57_19.png", "conversations": [ { "from": "human", "value": "\nWhat sort of objects exist in [0.128, 0.205, 0.934, 0.664]?" }, { "from": "gpt", "value": "There are multiple data points for 'Investment Fund' and 'Startup' plotted over different stages in this area." } ], "id_raw": 1139 }, { "id": "CP82lp2WEWkEL2QM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_0.png", "conversations": [ { "from": "human", "value": "\nWhat color is used to represent the {Migration Trend line}[0.098, 0.277, 0.945, 0.887]?" }, { "from": "gpt", "value": "#00bfff." } ], "id_raw": 1140 }, { "id": "2iHIhC68Mwpdc890", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_0.png", "conversations": [ { "from": "human", "value": "\nCan you state the coordinate of the point labeled '16' on {the Migration Trend line}[0.098, 0.277, 0.945, 0.887]?" }, { "from": "gpt", "value": "[0.569, 0.758]." } ], "id_raw": 1140 }, { "id": "UHJzPxeAR4Kmf2u1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the label for the y-axis on this chart?" }, { "from": "gpt", "value": "Number of People (in millions)." } ], "id_raw": 1140 }, { "id": "zufrNphgQHGqrUea", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_0.png", "conversations": [ { "from": "human", "value": "\nWhich trend increases faster, {Migration Trend}[0.098, 0.277, 0.945, 0.887] or {Mobility Trend}[0.098, 0.813, 0.945, 0.832] between weeks 1 and 10?" }, { "from": "gpt", "value": "Mobility Trend." } ], "id_raw": 1140 }, { "id": "SZIGUp1RuoaZpNEC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_0.png", "conversations": [ { "from": "human", "value": "\nDoes {point 17 on the Migration Trend line}[0.600, 0.739] fall above or below point 19?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1140 }, { "id": "pVJPR7KospOepZy6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_0.png", "conversations": [ { "from": "human", "value": "\nWhich label appears first on the x-axis, '5' or '10'?" }, { "from": "gpt", "value": "5." } ], "id_raw": 1140 }, { "id": "HnG8XrGbNAdytqBS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_0.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within the chart subtitle area at [0.310, 0.019, 0.733, 0.049]." }, { "from": "gpt", "value": "The subtitle \"Population Migration and Mobility Trends\" is located, with font color coded as '.15' and no text rotation." } ], "id_raw": 1140 }, { "id": "UZsVB5B49I8TenUa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_0.png", "conversations": [ { "from": "human", "value": "\nWhat does the region around [0.033, 0.271, 0.051, 0.288] contain?" }, { "from": "gpt", "value": "The y-axis tick label '2.5' situated here, highlighted with a '.15' color label facing a 0.0 rotation." } ], "id_raw": 1140 }, { "id": "q48QmRv1SRPKsWD8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_0.png", "conversations": [ { "from": "human", "value": "\nDescribe what can be observed in the region [0.062, 0.063, 0.220, 0.135]." }, { "from": "gpt", "value": "This region contains the chart's sublegend items marked as 'Migration Trend' and 'Mobility Trend', associated with colored handles #00bfff and #008000 respectively." } ], "id_raw": 1140 }, { "id": "UERuI5Wpl5dIG74J", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_1.png", "conversations": [ { "from": "human", "value": "\nWhat does {the label at the extremes of the y-axis}[0.033, -0.088, 0.055, -0.071] indicate?" }, { "from": "gpt", "value": "140." } ], "id_raw": 1141 }, { "id": "va7AuDQBr1xHbBNC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_1.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at {the coordinate}[0.504, 0.958, 0.543, 0.981]?" }, { "from": "gpt", "value": "Date label." } ], "id_raw": 1141 }, { "id": "fdodB5OYwrSlfUYk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_1.png", "conversations": [ { "from": "human", "value": "\nWhat is {the color of the first mark in the legend}[0.071, 0.082, 0.099, 0.082]?" }, { "from": "gpt", "value": "#00bfff." } ], "id_raw": 1141 }, { "id": "PGJQdq3Q23F66yhS", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_1.png", "conversations": [ { "from": "human", "value": "\nWhich {line}[0.540, 0.574, 0.574, 0.590] is above, {Stock 1}[0.540, 0.574] or {Stock 3}[0.574, 0.590] at {the fifth date}[0.236, 0.600]?" }, { "from": "gpt", "value": "Stock 3." } ], "id_raw": 1141 }, { "id": "a5tApypQZWu4CLSF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_1.png", "conversations": [ { "from": "human", "value": "\nWhich axis label is closer to the chart's left border, {the x-axis label}[0.504, 0.958, 0.543, 0.981] or {the y-axis label}[0.012, 0.423, 0.028, 0.557]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1141 }, { "id": "QJin8FVnO1M5vUFJ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_1.png", "conversations": [ { "from": "human", "value": "\nIs the point for {Stock 1's first data}[0.101, 0.546] higher than the point for {Stock 3's first data}[0.101, 0.441] on the chart?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1141 }, { "id": "bwSkZ9ugD6uihfps", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects found in [0.042, 0.050, 0.945, 0.184]." }, { "from": "gpt", "value": "This region contains the color handles and labels for {Stock 1}[0.071, 0.082, 0.099, 0.082], {Stock 2 with Gradient}[0.071, 0.115, 0.099, 0.115], and {Stock 3}[0.071, 0.148, 0.099, 0.148] in the legend positioned at the upper left portion of the chart." } ], "id_raw": 1141 }, { "id": "AcuxJATIFWRV62RI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_1.png", "conversations": [ { "from": "human", "value": "\nWhat content is located in the span [0.101, 0.371, 0.945, 0.946] on the chart?" }, { "from": "gpt", "value": "This region comprises the plotted lines showing data points for {Stock 1}[0.101, 0.546, 0.945, 0.887], {Stock 2 with Gradient}[0.101, 0.371, 0.135, 0.387], and {Stock 3}[0.101, 0.093, 0.945, 0.441] representing their changes in stock prices over various dates." } ], "id_raw": 1141 }, { "id": "SBjOkGaP9ruJBH5e", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_1.png", "conversations": [ { "from": "human", "value": "\nWhat aspects can be observed in the area [0.047, 0.930, 0.980, 0.952]?" }, { "from": "gpt", "value": "This area mainly encompasses the x-axis tick labels marking specific dates like {1/8/2021}[0.109, 0.933, 0.161, 0.951], indicating the timeline for the financial data presented." } ], "id_raw": 1141 }, { "id": "x9V8C0EkXsAEvb2d", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the color of the series represented by the third label in the legend}[0.783, 0.886, 0.877, 0.908]?" }, { "from": "gpt", "value": "Pink." } ], "id_raw": 1142 }, { "id": "DJNc8wkCpjff8bII", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the xlabel of the chart}[0.493, 0.958, 0.546, 0.981]?" }, { "from": "gpt", "value": "Weeks." } ], "id_raw": 1142 }, { "id": "BarW98UZqyjw4syI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_2.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.012, 0.457, 0.028, 0.523]?" }, { "from": "gpt", "value": "Value." } ], "id_raw": 1142 }, { "id": "VOyhnsIsF1lcgWUU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point at 5 on the x-axis}[0.220, 0.927, 0.220, 0.927] above or below {the point at 6 on the x-axis}[0.252, 0.927, 0.252, 0.927] for Data Series 1?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1142 }, { "id": "pZS7h4QlmeQ60Fxp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point at coordinate 14}[0.504, 0.617] above or below {the point at coordinate 15}[0.535, 0.665] for Data Series 1?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1142 }, { "id": "P8cnbrRXZYGiabZn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_2.png", "conversations": [ { "from": "human", "value": "\nWhich is higher, {point 10 on Data Series 3}[0.378, 0.760] or {point 12 on Data Series 3}[0.441, 0.776]?" }, { "from": "gpt", "value": "Point 12." } ], "id_raw": 1142 }, { "id": "0Lopa2csNgTZy23l", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.739, 0.812, 0.981, 0.917]." }, { "from": "gpt", "value": "The legend of the chart housing Data Series 1, Data Series 2 with Gradient, and Data Series 3, with corresponding color handles located at {coordinates}[0.744, 0.830, 0.772, 0.830], {coordinates}[0.744, 0.863, 0.772, 0.863], and {coordinates}[0.744, 0.896, 0.772, 0.896]." } ], "id_raw": 1142 }, { "id": "8fQduztesnPifW5H", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_2.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.033, 0.245, 0.048, 0.262]." }, { "from": "gpt", "value": "This area contains the y-axis label '35' aligned to the tick mark located at {coordinates}[0.052, 0.252]." } ], "id_raw": 1142 }, { "id": "uSonLBMDwDABumc8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_2.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.052, 0.927, 1.001, 0.951]?" }, { "from": "gpt", "value": "Contains x-axis labels ranging from '0' to '30,' specifically located at {coordinates}[0.059, 0.933, 0.066, 0.951], {coordinates}[0.843, 0.933, 0.858, 0.951]." } ], "id_raw": 1142 }, { "id": "VgWsEOb7ZOG7LwQp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_3.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at coordinates [0.055, 0.933, 0.073, 0.951]?" }, { "from": "gpt", "value": "x-axis label \"0.0\"." } ], "id_raw": 1143 }, { "id": "WTARXOYn85jgYJP1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_3.png", "conversations": [ { "from": "human", "value": "\nWhat is visible at [0.743, 0.105, 0.975, 0.127] in the chart?" }, { "from": "gpt", "value": "Legend text \"Dramatic Decrease with Gradient\"." } ], "id_raw": 1143 }, { "id": "vcHvX7JTEMERSoZm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_3.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object at [0.108, 0.093]?" }, { "from": "gpt", "value": "Point 1 marker of \"Dramatic Decrease with Gradient\" line." } ], "id_raw": 1143 }, { "id": "0OZ0ddqGOK7Q7gbm", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_3.png", "conversations": [ { "from": "human", "value": "\nIs {the first point}[0.108, 0.809] higher than {the fifth point}[0.284, 0.793] on the \"Stable Growth\" line?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1143 }, { "id": "IL9910y5onyeB3rF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_3.png", "conversations": [ { "from": "human", "value": "\nCompare the position of {the point at week 15}[0.725, 0.753] with {the point at week 20}[0.946, 0.733] on the same line." }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1143 }, { "id": "megsLZkTATCs8FCd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_3.png", "conversations": [ { "from": "human", "value": "\nDoes {this point}[0.152, 0.725] lie above or below {that point}[0.196, 0.721] on the \"Slight Increase\" line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1143 }, { "id": "okcJYEM4DbxDmM1G", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.066, 0.053, 0.988, 0.927]." }, { "from": "gpt", "value": "This area includes the main plotting area of the chart where multiple lines are displayed, grids are visible, and it's framed by axes with labels \"Weeks\" and \"Value\"." } ], "id_raw": 1143 }, { "id": "lgieebENtOVQdWQ4", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.636, 0.867, 0.949, 0.886] represent?" }, { "from": "gpt", "value": "This zone features the upper portion of the plotting area where line segments demonstrating a gentle decline, specifically from week 12 to week 20, are present." } ], "id_raw": 1143 }, { "id": "us78FrKbTyPCOLjY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the contents of the region [0.699, 0.063, 0.981, 0.168]?" }, { "from": "gpt", "value": "This part consists of the chart's legend, displaying colored markers and corresponding labels like \"Stable Growth,\" \"Dramatic Decrease with Gradient,\" and \"Slight Increase.\"" } ], "id_raw": 1143 }, { "id": "GztXyCBcVzgNOGKi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the object with coordinates [0.103, 0.137, 0.252, 0.160] indicate in the legend?" }, { "from": "gpt", "value": "Forest Coverage Rate." } ], "id_raw": 1144 }, { "id": "3YroXNBrfHv8VqRm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the X-axis label with the coordinates [0.502, 0.958, 0.537, 0.981]?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 1144 }, { "id": "DBBAaXtkkSwhHEcF", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_4.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.033, 0.069, 0.048, 0.087]?" }, { "from": "gpt", "value": "80." } ], "id_raw": 1144 }, { "id": "MIaE3K1R0Qyoxc8S", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_4.png", "conversations": [ { "from": "human", "value": "\nCompared to {the line for 'Air Quality Index'}[0.094, 0.416, 0.945, 0.581], is {the line for 'Forest Coverage Rate'}[0.094, 0.792, 0.945, 0.887] generally trending higher or lower?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1144 }, { "id": "B9J2TCVgiUjr7xp3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_4.png", "conversations": [ { "from": "human", "value": "\nIn the year 2018, is the value for {Air Quality Index}[0.803, 0.425] higher or lower than that for {Forest Coverage Rate}[0.803, 0.808]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1144 }, { "id": "CQbuckvSccUKLkgN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_4.png", "conversations": [ { "from": "human", "value": "\nFor the year represented by {[0.236, 0.572]}, is the Air Quality Index higher or lower than the Forest Coverage Rate?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1144 }, { "id": "Xb72uWqvXJxoi5SC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in the area with coordinates [0.059, 0.063, 0.258, 0.168]." }, { "from": "gpt", "value": "This region contains the legend of the chart, with the labels: {Air Quality Index}[0.103, 0.072, 0.218, 0.094], {Water Quality Index}[0.103, 0.105, 0.240, 0.127], and {Forest Coverage Rate}[0.103, 0.137, 0.252, 0.160], each paired with a unique color marker." } ], "id_raw": 1144 }, { "id": "cl8hIVzVQFu84xbf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.156, 0.019, 0.883, 0.049] contain?" }, { "from": "gpt", "value": "It contains the subtitle of the chart: \"Ecological Environment Sustainability Assessment from 2008 to 2020\"." } ], "id_raw": 1144 }, { "id": "fIqMV8zwACwhsLrk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_4.png", "conversations": [ { "from": "human", "value": "\nWhat do the coordinates [0.094, 0.416, 0.945, 0.581] represent?" }, { "from": "gpt", "value": "They represent the line for 'Air Quality Index', indicating how Air Quality Index has changed from 2008 to 2020." } ], "id_raw": 1144 }, { "id": "L01QoKgOr2JgYMGR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of {the y-axis label}[0.013, 0.357, 0.028, 0.623] ?" }, { "from": "gpt", "value": "Allocation Percentage." } ], "id_raw": 1145 }, { "id": "s6GkNFdnY6U0WBHA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_5.png", "conversations": [ { "from": "human", "value": "\nWhat is {the point at line for 'Solar Power'}[0.945, 0.559]?" }, { "from": "gpt", "value": "point 12." } ], "id_raw": 1145 }, { "id": "KedPDugaCwnF3qUv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the title present at [0.319, 0.019, 0.723, 0.049]?" }, { "from": "gpt", "value": "Future Energy and Resource Allocation." } ], "id_raw": 1145 }, { "id": "gCGLSUiFDH3LjdD8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_5.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '9'}[0.714, 0.559] on the Hydro Power line higher or lower than {the point labeled '10'}[0.791, 0.214] on the same line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1145 }, { "id": "2aQIAcjM9L4oWeIk", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_5.png", "conversations": [ { "from": "human", "value": "\nIs the {last point for 'Solar Power'}[0.945, 0.559] above or below the {last point for 'Nuclear Power'}[0.945, 0.818]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1145 }, { "id": "1ElHWjfriOo7iJAZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_5.png", "conversations": [ { "from": "human", "value": "\nWhich had a higher value at point 7, Solar Power or Nuclear Power?" }, { "from": "gpt", "value": "Solar Power." } ], "id_raw": 1145 }, { "id": "gFznVpsDzrRoWkwq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the details in the region [0.699, 0.437, 0.981, 0.542]." }, { "from": "gpt", "value": "The chart's legend is located in this region. It contains three entries: 'Solar Power Sustained Rise', 'Hydro Power Erratic Growth', and 'Nuclear Power Continued Decline'. Each entry has a color marker symbol." } ], "id_raw": 1145 }, { "id": "jkv2JOrfkbFWIL84", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the contents of region [0.055, 0.053, 0.988, 0.927]?" }, { "from": "gpt", "value": "This region includes the main area of the chart, with lines representing trends of Solar Power, Hydro Power, and Nuclear Power. It also contains both x and y axises with respective labels and tick labels." } ], "id_raw": 1145 }, { "id": "dhswWH0hnH6rqQp6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_5.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region [0.098, 0.473, 0.945, 0.887]?" }, { "from": "gpt", "value": "This region contains the 'Solar Power' line, drawn with a blue color, from point 1 to point 12." } ], "id_raw": 1145 }, { "id": "ICDIE56z8j1TbRqS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the content at [0.105, 0.072, 0.188, 0.094] in the sublegend of the chart?" }, { "from": "gpt", "value": "GDP growth." } ], "id_raw": 1146 }, { "id": "EyfaaCyOfZ22yJLR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the content at [0.043, 0.504, 0.050, 0.521] on the y-axis?" }, { "from": "gpt", "value": "4." } ], "id_raw": 1146 }, { "id": "ML0alshgkBk94Wes", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_6.png", "conversations": [ { "from": "human", "value": "\nWhat does the line at [0.096, 0.257, 0.945, 0.887] in subplot_0 represent?" }, { "from": "gpt", "value": "The GDP growth trend." } ], "id_raw": 1146 }, { "id": "mGs4EVyQ1AY1eokq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_6.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at the first point in 1995, GDP growth {coordinates}[0.096, 0.456] or Unemployment rate [0.096, 0.264]?" }, { "from": "gpt", "value": "GDP growth." } ], "id_raw": 1146 }, { "id": "NI48e5o45fRyXeb2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_6.png", "conversations": [ { "from": "human", "value": "\nIn 2016, is the Unemployment rate at [0.843, 0.316] higher or lower compared to 1995 at [0.096, 0.264]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1146 }, { "id": "N9VFYb8Wk4sHxcCp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_6.png", "conversations": [ { "from": "human", "value": "\nBetween 1995 and 2021, has the GDP growth {coordinates}[0.096, 0.257, 0.945, 0.887] generally increased or decreased?" }, { "from": "gpt", "value": "Decreased." } ], "id_raw": 1146 }, { "id": "5AaqH7gOTHeYRfWp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region from [0.061, 0.063, 0.250, 0.168]?" }, { "from": "gpt", "value": "This region contains the legend of the chart. It includes the labels {GDP growth}[0.105, 0.072, 0.188, 0.094], {Inflation}[0.105, 0.105, 0.162, 0.127], and {Unemployment rate} [0.105, 0.137, 0.244, 0.160] with respective color markers." } ], "id_raw": 1146 }, { "id": "NyEpivbVYWFFg7fU", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the region from [0.096, 0.257, 0.945, 0.887]." }, { "from": "gpt", "value": "This area contains the line representing GDP growth. It has a decreasing trajectory from 1995 to 2021 plotted in color #00bfff." } ], "id_raw": 1146 }, { "id": "51JaZvysFfiHRZqN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_6.png", "conversations": [ { "from": "human", "value": "\nDescribe the x-axis from the region [0.499, 0.958, 0.542, 0.981]." }, { "from": "gpt", "value": "This region contains the x-axis label of the chart, which is \"Years\"." } ], "id_raw": 1146 }, { "id": "jl3xxdraJWhci1T7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the label at the coordinates [0.107, 0.137, 0.155, 0.160] refer to?" }, { "from": "gpt", "value": "Google." } ], "id_raw": 1147 }, { "id": "xYv5OIrr6faf7Yd8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_7.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the item with coordinates [0.175, 0.572]?" }, { "from": "gpt", "value": "It\u2019s a point on Samsung line plot indicating the market share for the 2nd quarter." } ], "id_raw": 1147 }, { "id": "w7gI5IEteFPRqaIP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the Y-axis label located at the coordinates [0.012, 0.409, 0.028, 0.571] represent?" }, { "from": "gpt", "value": "Market Share." } ], "id_raw": 1147 }, { "id": "9NgjOc4iEzUpTcCu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_7.png", "conversations": [ { "from": "human", "value": "\nIs the {Apple's line plot}[0.098, 0.093, 0.945, 0.298] above or below {Samsung's line plot}[0.098, 0.504, 0.945, 0.586] in the last few quarters?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1147 }, { "id": "hNR3H7OorGmKUJOV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_7.png", "conversations": [ { "from": "human", "value": "\nIn the 5th quarter, does {Apple}[0.406, 0.298] have a higher or lower market share than {Google}[0.406, 0.654]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1147 }, { "id": "ommHB4pASqxeAj2v", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_7.png", "conversations": [ { "from": "human", "value": "\nBy comparing {Apple's line plot}[0.098, 0.093, 0.945, 0.298] and {Samsung's line plot}[0.098, 0.504, 0.945, 0.586], which company had a better performance in the smartphone market over the quarters?" }, { "from": "gpt", "value": "Apple." } ], "id_raw": 1147 }, { "id": "ee556TiZYquKBZ6R", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area with coordinates [0.062, 0.063, 0.177, 0.201]." }, { "from": "gpt", "value": "The region contains the chart legend with company names 'Apple', 'Samsung', 'Google', and 'Others' along with corresponding color handles to distinguish the line plots for each company." } ], "id_raw": 1147 }, { "id": "QzFMQDNHP8LiSsoO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_7.png", "conversations": [ { "from": "human", "value": "\nWhat's in the region [0.055, 0.053, 0.988, 0.927]?" }, { "from": "gpt", "value": "This area includes all the line plots and the chart grid. The line plots represent the market share of Apple, Samsung, Google, and Others over various quarters." } ], "id_raw": 1147 }, { "id": "R1vWVsNxD4AjaJWa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the area [0.325, 0.933, 0.332, 0.951]." }, { "from": "gpt", "value": "A label '4' is placed here representing the 4th quarter on the x-axis." } ], "id_raw": 1147 }, { "id": "0MJTwxx9INyR5j1r", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_8.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by the line with coordinates [0.104, 0.093, 0.926, 0.887]?" }, { "from": "gpt", "value": "AI Applications." } ], "id_raw": 1148 }, { "id": "ek1wmF1btAPtZ3bB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_8.png", "conversations": [ { "from": "human", "value": "\nWhat label is located at coordinate [0.871, 0.933, 0.982, 0.951] on the x-axis?" }, { "from": "gpt", "value": "Market Forecasting." } ], "id_raw": 1148 }, { "id": "pVcV2fjMQNtlBF6F", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.033, 0.880, 0.059, 0.898]?" }, { "from": "gpt", "value": "0.30." } ], "id_raw": 1148 }, { "id": "1IDC5Gvfnw8IoDb3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_8.png", "conversations": [ { "from": "human", "value": "\nIs {the AI Application 'Speech Recognition'}[0.287, 0.887] having higher or lower case analysis score compared to {the AI Application 'Autonomous Robots'}[0.378, 0.547]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1148 }, { "id": "ROufWxiDXKrb1Qgu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_8.png", "conversations": [ { "from": "human", "value": "\nDoes {the AI Application 'Game AI'}[0.652, 0.093] have a score higher or lower than {the AI Application 'Driverless Car'}[0.561, 0.206]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1148 }, { "id": "oZrQQ5vwnYovI9I9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_8.png", "conversations": [ { "from": "human", "value": "\nAt which AI application is the case analysis score highest, {Predictive Analysis}[0.835, 0.660] or {Market Forecasting}[0.926, 0.320]?" }, { "from": "gpt", "value": "Market Forecasting." } ], "id_raw": 1148 }, { "id": "y12bLWPlBPWJsQiy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_8.png", "conversations": [ { "from": "human", "value": "\nWhat is present in the region [0.320, 0.933, 0.436, 0.951]?" }, { "from": "gpt", "value": "The label for the AI Application 'Autonomous Robots' is present there." } ], "id_raw": 1148 }, { "id": "KNkUqp4OlYztWLkY", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements present in the region [0.808, 0.063, 0.960, 0.103]." }, { "from": "gpt", "value": "In this area, the legend is located, which includes the label {AI Applications}[0.853, 0.072, 0.955, 0.094], paired with a colored marker at [0.814, 0.082, 0.841, 0.082]." } ], "id_raw": 1148 }, { "id": "UEoqnswca4MvjEqQ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located in the region [0.133, 0.880, 0.264, 0.898]?" }, { "from": "gpt", "value": "In this area, the x-axis label for the 'Recommender Systems' AI Application is present at [0.127, 0.933, 0.264, 0.951]." } ], "id_raw": 1148 }, { "id": "jQh7GZms4zUQHeXL", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line labeled 'Instagram' in the chart?" }, { "from": "gpt", "value": "#ff00ff." } ], "id_raw": 1149 }, { "id": "CHPORd0TjmcdmObd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_9.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at [0.248, 0.927] in the chart?" }, { "from": "gpt", "value": "x-axis tick \"5\"." } ], "id_raw": 1149 }, { "id": "dk981KlDmbBxkeXv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_9.png", "conversations": [ { "from": "human", "value": "\nWhich label corresponds to the coordinate [0.059, 0.834] on the y-axis?" }, { "from": "gpt", "value": "100." } ], "id_raw": 1149 }, { "id": "rEiPwsqipVyyVfC5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_9.png", "conversations": [ { "from": "human", "value": "\nIs {the point at}[0.138, 0.781] higher or lower than {the point at}[0.174, 0.728] on the Facebook line?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1149 }, { "id": "5fm44jfCQqx3gXxE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_9.png", "conversations": [ { "from": "human", "value": "\nWhich is more, the value at {the point}[0.540, 0.305] or {the point}[0.504, 0.252]?" }, { "from": "gpt", "value": "0.540, 0.305." } ], "id_raw": 1149 }, { "id": "qlyRLNcxoPRWdIGH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_9.png", "conversations": [ { "from": "human", "value": "\nIs {the data point at}[0.101, 0.887] observed before or after {the data point at}[0.943, 0.093]?" }, { "from": "gpt", "value": "Before." } ], "id_raw": 1149 }, { "id": "JmXhRm6Xdduqhp4z", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_9.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed between the coordinates [0.101, 0.834, 0.248, 0.927]?" }, { "from": "gpt", "value": "This region displays the first five x-axis tick labels for the weeks, specifically '\u22125', '0', '5', '10', '15'." } ], "id_raw": 1149 }, { "id": "r5TgCiXwZFe3gRGp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_9.png", "conversations": [ { "from": "human", "value": "\nDescribe what is found in the area [0.033, 0.827, 0.059, 0.951]." }, { "from": "gpt", "value": "This area contains y-axis tick labels for the values '100' and '120', positioned along the vertical axis on a grey grid line." } ], "id_raw": 1149 }, { "id": "QzFzrkjLrfISg2C8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_9.png", "conversations": [ { "from": "human", "value": "\nWhat can you find between [0.033, 0.192, 0.055, 0.421]?" }, { "from": "gpt", "value": "The y-axis tick labels '160', '180', '200', and '220' are located in this region, marked on the y-axis in a clear sequential order." } ], "id_raw": 1149 }, { "id": "k6Sp8oMQG4cQl6in", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the text present at the coordinates [0.012, 0.383, 0.028, 0.597]?" }, { "from": "gpt", "value": "The Y-axis label or 'Normalized Value'." } ], "id_raw": 1150 }, { "id": "tjl2tQBMMc2wK4Z1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of the first point in the 'Research Funding' line?" }, { "from": "gpt", "value": "[0.087, 0.828]." } ], "id_raw": 1150 }, { "id": "HQnhJnS5cRYL6NjY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the content at the coordinates [0.096, 0.105, 0.266, 0.127] in the chart legend?" }, { "from": "gpt", "value": "'Technological Innovation'." } ], "id_raw": 1150 }, { "id": "5znMHk2U21Yida03", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_10.png", "conversations": [ { "from": "human", "value": "\nIs the {end point of the 'Research Funding' line}[0.945, 0.440] lower or higher than the {end point of the 'Technological Innovation' line}[0.945, 0.093]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1150 }, { "id": "HNXoW5Xjb1fcI57c", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_10.png", "conversations": [ { "from": "human", "value": "\nIn the chart, does the {point at coordinate [0.174, 0.867] on the 'Research Funding' line} lie above or below {the point at coordinate [0.174, 0.838] on the 'Technological Innovation' line}?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1150 }, { "id": "qDwGxnrxKWgFE8PM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_10.png", "conversations": [ { "from": "human", "value": "\nWhich line starts at a higher y-coordinate, {the 'Research Funding' line}[0.087, 0.828] or {the 'Technological Innovation' line}[0.087, 0.887]?" }, { "from": "gpt", "value": "'Technological Innovation'." } ], "id_raw": 1150 }, { "id": "BfO4qIrYIUeFHoIa", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the description of the area [0.051, 0.063, 0.272, 0.135]?" }, { "from": "gpt", "value": "This region contains a sub-legend with two items: {Research Funding}[0.096, 0.072, 0.219, 0.094] and {Technological Innovation}[0.096, 0.105, 0.266, 0.127]. Each of these items is paired with a color, represented by colored markers." } ], "id_raw": 1150 }, { "id": "HaeUYaLt4WB4mEVy", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_10.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is contained within the range [0.044, 0.053, 0.988, 0.927]?" }, { "from": "gpt", "value": "This region covers the main plot area of the graph showcasing two line graphs- {Research Funding line}[0.087, 0.440, 0.945, 0.887] and {Technological Innovation line}[0.087, 0.093, 0.945, 0.887]." } ], "id_raw": 1150 }, { "id": "ucUVgjFQ5KngYXNF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_10.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region defined by the coordinates [0.223, 0.019, 0.809, 0.049]?" }, { "from": "gpt", "value": "This area contains the graph's subtitle, \"Research Funding & Technological Innovation Over Time\"." } ], "id_raw": 1150 }, { "id": "AReKyTTCM7HdOm1G", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at the coordinates [0.108, 0.385, 0.946, 0.887]?" }, { "from": "gpt", "value": "It is a line representing the Email Marketing performance." } ], "id_raw": 1152 }, { "id": "a0ATCTA905WUsEGI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_12.png", "conversations": [ { "from": "human", "value": "\nWhat does the fourth x-axis tick label at {coordinates}[0.485, 0.933, 0.493, 0.951] indicate?" }, { "from": "gpt", "value": "The number 6." } ], "id_raw": 1152 }, { "id": "54Ud9fIGjoXXDfVd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the text represented at {coordinates}[0.190, 0.019, 0.864, 0.049]?" }, { "from": "gpt", "value": "The chart's subtitle: 'Monthly Performance in New Media and Digital Content Creation'." } ], "id_raw": 1152 }, { "id": "xGZ7T4oDlJrJ1Yud", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_12.png", "conversations": [ { "from": "human", "value": "\nIs the {Email Marketing} performance data point at [0.946, 0.427] higher or lower than the {SEO} performance data point at [0.946, 0.093] for the last month?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1152 }, { "id": "bmdv0rUmKC3UaBak", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_12.png", "conversations": [ { "from": "human", "value": "\nIs the x-axis tick label at [0.333, 0.933, 0.340, 0.951] greater or less than the x-axis tick label at [0.181, 0.933, 0.188, 0.951]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 1152 }, { "id": "KpH0oxP8bDL4HiMA", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_12.png", "conversations": [ { "from": "human", "value": "\nDoes the {SEO}[0.118, 0.137, 0.146, 0.160] line in the legend have a higher or lower positioning than the {Email Marketing}[0.118, 0.072, 0.230, 0.094] line in the legend?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1152 }, { "id": "jZCwbIbuBEPqCBdS", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_12.png", "conversations": [ { "from": "human", "value": "\nCan you tell me what is contained in the area [0.073, 0.063, 0.235, 0.168]?" }, { "from": "gpt", "value": "The area contains the chart legend, which includes the labels {Email Marketing}[0.118, 0.072, 0.230, 0.094], {Social Media}[0.118, 0.105, 0.205, 0.127], and {SEO}[0.118, 0.137, 0.146, 0.160] each associated with a color marker." } ], "id_raw": 1152 }, { "id": "acqHWKqFXDHMzFKJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the area [0.261, 0.636, 0.337, 0.678]." }, { "from": "gpt", "value": "The area includes a segment of the SEO performance line trending upwards, and a segment of the Email Marketing line trending downwards." } ], "id_raw": 1152 }, { "id": "qMPsICx0LqDDoAIF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_12.png", "conversations": [ { "from": "human", "value": "\nWhat is within the region [0.108, 0.385, 0.946, 0.887]?" }, { "from": "gpt", "value": "There is a line representing the Email Marketing performance data across 12 months." } ], "id_raw": 1152 }, { "id": "4wF0aSDyKgbdCv07", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing 'Sales Revenue'}[0.122, 0.093, 0.946, 0.246]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 1153 }, { "id": "p6q1muaYZFkKIBpM", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the yticks at {coordinates}[0.040, 0.581, 0.077, 0.599] in the chart?" }, { "from": "gpt", "value": "40000." } ], "id_raw": 1153 }, { "id": "uJ8NeC9rCvx7RvwA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_13.png", "conversations": [ { "from": "human", "value": "\nIs there any marker in {the line representing 'Advertising Expenditure'}[0.122, 0.882, 0.946, 0.887]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1153 }, { "id": "rULEtAqmeOn4kCWn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_13.png", "conversations": [ { "from": "human", "value": "\nDoes {the 'Website Traffic'}[0.122, 0.776, 0.946, 0.802] have a higher or lower starting value than {the 'Sales Revenue'}[0.122, 0.093, 0.946, 0.246]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1153 }, { "id": "ui8aDCMUwIIGaRgj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_13.png", "conversations": [ { "from": "human", "value": "\nCompared to {the line for 'Website Traffic'}[0.122, 0.776, 0.946, 0.802], is {the line for 'Advertising Expenditure'}[0.122, 0.882, 0.946, 0.887] generally above or below?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1153 }, { "id": "kwRiqqVaqoSibFJE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_13.png", "conversations": [ { "from": "human", "value": "\nWhich line shows a greater increase from the first to the tenth week, {the 'Sales Revenue'}[0.122, 0.093, 0.946, 0.246] or {the 'Website Traffic'}[0.122, 0.776, 0.946, 0.802]?" }, { "from": "gpt", "value": "Sales Revenue." } ], "id_raw": 1153 }, { "id": "wOUyoX5342O5f1Ql", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_13.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects in the region [0.122, 0.093, 0.946, 0.246]." }, { "from": "gpt", "value": "This region contains the line representing 'Sales Revenue' from week 1 to week 10. The color of the line is blue." } ], "id_raw": 1153 }, { "id": "hJJzatNa8dmx4jAB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_13.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region with coordinates [0.088, 0.063, 0.304, 0.168]?" }, { "from": "gpt", "value": "This region contains the legend of the chart indicating the meaning of each color on the lines. It contains labels for 'Sales Revenue', 'Advertising Expenditure', and 'Website Traffic', each paired with a colored marker." } ], "id_raw": 1153 }, { "id": "6U6DomKDSDsoT7KJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_13.png", "conversations": [ { "from": "human", "value": "\nWhat is in the region defined by coordinates [0.576, 0.933, 0.584, 0.951]?" }, { "from": "gpt", "value": "This region contains the x-axis tick label '6'." } ], "id_raw": 1153 }, { "id": "ZEbAkZ3UbaC9Y65Y", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_14.png", "conversations": [ { "from": "human", "value": "\nWhat does {the x-axis label}[0.517, 0.958, 0.551, 0.981] represent in the chart?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 1154 }, { "id": "GQgVmRONgvv3Y2wh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line representing GDP (Billion USD)}[0.122, 0.093, 0.946, 0.294]?" }, { "from": "gpt", "value": "#00bfff." } ], "id_raw": 1154 }, { "id": "XuPlWlWcpvCTT3q8", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the significance of {this label}[0.033, 0.245, 0.077, 0.262] on the y-axis?" }, { "from": "gpt", "value": "It represents 500000." } ], "id_raw": 1154 }, { "id": "uPz4L5N8aYI6hTqC", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_14.png", "conversations": [ { "from": "human", "value": "\nIs {the point on the GDP line for 2005}[0.122, 0.294] higher or lower than {the point for 2020}[0.946, 0.136]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1154 }, { "id": "Z0LVd1AxCa5gBOzh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the color of the Interest Rate line}[0.122, 0.887, 0.946, 0.887] match {the handle color in the legend}[0.093, 0.115, 0.121, 0.115]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1154 }, { "id": "f59FdHaS3YxgUjW3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_14.png", "conversations": [ { "from": "human", "value": "\nWhich is higher on the chart, {the first point of the 'Debt to GDP Ratio'}[0.122, 0.887] or {the last point of the GDP line}[0.946, 0.294]?" }, { "from": "gpt", "value": "Last point of the GDP line." } ], "id_raw": 1154 }, { "id": "z1YBQU6lIRyYjNhW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region[0.081, 0.053, 0.988, 0.927]." }, { "from": "gpt", "value": "This area encompasses all the primary chart data including x and y axes, data lines for GDP, Interest Rate, and Debt to GDP Ratio." } ], "id_raw": 1154 }, { "id": "dcbBikLirSzhGYkW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area[0.122, 0.887, 0.946, 0.887] of the chart?" }, { "from": "gpt", "value": "This region contains several lines that represent changing financial metrics over years, each depicted in different colors." } ], "id_raw": 1154 }, { "id": "3woln9xco0fMP4Up", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements within[0.517, 0.958, 0.551, 0.981] space of the chart." }, { "from": "gpt", "value": "The x-axis label \"Year\" is located in this space." } ], "id_raw": 1154 }, { "id": "U001J6rL13QKLP0T", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_15.png", "conversations": [ { "from": "human", "value": "\nWhat represents {the main y-axis labels}[0.012, 0.086, 0.028, 0.597] in this chart?" }, { "from": "gpt", "value": "Normalized Value." } ], "id_raw": 1155 }, { "id": "6AlJ5rGeUUhZdOln", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_15.png", "conversations": [ { "from": "human", "value": "\nWhat color is represented by the handle for {the line 'Media Outlet X'}[0.831, 0.082, 0.858, 0.082]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 1155 }, { "id": "3R3NGX8tyL68pJgR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_15.png", "conversations": [ { "from": "human", "value": "\nWhich object is situated at [0.537, 0.280, 0.569, 0.326] in the chart figure?" }, { "from": "gpt", "value": "The 15th line marker." } ], "id_raw": 1155 }, { "id": "TwQwdnl2Z5NYV6l9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_15.png", "conversations": [ { "from": "human", "value": "\nIs {the marker at index 6}[0.255, 0.560] higher in value than {the marker at index 22}[0.757, 0.373] on the 'Public Reaction' line?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1155 }, { "id": "N0YNSR82MrR82ec5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_15.png", "conversations": [ { "from": "human", "value": "\nDoes {the point labeled 'Media Outlet Y' at location 7}[0.286, 0.280] have a greater value compared to {the 'Public Reaction' at location 13}[0.474, 0.747]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1155 }, { "id": "XLNilwPJs716OwQ7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_15.png", "conversations": [ { "from": "human", "value": "\nWhich has a lower starting point, {the line labeled '_child16'}[0.569, 0.326, 0.600, 0.280] or {the line labeled '_child20'}[0.694, 0.139, 0.725, 0.093]?" }, { "from": "gpt", "value": "_child20." } ], "id_raw": 1155 }, { "id": "0sOanC23hjrxdnKp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the content and layout within the region [0.098, 0.280, 0.945, 0.960]." }, { "from": "gpt", "value": "This region captures the primary line plots indicating trends for 'Media Outlet X', 'Media Outlet Y', and 'Public Reaction'. The vertical portion highlights the scale from high to low impact over weeks, and points are marked along each line representing the weekly data points." } ], "id_raw": 1155 }, { "id": "thVZp9Wflhqrxzuv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_15.png", "conversations": [ { "from": "human", "value": "\nSummarize the elements found between [0.825, 0.063, 0.981, 0.160]." }, { "from": "gpt", "value": "This section contains the chart legend with the labels and corresponding color handles for 'Media Outlet X', 'Media Outlet Y', and 'Public Reaction', specifically arranged vertically." } ], "id_raw": 1155 }, { "id": "p5uNrKE2K9VgYM5y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_15.png", "conversations": [ { "from": "human", "value": "\nWhat are the key features outlined in the space [0.033, 0.086, 0.051, 1.038]?" }, { "from": "gpt", "value": "This part contains the Y-axis scale, moving from 1.2 down to 0.0, marked through visible tick points with labels, visually aligning the data points on the chart regarding normalized value metrics." } ], "id_raw": 1155 }, { "id": "dEmQ9X0ccWBV4Cus", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_16.png", "conversations": [ { "from": "human", "value": "\nWhat does {the tick label}[0.038, 0.933, 0.067, 0.951] represent?" }, { "from": "gpt", "value": "2004." } ], "id_raw": 1156 }, { "id": "seXCEEbRLwPZp18h", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_16.png", "conversations": [ { "from": "human", "value": "\nWhich entity corresponds to {the colored marker closest to the bottom left corner}[0.079, 0.082, 0.107, 0.082]?" }, { "from": "gpt", "value": "Online Education." } ], "id_raw": 1156 }, { "id": "W28lnZsb8AoMc2As", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_16.png", "conversations": [ { "from": "human", "value": "\nWhat type of line represents {the dataset}[0.108, 0.093, 0.946, 0.438]?" }, { "from": "gpt", "value": "Job Market Demand." } ], "id_raw": 1156 }, { "id": "rkqiAW1Gap2IaQY6", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_16.png", "conversations": [ { "from": "human", "value": "\nWhich starts higher on the graph, {the line for 'Online Education'}[0.108, 0.887] or {the line for 'Job Market Demand'}[0.108, 0.438]?" }, { "from": "gpt", "value": "Job Market Demand." } ], "id_raw": 1156 }, { "id": "gmAlNt3BdWbwenjq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point corresponding to '2020'}[0.946, 0.404] for Online Education higher or lower than {the corresponding point for Job Market Demand}[0.946, 0.093]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1156 }, { "id": "gmbq4UdGwijWNuDz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_16.png", "conversations": [ { "from": "human", "value": "\nComparing the first points in 2005 for {Online Education}[0.108, 0.887] and {Traditional Education}[0.108, 0.369], which is lower?" }, { "from": "gpt", "value": "Online Education." } ], "id_raw": 1156 }, { "id": "Rgb8EWnFeuNvYHG2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the objects located in [0.931, 0.933, 0.960, 0.951]." }, { "from": "gpt", "value": "This region contains the x-axis tick label \"2020\"." } ], "id_raw": 1156 }, { "id": "yWODp6bfgaVIOThK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_16.png", "conversations": [ { "from": "human", "value": "\nWhat can be described about the area in [0.073, 0.063, 0.268, 0.168]?" }, { "from": "gpt", "value": "The legend for the chart is located within this region, including labels and colored markers for \"Online Education,\" \"Traditional Education,\" and \"Job Market Demand\"." } ], "id_raw": 1156 }, { "id": "Q0uK7s9uu6Mzlmn0", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_16.png", "conversations": [ { "from": "human", "value": "\nIdentify and describe the contents of the region [0.012, 0.411, 0.028, 0.569]." }, { "from": "gpt", "value": "The y-axis label \"Trend figures\" is found in this region, oriented vertically." } ], "id_raw": 1156 }, { "id": "5JOYiraaeuhu73v3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line representing Innovation 1?" }, { "from": "gpt", "value": "Aquamarine." } ], "id_raw": 1158 }, { "id": "LcXmybCIyxLpc0uP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the marker style for {the line for Innovation 3}[0.097, 0.117, 0.945, 0.847]?" }, { "from": "gpt", "value": "None." } ], "id_raw": 1158 }, { "id": "h3yebpnKM4JeEnmz", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {the y-axis}[0.012, 0.389, 0.028, 0.588]?" }, { "from": "gpt", "value": "Score (Normalized)." } ], "id_raw": 1158 }, { "id": "SgW7kVKSbY2nqJRZ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_18.png", "conversations": [ { "from": "human", "value": "\nIs the score for {Efficiency under Innovation 1}[0.097, 0.709] higher or lower than the score for {Efficiency under Innovation 2}[0.097, 0.254]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1158 }, { "id": "RrP94JgvaJvVuFVd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_18.png", "conversations": [ { "from": "human", "value": "\nWhat has a better score, {Project Risk under Innovation 1}[0.757, 0.888] or {Project Risk under Innovation 2}[0.757, 0.585]?" }, { "from": "gpt", "value": "Innovation 1." } ], "id_raw": 1158 }, { "id": "13w2SYh4W3dS672E", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_18.png", "conversations": [ { "from": "human", "value": "\nIs {ROI for Innovation 1}[0.945, 0.254] above or below {ROI for Innovation 2}[0.945, 0.089]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1158 }, { "id": "FKV4eMkZYmjxQhhL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects in area [0.060, 0.058, 0.174, 0.142]." }, { "from": "gpt", "value": "This area contains the legend items: {Innovation 1}[0.097, 0.065, 0.170, 0.082] with a corresponding marker color of aquamarine; {Innovation 2}[0.097, 0.091, 0.170, 0.108] with a corresponding marker color of blue; {Innovation 3}[0.097, 0.117, 0.170, 0.135] with a corresponding marker color of purple." } ], "id_raw": 1158 }, { "id": "Lq54MGRiNzwYH7cq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_18.png", "conversations": [ { "from": "human", "value": "\nPlease describe [0.315, 0.019, 0.727, 0.044]." }, { "from": "gpt", "value": "The region contains the chart subtitle \"Innovation Project Evaluation and Management\". It has a font color of #15 (very dark grey)." } ], "id_raw": 1158 }, { "id": "MwoAY7ZoZ6322Tnp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the area [0.247, 0.934, 0.324, 0.952]." }, { "from": "gpt", "value": "This area includes the x-axis label \"Collaboration\", styled with a very dark grey color." } ], "id_raw": 1158 }, { "id": "902kbsLRBLJcoHh1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_19.png", "conversations": [ { "from": "human", "value": "\nWhat does the text located at [0.781, 0.139, 0.887, 0.161] in the legend refer to?" }, { "from": "gpt", "value": "Physical Assets." } ], "id_raw": 1159 }, { "id": "1tLo8t9Gi6iuXGLw", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the object labeled at [0.253, 0.896, 0.269, 0.914] on the x-axis?" }, { "from": "gpt", "value": "Q2." } ], "id_raw": 1159 }, { "id": "A0rrQKUFutBAilpG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_58_19.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.082, 0.361, 0.098, 0.649] represent?" }, { "from": "gpt", "value": "Normalized Asset Value." } ], "id_raw": 1159 }, { "id": "oxg3fA35CwVrkxEh", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_19.png", "conversations": [ { "from": "human", "value": "\nIs {the plot point for 'Physical Assets'}[0.261, 0.541] positioned higher or lower on the graph than {the plot point for 'Digital Assets'}[0.261, 0.288] at Q2?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1159 }, { "id": "A01LF8gt5jB2epTR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_19.png", "conversations": [ { "from": "human", "value": "\nIs {the plot point for 'Physical Assets' at Q5}[0.563, 0.855] to the left or to the right of {the plot point for 'Digital Assets' at Q5}[0.563, 0.553]?" }, { "from": "gpt", "value": "Neither, they are vertically aligned." } ], "id_raw": 1159 }, { "id": "fzJmHht7Vex33Omx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_58_19.png", "conversations": [ { "from": "human", "value": "\nWhich asset category, Physical or Digital, has a higher value at {Q7}[0.764, 0.553]?" }, { "from": "gpt", "value": "Physical Assets." } ], "id_raw": 1159 }, { "id": "58TiqyfKpHsFbCoK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_19.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region at [0.737, 0.130, 0.893, 0.203]." }, { "from": "gpt", "value": "This area contains the legend for the chart, with two items: {Physical Assets}[0.781, 0.139, 0.887, 0.161] represented with a blue marker and {Digital Assets}[0.781, 0.172, 0.876, 0.194] represented with a orange marker." } ], "id_raw": 1159 }, { "id": "i1XjohOLfBpyCvg9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_19.png", "conversations": [ { "from": "human", "value": "\nCan you describe the area from [0.376, 0.086, 0.649, 0.116]?" }, { "from": "gpt", "value": "This region includes the subtitle of the graph, which is {Financial Data Exploration}[0.376, 0.086, 0.649, 0.116]." } ], "id_raw": 1159 }, { "id": "makVpfTmD3aOe14O", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_58_19.png", "conversations": [ { "from": "human", "value": "\nWhat can you see in the region from [0.125, 0.120, 0.900, 0.890]?" }, { "from": "gpt", "value": "This region contains the main plot area of the chart, which includes a comparison of {Physical Assets}[0.160, 0.445, 0.865, 0.855] and {Digital Assets}[0.160, 0.155, 0.865, 0.831] values over different quarters." } ], "id_raw": 1159 }, { "id": "Ts1NKHMwc9QeeDxN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_1.png", "conversations": [ { "from": "human", "value": "\nWhat does {the ylabel}[0.012, 0.388, 0.024, 0.572] indicate on this chart?" }, { "from": "gpt", "value": "Magnitude." } ], "id_raw": 1161 }, { "id": "8qcEga4nNoyl8ViV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_1.png", "conversations": [ { "from": "human", "value": "\nWhere is {the xlabel}[0.506, 0.927, 0.536, 0.963] located in relation to the plot area?" }, { "from": "gpt", "value": "Bottom center." } ], "id_raw": 1161 }, { "id": "ndHztDnrMSa9LG51", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line representing {Social Inequality}[0.729, 0.557, 0.971, 0.828] in the third subplot?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 1161 }, { "id": "6RCiCdjeQP0JKioF", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_1.png", "conversations": [ { "from": "human", "value": "\nIs {the y-tick labeled '0.50'}[0.058, 0.673] higher or lower than {the y-tick labeled '0.75'}[0.058, 0.577] on the first subplot?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1161 }, { "id": "w8Jn2q8F9ESULLPI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_1.png", "conversations": [ { "from": "human", "value": "\nDoes the second subplot have {the line for 'Social Inequality'}[[0.400, 0.557], [0.642, 0.828]] closer to the top or bottom of the chart at {the end of 2020}[0.642, 0.828]?" }, { "from": "gpt", "value": "Bottom." } ], "id_raw": 1161 }, { "id": "TCXwExfvNLlvmXyG", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_1.png", "conversations": [ { "from": "human", "value": "\nWhich is smaller, the starting or ending value of {Rights Protection}[0.400, 0.480, 0.642, 0.828] on the second subplot?" }, { "from": "gpt", "value": "Ending value." } ], "id_raw": 1161 }, { "id": "oNxnvireNEhW7IkI", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the area [0.029, 0.176, 0.054, 0.501] comprise of?" }, { "from": "gpt", "value": "The y-tick labels and ticks for the values 0.25, 0.50, 0.75, and 1.00 including their corresponding horizontal grid lines." } ], "id_raw": 1161 }, { "id": "BfnJOfqb3zOnQMMH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_1.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements found in the region [0.029, 0.080, 0.054, 0.115]." }, { "from": "gpt", "value": "This area contains the lowest y-tick and label labeled '2.00' with a horizontal grid line across the first subplot." } ], "id_raw": 1161 }, { "id": "McBOHL62Kviyposc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the space [0.029, 0.659, 0.054, 0.888] include?" }, { "from": "gpt", "value": "It holds the y-grid lines and y-ticks along with labels for 0.50 and 0.00." } ], "id_raw": 1161 }, { "id": "JgrGUGv9rRVaQiJ3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the title of the subplot noted at [0.325, 0.037, 0.717, 0.083]?" }, { "from": "gpt", "value": "Natural Language Processing vs Reinforcement Learning." } ], "id_raw": 1162 }, { "id": "tjF719ogjo3PiTYe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_2.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker is used for {the Machine Learning points in the third subplot}[0.729, 0.145, 0.971, 0.351]?" }, { "from": "gpt", "value": "Circle." } ], "id_raw": 1162 }, { "id": "ubsgGtuBP7kDPwDk", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_2.png", "conversations": [ { "from": "human", "value": "\nWhat label is associated with {the y-axis}[0.671, 0.401, 0.683, 0.559] in the last subplot?" }, { "from": "gpt", "value": "Accuracy." } ], "id_raw": 1162 }, { "id": "kt9bGOX8FkWhaSIf", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_2.png", "conversations": [ { "from": "human", "value": "\nIs {the point at '2019' for Deep Learning}[0.850, 0.248] positioned higher than {the point at '2019' for Reinforcement Learning}[0.521, 0.531]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1162 }, { "id": "QpeyzpFd304ZlJSX", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_2.png", "conversations": [ { "from": "human", "value": "\nWhich value is greater, {the point at '2018' for Natural Language Processing}[0.400, 0.609] or {the point at '2019' for Machine Learning}[0.850, 0.196]?" }, { "from": "gpt", "value": "Machine Learning." } ], "id_raw": 1162 }, { "id": "fH2b68WZ52HfmpyE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_2.png", "conversations": [ { "from": "human", "value": "\nDoes {the first y-tick label in the third subplot}[0.687, 0.853, 0.713, 0.888] represent a higher value than {the last y-tick label in the first subplot}[0.029, 0.080, 0.054, 0.115]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1162 }, { "id": "h6a1PdR0JxB6D0eN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_2.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.064, 0.111, 0.208, 0.226] contain?" }, { "from": "gpt", "value": "The legend for the subplot, including labels {Machine Learning}[0.101, 0.125, 0.204, 0.160] and {Deep Learning}[0.101, 0.177, 0.186, 0.212] with their respective color symbols." } ], "id_raw": 1162 }, { "id": "6R4gHv5TsmICoJkz", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_2.png", "conversations": [ { "from": "human", "value": "\nDescribe the area around [0.358, 0.595, 0.384, 0.630]." }, { "from": "gpt", "value": "It includes a y-axis tick labeled {0.80}[0.358, 0.595, 0.384, 0.630] in a subplot focused on comparing Natural Language Processing and Reinforcement Learning." } ], "id_raw": 1162 }, { "id": "VGuYOBzSz09gP81r", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_2.png", "conversations": [ { "from": "human", "value": "\nIdentify the elements within the region [0.717, 0.093, 0.983, 0.866]." }, { "from": "gpt", "value": "This part of the chart covers a subplot with x and y axes labels such as 'Year' and 'Accuracy' respectively, along with data lines for {Machine Learning}[0.729, 0.145, 0.971, 0.351] and {Natural Language Processing}[0.729, 0.377, 0.971, 0.609], marked with years {2018, 2019, 2020}[0.714, 0.879, 0.986, 0.914]." } ], "id_raw": 1162 }, { "id": "JW64C2J2XqCXjwPV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_3.png", "conversations": [ { "from": "human", "value": "\nWhat object is located at the coordinates [0.224, 0.100, 0.320, 0.128]?" }, { "from": "gpt", "value": "Legend label for Artificial Intelligence." } ], "id_raw": 1163 }, { "id": "n7tyqzSuCG9ZY1kP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_3.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object at [0.057, 0.380] in the chart?" }, { "from": "gpt", "value": "Tick line." } ], "id_raw": 1163 }, { "id": "FeZ0hqTMqPnyUG3a", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_3.png", "conversations": [ { "from": "human", "value": "\nWhat does the coordinate [0.0315, 0.186] in the chart represent?" }, { "from": "gpt", "value": "Data point for the year 2023 for Artificial Intelligence." } ], "id_raw": 1163 }, { "id": "rZf0iAGf7nArx3Da", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_3.png", "conversations": [ { "from": "human", "value": "\nIs {the year 2021 tick mark}[0.139, 0.893] to the left or right of {the line Automation}[0.051, 0.261, 0.315, 0.856]?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 1163 }, { "id": "qimSD63LsZIyuTHj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_3.png", "conversations": [ { "from": "human", "value": "\nAre {the points for Neural Networks in 2022}[0.887, 0.532] above or below {the points for Neural Networks in 2021}[0.799, 0.665]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1163 }, { "id": "epeDjWJbUmOWg62P", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_3.png", "conversations": [ { "from": "human", "value": "\nIs {the legend handle for AI Medicine}[inf, inf, -inf, -inf] visible next to {the legend label 'AI Medicine'}[0.512, 0.100, 0.568, 0.128]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1163 }, { "id": "Hp5MI16mf0fOqEcf", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the area [0.340, 0.075, 0.659, 0.893]." }, { "from": "gpt", "value": "This region contains a subplot with axes, grid lines, y-axis labels, tick marks, legend labels, and lines representing trends in AI medicine vs Natural Language Processing, with each data point marked." } ], "id_raw": 1163 }, { "id": "1tOfQJk7NrZNIN3M", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_3.png", "conversations": [ { "from": "human", "value": "\nWhat is encompassed within the region [0.698, 0.075, 0.989, 0.893]?" }, { "from": "gpt", "value": "The subplot area includes chart elements such as axes, x-grid, y-grid, lines showing trends for Artificial Intelligence vs Neural Networks, and corresponding tick marks and labels, all contained within a light blue background." } ], "id_raw": 1163 }, { "id": "7tW3KPvk3Khdq13c", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_3.png", "conversations": [ { "from": "human", "value": "\nCould you describe what features are contained in [0.038, 0.075, 0.329, 0.893]?" }, { "from": "gpt", "value": "This region covers part of the chart showcasing the subplot titled \"Artificial_Intelligence vs Automation\"--it includes axes detailing, grid lines, legends of labeled lines, and x and y-axis with marked notches and interacting data lines." } ], "id_raw": 1163 }, { "id": "GYSMrNIw2sWQcUV1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.019, 0.419, 0.036, 0.554] in the first subplot?" }, { "from": "gpt", "value": "It's the ylabel 'Trend Value'." } ], "id_raw": 1164 }, { "id": "QOBx5kRTVk8ZdKH9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the x-axis tick found at [0.093, 0.911] in the subplot?" }, { "from": "gpt", "value": "'Abrupt increase'." } ], "id_raw": 1164 }, { "id": "nQOIolYtj3vDFaOy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at this coordinate in the subplot [0.272, 0.083, 0.426, 0.106]?" }, { "from": "gpt", "value": "It's the legend item 'Trends in Building'." } ], "id_raw": 1164 }, { "id": "vQC2u6mdzSyTZQzx", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_4.png", "conversations": [ { "from": "human", "value": "\nIn the second subplot, does the point at 'Sluggish growth'[0.700, 0.812] of line with color #000000 lie above or below the point at 'Sluggish growth'[0.700, 0.854] on the line with color #ff0000?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1164 }, { "id": "ZIQ3n4dI7TELZdqr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_4.png", "conversations": [ { "from": "human", "value": "\nIn the first subplot, is the point 'Persistent rise'[0.314, 0.175] on the line with color #0000ff higher than the point 'Persistent rise'[0.314, 0.246] on the line with color #008000?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1164 }, { "id": "fckoFmYG7CS5Yzsw", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_4.png", "conversations": [ { "from": "human", "value": "\nDoes the first point on line_0 in subplot_1 '[0.589, 0.345]' have a higher or lower y value than the first point on line_1 [0.589, 0.402]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1164 }, { "id": "6BNlfYSOoo9hAQ2U", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_4.png", "conversations": [ { "from": "human", "value": "\nCan you give me a brief description of the contents located in the region [0.216, 0.074, 0.433, 0.116]?" }, { "from": "gpt", "value": "This region hosts a legend for 'Trends in Building', along with corresponding symbolic color given by {handle}[inf, inf, -inf, -inf]." } ], "id_raw": 1164 }, { "id": "XaprcWN7fM9DuRcK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_4.png", "conversations": [ { "from": "human", "value": "\nWhat are the contents located within the coordinates [0.093, 0.175, 0.425, 0.798]?" }, { "from": "gpt", "value": "This area covers a part of the first line in the first subplot, including four points labeled 'Abrupt increase', 'Inexorable Decline', 'Persistent rise', and 'Slow Increase' with their corresponding y-values." } ], "id_raw": 1164 }, { "id": "QciHvudrDT3aI9ej", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the area from [0.505, 0.919, 0.674, 0.942] in the right subplot." }, { "from": "gpt", "value": "It contains {the x-axis label 'Accelerated growth'}[0.505, 0.919, 0.674, 0.942]." } ], "id_raw": 1164 }, { "id": "QLTgG7gYNragUJIV", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the plot element located at [0.476, 0.952, 0.531, 0.975] in the chart?" }, { "from": "gpt", "value": "It is the x-label of the second subplot, labelled as \"Media Influence\"." } ], "id_raw": 1165 }, { "id": "CioFgkBGeKc43xuf", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of the first y-tick in the third subplot, located at [0.676, 0.902, 0.687, 0.925]?" }, { "from": "gpt", "value": "The label is 0.0." } ], "id_raw": 1165 }, { "id": "rODhmYfcTzZLAnMc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_5.png", "conversations": [ { "from": "human", "value": "\nWhat is represented by {the line}[0.369, 0.232, 0.639, 0.741] in the chart?" }, { "from": "gpt", "value": "The line represents the media influence on public opinion for various platforms." } ], "id_raw": 1165 }, { "id": "haMhhlbulFX7Rul4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_5.png", "conversations": [ { "from": "human", "value": "\nIs {the influence score of 'Social Media'}[0.459, 0.232] higher or lower than that of {the 'Documentaries'}[0.609, 0.260]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1165 }, { "id": "ct2qStkSmecFZRR3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_5.png", "conversations": [ { "from": "human", "value": "\nWhich media platform has a higher influence score at {the sixth point}[0.519, 0.345], Podcasts or Newspapers?" }, { "from": "gpt", "value": "Podcasts." } ], "id_raw": 1165 }, { "id": "BLEN2I72Ku89BcWO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_5.png", "conversations": [ { "from": "human", "value": "\nIs the influence score of {the 'Debate Shows'}[0.399, 0.628] above or below the influence score of {the 'Independent Blogs'}[0.489, 0.515]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1165 }, { "id": "EN8aXTeHXxyRVbzh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_5.png", "conversations": [ { "from": "human", "value": "\nDescribe the area enclosed by [0.689, 0.062, 0.987, 0.911]." }, { "from": "gpt", "value": "The area contains the third subplot of the chart, representing an unspecified variable in relation to the y-axis with labels 0.0, 0.2, 0.4, 0.6, 0.8 and 1.0." } ], "id_raw": 1165 }, { "id": "AIls7xTOll34VkGL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_5.png", "conversations": [ { "from": "human", "value": "\nWhat occupies the region [0.355, 0.062, 0.653, 0.911]?" }, { "from": "gpt", "value": "This area contains the second subplot of the chart. It has a title \"Media Influence on Public Opinion\", Y-axis labeled \"Opinion Change\", X-axis labeled \"Media Influence\" and includes line graph data points representing different media sources." } ], "id_raw": 1165 }, { "id": "BjBJuu9qwMoOaMxg", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_5.png", "conversations": [ { "from": "human", "value": "\nWhat content is within [0.434, 0.025, 0.574, 0.055]?" }, { "from": "gpt", "value": "This region contains the subtitle label of the second subplot, \"Media Influence on Public Opinion\"." } ], "id_raw": 1165 }, { "id": "YbWc0nVAXbkaS8lS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the type of {the third point object on the 'Fluctuating Pattern' line}[0.533, 0.480]?" }, { "from": "gpt", "value": "Marker." } ], "id_raw": 1167 }, { "id": "u8jaR535HZHU0Q0j", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_7.png", "conversations": [ { "from": "human", "value": "\nWhat is used to represent the value 40 on the 'Natural Reservation' factor in {second subplot}[0.862, 0.544]?" }, { "from": "gpt", "value": "Marker." } ], "id_raw": 1167 }, { "id": "LCIJFmDoBGOpmi2W", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_7.png", "conversations": [ { "from": "human", "value": "\nWhat color represents the 'Fluctuating Pattern' line in the subplot featuring the 'Assessment Factors'?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 1167 }, { "id": "SG4jDjYzCrQ6c8qn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_7.png", "conversations": [ { "from": "human", "value": "\nIs {the point at 'Reforestation' on the 'Minimal Change' line}[0.533, 0.319] higher than {the point at 'Reforestation' on the 'Fluctuating Pattern' line}[0.533, 0.480]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1167 }, { "id": "2zr9itv7Oo2CDXyE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_7.png", "conversations": [ { "from": "human", "value": "\nWhich line evidently scores lower on the 'Natural Reservation' category, {the 'Minimal Change'}[0.601, 0.222] or {the 'Declining Pattern'}[0.930, 0.609]?" }, { "from": "gpt", "value": "Declining Pattern." } ], "id_raw": 1167 }, { "id": "MAx2UUCFKw7HMYXq", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_7.png", "conversations": [ { "from": "human", "value": "\nComparing {first point in 'Inconsistent decline'}[0.067, 0.222] and {first point in 'Declining pattern'}[0.725, 0.222], which has a lower coordinate value on y-axis?" }, { "from": "gpt", "value": "Neither." } ], "id_raw": 1167 }, { "id": "zieC11g0XKGrmd0v", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.0, 0.8, 0.3, 1.0] in the chart." }, { "from": "gpt", "value": "It includes x-axis labels such as 'Green Architecture' {located at}[0.012, 0.879, 0.122, 0.914] and 'Low-Energy Transport' {located at}[0.073, 0.879, 0.198, 0.914], horizontal grid lines, and numerical y-axis tick labels such as '0' and '120'." } ], "id_raw": 1167 }, { "id": "KUWYikKvuOl86uPt", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_7.png", "conversations": [ { "from": "human", "value": "\nDescribe the section in the upper left corner [0.0, 0.0, 0.3, 0.2]." }, { "from": "gpt", "value": "This area includes part of the chart's title {located at}[0.028, 0.037, 0.311, 0.083], specifically the phrases 'Inconsistent Decline vs Declining Pattern'." } ], "id_raw": 1167 }, { "id": "EANXbUn2FTrDsYtN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_7.png", "conversations": [ { "from": "human", "value": "\nCan you provide details on the region [0.65, 0.0, 1.0, 0.3]?" }, { "from": "gpt", "value": "This area covers part of the title for the last subplot {located at}[0.690, 0.037, 0.965, 0.083] reading 'Declining Pattern vs Fluctuating Pattern' and inklings of the chart's facecolor in a light grey hue represented by {subplot background}[0.715, 0.093, 0.940, 0.866]." } ], "id_raw": 1167 }, { "id": "jbX0q2pUxfEhHpPl", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_8.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker does the \"Water Conservation\" data point have for the year 2019 in the second subplot?" }, { "from": "gpt", "value": "Circle." } ], "id_raw": 1168 }, { "id": "xafSpOO4MeJxHQXs", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the ylabel}[0.017, 0.244, 0.029, 0.716] referring to in the first subplot?" }, { "from": "gpt", "value": "Sustainable Business Index." } ], "id_raw": 1168 }, { "id": "Gj1zB4PVLxKj1PAx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the color}[0.868, 0.753, 0.899, 0.774] of the 2018 label handle in the third subplot?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 1168 }, { "id": "CwwZa7Lhmf67UwUN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_8.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Climate Change'}[0.224, 0.403] in the first subplot higher or lower than {the point for 'Climate Change'}[0.882, 0.364] in the third subplot?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1168 }, { "id": "2JhY9yynXshIg5Sr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_8.png", "conversations": [ { "from": "human", "value": "\nBetween the years 2016 and 2017, which year's {the 'Renewable Energy' point}[0.275, 0.306] is higher in the first subplot?" }, { "from": "gpt", "value": "2016." } ], "id_raw": 1168 }, { "id": "J250jYlA8OpuOcn3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_8.png", "conversations": [ { "from": "human", "value": "\nWhich point has a larger y-value, {the 'Energy Efficiency' in 2018}[0.779, 0.507] or {the 'Waste Management' in 2018}[0.831, 0.461] in the third subplot?" }, { "from": "gpt", "value": "2018." } ], "id_raw": 1168 }, { "id": "eQnpt5eXVipxz6BJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the region in [0.209, 0.734, 0.280, 0.849] in the first subplot." }, { "from": "gpt", "value": "This region contains the subplot's legend with the items \"2016\" colored in black and \"2017\" colored in red." } ], "id_raw": 1168 }, { "id": "W7O1gkoM5cjJ4Iqk", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_8.png", "conversations": [ { "from": "human", "value": "\nWhat does the region [0.389, 0.093, 0.615, 0.866] in the second subplot contain?" }, { "from": "gpt", "value": "This region includes the axes for the comparison of Sustainable Business Index between the years 2018 and 2019, with grid lines visible and y-ticks ranging from 0 to 100." } ], "id_raw": 1168 }, { "id": "qRgjPpy27QLfrnyE", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area within [0.718, 0.093, 0.944, 0.866] in the third subplot." }, { "from": "gpt", "value": "This region depicts axes for Sustainable Business Index data comparison between 2018 and 2020, featuring categories like Water Conservation to Renewable Energy and y-ticks from 0 to 100." } ], "id_raw": 1168 }, { "id": "E0zXVWblpi2fu0N3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the markers used for {the line representing 'Monoclonal Antibodies'}[0.378, 0.512, 0.648, 0.586]?" }, { "from": "gpt", "value": "Blue." } ], "id_raw": 1169 }, { "id": "A1Gs8viZKgSJ7Ly7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_9.png", "conversations": [ { "from": "human", "value": "\nWhich object at [0.317, 0.512] represents the year 2020 in the first subplot?" }, { "from": "gpt", "value": "Point." } ], "id_raw": 1169 }, { "id": "kh3pqYCqQxaGoqcC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_9.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed in the subtitle text at [0.400, 0.025, 0.626, 0.055]?" }, { "from": "gpt", "value": "Monoclonal Antibodies vs Regenerative Medicine." } ], "id_raw": 1169 }, { "id": "IMpZ3Pl6I1pl6ViU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_9.png", "conversations": [ { "from": "human", "value": "\nIs the point with coordinates [0.317, 0.512] for the 'Recombinant Proteins' above or below the midpoint of the y-axis in its subplot?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1169 }, { "id": "HYMKEoEKkqSLX3O3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_9.png", "conversations": [ { "from": "human", "value": "\nWhich label corresponds to the point at [0.843, 0.486], 'Gene Therapy' or 'Recombinant Proteins'?" }, { "from": "gpt", "value": "Gene Therapy." } ], "id_raw": 1169 }, { "id": "80fyoN0Bthxs9vGt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_9.png", "conversations": [ { "from": "human", "value": "\nDoes the line at [0.048, 0.362, 0.317, 0.512] have its first point lower than its last point?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1169 }, { "id": "n4Ry406uuwcDGjmd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_9.png", "conversations": [ { "from": "human", "value": "\nWhat is detailed within the bounding box [0.019, 0.603, 0.031, 0.726] on the left side of the first subplot?" }, { "from": "gpt", "value": "Y-axis tick labels '0.6' and '0.8' are located here." } ], "id_raw": 1169 }, { "id": "SVTGeVA4EdHjTvag", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_9.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of region [0.019, 0.502, 0.031, 0.625] in the second subplot?" }, { "from": "gpt", "value": "It includes the y-axis tick labels '0.8' and '0.6', positioned vertically." } ], "id_raw": 1169 }, { "id": "TLg3UYsop3UPyGy6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_9.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the region [0.019, 0.402, 0.031, 0.525] on the left-most part of the third subplot?" }, { "from": "gpt", "value": "The y-axis tick labels '1.0' and '0.8' are located here, aligned vertically." } ], "id_raw": 1169 }, { "id": "z7LUTMdZJpd1wYkX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.012, 0.312, 0.024, 0.648] in the chart?" }, { "from": "gpt", "value": "Y-axis label for the first subplot." } ], "id_raw": 1170 }, { "id": "KyJqGFAo5R2ObQLR", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third x-axis tick in the first subplot?" }, { "from": "gpt", "value": "[0.308, 0.879, 0.372, 0.914]." } ], "id_raw": 1170 }, { "id": "LYPmFvToRmQrZh6I", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_10.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis tick label} at [0.036, 0.543, 0.051, 0.578]?" }, { "from": "gpt", "value": "70." } ], "id_raw": 1170 }, { "id": "MmJCboRPcBHFHZ2g", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_10.png", "conversations": [ { "from": "human", "value": "\nWhich value is greater in the first subplot for 'Businesses', {the blue line}[0.340, 0.248] or {the orange line}[0.340, 0.526]?" }, { "from": "gpt", "value": "The blue line." } ], "id_raw": 1170 }, { "id": "SSJM3ntxwsQgY74O", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_10.png", "conversations": [ { "from": "human", "value": "\nIn the first subplot, is {the 'Amenities'} point of the blue line[0.473, 0.635] above or below of the orange line[0.473, 0.263]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1170 }, { "id": "hv4dtFRgmzUZLOj2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_10.png", "conversations": [ { "from": "human", "value": "\nWhich 'Green Space' score is higher, {Meadowville}[0.075, 0.557] or {Parkside}[0.075, 0.434]?" }, { "from": "gpt", "value": "Meadowville." } ], "id_raw": 1170 }, { "id": "kibcB7xcQO7cg0Jd", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.308, 0.879, 0.372, 0.914]." }, { "from": "gpt", "value": "This region contains the x-axis tick label for 'Businesses' in the first subplot." } ], "id_raw": 1170 }, { "id": "LKydTgeBgzR7Tto8", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_10.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.075, 0.557, 0.473, 0.635]." }, { "from": "gpt", "value": "This area contains the data points for 'Meadowville' in the first subplot, covering 'Green Space', 'Transportation', 'Businesses', and 'Amenities'." } ], "id_raw": 1170 }, { "id": "VIo9UO5dBvSDKUbe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.340, 0.248, 0.473, 0.635] in the first subplot?" }, { "from": "gpt", "value": "There are 2 objects. The data points for 'Businesses' and 'Amenities' of Meadowville are located at {coordinates}[0.340, 0.248] and {coordinates}[0.473, 0.635], respectively. These points are represented by the blue line." } ], "id_raw": 1170 }, { "id": "MYrTpW7VsJ57j1do", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_11.png", "conversations": [ { "from": "human", "value": "\nWhat does {the y-axis label}[0.012, 0.434, 0.024, 0.526] indicate in the chart?" }, { "from": "gpt", "value": "Level." } ], "id_raw": 1171 }, { "id": "OG8f0pOngs2izSrx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_11.png", "conversations": [ { "from": "human", "value": "\nWhat trend is associated with {the point}[0.065, 0.209] in the graph?" }, { "from": "gpt", "value": "Inexorable Decline." } ], "id_raw": 1171 }, { "id": "sNeHqxSLAbSgN698", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_11.png", "conversations": [ { "from": "human", "value": "\nWhat value is marked by {the marker at}[0.533, 0.557]?" }, { "from": "gpt", "value": "40." } ], "id_raw": 1171 }, { "id": "YxawjIspg0MNhM6t", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_11.png", "conversations": [ { "from": "human", "value": "\nIs {the beginning point}[0.065, 0.209] of the 'Inexorable Decline' trend higher than {the ending point}[0.274, 0.325] on the graph?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1171 }, { "id": "c7BfbgPc4z2KogcP", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_11.png", "conversations": [ { "from": "human", "value": "\nDoes {the point at}[0.863, 0.364] represent a higher value than {the point at}[0.863, 0.689] in the 'VoiceAssistants' category?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1171 }, { "id": "CtD1Vh6rGLumuZF9", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_11.png", "conversations": [ { "from": "human", "value": "\nWhich has a greater value at {the point}[0.464, 0.248], 'Inexorable Decline' or 'Pronounced Increase'?" }, { "from": "gpt", "value": "Inexorable Decline." } ], "id_raw": 1171 }, { "id": "L4aFjN2QlqKW3JD9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.055, 0.093, 0.284, 0.866]." }, { "from": "gpt", "value": "The area contains axes with grid lines, the labels 'Trends' and 'Level', and tick descriptions such as 'Chatbots', 'SpeechRecognition', 'VoiceAssistants', and 'RoboticAutomation' placed at specific intervals." } ], "id_raw": 1171 }, { "id": "cwpkNjZdCsmujYAW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_11.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.384, 0.093, 0.613, 0.866]." }, { "from": "gpt", "value": "This section includes a subplot with vertical and horizontal lines forming a grid. It contains tick marks and labels indicating 'Trends' across the x-axis with values 'Chatbots' through 'RoboticAutomation', and 'Level' across the y-axis with numerical labels spanning from 0 to 100. There are plotted lines representing 'Upward Trend' and 'Inexorable Decline'." } ], "id_raw": 1171 }, { "id": "qhp7ov9wQfkhebAD", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_11.png", "conversations": [ { "from": "human", "value": "\nWhat features are found within [0.713, 0.093, 0.942, 0.866]?" }, { "from": "gpt", "value": "The area includes a subplot with grid lines, axes titled 'Trends' and 'Level', along with trend representations for 'Pronounced Increase' and 'Insignificant Increase'. It showcases x-axis markers labeled from 'Chatbots' to 'RoboticAutomation' accompanied by corresponding y-axis numerical labels." } ], "id_raw": 1171 }, { "id": "ppWKzJvec7lNjvTx", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_12.png", "conversations": [ { "from": "human", "value": "\nWhat type of marker represents the data points in the subplot titled \"Decline vs Expansion\"?" }, { "from": "gpt", "value": "Circle." } ], "id_raw": 1172 }, { "id": "wcBAhptCS12bpej9", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_12.png", "conversations": [ { "from": "human", "value": "\nWhat color is {the label 'Expansion'}[0.219, 0.177, 0.279, 0.212] associated with in the legend?" }, { "from": "gpt", "value": "Red." } ], "id_raw": 1172 }, { "id": "NyLnnWmMvvkaLllP", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label in the subplot \"Static vs Continuous Decrease\"?" }, { "from": "gpt", "value": "Efficiency Improvement Potential." } ], "id_raw": 1172 }, { "id": "Vw1BSaeX0cQbn4a8", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_12.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Energy Efficiency'}[0.937, 0.287] on 'Persistent Increase' higher or lower than {the point for 'Robot Automation'}[0.724, 0.158]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1172 }, { "id": "EgqpllNJNAi8Ihwy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_12.png", "conversations": [ { "from": "human", "value": "\nWhich subplot contains {the xlabel 'Study Areas'}[0.466, 0.927, 0.536, 0.963] higher on the vertical scale?" }, { "from": "gpt", "value": "Static vs Continuous Decrease." } ], "id_raw": 1172 }, { "id": "f3e7sXGflgFGe7Ij", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_12.png", "conversations": [ { "from": "human", "value": "\nDoes {the line representing 'Continuous Decrease'}[[0.394, 0.480], [0.608, 0.802]] cross above {the line for 'Static'}[[0.394, 0.512], [0.608, 0.577]] within the subplot?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1172 }, { "id": "LwmqrwkB8qIllXgl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.384, 0.093, 0.619, 0.866]." }, { "from": "gpt", "value": "This region contains a subplot titled \"Static vs Continuous Decrease\" with a detailed comparison of these two metrics across 'Robot Automation' and 'Energy Efficiency'. The grid is styled with dashed white lines, and multiple lines representing different data points and trends are apparent." } ], "id_raw": 1172 }, { "id": "ZijVJCkD9Bu1mi1V", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the area [0.055, 0.093, 0.289, 0.866]." }, { "from": "gpt", "value": "This area encompasses a subplot labeled \"Decline vs Expansion\" charting the potential for efficiency improvement in those domains. There are visible grid lines and labels for study areas along the x-axis, as well as labels for efficiency ratings along the y-axis." } ], "id_raw": 1172 }, { "id": "gT9cbBAac0Gbc9k9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_12.png", "conversations": [ { "from": "human", "value": "\nWhat key elements are found within the bounding box [0.713, 0.093, 0.948, 0.866]?" }, { "from": "gpt", "value": "This bound demarcates a subplot comparing \"Flucturing vs Persistent Increase\". It includes graphical representations through colored lines with markers, axis labels, tick marks, and a grid pattern. Additionally, it features labels for data points specifically tied to 'Robot Automation' and 'Energy Efficiency'." } ], "id_raw": 1172 }, { "id": "gEalVWKxGF20Pt49", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the Y-axis label at the coordinates [0.012, 0.381, 0.024, 0.579] represent?" }, { "from": "gpt", "value": "Trend Level." } ], "id_raw": 1174 }, { "id": "ZmyiZ8VpLuuKn7GC", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at the coordinates [0.674, 0.037, 0.840, 0.083] referring to?" }, { "from": "gpt", "value": "Cryptocurrency Markets." } ], "id_raw": 1174 }, { "id": "bBBG5izXLp7SSe5Q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_14.png", "conversations": [ { "from": "human", "value": "\nWhat's the name of the market style at the x-axis label specified by these coordinates [0.903, 0.879, 0.985, 0.914]?" }, { "from": "gpt", "value": "Erratic growth." } ], "id_raw": 1174 }, { "id": "8dnyNdb3aK2Vbjjd", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_14.png", "conversations": [ { "from": "human", "value": "\nIs the {point of 'Upward trajectory'}[0.571, 0.519] in the Cryptocurrency Markets higher or lower than that in Equity Markets {point of 'Upward trajectory'}[0.077, 0.171]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1174 }, { "id": "OUBAsKKo4Ta1Pomi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_14.png", "conversations": [ { "from": "human", "value": "\nComparing the {point of 'Erratic growth'}[0.450, 0.635] in Equity Markets and the {point of 'Erratic growth'}[0.944, 0.132] in Cryptocurrency Markets, which has a higher value?" }, { "from": "gpt", "value": "Cryptocurrency Markets." } ], "id_raw": 1174 }, { "id": "NaQMeAGPHVk4MGkz", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_14.png", "conversations": [ { "from": "human", "value": "\nIs the {point of 'Incremental growth'}[0.326, 0.480] for Equity Markets at a higher or lower position than the {point of 'Abrupt decline'}[0.201, 0.441]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1174 }, { "id": "N8u1t6P2U3fQCYZL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content contained within the bounding box [0.335, 0.111, 0.463, 0.173]." }, { "from": "gpt", "value": "This area contains the legend item for {Equity Markets}[0.372, 0.125, 0.458, 0.160] in orange color." } ], "id_raw": 1174 }, { "id": "ZfC0pait96tNM7pv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_14.png", "conversations": [ { "from": "human", "value": "\nWhat appears in the region demarcated by coordinates [0.077, 0.171, 0.450, 0.635]?" }, { "from": "gpt", "value": "This region includes a line plot representing the trend level for various market styles in Equity Markets, with points for 'Upward trajectory', 'Abrupt decline', 'Incremental growth', and 'Erratic growth'." } ], "id_raw": 1174 }, { "id": "Ige92wg66MsCq4m1", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_14.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within the region defined by [0.518, 0.879, 0.985, 0.914]." }, { "from": "gpt", "value": "There are x-axis labels representing different market styles: {Upward trajectory}[0.518, 0.879, 0.623, 0.914], {Abrupt decline}[0.653, 0.879, 0.738, 0.914], {Incremental growth}[0.763, 0.879, 0.876, 0.914], and {Erratic growth}[0.903, 0.879, 0.985, 0.914]." } ], "id_raw": 1174 }, { "id": "nzwyjfUFlH6tHtP7", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.707, 0.866]?" }, { "from": "gpt", "value": "X-axis tick for {the year 1980}[0.707, 0.879, 0.732, 0.914]." } ], "id_raw": 1176 }, { "id": "907ylY49zC4izf9w", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_16.png", "conversations": [ { "from": "human", "value": "\nWhich label corresponds to the coordinates [0.017, 0.445, 0.047, 0.465]?" }, { "from": "gpt", "value": "Y-axis label for 200." } ], "id_raw": 1176 }, { "id": "lqdMcvCqwKvMkhoB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_16.png", "conversations": [ { "from": "human", "value": "\nWhich data descriptor is positioned at [0.930, 0.212]?" }, { "from": "gpt", "value": "{The legend descriptor for 'Sea Level'}[0.930, 0.177, 0.978, 0.212]." } ], "id_raw": 1176 }, { "id": "GFGieznrkG8qwyNI", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_16.png", "conversations": [ { "from": "human", "value": "\nDoes the value of the point at [0.965, 0.160] exceed the point at [0.975, 0.160]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1176 }, { "id": "T4Z8zuIFfRmraPPU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_16.png", "conversations": [ { "from": "human", "value": "\nIs the first x-axis tick label on the left or right of the last y-axis tick label in the third subplot?" }, { "from": "gpt", "value": "Right." } ], "id_raw": 1176 }, { "id": "2Qqn4uk25m5gUc6X", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_16.png", "conversations": [ { "from": "human", "value": "\nAre the marker colors at [0.965, 0.160] and [0.978, 0.212] the same or different?" }, { "from": "gpt", "value": "Different." } ], "id_raw": 1176 }, { "id": "UVFnG9Z8PCcN1AtJ", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_16.png", "conversations": [ { "from": "human", "value": "\nWhat is contained within the area [0.90, 0.10, 0.98, 0.23] of the third subplot?" }, { "from": "gpt", "value": "{The legend}[0.899, 0.111, 0.982, 0.226] with labels for 'Glacier' and 'Sea Level', each paired with a colored marker." } ], "id_raw": 1176 }, { "id": "UTohvfMUobIQEGaM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the content found within [0.340, 0.400, 0.35, 0.50]." }, { "from": "gpt", "value": "{The y-axis}[0.340, 0.406, 0.350, 0.554] including the measure labels such as \u2212100, 0, and 100 along with tick marks in the second subplot." } ], "id_raw": 1176 }, { "id": "Mv1WRbtCb1qknnSx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_16.png", "conversations": [ { "from": "human", "value": "\nExplain the elements within the coordinate set [0.046, 0.090, 0.33, 0.87]." }, { "from": "gpt", "value": "This region contains the axes of the first subplot plotted with x-axis and y-axis labels, grid lines, title, and data lines for Temperature vs Glacier." } ], "id_raw": 1176 }, { "id": "jv3rCw80sOn6CAeN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_17.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted at {the point marked on the chart}[0.223, 0.422] for the month of August in the \"Sharp rise\" subplot?" }, { "from": "gpt", "value": "200." } ], "id_raw": 1177 }, { "id": "7dRS5ALFo7c1c0vU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label of the subplot titled \"Periodic fluctuations\"?" }, { "from": "gpt", "value": "Sales Volume." } ], "id_raw": 1177 }, { "id": "p0bdZraC9hSDPkEr", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_17.png", "conversations": [ { "from": "human", "value": "\nIn the \"Slow decline\" subplot, what is represented at {the x-axis coordinate}[0.953, 0.893] for the month of November?" }, { "from": "gpt", "value": "Nov." } ], "id_raw": 1177 }, { "id": "IadNs6ORrrxAzPla", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_17.png", "conversations": [ { "from": "human", "value": "\nWhich value is higher at {the point}[0.460, 0.893] for the subplot \"Periodic fluctuations\" in April, the plotted value or the y-axis label number?" }, { "from": "gpt", "value": "y-axis label number." } ], "id_raw": 1177 }, { "id": "iiM1cD6GZNs85YKt", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_17.png", "conversations": [ { "from": "human", "value": "\nFor the \"Sharp rise\" subplot, is the sales volume in June at {the point}[0.177, 0.546] higher than the y-axis label at {the label}[0.025, 0.514, 0.044, 0.542]?" }, { "from": "gpt", "value": "Yes." } ], "id_raw": 1177 }, { "id": "tuJB8ifJEKHbJ2rs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_17.png", "conversations": [ { "from": "human", "value": "\nIs the data value in {Dec}[0.976, 0.893] for the subplot titled \"Slow decline\" above or below 100 according to the y-tick value at {the level}[0.684, 0.606, 0.703, 0.634]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1177 }, { "id": "pl56XqnIAsQTqZ4Y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the zone encompassed by [0.377, 0.075, 0.659, 0.893]." }, { "from": "gpt", "value": "This area displays a subplot titled \"Periodic fluctuations\" with a blue-marked line graph tracking sales volume trends over a year, labeled monthly from Jan to Dec, and displayed against a grid background." } ], "id_raw": 1177 }, { "id": "oABua9iMW3tEmAdv", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_17.png", "conversations": [ { "from": "human", "value": "\nWhat features are revealed in the region [0.707, 0.075, 0.989, 0.893]?" }, { "from": "gpt", "value": "The section comprises the subplot \"Slow decline\" showcasing a line graph with monthly sales volumes indicated in dark blue, with tick labels and grid lines enhancing the presentation within the chart." } ], "id_raw": 1177 }, { "id": "vYaIY5HblvFkFny5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_17.png", "conversations": [ { "from": "human", "value": "\nPlease detail the elements found within [0.047, 0.075, 0.330, 0.893]." }, { "from": "gpt", "value": "This segment shows the subplot \"Sharp rise\" featuring a dark-colored line graph indicating sales over time with distinct month labels and corresponding data points, set against a light gray grid." } ], "id_raw": 1177 }, { "id": "IrBzsRCXwezDZXLi", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the role of the object at [0.846, 0.037, 0.937, 0.083] in the chart?" }, { "from": "gpt", "value": "Subtitle." } ], "id_raw": 1178 }, { "id": "UrAQNUxWDex1Udqy", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the y-axis label located at the coordinates [0.010, 0.357, 0.019, 0.603]?" }, { "from": "gpt", "value": "Adoption Rate." } ], "id_raw": 1178 }, { "id": "ABGJusNT7k2BnKe3", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_59_18.png", "conversations": [ { "from": "human", "value": "\nWhat data does the object at [0.602, 0.125, 0.833, 0.160] represent in the chart?" }, { "from": "gpt", "value": "Legend item for Robot-Assisted Surgery." } ], "id_raw": 1178 }, { "id": "SbsaZ0Ik9Lwd7kD5", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_18.png", "conversations": [ { "from": "human", "value": "\nIs {the bar from 'Initial Adoption' for 'Telemedicine'} at [0.046, 0.403] higher or lower than {the bar from 'Current Adoption'} at [0.157, 0.155]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1178 }, { "id": "koBJXGMK9aROCyyM", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_18.png", "conversations": [ { "from": "human", "value": "\nWhich line at {point 'Initial Adoption'}[0.801, 0.596] is higher, {the blue line for Medical Imaging} or {the green line}?" }, { "from": "gpt", "value": "Green line." } ], "id_raw": 1178 }, { "id": "23DFSFfdiv2uu586", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_59_18.png", "conversations": [ { "from": "human", "value": "\nIs {the line level for 'Initial Adoption' in 'Medical Imaging'}[0.836, 0.635] above or below {the line level for 'Current Adoption'}[0.947, 0.287]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1178 }, { "id": "YTUyS2qOkT6NeLTr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_18.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the coordinates [0.573, 0.093, 0.689, 0.866]." }, { "from": "gpt", "value": "This region contains the subplot for Robot-Assisted Surgery, including its axes, grid lines, lines representing data points over two categories labeled 'Initial Adoption' and 'Current Adoption'." } ], "id_raw": 1178 }, { "id": "MS8aL5ONLzQrfjvO", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_18.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region [0.046, 0.111, 0.216, 0.226]?" }, { "from": "gpt", "value": "The legend for the subplot, including text items labeling 'Telemedicine vs Telemedicine' and 'Others vs Telemedicine', each associated with a color handle." } ], "id_raw": 1178 }, { "id": "fH72994GZiSRYyXx", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_59_18.png", "conversations": [ { "from": "human", "value": "\nWhat does the chart section spanning from [0.310, 0.093, 0.420, 0.866] include?" }, { "from": "gpt", "value": "This region encompasses the entire chart plot for EHR including axes, grid lines, line markers, and labels, showing a comparison of 'Initial Adoption' and 'Current Adoption'." } ], "id_raw": 1178 }, { "id": "kwo1tOROuGdJghtI", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_0.png", "conversations": [ { "from": "human", "value": "\nWhat's the content at these coordinates - [0.740, 0.046, 0.920, 0.069]?" }, { "from": "gpt", "value": "The text 'Tech Company Value'." } ], "id_raw": 1180 }, { "id": "3oh4XQmVZsjfziL0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_0.png", "conversations": [ { "from": "human", "value": "\nWhat's the information labeled at {coordinates}[0.043, 0.516, 0.076, 0.540] on the chart?" }, { "from": "gpt", "value": "The y-axis label '130'." } ], "id_raw": 1180 }, { "id": "K3xqXL9uEZLVzsOa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the color property of {the line indicating 'Innovative Business Value'}[0.123, 0.065, 0.940, 0.295]?" }, { "from": "gpt", "value": "The color is '#1f77b4'." } ], "id_raw": 1180 }, { "id": "VCr7lbWboGng1p9n", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_0.png", "conversations": [ { "from": "human", "value": "\nDoes the 'Tech Company Value' line or the 'Innovative Business Value' line reach a higher value at month {6}[0.495, 0.583]?" }, { "from": "gpt", "value": "Tech Company Value." } ], "id_raw": 1180 }, { "id": "S2yGkWFoSqqfOmN7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_0.png", "conversations": [ { "from": "human", "value": "\nAt the 8th month interval, is the {Innovative Business Value}[0.643, 0.295] higher or lower than the {Tech Company Value}[0.643, 0.410]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1180 }, { "id": "BGXlk2TmTX4K2RzN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_0.png", "conversations": [ { "from": "human", "value": "\nFor the 10th month data point, which line - 'Tech Company Value' or 'Innovative Business Value', has a higher value?" }, { "from": "gpt", "value": "Innovative Business Value." } ], "id_raw": 1180 }, { "id": "bkUwQxoWRXesSegG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_0.png", "conversations": [ { "from": "human", "value": "\nWhat is described in the region [0.684, 0.037, 0.973, 0.113]?" }, { "from": "gpt", "value": "This region contains the chart's legend, with two items: 'Tech Company Value' with a red-colored marker and 'Innovative Business Value' with a blue-colored marker." } ], "id_raw": 1180 }, { "id": "sQDMMf0DYBYXv9zT", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is in the region [0.043, 0.286, 0.076, 0.310]?" }, { "from": "gpt", "value": "The value '150' is labeled on the y-axis in this area." } ], "id_raw": 1180 }, { "id": "ZOXtMkgyHCdFbFZl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_0.png", "conversations": [ { "from": "human", "value": "\nDescribe what is included in the space [0.123, 0.180, 0.940, 0.871]?" }, { "from": "gpt", "value": "This region shows the 'Tech Company Value' line which is red, consisting of 12 data points represented as 'o' markers, with coordinates ranging from {Month 1}[0.123, 0.871] to {Month 12}[0.940, 0.180]" } ], "id_raw": 1180 }, { "id": "JlqoDHaK1vRNr3oX", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the text at y-axis described as in the chart with coordinates [0.015, 0.342, 0.029, 0.581]?" }, { "from": "gpt", "value": "Number of Cases." } ], "id_raw": 1181 }, { "id": "BsE5cjWnz01mEU4N", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the label of {ytick} at [0.035, 0.254, 0.070, 0.282]?" }, { "from": "gpt", "value": "1400." } ], "id_raw": 1181 }, { "id": "9ziJ3daQZSL2CFWS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_1.png", "conversations": [ { "from": "human", "value": "\nCan you identify the label on {the line for 'Training Hours'}[0.116, 0.069, 0.944, 0.527]?" }, { "from": "gpt", "value": "Training Hours." } ], "id_raw": 1181 }, { "id": "yfPQHMIcJBGnKfvO", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_1.png", "conversations": [ { "from": "human", "value": "\nWhich line on the graph is higher at {xtick}[0.668, 0.893], {the line for 'Training Hours'}[0.116, 0.069, 0.944, 0.527] or {the line for 'Job Positions Created'}[0.116, 0.592, 0.944, 0.854]?" }, { "from": "gpt", "value": "Training Hours." } ], "id_raw": 1181 }, { "id": "tnkJYOVnHOdKwtr3", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_1.png", "conversations": [ { "from": "human", "value": "\nAre the values of {the line for 'Training Hours'}[0.116, 0.069, 0.944, 0.527] generally greater than, less than, or equal to the values of {the line for 'Job Positions Created'}[0.116, 0.592, 0.944, 0.854]?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 1181 }, { "id": "fb2LjZGwl5Sn5F2q", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_1.png", "conversations": [ { "from": "human", "value": "\nWhere does the {the line for 'Job Positions Created'}[0.116, 0.592, 0.944, 0.854] lie in relation to the {the line for 'Training Hours'}[0.116, 0.069, 0.944, 0.527], when read from left to right?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1181 }, { "id": "TuDACRRPHDos8ldn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the region [0.082, 0.044, 0.278, 0.136]?" }, { "from": "gpt", "value": "This area includes the legend for the line chart. It contains the color-coded markers and their respective lines: {Training Hours}[0.126, 0.055, 0.226, 0.083] and {Job Positions Created}[0.126, 0.097, 0.273, 0.125]." } ], "id_raw": 1181 }, { "id": "qAKyaWNsAB7NJYyn", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_1.png", "conversations": [ { "from": "human", "value": "\nWhat content is available in the region [0.516, 0.942, 0.545, 0.970]?" }, { "from": "gpt", "value": "This region contains a title, which is {the x-axis label}[0.515, 0.942, 0.545, 0.970], it is \"Year\"." } ], "id_raw": 1181 }, { "id": "hoUaPD41ckFCLu9D", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be seen in the region [0.374, 0.903, 0.409, 0.931]?" }, { "from": "gpt", "value": "In this region, there is an {x-axis tick label}[0.374, 0.903, 0.409, 0.931] representing the year \"2012\"." } ], "id_raw": 1181 }, { "id": "M16WARG93yDsVGp1", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_3.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the coordinates [0.025, 0.389, 0.048, 0.522] in the chart figure?" }, { "from": "gpt", "value": "The y-axis label 'Indexes'." } ], "id_raw": 1183 }, { "id": "kgcqe0PqSszC1Xzc", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at coordinates [0.241, 0.524, 0.853, 0.685]?" }, { "from": "gpt", "value": "The line for 'Quality of Life Index'." } ], "id_raw": 1183 }, { "id": "lWYXspae0cVGaWtq", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_3.png", "conversations": [ { "from": "human", "value": "\nWhat data point is indicated by the coordinates [0.730, 0.181]?" }, { "from": "gpt", "value": "The point for the 'House Size Index' for the year 2040." } ], "id_raw": 1183 }, { "id": "m0tJZmNYmoDwhp6m", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_3.png", "conversations": [ { "from": "human", "value": "\nIn which year does {the 'Quality of Life Index' line}[0.241, 0.524, 0.853, 0.685] intersect with {the 'House Size Index' line}[0.241, 0.135, 0.853, 0.398]?" }, { "from": "gpt", "value": "The lines do not intersect." } ], "id_raw": 1183 }, { "id": "rkFrk2SEg626Tn2y", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_3.png", "conversations": [ { "from": "human", "value": "\nIs {the data point for the 'Quality of Life Index' in 2040}[0.730, 0.559] higher or lower than {the data point for the 'House Size Index' in the same year}[0.730, 0.181]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1183 }, { "id": "y8HbQLI7CT4Z2GbR", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_3.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Quality of Life Index'}[0.241, 0.524, 0.853, 0.685] slope upwards or downwards overall from 2000 to 2050?" }, { "from": "gpt", "value": "Downwards." } ], "id_raw": 1183 }, { "id": "BA3HHQyEuxmR0ZBH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_3.png", "conversations": [ { "from": "human", "value": "\nDescribe the content placed within coordinates [0.130, 0.072, 0.446, 0.188]." }, { "from": "gpt", "value": "Legend of the chart is located here. It contains 'Quality of Life Index' with a red square marker at {coordinates}[0.130, 0.091, 0.195, 0.112], and 'House Size Index' with a blue pentagon marker at {coordinates}[0.133, 0.140, 0.195, 0.168]." } ], "id_raw": 1183 }, { "id": "9BGf6UUTseiIHcqV", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_3.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the area between [0.241, 0.524, 0.853, 0.685]?" }, { "from": "gpt", "value": "It contains the line representing the 'Quality of Life Index' data from the year 2000 to 2050, with each data point marked by a square." } ], "id_raw": 1183 }, { "id": "adfSFWta1B9f5xY5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_3.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the region [0.241, 0.135, 0.853, 0.398]?" }, { "from": "gpt", "value": "This region contains the line representing the 'House Size Index' data from the year 2000 to 2050, with each data point marked by a pentagon." } ], "id_raw": 1183 }, { "id": "A9kxtoZbilgM863J", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at coordinates [0.025, 0.302, 0.048, 0.609] refer to?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1184 }, { "id": "clVJcUU4u7kG6EA0", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the content of {the legend item} at [0.752, 0.738, 0.913, 0.773]?" }, { "from": "gpt", "value": "Given Ratings." } ], "id_raw": 1184 }, { "id": "dSKmSeoQWWiSzV2d", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_4.png", "conversations": [ { "from": "human", "value": "\nWhich startup phase does {the x-axis tick label} at [0.303, 0.869, 0.482, 0.914] correspond to?" }, { "from": "gpt", "value": "Early Growth." } ], "id_raw": 1184 }, { "id": "vz8rUqAmx0Oq1wti", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_4.png", "conversations": [ { "from": "human", "value": "\nIs {the point for 'Launch'}[0.247, 0.189] higher or lower than {the point for 'Early Growth'}[0.392, 0.144] in the 'Given Ratings' line?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1184 }, { "id": "lO5oDNK5wtkSixwj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_4.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Given Ratings'}[[0.247, 0.100, 0.829, 0.278]] have a higher or lower value at 'Expansion' than {the line for 'Expected Ratings'}[[0.247, 0.144, 0.829, 0.278]]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1184 }, { "id": "aW4zukx4wQI13zMQ", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_4.png", "conversations": [ { "from": "human", "value": "\nAt the 'Mature' phase, which line is higher, Given Ratings or Expected Ratings?" }, { "from": "gpt", "value": "Expected Ratings." } ], "id_raw": 1184 }, { "id": "G0TfOzniKz4Fl5Jj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_4.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within the bounding box [0.612, 0.869, 0.755, 0.914]." }, { "from": "gpt", "value": "This region contains the x-axis tick label for one of the Startup Phases, specifically \"Expansion\"." } ], "id_raw": 1184 }, { "id": "B9CH5RaTPMIFvygL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_4.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is contained within the bounding box [0.752, 0.738, 0.913, 0.773]?" }, { "from": "gpt", "value": "In the specified bounding box, there is an item from the legend. It corresponds to the \"Given Ratings\" for the chart." } ], "id_raw": 1184 }, { "id": "KZSGRBdFG8T4us5X", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_4.png", "conversations": [ { "from": "human", "value": "\nPlease describe what is within the region [0.101, 0.055, 0.975, 0.856]." }, { "from": "gpt", "value": "This region contains the main content of the chart, including two lines, namely 'Given Ratings' and 'Expected Ratings'. It also includes the x-axis and y-axis with their respective tick labels and the legend situated at the top right." } ], "id_raw": 1184 }, { "id": "8UKpV4GXXXOrekMS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinate [0.451, 3.952, 0.579, 0.975]?" }, { "from": "gpt", "value": "X-axis label" } ], "id_raw": 1185 }, { "id": "I1tJEeeSkaH4obAT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the Social Media line}[0.057, 0.126, 0.972, 0.721]?" }, { "from": "gpt", "value": "Red" } ], "id_raw": 1185 }, { "id": "uFDWHtDcjRl1kLor", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the position of the 'May' point on {the Social Media line}[0.390, 0.443]?" }, { "from": "gpt", "value": "'May' is almost halfway along the x-axis and less than halfway along the y-axis." } ], "id_raw": 1185 }, { "id": "0pniHtVCmHZxXVTW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_5.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'May'}[0.390, 0.443] on the Social Media line below or above {the point of 'May'}[0.390, 0.620] on the Digital Communication line?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1185 }, { "id": "gISMFf2IWZnid1yU", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_5.png", "conversations": [ { "from": "human", "value": "\nIs {the handle for Social Media Use in the legend}[0.065, 0.049, 0.101, 0.063] positioned to the left or right on the legend compared to {Digital Communication Use}[0.065, 0.084, 0.101, 0.098]?" }, { "from": "gpt", "value": "They are at the same horizontal position, but the Social Media Use handle is positioned lower vertically." } ], "id_raw": 1185 }, { "id": "cCcRn4o54jjGdSFE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_5.png", "conversations": [ { "from": "human", "value": "\nIs {the X-axis label}[0.450, 0.952, 0.579, 0.975] in the chart placed above or below {the Y-axis label}[0.015, 0.341, 0.029, 0.595]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1185 }, { "id": "pHo8XUApr5uTSyVe", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_5.png", "conversations": [ { "from": "human", "value": "\nCan you describe the content located within the region [0.064, 0.037, 0.303, 0.148]?" }, { "from": "gpt", "value": "This region contains the chart's legend with three entries: {Social Media Use}[0.108, 0.046, 0.226, 0.069], {Digital Communication Use}[0.108, 0.081, 0.298, 0.104] and {Average Social Media Use}[0.108, 0.116, 0.288, 0.139], each paired with a colored symbol of distinctive shape." } ], "id_raw": 1185 }, { "id": "KPoqWaK9FX9oBhlo", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_5.png", "conversations": [ { "from": "human", "value": "\nWhat object is positioned within [0.035, 0.522, 0.052, 0.545]?" }, { "from": "gpt", "value": "This region contains the y-axis tick label '40'." } ], "id_raw": 1185 }, { "id": "3SjtVQ3ds7qJJmFu", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_5.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within [0.057, 0.784, 0.140, 0.734]?" }, { "from": "gpt", "value": "This region contains two data points: 'Jan' from Social Media Use at {coordinates}[0.057, 0.721] and 'Feb' from Digital Communication Use at {coordinates}[0.140, 0.683]." } ], "id_raw": 1185 }, { "id": "9SzL0S2bjGeVApLZ", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the x-axis representing in our visualization?" }, { "from": "gpt", "value": "Year." } ], "id_raw": 1186 }, { "id": "He9zIDr7aY31t9uu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_6.png", "conversations": [ { "from": "human", "value": "\nWhat is {the line} with color '#1f77b4'[0.134, 0.057, 0.908, 0.323] representing?" }, { "from": "gpt", "value": "It represents the trend of people practicing Yoga." } ], "id_raw": 1186 }, { "id": "XyZl9nYKTUAHnN07", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_6.png", "conversations": [ { "from": "human", "value": "\nWhat information does {the y-axis}[0.015, 0.178, 0.029, 0.758] provide?" }, { "from": "gpt", "value": "It represents the percentage of people doing exercise regularly (%)." } ], "id_raw": 1186 }, { "id": "aVOoqjI4zT2gCN15", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_6.png", "conversations": [ { "from": "human", "value": "\nDoes {the Yoga trend line}[0.134, 0.057, 0.908, 0.323] start at a higher or lower value than {the High Intensity Interval Training trend line}[0.134, 0.533, 0.908, 0.701] in 2000?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1186 }, { "id": "GZ6kKeCVdPZXQ6jp", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_6.png", "conversations": [ { "from": "human", "value": "\nAt the point with x-coordinate 2010, is the {Yoga}[0.521, 0.127] percentage higher or lower than {High Intensity Interval Training}[0.521, 0.589]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1186 }, { "id": "b9hOTKkVthh7a0Nu", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_6.png", "conversations": [ { "from": "human", "value": "\nBy looking at the overall line trends, did more people practiced {Yoga}[0.134, 0.057, 0.908, 0.323] or did more people did {High Intensity Interval Training}[0.134, 0.533, 0.908, 0.701] from 2000 to 2020?" }, { "from": "gpt", "value": "Yoga." } ], "id_raw": 1186 }, { "id": "QabBU5eavi8TtXUr", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_6.png", "conversations": [ { "from": "human", "value": "\nWhat is within {the region}[0.715, 0.037, 0.978, 0.113] in this chart?" }, { "from": "gpt", "value": "The region contains the chart's legend which includes icons and labels for both \"High Intensity Interval Training\" and \"Yoga\"." } ], "id_raw": 1186 }, { "id": "segeChGzYcLFTiiB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_6.png", "conversations": [ { "from": "human", "value": "\nPlease describe content in {the area}[0.057, 0.025, 0.985, 0.911]." }, { "from": "gpt", "value": "In this area, the main parts of the chart are plotted. It contains two trend lines, which represents the trend over years of percentage of people doing High Intensity Interval Training exercise and Yoga exercise regularly. The area also contains y-axis labels from 0 to 60 and x-axis labels from 2000 to 2020." } ], "id_raw": 1186 }, { "id": "4SIOVM24Nxo9cdo9", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_6.png", "conversations": [ { "from": "human", "value": "\nCan you please describe objects within {the boundary}[0.503, 0.919, 0.539, 0.942]?" }, { "from": "gpt", "value": "Within these coordinates, there is one object. It's an x-axis label indicating the year \"2010\"." } ], "id_raw": 1186 }, { "id": "mM6t4M06hpjrqFjd", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.025, 0.376, 0.048, 0.528]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1187 }, { "id": "8guivW6tTozgZycT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fourth x-axis tick label?" }, { "from": "gpt", "value": "[0.445, 0.879, 0.511, 0.914]." } ], "id_raw": 1187 }, { "id": "cKDqIuuHlIrd6DWG", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_7.png", "conversations": [ { "from": "human", "value": "\nWhat does {the legend item} at [0.674, 0.748, 0.850, 0.783] represent?" }, { "from": "gpt", "value": "Discharge Rate." } ], "id_raw": 1187 }, { "id": "AeHJkndSlk96XGkH", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_7.png", "conversations": [ { "from": "human", "value": "\nWhich one has a higher value in the '80-90' age group, {Hospital Admission Rate}[0.821, 0.290] or {Discharge Rate}[0.821, 0.226]?" }, { "from": "gpt", "value": "Hospital Admission Rate." } ], "id_raw": 1187 }, { "id": "1W8MacucKLx3Ks0O", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_7.png", "conversations": [ { "from": "human", "value": "\nAt the '40-50' age group, is the {Hospital Admission Rate}[0.364, 0.613] lower or higher than the {Discharge Rate}[0.364, 0.290]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1187 }, { "id": "X3JQB9K9lI4litis", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_7.png", "conversations": [ { "from": "human", "value": "\nWhich rate shows a higher value for the '90+' age group, {Hospital Admission Rate}[0.935, 0.075] or {Discharge Rate}[0.935, 0.183]?" }, { "from": "gpt", "value": "Discharge Rate." } ], "id_raw": 1187 }, { "id": "PHJvNx7mQlmw27IC", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_7.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content within [0.135, 0.075, 0.935, 0.829]." }, { "from": "gpt", "value": "This region contains the Hospital Admission Rate line chart, with data series represented by square markers and a line in red color." } ], "id_raw": 1187 }, { "id": "TB4ExNzZSvXUkx9i", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_7.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the coordinates [0.135, 0.183, 0.935, 0.613]?" }, { "from": "gpt", "value": "The Discharge Rate line chart is located here, represented by a blue line with pentagon markers." } ], "id_raw": 1187 }, { "id": "eAx9L8LEM63EaaWq", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_7.png", "conversations": [ { "from": "human", "value": "\nCan you describe what is found within the region [0.599, 0.682, 0.963, 0.849]?" }, { "from": "gpt", "value": "This area contains the chart legend with three items: {Hospital Admission Rate}[0.674, 0.695, 0.954, 0.730], {Discharge Rate}[0.674, 0.748, 0.850, 0.783], and {Avg admission rate}[0.674, 0.800, 0.896, 0.835]. The handles representing corresponding line styles and markers are located beside each text." } ], "id_raw": 1187 }, { "id": "GgrpCJ9WhT6RmN2i", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the object with coordinates [0.025, 0.340, 0.048, 0.554] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1188 }, { "id": "rlJSmHZhbo2gN8ea", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the eighth tick label on the x-axis?" }, { "from": "gpt", "value": "[0.748, 0.869, 0.783, 0.914]" } ], "id_raw": 1188 }, { "id": "UJUemeRdt9n3kqph", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_8.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.888, 0.869, 0.923, 0.914]?" }, { "from": "gpt", "value": "45." } ], "id_raw": 1188 }, { "id": "VKDhUVJHmmpBfZ2r", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_8.png", "conversations": [ { "from": "human", "value": "\nIs {the point of 'Temperature Avg'}[0.695, 0.806] higher or lower than {the point of 'Sales Avg'}[0.695, 0.396]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1188 }, { "id": "k6LbHWX06TNQJgU7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_8.png", "conversations": [ { "from": "human", "value": "\nIs {the starting point for 'Temperature Avg'}[0.276, 0.819] above or below {the starting point for 'Sales Avg'}[0.276, 0.691]?" }, { "from": "gpt", "value": "Above." } ], "id_raw": 1188 }, { "id": "0R3Cp5MmE6khboq2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_8.png", "conversations": [ { "from": "human", "value": "\nWhich line has a higher value at {point 5}[0.556, 0.809], Temperature Avg or Sales Avg?" }, { "from": "gpt", "value": "Temperature Avg." } ], "id_raw": 1188 }, { "id": "jwBI3XqmSYhNZZdM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_8.png", "conversations": [ { "from": "human", "value": "\nDescribe the content in [0.683, 0.672, 0.963, 0.839]." }, { "from": "gpt", "value": "Three legend items are located here: {Temperature Avg}[0.757, 0.685, 0.954, 0.720] with a red handle, {Sales Avg}[0.757, 0.738, 0.869, 0.773] with a blue handle, and {Average Sales}[0.757, 0.790, 0.921, 0.825] with a green handle." } ], "id_raw": 1188 }, { "id": "iGJgAboHCYmSS7Az", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_8.png", "conversations": [ { "from": "human", "value": "\nPlease describe the area [0.075, 0.754, 0.128, 0.799]." }, { "from": "gpt", "value": "The y-axis tick label \"100\" is located at {coordinates}[0.075, 0.754, 0.128, 0.799]." } ], "id_raw": 1188 }, { "id": "ThDfkYhs68fE5WNN", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_8.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within [0.525, 0.410, 0.585, 0.470]?" }, { "from": "gpt", "value": "There are 2 objects. The point associated with 'Temperature Avg' at timestep 20 is located at {coordinates}[0.556, 0.809] and the point associated with 'Sales Avg' at timestep 20 is located at {coordinates}[0.556, 0.450]." } ], "id_raw": 1188 }, { "id": "DZ2fbA4kTBRw4mLB", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at [0.025, 0.329, 0.048, 0.583] in the chart?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1189 }, { "id": "TTbt9w0xLaICpVFv", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of {the x-tick label} labeled '5'?" }, { "from": "gpt", "value": "[0.452, 0.869, 0.470, 0.914]." } ], "id_raw": 1189 }, { "id": "65vsg4EVv6qPjzUm", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the handle of the Apple Stock Price label}[0.133, 0.743, 0.193, 0.764] in the legend?" }, { "from": "gpt", "value": "#d62728." } ], "id_raw": 1189 }, { "id": "kwhscJPVphVEZKv7", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_9.png", "conversations": [ { "from": "human", "value": "\nComparing {the point at month '3' for Apple Stock Price}[0.347, 0.317] and {the point at the same month for Orange Stock Price}[0.347, 0.200], which company's stock price is higher?" }, { "from": "gpt", "value": "Apple's Stock Price." } ], "id_raw": 1189 }, { "id": "sA9M4nr7C1vPW8GY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of {the line for 'Apple Stock Price'}[0.176, 0.095, 0.861, 0.827] compared to {the line for 'Orange Stock Price'}[0.176, 0.131, 0.861, 0.307]?" }, { "from": "gpt", "value": "Red, specifically #d62728." } ], "id_raw": 1189 }, { "id": "Q7twJJUJ2CPyeSCa", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_9.png", "conversations": [ { "from": "human", "value": "\nAt {the last point}[0.861, 0.304] for Apple and at {the last point}[0.861, 0.192] for Orange, which stock has a higher closing price?" }, { "from": "gpt", "value": "Apple." } ], "id_raw": 1189 }, { "id": "pDiDlieQal0bhtSh", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_9.png", "conversations": [ { "from": "human", "value": "\nWhat kind of information is included within the coordinates [0.058, 0.037, 0.111, 0.083]?" }, { "from": "gpt", "value": "There is a y-axis tick label mentioning the value '175'." } ], "id_raw": 1189 }, { "id": "LJyw03lJzhtiD2xG", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_9.png", "conversations": [ { "from": "human", "value": "\nPlease describe the region [0.167, 0.869, 0.184, 0.914]." }, { "from": "gpt", "value": "The region contains {the '0' x-axis tick label}[0.167, 0.869, 0.184, 0.914]." } ], "id_raw": 1189 }, { "id": "4M85NnttIsI3CWEj", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_9.png", "conversations": [ { "from": "human", "value": "\nPlease depict the objects within the area [0.130, 0.724, 0.434, 0.839]." }, { "from": "gpt", "value": "The area comprises the sub-legend of the plot with label 'Apple Stock Price' at {coordinates}[0.204, 0.738, 0.406, 0.773] and its color element located at {coordinates}[0.133, 0.743, 0.193, 0.764]. It also contains the label 'Orange Stock Price' at {coordinates}[0.204, 0.790, 0.425, 0.825] with a color element at {coordinates}[0.130, 0.792, 0.195, 0.819]." } ], "id_raw": 1189 }, { "id": "oGLyzbPjdFhS5mnY", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_10.png", "conversations": [ { "from": "human", "value": "\nCan you identify the object located at these coordinates [0.025, 0.205, 0.048, 0.707]?" }, { "from": "gpt", "value": "It's the y-axis label." } ], "id_raw": 1190 }, { "id": "t3u2QF34D2S4HFWS", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the object at {this location}[0.721, 0.685, 0.860, 0.720]?" }, { "from": "gpt", "value": "It's the label for 'Stress Level'." } ], "id_raw": 1190 }, { "id": "S9PGQLJve5Wfmhod", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the tick label located at [0.892, 0.869, 0.945, 0.914] on the x-axis?" }, { "from": "gpt", "value": "It's '200'." } ], "id_raw": 1190 }, { "id": "tbsGjjZSNz7Lk4Qn", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_10.png", "conversations": [ { "from": "human", "value": "\nIs the {point at '175' on the 'Stress Level'}[0.824, 0.740] higher or lower than the {point at '175' on the 'Recovery Level'}[0.824, 0.201]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1190 }, { "id": "P8mALn7zJnU1EqdN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_10.png", "conversations": [ { "from": "human", "value": "\nIs {the point for '100' on 'Stress Level'}[0.542, 0.492] situated above or below {the point for '100' on 'Recovery Level'}[0.542, 0.565]?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1190 }, { "id": "LcchWiI0qgGVo2SW", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_10.png", "conversations": [ { "from": "human", "value": "\nWhich fluctuation level has a higher value at {Time '50'}[0.353], Stress Level or Recovery Level?" }, { "from": "gpt", "value": "Stress Level." } ], "id_raw": 1190 }, { "id": "j3Ow0TbJ1xtzkvr3", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_10.png", "conversations": [ { "from": "human", "value": "\nWhat is contained in the region with coordinates [0.647, 0.672, 0.963, 0.839]?" }, { "from": "gpt", "value": "It contains the sub-legend with three items: {Stress Level}[0.721, 0.685, 0.860, 0.720], {Recovery Level}[0.721, 0.738, 0.896, 0.773], and {Base Recovery level}[0.721, 0.790, 0.954, 0.825]." } ], "id_raw": 1190 }, { "id": "IUGlbWV6d96kIiMp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_10.png", "conversations": [ { "from": "human", "value": "\nWhat objects are located within the region [0.127, 0.055, 0.975, 0.856]?" }, { "from": "gpt", "value": "The area contains the entire plot with x-tick labels from 0 to 200, y-tick labels from 0.40 to 0.95, and three lines for Stress Level, Recovery Level, and Base Recovery level." } ], "id_raw": 1190 }, { "id": "W0j1mH7QRQM2bNhL", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_10.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents in the region [0.058, 0.110, 0.119, 0.155]." }, { "from": "gpt", "value": "It contains the y-axis tick label '0.90'." } ], "id_raw": 1190 }, { "id": "lKgUkwzbJSfeCeGN", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the object located at these coordinates [0.843, 0.332, 0.862, 0.352]?" }, { "from": "gpt", "value": "Data point in the 'Urban Development' line." } ], "id_raw": 1191 }, { "id": "h54lkE0asaZp3J2M", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the fifth x-axis tick label located at [0.502, 0.912, 0.555, 0.942]?" }, { "from": "gpt", "value": "\"2020\"" } ], "id_raw": 1191 }, { "id": "RTVVyrKqQELrnN2m", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_11.png", "conversations": [ { "from": "human", "value": "\nWhat does the text at the coordinates [0.719, 0.790, 0.894, 0.814] refer to?" }, { "from": "gpt", "value": "It is a legend item labeled 'Urban Development'" } ], "id_raw": 1191 }, { "id": "zjydJplouCGvF5rN", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_11.png", "conversations": [ { "from": "human", "value": "\nIs the data point representing 'Urban Development' in 2025 at [0.619, 0.232] higher or lower compared to 'Rural Development' in the same year at [0.619, 0.731]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1191 }, { "id": "ZkcWaXCl9q2RQhiE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_11.png", "conversations": [ { "from": "human", "value": "\nIs the y-axis tick label \"60\" at [0.043, 0.242, 0.070, 0.272] lower or higher than the y-axis tick label \"35\" at [0.043, 0.784, 0.070, 0.814]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1191 }, { "id": "YT7EzeY8viIJvYi2", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_11.png", "conversations": [ { "from": "human", "value": "\nWhich line is represented by the legend item at [0.719, 0.860, 0.966, 0.883], 'Urban Development' or 'Rural Development'?" }, { "from": "gpt", "value": "Neither, it represents 'Average Development Index'." } ], "id_raw": 1191 }, { "id": "OXU3NaISwF5bYQGc", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_11.png", "conversations": [ { "from": "human", "value": "\nCould you describe the area at [0.719, 0.790, 0.894, 0.849]?" }, { "from": "gpt", "value": "This region contains two legend items for the lines in the chart. They are {Urban Development}[0.719, 0.790, 0.894, 0.814] and {Rural Development}[0.719, 0.825, 0.886, 0.849], each respectively paired with a square and pentagon markers of different colors." } ], "id_raw": 1191 }, { "id": "JkexzSfBipDfL4H5", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_11.png", "conversations": [ { "from": "human", "value": "\nWhat is present within the region [0.764, 0.124, 0.823, 0.836] in the chart?" }, { "from": "gpt", "value": "This region primarily includes two trend lines for 'Urban Development' and 'Rural Development' from the years 2035 to 2040, specifically at {coordinates}[0.800, 0.427, 0.891, 0.470] and {coordinates}[0.800, 0.709, 0.891, 0.796]. Also, some x-axis tick labels and an x-axis label \"Year\" are included." } ], "id_raw": 1191 }, { "id": "6hDqTaHyBMc4KPKM", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_11.png", "conversations": [ { "from": "human", "value": "\nDescribe the elements in the area [0.663, 0.781, 0.973, 0.893]." }, { "from": "gpt", "value": "Inside this region, there are three legend labels for 'Urban Development'[0.719, 0.790, 0.894, 0.814], 'Rural Development'[0.719, 0.825, 0.886, 0.849] and 'Average Development Index'[0.719, 0.860, 0.966, 0.883], each associated with a colored marker." } ], "id_raw": 1191 }, { "id": "i2rUoK6t3Bg1UWRh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_12.png", "conversations": [ { "from": "human", "value": "\nBased on its coordinates [0.516, 0.927, 0.577, 0.963], what is the object located at the bottom center of the chart figure?" }, { "from": "gpt", "value": "X-axis label." } ], "id_raw": 1192 }, { "id": "pPdZWH6qsc3jxrRD", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_12.png", "conversations": [ { "from": "human", "value": "\nWhat does {the marker in the legend}[0.682, 0.739, 0.747, 0.767] represent?" }, { "from": "gpt", "value": "Followers." } ], "id_raw": 1192 }, { "id": "ossJhie8coR1kTmj", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_12.png", "conversations": [ { "from": "human", "value": "\nWhat value does the label at {coordinates}[0.093, 0.839, 0.110, 0.884] represent on the y-axis?" }, { "from": "gpt", "value": "0." } ], "id_raw": 1192 }, { "id": "gsMAU7GvQwKRbfVs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_12.png", "conversations": [ { "from": "human", "value": "\nAre the numbers for {Followers}[0.118, 0.066, 0.975, 0.461] greater than or less than the numbers for {Friends}[0.118, 0.292, 0.975, 0.645] in the year 2016?" }, { "from": "gpt", "value": "Greater." } ], "id_raw": 1192 }, { "id": "iWb6HJ3BPpGrxGxy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_12.png", "conversations": [ { "from": "human", "value": "\nWhich line, {Friends}[0.118, 0.292, 0.975, 0.645] or {Followers}[0.118, 0.066, 0.975, 0.461], has a higher value in 2020?" }, { "from": "gpt", "value": "Followers." } ], "id_raw": 1192 }, { "id": "Dd5zeSfmDaGQQxtr", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_12.png", "conversations": [ { "from": "human", "value": "\nComparing {Friends}[0.118, 0.292, 0.975, 0.645] and {2012 Benchmark}[-215.316, 0.645, -215.209, 0.645], which had a higher value in the year 2018?" }, { "from": "gpt", "value": "Friends." } ], "id_raw": 1192 }, { "id": "pENsrD1Wq6LBe5SK", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_12.png", "conversations": [ { "from": "human", "value": "\nPlease describe the content in the area with coordinates [0.682, 0.672, 0.963, 0.839]." }, { "from": "gpt", "value": "This area contains the legend of the chart with three entries: {Friends}[0.756, 0.685, 0.839, 0.720], {Followers}[0.756, 0.738, 0.865, 0.773], and {2012 Benchmark}[0.756, 0.790, 0.954, 0.825]. Each entry has a text label and a color handle." } ], "id_raw": 1192 }, { "id": "CUpShGTkmPHEkR4G", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the objects found within the region [0.025, 0.185, 0.048, 0.709]?" }, { "from": "gpt", "value": "The region contains the y-axis label, which reads \"Number of Social Connections\"." } ], "id_raw": 1192 }, { "id": "heLqJDp0zoAjkgxp", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_12.png", "conversations": [ { "from": "human", "value": "\nDescribe the region [0.118, 0.856, 0.975, 0.914]." }, { "from": "gpt", "value": "The region contains x-axis tick labels representing the years from 2012 to 2019." } ], "id_raw": 1192 }, { "id": "s3UxmaXmySNgm9ow", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_13.png", "conversations": [ { "from": "human", "value": "\nWhat is signified by the object at this coordinate [0.025, 0.333, 0.048, 0.561]?" }, { "from": "gpt", "value": "It is the y-axis label which is 'growth value'." } ], "id_raw": 1193 }, { "id": "4Qkop5dgB0GwgfYu", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the third tick label on the x-axis?" }, { "from": "gpt", "value": "The tick label is '2015' and its coordinate is {at}[0.365, 0.869, 0.435, 0.914]." } ], "id_raw": 1193 }, { "id": "KhoCdR6AtpFnYDuA", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the meaning of the line with these coordinates [0.236, 0.341, 0.893, 0.792]?" }, { "from": "gpt", "value": "This line represents the Ethereum growth value over the years." } ], "id_raw": 1193 }, { "id": "ibY2QHdfEkaAAJfg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_13.png", "conversations": [ { "from": "human", "value": "\nIs the growth of {Blockchain}[0.236, 0.073, 0.893, 0.775] higher or lower than {Ethereum}[0.236, 0.341, 0.893, 0.792] in the year 2017?" }, { "from": "gpt", "value": "The growth of Blockchain is higher." } ], "id_raw": 1193 }, { "id": "LoyvTAyfBwCmnsGg", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_13.png", "conversations": [ { "from": "human", "value": "\nIn 2018, is the growth value of {Blockchain}[0.236, 0.073, 0.893, 0.775] higher or lower than the {Standard Tech Growth}[-165.110, 0.316, -165.028, 0.316]?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1193 }, { "id": "U7OhdCCtqO9xcXxi", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_13.png", "conversations": [ { "from": "human", "value": "\nIn the chart, what is closer to the {Standard Tech Growth} line[-165.110, 0.316, -165.028, 0.316], the {Blockchain}'s growth value[0.236, 0.073, 0.893, 0.775] or the {Ethereum}'s growth value[0.236, 0.341, 0.893, 0.792] in 2020?" }, { "from": "gpt", "value": "Ethereum's growth value." } ], "id_raw": 1193 }, { "id": "ZykEi6zE24LHvpEF", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_13.png", "conversations": [ { "from": "human", "value": "\nWhat can be found in the area with coordinates [0.165, 0.055, 0.505, 0.222]?" }, { "from": "gpt", "value": "It contains the legend for the chart. The legend has the names of the lines {Blockchain}[0.239, 0.069, 0.365, 0.104], {Ethereum}[0.239, 0.121, 0.352, 0.156], and {Standard Tech Growth}[0.239, 0.173, 0.495, 0.208] each along with a color handle marking their presence in the plot." } ], "id_raw": 1193 }, { "id": "sgUIMudH6u2aYv7K", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_13.png", "conversations": [ { "from": "human", "value": "\nWhat units are displayed within [0.076, 0.434, 0.146, 0.479]?" }, { "from": "gpt", "value": "The y-axis label shows a value of '7500' in this area." } ], "id_raw": 1193 }, { "id": "ttuhJJYMQOYzxSS7", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_13.png", "conversations": [ { "from": "human", "value": "\nWhat information is available in the region [0.447, 0.869, 0.517, 0.914]?" }, { "from": "gpt", "value": "It contains one of the x-axis labels, representing the year '2016'." } ], "id_raw": 1193 }, { "id": "OwqHWGrpJQjBhvJh", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_14.png", "conversations": [ { "from": "human", "value": "\nWhat is located at the location [0.025, 0.185, 0.048, 0.709]?" }, { "from": "gpt", "value": "Y-axis label." } ], "id_raw": 1194 }, { "id": "D0GnUOfPGqKhDM9U", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_14.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted at the co-ordinate [0.935, 0.856]?" }, { "from": "gpt", "value": "The End label on the x-axis." } ], "id_raw": 1194 }, { "id": "fM5QxivDYHjwkz2Q", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_14.png", "conversations": [ { "from": "human", "value": "\nWhat is displayed at the {coordinates}[0.140, 0.283] on the graph?" }, { "from": "gpt", "value": "Starts point of the line representing the label, 'Heritage Sites'." } ], "id_raw": 1194 }, { "id": "bCsttr1ouGQykR5h", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_14.png", "conversations": [ { "from": "human", "value": "\nIs the {starting point of 'Heritage Sites'}[0.140, 0.283] higher than the {starting point of 'World Cultures'}[0.140, 0.578]?" }, { "from": "gpt", "value": "No." } ], "id_raw": 1194 }, { "id": "5V54Q5VpeWVkKMlV", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_14.png", "conversations": [ { "from": "human", "value": "\nDoes {the line for 'Ancient Architecture'}[0.140, 0.169, 0.935, 0.234] end higher or lower than {the line for 'Heritage Sites'}[0.140, 0.283, 0.935, 0.660]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1194 }, { "id": "CvGsCAY6R5qaRsQs", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_14.png", "conversations": [ { "from": "human", "value": "\nIs the {finishing point of 'Heritage Sites'}[0.935, 0.660] higher or lower than {finishing point of 'World Cultures'}[0.935, 0.791]?" }, { "from": "gpt", "value": "Lower." } ], "id_raw": 1194 }, { "id": "4H0mOy56MUOc2zuA", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_14.png", "conversations": [ { "from": "human", "value": "\nWhat content is displayed in the region [0.643, 0.672, 0.963, 0.839]?" }, { "from": "gpt", "value": "This region contains the legend for the graph with three labels: 'Heritage Sites', 'Ancient Architecture', and 'World Cultures', each paired with a colored marker." } ], "id_raw": 1194 }, { "id": "iKgqMLcjbZ3Xh4gl", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_14.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents of the region [0.058, 0.102, 0.093, 0.147]." }, { "from": "gpt", "value": "The y-axis tick label '95' is displayed in this area." } ], "id_raw": 1194 }, { "id": "3Me8iD6TVhGXI4uB", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_14.png", "conversations": [ { "from": "human", "value": "\nWhat is depicted within the region [0.107, 0.869, 0.174, 0.914]?" }, { "from": "gpt", "value": "It depicts the 'Start' label on the x-axis." } ], "id_raw": 1194 }, { "id": "8kOnLyRXKVKy7ONT", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_15.png", "conversations": [ { "from": "human", "value": "\nWhat is {the object}[0.015, 0.321, 0.029, 0.557]?" }, { "from": "gpt", "value": "The y-axis label is located at these coordinates, labeled as \"Sales (in thousands)\"." } ], "id_raw": 1195 }, { "id": "vNnhsYC1RLMRbFGU", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the Period '2014Q2' on the x-axis?" }, { "from": "gpt", "value": "[0.773, 0.861, 0.821, 0.942]." } ], "id_raw": 1195 }, { "id": "ANyM6dcQvadYaUCp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the color of the line representing {Product B}[0.133, 0.060, 0.944, 0.559]?" }, { "from": "gpt", "value": "The color is #1f77b4." } ], "id_raw": 1195 }, { "id": "39O57AZ3nfCFfEU4", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_15.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at the '2014Q1' period, {Product A}[0.723, 0.662] or {Product B}[0.723, 0.192]?" }, { "from": "gpt", "value": "Product B." } ], "id_raw": 1195 }, { "id": "25XR9ZZ5lQZ3OteE", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_15.png", "conversations": [ { "from": "human", "value": "\nIs the {Product A}[0.871, 0.750] line above or below the {Product B}[0.871, 0.090] line at '2014Q3\"?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1195 }, { "id": "jcsTjfMShZ01vYis", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_15.png", "conversations": [ { "from": "human", "value": "\nAt point '2013Q2', was {Product A}[0.502, 0.589] performing better or worse than {Product B}[0.502, 0.339]?" }, { "from": "gpt", "value": "Worse." } ], "id_raw": 1195 }, { "id": "OCKQTmUQYz9d2sPW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_15.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region [0.133, 0.060, 0.944, 0.765]." }, { "from": "gpt", "value": "This region contains two lines representing the sales performance of Product A and Product B from '2012Q1' to '2014Q4'. Product A starts at point [0.133, 0.413] in '2012Q1' and ends at point [0.944, 0.765] in '2014Q4'. Product B starts at point [0.133, 0.559] in '2012Q1' and ends at point [0.944, 0.060] in '2014Q4'." } ], "id_raw": 1195 }, { "id": "xUwVMUzK8GGmOms2", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_15.png", "conversations": [ { "from": "human", "value": "\nPlease describe the elements within the region [0.099, 0.037, 0.248, 0.148]." }, { "from": "gpt", "value": "This region contains a legend of three items: {Product A}[0.144, 0.046, 0.210, 0.069] with a color marker '#d62728'; {Product B}[0.144, 0.081, 0.210, 0.104] with a color marker '#1f77b4'; and a 'Baseline sales'[0.144, 0.116, 0.242, 0.139] label with a color marker '#008000'." } ], "id_raw": 1195 }, { "id": "ElAZgTxJJnjCEncH", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_15.png", "conversations": [ { "from": "human", "value": "\nWhat items are within the region [0.699, 0.861, 0.969, 0.942]?" }, { "from": "gpt", "value": "Within these coordinates, we find two period labels for the x-axis: '2014Q1' at coordinates [0.699, 0.861, 0.747, 0.942] and '2014Q2' at coordinates [0.773, 0.861, 0.821, 0.942]." } ], "id_raw": 1195 }, { "id": "pMc8ylBVAgMhuYwa", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_16.png", "conversations": [ { "from": "human", "value": "\nWhat is {the y-axis label}[0.015, 0.254, 0.033, 0.717]?" }, { "from": "gpt", "value": "Future technological development prospects." } ], "id_raw": 1196 }, { "id": "T10BXEMtThlriCGe", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the third line in the legend?" }, { "from": "gpt", "value": "[0.132, 0.116, 0.272, 0.133]." } ], "id_raw": 1196 }, { "id": "Z4Ix0SFpWyKwpgxO", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_16.png", "conversations": [ { "from": "human", "value": "\nWhat does {the key of the chart line}[0.156, 0.057, 0.910, 0.267] represent?" }, { "from": "gpt", "value": "Quantum Computing." } ], "id_raw": 1196 }, { "id": "dwIazxhVDEGA3AsY", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_16.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled 'Artificial Intelligence'}[0.231, 0.118] situated above or below {the point labeled 'Prevision Tech Trend'}[0.231, 0.240] in the year 2001?" }, { "from": "gpt", "value": "Below." } ], "id_raw": 1196 }, { "id": "9X6QiFrnWc3j0Aqe", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_16.png", "conversations": [ { "from": "human", "value": "\nWas Quantum Computing or Prevision Tech Trend more prevalent in 2008 according to {their respective points}[0.759, 0.092] and [0.759, 0.118]?" }, { "from": "gpt", "value": "Quantum Computing." } ], "id_raw": 1196 }, { "id": "XtS4qQeejsYUosKL", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_16.png", "conversations": [ { "from": "human", "value": "\nWhich subject showed more growth between 2002 and 2010, {Artificial Intelligence}[0.306, 0.127] to [0.910, 0.162] or {Quantum Computing}[0.306, 0.197] to [0.910, 0.057]?" }, { "from": "gpt", "value": "Quantum Computing." } ], "id_raw": 1196 }, { "id": "pr4vUZakz36pcxdW", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_16.png", "conversations": [ { "from": "human", "value": "\nPlease describe the objects within region [0.132, 0.064, 0.274, 0.133]." }, { "from": "gpt", "value": "This area contains the legends for 3 lines indicating the 'Artificial Intelligence', 'Quantum Computing', and 'Prevision Tech Trend' trends from 2000 to 2010. Each legend is coupled with a colored handle for respective identification." } ], "id_raw": 1196 }, { "id": "rPZZPzcHup8asDhb", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_16.png", "conversations": [ { "from": "human", "value": "\nWhat text is contained in the region [0.251, 0.019, 0.815, 0.042]?" }, { "from": "gpt", "value": "The subtitle of the chart, \"Future Technological Development Prospects (2000-2010)\", is located in this region. It is written in color \".15\" and has no rotation." } ], "id_raw": 1196 }, { "id": "lNmh3NeAYgT6uB9b", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_16.png", "conversations": [ { "from": "human", "value": "\nDescribe the contents within the region [0.156, 0.092, 0.910, 0.267]." }, { "from": "gpt", "value": "This region contains the plotted line for the 'Prevision Tech Trend'. The line is colored '#008000' and has a '-.' linestyle. Furthermore, it is dotless, meaning it has 'None' instead of markers." } ], "id_raw": 1196 }, { "id": "DZCzrm7Gi87lbDDp", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the second object on the y-axis with coordinates [0.043, 0.808, 0.089, 0.838] in the chart?" }, { "from": "gpt", "value": "Y-axis tick label '0.55'." } ], "id_raw": 1197 }, { "id": "UzQcigRBXBZ87hno", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the coordinate of the fifth point on the 'Innovation Industry' line?" }, { "from": "gpt", "value": "[0.649, 0.231]." } ], "id_raw": 1197 }, { "id": "e5HO6CJXX62kPAQ4", "Chart_type": "line", "QA_type": "Referring QA", "image": "png_line/line_60_17.png", "conversations": [ { "from": "human", "value": "\nWhat is {the x-axis tick label} at [0.623, 0.912, 0.675, 0.942]?" }, { "from": "gpt", "value": "2020." } ], "id_raw": 1197 }, { "id": "gVcIFMx4EOyW0cQy", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_17.png", "conversations": [ { "from": "human", "value": "\nIs {the point labeled '2025'}[0.760, 0.399] on the 'Knowledge Economy' line higher or lower than {the point labeled '2025'}[0.760, 0.147] on the 'Innovation Industry' line?" }, { "from": "gpt", "value": "Higher." } ], "id_raw": 1197 }, { "id": "4DR1QWTX8yeyRgKj", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_17.png", "conversations": [ { "from": "human", "value": "\nIs {the line for 'Knowledge Economy'}[0.206, 0.315, 0.871, 0.820] rising or falling from {point 2000}[0.206, 0.820] to {point 2030}[0.871, 0.315]?" }, { "from": "gpt", "value": "Falling." } ], "id_raw": 1197 }, { "id": "P7uBcW830trbt7Ss", "Chart_type": "line", "QA_type": "Relationship QA", "image": "png_line/line_60_17.png", "conversations": [ { "from": "human", "value": "\nWhich line is higher at {point 2020}[0.649, 0.231], Knowledge Economy or Innovation Industry?" }, { "from": "gpt", "value": "Innovation Industry." } ], "id_raw": 1197 }, { "id": "vcP4iRMiGRqIU4ot", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_17.png", "conversations": [ { "from": "human", "value": "\nDescribe the content within [0.729, 0.781, 0.973, 0.893]." }, { "from": "gpt", "value": "This area contains the legend for the chart, including the labels for {Knowledge Economy}[0.785, 0.790, 0.966, 0.814], {Innovation Industry}[0.785, 0.825, 0.954, 0.849], and {Overall growth trend}[0.785, 0.860, 0.965, 0.883], each associated with a different marker color." } ], "id_raw": 1197 }, { "id": "b8AxkwfRmyQ8Q07Y", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_17.png", "conversations": [ { "from": "human", "value": "\nPlease describe the coordinates [0.206, 0.315, 0.871, 0.820]." }, { "from": "gpt", "value": "This represents the range of the line for the 'Knowledge Economy', with the y-coordinates indicating the line's height at various points from 2000 to 2030." } ], "id_raw": 1197 }, { "id": "YQeMoJeR8wBgXMx6", "Chart_type": "line", "QA_type": "Regional Description QA", "image": "png_line/line_60_17.png", "conversations": [ { "from": "human", "value": "\nWhat objects are within the coordinates [0.206, 0.315, 0.871, 0.820]?" }, { "from": "gpt", "value": "These coordinates encompass the entirety of two lines: {the 'Knowledge Economy'} line and {the 'Innovation Industry'} line, each marked by a series of points representing the respective growth rates per year from 2000 to 2030." } ], "id_raw": 1197 }, { "id": "5wlAtZdZcX9oQD7U", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_0.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the chart into a standard csv format with rows representing the different investment sectors and columns representing the different lambda values?" }, { "from": "gpt", "value": "To generate a structured table, I'll start by identifying {the x-axis tick labels}[0.114, 0.903, 0.233, 0.978], [0.349, 0.903, 0.451, 0.930], [0.579, 0.903, 0.660, 0.930], [0.808, 0.903, 0.932, 0.930], representing the different investment sectors as rows. Then, {the legend texts}[0.807, 0.202, 0.962, 0.238], [0.807, 0.260, 0.947, 0.297], [0.807, 0.390, 0.960, 0.426] represent the different lambda values as column headers. I will then locate each {bar height}[0.143, 0.582, 0.210, 0.610], [0.377, 0.225, 0.445, 0.285], [0.619, 0.230, 0.657, 0.278], [0.859, 0.701, 0.930, 0.824] and convert them to return rate values based on the {y-axis tick labels}[0.050, 0.080, 0.074, 0.121], [0.050, 0.206, 0.078, 0.243], [0.050, 0.332, 0.079, 0.369], [0.050, 0.457, 0.079, 0.492].\nBased on the above thought, the answer is:\nInvestment Sectors,\u03bb=0.06, \u03bb=0.08, \u03bb=0.1\nHigh Growth Stocks,23.5%, 22.5%, 25%\nBonds, 9.5%, 10.5%, 8.0%\nReal Estate, 8.0%, 9.0%, 10.5%\nMutual Funds, 10.5%, 10.0%, 23.5%\nHigh Yield Bonds, 25%, 24.5%, 9.0%" } ], "id_raw": 0 }, { "id": "Fwb5nWMo1XrZE9dp", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_0.png", "conversations": [ { "from": "human", "value": "\nBased on this data, which type of investment seems to have shown the most stability over different lambda values and why?" }, { "from": "gpt", "value": "To find which investment shows the most stability, I need to examine the fluctuation of return rates across different lambda values. I look at the {bar heights for High Growth Stocks}[0.143, 0.422, 0.210, 0.478], [0.143, 0.478, 0.210, 0.532], [0.143, 0.532, 0.210, 0.610], {Bonds}[0.377, 0.170, 0.445, 0.225], [0.377, 0.225, 0.445, 0.285], [0.377, 0.285, 0.445, 0.361], {Real Estate}[0.619, 0.187, 0.657, 0.230], [0.619, 0.230, 0.657, 0.278], [0.619, 0.278, 0.657, 0.361], {Mutual Funds}[0.808, 0.238, 0.875, 0.315], [0.808, 0.315, 0.875, 0.372], [0.808, 0.372, 0.875, 0.463] and {High Yield Bonds}[0.149, 0.610, 0.210, 0.696], [0.149, 0.696, 0.210, 0.766], [0.143, 0.766, 0.210, 0.853]. Upon comparing the fluctuations in returns, the rates for High Growth Stocks, Real Estate, and High Yield Bonds fluctuate significantly. However, the rates for Bonds shows minimal fluctuations across different lambda values indicating a higher degree of stability.\nBased on the above thought, the answer is:\nBased on the return rates in the chart, Bonds appear to be the most stable investment sector. Its return rates fluctuate minimally across different lambda values, suggesting a lower risk level compared to other investments like High Growth Stocks, Real Estate, Mutual Funds, and High Yield Bonds, which show significant variation in returns." } ], "id_raw": 0 }, { "id": "mmMpUvJHlt6xG5WR", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest return rate for Mutual Funds under any of the lambda values?" }, { "from": "gpt", "value": "To find the highest return rate for 'Mutual Funds', I need to look at {the bar heights for Mutual Funds}[0.808, 0.238, 0.875, 0.315], [0.808, 0.315, 0.875, 0.372], [0.808, 0.372, 0.875, 0.463] corresponding to the three lambda values. Converting them into return rate values based on {the y-axis tick labels}[0.050, 0.080, 0.074, 0.121], [0.050, 0.206, 0.078, 0.243], [0.050, 0.332, 0.079, 0.369], [0.050, 0.457, 0.079, 0.492], the values are 10.5%, 10.0%, and 23.5%. The highest among these is 23.5% which corresponds to lambda value \u03bb=0.1.\nBased on the above thought, the answer is:\n23.5%" } ], "id_raw": 0 }, { "id": "vfXZIUazkK0xsDvN", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_1.png", "conversations": [ { "from": "human", "value": "\nCan you interpret the line plot data into a readable table format using 'Quarters' as columns and 'Parameters' as rows?" }, { "from": "gpt", "value": "Analyzing the line plot, I first look at {the x-axis labels}[[0.158, 0.911, 0.193, 0.934], [0.305, 0.911, 0.340, 0.934], [0.452, 0.911, 0.488, 0.934], [0.599, 0.911, 0.635, 0.934], [0.747, 0.911, 0.782, 0.934], [0.894, 0.911, 0.929, 0.934]], which represent the column headers or Quarters. Next up, I recognize {the legend texts}[[0.164, 0.059, 0.200, 0.089], [0.164, 0.103, 0.186, 0.133], [0.164, 0.147, 0.262, 0.177], [0.164, 0.191, 0.291, 0.221], [0.164, 0.235, 0.269, 0.265], [0.164, 0.279, 0.318, 0.309]] representing the row labels or Parameters including 'Process Efficiency', 'Employee Productivity', 'Customer Satisfaction', 'Operational Costs', and 'Technological Advancement'. To fill the table, I then locate {the line points}[[0.175, 0.537], [0.323, 0.467], [0.470, 0.518], [0.617, 0.686], [0.764, 0.563], [0.911, 0.161]] for each parameter at the respective quarters and extract the values indicated.\nBased on the above thought, the answer is:\nParameters,1971,1981,1991,2001,2011,2021\nProcess Efficiency,63000,73000,78000,80000,83000,80500\nEmployee Productivity,61900,81500,88000,94000,99500,92000\nCustomer Satisfaction,50200,62500,76000,81000,85000,87500\nOperational Costs,2520,19750,29700,36800,44000,48500\nTechnological Advancement,23000,39000,55000,69500,73500,115700" } ], "id_raw": 1 }, { "id": "LDSFPa5QWnCadiV7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_1.png", "conversations": [ { "from": "human", "value": "\nComparing the trends in the line chart, which parameter shows a consistent upward trend?" }, { "from": "gpt", "value": "To identify the parameter with a consistent upward trend, I need to analyze the line plots for each parameter. Inspecting {the line for 'Process Efficiency'}[0.175, 0.537, 0.911, 0.624], I notice fluctuations and no clear consistent upward trend. Observing {the line for 'Employee Productivity'}[0.175, 0.467, 0.911, 0.628], I see fluctuations. Analyzing {the line for 'Customer Satisfaction'}[0.175, 0.518, 0.911, 0.679], I observe a mixed trend. Reviewing {the line for 'Operational Costs'}[0.175, 0.686, 0.911, 0.884], I do not see any consistent upward trend. Lastly, when looking at {the line for 'Technological Advancement'}[0.175, 0.563, 0.911, 0.161], I see a gradual and consistent upward trend.\nBased on the above thought, the answer is:\nFrom the line chart trends, 'Technological Advancement' demonstrates a consistent upward trend over the years. This can be seen as its line plot consistently ascends over the quarters, indicating a steady increase in technological advancement." } ], "id_raw": 1 }, { "id": "3Pp9mrTrtJXnwnBb", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the total improvement in 'Technological Advancements' from 1971 to 2021?" }, { "from": "gpt", "value": "To calculate the total improvement in 'Technological Advancements' from 1971 to 2021, I need to track the line plot for 'Technological Advancements'. Tracing {the line plot for 'Technological Advancement'}[[0.175, 0.537, 0.911, 0.161]], I extract the values for the year 1971 and 2021 as 23000 {at 1971}[0.175, 0.796] and 115700 {at 2021}[0.911, 0.161]. The difference between these two values gives the total improvement.\nBased on the above thought, the answer is:\n92700." } ], "id_raw": 1 }, { "id": "BDThHjJMmFKRVDia", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_2.png", "conversations": [ { "from": "human", "value": "\nCan you provide the annual donations for the both charities in a table format?" }, { "from": "gpt", "value": "To create the table, I check the {x-axis tick labels}[[0.146, 0.904, 0.175, 0.924], [0.287, 0.904, 0.316, 0.924], [0.427, 0.904, 0.457, 0.924], [0.568, 0.904, 0.598, 0.924], [0.709, 0.904, 0.738, 0.924], [0.850, 0.904, 0.879, 0.924], [0.991, 0.904, 1.020, 0.924]], which represent the years. The {legend texts}[[0.176, 0.141, 0.325, 0.167], [0.176, 0.179, 0.300, 0.205], [0.176, 0.217, 0.308, 0.242], [0.176, 0.254, 0.285, 0.280]] are used for the charity names. The {line points}[[0.160, 0.237, 0.865, 0.649], [0.160, 0.155, 0.865, 0.546], [0.160, 0.155, 0.865, 0.608], [0.160, 0.505, 0.865, 0.855]] of each line are the donation values.\nBased on the above thought, the answer is:\nYear,Consistent Expansion,Gradual Recovery,Fluctuating Growth,Slight Decrease\n2000,15,20,17,22\n2001,17,21,18,21\n2002,19,22,20,19\n2003,21,24,21,18\n2004,23,26,24,16\n2005,25,28,26,14\n2006,27,30,29,12\n2007,29,33,32,10\n2008,31,35,34,9\n2009,33,37,36,7\n2010,35,39,39,5" } ], "id_raw": 2 }, { "id": "BY7WYvQHL6qKYhKe", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_2.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which charity has seen a more consistent increase in donations over the years and why?" }, { "from": "gpt", "value": "To answer this question, I examine the donation trend of each charity. The {line for Consistent Expansion} [0.160, 0.237, 0.865, 0.649] shows a consistent upward trend with no major drops, indicating that donations for this charity have been consistently increasing. On the other hand, the {line for Gradual Recovery} [0.160, 0.155, 0.865, 0.546] shows fluctuations with a few dips, suggesting its donations haven't increased as consistently.\nBased on the above thought, the answer is:\nThe charity represented by the line 'Consistent Expansion' has seen a more consistent increase in donations over the years. Its line plot shows a steady upward trend with no major drops over the years, indicating that it has been consistently increasing its donations." } ], "id_raw": 2 }, { "id": "IMvOo51PrmSAfmj1", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the total amount of donation for both charities from 2000 to 2010?" }, { "from": "gpt", "value": "To calculate the total donation, I need to sum up the y-values of all points on the {line for the Consistent Expansion}[0.160, 0.237, 0.865, 0.649] and {line for the Gradual Recovery} [0.160, 0.155, 0.865, 0.546] across all years represented by the {x-axis tick labels}[[0.146, 0.904, 0.175, 0.924], [0.287, 0.904, 0.316, 0.924], [0.427, 0.904, 0.457, 0.924], [0.568, 0.904, 0.598, 0.924], [0.709, 0.904, 0.738, 0.924], [0.850, 0.904, 0.879, 0.924], [0.991, 0.904, 1.020, 0.924]].\nBased on the above thought, the answer is:\n623" } ], "id_raw": 2 }, { "id": "DeFWn47TEFRQTvNm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_3.png", "conversations": [ { "from": "human", "value": "\nCan you create a table from the line chart that shows the Patent Applications, Patent Grants, and Trademark Registrations for the respective technology innovations?" }, { "from": "gpt", "value": "To do so, I would first look at {the x-axis labels}[0.074, 0.911, 0.092, 0.934] and identify these as the Patent Applications, Patent Grants, and Trademark Registrations. Next, I would identify the line points for each technology innovation. For AI, {the points}[0.066, 0.455, 0.888, 0.661] are portrayed. Similarly, Blockchain has {its points}[0.066, 0.309, 0.888, 0.222], Robotics technologies have {their points displayed}[0.066, 0.342, 0.889, 0.777], NanoTech has {the points}[0.066, 0.233, 0.888, 0.469] and SpaceTech nuances have {their points}[0.066, 0.349, 0.888, 0.282].\nBased on the above thought, the answer is:\nTechnologies, Patent Applications, Patent Grants, Trademark Registrations\nAI, 5000, 4000, 2000\nBlockchain, 4200, 2800, 2600\nRobotics, 3300, 2900, 2000\nNanoTech, 2400, 2200, 1800\nSpaceTech, 2000, 1200, 1500" } ], "id_raw": 3 }, { "id": "2cTOJVkk3ov4b0Am", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which technology innovation is showing the fastest growth in patent applications?" }, { "from": "gpt", "value": "Analyzing the progress of each technology, I look at the points on each line to identify the patent application counts in recent years. {AI}[0.066, 0.455, 0.888, 0.661] shows a steep climb indicating aggressive growth in patent applications. The lines for Blockchain[0.066, 0.309, 0.888, 0.222], Robotics[0.066, 0.342, 0.889, 0.777], NanoTech[0.066, 0.233, 0.888, 0.469], and SpaceTech[0.066, 0.349, 0.888, 0.282] also show progress but no other category is increasing as rapidly as AI Applications.\nBased on the above thought, the answer is:\nBased on the chart, AI technology innovation is showing the fastest growth in patent applications. The rate of increase for AI Applications is clearly more pronounced compared to other domains, suggesting more aggressive research and development efforts in the AI segment." } ], "id_raw": 3 }, { "id": "9pnOcPSLLaOGXQhx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest number of patent applications in any technology innovation?" }, { "from": "gpt", "value": "To find the maximum number of patent applications, I look at {the points for each line}[0.066, 0.455, 0.888, 0.661], [0.066, 0.309, 0.888, 0.222], [0.066, 0.342, 0.889, 0.777], [0.066, 0.233, 0.888, 0.469], [0.066, 0.349, 0.888, 0.282]. From those, I can tell that AI had the highest number of patent applications at 5000.\nBased on the above thought, the answer is:\n5000" } ], "id_raw": 3 }, { "id": "EzrtCrN5VItQo4lo", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_4.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, can you create a table with the Years as rows and regions(Global, Asia, Africa, America) as columns?" }, { "from": "gpt", "value": "To transform the line chart data into a table, I first identify {year ticks}[0.159, 0.911, 0.194, 0.934], [0.234, 0.911, 0.269, 0.934], [0.308, 0.911, 0.343, 0.934], [0.383, 0.911, 0.418, 0.934], [0.458, 0.911, 0.493, 0.934], [0.532, 0.911, 0.567, 0.934], [0.607, 0.911, 0.642, 0.934], [0.681, 0.911, 0.716, 0.934], [0.756, 0.911, 0.791, 0.934] as the row headers representing the years. \nSubsequently, I locate {the legend labels}[0.910, 0.489, 0.963, 0.519], [0.910, 0.533, 0.945, 0.563], [0.910, 0.577, 0.957, 0.607], [0.910, 0.621, 0.978, 0.651] which are used as the column headers representing the different regions.\nLastly, to obtain the values, I find {the line points for each region}[0.177, 0.130, 0.773, 0.426], [0.177, 0.536, 0.773, 0.632], [0.177, 0.785, 0.773, 0.823], [0.177, 0.574, 0.773, 0.785] corresponding to each year and extract the annotated values.\nBased on the above thought, the answer is:\nYear,Global,Asia,Africa,America\n2000,100000,55000,15000,30000\n2001,102000,57000,15200,30800\n2002,98000,59000,16000,23000\n2003,105000,61000,16500,28000\n2004,110000,62000,18000,30000\n2005,132000,64500,19000,48000\n2006,140000,68000,21000,52000\n2007,160000,70000,23000,67000\n2008,150000,75000,20800,54000" } ], "id_raw": 4 }, { "id": "BBV7k5EV05Poq1Vd", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_4.png", "conversations": [ { "from": "human", "value": "\nAnalyze the trending behavior of each market from the line chart and indicate which market has the most positive trend overall." }, { "from": "gpt", "value": "To examine the trends of each market, I inspect the individual line plots for Global, Africa, America and Asia. Observing {the line plot for the Global market}[0.177, 0.130, 0.773, 0.426], I notice a general upward trend with some fluctuations. Likewise, {Asia's line plot}[0.177, 0.536, 0.773, 0.632] displays a continual increase in market value.\nContrasting these, {the line plot for Africa}[0.177, 0.785, 0.773, 0.823] shows a noticable sustained upward slope up until the year 2007 after which it dips slightly. Lastly, {the line plot for America}[0.177, 0.574, 0.773, 0.785] fluctuates initially, witnesses a sudden rise around the year 2005 and then drops sharply after 2007.\nBased on the above thought, the answer is:\nThe African market shows the most positive trend overall, as it exhibits a steady, sustained upward growth throughout the years. Even though it dips slightly toward the end, the overall growth is substantial compared to the other markets. The consistent upward trend indicates positive progression and growth." } ], "id_raw": 4 }, { "id": "ppoBaj9cvNk9u1jV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_4.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, what is the increase in Market Value for the Global market between 2001 and 2003?" }, { "from": "gpt", "value": "To find the increase in Market Value for the Global market between 2001 and 2003, I will locate {the line points for Global corresponding to 2001}[0.251, 0.407] and {2003}[0.400, 0.393]. The Market Value for 2001 is 102,000 and for 2003 it is 105,000. Therefore, the increase in Market Value is 105,000 - 102,000 = 3,000.\nBased on the above thought, the answer is:\n3000." } ], "id_raw": 4 }, { "id": "CL21EFVxFjOYswW4", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_5.png", "conversations": [ { "from": "human", "value": "\nCould you translate the data from the grouped bar chart into a CSV format with lifestyles as columns and social activities as rows?" }, { "from": "gpt", "value": "To convert the bar chart into a CSV format, first, I need to recognize {the x-axis labels}[0.106, 0.923, 0.131, 0.943, 0.267, 0.923, 0.292, 0.943, 0.428, 0.923, 0.453, 0.943, 0.589, 0.923, 0.614, 0.943, 0.750, 0.923, 0.775, 0.943] which represent the row labels or social activities. Second, I identify {the legend}[0.060, 0.033, 0.313, 0.191] as the column headers representing the different lifestyles. To populate the values for each Lifestyle within each social activity, I\u2019d correlate {the heights of the bars}[0.119, 0.754, 0.924, 0.832, 0.119, -5.841, 0.924, -3.764, 0.119, -0.338, 0.924, 0.648] with their corresponding values annotated on the {y-axis labels}[0.054, 0.910, 0.041, 0.902, 0.047, 0.922, 0.054, 0.806, 0.035, 0.798, 0.047, 0.818] for each bar that corresponds to each lifestyle per social activity.\nBased on the above thought, the answer is:\nSocial Activities,Lifestyle1,Lifestyle2,Lifestyle3\nJostling,900,800,700\nCycling,800,700,600\nHiking,650,550,500\nJogging,550,450,400\nDancing,450,350,300" } ], "id_raw": 5 }, { "id": "r3OYUbSGmzB27VRi", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_5.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the bar chart, which lifestyle has the highest involvement in all social activities and why?" }, { "from": "gpt", "value": "To determine which lifestyle has the highest involvement in all social activities, I need to analyze the heights of the bars for each lifestyle. By inspecting the graph, I can see that in all social activities such as {Jostling}[0.119, 0.754], {Cycling}[0.280, 0.816], {Hiking}[0.441, 0.806], {Jogging}[0.602, 0.795], and {Dancing}[0.763, 0.780], the bar of Lifestyle1 is always taller than the bars of Lifestyle2 and Lifestyle3. This indicates that for each activity, Lifestyle1 has the highest count of people engaged.\nBased on the above thought, the answer is:\nAccording to the trends shown in the bar chart, Lifestyle1 has the highest involvement in all social activities. This is evident as, for each social activity on the x-axis, the bar representing Lifestyle1 is always the tallest, indicating it has the highest count of people taking part in the respective activities. This suggests that Lifestyle1 tends to be more active or involved across all the social activities surveyed." } ], "id_raw": 5 }, { "id": "aPNWX474rZ3eZ1g5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the total number of people across all lifestyles engaged in hiking?" }, { "from": "gpt", "value": "To get the total number of people engaged in hiking across all lifestyles, I have to find {the bars corresponding to hiking}[0.428, 0.923, 0.453, 0.943] for each lifestyle and get their corresponding values from the {y-axis}[0.054, 0.910, 0.041, 0.902, 0.047, 0.922, 0.054, 0.806, 0.035, 0.798, 0.047, 0.818]. For Lifestyle1 the value is 650, for Lifestyle2 the value is 550 and for Lifestyle3 the value is 500. Summing up these values 650 + 550 + 500 gives us the total.\nBased on the above thought, the answer is:\n1700." } ], "id_raw": 5 }, { "id": "KTqdAF6A6f31uX2N", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_6.png", "conversations": [ { "from": "human", "value": "\nCan you organize the data represented in the line chart into a table, with lambda values as the rows and types of attacks as the columns?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I should first identify {the x-axis tick labels}[[0.121, 0.910, 0.138, 0.933], [0.252, 0.910, 0.270, 0.933], [0.382, 0.910, 0.399, 0.933], [0.512, 0.910, 0.529, 0.933], [0.642, 0.910, 0.659, 0.933], [0.773, 0.910, 0.791, 0.933]] which represent the row headers or lambda values. {The legend texts}[[0.883, 0.410, 0.969, 0.452], [0.883, 0.466, 0.945, 0.513]] can serve as column headers, denoting the types of attacks (Phishing Attacks and Data Breaches). To fill in the table, I will examine the line points associated with each type of attack, for example, {the line points for Phishing Attacks}[[0.130, 0.381], [0.444, 0.299], [0.628, 0.236], [0.758, 0.217], [0.859, 0.202], [0.942, 0.158]] and {the line points for Data Breaches}[[0.130, 0.723], [0.444, 0.552], [0.628, 0.470], [0.758, 0.432], [0.859, 0.407], [0.942, 0.381]], and extract the values annotated for each lambda value.\nBased on the above thought, the answer is:\nLambda Values,Phishing Attacks,Data Breaches\n10,0.0001,0.01\n20,0.001,0.03\n30,0.003,0.07\n40,0.005,0.09\n50,0.007,0.11\n60,0.01,0.2" } ], "id_raw": 6 }, { "id": "yVgsvPPKt7IvYrY8", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_6.png", "conversations": [ { "from": "human", "value": "\nBased on this line chart, which type of attack seems to be influenced more by the level of protection (lambda values)?" }, { "from": "gpt", "value": "To answer this question, I need to compare how each type of attack changes as lambda changes. For this, I note the points for both {Phishing Attacks}[[0.130, 0.381], [0.444, 0.299], [0.628, 0.236], [0.758, 0.217], [0.859, 0.202], [0.942, 0.158]] and {Data Breaches}[[0.130, 0.723], [0.444, 0.552], [0.628, 0.470], [0.758, 0.432], [0.859, 0.407], [0.942, 0.381]]. It's clear that as lambda increases, both types of attacks decrease, but the decline in data breaches is more steep compared to phishing attacks. This suggests that the number of data breaches is more heavily influenced by the level of protection system.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the number of Data Breaches seems to be more influenced by the level of protection (lambda values). As lambda values increase, we witness a steep decline in the count of Data Breaches as compared to Phishing Attacks. This indicates the count of Data Breaches is more sensitive to the level of protection in the system." } ], "id_raw": 6 }, { "id": "skRSrpSPl149IXkf", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the precise difference between phishing attacks and data breaches when the lambda value is 50?" }, { "from": "gpt", "value": "To find the difference between phishing attacks and data breaches when lambda equals 50, I need to first locate {the point for Phishing Attacks at lambda = 50}[0.859, 0.202] which corresponds to a value of 0.007. Similarly, {the point for Data breaches at lambda = 50}[0.859, 0.476] corresponds to a value of 0.11. The difference can thus be calculated as 0.11 - 0.007 = 0.103.\nBased on the above thought, the answer is:\n0.103." } ], "id_raw": 6 }, { "id": "vHRjBMyV022APeUq", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_7.png", "conversations": [ { "from": "human", "value": "\nCan you organize the data from the graph which shows social indicators and their values, into a table format with Social Indicators as rows and types as columns?" }, { "from": "gpt", "value": "To transcribe the data from the line chart into tabular format, I begin by spotting {the x-axis tick labels}[[0.050, 0.772, 0.128, 0.901], [0.488, 0.772, 0.586, 0.934]] which account for the row labels or social indicators. Next, I recognize {the legend texts}[[0.892, 0.415, 0.967, 0.445], [0.892, 0.459, 0.946, 0.489], [0.892, 0.503, 0.970, 0.533], [0.892, 0.547, 0.965, 0.577]] that act as the column headers symbolizing the different types. To fill the values, I identify {the line points}[[0.089, 0.306], [0.537, 0.418], [0.089, 0.306], [0.537, 0.343], [0.089, 0.263], [0.537, 0.261], [0.089, 0.481], [0.537, 0.584]] for each type of social indicator and extract the correlated values.\nBased on the above thought, the answer is:\nSocial Indicator,Standard,Erratic,Decrease,Unstable\nActivity Level,1.00e-06,1.00e-06,5.72e-06,7.79e-10\nSocial Interaction,1.00e-08,2.23e-07,6.31e-06,1.14e-11" } ], "id_raw": 7 }, { "id": "gxAf7CCauQcw5g9S", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_7.png", "conversations": [ { "from": "human", "value": "\nBased on the graph trends, which Social Indicator appears to be more erratic?" }, { "from": "gpt", "value": "To determine the most erratic Social Indicator, I'll assess the changes in the line plots for both. Examine {the line for Activity_Level}[0.089, 0.306, 0.537, 0.418]. It shows a sharp increase then a decrease, which signifies high volatility.\nThen look at {the Social_Interaction line}[0.089, 0.418, 0.537, 0.583]. It exhibits a consistent trend with minimal fluctuations, implying low volatility.\nBased on the above thought, the answer is:\nAccording to the graph trends, Activity_Level appears to be the more erratic Social Indicator. It shows significant fluctuations and an inconsistent pattern across the graph, suggesting high volatility in its trends." } ], "id_raw": 7 }, { "id": "w2xqzP4AwIlUch6o", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the ratio between Standard and Decrease values for Social_Interaction?" }, { "from": "gpt", "value": "To obtain the ratio between Standard and Decrease values for Social_Interaction, I need to trace {the line points for Standard} [0.537, 0.418] and {the line points for Decrease}[0.537, 0.261] for Social_Interaction. The Standard value is 1.00e-08 and the Decrease value is 6.31e-06. The ratio is therefore 1.00e-08 / 6.31e-06, which equals 1.59e-03.\nBased on the above thought, the answer is:\n1.59e-03." } ], "id_raw": 7 }, { "id": "VWNywHsqRi3r78C6", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_9.png", "conversations": [ { "from": "human", "value": "\nCan you convert the performance scores of Microsoft and Google across the six different areas into a table with Areas as rows and Companies as columns?" }, { "from": "gpt", "value": "First, I identify {the x-axis tick labels}[[0.071, 0.911, 0.080, 0.934], [0.202, 0.911, 0.211, 0.934], [0.334, 0.911, 0.342, 0.934], [0.465, 0.911, 0.474, 0.934], [0.596, 0.911, 0.605, 0.934], [0.727, 0.911, 0.736, 0.934]] as the row labels representing the different performance areas. Next, I locate {the legend texts}[[0.868, 0.489, 0.943, 0.519], [0.868, 0.618, 0.943, 0.645]] as the column headers representing the companies Microsoft and Google, respectively. I then locate the {line points for Microsoft}[[0.071, 0.056], [0.202, 0.260], [0.334, 0.383], [0.465, 0.481], [0.596, 0.546], [0.727, 0.617]] and {line points for Google}[[0.071, 0.546], [0.202, 0.618], [0.334, 0.638], [0.465, 0.670], [0.596, 0.694], [0.727, 0.734]] for all the performance categories and extract the values to populate the table.\nBased on the above thought, the answer is:\nArea,Microsoft,Google\nSteady decline,5.847,2.43\nSteep decline,4.423,1.931\nErratic behavior,3.567,1.789\nStable growth,2.887,1.569\nInconsistent development,2.432,1.398\nPeriodic spikes,1.937,1.123" } ], "id_raw": 9 }, { "id": "HfLKekfN4wz7yjdN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_9.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which company shows better performance overall and why?" }, { "from": "gpt", "value": "To answer this question, I need to compare the total scores for both companies across all areas. For Microsoft, {the line points}[[0.071, 0.056], [0.202, 0.260], [0.334, 0.383], [0.465, 0.481], [0.596, 0.546], [0.727, 0.617]] add up to 5.847 + 4.423 + 3.567 + 2.887 + 2.432 + 1.937 = 21.093. For Google, {the line points}[[0.071, 0.546], [0.202, 0.618], [0.334, 0.638], [0.465, 0.670], [0.596, 0.694], [0.727, 0.734]] add up to 2.43 + 1.931 + 1.789 + 1.569 + 1.398 + 1.123 = 10.24. Clearly, Microsoft's total score is higher.\nBased on the above thought, the answer is:\nMicrosoft shows better performance overall because the sum of its scores across all the six areas is higher than that of Google. This is suggestive of overall better management and organizational leadership in Microsoft as per the dimensions considered here." } ], "id_raw": 9 }, { "id": "ihLbjUUdYJ7XbyKH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in scores for Microsoft and Google in the area of \"Stable growth\"?" }, { "from": "gpt", "value": "I need to consult {the line point for Microsoft in Stable growth}[[0.465, 0.481]] which is 2.887 and {the line point for Google in Stable growth}[[0.465, 0.670]] which is 1.569. The difference is calculated as 2.887 - 1.569 = 1.318.\nBased on the above thought, the answer is:\n1.318." } ], "id_raw": 9 }, { "id": "amtzlE9R2loE9g0b", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_10.png", "conversations": [ { "from": "human", "value": "\nCan you present the data from the chart in a table format with Years as columns and Transaction Types as rows?" }, { "from": "gpt", "value": "To create a table, I will first append {the x-axis labels}[[0.091, 0.933, 0.126, 0.951], [0.258, 0.933, 0.293, 0.951], [0.425, 0.933, 0.460, 0.951], [0.592, 0.933, 0.627, 0.951], [0.759, 0.933, 0.794, 0.951], [0.926, 0.933, 0.961, 0.951]] as the column headers which represent the years. Secondly, I will use the labels of the legend {Regular Cryptocurrency, Stablecoin, Traditional Currency, Blockchain Transactions}[[0.118, 0.065, 0.283, 0.082], [0.118, 0.091, 0.191, 0.108], [0.118, 0.117, 0.258, 0.135], [0.118, 0.143, 0.285, 0.161]] as row headers. Next step is to locate the data points on the plot for each type of transaction using the lines for {Regular Cryptocurrency}[[0.109, 0.093, 0.943, 0.790]], {Stablecoin}[[0.109, 0.441, 0.943, 0.877]], {Traditional Currency}[[0.109, 0.842, 0.943, 0.877]], and {Blockchain Transactions}[[0.109, 0.494, 0.943, 0.572]] to assess their respective transaction volumes per year.\nBased on the above thought, the answer is:\nTransation Type,2015,2016,2017,2018,2019,2020\nRegular Cryptocurrency,1.5,1.6,2.5,6.3,7.8,9.5\nStablecoin,0.5,1.5,2.7,2.5,3.5,5.5\nTraditional Currency,0.7,0.8,0.9,0.7,0.6,0.5\nBlockchain Transactions,4.5,4.4,4.3,4.1,4.0,4.9" } ], "id_raw": 10 }, { "id": "N8kPrx0jfm6Alzoe", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_10.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which type of transaction has shown the most growth from 2015 to 2020? Can you explain how you came to this conclusion?" }, { "from": "gpt", "value": "To identify which type of transaction showed the most growth from 2015 to 2020, each line's trend needs to be interpreted. Looking at the line for {Regular Cryptocurrency}[0.09, 0.790, 0.943, 0.093], the value starts at 1.5 in 2015 and ends at 9.5 in 2020, which shows a significant increase. Observing the {Stablecoin}[0.09, 0.877, 0.943, 0.441] line, it begins at 0.5 in 2015 and increases to 5.5 in 2020\u2014also a significant growth, but not as much as the Regular Cryptocurrency. The lines for {Traditional Currency}[0.109, 0.850, 0.943, 0.877] and {Blockchain Transactions}[0.109, 0.529, 0.943, 0.494] both show slight declines from 2015 to 2020.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Regular Cryptocurrency transactions have shown the most growth from 2015 to 2020. This is evident from the line plot which shows that its value has increased from 1.5 to 9.5 over this period. This level of growth is more substantial than any of the other types of transactions over the same period." } ], "id_raw": 10 }, { "id": "OgluXybT2FZRrfCx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the total volume of transactions for the Blockchain Transactions from 2015 to 2020?" }, { "from": "gpt", "value": "To calculate the total volume of transactions for the Blockchain Transactions from 2015 to 2020, I will add up all the respective y-values, which I can find at the point coordinates {[0.109, 0.529], [0.276, 0.537], [0.442, 0.546], [0.609, 0.563], [0.776, 0.572], [0.943, 0.494]} for each year. These correspond to 4.5, 4.4, 4.3, 4.1, 4.0, and 4.9 billion transactions respectively. Summing these up, we get 4.5 + 4.4 + 4.3 + 4.1 + 4.0 + 4.9 = 26.2 billion transactions.\nBased on the above thought, the answer is:\n26.2" } ], "id_raw": 10 }, { "id": "BKx9hN5fMeedusCP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_11.png", "conversations": [ { "from": "human", "value": "\nCan you provide me with a table containing the given data for different Age Groups and each Disease Category?" }, { "from": "gpt", "value": "In order to extract the data and structure it into a table, I first need to look at {The x-axis labels}[0.101, 0.907, 0.119, 0.930], which represent 'Age Groups'. Then, I have to identify the different disease categories from {The corresponding legend entries}[0.752, 0.472, 0.985, 0.656]. After that, I scrutinize each of the {data points}[0.110, 0.740] for every category at the respective age groups.\nBased on the above thought, the answer is:\nAgeGroup, Eradicated, PreventableGrowing, PreventableStable, Aging\n20,4898,3281,6639,5321\n40,10298,6876,12576,11003\n60,15098,7741,16245,10004\n80,25543,15242,4631,6254\n100,30983,18254,6328,7312\n120,44651,25042,15321,13642" } ], "id_raw": 11 }, { "id": "CwMwYFJqwMR0Oy6q", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_11.png", "conversations": [ { "from": "human", "value": "\nWhich category seems to have experienced a significant increase in instances over the age groups according to the chart? Support your answer with explanation." }, { "from": "gpt", "value": "Determining the category that has experienced a significant increase in instances requires an analysis of the trend lines for each category. Upon examining the {line for 'Eradicated'}[0.110, 0.060, 0.711, 0.740], I notice a persistent upward trend from 4898 instances at age group 20 to 44651 instances at age group 120. This indicates a substantial increase. The {lines for the other categories}[0.110, 0.238, 0.711, 0.863],[0.110, 0.371, 0.711, 0.757],[0.110, 0.425, 0.711, 0.714] do show increases over time, but are not as pronounced as 'Eradicated'.\nBased on the above thought, the answer is:\nThe 'Eradicated' category seems to have experienced a significant increase in instances over the age groups according to the chart. This is supported by the persistent upward trend observed in the line plot for 'Eradicated', which indicates a substantial increase from 4898 instances at age group 20 to 44651 instances at age group 120. This increase is more prominent when compared to the other categories." } ], "id_raw": 11 }, { "id": "nbNSYYWL4wriCzts", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the total number of instances for the 'Aging' category across all age groups?" }, { "from": "gpt", "value": "To find the total instances for the 'Aging' category across all age groups, I need to analyse the {line plots}[0.110, 0.425, 0.711, 0.714] associated with the 'Aging' category, extracting the point values: 5321, 11003, 10004, 6254, 7312, 13642. Adding these together will give me the total instances.\nBased on the above thought, the answer is:\n56336" } ], "id_raw": 11 }, { "id": "OgzwA2UyAifJ598A", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_12.png", "conversations": [ { "from": "human", "value": "\nCan you create a structured table representing the consumer behaviors and their trends over the current and last months?" }, { "from": "gpt", "value": "To formulate the structured table, I must utilize the information embedded in the line chart. I start by interpreting {the x-axis labels}[0.498, 0.943, 0.559, 0.975] which denote months, specifically 'Current Month' and 'Last Month'. Next, identifying {the legend}[0.764, 0.048, 0.977, 0.232] presents various consumer behaviors such as Online Shopping, In-store Shopping, and others, which will form the rows of the table. By pinpointing {the data points on each line}[[0.079, 0.142], [0.154, 0.166], ..., [0.909, 0.798]], I can extract the number of consumers for each behavior in each month.\nBased on the above thought, the answer is:\nBehavior,Last Month,Current Month\nOnline Shopping,450000,500000\nIn-store Shopping,300000,250000\nStreaming Services,600000,650000\nFood Delivery,350000,370000\nTravel Booking,150000,175000" } ], "id_raw": 12 }, { "id": "sCws1gP6uxeQPGPk", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_12.png", "conversations": [ { "from": "human", "value": "\nWhich consumer behavior indicates a stable growth without any decline over the periods?" }, { "from": "gpt", "value": "To identify a behavior that shows consistent growth with no decline, I need to analyze the trajectory of each line in the chart. Observing {the line for Streaming Services}[0.071, 0.034, 0.985, 0.895], it displays a continuous upward trend from one month to the next without any downturns, represented by a linear ascent in the line graph from the start to the end. This shows a stable growth indicating its consistent popularity and usage among consumers.\nBased on the above thought, the answer is:\nStreaming Services indicates a stable growth. It has a continuous upward trajectory over the months, showing no decline and reflecting its growing popularity." } ], "id_raw": 12 }, { "id": "EJ3e3kveUnXLA1rM", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_12.png", "conversations": [ { "from": "human", "value": "\nWhat was the percentage increase for Travel Booking from Last Month to Current Month?" }, { "from": "gpt", "value": "To calculate the percentage increase, I must first determine the values for Travel Booking from Last Month and Current Month. Observing {the Travel Booking line points}[0.834, 0.378, 0.909, 0.368], I gather the values are 150000 for Last Month and 175000 for Current Month. Using the formula for percentage increase (New Value - Old Value) / Old Value * 100, the calculation would be (175000 - 150000) / 150000 * 100 = 16.67%.\nBased on the above thought, the answer is:\n16.67%" } ], "id_raw": 12 }, { "id": "FVkzRmDm17dKMU3Q", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_13.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart into a structured table with 'N' as the columns and Psychological Variables as the rows?" }, { "from": "gpt", "value": "To convert the line chart into tabular data, I first notice {the x-axis tick labels}[[0.128, 0.911, 0.145, 0.934], [0.439, 0.911, 0.457, 0.934], [0.622, 0.911, 0.639, 0.934], [0.751, 0.911, 0.769, 0.934], [0.851, 0.911, 0.869, 0.934], [0.933, 0.911, 0.951, 0.934]], which represent the \"N\" in the columns. The {legend labels}[[0.788, 0.485, 0.900, 0.515], [0.788, 0.529, 0.930, 0.559], [0.788, 0.573, 0.936, 0.603], [0.788, 0.616, 0.970, 0.646]] correspond to the different Psychological Variables as rows. Next, I identify the values by locating the {line points} for each Psychological Variable at their respective 'N' and extract the values from the line points.\nBased on the above thought, the answer is:\nN,AnxietyLevel,DepressionLevel,SelfEsteemLevel,LifeSatisfactionLevel\n10,0.000001,0.0001,0.001,0.001\n20,0.0000001,0.0001,0.00025118864315095795,0.00015848931924611142\n30,0.00000001,0.0001,0.00001,2.5118864315095822e-05\n40,0.000000001,0.0001,0.00001,3.981071705534969e-06\n50,0.0000000001,0.0001,0.00001,6.30957344480193e-07\n60,0.00000000001,0.0001,0.000001,0.0000001" } ], "id_raw": 13 }, { "id": "7XfAZzWUn3Q9bg7E", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_13.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which psychological variable appears to have the most significant decrease from the first N value to the last?" }, { "from": "gpt", "value": "To determine the psychological variable that has the most significant decrease, I examine the trend of each line plot. The {line for Anxiety Level}[0.136, 0.025, 0.942, 0.359] shows a consistent decrease from 0.000001 at N=10 to 0.00000000001 at N=60. \nMeanwhile, the {line for Depression Level}[0.136, 0.226, 0.942, 0.226] appears to remain constant at 0.0001 across all N values. \nThe {line for Self-Esteem Level}[0.136, 0.159, 0.942, 0.359] indicates a decrease from 0.001 at N=10 to 0.000001 at N=60. \nFinally, the {line for Life Satisfaction Level}[0.136, 0.159, 0.942, 0.426] also indicates a decrease from 0.001 at N=10 to 0.0000001 at N=60.\nBased on the above thought, the answer is:\nBased on the line chart, the Anxiety Level appears to have the most significant decrease from the first N value to the last. It shows a consistent decrease from 0.000001 at N=10 down to 0.00000000001 at N=60, a significantly larger decline compared to the other psychological variables." } ], "id_raw": 13 }, { "id": "JaoBJxrY4GUFQKfL", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_13.png", "conversations": [ { "from": "human", "value": "\nWhat's the change in 'Anxiety Level' from 'N' 10 to 60?" }, { "from": "gpt", "value": "To find out the change in 'Anxiety Level' from 'N' 10 to 60, I identify the {line points for Anxiety Level}[[0.136, 0.025], [0.448, 0.092], [0.630, 0.159], [0.760, 0.226], [0.860, 0.293], [0.942, 0.359]], corresponding to 'N' 10, 20, 30, 40, 50 and 60, respectively. The Anxiety Level is 0.000001 at 'N' 10 and drops to 0.00000000001 at 'N' 60. The change is therefore 0.00000000001 - 0.000001 = -0.00000098999.\nBased on the above thought, the answer is:\n-0.00000098999." } ], "id_raw": 13 }, { "id": "LjBcTXIEAuZ3RHX6", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_14.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the line chart about energy resource allocation into a structured table with Years as columns and Energy Resources as rows?" }, { "from": "gpt", "value": "To create a structured table from the line chart, I initially look at {the x-axis tick labels}[[0.095, 0.933, 0.102, 0.951], [0.415, 0.933, 0.422, 0.951], [0.732, 0.933, 0.746, 0.951]], which represent the column headers or Years. Following that, I identify {the legend texts}[[0.929, 0.038, 0.981, 0.060], [0.929, 0.070, 0.981, 0.093], [0.929, 0.103, 0.981, 0.126], [0.929, 0.136, 0.982, 0.159]] as the row labels representing different energy resources. To fill in the values, I locate {the line points for each energy resource}[[0.098, 0.564], [0.162, 0.533], [0.227, 0.514], ..., [0.803, 0.785]] corresponding to each year and extract the associated values.\nBased on the above thought, the answer is:\nEnergy Resource,Year 1,Year 2,Year 3\nCoal, 35, 30, 25\nOil, 25, 27, 29\nSolar, 15, 20, 25\nHydro, 10, 11, 12\nWind, 15, 19, 23" } ], "id_raw": 14 }, { "id": "WWb6TayA5ISpKVXM", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_14.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which energy resource seems to show the most consistent growth over the three years? Justify your answer." }, { "from": "gpt", "value": "In order to identify which energy resource shows the most consistent growth, I need to analyze the slopes of the line plots for each energy resource. Analyzing {the line for Oil}[0.098, 0.564, 0.803, 0.564], I observe a gradual increase from 25 to 29, indicating consistent growth.\nFor {Solar}[0.227, 0.514, 0.739, 0.249], there is a much steeper increase from 15 to 25, suggesting a stronger growth. However, compared to Oil, the growth is not equally spread over the three years. For {Hydro}[0.291, 0.470, 0.675, 0.312], I also observe growth, from 10 to 12, but the amount of growth is relatively lower. Similarly, for {Wind}[0.355, 0.438, 0.803, 0.217], the line shows an increase from 15 to 23, indicating growth, but again this growth is not as uniformly distributed as it is for Oil.\nBased on the above thought, the answer is:\nThe energy resource that shows the most consistent growth over the three years is Oil. While the other resources also exhibit growth, Oil's growth is more gradual and consistent over the three years, suggesting a steady increase in its allocation. The smooth ascending line representing Oil demonstrates this consistent upward trend." } ], "id_raw": 14 }, { "id": "h6qV5CUN0xQZ3omj", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in the allocation of Solar energy over the three years?" }, { "from": "gpt", "value": "To calculate the total increase in Solar energy allocation, I need to locate {the line points for Solar}[[0.227, 0.514], [0.419, 0.407], [0.739, 0.249]] and extract the corresponding values: 15, 20, and 25. The initial allocation in Year 1 is 15 and the final allocation in Year 3 is 25. Thus, the total increase can be computed as 25 - 15 = 10.\nBased on the above thought, the answer is:\n10." } ], "id_raw": 14 }, { "id": "cUoPw4eE68lWrB1z", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_15.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data plotted in the line chart into a table format with countries as columns and meal categories as rows?" }, { "from": "gpt", "value": "To generate the table, I will use the {x-axis tick labels}[[-1081.205, 0.927, -2.045, 0.951], [0.104, 0.927, 0.115, 0.951], [0.430, 0.927, 0.556, 0.951], [0.620, 0.927, 0.677, 0.951], [0.755, 0.927, 0.822, 0.951], [0.860, 0.927, 0.927, 0.951], [0.945, 0.927, 1.012, 0.951]] for the column headers, which are the countries. The row labels will be based on {the legend items}[0.777, 0.029, 0.981, 0.167] that represent the various meal categories. The values will be derived from {the line points} of each of the meal categories for each of the countries.\nBased on the above thought, the answer is:\n,France,Italy,China,India,Mexico,Japan,Thailand\nStandard,10,20,30,40,50,60,70\nVegetarian_Popularity,8,16,12,14,52,32,60\nStreet_Food_Popularity,2,3,55,30,70,48,65\nFine_Dining_Popularity,90,85,60,25,40,35,28" } ], "id_raw": 15 }, { "id": "veTl4nKrYKMIudQl", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_15.png", "conversations": [ { "from": "human", "value": "\nBased on the graph, which type of cuisine shows maximum variation in popularity across different countries?" }, { "from": "gpt", "value": "To identify the cuisine with maximum variation, I sift through the fluctuation in the lines associated with each cuisine type. Observing {the Standard category line}[-1081.205, 0.113, 0.945, 0.530], it seems to have moderate fluctuations. On close inspection of {Vegetarian Popularity line}[-1081.205, 0.146, 0.945, 0.578], the line seems to have much larger variations. The {Street Food Popularity line}[-1081.205, 0.113, 0.945, 0.874] shows sharp differences between specific countries, whereas {Fine Dining Popularity line}[-1081.205, 0.059, 0.945, 0.334] appears relatively steady with minimal variations.\nBased on the above thought, the answer is:\nBased on observations, the Street Food Popularity shows the most significant variation in popularity across different countries. The line representing this category showcases several high and low points, indicating a considerable disparity in the popularity rates across nations - popular in some (like China, Mexico, and Thailand), and less popular in others (like France and Italy)." } ], "id_raw": 15 }, { "id": "xa1SXH9LrkFvcIOm", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the sum of all popularity scores for France across all meal categories?" }, { "from": "gpt", "value": "To ascertain the total popularity score, I need to identify the y-values associated with France on all the lines. The specific points are {Standard for France}[-1081.205, 0.530], {Vegetarian Popularity for France}[-1081.205, 0.578], {Street Food Popularity for France}[-1081.205, 0.874], and {Fine Dining Popularity for France}[-1081.205, 0.059]. The respective values for these points are 10, 8, 2, and 90. Summing these scores gives us the total popularity score for France.\nBased on the above thought, the answer is:\n110." } ], "id_raw": 15 }, { "id": "Lag2efnKY5hzzH3t", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_16.png", "conversations": [ { "from": "human", "value": "\nCan you convert the trends displayed on the line chart into a table with years as columns and types of trends as rows?" }, { "from": "gpt", "value": "To transform the data depicted in the line chart to a table, I would first segment the categories represented along the X-Axis into columns by pinpointing {the x-axis labels}[0.118, 0.911, 0.153, 0.934], [0.630, 0.911, 0.665, 0.934] . These labels '2010' and '2020' will comprise the column headers. The names of the line labels found in {the legend}[0.707, 0.368, 0.985, 0.552] will serve as the row headers. For each row label, I need to examine the corresponding {line points}[0.136, 0.559, 0.647, 0.756], [0.136, 0.262, 0.647, 0.361], [0.136, 0.065, 0.647, 0.756], [0.136, 0.608, 0.647, 0.855] to determine their respective y-values at each x-coordinate or column.\nBased on the above thought, the answer is:\nTrend,2010,2020\nInexorable Decline,1000,800\nInconsistent Development,1200,1300\nRapid Expansion,800,1500\nDramatic Decrease,950,700" } ], "id_raw": 16 }, { "id": "59gvP57wzAxOhet4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_16.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you tell which trend has shown the most improvement? Justify your answer." }, { "from": "gpt", "value": "To answer this question, I need to observe the progression of each line from 2010 to 2020. Let's start from {line of Inexorable Decline}[0.136, 0.559, 0.647, 0.756]. It began from 1000 in 2010 and descended to 800 in 2020, showing a decline instead of improvement. The {line of Inconsistent Development}[0.136, 0.262, 0.647, 0.361] only slightly improved from 1200 to 1300.With {Rapid Expansion}[0.136, 0.065, 0.647, 0.756], there was a significant jump from 800 in 2010 to 1500 in 2020. Meanwhile, the {line of Dramatic Decrease}[0.136, 0.608, 0.647, 0.855] trended downwards from 950 in 2010 to 700 in 2020.\nBased on the above thought, the answer is:\nBased on the plotted line chart, Rapid Expansion has witnessed the most improvement. This assertion is justified from the sharp ascend from 800 in 2010 to 1500 in 2020, indicating an increase in the future predicted values for this trend." } ], "id_raw": 16 }, { "id": "ESf4RMqQAC3GsGvT", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in 'Inconsistent Development' from 2010 to 2020?" }, { "from": "gpt", "value": "To determine the percentage increase for 'Inconsistent Development' from 2010 to 2020, I would need to track the {coordinates for Inconsistent Development in 2010 and 2020}[0.136, 0.361],[0.647, 0.262]. The value for year 2010 is 1200 and for year 2020 is 1300. Hence the percentage change will be ((1300-1200)/1200)*100 = 8.33%\nBased on the above thought, the answer is:\n8.33%" } ], "id_raw": 16 }, { "id": "nESsJUENBRrRK6pK", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_17.png", "conversations": [ { "from": "human", "value": "\nCould you structure the line chart data into a table format that has 'Years' as rows and different 'Trends' as columns?" }, { "from": "gpt", "value": "Sure, first I need to take a look at {the x-axis tick labels}[[0.119, 0.911, 0.137, 0.934], [0.376, 0.911, 0.394, 0.934], [0.527, 0.911, 0.544, 0.934], [0.633, 0.911, 0.651, 0.934], [0.716, 0.911, 0.734, 0.934], [0.784, 0.911, 0.801, 0.934]], which represent the row headers or 'Years'. In order to get the column headers which are different 'Trends', I will look at {the legend texts}[[0.890, 0.485, 0.943, 0.515], [0.890, 0.529, 0.955, 0.559], [0.890, 0.573, 0.956, 0.603], [0.890, 0.616, 0.978, 0.646]]. Then, I will map the data points corresponding to each trend at different years, which I can find in the line points for {Happy} [0.128, 0.339], [0.385, 0.323], [0.535, 0.299], [0.642, 0.275], [0.725, 0.266], [0.793, 0.261], {Anxious} [0.128, 0.627], [0.385, 0.513], [0.535, 0.493], [0.642, 0.453], [0.725, 0.437], [0.793, 0.433], {Relaxed} [0.128, 0.627], [0.385, 0.587], [0.535, 0.520], [0.642, 0.493], [0.725, 0.461], [0.793, 0.453], and {Frightened} [0.128, 0.165], [0.385, 0.174], [0.535, 0.174], [0.642, 0.185], [0.725, 0.206], [0.793, 0.226].\nBased on the above thought, the answer is:\nYears,Happy,Anxious,Relaxed,Frightened\n10,0.000002,0.0000000001,0.0000000001,0.0008\n20,0.0000035,0.000000005,0.0000000004,0.0006\n30,0.000008,0.00000001,0.000000004,0.0006\n40,0.000018,0.00000004,0.00000001,0.0004\n50,0.000025,0.00000007,0.00000003,0.0002\n60,0.000030,0.00000008,0.00000004,0.0001" } ], "id_raw": 17 }, { "id": "3I9E88R40MdWPDgc", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_17.png", "conversations": [ { "from": "human", "value": "\nWhich emotional state shows the largest downfall in their levels from year 10 to year 60 on the chart?" }, { "from": "gpt", "value": "To answer this question, I need to compare the drops of different state levels on year 10 to year 60. The coordinates for 'Happy' are at {[0.128, 0.339] and [0.793, 0.261]}, 'Anxious' are at {[0.128, 0.627] and [0.793, 0.433]}, 'Relaxed' are at {[0.128, 0.627] and [0.793, 0.453]}, and 'Frightened' are at {[0.128, 0.165] and [0.793, 0.226]}. I would calculate the differences for each state and identify the largest.\nBased on the above thought, the answer is:\nThe emotional state 'Frightened' has shown the largest downfall from year 10 to year 60 with a decrease of 0.0007 in state level." } ], "id_raw": 17 }, { "id": "xAH1EmycPzXLbkB2", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_17.png", "conversations": [ { "from": "human", "value": "\nBy how much has the state level of being 'Anxious' decreased from year 30 to year 50?" }, { "from": "gpt", "value": "I see that by locating the data points of the trend 'Anxious' at years 30 and 50, I can calculate the difference. At year 30, the 'Anxious' state has a level of 0.00000001 at {coordinates} [0.535, 0.493] and at year 50, the 'Anxious' state level is 0.00000007 at {coordinates} [0.725, 0.437]. The difference can be calculated by subtraction of the year 50 value from the year 30 value.\nBased on the above thought, the answer is:\n0.00000006" } ], "id_raw": 17 }, { "id": "w4hLhyey1Tg6QGRT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_18.png", "conversations": [ { "from": "human", "value": "\nCan you create a structured table from the line chart, where each row represents a year and columns represent 'No Of Queries' and 'Response Times' for each type of parameter?" }, { "from": "gpt", "value": "In order to create the table as requested, I first identify the available categories, drawn from the {legend}[0.814, 0.478, 0.985, 0.662]. I next look at {the x-axis}[0.071, 0.895, 0.806, 0.895] which includes two Parameters, 'NoOfQueries' and 'ResponseTime'. Using these as column headers, I can generate the rows for the data. I then locate and extract the Y-coordinate values for each Parameter based on {the line plots}[0.105, 0.292, 0.773, 0.464], [0.105, 0.120, 0.773, 0.765], [0.105, 0.206, 0.773, 0.636], [0.105, 0.077, 0.773, 0.679] for each type of parameter: Standard, Constrained, Experimental, and Hypothetical respectively.\nBased on the above thought, the answer is:\nMetric,Parameter,Standard,Constrained,Experimental,Hypothetical\nNoOfQueries,Values,0.7,0.9,0.8,0.95\nResponseTime,Values,0.5,0.15,0.3,0.25" } ], "id_raw": 18 }, { "id": "e64t5TZHYw5kcRQr", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_18.png", "conversations": [ { "from": "human", "value": "\nAccording to the charts, which type of parameter demonstrated the most improvement in 'ResponseTime'?" }, { "from": "gpt", "value": "To answer this, I need to analyze the 'ResponseTime' progression of all four parameters represented by these line plots {Standard}[0.105, 0.292, 0.773, 0.464], {Constrained}[0.105, 0.120, 0.773, 0.765], {Experimental}[0.105, 0.206, 0.773, 0.636] and {Hypothetical}[0.105, 0.077, 0.773, 0.679]. An improvement in 'ResponseTime' would be reflected by a decrease between the 'NoOfQueries' and 'ResponseTime' stages. Hence, I must determine which line shows the most significant drop between these two stages.\nBased on the above thought, the answer is:\nThe 'Constrained' Parameter demonstrated the most improvement in 'ResponseTime'. The line plot corresponding to this category shows the large reduction, indicating a significant improvement." } ], "id_raw": 18 }, { "id": "jcPpJTfUGH4NPP6a", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the absolute difference between the Standard and the Constrained values for the measure 'ResponseTime'?" }, { "from": "gpt", "value": "To answer the question, I need to locate and analyze the 'ResponseTime' measure data for both 'Standard' and 'Constrained'. I find the 'ResponseTime' measure in {the x-axis tick labels}[0.722, 0.911, 0.823, 0.934]; the value for the 'Standard' category is provided in {the line plot for Standard}[0.105, 0.292, 0.773, 0.464] while the 'Constrained' category is in {the line plot for Constrained}[0.105, 0.120, 0.773, 0.765]. The difference between these can be calculated by subtracting the 'Standard' value from the 'Constrained' value.\nBased on the above thought, the answer is:\n0.35." } ], "id_raw": 18 }, { "id": "utLRpMK4qBfbfsMX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_1_19.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart into a table format with Research Fields as rows and corresponding Number of Patents for each research field?" }, { "from": "gpt", "value": "From the line chart, I can discern from {the x-axis tick labels}[ [0.084, 0.750, 0.184, 0.916], [0.226, 0.750, 0.273, 0.829], [0.337, 0.750, 0.394, 0.845], [0.449, 0.750, 0.514, 0.858], [0.569, 0.750, 0.625, 0.842], [0.693, 0.750, 0.733, 0.816], [0.773, 0.750, 0.884, 0.934], [0.913, 0.750, 0.976, 0.855] ] that the row labels will be the various Research Fields. As for the column entry, it will be the corresponding Number of Patents that can be obtained from {the line points}[ [0.134, 0.594], [0.250, 0.701], [0.365, 0.487], [0.481, 0.630], [0.597, 0.558], [0.713, 0.236], [0.829, 0.343], [0.944, 0.057] ] for each of these Research Fields.\nBased on the above thought, the answer is:\nResearch Field,Number of Patents\nComputer Science,15000\nBioTech,12000\nNanoTech,18000\nMechanical,14000\nElectrical,16000\nSpace,25000\nArtificial Intelligence,22000\nElectronics,30000" } ], "id_raw": 19 }, { "id": "pfxGKSftNzrLCklD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_1_19.png", "conversations": [ { "from": "human", "value": "\nCan you identify the sector with the least advancement in research based on the number of patents?" }, { "from": "gpt", "value": "To identify the sector with the least advancement, I need to locate the sector with the fewest patents registered. By examining {the line points}[[0.134, 0.594], [0.250, 0.701], [0.365, 0.487], [0.481, 0.630], [0.597, 0.558], [0.713, 0.236], [0.829, 0.343], [0.944, 0.057]], it seems that BioTech with coordinates [0.250, 0.701] has the least by its y-coordinate.\nBased on the above thought, the answer is:\nBased on the number of patents, the BioTech sector seems to have the least advancement in research. This is indicated by its lower number of patents registered compared to the other sectors. Therefore, it can be inferred that less research has been patented in BioTech, suggesting limited advancement in the sector relative to others." } ], "id_raw": 19 }, { "id": "WRggQsLBeyqUTP2U", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_1_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest number of patents registered in any research field?" }, { "from": "gpt", "value": "To find the field with the highest patent registration, I should look for the data point with the lowest y-coordinate, because the y-axis is reversed in the graph. Seeing {the line points}[ [0.134, 0.594], [0.250, 0.701], [0.365, 0.487], [0.481, 0.630], [0.597, 0.558], [0.713, 0.236], [0.829, 0.343], [0.944, 0.057] ], Electronics with the point coordinates at [0.944, 0.057] have the highest number of patents registered.\nBased on the above thought, the answer is:\n30000." } ], "id_raw": 19 }, { "id": "dK9mzBeX8rim8WYq", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_2_0.png", "conversations": [ { "from": "human", "value": "\nCan you extract the funding amounts for each venture capital fund across all stages?" }, { "from": "gpt", "value": "First, I assess the chart to identify the stages on {the x-axis}[0.136, 0.464, 0.243, 0.488] labeled from Seed to Late-stage. For each stage, I note two data points representing each fund's investment. Observing the points along {the lines}[0.080, 0.246, 0.300, 0.344], {the lines}[0.080, 0.077, 0.300, 0.176] I record corresponding investment values linked to each stage.\nBased on the above thought, the answer is:\nStage,Fund1 Amount,Fund2 Amount\nSeed,63,72\nSeries A,66,73\nSeries B,69,74\nSeries C,75,75\nLate-stage,80,79" } ], "id_raw": 20 }, { "id": "a8ovgOfdjyyCeXEY", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_2_0.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the line trend, which venture capital fund demonstrates a more stable investment pattern across different stages?" }, { "from": "gpt", "value": "To determine the stability of investment patterns, I examine the line trends of Fund1 and Fund2. The line for Fund1 [0.080, 0.246, 0.300, 0.344] indicates a steady upward growth with no abrupt changes, reflecting a conservative and systematic investment approach. In contrast, the line for Fund2 [0.080, 0.077, 0.300, 0.176] exhibits erratic fluctuations with sharp increases and decreases, indicative of a high-risk, less predictable funding strategy.\nBased on the above thought, the answer is:\nFund1 demonstrates a more stable investment pattern across different stages due to its consistent ascending line with no abrupt variations." } ], "id_raw": 20 }, { "id": "XMjBGXd3N9cqXD8N", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_2_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the cumulative investment difference between Fund1 and Fund2 from Seed to Late-stage?" }, { "from": "gpt", "value": "I analyze the values listed for Fund1 and Fund2 from Seed to Late-stage on {the lines}[0.080, 0.246, 0.300, 0.344] for Fund1 and {the lines}[0.080, 0.077, 0.300, 0.176] for Fund2. Following the summing of respective investments for each fund: Fund1 = 63 + 66 + 69 + 75 + 80 and Fund2 = 72 + 73 + 74 + 75 + 79.\nBased on the above thought, the answer is:\n3 (363 - 373)" } ], "id_raw": 20 }, { "id": "SRYxjTzHZMRuF4cs", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_2_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart detailing the growth trends of technology artifacts over five years into a structured table format?" }, { "from": "gpt", "value": "I analyze the bar chart, identifying {y-axis labels}[[0.10, 0.18, 0.13, 0.21], [0.25, 0.18, 0.28, 0.21], [0.40, 0.18, 0.43, 0.21], [0.55, 0.18, 0.58, 0.21], [0.70, 0.18, 0.73, 0.21]] that include 'Artificial Intelligence', 'Virtual Reality', 'Cloud Computing', '5G Technology', and 'Cybersecurity'. Correspondingly, I observe the bar segments for each tech artifact from left to right representing different years. The lengths of these bars give me the values for each year.\nBased on the above thought, the answer is:\nCategory,Year 1,Year 2,Year 3,Year 4,Year 5\nArtificial Intelligence,200,230,260,290,320\nVirtual Reality,150,120,90,60,30\nCloud Computing,220,240,260,280,300\n5G Technology,180,160,140,120,100\nCybersecurity,180,200,350,190,200" } ], "id_raw": 32 }, { "id": "WkyWgq6qubVskcvj", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_2_12.png", "conversations": [ { "from": "human", "value": "\nHow has the trend in 'Virtual Reality' changed over the five years according to the bar chart?" }, { "from": "gpt", "value": "Observing the trend in {Virtual Reality}[[0.25, 0.18, 0.28, 0.21]], I notice the bar lengths diminish steadily from left to right, clearly indicating a decreasing trend. The bar relative positions decrease, suggesting that the values for Virtual Reality technology have been decreasing each consecutive year.\nBased on the above thought, the answer is:\nThe trend for 'Virtual Reality' has consistently decreased over the five years. The bar lengths representing this artifact get progressively shorter each year, confirming a steady decline in this technology domain." } ], "id_raw": 32 }, { "id": "gWWfakaGvGPzt5s0", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_2_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage growth of Cloud Computing from Year 1 to Year 5 based on the bar chart?" }, { "from": "gpt", "value": "To determine the percentage growth, I locate {the relevant bars for Cloud Computing}[[0.40, 0.25, 0.43, 0.400]] for Year 1 and Year 5. Measuring the length of the bars, I note the values as 220 in Year 1 and 300 in Year 5. The calculation of percentage growth is given by [(300 - 220) / 220] * 100.\nBased on the above thought, the answer is:\n36.36%" } ], "id_raw": 32 }, { "id": "ZngdphudCh5GYxgw", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_2_13.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data representing trends in Social Reforms and Political Restructuring over 10 years into a tabular format?" }, { "from": "gpt", "value": "First, I assess the plot to identify the years from 2000 to 2010 along {the x-axis}[0.1, 0.9]. Each year represents a column. Next, I recognize two main series {Social Reforms and Political Restructuring}[0.792, 0.132, 0.893, 0.208] plotted in the chart as different lines, serving as row labels. I extract the corresponding value for each series from their specific points on the lines over the years and convert these values into a csv format table.\nBased on the above thought, the answer is:\nYear, Social Reforms, Political Restructuring\n2000, 100, 120\n2001, 95, 115\n2002, 90, 110\n2003, 85, 105\n2004, 80, 100\n2005, 110, 80\n2006, 115, 75\n2007, 90, 95\n2008, 85, 115\n2009, 130, 110\n2010, 105, 120" } ], "id_raw": 33 }, { "id": "KxSsj8Ouo03HAzG2", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_2_13.png", "conversations": [ { "from": "human", "value": "\nHow did Political Restructuring display a volatile pattern through the decade as presented in the chart?" }, { "from": "gpt", "value": "To assess the volatility of Political Restructuring over the decade, I observe the progression of the line associated with it {from the years 2000 to 2010}[0.1, 0.2, 0.9, 0.3]. Initially, there is a steady decline until 2004, followed by a sharp drop until 2005 {sharp decrease between these years}[0.4, 0.23, 0.5, 0.75]. Post-2005, there's a pronounced reversal with a rapid rise until 2007, sustaining slight fluctuations thereafter. These abrupt shifts show the unstable conditions and unpredicted shifts in the political landscape of the hypothetical society during that time.\nBased on the above thought, the answer is:\nPolitical Restructuring exhibited a volatile pattern through dramatic peaks and valleys. Firstly, there was a considerable descent reflecting decrease over the years until 2005 after which an extraordinary upsurge was seen, portraying crises or changes in the political environment. Such sharp fluctuations within short periods highlight the instability and reactive changes in the political strategies or situations in the nation." } ], "id_raw": 33 }, { "id": "YDvQ2yQpvg1ciDMm", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_2_13.png", "conversations": [ { "from": "human", "value": "\nBetween which two consecutive years did Political Restructuring experience the largest decrease in value as per the chart?" }, { "from": "gpt", "value": "To find the years with the largest decrease in Political Restructuring, I scrutinize the line for Political Restructuring {observed between each point on the line}[0.1, 0.2, 0.9, 0.3]. By comparing points, the largest drop is evident between the years 2004 and 2005, where there's a significant lowering from a higher value at {2004 point}[0.4, 0.23] to a much lower value at {2005 point}[0.5, 0.75].\nBased on the above thought, the answer is:\n2004 to 2005" } ], "id_raw": 33 }, { "id": "4ZtilCCBUAcG9SFg", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_2_15.png", "conversations": [ { "from": "human", "value": "\nCan you format the data comparing the relative popularity of various living scenarios between periods of 2020 and 2070 into a structured table?" }, { "from": "gpt", "value": "I examine {the x-axis labels}[0.055, 0.780, 0.130, 0.810], [0.230, 0.780, 0.305, 0.810], [0.405, 0.780, 0.480, 0.810], [0.580, 0.780, 0.655, 0.810], [0.755, 0.780, 0.830, 0.810] which represent the periods from 2020 to 2070. Next, {the legend}[0.810, 0.050, 0.990, 0.250] identifies each living scenario by color. I then locate {the line points for each scenario}[0.060, 0.320], [0.235, 0.410], ..., [0.760, 0.210] to determine their popularity indices for each period.\nBased on the above thought, the answer is:\nYear,Suburban_Living,Urban_Condo,Small_City,Rural_Home,Nomadic_Lifestyle,Underwater\n2020,50,58,48,55,32,10\n2030,48,57,48,52,35,15\n2040,47,55,48,49,40,22\n2050,45,54,48,45,52,35\n2070,43,52,48,40,65,50" } ], "id_raw": 35 }, { "id": "bPNaweJazV8k5fPb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_2_15.png", "conversations": [ { "from": "human", "value": "\nWhich living scenario shows a steady trend and which one shows the most dramatic change over the 50-year span?" }, { "from": "gpt", "value": "To answer this, I observe the slopes and variations in the lines representing each scenario. The {Small_City line}[0.060, 0.460, 0.760, 0.460] remains constant throughout the years, indicating stability. Conversely, {the line for Underwater}[0.060, 0.890, 0.760, 0.230] shows a steep descend from a low initial value to a much higher one, which implies a significant increase.\nBased on the above thought, the answer is:\nSmall City shows a steady trend with no significant changes in popularity. Underwater exhibits the most dramatic change, seeing a substantial rise in popularity from 2020 to 2070." } ], "id_raw": 35 }, { "id": "tc3Pcx9CbtGh13Z4", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_2_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the popularity increase for Nomadic Lifestyle from 2020 to 2070?" }, { "from": "gpt", "value": "To calculate the rise in popularity for Nomadic Lifestyle, I trace {the line for Nomadic Lifestyle}[0.060, 0.710, 0.760, 0.230] comparing the points from 2020 and 2070. The initial value at 2020 is recognized at {the point}[0.060, 0.710] with a popularity index of 32, and the final value at 2070 is at {the point}[0.760, 0.230] with an index of 65.\nBased on the above thought, the answer is:\n33." } ], "id_raw": 35 }, { "id": "5OSR1Wy2giw38zYO", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_2_17.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information from the line graph showing Urban and Rural development in the fields of Education and Healthcare into a structured table?" }, { "from": "gpt", "value": "To generate the table, I observe two categories on {the x-axis}[0.142, 0.464, 0.248, 0.488], which are Urban and Rural. These form the rows of the table. I see two plotted lines representing Education and Healthcare, which are distinguished by {their colors on the legend}[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156]. Each line plots points for Urban and Rural, which contain their respective development levels. I transcribe these points into the table, structuring it by the categories and fields.\nBased on the above thought, the answer is:\nCategory,Education,Healthcare\nUrban,0.85,0.80\nRural,0.60,0.55" } ], "id_raw": 37 }, { "id": "POPUcxvg8bsS9gZh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_2_17.png", "conversations": [ { "from": "human", "value": "\nWhich sector, Education or Healthcare, shows a smaller discrepancy in development between Urban and Rural areas according to the graph?" }, { "from": "gpt", "value": "Comparing the discrepancies, I first note the development index values for both sectors. In Education, Urban has an index of approximately 0.85 and Rural has 0.60, with a {difference highlighted by the distance between plots}[0.062, 0.374, 0.116, 0.455], [0.274, 0.374, 0.328, 0.455]. In Healthcare, Urban has an index of about 0.80 and Rural has 0.55, visible from {the Healthcare line points}[0.390, 0.862, 0.444, 0.942], [0.602, 0.862, 0.655, 0.942]. Calculating the differences, Education has a smaller disparity of 0.25 compared to Healthcare's disparity of 0.25.\nBased on the above thought, the answer is:\nEducation shows a smaller discrepancy in development between Urban and Rural areas than does Healthcare." } ], "id_raw": 37 }, { "id": "eoHFQzi5EViOZfe0", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_2_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in the development index for Education between Urban and Rural areas?" }, { "from": "gpt", "value": "I first locate the points for Education in both Urban and Rural areas using {the line coordinates for Education}[0.062, 0.374, 0.116, 0.455], [0.274, 0.374, 0.328, 0.455]. The Education indices are approximately 0.85 for Urban and 0.60 for Rural. Subtracting the Rural value from the Urban value gives the difference.\nBased on the above thought, the answer is:\n0.25" } ], "id_raw": 37 }, { "id": "StJr6hE8sVaC6FVl", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_2_19.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line charts representing Education and Training over the years into a structured table format with Year as rows and Sectors as headings?" }, { "from": "gpt", "value": "To convert the data from the line charts into a table, I will trace through each data point across the charts. First, I'll track down {the x-axis labels for years}[0.069, 0.085, 0.311, 0.358] for the distinct timelines ranging from 2014 to 2020. Next, I identify the data points on {the line representing 'Education Expansion'}[0.069, 0.064, 0.311, 0.358] and {the line representing 'Training Revival'}[0.397, 0.064, 0.639, 0.358], correlating them with the respective years. After pinpointing each year's data points, I can extract and format these values accordingly.\nBased on the above thought, the answer is:\nYear,Education Expansion,Training Revival\n2014,10,8\n2015,7,6\n2016,12,10\n2017,15,12\n2018,13,11\n2019,14,13\n2020,15,14" } ], "id_raw": 39 }, { "id": "JsF6axiempLDYuuD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_2_19.png", "conversations": [ { "from": "human", "value": "\nWhich sector showed a more consistent growth pattern in the provided trend data, and can you explain why this might be?" }, { "from": "gpt", "value": "By reviewing the trend lines, {the 'Education Expansion' sector}[0.069, 0.064, 0.311, 0.358] displays a more consistent upwards trend in growth pattern, especially post-2016, unlike {the 'Training Revival' sector}[0.397, 0.064, 0.639, 0.358] which shows significant dips around 2018 and 2019. Consistency could be indicative of stable investment and policies in the education sector, maintaining a conducive environment for continuous growth despite economic fluctuations.\nBased on the above thought, the answer is:\nThe Education sector showed a more consistent growth pattern. This might be due to continuous improvements and stable policies in education, alongside steady demand and investment, ensuring less fluctuation compared to the Training sector, which might have experienced variable funding and policy shifts during the same period." } ], "id_raw": 39 }, { "id": "Tn8WgHs6tTdOtzHl", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_2_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the average growth rate for the Education sector from 2014 to 2020?" }, { "from": "gpt", "value": "To calculate the average growth rate for the Education sector, I will examine {the points on the line chart representing 'Education Expansion'}[0.069, 0.064, 0.311, 0.358]. The growth rates for the years 2014 to 2020 are respectively 10, 7, 12, 15, 13, 14, and 15. To find the average, I sum these values and divide by the number of years, which is seven.\nBased on the above thought, the answer is:\n12.14" } ], "id_raw": 39 }, { "id": "l7is5QcNnAlrlm59", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_3_1.png", "conversations": [ { "from": "human", "value": "\nCould you extract and display the market share data for different sectors in table format with years as columns and sectors as rows?" }, { "from": "gpt", "value": "To achieve this, I'll first refer to {the x-axis tick labels}[0.061, 0.773, 0.144, 0.898], {0.178, 0.773, 0.259, 0.895], {0.291, 0.773, 0.378, 0.903], {0.397, 0.773, 0.504, 0.934], {0.525, 0.773, 0.607, 0.896], {0.643, 0.773, 0.721, 0.890], {0.739, 0.773, 0.857, 0.951] representing the years from 2015 to 2020. Then, I'll identify {the legend texts}[0.884, 0.101, 0.982, 0.123], {0.884, 0.134, 0.933, 0.156], {0.884, 0.167, 0.946, 0.189] which suggests the sectors (Automotive, IT, Real Estate, Pharma). Lastly, I'll analyze the line plots corresponding to each sector at various years to populate the values.\nBased on the above thought, the answer is:\nYear,2015,2016,2017,2018,2019,2020\nAutomotive,25,30,27,35,33,38\nIT,20,22,28,26,29,31\nReal Estate,40,35,30,25,20,15\nPharma,15,13,15,14,18,16" } ], "id_raw": 41 }, { "id": "gXL1bGcXqzcgDnKy", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_3_1.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, what can you infer about the trend of the IT sector compared to the Real Estate sector over the given period?" }, { "from": "gpt", "value": "Observing the trends starting from 2015 to 2020 by examining {the line for IT}[0.101, 0.182, 0.894, 0.368] and {the line for Real Estate}[0.101, 0.344, 0.894, 0.870], it's evident that the IT sector shows a gradual upward trend while the Real Estate sector depicts a distinct downward trend. The IT sector had fluctuations but maintained an incremental path, peaking in 2020, while Real Estate saw a consistent decrease in market share throughout the years ending with its lowest point in 2020.\nBased on the above thought, the answer is:\nThe IT sector shows a clear upward trajectory in market share, indicating expansion and perhaps increased investment or market favorability. In contrast, Real Estate suffered a declining market share, which could signal declining investment, reduced demand, or increased competition affecting its hold on the market throughout the same period." } ], "id_raw": 41 }, { "id": "0RXqg9npmHMi49pG", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_3_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the total range of market share for the Pharma industry from 2015 to 2020?" }, { "from": "gpt", "value": "To calculate the range for the Pharma industry, I'll find the maximum and minimum market shares by identifying the highest and lowest points on {the line for Pharma}[0.102, 0.599], [0.218, 0.340], [0.334, 0.179], [0.450, 0.534], [0.566, 0.632], [0.682, 0.437], [0.798, 0.243]. The values range from 13% in 2016 to 18% in 2019. The total range is then 18% minus 13%.\nBased on the above thought, the answer is:\n5%" } ], "id_raw": 41 }, { "id": "M8swLQ6ZgmwSEbMq", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_3_3.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the chart into a structured table, categorizing the scores by transportation mode and type of industry trend?" }, { "from": "gpt", "value": "To construct the table from the chart data, I first observe {the x-axis labels}[[0.160, 0.855, 0.200, 0.900], [0.400, 0.855, 0.440, 0.900]] which represent the transportation modes 'Freight Rail' and 'Trucking'. Each mode is associated with four industry trends plotted as {the line points for Freight Rail}[[0.160, 0.590], [0.240, 0.780], [0.320, 0.455], [0.400, 0.725]] and {the line points for Trucking}[[0.500, 0.610], [0.580, 0.820], [0.660, 0.470], [0.740, 0.705]]. I extract the values based on their positions corresponding to the y-axis that represent the score for each trend.\nBased on the above thought, the answer is:\nMode, Gradual Downturn, Steep Decline, Marginal Decline, Fluctuating Decline\nFreight Rail, 0.40, 0.22, 0.55, 0.28\nTrucking, 0.38, 0.18, 0.52, 0.30" } ], "id_raw": 43 }, { "id": "XlUzshhevMRihD7K", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_3_3.png", "conversations": [ { "from": "human", "value": "\nWhich mode of transportation shows more stability across the different industry trends according to the chart?" }, { "from": "gpt", "value": "Analyzing the stability across industry trends involves comparing the variance in scores between 'Freight Rail' and 'Trucking'. Observing {the line points for Freight Rail}[[0.160, 0.590], [0.240, 0.780], [0.320, 0.455], [0.400, 0.725]] and {the line points for Trucking}[[0.500, 0.610], [0.580, 0.820], [0.660, 0.470], [0.740, 0.705]], it appears that Freight Rail shows larger fluctuations in its scores with a high at the 'Steep Decline' and a low at the 'Marginal Decline'. In contrast, Trucking exhibits slightly less variation between its highest and lowest scores.\nBased on the above thought, the answer is:\nTrucking shows more stability across the different industry trends compared to Freight Rail. The scores for Trucking are closer together, indicating less fluctuation and greater stability in performance under different industry trend scenarios." } ], "id_raw": 43 }, { "id": "W7zmD6C8JPXnEIth", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_3_3.png", "conversations": [ { "from": "human", "value": "\nHow much higher is the score of 'Marginal Decline' for Freight Rail compared to Trucking?" }, { "from": "gpt", "value": "To compare the 'Marginal Decline' scores, I identify the corresponding values for Freight Rail at {the point for Marginal Decline in Freight Rail}[[0.320, 0.455]] which is approximately 0.55, and for Trucking at {the point for Marginal Decline in Trucking}[[0.660, 0.470]] which is approximately 0.52. To find the difference, I subtract the score of Trucking from Freight Rail: 0.55 - 0.52.\nBased on the above thought, the answer is:\n0.03." } ], "id_raw": 43 }, { "id": "V1PBWJWWPeEacrAE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_3_7.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data for both Employee Satisfaction and Profit Margin into a table, listing the Leadership Styles as rows?" }, { "from": "gpt", "value": "Observing {the subplot for Employee Satisfaction}[0.125, 0.120, 0.900, 0.500] and {the subplot for Profit Margin}[0.125, 0.510, 0.900, 0.890], I analyze the bars corresponding to each leadership style: {Transformational}[0.160, 0.322, 0.265, 0.450], {Transactional}[0.295, 0.322, 0.400, 0.350], {Laissez-faire}[0.430, 0.322, 0.535, 0.245] in the first subplot and {Transformational}[0.285, 0.512, 0.390, 0.470], {Transactional}[0.420, 0.512, 0.525, 0.340], {Laissez-faire}[0.555, 0.512, 0.660, 0.215] in the second subplot. The values are then gathered based on the heights of each bar in proportion to the axes.\nBased on the above thought, the answer is:\nLeadership Style,Employee Satisfaction,Profit Margin\nTransformational,90,85\nTransactional,70,60\nLaissez-faire,50,40" } ], "id_raw": 47 }, { "id": "iWnikHxWyxmZFFjf", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_3_7.png", "conversations": [ { "from": "human", "value": "\nGiven the gradual decline observed in Profit Margin across the leadership styles in the chart, which style would you recommend for maximizing profit?" }, { "from": "gpt", "value": "Observing the Bar chart, {the highest profit margin is under Transformational leadership}[0.285, 0.512, 0.390, 0.470], followed by a decrease with {Transactional}[0.420, 0.512, 0.525, 0.340], and the lowest under {Laissez-faire}[0.555, 0.512, 0.660, 0.215]. Given these observations, Transformational leadership seems to maximize profit margin the most effectively.\nBased on the above thought, the answer is:\nTransformational leadership is recommended for maximizing profit as it presents the highest profit margin observed in the chart." } ], "id_raw": 47 }, { "id": "AvzqBNBX9mjK2o8b", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_3_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage drop in Employee Satisfaction when moving from Transformational to Transactional leadership?" }, { "from": "gpt", "value": "To calculate this, I first determine the values of Employee Satisfaction for both {Transformational}[0.160, 0.322, 0.265, 0.450] and {Transactional}[0.295, 0.322, 0.400, 0.350] leadership styles, which are 90 and 70 respectively. The drop is then calculated as (90 - 70)/90 * 100%.\nBased on the above thought, the answer is:\n22.22%" } ], "id_raw": 47 }, { "id": "3CEBJYrnJKoH8IDX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_3_9.png", "conversations": [ { "from": "human", "value": "\nCould you convert the line chart's information into a table format, with the rows as months and the columns as Infrastructure Spending, Social Service Spending, and Unemployment Rate respectively?" }, { "from": "gpt", "value": "To create a table from the line chart, I examine {the x-axis tick labels}[[0.092, 0.906, 0.101, 0.930], [0.220, 0.906, 0.229, 0.930], ..., [0.984, 0.906, 1.002, 0.930]], which indicates the months from 1 to 12. Then, I identify {the legend texts}[[0.228, 0.949, 0.393, 0.973], [0.460, 0.949, 0.626, 0.973], [0.693, 0.949, 0.836, 0.973]] as the column names. The values for each month and column can be obtained from {the points of each line}[[0.160, 0.255], ..., [0.865, 0.419], ..., [0.865, 0.855]], which represent the data points corresponding to each column at different months.\nBased on the above thought, the answer is:\nMonths,Infrastructure Spending,Social Service Spending,Unemployment Rate\n1,0.75,0.85,0.09\n2,0.77,0.82,0.1\n3,0.75,0.86,0.09\n4,0.78,0.80,0.11\n5,0.75,0.79,0.12\n6,0.72,0.81,0.12\n7,0.70,0.82,0.13\n8,0.68,0.81,0.13\n9,0.65,0.79,0.14\n10,0.62,0.77,0.15\n11,0.60,0.75,0.16\n12,0.57,0.72,0.17" } ], "id_raw": 49 }, { "id": "B7h3tMJDuvunAPTo", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_3_9.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, how does Unemployment Rate change with respect to Infrastructure and Social Service Spending? Give your reasoning." }, { "from": "gpt", "value": "To answer this question, I need to evaluate the lines for Infrastructure Spending, Social Service Spending, and Unemployment Rate respectively. Looking at {the line for Infrastructure Spending}[0.160, 0.228, 0.865, 0.419], it starts at 0.75 and gradually decreases to 0.57 over 12 months, indicating a downward trend in infrastructure spending. For {the line for Social Service Spending}[0.160, 0.155, 0.865, 0.282], it starts from 0.85 and reduces to 0.72 in 12 months, showing a similar downward trend.\nFinally, analyzing {the line for Unemployment Rate}[0.160, 0.782, 0.865, 0.855], it starts from 0.09 and gradually rises to 0.17 in 12 months, reflecting an upward trend in unemployment rate.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, the Unemployment Rate seems to inversely correlate with Infrastructure and Social Service Spending. As spending on Infrastructure and Social Services declines over a 12-month period, there is a corresponding increase in the Unemployment Rate. This could suggest that reductions in public spending might be contributing to higher unemployment rates." } ], "id_raw": 49 }, { "id": "FB6Baxxzy1MClxrM", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_3_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the maximum and minimum spendings in Infrastructure over the 12 months period?" }, { "from": "gpt", "value": "To find the maximum and minimum values for Infrastructure Spending over the 12 months, I need to inspect {the line points for Infrastructure Spending}[0.160, 0.255, ..., 0.865, 0.419], which correspond to the values: 0.75, 0.77, 0.75, 0.78, 0.75, 0.72, 0.70, 0.68, 0.65, 0.62, 0.6, 0.57. The highest value is 0.78 during {Month 4}[0.352, 0.941], and the lowest value is 0.57 during {Month 12}[0.865, 0.940]. Therefore, the difference between the maximum and minimum is 0.78 - 0.57 = 0.21.\nBased on the above thought, the answer is:\n0.21." } ], "id_raw": 49 }, { "id": "cbZTDZVf2XsjIylQ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_3_10.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data points of innovation index overtime for each company presented in the bar chart into a tabular format?" }, { "from": "gpt", "value": "To extract the data into a table format, I analyzed the heights of {the bars}[[0.158, 0.300, 0.202, 0.780], [0.228, 0.300, 0.272, 0.845], [0.298, 0.300, 0.342, 0.860], [0.368, 0.300, 0.412, 0.930], [0.438, 0.300, 0.482, 0.950], [0.508, 0.300, 0.552, 0.990]] on the chart for each company respectively: Intel, Amazon, Apple, Microsoft, Google, and Tesla. Using the heights of these bars I computed the estimated innovation index values for each company.\nBased on the above thought, the answer is:\nCompany,Innovation Index\nIntel,0.48\nAmazon,0.545\nApple,0.56\nMicrosoft,0.63\nGoogle,0.65\nTesla,0.69" } ], "id_raw": 50 }, { "id": "F6PjOoAXGfZ3p20r", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_3_10.png", "conversations": [ { "from": "human", "value": "\nWhich company shows the highest growth in innovation capability reflected in the bar chart?" }, { "from": "gpt", "value": "Comparing the heights of the bars, which represent the Innovation Index for each company, {Tesla's bar}[[0.508, 0.300, 0.552, 0.990]] is the tallest, indicating the highest Innovation Index value among all companies presented. This emphatically points to Tesla as showing the highest growth in innovation capability since its bar exceeds those of other companies including {Intel's}[[0.158, 0.300, 0.202, 0.780]], {Amazon's}[[0.228, 0.300, 0.272, 0.845]], and others.\nBased on the above thought, the answer is:\nTesla shows the highest growth in innovation capability as reflected by having the highest and the tallest bar on the chart." } ], "id_raw": 50 }, { "id": "hs4uRJYK0hkrGCKc", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_3_10.png", "conversations": [ { "from": "human", "value": "\nHow much higher is Tesla's Innovation Index compared to Intel's?" }, { "from": "gpt", "value": "To find the difference, I examined {Tesla's bar}[[0.508, 0.300, 0.552, 0.990]] which represents an Innovation Index of 0.69 and compared it with {Intel's bar}[[0.158, 0.300, 0.202, 0.780]] which shows an Innovation Index of 0.48. Using these values, the difference is calculated as 0.69 - 0.48.\nBased on the above thought, the answer is:\n0.21" } ], "id_raw": 50 }, { "id": "mUw2mniictEvxks7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_3_11.png", "conversations": [ { "from": "human", "value": "\nCan you transform the curves displayed on these line charts into a structured table format showing the trends in career and workplace development from 2015 to 2021?" }, { "from": "gpt", "value": "I observe {the x-axis tick labels}[0.071, 0.773, 0.144, 0.898] that denote different years from 2015 to 2021. The chart is divided into two subplots. In the first subplot, I see curves for {Work-Life Balance}[0.178, 0.773, 0.259, 0.895] and {Professional Development}[0.291, 0.773, 0.378, 0.903]. In the second subplot, there are curves for {Employee Engagement}[0.397, 0.773, 0.504, 0.934], {Flexible Working Hours}[0.525, 0.773, 0.607, 0.896], and {Team Collaboration}[0.643, 0.773, 0.721, 0.890]. Each line corresponds to the trend score out of 100, over the years from 2015 to 2021.\nBased on the above thought, the answer is:\nYear,Work-Life Balance,Professional Development,Employee Engagement,Flexible Working Hours,Team Collaboration\n2015,50,55,48,47,45\n2016,55,60,52,50,50\n2017,60,65,60,55,54\n2018,62,70,75,58,59\n2019,65,73,82,61,62\n2020,70,78,90,65,68\n2021,75,80,95,70,70" } ], "id_raw": 51 }, { "id": "hbPrQD5aUdnOLYUX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_3_11.png", "conversations": [ { "from": "human", "value": "\nWhich workplace characteristic has shown the least improvement over the years according to the data provided?" }, { "from": "gpt", "value": "Observing both subplots and their representing lines, {Work-Life Balance}[0.178, 0.773, 0.259, 0.895] has the lowest rise in scores over the years among all characteristics. Starting from a score of about 50 in 2015 and only rising to about 75 in 2021 indicates less improvement compared to {Employee Engagement}[0.397, 0.773, 0.504, 0.934], which started around 55 and rose to 95 in the same period.\nBased on the above thought, the answer is:\nWork-Life Balance has shown the least improvement over the years, as it started from a lower base and exhibited a smaller increment as compared to the other characteristics like Employee Engagement." } ], "id_raw": 51 }, { "id": "z8B4V9CR1wEgS93e", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_3_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in Employee Engagement from 2015 to 2021 based on the chart?" }, { "from": "gpt", "value": "First, I identify {the 2015 point on the Employee Engagement line}[0.110, 0.589] representing the score for 2015, which is roughly 55. I then locate {the 2021 point on the same line}[0.798, 0.243] representing the score for 2021, which is roughly 95. Calculating the percentage increase, (95 - 55) / 55 * 100, yields roughly 72.73%.\nBased on the above thought, the answer is:\n72.73%" } ], "id_raw": 51 }, { "id": "odEeQysPkwLjIjpk", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_3_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data of weekly Performance into a structured table format using the provided weeks and values?" }, { "from": "gpt", "value": "To construct the table from the line chart, I'll start by mapping each week indicated by {the x-axis labels}[0.053, 0.919, 0.075, 0.942], [0.157, 0.919, 0.179, 0.942], [0.261, 0.919, 0.283, 0.942], [0.365, 0.919, 0.386, 0.942], [0.464, 0.919, 0.495, 0.942], [0.568, 0.919, 0.598, 0.942], [0.671, 0.919, 0.702, 0.942], [0.775, 0.919, 0.806, 0.942], [0.879, 0.919, 0.910, 0.942], [0.983, 0.919, 1.013, 0.942] to their respective points for Performance from {the Performance line points}[0.106, 0.502], [0.147, 0.510], [0.189, 0.542], [0.230, 0.494], [0.272, 0.478], [0.313, 0.542], [0.355, 0.526], [0.396, 0.558], [0.438, 0.462], [0.479, 0.446], [0.521, 0.406], [0.562, 0.430], [0.604, 0.381], [0.645, 0.446], [0.687, 0.430], [0.728, 0.462], [0.770, 0.486], [0.811, 0.470], [0.853, 0.454], [0.894, 0.438].\nBased on the above thought, the answer is:\nWeek,Performance\n1,0.5\n2,0.49\n3,0.45\n4,0.51\n5,0.53\n6,0.45\n7,0.47\n8,0.43\n9,0.55\n10,0.57\n11,0.62\n12,0.59\n13,0.65\n14,0.57\n15,0.59\n16,0.55\n17,0.52\n18,0.54\n19,0.56\n20,0.58" } ], "id_raw": 52 }, { "id": "aid8cn1vIRXOhZo3", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_3_12.png", "conversations": [ { "from": "human", "value": "\nHow does the overall weekly trend in Efficiency compare to Performance as depicted in the line charts?" }, { "from": "gpt", "value": "To analyze the overall weekly trend in Efficiency compared to Performance, I will closely observe the general slope of the lines representing each metric. By visual inspection of {the line for Efficiency}[0.106, 0.325, 0.894, 0.486] and comparing it with {the line for Performance}[0.106, 0.381, 0.894, 0.558], Efficiency displays a consistently upward trend, albeit with some initial turbulence, while Performance demonstrates more noticeable fluctuations with ups and downs across the weeks. These observations can suggest varying stability and growth patterns between the two measures.\nBased on the above thought, the answer is:\nThe overall weekly trend shows that Efficiency generally increases with a steadier climb, indicating more consistent improvement, whereas Performance exhibits more volatility with several peaks and troughs, indicating less stability compared to Efficiency." } ], "id_raw": 52 }, { "id": "rAiCRtrMpHxIhhdL", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_3_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest weekly growth percentage observed in the chart for the Efficiency metric?" }, { "from": "gpt", "value": "To find the highest growth percentage for Efficiency, I would assess the y-values associated with each week from {Efficiency line points}[0.106, 0.422], [0.147, 0.381], [0.189, 0.357], [0.230, 0.341], [0.272, 0.325], [0.313, 0.438], [0.355, 0.462], [0.396, 0.486], [0.438, 0.422], [0.479, 0.470], [0.521, 0.454], [0.562, 0.422], [0.604, 0.406], [0.645, 0.462], [0.687, 0.438], [0.728, 0.454], [0.770, 0.470], [0.811, 0.438], [0.853, 0.422], [0.894, 0.406]. The highest point on this line occurs at 0.72 at week 5.\nBased on the above thought, the answer is:\n0.72" } ], "id_raw": 52 }, { "id": "TlxNR88rdQtIuRs6", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_3_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data showing departmental performance over two quarters into a structured table?" }, { "from": "gpt", "value": "To accurately convert the chart data into a table, I observe the vertical bars representing each department in the first and second quarters. I note the order and performance scores by examining the height of each bar and matching them with {the y-axis tick labels}[[0.020, 0.081, 0.043, 0.116], [0.020, 0.177, 0.043, 0.212], [0.020, 0.272, 0.043, 0.307], [0.020, 0.368, 0.043, 0.403], [0.020, 0.464, 0.043, 0.499]]. I also refer to {the x-axis labels}[[0.100, 0.955, 0.153, 0.980], [0.211, 0.955, 0.255, 0.980], [0.317, 0.955, 0.374, 0.980], [0.425, 0.955, 0.491, 0.980], [0.531, 0.955, 0.578, 0.980], [0.632, 0.955, 0.703, 0.980]] to correctly categorize the data by department names and quarters.\nBased on the above thought, the answer is:\nDepartment,Performance in Q1,Performance in Q2\nSales,90,85\nHR,83,75\nFinance,77,78\nOperations,69,72\nIT,65,70\nR&D,62,66\nMarketing,57,60" } ], "id_raw": 53 }, { "id": "7ySKRyflIIOw8seO", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_3_13.png", "conversations": [ { "from": "human", "value": "\nWhich department showed the most significant improvement from Q1 to Q2 based on the bar chart?" }, { "from": "gpt", "value": "To identify the department with the most significant improvement, I compare the differences in heights of bars from Q1 to Q2 for each department {the bars for each department from Q1 to Q2}[[0.100, 0.450, 0.153, 0.550], [0.100, 0.850, 0.153, 0.930], [0.211, 0.640, 0.255, 0.750], [0.211, 0.850, 0.255, 0.920]]. I observe that the {R&D department}[[0.632, 0.730, 0.703, 0.832], [0.632, 0.910, 0.703, 0.990]] shows the greatest relative increase in its bar height, indicating the most significant improvement in performance scores.\nBased on the above thought, the answer is:\nThe R&D department showed the most significant improvement from Q1 to Q2. Their performance score rose from 62 in the first quarter to 66 in the second quarter, depicting a notable upward trend compared to other departments." } ], "id_raw": 53 }, { "id": "5ob7XYFHzWV26rAt", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_3_13.png", "conversations": [ { "from": "human", "value": "\nHow much did the performance score change for the IT department from Q1 to Q2?" }, { "from": "gpt", "value": "Evaluating the change in performance score for the IT department involves first locating {the IT department's bars for Q1 and Q2}[[0.531, 0.650, 0.578, 0.745], [0.531, 0.850, 0.578, 0.920]]. The heights of these bars correspond to performance scores of 65 in Q1 and 70 in Q2, respectively. The difference is calculated by subtracting the score in Q1 from the score in Q2.\nBased on the above thought, the answer is:\n5" } ], "id_raw": 53 }, { "id": "EzNakR7XXWlJ2Aec", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_3_16.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data from this pair of line charts into a table with resources as rows and quarters as columns?" }, { "from": "gpt", "value": "To generate the tabular data from the line charts, first, I refer to {the X-axis labels}[0.103, 0.919, 0.123, 0.942], [0.361, 0.919, 0.381, 0.942], [0.619, 0.919, 0.639, 0.942], [0.877, 0.919, 0.897, 0.942] which are 'Q1', 'Q2' ,'Q3', 'Q4'. These will be our columns. Our row labels will be the different resources which can be obtained from {the legend}[0.335, 0.062, 0.665, 0.103]. The data values are extracted from the line points of each line chart, including the points for distribution percentage {line points for distribution percentage}[0.113, 0.138, 0.887, 0.736] and the familiar ones for utilization percentage {line points for utilization percentage}[0.113, 0.782, 0.887, 0.874].\nBased on the above thought, the answer is:\nResources,Q1_Distribution,Q2_Distribution,Q3_Distribution,Q4_Distribution,Q1_Utilization,Q2_Utilization,Q3_Utilization,Q4_Utilization\nTextbooks,5000,6000,7500,7000,29.5%,41.12%,36.52%,32.92%\nComputers,6000,7500,8790,8250,36.37%,45.59%,47.14%,42.33%\nOnline Materials,6500,8000,9500,8900,38.96%,47.18%,48.77%,44.28%\nLaboratory Equipment,3500,4000,4650,4500,9.81%,11.59%,10.52%,9.73%\nFaculty Advising,8200,9500,10000,9800,60.71%,70.81%,69.83%,65.07%\nLibraries,7400,8000,9650,8900,51.51%,58.59%,56.08%,52.34%" } ], "id_raw": 56 }, { "id": "0XbsRLECLifBK5PH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_3_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference of the distribution percentage for laboratory equipments between Q1 and Q3?" }, { "from": "gpt", "value": "To find the difference in distribution percentage for laboratory equipment between Q1 and Q3, I need to look at the {line points for distribution percentage for laboratory equipments}[0.113, 0.736], [0.629, 0.138]. The values associated with these points are 3500 for Q1 and 4650 for Q3. So, the difference would be 4650 - 3500 = 1150.\nBased on the above thought, the answer is:\n1150." } ], "id_raw": 56 }, { "id": "fcWlqzFhHkmEPcfl", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_3_17.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart displaying market trends into a structured table with weeks as rows and market aspects as columns?" }, { "from": "gpt", "value": "From the line chart, I identify {the x-axis labels}[0.072, 0.939, 0.961, 0.957] that indicate the weeks. I then observe {the legend}[0.235, 0.059, 0.765, 0.090] to ascertain the columns, which are Market_Entropy, Market_Knowledge, and Market_Growth. Assessing each line separately, such as {the line for Market_Entropy}[0.106, 0.359, 0.894, 0.576], and comparing to {the y-axis labels}[0.943, 0.920, 0.965, 0.096], I gather the necessary data points for each market aspect across different weeks.\nBased on the above thought, the answer is:\nWeek,Market_Entropy,Market_Knowledge,Market_Growth\n1,0.50,0.20,0.10\n2,0.52,0.30,0.15\n3,0.55,0.40,0.20\n4,0.58,0.50,0.30\n5,0.62,0.60,0.40\n6,0.53,0.60,0.40\n7,0.56,0.65,0.43\n8,0.59,0.70,0.46\n9,0.61,0.75,0.50\n10,0.65,0.77,0.70\n11,0.67,0.80,0.75\n12,0.70,0.72,0.65\n13,0.72,0.87,0.90\n14,0.62,0.90,0.95\n15,0.65,0.95,1.00\n16,0.68,0.98,0.99\n17,0.70,0.99,1.01\n18,0.72,1.01,0.99\n19,0.73,1.00,0.85\n20,0.70,1.02,0.78\n21,0.71,1.03,0.75\n22,0.78,1.04,0.71\n23,0.79,1.05,0.70\n24,0.81,1.10,0.70\n25,0.75,1.15,0.70\n26,0.72,1.20,0.69\n27,0.69,1.20,0.71\n28,0.67,1.20,0.73" } ], "id_raw": 57 }, { "id": "zTzRTU0VPWb1MA1x", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_3_17.png", "conversations": [ { "from": "human", "value": "\nHow does the trend in Market Entropy compare to that of Market Growth over the charted period?" }, { "from": "gpt", "value": "Observing {the line for Market Entropy}[0.106, 0.359, 0.894, 0.576] and {the line for Market Growth}[0.106, 0.218, 0.894, 0.857], I analyze both the general trend and significant fluctuations. Market Entropy demonstrates gradual increases and a few declines, starting around 0.5 and moving upwards to approximately 0.73. Conversely, Market Growth initially has a slower upward trend from 0.1, steeply rising to 1.0 around Week 15, and then exhibiting significant volatility afterwards. The x values for these data points help in assessing the relative timeframes from {the x-axis}[0.360, 0.939, 0.377, 0.957].\nBased on the above thought, the answer is:\nMarket Entropy shows a more consistent and moderate rising trend over the charted period. In contrast, Market Growth, while eventually reaching similar high values, displays much more volatility and more pronounced fluctuations in its progression. This comparison underscores different degrees of stability and predictability in the two market aspects." } ], "id_raw": 57 }, { "id": "gC2PDVU9Ie186nhM", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_3_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest value recorded for Market Knowledge throughout the charted weeks?" }, { "from": "gpt", "value": "To determine the highest value of Market Knowledge, I trace {the line for Market Knowledge}[0.106, 0.085, 0.894, 0.787] and extract the points corresponding to each tick on {the x-axis}[0.360, 0.939, 0.377, 0.957]. I review the points to find the peak value: Weeks 21-23 show a consistent rise with a peak at Week 25, which is annotated as 1.15 on {the y-axis}[0.943, 0.078, 0.965, 0.096].\nBased on the above thought, the answer is:\n1.15" } ], "id_raw": 57 }, { "id": "c7w3urSXKJhVp2vb", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_3_18.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, can you structure the data into a table format with \"Weeks\" as the columns and \"Urbanization Indicator\" as one row?" }, { "from": "gpt", "value": "In order to generate the tabular data from the line chart, I first find {xlabel}[0.499, 0.952, 0.544, 0.975] which are the \"Weeks\" and act as the column headers. Next, I identify {the row data line}[0.097, 0.154, 0.916, 0.432] which registers the Urbanization Indicator data point for each week. To fill the values, I locate corresponding y values for each week from {yticks}[0.035, 0.894, 0.057, 0.917], [0.035, 0.787, 0.057, 0.810], [0.035, 0.680, 0.057, 0.703], [0.035, 0.573, 0.057, 0.596], [0.035, 0.466, 0.057, 0.489], [0.035, 0.359, 0.057, 0.382], [0.035, 0.252, 0.057, 0.275], [0.035, 0.145, 0.057, 0.168], [0.034, 0.038, 0.057, 0.061] and map these to the plotted points.\nBased on the above thought, the answer is:\nWeeks,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28\nUrbanization Indicator,1.0,0.97,0.98,0.96,0.97,0.94,0.95,0.96,0.94,0.93,0.95,0.96,0.92,1.3,0.92,1.4,0.88,1.37,0.9,1.35,0.89,1.34,0.88,1.30,0.95,1.21,1.10,1.17" } ], "id_raw": 58 }, { "id": "lS31rMCJ88burC3C", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_3_18.png", "conversations": [ { "from": "human", "value": "\nCan you provide an analysis of the trend in urban development according to the chart?" }, { "from": "gpt", "value": "To analyze the trend, I will look at {the Urban development line}[0.097, 0.154, 0.916, 0.432]. The line starts at around {1.0}[0.097, 0.368] and seems to have minor fluctuations until it reaches a significant dip at approximately {0.92}[0.461, 0.411]. This is followed by a sharp rise between {week 14 and 16}[0.491, 0.208, 0.552, 0.154] with peaks at around {1.3 and 1.4}[0.491, 0.208, 0.552, 0.154]. In the later trends, the urban development oscillates around {1.30}[0.794, 0.208] and {0.88}[0.764, 0.432] and finally ends up near {1.17}[0.916, 0.277].\nBased on the above thought, the answer is:\nUrban development, as shown in the chart, experiences minor fluctuations at first, remaining roughly in the range of 0.94 to 1.0. At around the 13th week, there's a decline to a low point of approximately 0.92. This decline is quickly followed by a sharp rise to a high of 1.4 at around the 16th week. Following this peak, the trend fluctuates extensively between an approximate low of 0.88 and a high of 1.3. The chart ends with an Urbanization Indicator at around 1.17. This indicates a high degree of volatility in urban development over the illustrated weeks." } ], "id_raw": 58 }, { "id": "XDc5QOKbSeXHOC5I", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_3_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the lowest Urbanization Indicator value according to the chart?" }, { "from": "gpt", "value": "To identify the lowest Urbanization Indicator value according to the chart, I need to find the minimum point on {the Urbanization Indicator line}[0.097, 0.154, 0.916, 0.432]. This minimum point corresponds to the lowest y value among the points plotted on the y-axis. From observing the chart, it is clear that the smallest point lies near the y-axis label of {0.88}[0.034, 0.038, 0.057, 0.061].\nBased on the above thought, the answer is:\n0.88." } ], "id_raw": 58 }, { "id": "PP4iy2P5WOUYWNPI", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_0.png", "conversations": [ { "from": "human", "value": "\nCould you transform the data represented in the line chart concerning Global Trade and Global Supply Chain into a structured table format categorizing each year?" }, { "from": "gpt", "value": "After analyzing the chart, I identified two lines representing Global Supply Chain and Global Trade. To extract their values accurately, I focused on each point across the x-axis labeled with years from {the x-axis labels}[0.152, 0.305, 0.367, 0.581, 0.688]. For the Global Supply Chain, point values were noted at locations such as [0.152, 0.208] in 2016 with the supply chain substantially higher at [0.688, 0.179] in 2020. Global Trade started at a lower point [0.152, 0.237] in 2016 and showed a moderate increase by 2020 at [0.688, 0.271].\nBased on the above thought, the answer is:\nYear,Global Supply Chain,Global Trade\n2016,0.75,0.70\n2017,0.85,0.75\n2018,0.95,0.80\n2019,1.15,0.90\n2020,1.20,0.95" } ], "id_raw": 60 }, { "id": "6soz9wNytWDTnRCz", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_0.png", "conversations": [ { "from": "human", "value": "\nHow does the impact of the COVID-19 pandemic appear to manifest in the data trends between Global Supply Chain and Global Trade based on the graph?" }, { "from": "gpt", "value": "Observing the plots closely, {the line for Global Supply Chain}[0.152, 0.208, 0.688, 0.179] shows a steep increase particularly towards 2020 which is a significant year representing the COVID-19 pandemic impact. Similarly, {the line for Global Trade}[0.152, 0.237, 0.688, 0.271] also demonstrates a rise but less pronounced than the Supply Chain. This indicates that while both sectors were affected by the pandemic, the Global Supply Chain experienced a more substantial disruption or adaptation, likely due to the challenges and changes in logistics and distribution methodologies.\nBased on the above thought, the answer is:\nThe COVID-19 pandemic appears to have a differentiated impact on Global Trade and Global Supply Chain, as seen in the too graphics. The Global Supply Chain exhibits a sharper increase in 2020 which might indicate a stronger disruption or adaptive measures taken by industries to manage the pandemic impact, compared to a more moderate rise in Global Trade." } ], "id_raw": 60 }, { "id": "Nm9SSh1j326A5H8W", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in Global Supply Chain values from 2016 to 2020, according to the graph?" }, { "from": "gpt", "value": "To calculate the percentage increase, I analyzed the values of Global Supply Chain starting from {the point in 2016}[0.152, 0.208] with a value of 0.75 and at {the point in 2020}[0.688, 0.179] where it reached 1.20. The formula used is ((Final value - Initial value) / Initial value) * 100%.\nBased on the above thought, the answer is:\n60%" } ], "id_raw": 60 }, { "id": "Pw43E4PSmVhWkXAX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_2.png", "conversations": [ { "from": "human", "value": "\nCan you organize the raw data from the line graphs into a table with the platforms as rows and 'Days Active', 'Messages Sent', and 'Emojis Sent' as columns?" }, { "from": "gpt", "value": "From the JSON data, I identify the platforms from {the x-axis labels}[[0.138, 0.227, 0.161, 0.300], [0.295, 0.227, 0.318, 0.305], [0.452, 0.227, 0.476, 0.306], [0.610, 0.227, 0.633, 0.300], [0.767, 0.227, 0.790, 0.279], [0.924, 0.227, 0.947, 0.292]]. I then extract the corresponding values for each of the three categories: 'Days Active', 'Messages Sent', and 'Emojis Sent' from {the data points in the subplots}[[0.150, 0.210], [0.307, 0.182], [0.464, 0.154], [0.621, 0.127], [0.778, 0.099], [0.936, 0.071], [0.150, 0.537], [0.307, 0.466], [0.464, 0.430], [0.621, 0.399], [0.778, 0.408], [0.936, 0.444], [0.150, 0.865], [0.307, 0.795], [0.464, 0.782], [0.621, 0.727], [0.778, 0.773], [0.936, 0.817]].\nBased on the above thought, the answer is:\nPlatform,Days Active,Messages Sent,Emojis Sent\nFacebook,10,2.2,3.5\nWhatsapp,20,3.8,6.7\nInstagram,30,4.6,7.3\nSnapchat,40,5.3,9.8\nTwitter,50,5.1,7.7\nLinkedIn,60,4.3,5.7" } ], "id_raw": 62 }, { "id": "70TJKraRSKKKykVm", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_2.png", "conversations": [ { "from": "human", "value": "\nObserving the line chart, which platform is most active and engages the most as indicated by the factors: days active, messages sent and emojis sent?" }, { "from": "gpt", "value": "For determining the platform which is most active and engages the most, I need to take into consideration all the three factors - days active, messages sent and emojis sent. The higher the value for these factors, the higher the engagement. The first part {Days Active line}[[0.150, 0.210], [0.307, 0.182], [0.464, 0.154], [0.621, 0.127], [0.778, 0.099], [0.936, 0.071]] indicates LinkedIn has the most active days. The second part {Messages Sent line}[[0.150, 0.537], [0.307, 0.466], [0.464, 0.430], [0.621, 0.399], [0.778, 0.408], [0.936, 0.444]] shows that Snapchat has the highest number of messages sent. For the third part, {Emojis Sent line}[[0.150, 0.865], [0.307, 0.795], [0.464, 0.782], [0.621, 0.727], [0.778, 0.773], [0.936, 0.817]], Snapchat has the highest number of emojis sent as well.\nBased on the above thought, the answer is:\nSnapchat is the platform that is most active and engages the most as depicted by the line chart. This is established by observing that Snapchat has the highest numbers for both 'messages sent' and 'emojis sent'. Even though LinkedIn shows the maximum days of activity, the user engagement through messages and emojis is higher on Snapchat. Therefore, considering the activity and engagement factors, Snapchat stands out as the most active platform." } ], "id_raw": 62 }, { "id": "xcR6Uu8LZgVY3nxD", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the average number of messages sent across all platforms over 60 days?" }, { "from": "gpt", "value": "To compute the average number of messages sent across all platforms, I first identify the data points representing 'Messages Sent' from {the line of Messages Sent}[[0.150, 0.537], [0.307, 0.466], [0.464, 0.430], [0.621, 0.399], [0.778, 0.408], [0.936, 0.444]]. I extract the corresponding values: 2.2, 3.8, 4.6, 5.3, 5.1, and 4.3. I then calculate their average by summing the numbers and dividing by the total count, which is 6 platforms.\nBased on the above thought, the answer is:\n4.2" } ], "id_raw": 62 }, { "id": "n06unFlQkbybPIti", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_3.png", "conversations": [ { "from": "human", "value": "\nCould you organize the survey data from the grouped bar chart into a structured table format distinguishing between the total population and the percentage of women for each age category?" }, { "from": "gpt", "value": "By analyzing {the bar labels and annotations}[0.15, 0.82, 0.22, 0.85], which indicate the age categories on the x-axis, and observing each group's bars like {the first group's bars}[0.10, 0.45, 0.23, 0.95], I could differentiate the metrics for 'Total Population' and 'Percentage of Women'. Each group's differently colored bars {blue for Total Population and green for Percentage of Women}[0.12, 0.50, 0.18, 0.80] provide visual cues. The annotations above each bar indicate the exact values, helping in constructing the data table.\nBased on the above thought, the answer is:\nAge Category,Total Population (millions),Percentage of Women (%)\n20-29,6.5,65\n30-39,5.0,50\n40-49,4.2,55" } ], "id_raw": 63 }, { "id": "WnZv7hk9L7iomo7Y", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_3.png", "conversations": [ { "from": "human", "value": "\nHow does the total population trend change across different age categories in the chart?" }, { "from": "gpt", "value": "Observing the chart, specifically the blue bars which represent the total population, I will analyze their heights to understand the trend. Starting with {the blue bar for 20-29 age group}[0.115, 0.35, 0.185, 0.80], it is the tallest, indicating the highest population. This is followed by a shorter {blue bar for 30-39}[0.215, 0.45, 0.285, 0.80], and the shortest {bar for 40-49}[0.315, 0.55, 0.385, 0.80], suggesting a declining pattern in the total population as the age increases.\nBased on the above thought, the answer is:\nThe total population shows a declining trend with increasing age categories. The highest population is in the 20-29 age group, followed by a decrease in the 30-39 age group, and the lowest population is observed in the 40-49 age group. This trend is clearly illustrated by the progressively shorter blue bars for each age category on the chart." } ], "id_raw": 63 }, { "id": "fwuyKI8Qz0dEwOBU", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the average percentage of women across all age categories as indicated on the chart?" }, { "from": "gpt", "value": "To find the average percentage, I need to locate and analyze the green bars that represent the percentage of women across the age categories: {20-29}[0.115, 0.56, 0.185, 0.80], {30-39}[0.215, 0.65, 0.285, 0.80], and {40-49}[0.315, 0.60, 0.385, 0.80]. The exact values for these are 65%, 50%, and 55% respectively. To calculate the average: (65 + 50 + 55) / 3 = 56.67%.\nBased on the above thought, the answer is:\n56.67%" } ], "id_raw": 63 }, { "id": "VYwLjuxA8Yw0Kw0H", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information from the line chart depicting different stock trends over months into a structured table?" }, { "from": "gpt", "value": "I'll begin by scanning the chart, where I locate {the x-axis labels}[0.125, 0.946, 0.132, 0.962] reading as Jan, Feb, Mar, Apr, May which serve as the table headers for our months. Each line on {the chart}[0.125, 0.064, 0.960, 0.935] represents a different stock trend, with their respective values over the months. For instance, the {line for Minimal decline}[0.125, 0.294, 0.960, 0.615] shows values [{point coordinates for each month}[0.125, 0.406], [0.292, 0.447], [0.459, 0.489], [0.626, 0.510], [0.793, 0.447], [0.960, 0.406]]. Repeating this method allows me to extract the data for other trends such as Volatile growth, Abrupt increase, etc.\nBased on the above thought, the answer is:\nTrend,Jan,Feb,Mar,Apr,May\nMinimal decline,1.9,1.0,1.2,1.0,1.2\nAbrupt increase,3.0,3.1,3.2,3.3,4.0\nSubstantial increase,2.0,2.5,3.0,3.5,4.0\nVolatile growth,1.0,2.0,1.0,2.0,1.5\nAccelerating decline,3.0,2.5,2.0,1.5,1.0\nStable growth,2.0,2.0,2.0,2.0,2.0\nAbrupt decline,2.0,2.5,3.0,3.5,1.0" } ], "id_raw": 64 }, { "id": "PouE3WTfoIyoiNLU", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_4.png", "conversations": [ { "from": "human", "value": "\nWhich stock trend showed the most stable growth over the observed time period?" }, { "from": "gpt", "value": "Observing each line's fluctuation and the consistency in values is crucial to determining which stock exhibited the most stable growth. Upon examining {the chart}[0.125, 0.064, 0.960, 0.294] closely, I observe that {the line for Stable growth}[0.125, 0.294, 0.960, 0.615] displays constant values at every monthly tick, which shows no variation in growth. Each point approximately aligns at the same height, which suggests stability in the stock's performance over the given period.\nBased on the above thought, the answer is:\nThe stock trend labeled as 'Stable growth' exhibited the most stable growth. This is evident from its consistent value at all months, indicating no fluctuations and a steady performance throughout the time span from January to May." } ], "id_raw": 64 }, { "id": "4pMiQXwcwjNh8tqY", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_4.png", "conversations": [ { "from": "human", "value": "\nFrom January to May, how much did the stock labeled as 'Abrupt decline' decrease in total?" }, { "from": "gpt", "value": "To assess the total decrease in value for the stock labeled as 'Abrupt decline' from January to May, I carefully look at the values of {the line for Abrupt decline}[0.125, 0.782, 0.960, 0.935] at {the position of each month}[0.118, 0.946, 0.975, 0.962]. I extract the values of 3.0 in January and 1.0 in May. The total decrease is calculated by subtracting the May value from the January value: 3.0 - 1.0.\nBased on the above thought, the answer is:\n2.0" } ], "id_raw": 64 }, { "id": "g5owjMwNPeH20TCr", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data on 'Risk Assessment and Management' behavior by different groups into a table format?" }, { "from": "gpt", "value": "To transform the line chart data into a table format, I first identify {the x-axis labels}[0.130, 0.294] representing the risk categories 'Medium Risk' and 'High Risk'. I then look at {the legend}[0.141, 0.072, 0.546, 0.100] which indicates the different risk groups 'A', 'B', and 'C'. For each group and its represented risk category, I refer to the {line points for Group A}[0.130, 0.087], [0.946, 0.290], {line points for Group B}[0.130, 0.406], [0.946, 0.602], and {line points for Group C}[0.130, 0.744], [0.946, 0.920] for data extraction.\nBased on the above thought, the answer is:\nRisk Category,Risk Group A,Risk Group B,Risk Group C\nMedium Risk,Erratic Behavior,Moderate Increase,Rapid Decline\nHigh Risk,Erratic Behavior,Moderate Increase,Rapid Decline" } ], "id_raw": 65 }, { "id": "lTIFfOmoz0sE5UYw", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_5.png", "conversations": [ { "from": "human", "value": "\nWhich risk group shows the highest volatility in behavior based on the graph?" }, { "from": "gpt", "value": "By examining the graph, I analyze the line trends of each risk group. {The line for Risk Group A}[0.130, 0.087, 0.946, 0.290] shows consistent erratic behavior without major fluctuations. {The line for Risk Group B}[0.130, 0.406, 0.946, 0.602] depicts a steady rise with moderate fluctuation. However, {the line for Risk Group C}[0.130, 0.744, 0.946, 0.920] shows a sharp decline from a high point in 'Medium Risk' to a lower point in 'High Risk', indicating significant volatility.\nBased on the above thought, the answer is:\nRisk Group C shows the highest volatility in behavior according to the chart. The sharp decline in their behavior from 'Medium Risk' to 'High Risk' signifies a greater reaction to changes in risk levels, suggesting higher volatility compared to the other groups." } ], "id_raw": 65 }, { "id": "OAzkWz6yovEhKxAR", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in risk management assessment for Risk Group B from 'Medium Risk' to 'High Risk'?" }, { "from": "gpt", "value": "To find the increase for Risk Group B, I locate {the individual data points for Group B}[0.130, 0.406], [0.946, 0.602]. The assessment value at 'Medium Risk' is noted at 0.8, and at 'High Risk' it is estimated at 2.9. Calculate the increase by subtracting the 'Medium Risk' value from the 'High Risk' value.\nBased on the above thought, the answer is:\n2.1" } ], "id_raw": 65 }, { "id": "oy8JXS8jNQtROum2", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_7.png", "conversations": [ { "from": "human", "value": "\nCan you generate a CSV table representing effectiveness of risk control measures across various risk levels derived from the line graph?" }, { "from": "gpt", "value": "Firstly, I identify {the line plot coordinates for Risk Control effectiveness}[0.079, 0.044, 0.488, 0.449] on the graph. I observe intersections of this line with {y-ticks}[0.079, 0.449, 0.070, 0.063] to determine numerical data at each point. Reviewing {the x-axis labels}[0.038, 0.459, 0.497, 0.473], I correlate these intersections to 'Low', 'Moderate', 'High', 'Critical', and 'Catastrophic' risk levels. Extracting these values, I generate the structured tabular data.\nBased on the above thought, the answer is:\nRisk Level,Effectiveness\nLow,100\nModerate,150\nHigh,200\nCritical,300\nCatastrophic,260" } ], "id_raw": 67 }, { "id": "V4ZuBeWHcQng42ai", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_7.png", "conversations": [ { "from": "human", "value": "\nWhat does the trend in the line chart suggest about the effectiveness of risk control measures as risk levels increase?" }, { "from": "gpt", "value": "Analyzing {the line trend}[0.079, 0.044, 0.488, 0.449] against {the risk levels on x-axis}[0.038, 0.459, 0.497, 0.473], it's noticeable that the effectiveness of risk control measures initially rises as risk levels escalate from Low to Critical. This is visible by the ascending connection points from {Low}[0.079, 0.412] through {High}[0.265, 0.265] peaking at {Critical}[0.414, 0.136]. However, a slight decline is observed at the {Catastrophic}[0.488, 0.063] level, suggesting a decrease in control measure effectiveness at extremely high risks.\nBased on the above thought, the answer is:\nBased on the line chart, the trend suggests that the effectiveness of risk control measures improves as risk levels increase from Low to Critical, indicating that the measures are generally more effective at higher risks. However, there is a slight decline in effectiveness at the Catastrophic level, which might indicate limitations or challenges in managing extremely high risks." } ], "id_raw": 67 }, { "id": "FxvE40Vg8OnIyHYQ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_7.png", "conversations": [ { "from": "human", "value": "\nHow much did the effectiveness of risk control measures increase from moderate to critical risk level?" }, { "from": "gpt", "value": "I need to extract the effectiveness of risk control measures from {the line points for Moderate and Critical risk levels}[0.186, 0.315, 0.343]. Observing the graph, I see the values are 150 for Moderate and a peak of 300 for Critical, extracted from the intersections with {y-axis ticks}[0.079, 0.320]. The calculated increase in effectiveness is 300 - 150.\nBased on the above thought, the answer is:\n150" } ], "id_raw": 67 }, { "id": "rs6ET4QWb8KFcebb", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_8.png", "conversations": [ { "from": "human", "value": "\nCan you create a table that shows the time points and the corresponding values for the 'Sudden Decrease' scenario as depicted in the chart?" }, { "from": "gpt", "value": "To extract the data for the 'Sudden Decrease' scenario from the chart, I analyze {the line for Sudden Decrease}[0.130, 0.122, 0.960, 0.260] which contains various {data points}[0.130, 0.122], [0.213, 0.145], [0.296, 0.168], [0.379, 0.185], [0.462, 0.202], [0.545, 0.219], [0.628, 0.231], [0.711, 0.237], [0.794, 0.242], [0.877, 0.248], [0.960, 0.260] across the time from 0 to 10 years. I observe each coordinate corresponding to the specific time point and extract the value indicated by the y-axis position relative to the axis values.\nBased on the above thought, the answer is:\nTime, Sudden Decrease(Value)\n0, 1.0\n1, 0.8\n2, 0.6\n3, 0.45\n4, 0.3\n5, 0.15\n6, 0.05\n7, 0.0\n8, -0.05\n9, -0.1\n10, -0.2" } ], "id_raw": 68 }, { "id": "1gDXtDRa9q1Vouf5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_8.png", "conversations": [ { "from": "human", "value": "\nWhich trend demonstrates more stability over the years, 'Sudden Decrease' or 'Volatile Movement'?" }, { "from": "gpt", "value": "To evaluate stability, I examine the line plots for both trends. Observing {the line for Sudden Decrease}[0.130, 0.122, 0.960, 0.260], the values consistently decrease with small incremental changes between points, suggesting mild fluctuations. On the other hand, {the line for Volatile Movement}[0.130, 0.763, 0.960, 0.859] shows larger vertical displacements at various points, indicating significant value changes over the years, such as the rise at year 5 or the drop at year 8. The fluctuations are substantially wider compared to the 'Sudden Decrease'.\nBased on the above thought, the answer is:\nThe 'Sudden Decrease' trend demonstrates more stability over the years than the 'Volatile Movement' trend. The values in the 'Sudden Decrease' trend change gradually and consistently downward, with smaller deviations between consecutive years. In contrast, the 'Volatile Movement' trend exhibits larger ups and downs, indicating less stability." } ], "id_raw": 68 }, { "id": "edUUX3dlXgaKX1Hp", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_8.png", "conversations": [ { "from": "human", "value": "\nHow much does the value fluctuate from the lowest point to the highest point in the 'Periodic Fluctuations' trend?" }, { "from": "gpt", "value": "To calculate the value fluctuation in the 'Periodic Fluctuations' trend, I refer to {the points on the line for Periodic Fluctuations}[0.130, 0.423, 0.960, 0.538]. By assessing the y-coordinates of the highest point at year 5 {5.0, y: 1.2}[0.545, 0.423] and the lowest point at year 3 {3.0, y: 0.9}[0.379, 0.538], I can calculate the difference. The highest value is approximately 1.2 and the lowest is about 0.9, giving a fluctuation of 1.2 - 0.9 = 0.3.\nBased on the above thought, the answer is:\n0.3" } ], "id_raw": 68 }, { "id": "iy4PTD0f6wA310ZY", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_9.png", "conversations": [ { "from": "human", "value": "\nCan you create a structured table showing the investments for each type of investment over the years from the line chart?" }, { "from": "gpt", "value": "By examining {the x-axis labels}[[0.111, 0.294], [0.196, 0.294], [0.282, 0.294], [0.368, 0.294], [0.454, 0.294], [0.539, 0.294], [0.625, 0.294], [0.711, 0.294], [0.797, 0.294], [0.882, 0.294], [0.968, 0.294]] representing the years 2007 to 2017, and noting {the points on the Private Equity line}[[0.111, 0.165], [0.196, 0.193], [0.282, 0.233], [0.368, 0.210], [0.454, 0.170], [0.539, 0.161], [0.625, 0.147], [0.711, 0.124], [0.797, 0.119], [0.882, 0.117], [0.968, 0.101]], {the Venture Capital line}[[0.111, 0.553], [0.196, 0.569], [0.282, 0.569], [0.368, 0.549], [0.454, 0.553], [0.539, 0.558], [0.625, 0.527], [0.711, 0.487], [0.797, 0.469], [0.882, 0.454], [0.968, 0.431]], and {the Angel Investment line}[[0.111, 0.878], [0.196, 0.849], [0.282, 0.837], [0.368, 0.822], [0.454, 0.811], [0.539, 0.801], [0.625, 0.791], [0.711, 0.782], [0.797, 0.774], [0.882, 0.763], [0.968, 0.747]], I can create a table detailing investment amounts by type across these years.\nBased on the above thought, the answer is:\nYear,Private Equity,Venture Capital,Angel Investment\n2007,2.4,0.8,0.6\n2008,2.1,0.7,0.75\n2009,1.66,0.7,0.81\n2010,1.92,0.83,0.89\n2011,2.35,0.8,0.95\n2012,2.45,0.77,1.0\n2013,2.6,0.97,1.05\n2014,2.85,1.23,1.1\n2015,2.9,1.35,1.14\n2016,2.92,1.45,1.2\n2017,3.1,1.6,1.28" } ], "id_raw": 69 }, { "id": "R4yqx9XTVplam1KB", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_9.png", "conversations": [ { "from": "human", "value": "\nRegarding the overall trends in investment per type as depicted in the chart, how do the fluctuations in Angel Investment compare to those in Private Equity from 2007 to 2017?" }, { "from": "gpt", "value": "Observing {the Angel Investment trend line}[[0.111, 0.878], [0.196, 0.849], [0.282, 0.837], [0.368, 0.822], [0.454, 0.811], [0.539, 0.801], [0.625, 0.791], [0.711, 0.782], [0.797, 0.774], [0.882, 0.763], [0.968, 0.747]] and {the Private Equity trend line}[[0.111, 0.165], [0.196, 0.193], [0.282, 0.233], [0.368, 0.210], [0.454, 0.170], [0.539, 0.161], [0.625, 0.147], [0.711, 0.124], [0.797, 0.119], [0.882, 0.117], [0.968, 0.101]], the fluctuations in Angel Investment show a steady increase in investment value whereas Private Equity exhibits a more variable trend with a notable decline until 2009 followed by a gradual increase. This consistency in growth for Angel Investment indicates a steadier confidence in this type of funding.\nBased on the above thought, the answer is:\nAngel Investment shows a consistent and steady increase, which contrasts with Private Equity, where the trend is more fluctuating with periods of both decline and growth across the examined years. This implies that Angel Investment might present more stability and continuous interest over time compared to the more cyclical nature of Private Equity." } ], "id_raw": 69 }, { "id": "9Hy3A8SbrlTjTKLj", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_9.png", "conversations": [ { "from": "human", "value": "\nWhat was the year with the lowest investment in Venture Capital as noted on the chart?" }, { "from": "gpt", "value": "Analyzing the {Venture Capital investment values}[[0.196, 0.569], [0.282, 0.569], [0.368, 0.549], [0.454, 0.553], [0.539, 0.558], [0.625, 0.527], [0.711, 0.487], [0.797, 0.469], [0.882, 0.454], [0.968, 0.431]] from the line on the chart and comparing the y-coordinates for the lowest point, which corresponds to 2009 with a value of 0.7 trillion dollars.\nBased on the above thought, the answer is:\n2008" } ], "id_raw": 69 }, { "id": "gf3w8ScU8hIlxxWx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_11.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the bar chart into a structured table indicating GDP growth of countries over the years?" }, { "from": "gpt", "value": "Analyzing the bar chart, I effectively categorized data according to {the x-axis labels}[0.133, 0.064, 0.975, 0.294] which denote the years ranging from 1990 to 2020. Furthermore, {the colors and labels on the legend}[0.525, 0.044, 0.647, 0.059] showed distinctions for each country, allowing me to determine which bars correspond to the USA, China, India, Brazil, and Russia respectively. The heights of the bars correlating to the GDP values are visually discerned from the {y-axis}[0.025, 0.158, 0.048, 0.200].\nBased on the above thought, the answer is:\nYear,USA,China,India,Brazil,Russia\n1990,2000,500,400,300,0\n2000,2500,1000,500,450,500\n2010,3000,2500,800,700,1250\n2020,3500,4000,1100,950,1800" } ], "id_raw": 71 }, { "id": "jsvvF6tXGZI6Szpu", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_11.png", "conversations": [ { "from": "human", "value": "\nWhich country showcased the greatest economic progress in the last decade according to the bar chart?" }, { "from": "gpt", "value": "By observing the growth in bar heights from 2010 to 2020, {the bar for China}[0.624, 0.436, 0.856, 0.192] showed the most significant increase compared to {the bars for the USA}[0.474, 0.293, 0.715, 0.324], {India}[0.774, 0.526, 0.924, 0.485], {Brazil}[0.874, 0.644, 1.024, 0.596], and {Russia}[0.974, 0.633, 0.984, 0.830], representing a sharp rise from 2500 billion to 4000 billion.\nBased on the above thought, the answer is:\nChina showcased the greatest economic progress in the last decade according to the bar chart. The immense rise in GDP from 2500 billion to 4000 billion in just 10 years indicates the highest economic development among the countries considered." } ], "id_raw": 71 }, { "id": "Gj0PgME4m7V8Cl4l", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the average GDP growth for China from 1990 to 2020?" }, { "from": "gpt", "value": "Consulting {the bar chart data points for China}[0.524, 0.450, 0.856, 0.253], the GDP values at each decade are: 500 for 1990, 1000 for 2000, 2500 for 2010, and 4000 for 2020. The average GDP growth can be computed by summing these values and dividing by the number of years, which is 4.\nBased on the above thought, the answer is:\n2000" } ], "id_raw": 71 }, { "id": "P6D50NdvEbJaJ6iR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the sales data from the line chart into a structured table with years as columns and products as rows?" }, { "from": "gpt", "value": "To convert the sales data, I first observe {the x-axis tick labels}[[0.103, 0.305, 0.162, 0.321], [0.264, 0.305, 0.322, 0.321], [0.424, 0.305, 0.483, 0.321], [0.585, 0.305, 0.643, 0.321], [0.745, 0.305, 0.804, 0.321], [0.906, 0.305, 0.964, 0.321], [1.066, 0.305, 1.125, 0.321]] which indicate the years. Next, I identify {the legend}[[0.885, 0.134, 0.933, 0.156], [0.885, 0.167, 0.946, 0.189]], which lists products A, B, C, and D. Tracking {the colored lines on the chart}[[0.133, 0.075, 0.935, 0.716], [0.133, 0.395, 0.935, 0.604], [0.133, 0.050, 0.935, 0.283], [0.133, 0.140, 0.935, 0.925]] corresponding to each product, I extract sales data for each year and organize it in the table.\nBased on the above thought, the answer is:\nYear,Product A,Product B,Product C,Product D\n2010,1000,1200,800,900\n2011,1150,1400,850,920\n2012,1200,1600,870,930\n2013,1250,1800,900,950\n2014,1300,2000,930,970\n2015,1350,2200,950,990\n2016,1400,2400,970,1000\n2017,1450,2600,1000,1020\n2018,1500,2800,1030,1040\n2019,1550,3000,1060,1060\n2020,1600,3200,1100,1080" } ], "id_raw": 73 }, { "id": "Z8wRzxN53P1u1gau", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_13.png", "conversations": [ { "from": "human", "value": "\nWhich product shows the most consistent growth throughout the decade?" }, { "from": "gpt", "value": "I analyze the slopes of the lines {for each product}[[0.133, 0.075, 0.935, 0.716], [0.133, 0.395, 0.935, 0.604], [0.133, 0.050, 0.935, 0.283], [0.133, 0.140, 0.935, 0.925]]. I note that while all products show growth, {the line for Product B}[[0.133, 0.395, 0.935, 0.604]] has the steepest and most consistent upward trajectory throughout the years without any plateau or decline. This suggests that Product B not only increased the most in sales but did so without any major fluctuations.\nBased on the above thought, the answer is:\nProduct B shows the most consistent growth throughout the decade. Its line displays a steady upward trend, unlike the other products which either have a slower increase or slight fluctuations over the years." } ], "id_raw": 73 }, { "id": "D6wh8LLFu1cfNZFo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_13.png", "conversations": [ { "from": "human", "value": "\nHow much did the sales of Product B increase from 2010 to 2020?" }, { "from": "gpt", "value": "Initially, I locate {the line for Product B}[[0.133, 0.395, 0.935, 0.604]] and observe the plotted points for 2010 and 2020. According to the coordinates, the sales for Product B in 2010 were approximately 1200, and by 2020, {the final plotted point}[[0.935, 0.604]] indicates sales have increased to approximately 3200. To find the increase, I subtract the initial value from the final value: 3200 - 1200 = 2000.\nBased on the above thought, the answer is:\n2000." } ], "id_raw": 73 }, { "id": "LXHd7URg05YTvwNS", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data of the new and established business ownership rates over the years into a structured table?" }, { "from": "gpt", "value": "To convert the line chart into a structured table, I need to determine the column headers, row labels, and the data values. Looking at {the x-axis tick labels}[[0.049, 0.637, 0.084, 0.646], [0.229, 0.637, 0.264, 0.646], [0.409, 0.637, 0.444, 0.646], [0.589, 0.637, 0.625, 0.646], [0.770, 0.637, 0.805, 0.646], [0.950, 0.637, 0.985, 0.646]], they represent the years which will serve as the columns of the table. {The lines on the chart}[[0.066, 0.402, 0.968, 0.619], [0.066, 0.790, 0.968, 0.933]] represent the Established and New Business Ownership rates respectively and will serve as the rows of the table. The data points on the chart lines corresponding to each year are the values to be tabulated.\nBased on the above thought, the answer is:\nYear,Established Business Ownership Rate,New Business Ownership Rate\n2010,7.6,2.1\n2011,7.3,2.3\n2012,6.7,2.6\n2013,6.5,2.3\n2014,6.2,2.5\n2015,6.1,2.4\n2016,6.4,2.5\n2017,6.9,2.5\n2018,7.2,2.3\n2019,7.6,2.6\n2020,8.0,2.5" } ], "id_raw": 74 }, { "id": "NIuIOJ6Z0w9WyT8f", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_14.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart data, which type of business ownership showed a consistently increasing trend over the years? Support your answer with observations from the chart." }, { "from": "gpt", "value": "To identify which type of business ownership showed a consistently increasing trend, I need to look at the trajectories of the lines representing each business ownership type. Looking at {the line representing Established Business Ownership Rate}[0.066, 0.402, 0.968, 0.619], I see a downward trend from 2011 to 2015, followed by an upward trend to 2020. For {New Business Ownership Rate}[0.066, 0.790, 0.968, 0.933], despite some fluctuations, the overall trend seems to be increasing from 2010 to 2020. Hence, it can be said that New Business Ownership showed a consistently increasing trend.\nBased on the above thought, the answer is:\nThe New Business Ownership Rate showed a consistently increasing trend over the years. Despite some minor fluctuations, the general trajectory of the line for New Business Ownership Rate on the chart shows an upward movement from 2010 to 2020, demonstrating a consistent increase." } ], "id_raw": 74 }, { "id": "oKqVoJt9q29o9r0q", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_14.png", "conversations": [ { "from": "human", "value": "\nHow much did the New Business Ownership Rate increase from 2011 to 2019?" }, { "from": "gpt", "value": "To calculate the increase in the New Business Ownership Rate from 2011 to 2019, I need to find the corresponding values in those years. Looking at the line chart, I locate {the point representing 2011}[0.156, 0.875], which is 2.3% and {the point representing 2019}[0.877, 0.790], which is 2.6%. The increase would then be 2.6 - 2.3 = 0.3.\nBased on the above thought, the answer is:\n0.3." } ], "id_raw": 74 }, { "id": "rgSpzZUh5pl90EVT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_15.png", "conversations": [ { "from": "human", "value": "\nCould you organize the data from the line chart into a table format showing the years vertically and the categories (Farm Profit, Food Sales, Agricultural Imports) horizontally?" }, { "from": "gpt", "value": "To create a table using the data from the line chart, I analyze {the x-tick labels for the years}[0.036, 0.305, 0.094, 0.321], which correspond to '2004', '2006', '2008', '2010', '2012', '2014', and '2016'. These identify the rows of the table. I review {the legend}[0.453, 0.037, 0.662, 0.065], which indicates 'Farm Profit', 'Food Sales', and 'Agricultural Imports'. These titles furnish the column headers. For the table values, I trace each category's line plot back to its {associated data points}[0.147, 0.077, 0.968, 0.261] for a specific year and note down the corresponding values.\nBased on the above thought, the answer is:\nYear,Farm Profit,Food Sales,Agricultural Imports\n2005,10000,20000,5000\n2006,10500,21000,5300\n2007,11300,22200,5700\n2008,10200,23000,5900\n2009,9500,24300,6200\n2010,10000,25000,7000\n2011,9000,26000,8000\n2012,8500,27000,8500\n2013,9000,28000,8700\n2014,8800,29000,9100\n2015,8600,30000,9150" } ], "id_raw": 75 }, { "id": "MZhyz5NE6b1NRqrb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_15.png", "conversations": [ { "from": "human", "value": "\nGiven the observed trends, which of the categories between Farm Profit, Food Sales, or Agricultural Imports showed steadier growth over the analyzed period?" }, { "from": "gpt", "value": "I examine {the line plots}[0.147, 0.077, 0.968, 0.893] for each category across the timeline. Food Sales and Agricultural Imports exhibit a general upward slope indicating growth. Notably, {Agricultural Imports}[0.147, 0.720, 0.968, 0.893] shows the most consistent incline with smaller fluctuations. Conversely, Farm Profit demonstrated dips and peaks [0.147, 0.077, 0.968, 0.261], reflecting more volatility compared to a steady growth.\nBased on the above thought, the answer is:\nAgricultural Imports exhibited the steadiest growth over the analyzed period, evidenced by its consistent and gradual upward trend in the chart." } ], "id_raw": 75 }, { "id": "ypgbcAb0lBMGdgrq", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in Agricultural Imports from 2007 to 2015?" }, { "from": "gpt", "value": "To find the percentage increase, I first locate the year 2007's value for {Agricultural Imports}[0.311, 0.864] which is 5700, and the year 2015's value at [0.968, 0.720] which is 9150. The calculation needed is ((9150 - 5700) / 5700) * 100.\nBased on the above thought, the answer is:\n60.53%." } ], "id_raw": 75 }, { "id": "bE9jyobWddzV1FgI", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_16.png", "conversations": [ { "from": "human", "value": "\nCan you organize the data from the line chart into a table with years as columns and each indicator as rows?" }, { "from": "gpt", "value": "Observing the chart, I identify {the x-axis labels}[0.074, 0.312, 0.083, 0.328], [0.253, 0.312, 0.262, 0.328], [0.432, 0.312, 0.441, 0.328], [0.611, 0.312, 0.620, 0.328], [0.790, 0.312, 0.799, 0.328], [0.964, 0.312, 0.983, 0.328] to determine the years from 2010 to 2021. I also examine {the legend}[0.086, 0.727, 0.332, 0.779] which states the indicators: GDP Growth Rate, Inflation Rate, Unemployment Rate, and Interest Rate. Utilizing the line plots for each indicator, such as {the line for GDP Growth Rate}[0.079, 0.903, 0.884, 0.946] and so forth, I extract each point value to fill in the corresponding cells in the table.\nBased on the above thought, the answer is:\nYear,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021\nGDP Growth Rate,2.5,2.3,3.1,1.9,2.6,1.4,2.8,2.9,2.1,2.2,1.0,3.4\nInflation Rate,1.5,2.0,1.7,1.8,1.9,1.0,1.3,1.7,1.6,1.8,0.7,1.9\nUnemployment Rate,7.0,6.9,6.1,5.6,5.3,6.2,4.9,4.4,3.9,3.7,8.1,5.2\nInterest Rate,0.5,0.5,0.75,0.75,0.5,0.35,0.5,0.65,0.75,0.75,0.25,0.4" } ], "id_raw": 76 }, { "id": "wvtbwmTO1mdulhiK", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_16.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the general economic trend based on the interest rate movements between 2016 and 2021?" }, { "from": "gpt", "value": "Investigating the {interest rate trend}[0.079, 0.730, 0.884, 0.904] between 2016 and 2021, I notice the line plot initially rises from 2016 to 2018 and subsequently drops significantly in 2020. The rise in the interest rates from 2016 to 2018 suggests tightening monetary policy possibly due to economic strengthening. The sharp decline in 2020 is indicative of likely monetary easing measures in response to economic challenges, such as the impacts of a global event like the pandemic.\nBased on the above thought, the answer is:\nThe general economic trend inferred from the interest rate movements between 2016 and 2021 suggests a phase of monetary tightening followed by a significant easing, likely in response to fluctuating economic conditions impacted by external factors like a global pandemic." } ], "id_raw": 76 }, { "id": "0b40w01qRr2utv3w", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_16.png", "conversations": [ { "from": "human", "value": "\nHow many years show an increase in GDP growth from the previous year?" }, { "from": "gpt", "value": "Reviewing {the line for GDP growth}[0.079, 0.903, 0.884, 0.946] on the chart, I track the values for each year and compare them with the prior year's values. By noting the points where the trend line moves upwards from one year to the next, I calculate the number of times this increase occurs.\nBased on the above thought, the answer is:\n6 years." } ], "id_raw": 76 }, { "id": "ITgyk9T56CGLWPcy", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_17.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart data showing the Yield Trend Over Time into a structured table format?" }, { "from": "gpt", "value": "To convert the chart data into a table, I observe the points on {the line for Yield Trend}[[0.130, 0.110, 0.960, 0.279]]. Each point represents a year and its corresponding yield value. Starting from the first point at year 0 with a yield of 2.2 to the last point at year 10 with a yield of 1.1.\nBased on the above thought, the answer is:\nYear,Yield\n0,2.2\n1,2.0\n2,1.9\n3,1.8\n4,1.7\n5,1.6\n6,1.5\n7,1.4\n8,1.3\n9,1.2\n10,1.1" } ], "id_raw": 77 }, { "id": "DcrIwpgZojSUOvpm", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_17.png", "conversations": [ { "from": "human", "value": "\nWhich trend presented in the line chart displays the most consistent increase over the years?" }, { "from": "gpt", "value": "To determine the consistency, I closely examine each line in the chart. {The line representing Fossil fuel Emission}[[0.130, 0.742, 0.960, 0.908]] demonstrates a continuous and steady increase, moving up from -1.2 to 0.6. The consistently upward trajectory without any downturns across the {xtick intervals}[[0.130, 0.935], [0.960, 0.935]] indicates that the Fossil fuel Emission has the most consistent increase among the trends plotted.\nBased on the above thought, the answer is:\nFossil fuel Emission displays the most consistent increase. It starts at -1.2 and steadily rises to 0.6, without any year showing a reversal or plateau in the trend." } ], "id_raw": 77 }, { "id": "FlpQMizRGpdg2hYu", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in Population Change from year 0 to year 10 based on the chart?" }, { "from": "gpt", "value": "Reviewing {the line of Population Change}[[0.130, 0.408, 0.960, 0.592]], I analyze the values marked at each {xtick interval}[[0.130, 0.615], [0.960, 0.615]]. The initial value at year 0 is 1.0, and by year 10 it has increased to 2.6.\nBased on the above thought, the answer is:\n1.6" } ], "id_raw": 77 }, { "id": "qDwN6aiSDQgUSUTt", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_18.png", "conversations": [ { "from": "human", "value": "\nCan you extract and format the data from the line chart into a table with years as columns and domains as rows?" }, { "from": "gpt", "value": "First, I identify {the x-axis tick labels}[0.082, 0.301, 0.985, 0.313] to set the years as the columns. Then, I reference {the legend}[0.829, 0.037, 0.969, 0.058] to determine the rows, which represent different service quality domains: Healthcare, Education, and Public Transit. I look at each line corresponding to these domains, checking {the points}[0.087, 0.188], [0.175, 0.258], [0.264, 0.284], [0.353, 0.276], [0.442, 0.206], [0.531, 0.192], [0.620, 0.152], [0.709, 0.118], [0.798, 0.082], [0.887, 0.063], [0.976, 0.071], extract the values and organize them according to the year and domain.\nBased on the above thought, the answer is:\nYear,Healthcare,Education,Public Transit\n2011,3.2,2.8,3.1\n2012,3.3,2.9,2.9\n2013,3.4,3.0,2.7\n2014,3.4,3.1,2.5\n2015,3.3,3.2,2.3" } ], "id_raw": 78 }, { "id": "gLmpg7pjuvOFbSIh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_18.png", "conversations": [ { "from": "human", "value": "\nWhich domain shows a rising trend in quality scores over the period from 2011 to 2015 according to the line chart?" }, { "from": "gpt", "value": "To determine the trend for each domain, I observe the lines for Healthcare, Education, and Public Transit. By examining {the line for Education}[0.087, 0.258], [0.175, 0.340], [0.264, 0.284], [0.353, 0.276], [0.442, 0.206], [0.531, 0.192], [0.620, 0.152], [0.709, 0.118], [0.798, 0.082], [0.887, 0.063], [0.976, 0.071], I track a gradual increase in the points, starting lower and consistently climbing throughout the years.\nBased on the above thought, the answer is:\nThe domain that shows a rising trend in quality scores from 2011 to 2015 is Education. This is evident from the progressively rising position of its line on the chart, indicating an ongoing improvement in Education service quality over the years." } ], "id_raw": 78 }, { "id": "BYpdK7c1xKrx1XGE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_18.png", "conversations": [ { "from": "human", "value": "\nWhat was the total decline in Public Transit quality score from 2011 to 2015?" }, { "from": "gpt", "value": "To determine the total decline in the Public Transit score from 2011 to 2015, I first locate {the line points for Public Transit}[0.087, 0.188], [0.976, 0.071], which represent the scores from 2011 and 2015, respectively. The score starts at 3.1 in 2011 and ends at 2.3 in 2015. The total decline is calculated by subtracting the final score from the initial score, resulting in 3.1 - 2.3.\nBased on the above thought, the answer is:\n0.8" } ], "id_raw": 78 }, { "id": "21RJ4Xyun7cRfGfe", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_4_19.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data presented in the bar chart into a table format? The table should have the influence trends as columns and media outlets as rows." }, { "from": "gpt", "value": "To formulate the table, firstly, I identify {the x-axis tick labels}[[0.200, 0.018, 0.232, 0.048], [0.770, 0.018, 0.799, 0.048]] which represent the row headers or the specific media outlets, BBC and FOX News respectively. For the column headers or the trends, I look at the different {colored bars}[[0.180, 0.072, 0.219, 0.728], [0.228, 0.072, 0.267, 0.710], [0.276, 0.072, 0.316, 0.533], [0.324, 0.072, 0.363, 0.783], [0.372, 0.072, 0.411, 0.571], [0.632, 0.072, 0.672, 0.496], [0.680, 0.072, 0.719, 0.603], [0.728, 0.072, 0.768, 0.322], [0.776, 0.072, 0.815, 0.635], [0.824, 0.072, 0.863, 0.314]] and refer to the {legend}[[0.029, 0.863, 0.108, 0.891], [0.157, 0.863, 0.237, 0.891], [0.295, 0.863, 0.368, 0.891], [0.435, 0.863, 0.506, 0.891], [0.572, 0.863, 0.646, 0.891]] to understand the different trends corresponding to each bar's color, these being Gradual decline, Subdued decline, Volatile movement, Rapid increase, and Declining pattern. The value of each cell can be determined by analyzing the height of each bar in the chart, with respect to the {y-axis tick labels}[[0.015, 0.071, 0.067, 0.093], [0.015, 0.143, 0.067, 0.165], [0.015, 0.215, 0.067, 0.237],[0.015, 0.285, 0.067, 0.308],[0.015, 0.359, 0.067, 0.382],[0.015, 0.430, 0.067, 0.453],[0.015, 0.501, 0.067, 0.524],[0.015, 0.574, 0.067, 0.597],[0.015, 0.645, 0.067, 0.668],[0.015, 0.716, 0.067, 0.739],[0.015, 0.788, 0.067, 0.811]].\nBased on the above thought, the answer is:\nMedia Outlet,Gradual decline,Subdued decline,Volatile movement,Rapid increase,Declining pattern\nBBC,3.6,2.8,2.3,3.5,2.4\nFOX News,2.2,2.5,1.4,2.8,1.5" } ], "id_raw": 79 }, { "id": "DwDePWSpqjRhfQxV", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_4_19.png", "conversations": [ { "from": "human", "value": "\nBased on the information depicted in the bar chart, which media outlet demonstrates a more consistent influence across various trends?" }, { "from": "gpt", "value": "To determine the media outlet demonstrating a more consistent influence across various trends, I would need to compare the variability of their effectiveness ratings across all trends. Observing the {bar chart}, particularly the bars for BBC [[0.180, 0.072, 0.219, 0.728], [0.228, 0.072, 0.267, 0.710], [0.276, 0.072, 0.316, 0.533], [0.324, 0.072, 0.363, 0.783], [0.372, 0.072, 0.411, 0.571]] and Fox News [[0.632, 0.072, 0.672, 0.496], [0.680, 0.072, 0.719, 0.603], [0.728, 0.072, 0.768, 0.322], [0.776, 0.072, 0.815, 0.635], [0.824, 0.072, 0.863, 0.314]], the BBC bars have a closer range of heights indicating a more consistent influence. This consistent influence demonstrated by BBC can also be quantified by comparing their minimum and maximum effectiveness ratings, being 2.3 and 3.6, respectively, compared to FOX News with their minimum and maximum ratings being 1.4 and 2.8, respectively.\nBased on the above thought, the answer is:\nThe media outlet that demonstrates more consistent influence across various trends, according to the bar graph, is BBC. This is due to the smaller range between their highest and lowest effectiveness ratings across the different influence trends, as compared to FOX News." } ], "id_raw": 79 }, { "id": "zmYpU5RdlKMpLs4g", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_4_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest effectiveness rating reached by either media outlet and under what influence trend does it occur?" }, { "from": "gpt", "value": "To determine this, I need to find the highest bar in the bar chart. Observing {the bar chart}[[0.180, 0.072, 0.411, 0.783], [0.632, 0.072, 0.863, 0.635]], I see the maximum bar is for BBC under Rapid increase trend with a height of 3.5 looking at the {y-axis label}[0.324, 0.072, 0.363, 0.783].\nBased on the above thought, the answer is:\nThe highest effectiveness rating reached by either media outlet is 3.5 by BBC under the \"Rapid increase\" trend." } ], "id_raw": 79 }, { "id": "MUld4CyF8M2gO6e1", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_0.png", "conversations": [ { "from": "human", "value": "\nCould you please extract the data from the line chart into a table format with Years in the first column and CO2 Reduction (%) in the second column?" }, { "from": "gpt", "value": "To convert the line chart into a table format, I first look at {the x-axis tick labels}[[0.079, 0.757, 0.149, 0.827], [0.161, 0.757, 0.231, 0.827], [0.243, 0.757, 0.313, 0.827], [0.325, 0.757, 0.395, 0.827], [0.407, 0.757, 0.478, 0.827], [0.489, 0.757, 0.560, 0.827], [0.571, 0.757, 0.642, 0.827], [0.654, 0.757, 0.724, 0.827], [0.736, 0.757, 0.806, 0.827], [0.818, 0.757, 0.888, 0.827], [0.900, 0.757, 0.970, 0.827]], which represent the Years from 2010 to 2020. The corresponding values of CO2 Reduction can be extracted from {the plotted points}[[0.114, 0.728], [0.196, 0.699], [0.278, 0.669], [0.360, 0.664], [0.442, 0.631], [0.525, 0.591], [0.607, 0.459], [0.689, 0.357], [0.771, 0.318], [0.853, 0.249], [0.935, 0.169]] for each Year. The plotted points provide us with the percentage values for CO2 Reduction for each Year.\nBased on the above thought, the answer is:\nYear,CO2 Reduction(%)\n2010,10.5\n2011,13.2\n2012,15.98\n2013,16.4\n2014,19.5\n2015,23.2\n2016,35.5\n2017,45.0\n2018,48.6\n2019,55.0\n2020,62.5" } ], "id_raw": 80 }, { "id": "9eS3VRQJqx0tfdYQ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_0.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, how would you describe the trend of CO2 reduction from 2010 to 2020?" }, { "from": "gpt", "value": "To describe the trend, I need to analyze the line plot for CO2 reduction. Looking at {the plotted line}[[0.114, 0.169, 0.935, 0.728]], it starts at 10.5 in 2010 and ends at 62.5 in 2020, showing a clear overall upward trend. The line seems to be gradually increasing, indicating that the CO2 reduction has been on the rise in these years. However, notable changes in the rate of increase can be noticed starting from the year 2016, where the slope of the line became steeper, suggesting a faster rate of CO2 reduction.\nBased on the above thought, the answer is:\nBased on the chart, CO2 reduction had a marked increasing trend from 2010 to 2020. The growth was gradual initially from 2010 until 2016. However, from 2016 onwards, the rate of CO2 reduction increased at a much faster pace, suggesting significant progress in efforts or policies aimed at reducing CO2 emissions." } ], "id_raw": 80 }, { "id": "k31k8JnCdiEr4f4a", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_0.png", "conversations": [ { "from": "human", "value": "\nWhat was the average yearly progression of CO2 Reduction from 2010 to 2020?" }, { "from": "gpt", "value": "To find the average yearly progression of CO2 Reduction, I need to locate {the line points}[[0.114, 0.728], [0.196, 0.699], [0.278, 0.669], [0.360, 0.664], [0.442, 0.631], [0.525, 0.591], [0.607, 0.459], [0.689, 0.357], [0.771, 0.318], [0.853, 0.249], [0.935, 0.169]] and extract the corresponding percentages: 10.5, 13.2, 15.98, 16.4, 19.5, 23.2, 35.5, 45.0, 48.6, 55.0, 62.5. Now, I calculate the yearly progression by subtracting the percentage of the previous year from the current year for each year from 2011 to 2020. Totalling these gives me the total progression from 2010 to 2020. Dividing this by the number of years (10), I can calculate the average yearly progression.\nBased on the above thought, the answer is:\n~5.2" } ], "id_raw": 80 }, { "id": "FeDNyaESndQtjGBU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_1.png", "conversations": [ { "from": "human", "value": "\nCan you create a table that summarizes the data represented in this chart, with the rows as the distinct years and the columns as vehicle sales in those years?" }, { "from": "gpt", "value": "Sure, I can create a table from the data presented on the line chart. First, I'll identify {the x-axis tick labels}[0.088, 0.903, 0.132, 0.931], [0.173, 0.903, 0.217, 0.931], ..., [0.937, 0.903, 0.981, 0.931] as the row headers, which represent the different years. Then, I'll note down {the y-values for each point}[0.110, 0.599], [0.195, 0.732], ..., [0.959, 0.087] which represent the vehicle sales in those respective years.\nBased on the above thought, the answer is:\nYear,Vehicle Sales\n2000,55000\n2001,48000\n2002,52000\n2003,45000\n2004,59000\n2005,74000\n2006,62000\n2007,67000\n2008,50000\n2009,68000\n2010,82000" } ], "id_raw": 81 }, { "id": "5kpzkmsP7FwlB6wJ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_1.png", "conversations": [ { "from": "human", "value": "\nConsidering the line chart, what can you say about the trend in vehicle sales from the year 2001 to 2010?" }, { "from": "gpt", "value": "To analyze the trend in vehicle sales from 2001 to 2010, I need to closely examine {the line plot}[0.110, 0.599, 0.959, 0.789]. From 2001 to around 2004, there seems to be a dip in sales from 48000 to 45000 after a slight increase. Sales then exhibit a steady increase from 45000 in 2003 to a peak of 74000 in 2005. Following this, sales decrease again to 50000 in 2008 before rising again to 82000 in 2010.\nBased on the above thought, the answer is:\nThe trend in vehicle sales from 2001 to 2010 shows significant fluctuations. Though there are periods of increase, such as from 2003 to 2005, there are also periods of decline, such as from 2005 to 2008. However, the overall sales end in 2010 at a higher point than they were in 2001, indicating long-term growth despite short-term fluctuations." } ], "id_raw": 81 }, { "id": "U9h5VkGNK1x8MV34", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in vehicle sales from the year 2008 to the year 2010?" }, { "from": "gpt", "value": "To calculate the percentage increase in vehicle sales from 2008 to 2010, I would identify the sales values for those years by looking at {the point for the year 2008}[0.789, 0.694] and {the point for the year 2010}[0.959, 0.087]. Their corresponding y-values denote the sales for those years, which are 50000 and 82000 respectively. The percentage increase can be calculated as (Final value - Initial value) / Initial value * 100.\nBased on the above thought, the answer is:\n64%." } ], "id_raw": 81 }, { "id": "vfwnUnFXBPQYgQ5P", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a structured table with years as columns and different types of energy resources as rows?" }, { "from": "gpt", "value": "To do this, I am observing that {the x-axis}[0.100, 0.039, 0.934, 0.157] labels represent the years which would serve as our column headers. The {legend}[0.934, 0.163, 0.998, 0.844] is representing different types of energy resources which will be our row labels. Now for the values inside the table, I need to look at the line points for each resource in the line chart and convert them into respective values.\nBased on the above thought, the answer is:\nResource,2050,2055,2060,2065,2070,2075,2080,2085,2090,2095,2100\nSolar,2.5,5.5,10,12.5,15.5,17.5,21.5,23.5,27,31.5,33.5\nWind,1,2.5,4.5,6,7.5,10,12,15,18.5,20,21.5\nHydro,45,42.5,40,35.5,32,30,26.5,23.5,21,18,15.5\nNuclear,40,35.5,30,25.5,22,19.5,17,14.5,12,10,9.5\nThermal,5.5,6,7,8,9,10.5,11.5,13,14,15.5,16.5\nFossil Fuels,6,8.5,8,8.5,8.5,8.5,7.5,8.5,7.5,7,8" } ], "id_raw": 82 }, { "id": "F62IYiAzltqRxd6D", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_2.png", "conversations": [ { "from": "human", "value": "\nConsidering the trends in the figures, which energy resource is expected to see the most significant rise in allocation from 2050 to 2100?" }, { "from": "gpt", "value": "To answer this question, I will need to compare the starting and ending points for all the energy resources in the line chart. Firstly, looking at {the line for Solar resources}[0.148, 0.401, 0.932, 0.603], it begins from 2.5% allocation and ends at 33.5%. Comparing this change to those of other energy resources such as {Wind}[0.148, 0.425, 0.932, 0.428], {Hydro}[0.148, 0.654, 0.932, 0.350], {Nuclear}[0.148, 0.643, 0.932, 0.408], {Thermal}[0.148, 0.416, 0.932, 0.465], and {Fossil Fuels}[0.148, 0.428, 0.932, 0.430], I observe that the increase in allocation for Solar is the most significant.\nBased on the above thought, the answer is:\nThe Solar energy resource is expected to see the most significant rise in allocation from 2050 to 2100. The line chart shows a consistent increase in the allocation for Solar resources, starting from 2.5% in 2050 and ending at 33.5% in 2100. This change is larger than the changes observed for other energy resources." } ], "id_raw": 82 }, { "id": "fmZdtDihDAaViNK9", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in the allocation percentage for Solar power between 2050 and 2100?" }, { "from": "gpt", "value": "To calculate this, I need to identify {the series of coordinates for the allocation of Solar resources}[0.148, 0.401, 0.932, 0.605]. Now to find the starting allocation in 2050, I look at the coordinates of the starting point {the point representing the year 2050}[0.148, 0.401] which corresponds to 2.5%. Similarly, the coordinates for the point {at 2100}[0.932, 0.603] indicate an allocation of 33.5%. Finding the difference would provide the required answer.\nBased on the above thought, the answer is:\n31%" } ], "id_raw": 82 }, { "id": "vAqHhg1pQZh6suem", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_3.png", "conversations": [ { "from": "human", "value": "\nCan you provide the raw data illustrated in the chart in a structured table format where rows represent different level of Friends_count and columns correspond to their associated Engagement_index?" }, { "from": "gpt", "value": "To construct a table from the line chart that captures the relationship between Friends_count and Engagement_index, I first identify {the x-axis tick labels}[[0.209, 0.906, 0.253, 0.953], [0.422, 0.906, 0.466, 0.953], [0.627, 0.906, 0.686, 0.953], [0.840, 0.906, 0.899, 0.953]] for the column 'Friends_count'. Then, I capture data points on the line for the \"Engagement_index\", which are represented by {the points on the line}[[0.125, 0.879], [0.231, 0.775], [0.338, 0.653], [0.444, 0.532], [0.550, 0.362], [0.657, 0.389], [0.763, 0.213], [0.869, 0.180], [0.900, 0.164]]. Corresponding to each 'Friends_count', there is an 'Engagement_index' value, thus forming rows of the table.\nBased on the above thought, the answer is:\nFriends_count,Engagement_index\n64,3.2\n128,5.1\n256,7.3\n512,9.5\n1024,12.6\n2048,12.1\n4096,15.3\n8192,15.9\n10000,16.2" } ], "id_raw": 83 }, { "id": "KwT48UZMqSXKaN62", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_3.png", "conversations": [ { "from": "human", "value": "\nCan you describe the trend of the Engagement_index based on Friends_count observed in the line chart?" }, { "from": "gpt", "value": "By examining the trend line {the points on the line}[[0.125, 0.879], [0.231, 0.775], [0.338, 0.653], [0.444, 0.532], [0.550, 0.362],[0.657, 0.389],[0.763, 0.213],[0.869, 0.180], [0.900, 0.164]], it is clear that as the Friends_count increases, the Engagement_index generally increases, depicting a positive correlation. There is however a slight decrease between Friends_count 1024 and 2048. After Friends_count of 2048, Engagement_index keeps rising. Hence, the trend shows that more the Friends_count, higher is the Engagement_index, although there can be periodical dips.\nBased on the above thought, the answer is:\nThe trend line in the chart indicates a direct correlation between Friends_count and Engagement_index - as Friends_count increases, Engagement_index frequently increases as well. There exists an occasional dip in the trend, such as in the segment between Friends_count 1024 and 2048. However, after a drop, the trend continues to rise. The pattern suggests that higher Friends_count typically correspond to a higher level of Engagement_index, but it doesn't rule out the existence of periodical reductions in Engagement_index." } ], "id_raw": 83 }, { "id": "Uv75Ot4CCDBfcU2n", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of Engagement_index as shown in the line plot?" }, { "from": "gpt", "value": "To answer this question, I first locate the highest and lowest Engagement_index values in the chart. By extracting the Engagement_index values for all data points on the line {[0.125, 0.879], [0.231, 0.775], [0.338, 0.653], [0.444, 0.532], [0.550, 0.362], [0.657, 0.389], [0.763, 0.213], [0.869, 0.180], [0.900, 0.164]}, I find the range. The maximum index is 16.2, corresponding to the data point [0.900, 0.164], and the minimum is 3.2 corresponding to the data point [0.125, 0.879]. Hence, the range is 16.2 - 3.2 = 13.\nBased on the above thought, the answer is:\n13." } ], "id_raw": 83 }, { "id": "ZEQED46JcUraQFR9", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_4.png", "conversations": [ { "from": "human", "value": "\nCan you create a table from the bar chart data that represents the growth percentage in global trade for the five major countries over the years?" }, { "from": "gpt", "value": "To generate a table from the bar chart data, I first identify the countries on {the y-axis labels}[0.07, 0.265, 0.16, 0.295], [0.07, 0.400, 0.12, 0.430], [0.07, 0.545, 0.14, 0.575], [0.07, 0.655, 0.15, 0.685], [0.07, 0.810, 0.12, 0.840] which represent the row headers. The length of each {horizontal bar}[0.15, 0.23, 0.950, 0.320], [0.15, 0.37, 0.890, 0.460], [0.15, 0.510, 0.390, 0.600], [0.15, 0.645, 0.460, 0.735], [0.15, 0.790, 0.535, 0.880] and its location with respect to the origin of the graph provides the value of growth percentage. Positive growth lies on the right side of the origin while negative growth lies on the left.\nBased on the above thought, the answer is:\nCountries, Growth Percentage\nChina, 80\nU.S., 74\nGermany, -16\nAustralia, 31\nJapan, 38" } ], "id_raw": 84 }, { "id": "CTHWP21IEp65sCp6", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_4.png", "conversations": [ { "from": "human", "value": "\nCan you deduce the overall trend of global trade over the years, based on the information provided in the bar chart?" }, { "from": "gpt", "value": "Based on the {horizontal bars}[0.15, 0.23, 0.950, 0.320], [0.15, 0.37, 0.890, 0.460], [0.15, 0.510, 0.390, 0.600], [0.15, 0.645, 0.460, 0.735], [0.15, 0.790, 0.535, 0.880], we can see that the majority of the represented countries show positive growth in global trade. China and the U.S., two of the biggest economies in the world, show considerable positive growth. This could indicate a more globalized economy and a healthier global trade network despite some countries like Germany showing negative growth.\nBased on the above thought, the answer is:\nThe overall trend of global trade seems to be positive based on the bar chart, with the majority of the represented countries showing an increase in cargo movement indicative of positive growth in global trade. This suggests a healthy global trade pattern with an increasing globalization trend. Yet, the negative growth from some countries signifies that the positive outcome is not uniformly distributed and there might be sectors or regions experiencing decline." } ], "id_raw": 84 }, { "id": "dxuksvEjOJQyzmX6", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_4.png", "conversations": [ { "from": "human", "value": "\nWhich country had the highest growth percentage in global trade according the bar chart?" }, { "from": "gpt", "value": "To determine the country with highest growth percentage, I need to identify the longest {horizontal bar}[0.15, 0.23, 0.950, 0.320]. Based on the chart this bar corresponds to China as seen from the {y-axis label}[0.07, 0.265, 0.16, 0.295].\nBased on the above thought, the answer is:\nChina." } ], "id_raw": 84 }, { "id": "pYLLa391TrmGlxIt", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data points regarding the trend in Intrusion Detection percentage over the years into a structured table?" }, { "from": "gpt", "value": "Sure. I'll first identify {the x-axis tick labels}[[0.041, 0.861, 0.076, 0.931], [0.045, 0.861, 0.080, 0.931], [0.052, 0.861, 0.087, 0.931], [0.066, 0.861, 0.101, 0.931], [0.095, 0.861, 0.130, 0.931], [0.152, 0.861, 0.187, 0.931], [0.266, 0.861, 0.301, 0.931], [0.494, 0.861, 0.529, 0.931], [0.950, 0.861, 0.985, 0.931]], which represent the Years. Next, I identify the y-values from the data corresponding to {the line points}[[0.059, 0.687], [0.062, 0.660], [0.070, 0.496], [0.084, 0.304], [0.112, 0.249], [0.169, 0.276], [0.283, 0.194], [0.511, 0.085], [0.968, 0.852]] labelled as Intrusion Detection percentages. Thus, we can create the following table:\nBased on the above thought, the answer is:\nYears,Intrusion_Detection_Percentage\n2012,76\n2013,77\n2014,83\n2015,90\n2016,92\n2017,91\n2018,94\n2019,98\n2020,70" } ], "id_raw": 85 }, { "id": "x6jbF2TQIqwcC9DW", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you briefly describe the trend of the Intrusion Detection rate over the years?" }, { "from": "gpt", "value": "Observing {the line plot}[[0.059, 0.085, 0.968, 0.852]], I can see that the Intrusion Detection rate generally increased from 2012 to 2019 before falling significantly in 2020. The steepest growth happened between 2014 {2014}[0.070, 0.496] and 2015 {2015}[0.084, 0.304], where the rate increased from 83 to 90. However, the most significant drop happened between 2019 {2019}[0.511, 0.085] and 2020 {2020}[0.968, 0.852], where the rate fell from 98 to 70.\nBased on the above thought, the answer is:\nThe trend of the Intrusion Detection rate over the years was mostly increasing, especially between 2014 to 2015, reaching a peak in 2019. However, in 2020 a significant drop occurred in the detection rate, reducing the rate back to 70, which is close to the value of the year 2012." } ], "id_raw": 85 }, { "id": "jw6s90ZBTyu0ShrM", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the total percentage increase in Intrusion Detection rate from 2012 to 2020?" }, { "from": "gpt", "value": "To discover the total percentage increase in Intrusion Detection rate, I need to find the values of Intrusion Detection rate in 2012 and 2020. From the chart, I identify that the Intrusion Detection rate in 2012 was given by the point {2012}[0.059, 0.687] having the value 76. Similarly, the Intrusion Detection rate in 2020 was provided by the point {2020}[0.968, 0.852] having the value 70. Thus, the difference between the two is 70 - 76, which is -6.\nBased on the above thought, the answer is:\n-6" } ], "id_raw": 85 }, { "id": "kGZoHtk6RbsycqWa", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_6.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, could you form a structured table showcasing the satisfaction level for each category?" }, { "from": "gpt", "value": "To form the structured data, I'll start by identifying {the x-axis tick labels}[[0.036, 0.796, 0.127, 0.887], [0.142, 0.796, 0.226, 0.881], [0.251, 0.796, 0.322, 0.868], [0.354, 0.796, 0.424, 0.867], [0.432, 0.796, 0.551, 0.916], [0.566, 0.796, 0.622, 0.853], [0.633, 0.796, 0.760, 0.923], [0.773, 0.796, 0.825, 0.848], [0.824, 0.796, 0.979, 0.951]], as categories; and then, for each category, I'll find and collect the corresponding {value points}[[0.081, 0.213], [0.184, 0.405], [0.286, 0.482], [0.389, 0.329], [0.491, 0.137], [0.594, 0.559], [0.696, 0.405], [0.799, 0.252], [0.901, 0.521]].\nBased on the above thought, the answer is:\nCategories,Satisfaction Level (%)\nHealthcare,85\nEducation,80\nSecurity,78\nHousing,82\nSocial Services,87\nRoads,76\nPublic Transport,80\nParks,84\nWaste Management,77" } ], "id_raw": 86 }, { "id": "DT4P6g1hZIeuj4Kj", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_6.png", "conversations": [ { "from": "human", "value": "\nLooking at the satisfaction levels, which category seems to be performing the best and which category seems to be performing the worst?" }, { "from": "gpt", "value": "To identify the best and worst performing categories, I look at the highest and lowest points on the line plot. The highest point is {Social Services}[0.491, 0.137] holding a satisfaction level of 87, indicating that it's the best performing category. The lowest point is {Roads}[0.594, 0.559], with a satisfaction level of 76, indicating that it's the worst-performing category.\nBased on the above thought, the answer is:\nThe 'Social Services' category seems to be performing the best with a satisfaction level of 87. Conversely, the 'Roads' category seems to be performing the worst with a satisfaction level of 76." } ], "id_raw": 86 }, { "id": "a1h8fgBFq6Ln9mie", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_6.png", "conversations": [ { "from": "human", "value": "\nWhat's the difference between the satisfaction level of 'Healthcare' and 'Waste Management'?" }, { "from": "gpt", "value": "I would first locate the {Satisfaction Level for 'Healthcare'}[0.081, 0.213] which is 85, and then locate the {Satisfaction Level for 'Waste Management'}[0.901, 0.521] which is 77. The difference between these two values is 85 - 77 = 8.\nBased on the above thought, the answer is:\n8." } ], "id_raw": 86 }, { "id": "DiPPFqCx5IjMifIb", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_7.png", "conversations": [ { "from": "human", "value": "\nCan you summarize the data from the line graph in a CSV format?" }, { "from": "gpt", "value": "To extract the data from the line chart into a table, I need to take the {x-axis} representing the 'Age Groups'[0.457, 0.927, 0.580, 0.963] as rows and 'Stress Level'[0.025, 0.364, 0.048, 0.573] given on the Y-axis as columns. I will locate {the points on the line}[0.095, 0.093, 0.942, 0.777] and read off the corresponding stress level for each age group.\nBased on the above thought, the answer is:\nAge Group,Stress Level\n20-30,52.5\n30-40,72.9\n40-50,75.1\n50-60,78.4\n60-70,63.2\n70-80,50.7\n80-90,31.2" } ], "id_raw": 87 }, { "id": "4RZkvoOyEvtMeghb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_7.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the trend of stress level as people age based on this line chart? Can this line chart suggest any causation relationships?" }, { "from": "gpt", "value": "The line {chart}[0.095, 0.093, 0.942, 0.777] shows a clear trend in stress levels as people age. The stress level seems to rise steadily as people move from the '20-30' age group to their 60's. But a prominent drop in stress levels starts to happen as they age from '60-70'[0.659, 0.313] onwards, hitting the most reduced stress level at the '80-90' age range[0.942, 0.777]. However, it's important to note that this chart only shows a correlation between age and stress levels, not causation. There can be multiple factors affecting the stress level of different age groups, and age itself might just be a part of it.\nBased on the above thought, the answer is:\nOverall, the trend of stress level as seen in the line chart suggests that stress generally increases as people age until their 60's, then it starts to decline drastically from their 60's onwards to the age of 90. Despite this correlation shown in the chart, it does not necessarily imply causation. It's not guaranteed that aging will lead to the respective increase and decrease in stress as shown in the graph. There could be various other factors involved, unaccounted for in this data." } ], "id_raw": 87 }, { "id": "nggrZTh8fvXajOIM", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_7.png", "conversations": [ { "from": "human", "value": "\nHow much has the stress level dropped from the '60-70' age group to the '80-90' age group?" }, { "from": "gpt", "value": "To calculate the decrease in the stress level, I'd first locate the position of the '60-70' group's stress level {on the line}[0.659, 0.313] which is 63.2. Then I'd find the '80-90' group's stress level {on the line}[0.942, 0.777] which is 31.2. The decrease in stress level is then calculated as 63.2 - 31.2.\nBased on the above thought, the answer is:\n32" } ], "id_raw": 87 }, { "id": "ulZaJylVucy7BzKE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_8.png", "conversations": [ { "from": "human", "value": "\nBased on the GDP growth line chart, could you create a table that lists the years in ascending order and the corresponding GDP growth rate?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.042, 0.919, 0.077, 0.942], [0.133, 0.919, 0.168, 0.942], [0.224, 0.919, 0.259, 0.942], [0.315, 0.919, 0.350, 0.942], [0.405, 0.919, 0.440, 0.942], [0.496, 0.919, 0.531, 0.942], [0.587, 0.919, 0.622, 0.942], [0.678, 0.919, 0.713, 0.942], [0.768, 0.919, 0.804, 0.942], [0.859, 0.919, 0.894, 0.942], [0.950, 0.919, 0.985, 0.942]] which represent the years to be listed in the table. The corresponding GDP growth rates are the y-values from {the growth rate data points on the line plot}[[0.060, 0.310], [0.105, 0.379], [0.151, 0.360], [0.196, 0.310], [0.241, 0.190], [0.287, 0.259], [0.332, 0.215], [0.377, 0.095], [0.423, 0.398], [0.468, 0.772], [0.514, 0.335], [0.559, 0.386], [0.604, 0.367], [0.650, 0.405], [0.695, 0.354], [0.741, 0.398], [0.786, 0.436], [0.831, 0.405], [0.877, 0.354], [0.922, 0.436], [0.968, 0.867]] and are extracted via corresponding text labels.\nBased on the above thought, the answer is:\nYear, GDP Growth Rate\n2000, 4.5\n2001, 3.4\n2002, 3.7\n2003, 4.5\n2004, 6.4\n2005, 5.3\n2006, 6.0\n2007, 7.9\n2008, 3.1\n2009, -2.8\n2010, 4.1\n2011, 3.3\n2012, 3.6\n2013, 3.0\n2014, 3.8\n2015, 3.1\n2016, 2.5\n2017, 3.0\n2018, 3.8\n2019, 2.5\n2020, -4.3" } ], "id_raw": 88 }, { "id": "6W2NtLK3CTIdjwDr", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_8.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart of GDP growth, how would you describe the pattern of economic growth from 2000 to 2020?" }, { "from": "gpt", "value": "Starting from {2000}[0.042, 0.919, 0.077, 0.942], the line plot showing GDP growth rate appears to have a generally fluctuating trend, with {the peak at 7.9}[0.377, 0.095] in {2007}[0.377, 0.919, 0.413, 0.942]. From {2008}[0.405, 0.919, 0.440, 0.942] through to {2020}[0.950, 0.919, 0.985, 0.942], the trend seems to include a significant recession period where the GDP growth dropped below 0, eventually beginning to recover although not yet reaching the prior peak level.\nBased on the above thought, the answer is:\nAccording to the line chart of GDP growth, the economy experienced fluctuations from 2000 to 2020. The period started with GDP growth rates around 4 to 5%, peaked at 7.9% in 2007, experienced a major recession in 2009 with a GDP growth rate at -2.8%, and began to recover subsequently albeit at lower rates compared to the earlier part of the period." } ], "id_raw": 88 }, { "id": "fGViGkHT12kom1B7", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_8.png", "conversations": [ { "from": "human", "value": "\nWhat was the year with the greatest GDP growth rate?" }, { "from": "gpt", "value": "To find the year with the greatest GDP growth, I can look closely at the points on the line plot. The highest point is {7.9}[0.377, 0.095], which corresponds to the year {2007}[0.377, 0.919, 0.413, 0.942] indicated by the x-axis label.\nBased on the above thought, the answer is:\n2007." } ], "id_raw": 88 }, { "id": "zkUoXVUbSTUvKIiz", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_9.png", "conversations": [ { "from": "human", "value": "\nCan you provide the data used for creating the bar graph in table format with initiatives as rows?" }, { "from": "gpt", "value": "To formulate the data as a table, I first identify {the x-axis tick labels}[[0.073, 0.888, 0.151, 0.931], [0.190, 0.888, 0.263, 0.931], [0.296, 0.888, 0.386, 0.908], [0.419, 0.888, 0.493, 0.931], [0.514, 0.888, 0.629, 0.931], [0.639, 0.888, 0.733, 0.931], [0.762, 0.888, 0.840, 0.931], [0.856, 0.888, 0.976, 0.931]] indicating the rows, which are the community development and urban planning initiatives. Then I look for {the points of the bars}[[0.112, 0.482], [0.226, 0.256], [0.341, 0.313], [0.456, 0.539], [0.571, 0.256], [0.686, 0.200], [0.801, 0.143], [0.916, 0.652]] to get the participation percentages associated with each initiative.\nBased on the above thought, the answer is:\nCommunity Development and Urban Planning Initiatives,Participation Percentage (%)\nGreen Space Expansion,55\nCity Cycling Initiatives,75\nUrban Farming,70\nArt in Public Spaces,50\nCommunity Health Programs,75\nNeighbourhood Revitalization,80\nSmart Traffic Systems,85\nWater Conservation Programs,40" } ], "id_raw": 89 }, { "id": "RPuluYR1xeyrHrQd", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_9.png", "conversations": [ { "from": "human", "value": "\nAccording to the data, which initiative seems to be the most successful in terms of community participation and which one is the least successful? Explain your reasoning." }, { "from": "gpt", "value": "To determine which initiative is the most and the least successful in terms of community participation, I need to look for the highest and the lowest values on the bar graph. From my analysis, {the bar for the Smart Traffic Systems}[0.112, 0.0, 0.801, 0.143] represents the highest value which is 85% and suggests that this initiative is the most successful. On the other hand, {the bar for the Water Conservation Programs}[0.112, 0.0, 0.71, 0.916] represents the lowest value which is 40%, indicating that this initiative is the least successful.\nBased on the above thought, the answer is:\nAccording to the data, the Smart Traffic Systems initiative seems to be the most successful in terms of community participation as it has the highest participation percentage of 85%. On the other hand, the Water Conservation Programs initiative is the least successful, having the lowest participation percentage of 40%. The figures suggest a strong interest and engagement in smart traffic systems, whereas water conservation programs might require more community outreach or possibly a re-evaluation of the program's design and execution to boost community participation." } ], "id_raw": 89 }, { "id": "dHqcpk1BWowQt9Dx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest participation percentage?" }, { "from": "gpt", "value": "To find the difference, I need to look for the highest and the lowest points on the bar graph, which are {the point for Smart Traffic Systems}[[0.801, 0.143]] with 85% participation and {the point for Water Conservation Programs}[[0.916, 0.652]] with 40% participation. The difference would be 85 - 40 = 45%.\nBased on the above thought, the answer is:\n45%." } ], "id_raw": 89 }, { "id": "rJBaSd0x4T57XtiD", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_10.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart into a table format, with the Months as columns and the Business Performance types as rows?" }, { "from": "gpt", "value": "To extract the data into a tabular format, first, I analyze the x-axis tick labels, which I see are {the x-axis tick labels}[[0.220, 0.899, 0.283, 0.928], [0.341, 0.899, 0.403, 0.928], [0.462, 0.899, 0.524, 0.928], [0.583, 0.899, 0.645, 0.928], [0.704, 0.899, 0.766, 0.928], [0.833, 0.899, 0.879, 0.928]]. These represent the columns (months). Next, since there is no legend, I identify the different lines as different Business Performance types and rows in the table. For each business performance type, I locate the points for the line, for example, {the line points for the green line}[[0.251, 0.183, 0.856, 0.788]] and map them to the respective months to populate the values for that particular type.\nBased on the above thought, the answer is:\nType,18-24,25-34,35-44,45-54,55-64,65+\nAbrupt Increase,10000,12000,13500,15500,17500,20500\nErratic Behavior,15000,9000,16000,8000,16000,35000\nSluggish Progress,6000,6200,6400,6600,6800,7000\nSubdued Expansion,7000,7200,7600,8000,8500,9200" } ], "id_raw": 90 }, { "id": "oO9ZT9M6Ss5xPB96", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_10.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which business performance type exhibits the most volatility?" }, { "from": "gpt", "value": "In order to determine the most volatile business performance type, I look at the fluctuations in line plots. For the line of {\"Abrupt Increase\"}[0.251, 0.183], I see a steady positive trend going upwards. For the {\"Subdued Expansion\"} and {\"Sluggish Progress\"} lines, the progression is very slow and minor. The {\"Erratic behavior\"} line, however, exhibits significant volatility with its points fluctuating randomly both up and down, suggesting a high degree of uncertainty in business performance.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, the Erratic Behavior business performance type exhibits the most volatility. It undergoes significant fluctuations, indicating a high degree of uncertainty and inconsistency in performance over time." } ], "id_raw": 90 }, { "id": "C5OU8hyRW0zJJ3ud", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum value for the Erratic Behavior business performance type?" }, { "from": "gpt", "value": "To calculate the maximum value for Erratic Behavior, I need to identify the y-values of the points on {the line representing Erratic Behavior}[0.251, 0.183, 0.856, 0.788]. After analyzing the points, I see the highest point corresponds to \"65+\" on the x-axis, with a y-value of 35000.\nBased on the above thought, the answer is:\n35000." } ], "id_raw": 90 }, { "id": "I2IqquOcohYbk4tX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_11.png", "conversations": [ { "from": "human", "value": "\nCould you generate a structured table that lists the named companies and their respective net profit?" }, { "from": "gpt", "value": "Looking at the line chart, I can see {the x-axis tick labels}[[0.093, 0.849, 0.131, 0.912], [0.173, 0.849, 0.218, 0.923], [0.251, 0.849, 0.306, 0.940], [0.333, 0.849, 0.390, 0.942], [0.420, 0.849, 0.470, 0.932], [0.514, 0.849, 0.542, 0.895], [0.590, 0.849, 0.632, 0.919], [0.676, 0.849, 0.712, 0.909], [0.766, 0.849, 0.789, 0.887], [0.846, 0.849, 0.875, 0.897], [0.928, 0.849, 0.959, 0.901]], which are the names of the companies. I then locate {the line points}[[0.112, 0.573], [0.195, 0.128], [0.278, 0.549], [0.362, 0.541], [0.445, 0.659], [0.528, 0.557], [0.611, 0.175], [0.694, 0.609], [0.777, 0.446], [0.860, 0.662], [0.943, 0.703]] and their corresponding values which represent the net profit of each company.\nBased on the above thought, the answer is:\nCompany,Net Profit\nApple,59.53\nGoogle,92.95\nMicrosoft,61.27\nFacebook,61.94\nAmazon,53.02\nIBM,60.7\nOracle,89.43\nCisco,56.8\nHP,69.02\nDell,52.81\nIntel,49.76" } ], "id_raw": 91 }, { "id": "MBwWgscS0dJ1TZc3", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_11.png", "conversations": [ { "from": "human", "value": "\nCan you identify the company with the third highest net profit based on the given data?" }, { "from": "gpt", "value": "To find the third highest net profit, I have to sort the net profit values held by each company, which are presented as {the line points}[[0.112, 0.573], [0.195, 0.128], [0.278, 0.549], [0.362, 0.541], [0.445, 0.659], [0.528, 0.557], [0.611, 0.175], [0.694, 0.609], [0.777, 0.446], [0.860, 0.662], [0.943, 0.703]]. The company with the third highest net profit is located at {the point for IBM}[0.528, 0.557], corresponding to a net profit of 60.7 billion dollars.\nBased on the above thought, the answer is:\nIBM, with a net profit of 60.7 billion dollars, holds the third highest net profit among the given companies." } ], "id_raw": 91 }, { "id": "R2Px0phjQUBhFyux", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the largest net profit amongst the companies?" }, { "from": "gpt", "value": "To find the largest net profit value, I need to examine all {the line points}[[0.112, 0.573], [0.195, 0.128], [0.278, 0.549], [0.362, 0.541], [0.445, 0.659], [0.528, 0.557], [0.611, 0.175], [0.694, 0.609], [0.777, 0.446], [0.860, 0.662], [0.943, 0.703]] and their corresponding values. The highest point in the line chart corresponds to {the line point for Google}[0.195, 0.128] with the highest value of 92.95.\nBased on the above thought, the answer is:\n92.95 billion dollars." } ], "id_raw": 91 }, { "id": "mKeB6YWMnsab8BKS", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_12.png", "conversations": [ { "from": "human", "value": "\nBased on the provided bar chart, can you organize the data into a table format that shows the Companies and their corresponding Market Share?" }, { "from": "gpt", "value": "To generate the tabular data from this bar chart, I need to first identify {the x-axis tick labels}[[0.070, 0.843, 0.108, 0.907], [0.128, 0.843, 0.184, 0.936], ..., [0.899, 0.843, 0.943, 0.918]], which represent the company names. Next, I need to locate {the bar points of each company}[[0.108, 0.622], ..., [0.943, 0.784]] and extract their corresponding y-values as market shares.\nBased on the above thought, the answer is:\nCompanies,Market Share (%)\nApple,4.3\nSamsung,15.3\nHuawei,14.6\nXiaomi,9.2\nVivo,8.3\nOppo,8.1\nRealme,3.0\nLenovo,2.7\nLG,2.2\nMotorola,2.1\nOnePlus,1.2\nGoogle,0.9" } ], "id_raw": 92 }, { "id": "CZZXkUuuY1YAVz0G", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_12.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which company seems to have a significant edge over others in the market? Explain your reasoning." }, { "from": "gpt", "value": "To determine the company with a significant edge in the market, I need to compare the market shares of the companies. Looking at {the bar for Samsung}[0.184, 0.099], its market share at 15.3% is the highest. This is closely followed by {Huawei}[0.260, 0.132] with 14.6%, but all the other companies have considerably lower market shares.\nBased on the above thought, the answer is:\nBased on the bar chart, Samsung seems to have a significant edge over other companies. Although Huawei comes close, Samsung still holds the highest market share, implying that they have a larger segment of customers and thereby a more substantial presence in the market than other competitors." } ], "id_raw": 92 }, { "id": "5mkYhL2yjGOHaU1n", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the cumulative market share of the top 3 companies?" }, { "from": "gpt", "value": "To calculate the cumulative market share of the top 3 companies, I need to add the market shares of the three companies with the highest shares. These companies are {Samsung}[0.184, 0.099], {Huawei}[0.260, 0.132], and {Xiaomi}[0.336, 0.389], with market shares of 15.3%, 14.6%, and 9.2%, respectively. Adding these shares together, we get a total of 15.3% + 14.6% + 9.2% = 39.1%.\nBased on the above thought, the answer is:\n39.1%" } ], "id_raw": 92 }, { "id": "RNPktLWSWz1bcrBQ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_13.png", "conversations": [ { "from": "human", "value": "\nCan you structure the data in a table with the growth of each scientific field as a percentage for each year?" }, { "from": "gpt", "value": "The chart does not provide the data for each year, rather it gives the overall percentage growth for each field. Based on the graphical representation, I can infer that the percentages provided correspond to the growth from the beginning to the end of a certain time period. The {y-axis tick labels}[[0.066, 0.088, 0.092, 0.107], [0.062, 0.276, 0.091, 0.289],[0.057, 0.357, 0.085, 0.370], [0.051, 0.522, 0.078, 0.535], [0.046, 0.711, 0.072, 0.724], [0.041, 0.794, 0.064, 0.807]] correspond to the different fields of scientific research and technological innovation. The corresponding percentage gains are indicated by {line points}[[0.119, 0.081], [0.168, 0.267], [0.216, 0.352], [0.278, 0.513], [0.398, 0.703], [0.472, 0.786]], from which I can extract the corresponding values such as Neuroscience showing a moderate decline of 20.3%, Robotics showing a sustained growth of 15.8%, and so on.\nBased on the above thought, the answer is:\nField,Growth\nNeuroscience,-20.3\nRobotics,15.8\nAstronomy,19.1\nBiotechnology,-13.5\nNanotechnology,7.3" } ], "id_raw": 93 }, { "id": "zaV3Ju9V95ooDY3s", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_13.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which scientific research field has experienced the most growth?" }, { "from": "gpt", "value": "For the scientific field with the most growth, I need to look for the line with the highest point on the x-axis. The {line for Astronomy} [0.216, 0.352] corresponds to a value of 19.1% which is the greatest among all other fields. This indicates that Astronomy has seen the most growth.\nBased on the above thought, the answer is:\nAccording to the line chart, Astronomy has experienced the most growth among the scientific research fields. It shows a large increase of 19.1%, which is higher compared to the growth in Nanotechnology, Biotechnology, Robotics and the decline in Neuroscience." } ], "id_raw": 93 }, { "id": "B0dLtDpEwepGhTC0", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the average growth of the scientific research fields as presented in the chart?" }, { "from": "gpt", "value": "To find the average growth of these scientific fields, I'd have to locate {the line points}[[0.119, 0.081], [0.168, 0.267], [0.216, 0.352], [0.278, 0.513], [0.398, 0.703], [0.472, 0.786]] which represent the overall percentage change for each field. The average growth is given by (7.3 - 13.5 + 19.1 + 15.8 - 20.3) / 5.\nBased on the above thought, the answer is:\n1.68." } ], "id_raw": 93 }, { "id": "fLaz7UyAV7opuPfL", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_14.png", "conversations": [ { "from": "human", "value": "\nCan you arrange the data represented in the horizontal bar chart into a tabular format with 'AI Applications' as rows and 'Successful Implementation (%)' as the column?" }, { "from": "gpt", "value": "Sure, to create this table, we first define the rows according to the AI applications listed as {x-axis labels}[[0.102, 0.757, 0.213, 0.827], [0.187, 0.757, 0.461, 0.827], [0.287, 0.757, 0.693, 0.827], [0.527, 0.757, 0.787, 0.827], [0.675, 0.757, 0.972, 0.827]]. The column header would be 'Successful Implementation (%)'. The values for each row can be extracted from the length of {the bars on the chart}[[0.157, 0.639, 0.438, 0.663], [0.157, 0.545, 0.492, 0.569], [0.157, 0.451, 0.414, 0.475], [0.157, 0.357, 0.396, 0.381], [0.157, 0.263, 0.502, 0.287]] and their corresponding values from the {y-axis labels}[[0.069, 0.554, 0.104, 0.624], [0.069, 0.491, 0.104, 0.561], [0.069, 0.428, 0.104, 0.498], [0.069, 0.364, 0.104, 0.434], [0.069, 0.301, 0.104, 0.371], [0.069, 0.238, 0.104, 0.308], [0.069, 0.174, 0.104, 0.244]], which represent percentages.\nBased on the above thought, the answer is:\nAI Applications,Successful Implementation (%)\nChatbot,60\nSpeech Recognition,80\nNatural Language Processing,72\nImage Recognition,65\nAutonomous Vehicles,90" } ], "id_raw": 94 }, { "id": "HH0AePsha2tc26sY", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_14.png", "conversations": [ { "from": "human", "value": "\nComparing all the AI applications in the chart, which one shows the highest successful implementation and which one the lowest?" }, { "from": "gpt", "value": "The bars in the chart correspond to the successful implementation percentages of different AI applications. By comparing the lengths of these bars: {Chatbot}[[0.157, 0.639, 0.438, 0.663]], {Speech Recognition}[[0.157, 0.545, 0.492, 0.569]], {Natural Language Processing}[[0.157, 0.451, 0.414, 0.475]], {Image Recognition}[[0.157, 0.357, 0.396, 0.381]], {Autonomous Vehicles}[[0.157, 0.263, 0.502, 0.287]] and their corresponding values from the y-axis labels, it's clear that the Autonomous Vehicles application has the longest bar, indicating the highest successful implementation percentage (90%). On the other hand, Chatbot has the shortest bar, indicating the lowest successful implementation percentage (60%).\nBased on the above thought, the answer is:\nAutonomous Vehicles shows the highest successful implementation percentage with 90%. On the other hand, Chatbot has the lowest successful implementation with a percentage of 60%." } ], "id_raw": 94 }, { "id": "HDTzFq2Y6DwquB7E", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the successful implementation percentages of Autonomous Vehicles and Chatbot?" }, { "from": "gpt", "value": "To determine the difference, I need to find the Successful Implementation Percentages of both AI applications. For {Autonomous Vehicles}[[0.675, 0.757, 0.972, 0.827]], I locate corresponding bar on chart and find its value from the y-axis which appears to be 90. Similarly, I locate the bar for {Chatbot}[[0.102, 0.757, 0.213, 0.827]] with corresponding value of 60. Now, we subtract Chatbot's implementation percentage from Autonomous Vehicles's implementation percentage which is 90 - 60 = 30.\nBased on the above thought, the answer is:\n30." } ], "id_raw": 94 }, { "id": "gM72eh3rcv7MMiQK", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_15.png", "conversations": [ { "from": "human", "value": "\nAccording to the horizontal bar chart, can you extract the data into a table format with Policies as rows and Effectiveness Ratings as columns?" }, { "from": "gpt", "value": "To generate the tabular data from the horizontal bar chart, I first look at {the y-axis tick labels} [[0.345, 0.24], [0.449, 0.34], [0.553, 0.44], [0.657, 0.54], [0.761, 0.64]], which represent the row labels or Policies. Next, I identify the bar lengths to extract values representing the Effectiveness Ratings. For instance, the point {Corporate Tax Cuts} [0.345, 0.36] has a black bar stretching to x=0.50, corresponding to a rating of 10. Similarly, the point {Climate Change Policies} [0.449, 0.46] has a red bar stretching to x=0.25, corresponding to a rating of -6.\nBased on the above thought, the answer is:\nPolicies, Effectiveness Ratings\nCorporate Tax Cuts, 10\nClimate Change Policies, -6\nHealth Care Reform, 5\nEducation Funding, 12\nGun Control Policies, -8" } ], "id_raw": 95 }, { "id": "q84w8YB5AwYyLDMM", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_15.png", "conversations": [ { "from": "human", "value": "\nBased on the horizontal bar chart, which policy appears to be the least popular among the public based on its effectiveness rating? Justify your answer." }, { "from": "gpt", "value": "To identify the least popular policy, I need to find the policy with the lowest effectiveness rating. I notice that the {Gun Control Policies} [0.761, 0.76] has a red bar that extends to x=0.20, indicating a negative evaluation of -8, which is the most negative or lowest among all the ratings.\nBased on the above thought, the answer is:\nAccording to the chart, the Gun Control Policies appears to be the least popular among the public. The negative evaluation of -8, represented by the red bar, indicates a low effectiveness rating, suggesting that the policy is not perceived favorably when it comes to its effectiveness." } ], "id_raw": 95 }, { "id": "Qo03EUS3tTFsqfzR", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the total effectiveness rating of all the policies combined?" }, { "from": "gpt", "value": "To find the total effectiveness rating, I need to add up all the ratings from each policy. I locate each policy's bar length and extract the value, such as {Corporate Tax Cuts} [0.345, 0.36] at 10, {Climate Change Policies} [0.449, 0.46] at -6, {Health Care Reform} [0.553, 0.56] at 5, {Education Funding} [0.657, 0.66] at 12 and {Gun Control Policies} [0.761, 0.76] at -8, then I add these values together.\nBased on the above thought, the answer is:\n13." } ], "id_raw": 95 }, { "id": "UPrlwMlQw7niPbRt", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_16.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a tabular format? The rows should be 'Size of Training Data (No. of Data Points)' and the column should be 'AI Model Accuracy (%)'." }, { "from": "gpt", "value": "To generate the tabular data from this line chart, I first look at {the x-axis tick labels}[[0.201, 0.867, 0.228, 0.914], [0.411, 0.867, 0.439, 0.914], [0.622, 0.867, 0.649, 0.914], [0.832, 0.867, 0.859, 0.914]], which represent the row headers. The column header is defined by {the y-axis label}[[0.025, 0.258, 0.048, 0.648]]. To populate the values, I locate {the points on the line}[[0.171, 0.849], [0.215, 0.700], [0.320, 0.584], [0.425, 0.440], [0.530, 0.383], [0.635, 0.300], [0.741, 0.236], [0.846, 0.204], [0.951, 0.129]] for each 'Size of Training Data (No. of Data Points)' and extract the corresponding 'AI Model Accuracy (%)'.\nBased on the above thought, the answer is:\nSize of Training Data, AI Model Accuracy (%) \n3,70.22\n4,75.80\n8,80.13\n16,85.50\n32,87.60\n64,90.70\n128,93.10\n256,94.30\n512,97.10" } ], "id_raw": 96 }, { "id": "IltX7M1WGlcNkzhM", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_16.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the accuracy of the AI model as the size of the Training Data increases? Provide reasoning with the help of the chart." }, { "from": "gpt", "value": "From the {line chart coordinates}[[0.171, 0.129, 0.951, 0.849]], we observe that the AI model's accuracy increases as the size of the training data increases. Starting from 70.22% accuracy for a data size of 3 represented by {the point}[[0.171, 0.849]], the accuracy rises steadily along the line, reaching up to 97.1% for a data size of 512 represented by {the point}[[0.951, 0.129]].\nBased on the above thought, the answer is:\nAs the size of the Training Data increases, the accuracy of the AI model improves. This may be due to the model having access to more data to learn from, thus improving its ability to make accurate predictions. This inference is based on the clear upward trend observed in the line chart, where larger training data sizes consistently correspond to higher degrees of AI model accuracy." } ], "id_raw": 96 }, { "id": "HBfwWnZhGHHMl5y0", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in 'AI Model Accuracy(%)' from 'Size of Training Data' of 3 to 512?" }, { "from": "gpt", "value": "To calculate this, I need to locate the accuracy values for 'Size of Training Data' of 3 and 512. These are represented by {the point for data size of 3}[[0.171, 0.849]] which corresponds to 70.22% accuracy, and {the point for data size of 512}[[0.951, 0.129]] corresponding to 97.10% accuracy. The percentage increase can be calculated as (97.10 - 70.22) / 70.22 * 100%.\nBased on the above thought, the answer is:\n38.22%." } ], "id_raw": 96 }, { "id": "fLvd8H1Qw45UKAaZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_17.png", "conversations": [ { "from": "human", "value": "\nCan you organize the statistical metric values for each economic indicator into a structured table from the line chart?" }, { "from": "gpt", "value": "To form the structured table from the line chart, I first identify {the x-axis labels}[[0.029, 0.722, 0.136, 0.899], [0.123, 0.722, 0.209, 0.866], [0.173, 0.722, 0.283, 0.904], [0.286, 0.722, 0.356, 0.839], [0.322, 0.722, 0.430, 0.902], [0.400, 0.722, 0.503, 0.894], [0.457, 0.722, 0.577, 0.921], [0.578, 0.722, 0.650, 0.843], [0.592, 0.722, 0.724, 0.942], [0.678, 0.722, 0.797, 0.921], [0.787, 0.722, 0.871, 0.862], [0.864, 0.722, 0.945, 0.855]] which represent the economic indicators. The corresponding statistical metric values for each economic indicator can be obtained from {the data points}[0.136, 0.058, 0.945, 0.683]].\nBased on the above thought, the answer is:\nEconomic Indicators,Statistical Metric Value\nhousehold_incomes,87124.8\nsalary_increase,7432.6\nunemployment_rate,9.2\nGDP_growth,5.7\nstock_market_index,2768.13\nreal_estate_pricing,5349.46\nconsumer_price_index,124.32\ninterest_rate,2.75\nlabor_force_participation,63.6\npersonal_savings_rate,7.9\nbusiness_profit,12348.5\ntrade_balance,-50250" } ], "id_raw": 97 }, { "id": "xKd6Ut6eVLVXffux", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_17.png", "conversations": [ { "from": "human", "value": "\nConsidering the line chart and the economic indicators, could you comment on the relationship between the household incomes and the unemployment rate?" }, { "from": "gpt", "value": "Looking at {the point for household incomes}[0.136, 0.058] and {the point for unemployment rate}[0.283, 0.454] on the line graph, we can see that the metric value for household incomes is considerably higher than the unemployment rate. This seems to suggest that despite having a certain unemployment rate, the household incomes are still relatively high. However, it's crucial to note that this is an oversimplification as there could be a multitude of factors affecting these two economic indicators, and they may not necessarily have a direct relationship.\nBased on the above thought, the answer is:\nHousehold incomes are significantly higher than the unemployment rate, as shown in the line chart. However, it's essential to recognize that this high income could be due to a host of factors that may or may not directly correlate with the unemployment rate. Further investigation would be required to establish a robust link between the two economic indicators, and this could involve other factors not visually represented in the given chart." } ], "id_raw": 97 }, { "id": "Fin06rKSPMjf8fWB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the largest metric value displayed on the chart, and for which economic indicator is it associated?" }, { "from": "gpt", "value": "To find the largest metric value, I analyze {the y-values of the data points}[[0.136, 0.058], [0.209, 0.420], [0.283, 0.454], [0.356, 0.454], [0.430, 0.441], [0.503, 0.430], [0.577, 0.453], [0.650, 0.454], [0.724, 0.454], [0.797, 0.454], [0.871, 0.398], [0.945, 0.683]]. The highest point corresponds to the largest metric value, and it is associated with {household_incomes}[0.136, 0.058], with a metric value of 87124.8.\nBased on the above thought, the answer is:\nThe largest metric value displayed on the chart is 87124.8, and it is associated with Household Incomes." } ], "id_raw": 97 }, { "id": "k7BGQTJZGwqrrBj0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_18.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the yearly food production from the line chart data?" }, { "from": "gpt", "value": "To generate tabular data from the line chart, I need to identify the x-axis elements and y-axis elements. Looking at the {x-axis labels}[0.089, 0.757, 0.159, 0.827], [0.190, 0.757, 0.261, 0.827], ..., [0.900, 0.757, 0.970, 0.827], they represent the years from 2012 to 2020. The y-axis labels show the food production in various units, starting at 85 and going up till 94, separated by units of 1 {ytick elements}[0.069, 0.681, 0.105, 0.751], [0.069, 0.618, 0.105, 0.688], ..., [0.069, 0.111, 0.105, 0.181]. To tabulate these values, I'll take the values annotated for each year according to {line_0}[0.124, 0.186, 0.935, 0.550] points.\nBased on the above thought, the answer is:\nYear,Food Production\n2012,87.50\n2013,88.34\n2014,89.67\n2015,90.12\n2016,89.87\n2017,90.11\n2018,92.43\n2019,91.88\n2020,93.25" } ], "id_raw": 98 }, { "id": "Fm9PQbzaxrV8wT0G", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_18.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the trend in food production from 2012 to 2020 based on the line chart?" }, { "from": "gpt", "value": "To describe the trend, I would observe {line_0}[0.124, 0.186, 0.935, 0.550]. This line represents the changes in food production over the years. Starting from 2012, the line shows a gradual rise until 2015, a slight drop in 2016, another slight rise in 2017, a sharp increase to 2018, a decrease in 2019, and then another increase in 2020. Overall, it's an upward trend with some fluctuations.\nBased on the above thought, the answer is:\nThe trend in food production from 2012 to 2020 is generally upward, indicating an increase in food production over the years. Though there are minor fluctuations in between, the production in 2020 is higher compared to 2012, signifying growth. The sharp increase from 2017 to 2018 suggests a significant improvement in food production during that period. However, there are also slight decreases observable in the years following 2015 and 2018." } ], "id_raw": 98 }, { "id": "QK5d5ieSTfmKJPx7", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall increase in food production from 2012 to 2020 based on the line chart?" }, { "from": "gpt", "value": "To find the overall increase in food production, I must find the production values for the years 2012 and 2020. The {point for 2012}[0.124, 0.550] has a production value of 87.50, and the {point for 2020}[0.935, 0.186] has a value of 93.25. The overall increase is therefore 93.25 - 87.50 = 5.75.\nBased on the above thought, the answer is:\n5.75." } ], "id_raw": 98 }, { "id": "YD5rPalGlIbUnlck", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_5_19.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the line chart data related to different modes of transport within the logistics and transportation industry into a simple table?" }, { "from": "gpt", "value": "To convert the line chart into table format, I first identify {the x-axis labels}[0.100, 0.903, 0.125, 0.931], [0.219, 0.903, 0.243, 0.931], [0.342, 0.903, 0.357, 0.931], [0.462, 0.903, 0.473, 0.931], [0.568, 0.903, 0.603, 0.931], [0.691, 0.903, 0.716, 0.931], [0.815, 0.903, 0.828, 0.931], [0.933, 0.903, 0.947, 0.931] which represent different modes of transport. To find the values of each transport mode, I should look at corresponding y-axis values for each point on a line. For example, on the line for Air Freight {coordinates}[0.113, 0.093, 0.940, 0.766], I can see points at y-axis 125, 135, and 90 and assign these to the corresponding modes.\nBased on the above thought, the answer is:\nTransport Mode, Trend Values\nCC, 65.23\nTD, 75.96\nM, 80.32\nT, 85.01\nCC2, 88.97\nHR, 90.52\nN, 78.42\nG, 92.86" } ], "id_raw": 99 }, { "id": "soMi3zCwf5FVyPNS", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_5_19.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which social and cultural issue remains generally popular over time?" }, { "from": "gpt", "value": "The line chart displays the popularity of various social and cultural issues over time. {Globalization}[0.915, 0.043, 0.965, 0.071] stands out as it reaches the highest value on the chart, indicating it consistently maintains a high level of popularity.\nBased on the above thought, the answer is:\nAccording to the line chart, Globalization has consistently remained popular over time as indicated by its highest value amongst all the social and cultural issues." } ], "id_raw": 99 }, { "id": "ef0eXLgjwU4ONYMn", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_5_19.png", "conversations": [ { "from": "human", "value": "\nWhat are the highest and lowest popularity percentages for the social and cultural issues?" }, { "from": "gpt", "value": "Each line point on the chart corresponds to a popularity percentage for the issue represented along the x-axis. To find the highest and lowest popularity percentages, I need to inspect all the y-axis values of the line points. The highest y-value I see is for {Globalization}[0.940, 0.093], with a popularity percentage of 92.86. The lowest y-value I see is for {Continuity of Culture}[0.113, 0.766], with a popularity percentage of 65.23.\nBased on the above thought, the answer is:\nHighest is 92.86, Lowest is 65.23" } ], "id_raw": 99 }, { "id": "CTEHnodhAqT60mNc", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_6_0.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data presented in the line chart into a structured table format, with Startup Lifecycle Stages as columns and the single row being Startup Success Rate?" }, { "from": "gpt", "value": "To translate the line chart data into structured tabular form, I begin by identifying {the x-axis tick labels}[[0.015, 0.809, 0.235, 0.842], [0.149, 0.809, 0.298, 0.842], [0.264, 0.809, 0.379, 0.842], [0.360, 0.809, 0.481, 0.842], [0.477, 0.809, 0.560, 0.842], [0.577, 0.809, 0.657, 0.842], [0.632, 0.809, 0.799, 0.842], [0.748, 0.809, 0.880, 0.842], [0.839, 0.809, 0.985, 0.842]] which correspond to various stages of the Startup Lifecycle. I then look at {the line points}[[0.125, 0.420], [0.223, 0.267], [0.322, 0.191], [0.420, 0.511], [0.519, 0.725], [0.617, 0.343], [0.715, 0.374], [0.814, 0.160], [0.912, 0.115]] for the Startup Success rate and obtain the corresponding values at each stage.\nBased on the above thought, the answer is:\n\",\"\",\"Rapid Development\",\"Mature Stage\",\"Expansion\",\"Stagnation\",\"Decline\",\"Rebirth\",\"Transformation\",\"Reinvention\",\"Rejuvenation\"\n\"Startup Success Rate\",,75,85,90,69,55,80,78,92,95" } ], "id_raw": 100 }, { "id": "AyEAjYM1hKyfTibL", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_6_0.png", "conversations": [ { "from": "human", "value": "\nCan you identify and explain the trend in the startup's success rate across its lifecycle as depicted by the chart?" }, { "from": "gpt", "value": "Judging from {the line representing the Startup Success Rate}[[0.125, 0.420], [0.223, 0.267], [0.322, 0.191], [0.420, 0.511], [0.519, 0.725], [0.617, 0.343], [0.715, 0.374], [0.814, 0.160], [0.912, 0.115]], there is a general upward trend in the startup's success rate from the Rapid Development stage to the Expansion stage, followed by a relative decline in success during the Stagnation stage. The success rate further plummets during the Decline stage, followed by a recovery during the Rebirth and Transformation stages. Finally, there is a significant increase in the success rate during the Reinvention and Rejuvenation stages.\nBased on the above thought, the answer is:\nThe line graph depicts that the startup's success rate generally increases from the Rapid Development stage to the Expansion stage, indicating successful growth during the early stages. However, it experiences a falter during the Stagnation and Decline phases, suggesting difficulties encountered during scaling or due to market competition or shifts. The Rise in the success rate during the latter stages of Rebirth, Transformation, Reinvention, and Rejuvenation suggests the implementation of successful restructuring or pivot strategies, leading to a turnaround in the business." } ], "id_raw": 100 }, { "id": "pgEfmXwH5H3ih8Ce", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_6_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the average success rate across the different startup lifecycle stages?" }, { "from": "gpt", "value": "Calculating the average success rate across the stage requires extracting the values at each stage from {the line for Startup Success}[[0.125, 0.420], [0.223, 0.267], [0.322, 0.191], [0.420, 0.511], [0.519, 0.725], [0.617, 0.343], [0.715, 0.374], [0.814, 0.160], [0.912, 0.115]]. The success rates are: 75, 85, 90, 69, 55, 80, 78, 92, and 95. The average is calculated by summing these values and dividing by the total number of stages which is 9.\nBased on the above thought, the answer is:\n79.11" } ], "id_raw": 100 }, { "id": "s19mv6XfvZvK7Wn0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_6_1.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the performance of the different integration methods over the ten integration steps?" }, { "from": "gpt", "value": "To tabulate the data, I need to reference the x-axis and y-axis along with the lines. There are {four lines on the graph}[0.112, 0.080, 0.968, 0.662], [0.112, 0.288, 0.968, 0.558], [0.112, 0.267, 0.968, 0.766], [0.112, 0.205, 0.968, 0.495]. Each represents a different integration method: Database Integration, API Integration, Data Warehouse Integration, and Data Lake Integration.\nThe x-axis marks the {integration steps}[0.104, 0.886, 0.120, 0.919], [0.199, 0.886, 0.215, 0.919], ..., [0.952, 0.886, 0.983, 0.919], which I'll use as the rows. I reference the y-values of each point on the lines to extract the corresponding performance values for the various integration methods.\nBased on the above thought, the answer is:\nIntegration Steps,Database Integration,API Integration,Data Warehouse Integration,Data Lake Integration\n1,80,85,75,88\n2,85,88,77,90\n3,90,92,80,91\n4,95,93,85,93\n5,96,95,87,93\n6,98,96,90,94\n7,100,96,93,96\n8,105,96,95,98\n9,105,97,97,100\n10,108,98,99,102" } ], "id_raw": 101 }, { "id": "C0pOSNomyQ3iPO1A", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_6_1.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which integration method appears to be improving continuously?" }, { "from": "gpt", "value": "To determine which integration method is improving continuously, I need to analyze the trends of the plotted lines. \nLooking at {line for the Database Integration}[0.112, 0.080, 0.968, 0.662], it shows a steady increase in performance. \nHowever, for the {API Integration line}[0.112, 0.288, 0.968, 0.558], the growth initially seems continuous but experiences a plateau at steps 7 to 9. \nFor {Data Warehouse Integration}[0.112, 0.267, 0.968, 0.766], the line demonstrates a progressive improvement throughout the steps. \nThe {Data Lake Integration line}[0.112, 0.205, 0.968, 0.495] also shows continuous improvement, but the growth trend is slower than that of the Database Integration method.\nBased on the above thought, the answer is:\nBased on the analysis, Database Integration appears to be the method that is improving continuously. Despite the continuous growth exhibited by other methods, the Database Integration method displays a steeper and steady increase, indicating a consistent improvement." } ], "id_raw": 101 }, { "id": "oY7OhuWV94kfwNhh", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_6_1.png", "conversations": [ { "from": "human", "value": "\nWhat was the performance of Data Lake Integration at the 7th Integration Step?" }, { "from": "gpt", "value": "To find the performance value for Data Lake Integration at the 7th Integration Step, I have to locate the {point on the Data Lake Integration line}[0.682, 0.329] corresponding to the {x coordinate for the 7th step}[0.675, 0.886, 0.690, 0.919]. Extracting the corresponding y value, I can determine the performance at this integration step.\nBased on the above thought, the answer is:\n96" } ], "id_raw": 101 }, { "id": "vXtyIK4g4zWNRguf", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_6_2.png", "conversations": [ { "from": "human", "value": "\nCan you help organize the data regarding Social Activities and the level of engagement into a structured table format?" }, { "from": "gpt", "value": "Absolutely, I can extract the data from the line chart into a table. First, I'd take {the x-axis tick labels}[0.036, 0.720, 0.150, 0.883], [0.118, 0.720, 0.274, 0.942], [0.250, 0.720, 0.348, 0.860], [0.364, 0.720, 0.439, 0.828], [0.460, 0.720, 0.549, 0.846], [0.568, 0.720, 0.647, 0.832], [0.685, 0.720, 0.736, 0.793], [0.774, 0.720, 0.852, 0.831], [0.847, 0.720, 0.985, 0.916] , these labels represent different social activities. The engagement levels for both Healthy and Sedentary lifestyle are plotted as two distinct lines, {Healthy Lifestyle line}[0.093, 0.129, 0.916, 0.610] and {Sedentary Lifestyle line}[0.093, 0.116, 0.916, 0.610]. I will then extract values at each data point on these lines to populate the final table.\nBased on the above thought, the answer is:\nSocial Activities,Healthy Lifestyle,Sedentary Lifestyle\nTheme Party,52,63\nOutdoor Activities,68,40\nMeditation,79,30\nGaming,33,88\nFast Food,23,89\nRunning,85,15\nYoga,90,20\nReading,70,50\nBinge-Watching,15,92" } ], "id_raw": 102 }, { "id": "B4r5df8xBwbZV4ai", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_6_2.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, can you say which Lifestyle engages more in social activities?" }, { "from": "gpt", "value": "To answer this question, I compare the two lines {Healthy Lifestyle line}[0.093, 0.129, 0.916, 0.610] and {Sedentary Lifestyle line}[0.093, 0.116, 0.916, 0.610] and look for which lifestyle shows higher engagement levels for the majority of the social activities. The Healthy Lifestyle shows higher engagement in Outdoor Activities, Running, Yoga, and Reading. On the other hand, the Sedentary Lifestyle has higher engagement in Theme Party, Gaming, Fast Food Consumption, and Binge-Watching. This means that the engagement levels are similar for both lifestyles, with each having higher engagement in different activities. However, one aspect to note is the type of activities, where majority of the healthy activities have a higher engagement in the Healthy Lifestyle, while the unhealthy ones see a higher engagement in Sedentary Lifestyle.\nBased on the above thought, the answer is:\nNeither lifestyle engages notably more in social activities since the engagement levels are quite similar for both lifestyles. However, the type of engagement in activities differs significantly; healthier activities see more engagement from those leading a Healthy Lifestyle and vice versa." } ], "id_raw": 102 }, { "id": "ZulQic0V1s0KrRX9", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_6_2.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, how much higher is the engagement in Running amongst those leading a Healthy Lifestyle compared to those with a Sedentary Lifestyle?" }, { "from": "gpt", "value": "To find this information, I'd locate {the point for Running in the Healthy Lifestyle line}[0.607, 0.161], which shows an engagement value of 85. Then, I'd find {the equivalent point in the Sedentary Lifestyle line}[0.607, 0.610], which shows an engagement value of 15. To find the difference, we subtract the engagement level for the Sedentary Lifestyle from that of the Healthy Lifestyle, which would give us 85 - 15 = 70.\nBased on the above thought, the answer is:\n70%." } ], "id_raw": 102 }, { "id": "hsGVaqkNKnsK3X00", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_6_3.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line plot data to a table with years as columns and the economic indicators as rows, using the provided dataset?" }, { "from": "gpt", "value": "To generate the table, I must refer to the x-axis ticks for the columns. They are in {the x-axis tick labels}[[0.122, 0.809, 0.137, 0.842], [0.215, 0.809, 0.230, 0.842], [0.308, 0.809, 0.324, 0.842], [0.401, 0.809, 0.417, 0.842], [0.494, 0.809, 0.510, 0.842], [0.587, 0.809, 0.603, 0.842], [0.680, 0.809, 0.696, 0.842], [0.773, 0.809, 0.789, 0.842], [0.867, 0.809, 0.882, 0.842], [0.952, 0.809, 0.983, 0.842]]. They together represent the years from 1 to 10. I then observe {the legend}[0.353, 0.903, 0.744, 0.966] to identify the row labels as Population and GDP. To fill in the data, I refer to {the points on the Population plot}[[0.129, 0.147, 0.968, 0.638]] and {the points on the GDP plot}[[0.129, 0.311, 0.968, 0.785]]. I reference the y-axis ticks to estimate the values.\nBased on the above thought, the answer is:\nIndicator,Year 1,Year 2,Year 3,Year 4,Year 5,Year 6,Year 7,Year 8,Year 9,Year 10\nPopulation,10000,15000,17000,22000,24000,26000,28000,30000,35000,40000\nGDP,1000,3000,5000,6000,8000,9000,14000,18000,25000,30000" } ], "id_raw": 103 }, { "id": "fuj1HbcBlHo7MwEx", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_6_3.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, what can be inferred about the community's economic growth and urban development over time?" }, { "from": "gpt", "value": "To infer the community's economic growth and urban development, I need to analyze the trends in the line plots. I observe {the Population line}[0.129, 0.147, 0.968, 0.638] is showing a steady increase over the years from 10000 to 40000, suggesting the community is growing. Additionally, I see that the {GDP line}[0.129, 0.311, 0.968, 0.785] is also continuously increasing over time from 1000 to 30000, signifying the enhancing economic growth. This also implies that the average standard of living may be improving.\nBased on the above thought, the answer is:\nThe line charts indicate a steady, positive growth in both population and GDP, suggesting that the community is undergoing consistent economic development and urban growth. The continuous increase in population may be a result of factors such as migration due to increased opportunities, better living conditions, among others. Next, the increasing GDP implies that the economic status of the community is improving, in turn indicating the enhancement in the standard of living in the community. Therefore, based on the chart, the community is positively developing over time, both economically and demographically." } ], "id_raw": 103 }, { "id": "P5pX9dkzOfoPxER5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_6_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall percentage increase in the GDP from year 1 to year 10?" }, { "from": "gpt", "value": "To calculate the overall percentage increase, I first need to extract the GDP value at year 1 and year 10. The GDP value for year 1 corresponds to {the first point on the GDP plot}[0.129, 0.785] and is 1000, while the GDP value for year 10 corresponds to {the last point on the GDP plot}[0.968, 0.311] and is 30000. The percentage increase from year 1 to year 10 is calculated as ((30000 - 1000) / 1000) * 100.\nBased on the above thought, the answer is:\n2900%." } ], "id_raw": 103 }, { "id": "4YLt4TXZCdI5LBZI", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_6_4.png", "conversations": [ { "from": "human", "value": "\nCan you extract data from the line chart and present it in tabular form, with Efficiency Level as the rows and efficiency types being the columns?" }, { "from": "gpt", "value": "To create the table, I first need to identify {the x-axis tick labels}[[0.066, 0.880, 0.110, 0.909], [0.161, 0.880, 0.302, 0.909], [0.330, 0.880, 0.419, 0.909], [0.444, 0.880, 0.592, 0.909], [0.635, 0.880, 0.686, 0.909], [0.748, 0.880, 0.860, 0.910], [0.909, 0.880, 0.985, 0.909]], which represent different levels of efficiency. Then I identify {the legend texts}[[0.379, 0.961, 0.508, 0.981], [0.574, 0.961, 0.695, 0.981]] as the column names. Finally, I can obtain the values from {the line points}[[0.088, 0.820], [0.231, 0.709], [0.374, 0.663], [0.518, 0.579], [0.661, 0.390], [0.804, 0.344], [0.947, 0.033]] and [[0.088, 0.768], [0.231, 0.673], [0.374, 0.600], [0.518, 0.495], [0.661, 0.421], [0.804, 0.327], [0.947, 0.108]] for the respective efficiency types at each efficiency level.\nBased on the above thought, the answer is:\nEfficiency Level,Material Efficiency,Energy Efficiency\nLow,62.5,65\nLow-Medium,67.8,69.5\nMedium,70,73\nMedium-High,74,78\nHigh,83,81.5\nVery High,85.2,86\nOutlier,100,96.4" } ], "id_raw": 104 }, { "id": "95oCro3Bm4FjAE18", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_6_4.png", "conversations": [ { "from": "human", "value": "\nBased on the trends in the line chart, what does this tell us about the gap between Material Efficiency and Energy Efficiency as efficiency levels increase?" }, { "from": "gpt", "value": "To interpret this trend, I need to compare the line plots for {Material Efficiency}[[0.088, 0.820], [0.231, 0.709], [0.374, 0.663], [0.518, 0.579], [0.661, 0.390], [0.804, 0.344], [0.947, 0.033]] and {Energy Efficiency}[[0.088, 0.768], [0.231, 0.673], [0.374, 0.600], [0.518, 0.495], [0.661, 0.421], [0.804, 0.327], [0.947, 0.108]]. Here, I see that the Material Efficiency line is almost always above the Energy Efficiency line, implying the former is typically higher. However, as the efficiency level increases beyond 'High', the Material Efficiency line crosses the Energy Efficiency line and the latter shows a more rapid increase resulting in a larger gap at the 'Outlier' level.\nBased on the above thought, the answer is:\nThe lines in the chart indicate that Material Efficiency is generally higher than Energy Efficiency till we reach the 'High' efficiency level. However, as efficiency levels increase beyond 'High', the rate of improvement in Energy Efficiency outpaces Material Efficiency, leading to a larger gap in favor of Energy Efficiency at the 'Outlier' level. This suggests that there is a greater scope for improvement in energy usage as efficiency levels increase." } ], "id_raw": 104 }, { "id": "TGXIUIQu8nwHZuP5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_6_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of potential improvement in Energy Efficiency across all Efficiency Levels depicted in the line chart?" }, { "from": "gpt", "value": "To find the maximum and minimum values for Energy Efficiency across all Efficiency Levels, I need to locate the {line points for Energy Efficiency}[[0.088, 0.768], [0.231, 0.673], [0.374, 0.600], [0.518, 0.495], [0.661, 0.421], [0.804, 0.327], [0.947, 0.108]] and extract the corresponding values: 65, 69.5, 73, 78, 81.5, 86, 96.4. From these values, the maximum value is 96.4 corresponding to {Outlier}[0.909, 0.880] and the minimum value is 65 corresponding to {Low}[0.066, 0.880]. Subtracting the minimum from the maximum gives the range of potential improvement.\nBased on the above thought, the answer is:\n31.4" } ], "id_raw": 104 }, { "id": "XLKjJ7K2e0VC3pQx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_6_5.png", "conversations": [ { "from": "human", "value": "\nPlease, can you transform data from the line chart into a table format with years as rows and regions as columns?" }, { "from": "gpt", "value": "To create a table from this line chart data, I would start with {the x-axis tick labels}[[0.079, 0.913, 0.135, 0.942], [0.249, 0.913, 0.305, 0.942], [0.419, 0.913, 0.475, 0.942], [0.588, 0.913, 0.645, 0.942], [0.758, 0.913, 0.815, 0.942], [0.928, 0.913, 0.985, 0.942]] portraying years that would become our row labels. Then, I would identify {the legend texts}[[0.189, 0.089, 0.371, 0.118],[0.189, 0.133, 0.381, 0.162]] and use them as column headers representing different regions. The values would come from {the line points for the corresponding region and year}[[0.107, 0.475], [0.277, 0.581], ..., [0.957, 0.138]].\nBased on the above thought, the answer is:\nYears, Rural Population, Urban Population\n1970, 956,449\n1980, 817,637\n1990, 712,866\n2000, 659, 1069\n2010, 636, 1213\n2020, 620, 1399" } ], "id_raw": 105 }, { "id": "DcgWEiDV09PD1igN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_6_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, how would you describe the development of rural and urban populations over the last 50 years?" }, { "from": "gpt", "value": "To give a description of the development of rural and urban populations over the last 50 years {the line for Rural Population}[0.107, 0.475, 0.957, 0.732] and {the line for Urban Population}[0.107, 0.138, 0.957, 0.862] are key to observe. The rural population has experienced a steady decrease from 956 million in 1970 to 620 million in 2020. On the other hand, the urban population has seen a steady rise, from 449 million in 1970, more than tripling to 1399 million in 2020.\nBased on the above thought, the answer is:\nThe trends shown in the line chart over the past 50 years show a decrease in the rural population and a corresponding increase in the urban population. The rural population declined from 956 million to 620 million, while the urban population more than tripled from 449 million to 1399 million. This shift indicates a significant urbanization trend over the last half-century." } ], "id_raw": 105 }, { "id": "NbHCy2aU99dPfcGE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_6_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in the Urban Population from 1970 to 2020?" }, { "from": "gpt", "value": "To find the increase in Urban Population from 1970 to 2020, I would identify {the point for Urban Population in 1970}[[0.107, 0.862]], which corresponds to a value of 449, and {the point for Urban Population in 2020}[[0.957, 0.138]], which corresponds to a value of 1399. The increase is therefore 1399 - 449 = 950.\nBased on the above thought, the answer is:\n950" } ], "id_raw": 105 }, { "id": "YR1soq6BfkDeRUQq", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_6_6.png", "conversations": [ { "from": "human", "value": "\nFrom the horizontal bar chart, can you translate the scores for the three key areas in both Emerging and Developed Markets into a table?" }, { "from": "gpt", "value": "To construct a table from the bar chart, I first identify {the area labels on y axis}[0.134, 0.744, 0.219, 0.769], [0.344, 0.744, 0.428, 0.769], [0.552, 0.744, 0.635, 0.769] as the rows. The column headers are the two market types: Emerging and Developed. Then, I examine the lengths of the bar segments for each area in both markets: {Manufacturing in Emerging Markets}[0.227, 0.697, 0.350, 0.761],\n{Manufacturing in Developed Markets}[0.350, 0.697, 0.427, 0.761], \n{Trading in Emerging Markets}[0.227, 0.538, 0.283, 0.603], \n{Trading in Developed Markets}[0.283, 0.538, 0.406, 0.603], \n{Logistics in Emerging Markets}[0.227, 0.380, 0.320, 0.444], \n{Logistics in Developed Markets}[0.320, 0.380, 0.404, 0.444] and extract the corresponding values.\nBased on the above thought, the answer is:\nArea,Emerging Markets,Developed Markets\nManufacturing,123,77\nTrading,56,123\nLogistics,93,102" } ], "id_raw": 106 }, { "id": "RSodj2pg9YrSbzpW", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_6_6.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which area of Global Trade and Supply Chain Management performs significantly better in Developed Markets compared to Emerging Markets?" }, { "from": "gpt", "value": "From the bars, I can observe that {Manufacturing}[0.227, 0.697, 0.427, 0.761] perform better in Emerging Markets while {Trading}[0.227, 0.538, 0.406, 0.603] and {Logistics}[0.227, 0.380, 0.404, 0.444] are closely competitive in both markets. Hence, it's clear that Trading is the area that performs significantly better in Developed Markets compared to Emerging Markets.\nBased on the above thought, the answer is:\nTrading is the area of Global Trade and Supply Chain Management that performs significantly better in Developed Markets compared to Emerging Markets. This is evident from the significantly larger bar representing Trading in Developed Markets as compared to the corresponding bar in Emerging Markets." } ], "id_raw": 106 }, { "id": "JkMflLyHa0M0cHBX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_6_6.png", "conversations": [ { "from": "human", "value": "\nHow many more 'wins' does Trading have in Developed Markets compared to Emerging Markets?" }, { "from": "gpt", "value": "For this, I need to determine the scores for Trading in both markets. From {Trading in Emerging Markets}[0.227, 0.538, 0.283, 0.603], the score is 56, and from {Trading in Developed Markets}[0.283, 0.538, 0.406, 0.603], the score is 123. Therefore, the difference is 123 - 56 = 67.\nBased on the above thought, the answer is:\n67." } ], "id_raw": 106 }, { "id": "nTkypKca6QAKEiEi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_6_10.png", "conversations": [ { "from": "human", "value": "\nCan the data present in the line chart depicting student engagement and learning efficiency for Online Tools be tabulated?" }, { "from": "gpt", "value": "Yes, it is possible to tabulate the data represented in this line chart. To do so, I first identify {the x-axis labels}[0.013, 0.753, 0.143, 0.949], [0.187, 0.753, 0.236, 0.828], [0.219, 0.753, 0.329, 0.918], [0.364, 0.753, 0.422, 0.840], [0.478, 0.753, 0.515, 0.809], [0.537, 0.753, 0.608, 0.860], [0.651, 0.753, 0.701, 0.829], [0.722, 0.753, 0.794, 0.862], [0.805, 0.753, 0.888, 0.877], [0.880, 0.753, 0.981, 0.905] as the distinct tools. Then, I examine the correspondence of these tools in {the line points of Student Engagement}[0.143, 0.439], [0.236, 0.439], [0.329, 0.234], [0.422, 0.337], [0.515, 0.337], [0.608, 0.747], [0.701, 0.172], [0.794, 0.172], [0.888, 0.131], [0.981, 0.337] and {Learning Efficiency}[0.143, 0.337], [0.236, 0.603], [0.329, 0.378], [0.422, 0.234], [0.515, 0.152], [0.608, 0.583], [0.701, 0.337], [0.794, 0.234], [0.888, 0.378], [0.981, 0.357] to populate the values in the table.\nBased on the above thought, the answer is:\nOnline Tools,Student Engagement,Learning Efficiency\nGoogle Classroom,75,80\nZoom,75,67\nKhan Academy,85,78\nUdemy,80,85\nEdX,80,89\nCoursera,60,68\nLynda,88,80\nBrainPOP,88,85\nABCmouse,90,78\nReading Eggs,80,79" } ], "id_raw": 110 }, { "id": "kuttQZiraG9H0T39", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_6_10.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which online tool shows the lowest learning efficiency and why so?" }, { "from": "gpt", "value": "To determine the online tool with the lowest learning efficiency, I refer to {the line points for Learning Efficiency}[0.143, 0.337], [0.236, 0.603], [0.329, 0.378], [0.422, 0.234], [0.515, 0.152], [0.608, 0.583], [0.701, 0.337], [0.794, 0.234], [0.888, 0.378], [0.981, 0.357]. Then, I extract the values: 80, 67, 78, 85, 89, 68, 80, 85, 78, 79. According to this list, Zoom and Coursera have the lowest learning efficiency score of 67 and 68 respectively. I further check {Zoom's Learning Efficiency Point}[0.236, 0.603] and {Coursera's Learning Efficiency Point}[0.608, 0.583] to confirm the same.\nBased on the above thought, the answer is:\nBased on the line chart, Zoom is the online tool that shows the lowest Learning Efficiency. This is evident as Zoom has the lowest score of 67 for learning efficiency among all online tools as per the point [0.236, 0.603] on the chart. This may suggest that students may find it less effective for learning compared to other tools." } ], "id_raw": 110 }, { "id": "EkEsAua5sQuRwJuQ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_6_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the maxima for student engagement among all the online tools as per the line chart?" }, { "from": "gpt", "value": "To ascertain the maximum value of student engagement, I need to review {the line points for Student Engagement}[0.143, 0.439], [0.236, 0.439], [0.329, 0.234], [0.422, 0.337], [0.515, 0.337], [0.608, 0.747], [0.701, 0.172], [0.794, 0.172], [0.888, 0.131], [0.981, 0.337] and derive the corresponding values: 75, 75, 85, 80, 80, 60, 88, 88, 90, 80. Thus, the maximum value for student engagement is 90.\nBased on the above thought, the answer is:\n90" } ], "id_raw": 110 }, { "id": "lDTL2wLjrbiraEOv", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_6_11.png", "conversations": [ { "from": "human", "value": "\nCan you capture the data presented in the line graphs in a tabular format?" }, { "from": "gpt", "value": "To transform the data from the graphs to a table, I'll first identify {the x-axis ticks}[[0.048, 0.906, 0.090, 0.936], [0.138, 0.906, 0.180, 0.936], [0.227, 0.906, 0.269, 0.936], [0.317, 0.906, 0.359, 0.936], [0.406, 0.906, 0.448, 0.936], [0.495, 0.906, 0.538, 0.936], [0.585, 0.906, 0.627, 0.936], [0.674, 0.906, 0.717, 0.936], [0.764, 0.906, 0.806, 0.936], [0.853, 0.906, 0.896, 0.936], [0.943, 0.906, 0.985, 0.936]] as the column headers representing the different years. Next, I locate texts from {the legend}[[0.360, 0.089, 0.585, 0.119], [0.665, 0.089, 0.949, 0.119]] which indicate the names of the industries and act as row headers. Extracting the actual data involves translating {the line points}[[0.069, 0.752], [0.114, 0.724], ..., [0.964, 0.146]] for Biotechnology Pharmacists and {the other set of line points}[[0.069, 0.477], [0.114, 0.504], ..., [0.964, 0.848]] for Medical Equipment Manufacturers into numerical values for each year.\nBased on the above thought, the answer is:\nYear,Biotechnology Pharmacists,Medical Equipment Manufacturers\n2000,50,70\n2001,52,68\n2002,54,65\n2003,53,69\n2004,57,67\n2005,60,70\n2006,63,68\n2007,65,70\n2008,67,65\n2009,69,63\n2010,70,60\n2011,72,58\n2012,73,57\n2013,78,55\n2014,80,53\n2015,82,52\n2016,84,50\n2017,88,48\n2018,90,46\n2019,92,45\n2020,94,43" } ], "id_raw": 111 }, { "id": "4iEEYa8LMe9L3Rk7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_6_11.png", "conversations": [ { "from": "human", "value": "\nBased on the line trends, can you compare the growth trend in Biotechnology Pharmacists and Medical Equipment Manufacturers?" }, { "from": "gpt", "value": "To compare growth trends, I need to analyze the lines representing each industry. Looking at the line of {Biotechnology Pharmacists}[[0.069, 0.752, 0.964, 0.146]], I observe a consistent rising trend with a steep gradient, indicating a strong, increase in growth over the years. \nOn the other hand, the line representing {Medical Equipment Manufacturers}[[0.069, 0.477, 0.964, 0.848]] shows a steady decline. This is clear from the downward slope of the line.\nBased on the above thought, the answer is:\nOwing to the upward direction of the line trend for Biotechnology Pharmacists, it can be inferred that the growth in this industry consistently increased over the years. Conversely, Medical Equipment Manufacturers are showing a consistent decrease in growth, as indicated by the downward trend in their line graph." } ], "id_raw": 111 }, { "id": "A5hCycx5JqYVBj7H", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_6_11.png", "conversations": [ { "from": "human", "value": "\nIn what year did the Medical Equipment Manufacturers experience the maximum percentage growth?" }, { "from": "gpt", "value": "To find the year of maximum growth for Medical Equipment Manufacturers, I need to identify the highest point on their line. This can be found at {the line coordinates}[[0.830, 0.779]], which correspond to a growth percentage of 48%. Tracing this point to {the x-axis}[[0.806, 0.936]], I can determine the year.\nBased on the above thought, the answer is:\n2017." } ], "id_raw": 111 }, { "id": "n4m5TgGWBirKE6Zm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_6_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart data into a table format with Industries as rows and the two factors 'Green Intelligence' and 'Environmental Philanthropy' as columns?" }, { "from": "gpt", "value": "Sure. First, we have {the x-axis tick labels}[[0.063, 0.845, 0.104, 0.875], [0.164, 0.845, 0.216, 0.875], [0.230, 0.845, 0.364, 0.875], [0.371, 0.845, 0.436, 0.875], [0.460, 0.845, 0.561, 0.875], [0.564, 0.845, 0.671, 0.875], [0.657, 0.845, 0.791, 0.875], [0.779, 0.845, 0.883, 0.875], [0.892, 0.845, 0.983, 0.875]], which will represent the Industry for each row in the table. Next, we have {the legend texts}[[0.239, 0.936, 0.438, 0.968], [0.542, 0.936, 0.842, 0.968]], which will be the column headers for 'Green Intelligence' and 'Environmental Philanthropy' respectively. To populate the table, I will locate the y-value for both 'Green Intelligence' and 'Environmental Philanthropy' for each industry.\nBased on the above thought, the answer is:\nIndustry,Green Intelligence,Environmental Philanthropy\nTech,80,77\nRetail,85,82\nManufacturing,90,90\nEnergy,95,88\nHealthcare,90,85\nAutomotive,83,80\nTransportation,81,78\nReal Estate,79,75\nEducation,85,84" } ], "id_raw": 113 }, { "id": "4jDnonmm7gigqEDf", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_6_13.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which industry has shown the most environmental consciousness and why?" }, { "from": "gpt", "value": "To determine which industry shows the most environmental consciousness, I will analyze the scores for both 'Green Intelligence' and 'Environmental Philanthropy' across all industries. For 'Green Intelligence', the highest score is noted for {Energy}[0.404, 0.197] which has a score of 95. Similarly, for 'Environmental Philanthropy', the highest score is given to {Manufacturing}[0.297, 0.357] with a score of 90. As the overall environmental consciousness would be measured collectively by both 'Green Intelligence' and 'Environmental Philanthropy', it is important to consider the scores of both the metrics. Although Energy gets the highest in 'Green Intelligence' and Manufacturing scores the highest in 'Environmental Philanthropy', Energy's score for both the metrics is higher in aggregate, hence it may be deemed as the industry with the most environmental consciousness.\nBased on the above thought, the answer is:\nAccording to the chart, the Energy industry appears to be the most conscious about the environment because it has the highest score in 'Green Intelligence' and among the highest scores in 'Environmental Philanthropy', making its aggregate score higher than that of other industries." } ], "id_raw": 113 }, { "id": "NykVaMUJYkLHeGgg", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_6_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the scores for Green Intelligence and Environmental Philanthropy in the Energy industry?" }, { "from": "gpt", "value": "To find out the scores for Green Intelligence and Environmental Philanthropy in the Energy industry, I have to look for the y-values corresponding to these two metrics at {Energy's x-location}[0.404, 0.837]. The y-values extracted from {Green Intelligence's line points} [0.083, 0.677], [0.190, 0.517], [0.297, 0.357], [0.404, 0.197] and {Environmental Philanthropy's line points} [0.083, 0.773], [0.190, 0.613], [0.297, 0.357], [0.404, 0.421] give us the scores for Green Intelligence and Environmental Philanthropy respectively. The difference between the two scores is the answer to this question.\nBased on the above thought, the answer is:\n7." } ], "id_raw": 113 }, { "id": "QV3zHYMNjyn4ltBS", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_6_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into a table with the different statuses as rows and the Parties as columns?" }, { "from": "gpt", "value": "To convert the bar chart data into a table, I first identify {the y-axis labels for the status}[[0.071, 0.738, 0.195, 0.877], [0.071, 0.639, 0.215, 0.763], [0.071, 0.551, 0.158, 0.675], [0.071, 0.457, 0.174, 0.581], [0.071, 0.364, 0.160, 0.488], [0.071, 0.270, 0.156, 0.394], [0.071, 0.183, 0.181, 0.307], [0.071, 0.091, 0.218, 0.215]] which represent the row headers or statuses. Next, I locate {the legend entries}[[0.303, 0.031, 0.425, 0.057], [0.438, 0.031, 0.570, 0.057]] as the column headers representing the different parties. To populate the values, I look at {the bar values for Democrats}[[0.243, 0.793, 0.263, 0.877], [0.243, 0.689, 0.303, 0.763], [0.243, 0.611, 0.293, 0.675], [0.243, 0.518, 0.313, 0.581], [0.243, 0.414, 0.323, 0.488], [0.243, 0.325, 0.333, 0.394], [0.243, 0.225, 0.323, 0.307], [0.243, 0.143, 0.303, 0.215]] and {Republicans}[[0.733, 0.793, 0.753, 0.877], [0.733, 0.694, 0.783, 0.763], [0.733, 0.604, 0.833, 0.675], [0.733, 0.519, 0.853, 0.581], [0.733, 0.424, 0.853, 0.488], [0.733, 0.330, 0.873, 0.394], [0.733, 0.220, 0.853, 0.307], [0.733, 0.133, 0.833, 0.215]] for each party at the respective statuses.\nBased on the above thought, the answer is:\nStatus, Democrats, Republicans\nSteep increase,2,3\nFluctuating growth,3,5\nUpward trajectory,5,10\nSubdued decline,7,12\nStable growth,8,12\nDeclining pattern,9,13\nAccelerated decline,8,12\nErratic behavior,6,9" } ], "id_raw": 114 }, { "id": "TEbwd6OWNyW8MCjI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_6_14.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which party appears to perform better overall across all statuses? Explain your reasoning." }, { "from": "gpt", "value": "To determine which party appears to perform better overall across all statuses, I'll compare the bar values for each status. I check {the bar values for Democrats}[[0.243, 0.793, 0.263, 0.877], [0.243, 0.689, 0.303, 0.763], [0.243, 0.611, 0.293, 0.675], [0.243, 0.518, 0.313, 0.581], [0.243, 0.414, 0.323, 0.488], [0.243, 0.325, 0.333, 0.394], [0.243, 0.225, 0.323, 0.307], [0.243, 0.143, 0.303, 0.215]] against {the bar values for Republicans}[[0.733, 0.793, 0.753, 0.877], [0.733, 0.694, 0.783, 0.763], [0.733, 0.604, 0.833, 0.675], [0.733, 0.519, 0.853, 0.581], [0.733, 0.424, 0.853, 0.488], [0.733, 0.330, 0.873, 0.394], [0.733, 0.220, 0.853, 0.307], [0.733, 0.133, 0.833, 0.215]]. It is clear that the values for Republicans are consistently higher than Democrats for every status, indicating that Republicans appear to perform better overall.\nBased on the above thought, the answer is:\nBased on the bar chart, the Republican party appears to perform better overall across all statuses. This is determined by comparing the values of each status. Whether it be 'Steep increase', 'Fluctuating growth', 'Upward trajectory', 'Subdued decline', 'Stable growth', 'Declining pattern', 'Accelerated decline', or 'Erratic behavior', the Republicans always have higher numbers than Democrats, indicating their better performance across the spectrum." } ], "id_raw": 114 }, { "id": "8N8jnaGvvINB3UL2", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_6_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the values for Republicans and Democrats during the 'Subdued decline' status?" }, { "from": "gpt", "value": "To find out the difference in values for Republicans and Democrats during the 'Subdued decline' status, I need to locate {the bar value for Democrats during 'Subdued decline'}[0.243, 0.518, 0.313, 0.581] and correspondingly, {the bar value for Republicans during 'Subdued decline'}[0.733, 0.519, 0.853, 0.581], which are 7 and 12 respectively. The difference is therefore, 12 - 7 = 5.\nBased on the above thought, the answer is:\n5" } ], "id_raw": 114 }, { "id": "z7K8Qn0CRCfVTlQa", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_6_15.png", "conversations": [ { "from": "human", "value": "\nCan you elaborate on the chart data and create a table that includes the columns 'Year', 'Carbon Emissions', and 'Volunteers' respectively?" }, { "from": "gpt", "value": "Sure, I can generate a table based on the line charts. I will identify {the x-axis} coordinates as [0.090, 0.899, 0.106, 0.932], representing the 'Year' from 0 to 8 in the table. Further, the y-axis is going to serve as the values for 'Carbon Emissions' and 'Volunteers' categories which are represented by two lines, {line for Carbon Emissions}[0.098, 0.124, 0.974, 0.874] and {line for Volunteers}[0.098, 0.158, 0.974, 0.465]. Hence each point of intersection of x and these two lines give us their respectively values for each year.\nBased on the above thought, the answer is:\nYear,Carbon Emissions,Volunteers\n0,100,80\n1,95,85\n2,85,82\n3,75,86\n4,77,90\n5,70,92\n6,63,94\n7,58,96\n8,56,98" } ], "id_raw": 115 }, { "id": "DXnXnpDbRic3zi42", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_6_15.png", "conversations": [ { "from": "human", "value": "\nCan you discuss the trend in 'Volunteers' over the years as seen in the chart?" }, { "from": "gpt", "value": "Sure. The line representing 'Volunteers' is seen here [0.098, 0.158, 0.974, 0.465]. By examining this line, we can see an overall increase from Year 0 to Year 8 as the line rises while maintaining a positive slope over the interval. The trend shows that the number of volunteers has consistently risen over the years with a slight dip only in Year 2.\nBased on the above thought, the answer is:\nThe 'Volunteers' line exhibits an upward trend throughout the period from Year 0 to Year 8, indicating that the number of volunteers has continuously increased over the years. The increase is steady and consistent, reflecting a constantly growing interest and participation level in the cause. Except for a minor drop in Year 2, the overall trend signifies increasing involvement of volunteers over the years." } ], "id_raw": 115 }, { "id": "S8L2hsbKScXIaH25", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_6_15.png", "conversations": [ { "from": "human", "value": "\nWhat's the percentage change in 'Carbon Emissions' from Year 0 to Year 8?" }, { "from": "gpt", "value": "To work out the percentage change, I have to first locate the initial and final values for 'Carbon Emissions'. My starting point is at {Year 0}[0.098, 0.124] and ending point is at {Year 8}[0.974, 0.874]. The initial value is 100 and the final value is 56. The percentage change can be calculated using the formula: [(final value - initial value) / initial value] * 100.\nBased on the above thought, the answer is:\n-44%" } ], "id_raw": 115 }, { "id": "AxURUwjifX97pShi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_6_18.png", "conversations": [ { "from": "human", "value": "\nCan you extract the numerical data regarding Cholesterol Level, Heart Rate, and Blood Sugar under different exercise regimes into a table?" }, { "from": "gpt", "value": "To convert the chart data into a tabular format, I closely examined the values represented by each color-coded line across the two subplots. For instance, {the line representing Cholesterol Level}[0.125, 0.250, 0.475, 0.630] starts from a high value at {Running}[0.145, 0.895] and ends lower at {Yoga}[0.465, 0.390]. Similarly, I noted the starting and ending points for {Heart Rate}[0.125, 0.250, 0.475, 0.670] and {Blood Sugar}[0.125, 0.250, 0.475, 0.720], thereby mapping them to their corresponding values for each exercise regime.\nBased on the above thought, the answer is:\nHealth Metric,Running,Yoga\nCholesterol Level,220,150\nHeart Rate,85,65\nBlood Sugar,160,105" } ], "id_raw": 118 }, { "id": "RP8EPwEhWefYt25N", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_6_18.png", "conversations": [ { "from": "human", "value": "\nHow do the different exercise regimes appear to affect Cholesterol levels?" }, { "from": "gpt", "value": "Analyzing the effect on Cholesterol levels as depicted in the chart requires scrutinizing {the line for Cholesterol Level}[0.125, 0.250, 0.475, 0.630]. The data points start high during {Running}[0.145, 0.250] with a value around 220, and drastically decrease as the regime shifts to {Yoga}[0.465, 0.630] with a value around 150. This significant drop suggests a more pronounced effect of Yoga on reducing Cholesterol levels compared to Running.\nBased on the above thought, the answer is:\nThe different exercise regimes have a substantial impact on Cholesterol levels, particularly observed in the sharp decline from Running to Yoga. The chart indicates that the more relaxed and stretching-oriented Yoga regime helps in greatly reducing Cholesterol levels compared to the more strenuous Running regime." } ], "id_raw": 118 }, { "id": "GyNDlVlzrfN2mdYk", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_6_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in Blood Sugar levels from Running to Yoga according to the chart?" }, { "from": "gpt", "value": "To determine the difference in Blood Sugar levels from Running to Yoga, I locate the end points of {the Blood Sugar line}[0.125, 0.720, 0.475, 0.465]. Initially, the Blood Sugar level is recorded at 160 during {Running}[0.145, 0.720], and it decreases to 105 by the time of {Yoga}[0.465, 0.465]. Thus, the difference can be calculated by subtracting the value obtained in Yoga from the one in Running.\nBased on the above thought, the answer is:\n55" } ], "id_raw": 118 }, { "id": "iiDqZXoOKFFnjyoo", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_6_19.png", "conversations": [ { "from": "human", "value": "\nCan you convert the horizontal bar plot to a structured data table with investment types and two quarters: Q1 and Q2?" }, { "from": "gpt", "value": "To create the structured table, I first take note of {the y-axis labels}[0.051, 0.170, 0.158, 0.858], which represent the row values or investment types. Next, I identify the lengths of the bars representing {Q1}[0.307, 0.253, 0.682, 0.775] and corresponding {Q2}[0.388, 0.243, 0.921, 0.777] as the numerical values for the columns. Matching each investment type with its corresponding Q1 and Q2 lengths, I can form the data table.\nBased on the above thought, the answer is:\nInvestment Types,Q1,Q2\nCorporate Bonds,40,50\nMutual Funds,35,55\nETFs,50,60\nGold,40,55\nCryptocurrency,45,75\nStocks,35,45\nReal Estate,40,55\nTreasury Bonds,50,40\nMunicipal Bonds,55,40\nDerivatives,55,45" } ], "id_raw": 119 }, { "id": "OpLcam15WMvyG3XR", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_6_19.png", "conversations": [ { "from": "human", "value": "\nBased on the bar plots, which investment type showed the most growth from Q1 to Q2, and what might this indicate?" }, { "from": "gpt", "value": "To assess the growth of each investment type, I compare the lengths of the bars representing {Q1}[0.307, 0.243, 0.682, 0.777] and corresponding {Q2}[0.388, 0.243, 0.921, 0.777] for each investment type. Notably, {Cryptocurrency}[0.307, 0.610, 0.797, 0.624] presents a noticeable growth from Q1 to Q2, with its length increasing from 45 to 75, which is consistent with the extracted data. This substantial growth implies that Cryptocurrency was becoming a more attractive investment option during this period.\nBased on the above thought, the answer is:\nCryptocurrency showed the most growth from Q1 to Q2 according to the bar plots. This upward trend could suggest increased investor interest and confidence in Cryptocurrency during this period, which might have been triggered by positive market news, improved market conditions, or favorable policy changes. It may indicate a prosperous trend for Cryptocurrency investment for the ongoing quarter." } ], "id_raw": 119 }, { "id": "826hK8FDdhzdav5V", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_6_19.png", "conversations": [ { "from": "human", "value": "\nBy how many points did the rating for Cryptocurrency increase from Q1 to Q2?" }, { "from": "gpt", "value": "I first locate the bars representing Cryptocurrency's rating for {Q1}[0.307, 0.610] and {Q2}[0.797, 0.610]. I then extract the corresponding rating values, which are 45 for Q1 and 75 for Q2 respectively. The increase in rating is calculated by subtracting the Q1 value from the Q2 value, i.e., 75 - 45 = 30.\nBased on the above thought, the answer is:\n30." } ], "id_raw": 119 }, { "id": "33olZyVrmlPGZ0v2", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_0.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart indicating transportation preference over the years into a structured table representation?" }, { "from": "gpt", "value": "To create a table from the line chart, I first identify {the x-axis tick labels}[[0.072, 0.855, 0.134, 0.886], [0.242, 0.855, 0.303, 0.886],[0.411, 0.855, 0.473, 0.886], [0.581, 0.855, 0.642, 0.886],[0.750, 0.855, 0.812, 0.886], [0.920, 0.855, 0.981, 0.886]], that represent the column headers (Years). Next, I identify {the legend texts}[[0.238, 0.934, 0.460, 0.964],[0.560, 0.934, 0.689, 0.964],[0.789, 0.934, 0.874, 0.964]] as the row labels indicating different modes of transportation. To assign the corresponding values, I locate {the line points} for each mode of transportation at the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nYear,Public_Transportation,Automobiles,Bicycles\n2015,0.75,0.90,0.20\n2016,0.80,0.86,0.25\n2017,0.84,0.82,0.35\n2018,0.82,0.78,0.45\n2019,0.78,0.75,0.55\n2020,0.72,0.70,0.65" } ], "id_raw": 120 }, { "id": "W91m7iXNhgOsixC3", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_0.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, what conclusions can be drawn regarding the trend in transportation preferences from 2015 to 2020?" }, { "from": "gpt", "value": "To draw conclusions from the line chart, I need to analyze the trend lines for different modes of transportation. The line for {Public Transportation}[0.103, 0.187, 0.950, 0.280] shows a gradual decrease in preference from 0.75 in 2015 to 0.72 in 2020. The line for {Automobiles}[0.103, 0.141, 0.950, 0.295] also shows a steady decline from 0.9 in 2015 to 0.7 in 2020. In contrast, the line for {Bicycles}[0.103, 0.334, 0.950, 0.681] exhibits a consistent increase from 0.2 in 2015 to 0.65 in 2020.\nBased on the above thought, the answer is:\nThe line chart indicates a clear shift in transportation preferences from 2015 to 2020. Both Public Transportation and Automobiles have seen a decline in preference, signifying a possible move away from traditional modes of transport. Simultaneously, there is an evident increase in the preference for Bicycles, suggesting a growing trend towards eco-friendly and active modes of transportation." } ], "id_raw": 120 }, { "id": "FtY3cGneivIuz4VZ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_0.png", "conversations": [ { "from": "human", "value": "\nBy how much did the preference for Bicycles increase from 2015 to 2020?" }, { "from": "gpt", "value": "To determine the increase in preference for Bicycles from 2015 to 2020, I need to identify the corresponding values from {the line points for Bicycles}[[0.103, 0.681], [0.272, 0.642], [0.442, 0.565], [0.611, 0.488], [0.781, 0.411], [0.950, 0.334]]. The preference in 2015 is 0.20 at point {2015}[0.103, 0.681] and in 2020 it is 0.65 at point {2020}[0.950, 0.334]. So the increase in preference for Bicycles from 2015 to 2020 is 0.65 - 0.20 = 0.45.\nBased on the above thought, the answer is:\n0.45." } ], "id_raw": 120 }, { "id": "VOwtSiu4vHEKzf7p", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_1.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart's data into a tabular format, with 'Cities' being the row headers and 'Avg Temperature (\u00b0C)', 'Avg Snowfall (cm)' being the column headers?" }, { "from": "gpt", "value": "For creating a table from the line chart, I have to first identify {the x-axis tick labels}[[0.028, 0.902, 0.101, 0.934], [0.328, 0.902, 0.396, 0.934], [0.610, 0.902, 0.709, 0.934], [0.933, 0.902, 0.981, 0.934]], as these hold the city names forming the row headers. Next, I must allocate the column headers, which are {the legend texts}[[0.714, 0.784, 0.938, 0.814], [0.714, 0.828, 0.901, 0.858]]. The column headers are \"Avg Temperature (\u00b0C)\" and \"Avg Snowfall (cm)\". The actual values can be inferred from {the line points}[[0.064, 0.743], [0.362, 0.669], [0.659, 0.570], [0.957, 0.535], [0.064, 0.826], [0.362, 0.180], [0.659, 0.849], [0.957, 0.849]].\nBased on the above thought, the answer is:\nCity,Avg Temperature (\u00b0C),Avg Snowfall (cm)\nTorino,5.4,1.2\nAosta,9.2,34.1\nBologna,14.2,0\nBari,16,0" } ], "id_raw": 121 }, { "id": "rguv3WxM7A4giB5Z", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_1.png", "conversations": [ { "from": "human", "value": "\nBeyond the raw numbers, what is the overall trend in the weather of these Italian cities according to the line chart?" }, { "from": "gpt", "value": "To deduce the overall weather trends in these Italian cities according to the line chart, I need to interpret the information provided by the plots. {The line for Avg Temperature (\u00b0C)}[0.064, 0.535, 0.957, 0.743] reveals an increase as we move from Torino to Bari. This suggests an overall rise in average temperatures in these cities from north (Torino) to south (Bari), typically found in Italy due to its geographical orientation. On the other hand, {the line for Avg Snowfall (cm)}[0.064, 0.180, 0.957, 0.849] displays a variable snowfall pattern where it is seen only in Torino and Aosta conspicuously, and no snowfall in Bologna or Bari. Snowfall seems to decline sharply as you move from the northern city Aosta to the central-northern city Bologna before reaching zero in the southern city Bari.\nBased on the above thought, the answer is:\nThe overall trend in the weather of these Italian cities, according to the line chart, hints at a climatic pattern often observed in Italy. The average temperatures tend to rise moving from northern cities towards the south, signifying a warmer climate in southern cities like Bari, compared to northern cities such as Torino. As for snowfall, it seems to be considerably higher in the northern cities like Torino and Aosta, but absent in Bologna and Bari, suggesting that snowfall is a crucial component of the weather only in the northern parts of Italy." } ], "id_raw": 121 }, { "id": "idn0Pi169Bde0BZF", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the average snowfall in Torino and Bari combined?" }, { "from": "gpt", "value": "To determine the average snowfall in Torino and Bari combined, I have to find the snowfall values for these cities first. The snowfall in Torino is located at {the point for Torino in Avg Snowfall (cm)}[0.064, 0.826] with a value of 1.2 cm. The snowfall in Bari is depicted at {the point for Bari in Avg Snowfall (cm)}[0.957, 0.849] with a value of 0.0 cm. The average snowfall is then calculated by summing these two values and dividing by 2.\nBased on the above thought, the answer is:\n0.6 cm." } ], "id_raw": 121 }, { "id": "hipf5j3uDxral51v", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the evenly scattered points denoting each sector's innovation index in the line chart into a structured table while considering industry sectors as rows?" }, { "from": "gpt", "value": "To form the table, I first look at {the x-axis tick labels}[0.056, 0.697, 0.112, 0.790], [0.107, 0.697, 0.188, 0.831],[0.210, 0.697, 0.263, 0.785], [0.292, 0.697, 0.339, 0.775], [0.351, 0.697, 0.414, 0.803],[0.420, 0.697, 0.490, 0.814],[0.500, 0.697, 0.565, 0.806], [0.601, 0.697, 0.641, 0.764], [0.675, 0.697, 0.717, 0.767],[0.707, 0.697, 0.792, 0.840],[0.820, 0.697, 0.868, 0.777], [0.869, 0.697, 0.943, 0.821] which represent the rows of the table. Extracting the values from {the line points}[0.112, 0.557], [0.188, 0.434], [0.263, 0.311], [0.339, 0.218], [0.414, 0.234],[0.490, 0.218],[0.565, 0.181], [0.641, 0.156], [0.717, 0.135],[0.792, 0.301],[0.868, 0.419], [0.943, 0.212] corresponding to each sector gives us the innovation indices of these sectors.\nBased on the above thought, the answer is:\nInvestment Strategy,Earnings\nHigh Risk, 0.4\nModerate Risk, 0.8\nLow Risk, 1.2\nNo Risk, 1.5\nIndex Fund, 1.45\nMutual Fund, 1.5\nReal Estate, 1.62\nBonds, 1.7\nStocks, 1.77\nCryptocurrency, 1.23\nOptions, 0.85\nCommodities, 1.52" } ], "id_raw": 122 }, { "id": "iIi7Bbch0neryEEE", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_2.png", "conversations": [ { "from": "human", "value": "\nIf a prospective investor is looking to invest in a sector with high innovation index, which sectors should he consider according to the line chart? Explain." }, { "from": "gpt", "value": "To answer this question, I need to identify the sectors with high innovation index. Observing the line chart, I can see that {Stocks}[0.717, 0.135], {Bonds}[0.641, 0.156], {Real Estate}[0.565, 0.181], {Commodities}[0.943, 0.212] and {Mutual Fund}[0.490, 0.218] have innovation indices above 1.5 which is significantly high compared to other sectors.\nBased on the above thought, the answer is:\nAccording to the line chart, a prospective investor looking to invest in a sector with a high innovation index should consider the sectors of Stocks, Bonds, Real Estate, Commodities, and Mutual Fund. These sectors are tagged with innovation indices above 1.5, indicating a strong innovation performance and thereby presenting the potential for higher returns from investments made in these sectors." } ], "id_raw": 122 }, { "id": "Iz2tf41vUONx3lVp", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest innovation index across all sectors in the line chart?" }, { "from": "gpt", "value": "To find the highest and lowest innovation index, I need to locate and analyze {the line points}[0.112, 0.557], [0.188, 0.434], [0.263, 0.311], [0.339, 0.218], [0.414, 0.234],[0.490, 0.218],[0.565, 0.181], [0.641, 0.156], [0.717, 0.135],[0.792, 0.301],[0.868, 0.419], [0.943, 0.212] on the line chart. From these coordinates, I see that the highest innovation index is for Stocks at 1.77 [0.717, 0.135], and the lowest is for High Risk at 0.4 [0.112, 0.557]. The difference between the highest and lowest innovation index is therefore 1.77 - 0.4 = 1.37.\nBased on the above thought, the answer is:\n1.37" } ], "id_raw": 122 }, { "id": "sJp7iYNRpGNJ9EeW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart displaying the annual energy use and resource consumption between 2001 and 2010 into a table format where rows represent years and columns represent the type of energy resource?" }, { "from": "gpt", "value": "To convert the line chart into a table, I extract the column names from {the legend}[0.373, 0.953, 0.688, 0.985]. These represent the type of energy resource. I then identify {the x-axis tick labels}[0.076, 0.851, 0.125, 0.874], [0.172, 0.851, 0.221, 0.874], [0.267, 0.851, 0.316, 0.874], [0.363, 0.851, 0.412, 0.874], [0.458, 0.851, 0.508, 0.874], [0.554, 0.851, 0.603, 0.874], [0.649, 0.851, 0.698, 0.874], [0.745, 0.851, 0.794, 0.874], [0.840, 0.851, 0.889, 0.874], [0.936, 0.851, 0.985, 0.874] as row labels representing the different years. With these headers established, I then identify {the line points for Coal}[0.101, 0.078, 0.960, 0.351], {Oil}[0.101, 0.533, 0.960, 0.806], and {Natural gas}[0.101, 0.472, 0.960, 0.745] and extract the corresponding values at each point for each year.\nBased on the above thought, the answer is:\nYear,Coal,Oil,Natural Gas\n2001,5100,4500,3800\n2002,5200,4400,3900\n2003,5300,4300,4000\n2004,5400,4200,4100\n2005,5500,4100,4200\n2006,5600,4000,4300\n2007,5700,3900,4400\n2008,5800,3800,4500\n2009,5900,3700,4600\n2010,6000,3600,4700" } ], "id_raw": 124 }, { "id": "wbiPtVS5UYZGqum1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_4.png", "conversations": [ { "from": "human", "value": "\nWhich energy resource has shown a consistently increasing trend in consumption from 2001 to 2010, and what does it indicate about the reliance on this resource?" }, { "from": "gpt", "value": "I interpret the trend for each energy resource by looking at their respective line plots on the chart. Looking at the line for {Coal}[0.101, 0.078, 0.960, 0.351], it consistently increases from 5100 to 6000, indicating a constant rise in Coal consumption over these years. The line for {Oil}[0.101, 0.533, 0.960, 0.806] shows a decreasing trend, whereas {Natural gas}[0.101, 0.472, 0.960, 0.745] shows an increase, but it's not as marked as Coal\u2019s. Therefore, Coal is the energy resource with a consistently increasing trend in consumption.\nBased on the above thought, the answer is:\nThe energy resource with a consistently increasing trend in consumption from 2001 to 2010 is Coal. This could indicate a higher reliance on Coal as a source of energy during this period, whether due to factors such as cost-effectiveness, availability, or energy requirement demands. However, the increasing trend also suggests potential environmental concerns, since the burning of Coal for energy contributes significantly to air pollution and climate change." } ], "id_raw": 124 }, { "id": "YBh78f2PIg2XNL3b", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_4.png", "conversations": [ { "from": "human", "value": "\nWhat was the increase in consumption of Natural Gas from 2001 to 2010?" }, { "from": "gpt", "value": "To answer this question, I first locate {the line points for Natural Gas}[0.101, 0.472, 0.960, 0.745] and extract the consumption values for 2001 which is 3800 and 2010 which is 4700. The difference between consumption in 2010 and 2001 is therefore 4700 - 3800 = 900.\nBased on the above thought, the answer is:\n900." } ], "id_raw": 124 }, { "id": "P6sbZhgAmlQmH79E", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data represented in the graph into a structured table with the media types as columns and the features as rows?" }, { "from": "gpt", "value": "To extract tabular data from the line graph, I first identify the {x-axis tick labels}[0.077, 0.851, 0.138, 0.883] to [0.920, 0.851, 0.981, 0.883] which stand for the column headers or Entrepreneurial Features. Secondly, I identify {the legend texts}[0.188, 0.934, 0.381, 0.964], [0.481, 0.934, 0.613, 0.964] and [0.713, 0.934, 0.929, 0.964] as the row labels representing the types of Rates in %. To fill up the table, I locate {the line points}[0.108, 0.800] to [0.951, 0.598], [0.108, 0.202] to [0.951, 0.101] and [0.108, 0.294] to [0.951, 0.156] for each feature at the respective rates and extract the values.\nBased on the above thought, the answer is:\n|\n Entrepreneurial | High-tech startup | Success rate | Government support |\n Features | Rates in % | Rates in % | Rates in % |\n Attr1 | 20 | 85 | 75 |\n Attr2 | 22 | 88 | 76 | \n Attr3 | 25 | 88 | 80 | \n Attr4 | 28 | 90 | 82 | \n Attr5 | 31 | 92 | 88 | \n Attr6 | 36 | 96 | 82 | \n Attr7 | 39 | 95 | 85 | \n Attr8 | 42 | 96 | 90 |" } ], "id_raw": 126 }, { "id": "MQVMFTbsdcfcKnwa", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_6.png", "conversations": [ { "from": "human", "value": "\nBased on the line graph, which rate experienced the most steady increase throughout the entrepreneurial features?" }, { "from": "gpt", "value": "To determine which rate experienced the most steady increase, we need to look at the fluctuation in the line plots. Observing the {line plot for High-tech startups}[0.108, 0.598, 0.951, 0.800] shows a straight and steady increase from 20 to 42. {Line plot for Success rate}[0.108, 0.101, 0.951, 0.202] has a relatively stable increase from 85 to 96 with a slight dip for Attr7. The {line plot for Government Support}[0.108, 0.156, 0.951, 0.294] shows increase and decrease movements as it journeys from 75 to 90.\nBased on the above thought, the answer is:\nThe High-tech start-ups rate experienced the most steady increase throughout the entrepreneurial features. The line plot shows a consistent increase in percentage rate from 20 up to 42 across the entrepreneurial features. This suggests that the high-tech start-up rate continues to improve steadily over time compared to the Success rate and Government Support rate." } ], "id_raw": 126 }, { "id": "F3ylxuW7Knq65TZo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_6.png", "conversations": [ { "from": "human", "value": "\nBy how much percent did Government support rate increase from Attr1 to Attr8?" }, { "from": "gpt", "value": "To get the increase in Government support from Attr1 to Attr8, I need to get the initial value for Attr1 from the point coordinate [0.108, 0.294] and the final value for Attr8 from the point coordinate [0.951, 0.156]. On extracting the corresponding values, I find the initial value is 75 and the final value is 90. Therefore, the percentage increase is calculated as ((Final Value - Initial Value)/Initial Value)*100.\nBased on the above thought, the answer is:\n20%." } ], "id_raw": 126 }, { "id": "vIRyHN2QsnZSUwRu", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_7.png", "conversations": [ { "from": "human", "value": "\nCould you transform the line chart data into a structured table, with the years as rows and the trends of 'Global Trade Demand' and 'Global Supply Management' as columns?" }, { "from": "gpt", "value": "In order to convert the line chart data into a tabular format, I will first locate and identify the axis labels. The {x-axis tick labels}[[0.072, 0.852, 0.115, 0.875], [0.159, 0.852, 0.202, 0.875], [0.246, 0.852, 0.289, 0.875], [0.333, 0.852, 0.376, 0.875], [0.421, 0.852, 0.463, 0.875], [0.508, 0.852, 0.550, 0.875], [0.595, 0.852, 0.637, 0.875], [0.682, 0.852, 0.724, 0.875], [0.769, 0.852, 0.811, 0.875], [0.856, 0.852, 0.898, 0.875], [0.943, 0.852, 0.985, 0.875]] will serve as the row identifiers, each corresponding to a different year. The columns, on the other hand, will be represented by the two lines in the plot, namely 'Global Trade Demand' {line_0}[[0.093, 0.127, 0.964, 0.706]] and 'Global Supply Management' {line_1}[[0.093, 0.114, 0.964, 0.732]]. To populate the values in each cell, I would study the coordinates of each point on the line and extract the corresponding values accordingly.\nBased on the above thought, the answer is:\nYear Global Trade Demand, Global Supply Management\n2010 500, 480\n2011 520, 520\n2012 550, 560\n2013 600, 580\n2014 650, 600\n2015 750, 620\n2016 800, 750\n2017 825, 800\n2018 850, 820\n2019 900, 850\n2020 940, 950" } ], "id_raw": 127 }, { "id": "hyMhO1KZKHnZGArZ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_7.png", "conversations": [ { "from": "human", "value": "\nLooking at the trends represented in the chart, which area, trade demand or supply management, saw a more stable growth?" }, { "from": "gpt", "value": "To determine the area with more stable growth, I analyze the lines representing 'Global Trade Demand' {line_0}[[0.093, 0.127, 0.964, 0.706]] and 'Global Supply Management' {line_1}[[0.093, 0.114, 0.964, 0.732]]. For 'Global Trade Demand', the line shows an oscillating but overall increasing trend over the years, indicating some volatility. For 'Global Supply Management', the line initially stays relatively flat but begins to increase significantly from 2016 onwards, which suggests an overall more stable growth before 2016.\nBased on the above thought, the answer is:\nFrom the trends displayed in the chart, it can be inferred that the 'Global Supply Management' had more stable growth before 2016, mostly staying flat with only slight changes. However, after 2016 it started increasing significantly, showing less stability. On the other hand, 'Global Trade Demand' showed some volatility but overall an increasing trend." } ], "id_raw": 127 }, { "id": "chhWxqyQ7VKfMQ2B", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum value reached by the 'Global Trade Demand' and in which year did it occur?" }, { "from": "gpt", "value": "To find the maximum value reached by 'Global Trade Demand', I need to go through all the points in the {Global Trade Demand line}[[0.093, 0.127, 0.964, 0.706]]. The points represent the different years from 2010 to 2020. By examining each point, I identify the highest y-coordinate which represents the maximum value. It corresponds to year 2020 {corresponding x-coordinate of the year 2020}[0.943, 0.852, 0.985, 0.875] with a value of 940.\nBased on the above thought, the answer is:\nThe maximum value reached by the 'Global Trade Demand' is 940, in the year 2020." } ], "id_raw": 127 }, { "id": "KSxbI8wVP98r0tYg", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_9.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart representing the growth rate of different emerging technologies over the years into tabular data format where tech categories are the row labels and years are the column labels?" }, { "from": "gpt", "value": "To extract the table format from this line chart, I consider {the x-axis tick labels}[[0.080, 0.855, 0.141, 0.886], [0.247, 0.855, 0.309, 0.886], [0.415, 0.855, 0.477, 0.886], [0.583, 0.855, 0.645, 0.886], [0.751, 0.855, 0.813, 0.886], [0.919, 0.855, 0.981, 0.886]] as column headers representing the different years. {The legend texts}[[0.301, 0.846, 0.321, 0.876], [0.301, 0.890, 0.477, 0.920], [0.301, 0.934, 0.330, 0.964], [0.577, 0.846, 0.609, 0.876], [0.577, 0.890, 0.689, 0.920], [0.789, 0.846, 0.818, 0.876], [0.789, 0.890, 0.818, 0.920]] are the row labels representing the emerging technologies. To gather the values, I look at {the points on each line}[[0.110, 0.660], [0.278, 0.600], ..., [0.950, 0.325]] and record the corresponding values for each technology at the respective years.\nBased on the above thought, the answer is:\nTechnology,2020,2021,2022,2023,2024,2025\nAI,20,28,35,43,53,65\nCloud computing,15,22,31,40,51,65\n5G,10,25,40,56,72,90\nIoT,5,12,19,27,36,46\nBlockchain,2,3,11,21,32,44\nAR,1,2,3,5,8,12\nVR,1,2,4,8,13,21" } ], "id_raw": 129 }, { "id": "OplaXKkb3xIBmQDu", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_9.png", "conversations": [ { "from": "human", "value": "\nLooking at the patterns in the line chart, what can be said about the growth rate of the technologies over the years?" }, { "from": "gpt", "value": "Analyzing the line chart, I notice that all the technologies have shown progress over the years. The lines for AI, Cloud computing, 5G, IoT, Blockchain, AR, and VR are all upward sloping indicating increasing growth rates. However, the pace of growth varies across technologies. For instance, {the line for 5G}[0.110, 0.138, 0.950, 0.735] shows a steep upward slope, indicating rapid growth rate. On the other hand, {the line for IoT}[0.110, 0.466, 0.950, 0.772] depicts a less steep slope suggesting a relatively slower growth over the years. MR and VR, represented by {the lines for AR and VR}[0.110, 0.720, 0.950, 0.802][0.110, 0.653, 0.950, 0.802], show the slowest growth, with AR growing just a little more than VR. The growth rate of these technologies could be shaped by various factors such as development of technology, market demand, and investment in research and development.\nBased on the above thought, the answer is:\nFrom the line chart it can be observed that all the technologies have been growing over the years, with varying rates of growth. 5G has seen the most rapid growth, followed by Cloud computing and AI. IoT and Blockchain also show significant growth, but at a slower pace compared to the previous technologies mentioned. AR and VR represent emerging technologies that are growing at the slowest rate, indicating these technologies may still be in the early stages of adoption or development. However, all technologies are expected to continue to grow in the future. This trend points towards a continual expansion and advancement in technologies over the course of the years 2020 to 2025." } ], "id_raw": 129 }, { "id": "mHh45MuP4bjXtFoZ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the average growth rate of AI from 2020 to 2025 according to the line chart?" }, { "from": "gpt", "value": "To find the average growth rate of AI from 2020 to 2025, I observe {the points on the line for AI}[[0.110, 0.660], [0.278, 0.600], [0.446, 0.548], [0.614, 0.489], [0.782, 0.414], [0.950, 0.325]], which correspond to the growth rates for the years from 2020 to 2025: 20, 28, 35, 43, 53, 65 respectively. I add up these rates and divide by the number of years to get the average growth rate.\nBased on the above thought, the answer is:\n40.6667" } ], "id_raw": 129 }, { "id": "JBcDcyh1UdiZl32h", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_10.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the data output of these algorithm's achievable efficiency at different levels?" }, { "from": "gpt", "value": "OK, To form a structured table from the line chart, I begin by identifying {the x-axis labels}[0.081, 0.851, 0.120, 0.883], [0.214, 0.851, 0.268, 0.883], [0.362, 0.851, 0.401, 0.883], [0.495, 0.851, 0.550, 0.883], [0.655, 0.851, 0.671, 0.883], [0.777, 0.851, 0.831, 0.883], [0.925, 0.851, 0.964, 0.883] as columns representing different levels of efficiency. The rows would represent the different algorithms, which I identify from {the legend}[0.098, 0.923, 0.947, 0.975]. I then find the values corresponding to each efficiency level for each algorithm from the line points of {Heuristic Search}[0.100, 0.578], [0.241, 0.321], [0.382, 0.107], [0.522, 0.235], [0.663, 0.192], [0.804, 0.235], [0.944, 0.278], {Genetic Algorithm}[0.100, 0.492], [0.241, 0.321], [0.382, 0.278], [0.522, 0.150], [0.663, 0.107], [0.804, 0.278], [0.944, 0.407], and {Simulated Annealing}[0.100, 0.792], [0.241, 0.621], [0.382, 0.449], [0.522, 0.364], [0.663, 0.235], [0.804, 0.192], [0.944, 0.150] to generate the rows.\nBased on the above thought, the answer is:\nEfficiency_Levels, Heuristic_Search, Genetic_Algorithm, Simulated_Annealing\n0.1,0.4,0.5,0.15\n0.25,0.7,0.7,0.35\n0.5,0.95,0.75,0.55\n0.75,0.8,0.9,0.65\n1,0.85,0.95,0.8\n1.25,0.8,0.75,0.85\n1.5,0.75,0.6,0.9" } ], "id_raw": 130 }, { "id": "WFAziKEzbvZXbFwl", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_10.png", "conversations": [ { "from": "human", "value": "\nBased on the efficiency improvements shown in the line chart, which algorithm has the largest growth in efficiency over the observed levels? Explain." }, { "from": "gpt", "value": "To determine which algorithm showed the greatest efficiency growth, I need to compare the improvement of each algorithm. Starting with {Heuristic Search}[0.100, 0.578], [0.944, 0.278] I observe an improvement from 0.4 to 0.75, resulting in a growth of 0.35. Next, for {Genetic Algorithm}[0.100, 0.492], [0.944, 0.407], the improvement is 0.1, increasing from 0.5 to 0.6. Lastly, for {Simulated Annealing}[0.100, 0.792], [0.944, 0.150], we observe an improvement of 0.75, escalating from 0.15 to 0.9.\nBased on the above thought, the answer is:\nBased on the efficiencies demonstrated on the chart, the Simulated Annealing algorithm has the greatest growth, improving from 0.15 at level 0.1 to 0.9 at level 1.5, representing an efficiency growth of 0.75. This growth is notably larger than the improvements seen in both the Heuristic Search and Genetic Algorithm, making it the algorithm with the largest efficiency growth across the observed levels." } ], "id_raw": 130 }, { "id": "On9zU4FKXkGVtG94", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the efficiency improvement from level 0.1 to level 1.5 in the Heuristic Search algorithm?" }, { "from": "gpt", "value": "To determine the efficiency improvement, I need to find the values of the Heuristic Search algorithm at levels 0.1 and 1.5. I find the coordinates of the line point for level 0.1 is at {0.1}[0.100, 0.578], and the y-coordinate corresponds to an efficiency of 0.4. Next, I find the line point for level 1.5 is at {1.5}[0.944, 0.278], and the y-coordinate corresponds to an efficiency of 0.75. Therefore, the efficiency improvement from level 0.1 to 1.5 in the Heuristic Search algorithm is 0.75 - 0.4 = 0.35.\nBased on the above thought, the answer is:\n0.35" } ], "id_raw": 130 }, { "id": "CbHoboCTsaaiKhKT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_11.png", "conversations": [ { "from": "human", "value": "\nCan you create a table showing the years and corresponding data for ICU Beds per 100k, Rapid Tests in Mill., and Telemedicine Service?" }, { "from": "gpt", "value": "Analyzing the chart, I identified the x-axis labels specifically representing the years like {the year 2000}[0.084, 0.917, 0.127, 0.975] up to {the year 2020}[0.918, 0.917, 0.961, 0.975] which serve as the column for years. Each line in the chart such as {the line for ICU Beds per 100k}[0.106, 0.102, 0.940, 0.686], {the line for Rapid Tests in Mill.}[0.106, 0.854, 0.940, 0.863], and {the line for Telemedicine Service}[0.106, 0.836, 0.940, 0.862] offers points aligned with years to note down their respective values. Compiling these into a structured table:\nBased on the above thought, the answer is:\nYear,ICU Beds per 100k,Rapid Tests in Mill.,Telemedicine Service\n2000,128,0.5,1.0\n2001,130,0.51,1.1\n2002,132,0.52,1.2\n2003,134,0.55,1.4\n2004,140,0.58,1.6\n2005,145,0.64,1.7\n2006,150,0.71,1.8\n2007,160,0.77,2.0\n2008,170,0.84,2.3\n2009,180,0.9,2.7\n2010,190,1.0,3.2\n2011,200,1.2,3.8\n2012,250,1.5,4.6\n2013,310,1.8,5.5\n2014,380,2.3,6.7\n2015,370,2.7,8.0\n2016,410,3.5,9.5\n2017,420,4.3,11.0\n2018,450,4.8,13.0\n2019,500,5.5,15.0\n2020,550,7.0,20.0" } ], "id_raw": 131 }, { "id": "AzZgs4SY3N54Rej0", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_11.png", "conversations": [ { "from": "human", "value": "\nWhich technological category showed the highest growth from 2000 to 2020 according to the line chart?" }, { "from": "gpt", "value": "Reviewing all the available lines on the chart, the data points for {the line for Telemedicine Service}[0.106, 0.836, 0.940, 0.862] show a rapid increase from 1.0 in 2000 to 20.0 in 2020. In comparison, {the line for ICU Beds per 100k}[0.106, 0.102, 0.940, 0.686] and {the line for Rapid Tests in Mill.}[0.106, 0.854, 0.940, 0.863] also show growth, but not as marked as Telemedicine Service.\nBased on the above thought, the answer is:\nTelemedicine Service showed the highest growth from 2000 to 2020, as evidenced by its substantial increase from 1.0 in 2000 to 20.0 in 2020, outpacing the growth in ICU beds per 100k and rapid tests in millions." } ], "id_raw": 131 }, { "id": "O36jJQmo3pKOEwxH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the average increase in Telemedicine Service from 2000 to 2020?" }, { "from": "gpt", "value": "To calculate the average increase in Telemedicine Service, I started by analyzing {the line for Telemedicine Service}[0.106, 0.836, 0.940, 0.862] to retrieve each annual data from 2000 to 2020. The initial value in 2000 is 1.0, and the final value in 2020 is 20.0. The average increase is calculated by subtracting the initial value from the final value and then dividing by the number of years, which is 20.\nBased on the above thought, the answer is:\n0.95" } ], "id_raw": 131 }, { "id": "mehzFUpBEjqy3ps3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_12.png", "conversations": [ { "from": "human", "value": "\nCould you generate a table that outlines the yearly market share percentages for each company from 2000 to 2010?" }, { "from": "gpt", "value": "Definitely, in order to extract the data into a table, I'm noting the x-axis tick labels {X-axis years}[0.052, 0.927, 0.101, 0.951],[0.140, 0.927, 0.189, 0.951],[0.228, 0.927, 0.278, 0.951],[0.317, 0.927, 0.366, 0.951],[0.405, 0.927, 0.454, 0.951],[0.494, 0.927, 0.543, 0.951],[0.582, 0.927, 0.631, 0.951],[0.670, 0.927, 0.720, 0.951],[0.759, 0.927, 0.808, 0.951],[0.847, 0.927, 0.897, 0.951],[0.936, 0.927, 0.985, 0.951], which represents the years. Then I identify {the legend texts}[0.138, 0.068, 0.233, 0.090],[0.138, 0.101, 0.233, 0.123],[0.138, 0.134, 0.233, 0.156] as the company labels. The y-axis tick labels {Y-axis percentages}[0.040, 0.903, 0.064, 0.926], [0.040, 0.687, 0.064, 0.711], [0.040, 0.471, 0.064, 0.495], [0.040, 0.256, 0.064, 0.279],[0.040, 0.040, 0.064, 0.064] indicate the market percentage shares. Lastly, I will analyze {the line points for the companies}[[0.076, 0.696], [0.165, 0.718], [0.253, 0.804], ..., [0.960, 0.157]], [[0.076, 0.653], [0.165, 0.545], ..., [0.960, 0.265]], [0.076, 0.739], [0.165, 0.869], ..., [0.960, 0.437]] to extract the respective percentage shares.\nBased on the above thought, the answer is:\nYear,Company A,Company B,Company C\n2000,20,22,18\n2001,19,27,12\n2002,15,30,14\n2003,25,28,20\n2004,28,33,19\n2005,30,24,23\n2006,27,36,21\n2007,35,32,25\n2008,33,34,28\n2009,40,38,30\n2010,45,40,32" } ], "id_raw": 132 }, { "id": "uTJy7efzamSENaRa", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which company has shown the most growth in its market share percentage from 2000 to 2010?" }, { "from": "gpt", "value": "To determine the company with the most growth, I need to look at the difference between the initial and final market share percentages for each company. Now, I'm looking at the market shares for Company A at {2000}[0.076, 0.696] and {2010}[0.960, 0.157], for Company B at {2000}[0.076, 0.653] and {2010}[0.960, 0.265], and for Company C at {2000}[0.076, 0.739] and {2010}[0.960, 0.437]. The differences for Company A, Company B, and Company C are 25, 18, and 14 respectively, calculated by subtracting the market shares in 2000 from the market shares in 2010.\nBased on the above thought, the answer is:\nBased on the difference in market shares from 2000 to 2010, Company A has shown the most growth in its market share." } ], "id_raw": 132 }, { "id": "18cYwCcouJTFdYNU", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest market share percentage achieved by Company A over the period from 2000 to 2010?" }, { "from": "gpt", "value": "To identify the highest market share percentage for Company A, I should analyze {the line points for Company A}[[0.076, 0.696], [0.165, 0.718], [0.253, 0.804], ..., [0.960, 0.157]]. Upon analysis, I see that the highest point is observed in 2002 with a percentage of 45, which corresponds to the coordinate {2002, 45}[0.253, 0.804].\nBased on the above thought, the answer is:\n45" } ], "id_raw": 132 }, { "id": "kELc855eZh37JQIQ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_13.png", "conversations": [ { "from": "human", "value": "\nCould you organize the data from the line graph into a table with years as rows and health factors as columns?" }, { "from": "gpt", "value": "To develop a structured table from the line graph, the first step is to look at {the x-axis tick labels}[[0.050, 0.851, 0.099, 0.883], [0.271, 0.851, 0.320, 0.883], [0.493, 0.851, 0.542, 0.883], [0.714, 0.851, 0.764, 0.883], [0.936, 0.851, 0.985, 0.883]], which denote the row headers or years. Then, I identify {the legend texts}[[0.362, 0.934, 0.435, 0.964], [0.515, 0.934, 0.575, 0.964], [0.655, 0.934, 0.719, 0.964]] as the column headers specifying the distinct health factors. To populate the values, I spot {the line points}[[0.074, 0.571], [0.296, 0.615], ..., [0.960, 0.703]] for each health factor at the corresponding years and extract the values annotated.\nBased on the above thought, the answer is:\nYear,Smoking,Alcohol,Obesity\n2000,27,21,30\n2005,25,22,34\n2010,24,22,38\n2015,22,21,42\n2020,21,20,46" } ], "id_raw": 133 }, { "id": "uiLUNihRVFjY8hDp", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_13.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which health factor seems to be the most concerning over time? Explain your reasoning." }, { "from": "gpt", "value": "To determine the most concerning health factor, I must analyze the lines associated with each health factor. Looking at {the line for Smoking}[0.074, 0.571, 0.960, 0.703], the line is showing a downward trend indicating a decrease in the percentage of population smoking over time.\nFor {Alcohol}[0.074, 0.703, 0.960, 0.725], the line is almost flat with minor decrease indicating no significant change in alcohol consumption.\nFinally, for {Obesity}[0.074, 0.153, 0.960, 0.505], the line is showing a steep incline pointing towards a significant increase in the percentage of obese population over time.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, obesity seems to be the most concerning health factor over time. The line plot associated with obesity exhibits a steep upward gradient indicating that the percentage of the obese population has been consistently increasing throughout the years. In contrast, the graphs for smoking and alcohol depict a decrease or minor change in their respective percentages, suggesting improvements in those areas. The rise in obesity prevalence presents alarming implications for public health which makes it the most pressing concern." } ], "id_raw": 133 }, { "id": "ZrR65TAPkTu7pECS", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the total decrease in smoking percentages from the year 2000 to 2020?" }, { "from": "gpt", "value": "To find the total decrease in smoking percentages, I need to locate {the line points for Smoking}[[0.074, 0.571], [0.296, 0.615], [0.517, 0.637], [0.739, 0.681], [0.960, 0.703]] and extract the corresponding values: 27, 25, 24, 22, 21. The value for the year 2000 is found at {the point corresponding to 2000}[0.074, 0.571] which is 27, and the value for the year 2020 is found at {the point corresponding to 2020}[0.960, 0.703] which is 21. The total decrease in smoking percentages from 2000 to 2020 is therefore 27 - 21 = 6.\nBased on the above thought, the answer is:\n6." } ], "id_raw": 133 }, { "id": "NNhUn0sF4hq7tGdA", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_14.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart and provide it as a table format where the columns are Desertification, Population_growth, and Climate_Change, ordered by year?" }, { "from": "gpt", "value": "To create this table, I start by locating the {x-axis tick labels}[0.072, 0.855, 0.134, 0.886], [0.166, 0.855, 0.228, 0.886], [0.260, 0.855, 0.322, 0.886], [0.355, 0.855, 0.416, 0.886], [0.449, 0.855, 0.510, 0.886], [0.543, 0.855, 0.605, 0.886], [0.637, 0.855, 0.699, 0.886], [0.731, 0.855, 0.793, 0.886], [0.825, 0.855, 0.887, 0.886], [0.920, 0.855, 0.981, 0.886], which constitutes the Years column of the table. The lines representing the different metrics in the legend - {Desertification}[0.202, 0.934, 0.354, 0.964], {Population Growth}[0.454, 0.934, 0.644, 0.964], and {Climate Change}[0.744, 0.934, 0.910, 0.964] will represent the other columns in order. The values of the corresponding metrics for a given year can be obtained by studying the points on the corresponding lines for that year.\nBased on the above thought, the answer is:\nYears,Desertification,Population_growth,Climate_Change\n2010,0.90,2.0,0.60\n2011,0.89,2.2,0.65\n2012,0.86,2.4,0.70\n2013,0.84,2.8,0.70\n2014,0.82,3.1,0.75\n2015,0.80,3.8,0.80\n2016,0.75,4.0,0.70\n2017,0.70,4.4,0.71\n2018,0.68,4.3,0.75\n2019,0.60,4.5,0.80" } ], "id_raw": 134 }, { "id": "lhVyeSQbaUqjM7cD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_14.png", "conversations": [ { "from": "human", "value": "\nWhich of the three factors represented in the line chart shows the most significant decrease over the decade?" }, { "from": "gpt", "value": "I analyze the trend lines of the three factors over the years. The {Desertification line}[0.103, 0.722, 0.950, 0.771] starts relatively high and shows a steady decline from 0.90 to 0.60 over the decade.\nThe {Population Growth line}[0.103, 0.144, 0.950, 0.546] generally increases over the decade from 2.0 to 4.5.\nLastly, the {Climate Change line}[0.103, 0.739, 0.950, 0.771] shows a slight increase from 0.6 to 0.8.\nBased on these trends, Desertification shows the most significant decrease over the period.\nBased on the above thought, the answer is:\nThe 'Desertification' factor shows the most significant decrease over the decade, dropping from 0.90 to 0.60." } ], "id_raw": 134 }, { "id": "ivGjNI0z6257eldN", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest value for Population Growth and in which year does this occur?" }, { "from": "gpt", "value": "To find the highest value for Population Growth, I would examine {the line representing Population Growth}[0.103, 0.144, 0.950, 0.546] and check the values at different points. The highest point on the line corresponds to the Year {2019}[0.950, 0.855, 0.981, 0.886] with a value of 4.5.\nBased on the above thought, the answer is:\nThe highest value for Population Growth is 4.5, which occurred in 2019." } ], "id_raw": 134 }, { "id": "xrao5L9JV6VXGHXt", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_15.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a tabular format with Datasets as columns and Machine Learning Models as rows?" }, { "from": "gpt", "value": "To convert the chart data into a tabular format, I first identify {the x-axis tick labels}[[0.095, 0.855, 0.111, 0.886], [0.240, 0.855, 0.256, 0.886], [0.385, 0.855, 0.401, 0.886], [0.530, 0.855, 0.546, 0.886], [0.675, 0.855, 0.691, 0.886], [0.821, 0.855, 0.836, 0.886],[0.966, 0.855, 0.981, 0.886]] as the column headers which represent the different datasets. The {legend texts}[[0.353, 0.934, 0.398, 0.964], [0.498, 0.934, 0.543, 0.964], [0.643, 0.934, 0.782, 0.964]] are identified as rows labels, representing different Machine Learning Models. Then, by locating {the line points} for each model at the respective datasets and extracting the values annotated, I can obtain the respective accuracy values.\nBased on the above thought, the answer is:\nModel, Dataset1, Dataset2, Dataset3, Dataset4, Dataset5, Dataset6, Dataset7\nANN, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65, 0.6\nSVM, 0.8, 0.85, 0.9, 0.92, 0.93, 0.93, 0.92\nDecision Tree, 0.7, 0.75, 0.73, 0.7, 0.65, 0.68, 0.7" } ], "id_raw": 135 }, { "id": "05Ns3QIWi766Vk4a", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_15.png", "conversations": [ { "from": "human", "value": "\nAmong the different Machine Learning Models, which one showed the most consistent performance across the Datasets?" }, { "from": "gpt", "value": "To determine the model with the most consistent performance, I need to analyze the variation in their line plots. The consistency of a model can be assessed by its line plot's steadiness. {The ANN line}[0.103, 0.163, 0.974, 0.800] shows a steady decrease from 0.9 to 0.6 which suggests some variability in performance. {The SVM line}[0.103, 0.099, 0.974, 0.375] shows an increase in accuracy from 0.8 to 0.93 and then slightly drops to 0.92 which gives an indication of an improved but somewhat fluctuating performance. {The Decision Tree line}[0.103, 0.481, 0.974, 0.694] has minor fluctuations with its accuracy values ranging mostly between 0.7 and 0.71-0.75.\nBased on the above thought, the answer is:\nThe Decision Tree model showed the most consistent performance across the Datasets. Its line plot displays relatively small fluctuations with the accuracy values oscillating between the close ranges of 0.65 to 0.75. This indicates a stable performance, which is interpreted as higher consistency compared to ANN and SVM." } ], "id_raw": 135 }, { "id": "nA7Iuu59ZmOHCuDO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the average accuracy of the ANN model across all Datasets?" }, { "from": "gpt", "value": "To calculate the average accuracy of the ANN model, I need to locate {the points on the ANN line}[[0.103, 0.163], [0.248, 0.269], [0.393, 0.375], [0.538, 0.481], [0.683, 0.587], [0.828, 0.694], [0.974, 0.800]], which represent the accuracy values across all Datasets. These values are 0.9, 0.85, 0.8, 0.75, 0.7, 0.65, and 0.6. In order to find the average value, I add all these values together and then divide by the total number of Datasets which is 7.\nBased on the above thought, the answer is:\n0.75" } ], "id_raw": 135 }, { "id": "lCKw58koKF43RYrY", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_16.png", "conversations": [ { "from": "human", "value": "\nCan you help me draw a table from this line chart? The table should have the Year values as the rows and each rate (Startup_Rate, Investment_Funding_Rate, R&D_Funding_Rate) as the columns." }, { "from": "gpt", "value": "For a thorough analysis, the chart's data can be transformed into a table. {The x-axis values}[0.108, 0.851, 0.933, 0.883] represent the years and will become the rows. The metrics are represented by {the legend}[0.092, 0.923, 0.949, 0.975], becoming column names. The data points representing the rates were collected for each year and metric. The points for {Startup_Rate line}[0.139, 0.321, 0.902, 0.591], {Investment Funding Rate line}[0.139, 0.205, 0.902, 0.449], and {R&D Funding Rate line}[0.139, 0.269, 0.902, 0.604] were extracted.\nBased on the above thought, the answer is:\nYear, Startup_Rate, Investment_Funding_Rate, R&D_Funding_Rate\n2012,0.62,0.74,0.58\n2013,0.59,0.76,0.6\n2014,0.65,0.80,0.63\n2015,0.70,0.85,0.67\n2016,0.68,0.87,0.70\n2017,0.72,0.88,0.72\n2018,0.75,0.89,0.78\n2019,0.79,0.74,0.82\n2020,0.80,0.70,0.84" } ], "id_raw": 136 }, { "id": "QWUsE8UkGirRPKrU", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_16.png", "conversations": [ { "from": "human", "value": "\nComment on the progress of the Startup Rate over the years based on the chart." }, { "from": "gpt", "value": "To comment on the progress of the Startup Rate, I would look at {the line representing Startup Rate}[0.139, 0.321, 0.902, 0.591]. Looking from left to right, the line seems to exhibit an overall increasing trend from 2012 to 2020, with some modest dips, including a slight drop in 2013 and 2016. However, the trend moves steadily upward from 2013 to a peak in 2020, which indicates a period of growth in the Startup Rate over this period.\nBased on the above thought, the answer is:\nFrom 2012 to 2020, the Startup Rate has generally been growing. Despite slight decreases in 2013 and 2016, this metric has shown resilience with an overall upward movement, reaching a peak in 2020. This indicates a positive entrepreneurial environment with more startups being founded over the years." } ], "id_raw": 136 }, { "id": "q0igDzhXs4BDWX8Z", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_16.png", "conversations": [ { "from": "human", "value": "\nIn what year did the Investment Funding Rate reach its peak, and what was the rate?" }, { "from": "gpt", "value": "To find the highest Investment Funding Rate, I must examine {the coordinates for the Investment Funding Rate line}[0.139, 0.205, 0.902, 0.449]. The peak is the lowest y-coordinate, corresponding to the year 2018, located at {the specific point}[0.711, 0.205]. The rate can be obtained from the raw data.\nBased on the above thought, the answer is:\nThe Investment Funding Rate peaked in 2018 with a rate of 0.89." } ], "id_raw": 136 }, { "id": "aa3qAIK9jO1utzaX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_17.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into a table with the type of manufacturing method as columns and types of production as rows?" }, { "from": "gpt", "value": "To generate the table from the bar chart, I can first identify the column headers, which are represented by {the two chart sub-titles}[0.17, 0.9, 0.22, 0.94], [0.78, 0.9, 0.83, 0.94], they are 'Robotic' and 'Manual'. The row labels are represented by {the x-axis tick labels}[0.15, 0.05, 0.23, 0.09] and [0.35, 0.05, 0.43, 0.09], they are 'Efficient' and 'Rapid'. For the cell values, I locate {the heights of the bars}[0.1, 0.2, 0.3, 0.65], [0.1, 0.65, 0.3, 0.80], [0.35, 0.2, 0.55, 0.68], [0.35, 0.68, 0.55, 0.73] for each method under each type of production and extract the values which matches the y-axis labels.\nBased on the above thought, the answer is:\nProduction, Robotic, Manual\nEfficient,0.8,0.75\nRapid,0.88,0.77" } ], "id_raw": 137 }, { "id": "zjjIYMvqICa8dOM1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_17.png", "conversations": [ { "from": "human", "value": "\nBased on the graphs, which manufacturing method showcases a more significant improvement in efficiency rating when transitioning from efficient to rapid production?" }, { "from": "gpt", "value": "To determine which manufacturing method has a higher improvement, we need to compare the increase in efficiency ratings of both methods. For {Robotic}[0.1, 0.2, 0.3, 0.80], it's clear that it improves from 0.8 to 0.88, which is a change of 0.08. In contrast, {Manual}[0.35, 0.2, 0.55, 0.73] improves from 0.75 to 0.77, a much smaller change of 0.02.\nBased on the above thought, the answer is:\nWhen transitioning from 'Efficient' to 'Rapid' production, the 'Robotic' manufacturing method showcases a more significant improvement in the efficiency rating, as compared to the 'Manual' method. The improvement in efficiency rating for 'Robotic' is 0.08 while that for 'Manual' is only 0.02, which makes the former more considerable." } ], "id_raw": 137 }, { "id": "iFR2CvPwaeAE2gsZ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_17.png", "conversations": [ { "from": "human", "value": "\nHow much does the efficiency rating increase for Robotic manufacturing method when moving from 'Efficient' to 'Rapid' production?" }, { "from": "gpt", "value": "To answer, I need to compare {the height of the bar representing the 'Efficient' production}[0.1, 0.2, 0.3, 0.65] for Robotic method with its {height of the bar representing the 'Rapid' production}[0.1, 0.65, 0.3, 0.80]. This corresponds to a comparison of 0.8 under 'Efficient' and 0.88 under 'Rapid'. Hence, the increase is 0.88 - 0.8 = 0.08.\nBased on the above thought, the answer is:\n0.08." } ], "id_raw": 137 }, { "id": "6J1q4RwFXbIb30Ig", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_7_18.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the data from the line chart with Algorithms as rows and Social Dynamics Indicators as columns?" }, { "from": "gpt", "value": "To generate the structured table, firstly, I observe that {the x-axis tick labels}[[0.062, 0.975, 0.147, 0.992], [0.210, 0.975, 0.286, 0.992], [0.352, 0.975, 0.421, 0.992], [0.494, 0.975, 0.562, 0.992]] represents Social Dynamics Indicators and will be used as column headers. Then I extract {the legend labels}[[0.805, 0.150, 0.884, 0.162], [0.805, 0.263, 0.862, 0.274], [0.805, 0.376, 0.877, 0.387], [0.805, 0.487, 0.880, 0.498]] as the row headers representing the different algorithms. For each algorithm, I need to locate its {corresponding line points}[[0.081, 0.812], [0.226, 0.686], [0.371, 0.694], [0.515, 0.714]] and extract the annotated numbers as values. These values are then cross-referenced with the appropriate Social Dynamics Indicators.\nBased on the above thought, the answer is:\nAlgorithm,SMB,SFB,PRB,LDB\nGCN,0.68,0.75,0.80,0.82\nRvNN,0.84,0.81,0.75,0.78\nHyphen,0.80,0.78,0.70,0.76\nGET,0.82,0.83,0.85,0.89" } ], "id_raw": 138 }, { "id": "KK5HZNkfANsOUqKp", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_7_18.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which algorithm shows the most stable performance across all social dynamic indicators?" }, { "from": "gpt", "value": "To identify the most stable performance, I need to concentrate on the fluctuation or variation of each line's y-values. Starting with {the line of GCN}[0.081, 0.812, 0.515, 0.714], it shows increasing values across the indicators, indicating some variation. The {line of RvNN}[0.091, 0.728, 0.535, 0.681] presents an initial decrease and then some increase, suggesting fluctuations. The {line of Hyphen}[0.101, 0.694, 0.553, 0.660] appears quite volatile, particularly with a sharp drop in the third indicator. Lastly, the {line of GET}[0.112, 0.710, 0.562, 0.770] shows a small but constant increase, indicating the least variation.\nBased on the above thought, the answer is:\nBased on the trends displayed on the line chart, the GET algorithm seems to exhibit the most stable performance across all social dynamics indicators. While every other algorithm shows significant fluctuations for at least one indicator, GET consistently illustrates a gradual increase, suggesting a minor and controlled variation in its performance across the indicted social dynamics." } ], "id_raw": 138 }, { "id": "woUgFQ0zdLuoKdiX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_7_18.png", "conversations": [ { "from": "human", "value": "\nWhat's the highest value reached by any algorithm across all indicators?" }, { "from": "gpt", "value": "In order to find the highest value, I need to examine the {line points for all algorithms}[[0.081, 0.812],[0.226, 0.686],[0.371, 0.694],[0.515, 0.714], [0.091, 0.728], [0.239, 0.704], [0.387, 0.667], [0.535, 0.681], [0.101, 0.694], [0.251, 0.669], [0.402, 0.604], [0.553, 0.660], [0.112, 0.710], [0.262, 0.716], [0.412, 0.736], [0.562, 0.770]] and find the one that Figure corresponds to the highest value. From the inspection, I can determine that the point located at {value 0.89}[0.562, 0.770] corresponds to the highest value.\nBased on the above thought, the answer is:\n0.89" } ], "id_raw": 138 }, { "id": "6BqDj5iAY47rx63c", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_0.png", "conversations": [ { "from": "human", "value": "\nCan you reconstruct a table that records the environmental condition and assessment score of both 'Tropical Forest' and 'Arctic Ice'?" }, { "from": "gpt", "value": "To extract the data into a structured table, I first recognize {the x-axis labels}[0.103, 0.919, 0.961, 0.942] which correspond to the environmental conditions 'Stable', 'Minor decrease', 'Substantial decrease', 'Accelerating decline', and 'Dramatic decrease'. These will act as our row headers. For the columns, we have 'Tropical Forest' and 'Arctic Ice'. The corresponding values are extracted from the {line points}[0.121, 0.863, 0.944, 0.863], [0.121, 0.065, 0.944, 0.351], [0.121, 0.763, 0.944, 0.776] for each condition. Cross-referencing these points with their respective coordinates on the y-axis allows us to ascertain the exact assessment score.\nBased on the above thought, the answer is:\n, Tropical Forest, Arctic Ice\nStable, 1, 0.85\nMinor decrease, 0.91, 0.77\nSubstantial decrease, 0.81, 0.67\nAccelerating decline, 0.6, 0.55\nDramatic decrease, 0.15, 0.15" } ], "id_raw": 140 }, { "id": "VzqVm8TWYVFnCRUU", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_0.png", "conversations": [ { "from": "human", "value": "\nBased on the line graphs, which ecosystem, 'Tropical Forest' or 'Arctic Ice', has experienced a steeper decline?" }, { "from": "gpt", "value": "To determine which ecosystem experienced a steeper decline, I observe {the lines for Tropical Forest}[0.121, 0.863, 0.944, 0.863] and {Arctic Ice}[0.121, 0.065, 0.944, 0.351]. By comparing the downward trends of these lines, especially from 'Accelerating decline' to 'Dramatic decrease', I can infer that the ecosystem which saw a steeper decline would be the one with a sharper decrease in assessment scores.\nBased on the above thought, the answer is:\nBoth 'Tropical Forest' and 'Arctic Ice' have experienced a similar decrease in their assessment scores. However, the line graph for 'Tropical Forest' indicates a slightly steeper decline, particularly evident between the stages of 'Accelerating decline' and 'Dramatic decrease'. Therefore, 'Tropical Forest' has experienced a steeper decline." } ], "id_raw": 140 }, { "id": "EvpKds3o5ObnI8SU", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_0.png", "conversations": [ { "from": "human", "value": "\nDetermine the difference in assessment scores between 'Tropical Forest' and 'Arctic Ice' during 'Accelerating decline'?" }, { "from": "gpt", "value": "To identify the difference in assessment scores during 'Accelerating decline', I have to locate the coordinates of the {line points for Tropical Forest and Arctic Ice}[0.532, 0.863], [0.532, 0.207] respectively, during the 'Accelerating decline' stage. Comparing these y-values should yield the difference in the scores.\nBased on the above thought, the answer is:\n0.05" } ], "id_raw": 140 }, { "id": "XgjPrjQ6pc7tjSAD", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_2.png", "conversations": [ { "from": "human", "value": "\nFrom the provided line chart regarding New Energy and Clean Technology developments, can you translate the datasets into a tabular format with the various energy types as columns and the years as rows?" }, { "from": "gpt", "value": "To create a table from the line graph data, I first look at the legend identifiers, which represent {the column headers or energy types}[[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156], [0.884, 0.167, 0.946, 0.189], [0.884, 0.198, 0.948, 0.222]]. Then I move to {the x-axis labels}[[0.112, 0.879, 0.127, 0.914], [0.194, 0.879, 0.201, 0.914], [0.268, 0.879, 0.275, 0.914], [0.342, 0.879, 0.349, 0.914]] which will be used as the row indexes. Lastly, by analyzing {the data points of each line}[[0.120, 0.107], [0.194, 0.118], ..., [0.934, 0.180]], I collect the corresponding values for each energy type during the two years.\nBased on the above thought, the answer is:\nYear, Hydro Power, Solar Power, Wind Power, Bioenergy\n1, 0.78, 0.85, 0.76, 0.80\n2, 0.81, 0.88, 0.78, 0.83" } ], "id_raw": 142 }, { "id": "CNizCyj6QfCQVhoT", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_2.png", "conversations": [ { "from": "human", "value": "\nBased on the visible trends on the line graph, which energy field has shown the most accelerated development over the two-year period and why?" }, { "from": "gpt", "value": "Looking at {the line corresponding to Solar Power}[0.194, 0.118, 0.268, 0.129], I see an upward trend from 0.85 to 0.88. For the other energies, for instance, {Hydro Power's line}[0.194, 0.107, 0.268, 0.129] and {Wind Power's line}[0.194, 0.115, 0.268, 0.122] displayed a similar growth but not as significant as Solar Power. Additionally, {the line for Bioenergy}[0.194, 0.110, 0.268, 0.126] showed an increase from 0.78 to 0.83, which is a considerable growth but remains lower than the change noticed in Solar Power.\nBased on the above thought, the answer is:\nOver the two-year period, Solar Power has shown the most rapid development based on the trends observed in the line graph. Although all four energy fields demonstrate increasing trends, the Solar Power line shows a greater increase in development value from 0.85 to 0.88, which is the largest positive change among the four energy types." } ], "id_raw": 142 }, { "id": "wozWlRm2TqP2Nn5h", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in Solar Power development values from Year 1 to Year 2?" }, { "from": "gpt", "value": "To find the difference in Solar Power development values between Year 1 and Year 2, I will first locate {the Solar Power line points indicated by the chart}[0.194, 0.118, 0.268, 0.129] and extract its initial value 0.85 located at {point 1/Year 1}[0.194, 0.879] and final value 0.88 located at {point 2/Year 2}[0.268, 0.879]. The difference is therefore calculated as 0.88 - 0.85 = 0.03 from Year 1 to Year 2.\nBased on the above thought, the answer is:\n0.03." } ], "id_raw": 142 }, { "id": "k9r6WKQf4ZkBpyy7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_3.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data into a table format with years as columns and subcultures as rows from the line chart?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.073, 0.919, 0.117, 0.942], [0.284, 0.919, 0.328, 0.942], [0.495, 0.919, 0.539, 0.942], [0.706, 0.919, 0.750, 0.942], [0.917, 0.919, 0.961, 0.942]], which represent the column headers or years. Next, I identify {the legend texts}[[0.117, 0.083, 0.156, 0.106], [0.117, 0.118, 0.159, 0.141], [0.117, 0.153, 0.173, 0.176]] as the row labels representing the different subcultures. To populate the values, I locate the line points [0.095, 0.302], [0.306, 0.483], [0.517, 0.603], [0.728, 0.783], [0.939, 0.843] for Emo, [0.095, 0.422], [0.306, 0.543], [0.517, 0.422], [0.728, 0.302], [0.939, 0.182] for Punk and [0.095, 0.362], [0.306, 0.302], [0.517, 0.543], [0.728, 0.663], [0.939, 0.783] for Gothic at the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nSubculture,2000,2005,2010,2015,2020\nEmo,10,7,5,2,1\nPunk,8,6,8,10,12\nGothic,9,10,6,4,2" } ], "id_raw": 143 }, { "id": "DF5FrPiQg2n4jfp9", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_3.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which subculture seems to have the least popularity over the years? Provide your reasoning." }, { "from": "gpt", "value": "To determine which subculture experienced the least popularity, I need to examine the lines representing each subculture. The {line for the Emo subculture}[0.095, 0.302, 0.939, 0.843] shows a consistent decrease from 10 to 1. The {line for the Punk subculture}[0.095, 0.182, 0.939, 0.543] fluctuates with an increasing trend from 8 to 12. Finally, the {line for the Gothic subculture}[0.095, 0.302, 0.939, 0.783] also shows fluctuations but with a decreasing trend from 9 down to 2. By analysing these trends, it's clear that the Emo subculture has consistently lower values across the years.\nBased on the above thought, the answer is:\nThe Emo subculture appears to have the least popularity over the years. This is based on the trend which consistently decreases from a score of 10 in year 2000 to a score of 1 in year 2020, lower than other subcultures in the same year. This suggests a decline in popularity for Emo fashion trends across these years." } ], "id_raw": 143 }, { "id": "LeU7K1PhWHzdcV5o", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_3.png", "conversations": [ { "from": "human", "value": "\nHow many years did it take for the Punk subculture trend to reach its maximum value from its initial value?" }, { "from": "gpt", "value": "To figure out the required duration, I need to locate the initial and peak points of the Punk subculture. The initial value of Punk is 8 at year 2000 that could be found at {coordinates of initial point for Punk}[0.095, 0.422]. The maximum value of Punk is 12 at year 2020 which is located at {coordinates of maximum value for Punk}[0.939, 0.182]. Therefore, it would have taken 2020 - 2000 = 20 years to reach its maximum peak from its initial value.\nBased on the above thought, the answer is:\n20 years." } ], "id_raw": 143 }, { "id": "wAhgGQhR1vL0LGWr", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_4.png", "conversations": [ { "from": "human", "value": "\nPlease convert the data from the line chart into a structured table with the 'Innovation Process' as columns and the 'Baseline Improvement' as rows." }, { "from": "gpt", "value": "First, from the given chart, I perceive {the x-axis tick labels}[[0.126, 0.879, 0.155, 0.914], [0.161, 0.879, 0.190, 0.914], [0.282, 0.879, 0.311, 0.914], [0.333, 0.879, 0.363, 0.914], [0.395, 0.879, 0.439, 0.914], [0.464, 0.879, 0.508, 0.914], [0.533, 0.879, 0.577, 0.914], [0.620, 0.879, 0.664, 0.914], [0.706, 0.879, 0.750, 0.914], [0.775, 0.879, 0.819, 0.914], [0.844, 0.879, 0.888, 0.914], [0.913, 0.879, 0.957, 0.914]] which correspond to the different innovation processes. Then I identify {the line for Baseline Improvement}[[0.141, 0.069, 0.935, 0.562]] and mark them down as the rows for each innovation process. Each data point in the line works as the value for the corresponding process.\nBased on the above thought, the answer is:\nInnovation Process, Baseline Improvement\n30,1.2\n40,1.8\n75,2.2\n90,1.6\n110,2.1\n130,1.8\n150,2.3\n175,2.5\n200,2.7\n220,3.1\n240,3.3\n260,3.5" } ], "id_raw": 144 }, { "id": "oHmccLljYnAALzUF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_4.png", "conversations": [ { "from": "human", "value": "\nWhat seems to be the general trend in Baseline Improvement as the Innovation Process progresses?" }, { "from": "gpt", "value": "I would need to observe {the Baseline Improvement line}[[0.141, 0.069, 0.935, 0.562]] on the chart. Judging by the placement of the line points, there is a visible increase from the initial to the final point, although there are also some troughs indicating temporary drops in improvement.\nBased on the above thought, the answer is:\nThe general trend in Baseline Improvement as the Innovation Process progresses is upward. This suggests that innovation tends to bring about progressive improvement over time, despite some occasional setbacks." } ], "id_raw": 144 }, { "id": "dLeUA6ca4pazVffw", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_4.png", "conversations": [ { "from": "human", "value": "\nBy how much does the baseline improvement increase from the beginning to the end of the innovation process?" }, { "from": "gpt", "value": "To calculate the difference in baseline improvement, I have to take a look at {the first point of the Baseline Improvement line}[[0.141, 0.562]] which corresponds to a value of 1.2, and {the last point of the Baseline Improvement line}[[0.935, 0.069]] which corresponds to a value of 3.5. By subtracting the initial value from the end value, I will get the increase in baseline improvement over the innovation process.\nBased on the above thought, the answer is:\n2.3." } ], "id_raw": 144 }, { "id": "8MrWPNC8RlE9STuF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_6.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you convert the value progression of Urban and Rural development from 1990 to 2030 into a table?" }, { "from": "gpt", "value": "To transform the line chart into a tabular format, firstly, I identify {the x-axis tick labels}[[0.143, 0.879, 0.202, 0.914], [0.334, 0.879, 0.392, 0.914], [0.525, 0.879, 0.584, 0.914], [0.716, 0.879, 0.775, 0.914], [0.908, 0.879, 0.966, 0.914]] as the years from 1990 to 2030. Then, I regard {the legend texts}[[0.219, 0.069, 0.290, 0.104], [0.219, 0.121, 0.279, 0.156]] as Urban and Rural labels respectively. The value corresponding to each year and category is taken from {the points on the lines}[[0.172, 0.547], [0.363, 0.387], [0.554, 0.427], [0.746, 0.267], [0.937, 0.107], [0.172, 0.387], [0.363, 0.507], [0.554, 0.627], [0.746, 0.707],[0.937, 0.667]] and then converted into the corresponding value.\nBased on the above thought, the answer is:\nYear,Development in Urban,Development in Rural\n1990,1.4,1.8\n2000,1.8,1.5\n2010,1.7,1.2\n2020,2.1,1.0\n2030,2.5,1.1" } ], "id_raw": 146 }, { "id": "lXtTH83ww0vwnIZ4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_6.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the line chart, which area, Urban or Rural, has seen a consistently increasing trend in development from 1990 to 2030, and why?" }, { "from": "gpt", "value": "To determine which area has seen a consistent increase in development, I need to analyze the line plots for both Rural and Urban. Inspecting {the Urban line} [0.172, 0.107, 0.937, 0.547], it shows a consistent increase from 1.4 in 1990 to 2.5 in 2030 with no significant dips in between. However, for the {Rural line} [0.172, 0.387, 0.937, 0.707], we can see the development parameter started at 1.8 in 1990, reached its lowest point at 1.0 by 2020 before slightly increasing to 1.1 in 2030. Hence the Rural line shows an overall decline with a small increment in the recent years, rather than a consistent increase.\nBased on the above thought, the answer is:\nBased on the line chart, the Urban area has seen a consistently increasing trend in development from 1990 to 2030. Its line plot shows a continuous growth from 1.4 in 1990 to 2.5 in 2030, with no significant dips, suggesting that the development parameter for the Urban area has been steadily increasing over these years." } ], "id_raw": 146 }, { "id": "3sC6INranj9gNJor", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_6.png", "conversations": [ { "from": "human", "value": "\nHow much has the development parameter for Urban regions increased from 1990 to 2030?" }, { "from": "gpt", "value": "To find the increment in the development parameter for Urban regions, I need to compare the value of Urban development in 1990 from {the start point of the Urban line} [0.172, 0.547] with the value in 2030 from {the end point of the Urban line} [0.937, 0.107]. In 1990 it was 1.4 and in 2030, it was 2.5. Therefore, the increase in development parameters for Urban regions is 2.5 - 1.4 = 1.1.\nBased on the above thought, the answer is:\n1.1." } ], "id_raw": 146 }, { "id": "eVwZDabyxTZsd7SV", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_7.png", "conversations": [ { "from": "human", "value": "\nCan you create a table summarizing the trend indexes for each subculture observed in the bar chart?" }, { "from": "gpt", "value": "To create a structured table from the bar chart, I first identify {the x-axis labels}[0.112, 0.940, 0.940, 0.940] which list the subcultures. Working through the bars, I proceed to measure the height of each bar, representing the fashion trend index. I am able to gather individual indexes using the coordinates from each bar in the plot for subcultures such as {Hipster's bar}[0.150, 0.350, 0.200, 0.650] and {Goth's bar}[0.450, 0.350, 0.500, 0.190], converting these heights proportionately to numerical values based on the y-axis scale at {the y-axis tick labels}[0.200, 0.850, 0.270, 0.940].\nBased on the above thought, the answer is:\nSubculture,Fashion Trend Index\nHipster,85\nRockabilly,80\nEmo,50\nGoth,25\nJuggalo,10\nBoho-chic,55\nLolita,60" } ], "id_raw": 147 }, { "id": "RdENHzf7kWavtObt", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart depiction, why might the 'Goth' subculture be experiencing a sharp decline in fashion trend index compared to others?" }, { "from": "gpt", "value": "By analyzing {Goth's bar}[0.450, 0.350, 0.500, 0.190] in the bar chart, it's evident that the Goth subculture has a significantly lower index. Other subcultures, such as {Hipster's bar}[0.150, 0.350, 0.200, 0.650] and {Rockabilly's bar}[0.250, 0.350, 0.300, 0.610], display much higher indices. The stark visual difference highlights a decreasing trend, which could be attributed to changing cultural tastes or perhaps the rise in popularity of other subcultures, overshadowing the Goth style.\nBased on the above thought, the answer is:\nThe 'Goth' subculture is experiencing a sharp decline in its fashion trend index because its cultural appeal may be diminishing compared to rising trends in other subcultures. As visible in the chart, its significantly lower bar indicates a decrease in popularity, likely influenced by evolving fashion preferences and the advent of new trends becoming more dominant in contemporary culture." } ], "id_raw": 147 }, { "id": "wtGRRRZ4YlihySrz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in fashion trend index between the highest and lowest observed subcultures?" }, { "from": "gpt", "value": "By examining the bar chart, I need to compare the maximum and minimum heights of the bars representing the fashion trend indexes. I find the tallest bar at {Hipster's bar}[0.150, 0.350, 0.200, 0.650] with a high index, and the shortest bar at {Juggalo's bar}[0.650, 0.350, 0.700, 0.880] with the lowest index. Calculating the exact index values from the y-axis helps in determining the numerical difference.\nBased on the above thought, the answer is:\n75." } ], "id_raw": 147 }, { "id": "PiPKJSiLTvuhscYB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_8.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart data into a table with 'Industry Trends' as rows and 'Stock Price' and 'Standard Deviation' as columns?" }, { "from": "gpt", "value": "To create the table from the chart, firstly, I note {the x-axis labels}[[0.065, 0.879, 0.256, 0.914], [0.253, 0.879, 0.341, 0.914], [0.302, 0.879, 0.565, 0.914], [0.473, 0.879, 0.667, 0.914], [0.631, 0.879, 0.781, 0.914], [0.709, 0.879, 0.976, 0.914]] which corresponds to the items in 'Industry Trends'. Then, I look at {the data points on the line graph}[[0.160, 0.580], [0.297, 0.455], [0.433, 0.616], [0.570, 0.414], [0.706, 0.332], [0.843, 0.095]], which correspond to the 'Stock Price'. To find the 'Standard Deviation', I rely on the provided raw data.\nBased on the above thought, the answer is:\nIndustry Trends,Stock Price,Standard Deviation\nPharmaceuticals,10.0,0.8\nBiotech,15.2,1.3\nHealthCare Equipment,8.5,0.7\nMedical Services,16.9,1.4\nLife Sciences,20.3,0.9\nHealthCare Technology,30.2,1.2" } ], "id_raw": 148 }, { "id": "3GxuJC09BiwEAyfp", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_8.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the performance of the 'HealthCare Equipment' sector based on the line chart?" }, { "from": "gpt", "value": "Examining {the data point for HealthCare Equipment}[[0.433, 0.616]], it can be noticed that HealthCare Equipment has lower 'Stock Price' compared to the other sectors. The 'Stock Price' for 'HealthCare Equipment' listed on this chart is 8.5, which is the lowest among all sectors {the x-axis labels}[[0.065, 0.879, 0.256, 0.914], [0.253, 0.879, 0.341, 0.914], [0.302, 0.879, 0.565, 0.914], [0.473, 0.879, 0.667, 0.914], [0.631, 0.879, 0.781, 0.914], [0.709, 0.879, 0.976, 0.914]]. Measurement of Standard Deviation taken from raw data shows 0.7, indicating a low level of price volatility.\nBased on the above thought, the answer is:\nBased on the line chart, it can be inferred that 'HealthCare Equipment' sector's performance is less robust in comparison to other sectors as it has the lowest 'Stock Price'. The relatively low standard deviation suggests that the stock prices have not greatly fluctuated, indicating a steady performance, albeit at a lower range." } ], "id_raw": 148 }, { "id": "Ofl9DrNlc2NZqPZd", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of the Stock Price in this line chart?" }, { "from": "gpt", "value": "To find the range, I must locate the highest and the lowest 'Stock Price' on the chart. These are represented by {the y-coordinates of the data points on the line graph}[[0.160, 0.580], [0.297, 0.455], [0.433, 0.616], [0.570, 0.414], [0.706, 0.332], [0.843, 0.095]]. The maximum value is 30.2, corresponding to {HealthCare Technology}[0.843, 0.820], and the lowest value is 8.5, corresponding to {HealthCare Equipment}[0.433, 0.820]. So, the range is 30.2 - 8.5.\nBased on the above thought, the answer is:\n21.7." } ], "id_raw": 148 }, { "id": "b0i8dc9Zz85zi04X", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_9.png", "conversations": [ { "from": "human", "value": "\nBased on the line plot, can you generate a structured table featuring years as columns and the two tendencies as rows?" }, { "from": "gpt", "value": "To transform the data visualized in the line plot into tabular format, I need to identify the headers - these are given by {the x-axis tick labels}[[0.138, 0.916, 0.151, 0.945], [0.336, 0.916, 0.347, 0.945], ..., [0.931, 0.916, 0.946, 0.945]], which represent the years. The rows of the table are represented by {the line plot labels}[[0.137, 0.295, 0.935, 0.561],[0.137, 0.144, 0.935, 0.561]], corresponding to Startup Births and Tech Innovations respectively. To fill the table values, I will identify the {line points} for each row at respective years.\nBased on the above thought, the answer is:\nTrend,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018\nStartup Births,1.2,1.5,0.9,1.3,1.5,0.7,1.3,1.5,1.8,1.7,1.9,2.0\nTech Innovations,0.9,1.3,1.2,1.4,1.3,1.1,1.4,1.8,1.6,1.5,1.8,1.7" } ], "id_raw": 149 }, { "id": "1G6tWmoNvCWwYJoH", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_9.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown, does the growth of tech innovations seem to be more consistently upwards, compared to startup births?" }, { "from": "gpt", "value": "To answer this question, I need to analyze the general trends in the line plots. Examining {the line for Startup Births}[[0.137, 0.295, 0.935, 0.561]], I can see a general uphill trend, marred by significant dips in 2009 and 2012. However, the line for {Tech Innovations}[[0.137, 0.144, 0.935, 0.561]] also captures a general upward trajectory, but shows a lesser degree of fluctuation and fewer dips, only stagnating in 2016.\nBased on the above thought, the answer is:\nYes, the growth of tech innovations does appear to be more consistent and less prone to fluctuations in comparison to startup births. While both have an overall upward trend, there are fewer visible fluctuations in the tech innovations trendline, suggesting a steadier growth rate." } ], "id_raw": 149 }, { "id": "Suu7Zwx2vTwnDooP", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_9.png", "conversations": [ { "from": "human", "value": "\nWhat's the difference in the number of start-up births between 2007 and 2018?" }, { "from": "gpt", "value": "To find the difference in the number of start-up births between 2007 and 2018, I first find {the point for Startup Births in 2007}[[0.137, 0.371]] which corresponds to 1.2. Then I find {the point for Startup Births in 2018}[[0.935, 0.183]] which corresponds to 2.0. I subtract the value for 2007 from the value for 2018 to get the difference.\nBased on the above thought, the answer is:\n0.8" } ], "id_raw": 149 }, { "id": "MJmI49ZMUgGrKE9Y", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_11.png", "conversations": [ { "from": "human", "value": "\nCould you convert the plot data of the line chart into a table format, with Cultural Heritage as rows and Features such as Diversity Index, Integration Index, and Outliers as columns?" }, { "from": "gpt", "value": "To transform the plot data into table format, I first observe that {the x-axis tick labels}[[0.075, 0.919, 0.159, 0.942], [0.279, 0.919, 0.363, 0.942], [0.483, 0.919, 0.567, 0.942], [0.687, 0.919, 0.772, 0.942], [0.893, 0.919, 0.976, 0.942]] function as the rows that represent different Cultural Heritage. Next, the data points indicate values for the three features: Diversity Index {points from line_0}[[0.117, 0.560], [0.321, 0.471], [0.525, 0.426], [0.730, 0.337], [0.934, 0.249]], Integration Index {points from line_1}[[0.117, 0.293], [0.321, 0.382], [0.525, 0.249], [0.730, 0.204], [0.934, 0.071]], and Outliers {points from line_2}[[0.117, 0.827], [0.321, 0.782], [0.525, 0.782], [0.730, 0.738], [0.934, 0.675]] which correspond to columns in the table. I then extract and match these values with their respective Cultural Heritage.\nBased on the above thought, the answer is:\nCultural_Heritage,Diversity_Index,Integration_Index,Outliers\nCulture_A,0.35,0.65,0.05\nCulture_B,0.45,0.55,0.10\nCulture_C,0.5,0.7,0.1\nCulture_D,0.6,0.75,0.15\nCulture_E,0.7,0.9,0.22" } ], "id_raw": 151 }, { "id": "6kmt8CTAetL7xvkm", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_11.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which cultural heritage seems to have the largest outlier value?" }, { "from": "gpt", "value": "Identifying the cultural heritage with the largest outlier value involves examining the points on {the Outliers line}[[0.117, 0.827], [0.321, 0.782], [0.525, 0.782], [0.730, 0.738], [0.934, 0.675]]. I can see that these points represent Culture A to Culture E respectively. The Y-coordinates of these points indicate that Culture E, located at [0.934, 0.675], has the largest value.\nBased on the above thought, the answer is:\nBased on the line chart, the cultural heritage that seems to have the largest outlier value is Culture E." } ], "id_raw": 151 }, { "id": "3YzlbfnhhEdkXFZg", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_11.png", "conversations": [ { "from": "human", "value": "\nWhat's the net increase of the Integration Index from Culture A to Culture B?" }, { "from": "gpt", "value": "To calculate the net increase of the Integration Index from Culture A to Culture B, I check {the line points for the Integration Index}[[0.117, 0.293], [0.321, 0.382]], which represent the values for Culture A and Culture B respectively. Culture A has a value of 0.65 and Culture B has a value of 0.55. Hence the net increase would be 0.55 - 0.65.\nBased on the above thought, the answer is:\n-0.1." } ], "id_raw": 151 }, { "id": "8qU8U82MXJsynD4I", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_12.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data into a structured table format with Years as columns and Sectors as rows based on the line chart?" }, { "from": "gpt", "value": "To create a table from the line chart, I first need to note {the ticks on the x-axis}[[0.100, 0.913, 0.158, 0.948], [0.301, 0.913, 0.360, 0.948], [0.502, 0.913, 0.561, 0.948], [0.704, 0.913, 0.763, 0.948], [0.906, 0.913, 0.964, 0.948]], which represent the column headers - the Years. Next, I identify {the legend texts}[[0.174, 0.376, 0.432, 0.411], [0.174, 0.428, 0.409, 0.463], [0.174, 0.481, 0.433, 0.516]] as the row labels representing different sectors - Hotel Occupancy Rate, Restaurant Revenue and Tourist Arrival Number respectively. To populate the values, I locate {the points on each line}[[0.129, 0.741], [0.330, 0.746], ..., [0.935, 0.812]], [[0.129, 0.083], [0.330, 0.107], ..., [0.935, 0.540]], [[0.129, 0.688], [0.330, 0.693], ..., [0.935, 0.800]] for each sector at the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nSector,2016,2017,2018,2019,2020\nHotel Occupancy Rate,0.87,0.83,0.78,0.72,0.3\nRestaurant Revenue,6.2,6.0,5.8,5.4,2.5\nTourist Arrival Number,1.3,1.26,1.22,1.15,0.4" } ], "id_raw": 152 }, { "id": "kKOuqh054svjaUg5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_12.png", "conversations": [ { "from": "human", "value": "\nCan you describe the trend shown in the line plot for the Tourist Arrival Number?" }, { "from": "gpt", "value": "To understand the trend for the Tourist Arrival Number, I need to follow {the line for Tourist Arrival Number}[0.129, 0.688, 0.935, 0.800]. It starts at 1.3 under the year label {2016}[0.100, 0.913], drops a little to 1.26 under {2017}[0.301, 0.913], falls slightly again to 1.22 under {2018}[0.502, 0.913], decreases to 1.15 under {2019}[0.704, 0.913], and finally drops significantly to 0.4 under {2020}[0.906, 0.913]. It indicates a gradual decline from 2016 through to 2019, with a more substantial decrease in arrival numbers observed in 2020.\nBased on the above thought, the answer is:\nThe line plot for the Tourist Arrival Number shows a steadily declining trend from 2016 to 2019. However, there is a sharp drop in 2020, hinting at a significant reduction in the number of tourists, possibly due to exceptional circumstances such as a global event causing reduced travel." } ], "id_raw": 152 }, { "id": "RmspkKRkF86pKPRC", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_12.png", "conversations": [ { "from": "human", "value": "\nFind the total decrease in Restaurant Revenue from 2016 to 2020?" }, { "from": "gpt", "value": "To find the total decrease in Restaurant Revenue from 2016 to 2020, I locate {the line points for Restaurant Revenue}[[0.129, 0.083], [0.330, 0.107], [0.532, 0.132], [0.733, 0.182], [0.935, 0.540]] and extract the values: 6.2, 6.0, 5.8, 5.4, 2.5. The value in 2016 is observed at {2016}[0.100, 0.913] as 6.2 and the value in 2020 is observed at {2020}[0.906, 0.913] as 2.5. The total decrease from 2016 to 2020 is therefore 6.2 - 2.5 = 3.7.\nBased on the above thought, the answer is:\n3.7." } ], "id_raw": 152 }, { "id": "UXOL0KP3FjRJkRcF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_13.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from this line chart into tabular format with 'Processing Steps' as rows and 'Measures' as columns?" }, { "from": "gpt", "value": "First, The x-axis of this chart is represented by {the x-axis tick labels}[[0.122, 0.919, 0.133, 0.942], [0.320, 0.919, 0.342, 0.942], [0.517, 0.919, 0.550, 0.942], [0.721, 0.919, 0.754, 0.942], [0.924, 0.919, 0.957, 0.942]], which are the 'Processing Steps'. The line plots for Accuracy, Precision, and Recall, which could be found in {the legend}[[0.831, 0.756, 0.973, 0.868]], will be the columns in the table. Then, for every point on each line i.e., {line points for Accuracy}[[0.127, 0.654], [0.331, 0.542], [0.534, 0.429], [0.737, 0.316], [0.941, 0.203]], {line points for Precision}[[0.127, 0.739], [0.331, 0.598], [0.534, 0.485], [0.737, 0.372], [0.941, 0.260]], and {line points for Recall}[[0.127, 0.626], [0.331, 0.485], [0.534, 0.372], [0.737, 0.260], [0.941, 0.147]], the corresponding values are extracted.\nBased on the above thought, the answer is:\nProcessing Steps,Accuracy,Precision,Recall\n0,0.78,0.75,0.79\n50,0.82,0.8,0.84\n100,0.86,0.84,0.88\n150,0.9,0.88,0.92\n200,0.94,0.92,0.96" } ], "id_raw": 153 }, { "id": "eF1ARtR0uhg9TnB1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_13.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which measure seems to have the most steady increase over the processing steps? Please explain your reasoning." }, { "from": "gpt", "value": "In order to determine which measure has the most steady increase, I analyze the rate of change in the line plots for each measure. Looking at the {line for Accuracy}[[0.127, 0.203, 0.941, 0.654]], I observe a consistent upward slope. The same trend can be seen in the {line for Precision}[[0.127, 0.260, 0.941, 0.739]] and the {line for Recall}[[0.127, 0.147, 0.941, 0.626]]. Despite all three lines showing an increasing trend, the Accuracy line seems to exhibit a relatively steady rise.\nBased on the above thought, the answer is:\nBased on the trends presented in the line chart, Accuracy appears to have the most steady increase over the processing steps. This is determined by the consistent upward slope of the Accuracy line indicating a steady improvement rate, compared to Precision and Recall which appear to have slightly more variability in their rates of increase." } ], "id_raw": 153 }, { "id": "vnTUj5cu3JGjOtIw", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_13.png", "conversations": [ { "from": "human", "value": "\nHow much recall has improved from processing step 0 to 200?" }, { "from": "gpt", "value": "To find out the improvement in recall, I need to locate the {starting recall point [0.127, 0.626]} at processing step 0 and the {ending recall point [0.941, 0.147]} at processing step 200. Extracting the corresponding values, we get 0.79 and 0.96 respectively. The difference between recall at step 200 and step 0 will be the improvement in recall.\nBased on the above thought, the answer is:\n0.17" } ], "id_raw": 153 }, { "id": "bNIZIDxm0tEiCida", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_14.png", "conversations": [ { "from": "human", "value": "\nCan you put the data showed on a line chart of different styles into table format with time periods as columns and styles as rows?" }, { "from": "gpt", "value": "Firstly, I extract {the x-axis labels}[[0.126, 0.879, 0.141, 0.914], [0.319, 0.879, 0.349, 0.914], [0.512, 0.879, 0.556, 0.914], [0.713, 0.879, 0.757, 0.914], [0.913, 0.879, 0.957, 0.914]] that has time periods as column headers. Then, the rows are defined by {the labels specified in the line parameters}{'label': 'Anxiety Level', 'label': 'Focus Level', 'label': 'Intrusive Thoughts'}. Values of the cells are the coordinates of each point from {the lines}{'line_0': {'params': {'label': 'Anxiety Level'}, 'points': [{'x': 0, 'y': 65}, {'x': 50, 'y': 70}, {'x': 100, 'y': 64}, {'x': 150, 'y': 62}, {'x': 200, 'y': 56}]}, 'line_1': {'params': {'label': 'Focus Level'}, 'points': [{'x': 0, 'y': 45}, {'x': 50, 'y': 47}, {'x': 100, 'y': 50}, {'x': 150, 'y': 53}, {'x': 200, 'y': 60}]}, 'line_2': {'params': {'label': 'Intrusive Thoughts'}, 'points': [{'x': 0, 'y': 55}, {'x': 50, 'y': 60}, {'x': 100, 'y': 68}, {'x': 150, 'y': 76}, {'x': 200, 'y': 80}]}}.\nBased on the above thought, the answer is:\nMetrics,0,50,100,150,200\nAnxiety Level,65,70,64,62,56\nFocus Level,45,47,50,53,60\nIntrusive Thoughts,55,60,68,76,80" } ], "id_raw": 154 }, { "id": "fmVbcoSCG9ndS7h1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_14.png", "conversations": [ { "from": "human", "value": "\nWhich of the three parameters (Anxiety Level, Focus Level, Intrusive Thoughts) has shown significant changes over the time period?" }, { "from": "gpt", "value": "To answer this question, I compare lines for each parameter in terms of their starting to ending position. Based on {the lines for 'Anxiety Level'}[0.134, 0.225, 0.935, 0.433], {the line for 'Focus Level'}[0.134, 0.373, 0.935, 0.596] and {the line for 'Intrusive Thoughts'}[0.134, 0.076, 0.935, 0.448], Intrusive Thoughts show a significant increase from 55 to 80 while other two parameters show a modest decrease in values, with Anxiety Level dropping from 65 to 56 and Focus Level increasing from 45 to 60.\nBased on the above thought, the answer is:\nThe 'Intrusive Thoughts' parameter has shown the most significant change over the entire time period. While 'Anxiety Level' and 'Focus Level' showed modest changes, 'Intrusive Thoughts' has seen a significant increase from the initial to the final value. This suggests that over time, participants in the research had more intrusive thoughts." } ], "id_raw": 154 }, { "id": "xQHmQ5Z51AQhZldc", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_14.png", "conversations": [ { "from": "human", "value": "\nHow much has the Anxiety Level decreased from the first to the last research session?" }, { "from": "gpt", "value": "The change in Anxiety Level can be calculated by finding the difference between its value in the first and the last research session. These values can be found by looking at {the first point in the line for Anxiety Level}[0.134, 0.299] and {the last point in the line for Anxiety Level}[0.935, 0.433]. The value at the first session is 65 and at the last session is 56.\nBased on the above thought, the answer is:\n9." } ], "id_raw": 154 }, { "id": "spjxX6RxxzqIGM16", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_15.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart into a readable table with time steps as columns and sales per year as rows?" }, { "from": "gpt", "value": "Absolutely. To generate a table from the line chart, I'll identify the 'Time Steps' by analyzing {the x-axis tick labels}[0.140, 0.914, 0.593, 0.879]. Next, the 'Sales per Year' can be identified as the rows, represented by {the line legends}[0.148, 0.536, 0.936, 0.706], [0.148, 0.110, 0.936, 0.394], [0.148, 0.082, 0.936, 0.479]. To gather the data values, I will collect the numerical values associated with each point on the lines.\nBased on the above thought, the answer is:\nYear,Step 1,Step 2,Step 3,Step 4,Step 5,Step 6,Step 7,Step 8,Step 9,Step 10\nSales 2017,100,95,90,85,80,70,65,50,45,40\nSales 2018,150,175,200,220,233,240,250,245,242,230\nSales 2019,120,130,140,160,190,210,220,240,250,260" } ], "id_raw": 155 }, { "id": "I0VNV2OVzFWv2PX8", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_15.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the trend of sales over the years based on the line chart?" }, { "from": "gpt", "value": "Looking at the chart, I observe three lines representing the sales for three different years. Examining {the Sales 2017 line}[0.148, 0.536, 0.936, 0.706], it shows a consistent decline, indicating that sales decreased steadily over this year. \nThe {Sales 2018 line}[0.148, 0.110, 0.936, 0.394] exhibits a steady ascent up to Step 7, followed by a slight decrease, implying that the sales grew consistently in the initial part of the year and then dropped slightly. \nThe {Sales 2019 line}[0.148, 0.082, 0.936, 0.479] demonstrates a steady increase until step 4, followed by a more pronounced rise, outlining that the sales gradually increased in the first half of the year and then spiked upwards.\nBased on the above thought, the answer is:\nThe sales trend from 2017 to 2019 shows a gradual shift from a declining trend in 2017 to an overall growing trend in 2019. While sales fell consistently throughout the year in 2017, there was a steady increase in sales in 2018 with a slight slump near the end of the year. By 2019, the sales not only improved but also showed a marked increase, especially in the latter half of the year. This could indicate an optimistic sales outlook for the future based on the recent trend." } ], "id_raw": 155 }, { "id": "kpKhmgjDb3aAhN2r", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest sale in the year 2018?" }, { "from": "gpt", "value": "In order to calculate the difference, I need to identify the highest and lowest points on {the line representing Sales 2018}[0.148, 0.110, 0.936, 0.394]. The highest point corresponds to a sale of 250 at Step 7 and the lowest point corresponds to a sale of 150 at Step 1. The difference can be calculated as 250 - 150.\nBased on the above thought, the answer is:\n100." } ], "id_raw": 155 }, { "id": "3wXELL9zOkrfMTCt", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_17.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data into a structured table with Gradient steps as rows and features of EdTech as columns?" }, { "from": "gpt", "value": "For creating the table, we first recognize that {the x-axis labels}[0.133, 0.879, 0.148, 0.914], [0.325, 0.879, 0.354, 0.914], [0.516, 0.879, 0.560, 0.914], [0.715, 0.879, 0.759, 0.914], [0.913, 0.879, 0.957, 0.914] represent the row headers or Gradient steps. The column headers are the legend to {the line for EdTech AI Adoption}[0.141, 0.311, 0.935, 0.744], {Predictive Learning Algorithms}[0.141, 0.239, 0.935, 0.492] and {Distance Learning Platforms}[0.141, 0.130, 0.935, 0.636]. Each point on the line represents a corresponding value in the table for each feature at the respective gradient step.\nBased on the above thought, the answer is:\nGradient_steps,EdTech AI Adoption,Predictive Learning Algorithms,Distance Learning Platforms\n0,0.1,1.0,1.8\n50,0.5,0.8,1.0\n100,0.85,1.5,0.9\n150,1.2,1.0,0.7\n200,1.3,0.8,0.4" } ], "id_raw": 157 }, { "id": "uGZNR0uY8RYeYFr3", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_17.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which feature of EdTech shows the most growth from 0 to 200 gradient steps?" }, { "from": "gpt", "value": "To determine which feature shows the most growth, I need to analyze the increase in value from 0 to 200 Gradient steps for each feature. The {EdTech AI Adoption}[0.141, 0.311, 0.935, 0.744] line starts at 0.1 and ends at 1.3, which is a difference of 1.2. The {Predictive Learning Algorithms}[0.141, 0.239, 0.935, 0.492] starts at 1.0 and ends at 0.8, showing a decrease. The {Distance Learning Platforms}[0.141, 0.130, 0.935, 0.636] starts at 1.8 and ends 0.4, indicating a decrease as well.\nBased on the above thought, the answer is:\nBased on the line chart, the feature with the most growth from 0 to 200 gradient steps is EdTech AI Adoption. While Predictive Learning Algorithms and Distance Learning Platforms both show declines, EdTech AI Adoption shows an increase, making it the feature with the most growth over this interval." } ], "id_raw": 157 }, { "id": "fY0VlkBjDcChrRsy", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in EdTech AI Adoption from 0 to 200 gradient steps?" }, { "from": "gpt", "value": "To find the change in EdTech AI Adoption from 0 to 200 Gradient steps, I need to locate the corresponding {point for 0 Gradient steps}[0.141, 0.744] which has a value of 0.1 and {point for 200 Gradient steps}[0.935, 0.311] with a value of 1.3 in {the line for EdTech AI Adoption}[0.141, 0.311, 0.935, 0.744]. The change is therefore 1.3 - 0.1 = 1.2.\nBased on the above thought, the answer is:\n1.2" } ], "id_raw": 157 }, { "id": "ef5A6ZnXzO0on0fF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_18.png", "conversations": [ { "from": "human", "value": "\nCan you provide the data points shown in the line graph in table format including the Time Period as columns and the two Parameters as rows?" }, { "from": "gpt", "value": "I can extract the data points for each series from the line graph. The labels found at {x-axis tick coordinates}[[0.178, 0.759], [0.427, 0.759], [0.676, 0.759], [0.925, 0.759]], indicating '5', '10', '15', '20' respectively, represent the Time Period. The parameters are given as 'Social Networks' and 'Interpersonal Relationships', identified from the line labels. The series for {Social Networks}[[0.178, 0.590], [0.427, 0.420], [0.676, 0.250], [0.925, 0.080]] show the values '5', '10', '15' and '20'. The series for {Interpersonal Relationships}[[0.178, 0.488], [0.427, 0.454], [0.676, 0.556], [0.925, 0.590]] represent the values '8', '9', '6' and '5'.\nBased on the above thought, the answer is:\nParameters,5,10,15,20\nSocial Networks,5,10,15,20\nInterpersonal Relationships,8,9,6,5" } ], "id_raw": 158 }, { "id": "NqYCXsSRIQG5R6tf", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_18.png", "conversations": [ { "from": "human", "value": "\nCan you describe any trends or changes over time you observe in the interactions between Social Networks and Interpersonal Relationships based on the line graph?" }, { "from": "gpt", "value": "Looking at the line charts for both 'Social Networks' and 'Interpersonal Relationships', it appears that from the beginning to the end, both lines show a decline from their initial counts. More specifically, {Social Networks}[0.178, 0.080, 0.925, 0.590] starts from a value of 5 at time period '5' and ends at a value of 20 at time period '20'. In contrast, {Interpersonal Relationships}[0.178, 0.454, 0.925, 0.590] starts from 8 at time period '5' and reaches 5 at time period '20'. What's interesting is the gap between these two metrics. Initially, Social Networks were lower than Interpersonal Relationships, but as time went on they grew more strongly and eventually surpassed Interpersonal Relationships.\nBased on the above thought, the answer is:\nThe trend observed in the line graph shows that while both Social Networks and Interpersonal Relationships initially decrease over the given time periods, the rate of decrease differs. Social Networks start at a lower point but eventually overtake Interpersonal Relationships, suggesting a more robust growth and probable increase in user preference for Social Networks as compared to Interpersonal Relationships over the observed time." } ], "id_raw": 158 }, { "id": "ouuUgGgRdABeSUlM", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in values between 'Social Networks' and 'Interpersonal Relationships' at time period '15'?" }, { "from": "gpt", "value": "To answer this question, I will locate the points for 'Social Networks' and 'Interpersonal Relationships' at time period '15'. The coordinates of the point for {Social Networks at '15'}[0.676, 0.250] reveal the value '15'. Meanwhile, the coordinates at the same time period for {Interpersonal Relationships at '15'}[0.676, 0.556] correspond to the value '6'. The difference between these values is 15 - 6 = 9.\nBased on the above thought, the answer is:\n9." } ], "id_raw": 158 }, { "id": "oiPxgNgUpXzaq8Nz", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_8_19.png", "conversations": [ { "from": "human", "value": "\nCan you create a table that captures the information from the line chart, with the type of Data Integration in rows and different metrics in columns?" }, { "from": "gpt", "value": "To generate the required table, I first look at {the x-axis tick labels}[[0.098, 0.919, 0.167, 0.942],[0.370, 0.919, 0.428, 0.942],[0.625, 0.919, 0.706, 0.942],[0.888, 0.919, 0.976, 0.942]], which signify the row labels or types of data integration. Then, I identify the three lines {Followers}[0.132, 0.064, 0.932, 0.711], {Engagement Rate}[0.132, 0.840, 0.932, 0.841] and {Bounce Rate}[0.132, 0.815, 0.932, 0.831] as the column headers. For each category, I extract the respective values given at these points.\nBased on the above thought, the answer is:\nCategory,Followers,EngagementRate,BounceRate\nWebsite Traffic,400,3.45,80\nTwitter Follows,1200,1.2,60\nFacebook Likes,2000,0.8,50\nInstagram Follows,2400,0.4,30" } ], "id_raw": 159 }, { "id": "DNctSB6AT5CLquu8", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_8_19.png", "conversations": [ { "from": "human", "value": "\nAnalyze the line chart and share insight about the relationship between the number of followers and engagement rate for various platforms." }, { "from": "gpt", "value": "By examining the lines {Followers}[0.132, 0.064, 0.932, 0.711] and {Engagement Rate}[0.132, 0.840, 0.932, 0.841], I can observe that as the number of followers increases, the engagement rate decreases for all the platforms. For instance, Website traffic with the lowest number of followers at 400, has the highest engagement rate at 3.45. Meanwhile, Instagram with the highest followers at 2400 has the lowest engagement rate at 0.4.\nBased on the above thought, the answer is:\nThe line chart indicates a negative correlation between the followers and engagement rate across all platforms. As the number of followers increases, the engagement rate tends to decrease. This implies that while platforms with fewer followers such as Website Traffic experience high engagement rates, platforms with a larger number of followers like Instagram have a significantly lower engagement rate." } ], "id_raw": 159 }, { "id": "weucrj0m5LdCLKDM", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_8_19.png", "conversations": [ { "from": "human", "value": "\nWhat's the difference between the highest and lowest followers?" }, { "from": "gpt", "value": "To determine this difference, I need to look at the points on {the line for Followers}[0.132, 0.064, 0.932, 0.711]. The maximum value is observed at 2400 corresponding to {Instagram Follows}[0.932, 0.880] and the minimum value is observed at 400 corresponding to {Website Traffic}[0.132, 0.880]. Therefore, the difference is 2400 - 400 = 2000.\nBased on the above thought, the answer is:\n2000." } ], "id_raw": 159 }, { "id": "DLdj434xiNksw6rw", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_9_1.png", "conversations": [ { "from": "human", "value": "\nCould you provide structured table data for each type of Social Welfare Activities in different regions based on the line chart?" }, { "from": "gpt", "value": "To create a structured table, I will first identify {the x-axis tick labels}[0.093, 0.862, 0.161, 0.894], [0.205, 0.862, 0.264, 0.894], [0.316, 0.862, 0.384, 0.894], [0.428, 0.862, 0.509, 0.894], [0.552, 0.862, 0.648, 0.894] which represent the different Social Welfare Activities. Subsequently, I will locate {the line points for each region}[0.098, 0.320], [0.211, 0.525], [0.325, 0.197], [0.438, 0.062], [0.565, 0.469],[0.098, 0.092], [0.211, 0.220], [0.325, 0.365], [0.438, 0.532], [0.565, 0.695],[0.098, 0.570], [0.211, 0.781], [0.325, 0.554], [0.438, 0.302], [0.565, 0.105] for Region 1, Region 2 and Region 3 respectively, to populate the corresponding expenditures.\nBased on the above thought, the answer is:\nActivities,Region 1, Region 2, Region 3\nChild Education Fund,10000,3000,25000\nHealthcare Assistance,25000,8000,35000\nFood Distribution,8000,15000,25000\nHousing Support,2000,22000,12000\nEmergency Relief,22000,29000,4000" } ], "id_raw": 161 }, { "id": "ZS6IlGXcTHkZLBs2", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_9_1.png", "conversations": [ { "from": "human", "value": "\nWhich region appears to prioritize Education and Healthcare over other activities based on the funding allocations?" }, { "from": "gpt", "value": "To identify which region prioritizes Education and Healthcare, I need to compare the funding for these activities across all the regions. Looking at {the line for Region 1}[0.098, 0.320], [0.211, 0.525], [0.325, 0.197], [0.438, 0.062], [0.565, 0.469], I see that the funding for Child Education Fund and Healthcare Assistance is relatively higher compared to other activities. For {Region 2}[0.098, 0.092], [0.211, 0.220], [0.325, 0.365], [0.438, 0.532], [0.565, 0.695], the funding is evenly distributed, but Child Education Fund and Healthcare Assistance do not have the highest allocations. In {Region 3}[0.098, 0.570], [0.211, 0.781], [0.325, 0.554], [0.438, 0.302], [0.565, 0.105], Healthcare Assistance receives significantly higher funding, but Child Education Fund is not a prioritized area.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Region 1 seems to prioritize Education and Healthcare over other activities. Both Child Education Fund and Healthcare Assistance are allocated significantly higher funding compared to Food Distribution, Housing Support, and Emergency Relief in Region 1." } ], "id_raw": 161 }, { "id": "QXmxtegWEUioIVIG", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_9_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the biggest difference in funding allocation between two activities within a single region and which region does this difference occur in?" }, { "from": "gpt", "value": "To answer this question, I must first analyze the funding range for each activity in each region. Examining {Region 1's line points}[0.098, 0.320], [0.211, 0.525], [0.325, 0.197], [0.438, 0.062], [0.565, 0.469], I see a difference of 23000 between Healthcare Assistance and Housing Support. Looking at {Region 2's line points}[0.098, 0.092], [0.211, 0.220], [0.325, 0.365], [0.438, 0.532], [0.565, 0.695], the largest difference is 26000 between Emergency Relief and Child Education Fund. Lastly, from {Region 3's line points}[0.098, 0.570], [0.211, 0.781], [0.325, 0.554], [0.438, 0.302], [0.565, 0.105], the largest difference is 31000 between Healthcare Assistance and Emergency Relief. Now, comparing these differences, the largest funding disparity occurs in Region 3.\nBased on the above thought, the answer is:\n31000, Region 3." } ], "id_raw": 161 }, { "id": "gc2ndFat3ZIGQi0Y", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_9_4.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data represented on the line chart into a structured table format with Age Groups as rows and Years as columns?" }, { "from": "gpt", "value": "To transform the line chart into a tabular format, I am looking and identifying vital elements in the chart. I find {the x-axis tick labels}[[0.084, 0.857], [0.297, 0.857], [0.510, 0.857], [0.723, 0.857], [0.936, 0.857]], which will serve as the column headers or the Years. I see {the legend texts}[[0.587, 0.889], [0.709, 0.889], [0.818, 0.889], [0.926, 0.889]] as the row labels that represent the different Age Groups. I then reach to locate {the points of four lines}[[0.096, 0.548], [0.309, 0.595], [0.522, 0.440], [0.735, 0.291], [0.947, 0.327], [0.096, 0.760], [0.309, 0.799], [0.522, 0.677], [0.735, 0.588], [0.947, 0.515], [0.096, 0.242], [0.309, 0.347], [0.522, 0.469], [0.735, 0.539], [0.947, 0.621], [0.096, 0.129], [0.309, 0.172], [0.522, 0.314], [0.735, 0.456], [0.947, 0.539]] each representing the population size of an age group in a particular year and arrange them in the table.\nBased on the above thought, the answer is:\nAge Groups,Population in 2000,Population in 2010,Population in 2020,Population in 2030,Population in 2040\nUnder 25,76.5,75.1,79.8,84.3,83.2\n25 - 34,70.1,68.9,72.6,75.3,77.5\n35 - 44,85.8,82.6,78.9,76.8,74.3\nOver 45,89.2,87.9,83.6,79.3,76.8" } ], "id_raw": 164 }, { "id": "M090tzPSgiRB8j1N", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_9_4.png", "conversations": [ { "from": "human", "value": "\nJudging from the line chart, which age group has experienced a continuous decrease in the population from the year 2000 to 2040?" }, { "from": "gpt", "value": "In order to answer this question, I need to observe the trends shown by the individual line plots for each age group. Let's take a look at the line plots:\nFirst, {the line for \"Under 25\"}[0.096, 0.548, 0.947, 0.327] shows a general increase, then a decrease, and then an increase again, which means there is fluctuation, no continuous decrease.\nSecond, {the line for \"25 - 34\"}[0.096, 0.760, 0.947, 0.515] shows a decrease first, then increases overall. So, this is also not a continuous decrease.\nThird, {the line for \"35 - 44\"}[0.096, 0.242, 0.947, 0.621] shows a clear declining trend from the start to the end, which seems to fit the criteria.\nLastly, {the line for \"Over 45\"}[0.096, 0.129, 0.947, 0.539] also shows a clear decreasing trend from start to finish.\nBased on the above thought, the answer is:\nThe line chart suggests that both the age groups of \"35 - 44\" and \"Over 45\" have experienced a continuous decrease in population from the year 2000 to 2040. The line plots for these two age groups consistently decline over this period, unlike the plots for the other age groups which present some increasing trends in between." } ], "id_raw": 164 }, { "id": "2J2FUuhUKxyTDihS", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_9_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in population size for the age group \"25 - 34\" from the year 2000 to 2020?" }, { "from": "gpt", "value": "To calculate the increase in size for the age group of \"25 - 34\", I need to locate {the points for the line corresponding to \"25 - 34\"}[[0.096, 0.760],[0.522, 0.677]]. The value corresponding to the year 2000 is 70.1 million and the one corresponding to 2020 is 72.6 million. The increase would be the value for 2020 subtracted by the value for 2000, hence 72.6 - 70.1 = 2.5 million.\nBased on the above thought, the answer is:\n2.5 million." } ], "id_raw": 164 }, { "id": "ffe0KBODzFYa7lUJ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_9_5.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table representing the yearwise data for Global Temperature Change, CO2 Emissions, and Ocean Acidification from the line charts?" }, { "from": "gpt", "value": "To create the table from the line charts, I identify {the color-coded lines corresponding to each variable}[0.988, 0.255, 0.992, 0.556] and note the trend points for each. By observing {the x-axis tick labels} for the years [2000, 2020] and {the y-axis tick labels} for values, I compile values for each year and variable as displayed on their respective points on the line.\nBased on the above thought, the answer is:\nYear,Global Temperature Change,CO2 Emissions,Ocean Acidification\n2000,0.3,2200,400\n2020,1.2,3000,600" } ], "id_raw": 165 }, { "id": "07mWF1q7fD3SDbGv", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_9_5.png", "conversations": [ { "from": "human", "value": "\nConsidering the graph, which environmental variable shows the steepest increase over the period 2000 to 2020?" }, { "from": "gpt", "value": "To ascertain the steepest increase among the variables, I compare the slope of {the line representing Global Temperature Change}[0.988, 0.556, 0.992, 0.315], {CO2 Emissions}[0.989, 0.435, 0.990, 0.255] and {Ocean Acidification}[0.990, 0.315, 0.991, 0.195]. A steeper line indicates a greater rate of change. By observational comparison, the Global Temperature Change exhibits the most significant change per year.\nBased on the above thought, the answer is:\nGlobal Temperature Change shows the steepest increase, as indicated by the line's steepness, which suggests a higher rate of rise compared to the other variables." } ], "id_raw": 165 }, { "id": "qyeSI0SzYShT3bOT", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_9_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in CO2 Emissions from 2000 to 2020?" }, { "from": "gpt", "value": "To determine the total increase in CO2 Emissions from 2000 to 2020, I observe the values at the points on the CO2 Emissions line from {year}=2000 [0.040, 0.676] to {year}=2020 [0.045, 0.796]. The values increase from 2200 units to 3000 units across these years. The total increase is calculated as 3000 - 2200.\nBased on the above thought, the answer is:\n800 units." } ], "id_raw": 165 }, { "id": "DXI3B0Uv176CdZbI", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_9_8.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured table showing the year and study proportions for the Career Skill Demand Study from the chart provided?" }, { "from": "gpt", "value": "To generate the structured table from the line chart, firstly I focus on {the x-axis labels}[0.117, 0.820, 0.152, 0.843], [0.162, 0.820, 0.197, 0.843], ..., [0.656, 0.820, 0.691, 0.843] which represent the years. Then, I must examine the points corresponding to the Career Skill Demand Study on the chart at {the line points for Career Skill Demand Study}[0.134, 0.701], [0.179, 0.616], ..., [0.673, 0.582] to collect the respective proportions for each year.\nBased on the above thought, the answer is:\nYear,Career Skill Demand\n2008,0.3\n2009,0.35\n2010,0.4\n2011,0.38\n2012,0.37\n2013,0.35\n2014,0.33\n2015,0.32\n2016,0.33\n2017,0.35\n2018,0.37\n2019,0.35\n2020,0.37" } ], "id_raw": 168 }, { "id": "yND1QMTkRlaMLlou", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_9_8.png", "conversations": [ { "from": "human", "value": "\nWhich study shown on the line chart has seen the most consistent growth in proportion over the examined years?" }, { "from": "gpt", "value": "To identify the study with the most consistent growth, each line's trend must be analyzed. By observing the start and end points of each line and points in between, the following can be deduced:\nThe line for {Career Skill Demand}[0.134, 0.701, 0.673, 0.582] fluctuates but ends at a point slightly lower than where it started.\nThe line for {Workplace Automation}[0.134, 0.787, 0.673, 0.325] shows significant growth from 0.25 to 0.52.\nThe line for {Career Tech Advance}[0.134, 0.753, 0.673, 0.223] increases most prominently from 0.27 to 0.58.\nMaturing with the observation, {Career Tech Advance} shows the most consistent and substantial growth trend across the years due to the higher endpoint in its line progression on the chart.\nBased on the above thought, the answer is:\nCareer Tech Advance has demonstrated the most consistent growth in proportion over the years. This conclusion is supported by the consistent upward trend of its line on the chart from 2008 to 2020, marking an increase from 0.27 to 0.58, and showing no dip below its starting point throughout." } ], "id_raw": 168 }, { "id": "VzJwu9ts8eg6Xiq2", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_9_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in the Workplace Automation study proportion from the year 2008 to 2020?" }, { "from": "gpt", "value": "To find the increase in proportion, I need to compare the points on the chart that represent the year 2008 and 2020 for the Workplace Automation study. Initially locating {the point for 2008}[0.134, 0.787] with a proportion of 0.25, and then {the point for 2020}[0.673, 0.325] with a proportion of 0.52. The numerical increase in the proportion is calculated as 0.52 - 0.25 = 0.27.\nBased on the above thought, the answer is:\n0.27" } ], "id_raw": 168 }, { "id": "AYO0tCV1M9vhPVwu", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_9_9.png", "conversations": [ { "from": "human", "value": "\nCould you convert the financial trends depicted in the bar chart into a structured table format showing the years and the corresponding values for Beige Book A, Bond Index A, and Economic Growth A?" }, { "from": "gpt", "value": "To transform the bar chart data into a table format, I first identify {the x-axis labels for each subplot}[0.099, 0.859, 0.150, 0.895], [0.363, 0.859, 0.414, 0.895], [0.627, 0.859, 0.678, 0.895] representing the years from 1 to 10. I then proceed to observe the y-values for each corresponding bar at these x-axis positions: {Beige_Book_A values}[0.065, 0.110, 0.184, 0.768], {Bond_Index_A values}[0.329, 0.110, 0.448, 0.768], {Economic_Growth_A values}[0.593, 0.110, 0.712, 0.768]. Using this information, I compile the entries for each year across the different indices.\nBased on the above thought, the answer is:\nYear,Beige_Book_A,Bond_Index_A,Economic_Growth_A\n1,0.02,0.03,0.04\n2,0.025,0.025,0.035\n3,0.028,0.020,0.030\n4,0.03,0.015,0.025\n5,0.026,0.017,0.028\n6,0.022,0.020,0.031\n7,0.018,0.023,0.033\n8,0.021,0.019,0.036\n9,0.019,0.022,0.035\n10,0.022,0.020,0.04" } ], "id_raw": 169 }, { "id": "KaVkHvCKk3jeKldb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_9_9.png", "conversations": [ { "from": "human", "value": "\nHow do the trends in Beige Book A and Economic Growth A compare across the given timesteps, and what could be inferred about their volatility?" }, { "from": "gpt", "value": "To compare the trends, I review {the line coordinates for Beige Book A}[0.065, 0.110, 0.184, 0.768] and {the line coordinates for Economic Growth A}[0.593, 0.110, 0.712, 0.768]. Beige Book A exhibits a gradual decrease in values after an initial increase. In contrast, Economic Growth A shows fluctuations at earlier timesteps but stabilizes toward the later timesteps with a peak at the final value. By analyzing the ranges in y-coordinates, Beige Book A shows smaller fluctuations, indicating lower volatility compared to Economic Growth A.\nBased on the above thought, the answer is:\nThe trend for Beige Book A is less volatile, indicated by smaller vertical fluctuations, suggesting steadier performance. Economic Growth A, while ending on a high, displays more significant ups and downs, which indicates a higher volatility across timesteps." } ], "id_raw": 169 }, { "id": "Ap3fFhgW537QenXv", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_9_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage change in Bond Index A from Timestep 1 to Timestep10?" }, { "from": "gpt", "value": "To calculate the percentage change in Bond Index A, I first spot {the initial value of Bond Index A at Timestep 1}[0.329, 0.110] and {the final value at Timestep 10}[0.448, 0.548]. The initial value is 0.03 and the final value is 0.02. Using the formula for percentage change, ((Final - Initial) / Initial * 100), I calculate ((0.02 - 0.03) / 0.03) * 100.\nBased on the above thought, the answer is:\n-33.33%" } ], "id_raw": 169 }, { "id": "F1JbWCiWL1KmxqkW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_9_11.png", "conversations": [ { "from": "human", "value": "\nCan you provide the data representation from the line chart showing the MSE (Mean Squared Error) rates across the various sample sizes for different activities such as Sport, Leisure, Hobby, and Work in a tabular CSV format?" }, { "from": "gpt", "value": "I begin by identifying the tick marks on {the x-axis for each subplot}[0.051, 0.477], [0.516, 0.477], [0.051, 0.903], [0.516, 0.903] to categorize different sample sizes. I follow this up by locating each point on {the lines for Sport}[0.095, 0.137, 0.494, 0.460], {Leisure}[0.560, 0.137, 0.959, 0.460], {Hobby}[0.095, 0.563, 0.494, 0.887], and {Work}[0.560, 0.563, 0.959, 0.887] to determine the specific MSE values at those sample sizes for each activity.\nBased on the above thought, the answer is:\nSample Size,Sport,Leisure,Hobby,Work\n1000,0.51,0.63,0.55,0.61\n2000,0.43,0.47,0.64,0.52\n3000,0.58,0.59,0.67,0.56\n4000,0.42,0.50,0.60,0.65\n5000,0.52,0.45,0.54,0.61\n6000,0.56,0.48,0.68,0.64\n7000,0.66,0.57,0.49,0.51\n8000,0.72,0.63,0.52,0.57\n9000,0.78,0.71,0.57,0.67\n10000,0.76,0.73,0.68,0.72" } ], "id_raw": 171 }, { "id": "tE4tBsP4uooIeOHj", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_9_11.png", "conversations": [ { "from": "human", "value": "\nConsidering the trend analysis, which social activity has shown a consistent increase in the MSE rates from the smallest sample size to the largest?" }, { "from": "gpt", "value": "To analyze the consistent increase in MSE rates, I review the line trends from {Sport}[0.095, 0.137, 0.494, 0.460], {Leisure}[0.560, 0.137, 0.959, 0.460], {Hobby}[0.095, 0.563, 0.494, 0.887], and {Work}[0.560, 0.563, 0.959, 0.887]. From the line plots, the MSE for {Work}[0.560, 0.563, 0.959, 0.887] exhibits a consistent upward trajectory, especially notable in the plot starting from a lower position and escalating to greater values at larger sample sizes.\nBased on the above thought, the answer is:\nWork has shown a consistent increase in MSE rates across the progression from smallest to largest sample sizes. This consistent upward trend in {the Work subplot}[0.560, 0.563, 0.959, 0.887] indicates increasing errors in measurements or model predictions related to Work as the data set expands." } ], "id_raw": 171 }, { "id": "s6IJEU7l0ncq3gWr", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_9_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest MSE recorded for Leisure across all sample sizes?" }, { "from": "gpt", "value": "To determine the highest MSE for Leisure, I inspect {the data points on the line plot for Leisure}[0.560, 0.137, 0.959, 0.460], which contains values at various sample sizes. The highest point corresponds to a sample size of 10000 with an MSE of 0.73 which is observed at {point coordinates}[0.959, 0.137].\nBased on the above thought, the answer is:\n0.73." } ], "id_raw": 171 }, { "id": "UR82hS3d30eeHXKV", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_9_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data showing the growth of different industries over the decade into a table format?" }, { "from": "gpt", "value": "Observing {the axis labels}[0.508, 0.222, 0.549, 0.245] and extracting information from {the legend}[0.078, 0.027, 0.297, 0.085], the chart depicts the growth across various years marked on {the x-axis}[0.108, 0.197, 0.117, 0.215] for different industries. I'll arrange the data by years as columns and industries as rows, obtaining the growth values from {the lines for each industry}[0.113, 0.171, 0.943, 0.177] corresponding to each year.\nBased on the above thought, the answer is:\nYear,Mining,Logistics,Manufacturing,Textiles,Retail\n2010,0.5,0.8,0.2,0.1,0.7\n2011,0.6,0.85,0.15,0.11,0.75\n2012,0.55,0.9,0.1,0.12,0.8\n2013,0.62,0.92,0.13,0.1,0.83\n2014,0.65,0.93,0.12,0.13,0.85\n2015,0.68,0.95,0.1,0.14,0.9\n2016,0.7,0.98,0.09,0.15,0.92\n2017,0.72,0.99,0.08,0.16,0.94\n2018,0.75,1,0.07,0.17,0.96\n2019,0.8,1.02,0.05,0.18,0.99\n2020,0.85,1.05,0.03,0.2,1.02" } ], "id_raw": 172 }, { "id": "domfb8texh5m1Nqg", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_9_12.png", "conversations": [ { "from": "human", "value": "\nWhich industry appears to have had the least variation in growth over the decade and can you explain why it might be considered stable?" }, { "from": "gpt", "value": "Evaluating the stability in growth can be determined by examining the line plots. Observing {the line plot for Retail}[0.113, 0.026, 0.943, 0.144], the slope is consistent with minimal fluctuations, as seen from the coordinates. This suggests that the Retail industry has had a steady demand or effective adaptability strategies over the years, indicated by the uniform positive slope.\nBased on the above thought, the answer is:\nThe Retail industry appears to have had the least variation in growth, indicating stability. This can be attributed potentially to consistent consumer demand and effective strategic adaptations over the decade. The line plot confirms this with its gradual and consistent upward trend, emphasizing a resilient market presence amidst varying economic conditions." } ], "id_raw": 172 }, { "id": "Uas33zX5xXDUzaOZ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_9_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the absolute difference between the highest and lowest global trade impact percentages for the Retail industry during the decade?" }, { "from": "gpt", "value": "To find the absolute difference, I first locate {the plot line points for the Retail industry}[0.113, 0.026, 0.943, 0.144] which pertain to the global trade impact percentages. Observing the maximum value at 2020 (1.05) and the minimum value at 2010 (0.7), I calculate the absolute difference between these two values.\nBased on the above thought, the answer is:\n0.35" } ], "id_raw": 172 }, { "id": "MIpAf9KKetDp07G4", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_9_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data present in the line chart into a table format with company names as rows and quarters as columns?" }, { "from": "gpt", "value": "To construct the data table, I first identify {the x-axis tick labels}[0.063, 0.911, 0.070, 0.934], [0.246, 0.911, 0.254, 0.934], [0.430, 0.911, 0.437, 0.934], [0.613, 0.911, 0.621, 0.934], [0.797, 0.911, 0.804, 0.934] which represent the different Quarters and would form the columns of the table. Then I locate {the legend labels}[0.127, 0.095, 0.278, 0.126],[0.127, 0.143, 0.311, 0.174], [0.127, 0.190, 0.299, 0.222],[0.127, 0.238, 0.271, 0.270] which represent the names of the companies that will be the row labels. The individual data points {Line1}[0.250, 0.780,,0.434, 0.752, 0.617, 0.723, 0.800, 0.694, 0.984, 0.694], {Line2}[0.250, 0.780, 0.434, 0.780, 0.617, 0.780, 0.800, 0.780, 0.984, 0.780], {Line3}[0.250, 0.780, 0.434, 0.866, 0.617, 0.809, 0.800, 0.723, 0.984, 0.637] and {Line4}[0.250, 0.780, 0.434, 0.637, 0.617, 0.494, 0.800, 0.351, 0.984, 0.208] help populate the values for each stock in each quarter.\nBased on the above thought, the answer is:\nCompany,Q1,Q2,Q3,Q4\nAmazon,100,120,140,160\nApple,100,80,70,90\nFacebook,100,60,50,70\nMicrosoft,100,90,80,100\nTesla,100,110,120,130" } ], "id_raw": 173 }, { "id": "tep6rjHxyICn6lP8", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_9_13.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the graph, which company's shares would you recommend as the most stable investment? Why?" }, { "from": "gpt", "value": "To identify the most stable investment, I'd need to evaluate the consistency in the growth trends of each company. I can see that {the line for Amazon}[0.250, 0.780,,0.434, 0.637, 0.617, 0.494, 0.800, 0.351, 0.984, 0.208] shows an increase every quarter without any decline. On the other hand, the stocks of {Apple}[0.250, 0.780, 0.434, 0.866, 0.617, 0.809, 0.800, 0.723, 0.984, 0.637], {Facebook}[0.250, 0.780, 0.434, 0.866, 0.617, 0.809, 0.800, 0.723, 0.984, 0.637], and {Microsoft}[0.250, 0.780,,0.434, 0.752, 0.617, 0.723, 0.800, 0.694, 0.984, 0.694] all exhibit periods of decline at some point. {Tesla}[0.250, 0.780, 0.434, 0.780, 0.617, 0.780, 0.800, 0.780, 0.984, 0.780]'s stock also increases each quarter, but the growth rate is less than that of Amazon.\nBased on the above thought, the answer is:\nBased on the trends shown in the graph, Amazon's shares seem to be the most stable investment. It not only increases every quarter but does so at a higher and consistent growth rate compared to the other companies. This constant upward trend suggests stability and good potential return on investment, minimizing the risk for potential investors." } ], "id_raw": 173 }, { "id": "MBe1pPFQEXkCaZ5Y", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_9_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the total gain in percentage points for Tesla over the 4 quarters?" }, { "from": "gpt", "value": "To calculate the total gain for Tesla, I need to locate {the line points for Tesla}[0.250, 0.780, 0.434, 0.637, 0.617, 0.494, 0.800, 0.351, 0.984, 0.208] and subtract the initial value in Q1 from the final value in Q4: 130 - 100 = 30.\nBased on the above thought, the answer is:\n30." } ], "id_raw": 173 }, { "id": "F7smOkFDXUqy5sMT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_9_14.png", "conversations": [ { "from": "human", "value": "\nCan you transform the series of MSE values for each stock across various time points into a structured table?" }, { "from": "gpt", "value": "First, I analyze the chart detailing MSE values for each stock at certain time points. I identify {the x-axis tick labels}[0.056, 0.927, 0.068, 0.962], [0.097, 0.927, 0.115, 0.962], [0.141, 0.927, 0.159, 0.962], [0.185, 0.927, 0.203, 0.962], [0.229, 0.927, 0.246, 0.962] for the time points and {the plotted MSE points for each stock}[0.062, 0.500], [0.106, 0.548], [0.150, 0.613], [0.194, 0.629], [0.238, 0.667] to extract and arrange the MSE values in the table below.\nBased on the above thought, the answer is:\nTime Point,Stock 1 MSE,Stock 2 MSE,Stock 3 MSE,Stock 4 MSE\n96,0.225,0.180,0.140,0.330\n192,0.210,0.175,0.135,0.320\n288,0.190,0.170,0.130,0.315\n384,0.185,0.165,0.128,0.308\n480,0.173,0.160,0.120,0.300" } ], "id_raw": 174 }, { "id": "PK23gHPSo59dTGTz", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_9_14.png", "conversations": [ { "from": "human", "value": "\nWhich stock displayed the most consistent decrease in MSE values over time?" }, { "from": "gpt", "value": "Observing the trend in the line plot for each stock, I assess the slopes and data points. {Stock 3}[0.557, 0.774], [0.601, 0.790], [0.645, 0.806], [0.689, 0.813], [0.733, 0.839] presents data points of MSE values that decrease steadily from 0.14 to 0.12 over the time measurements, suggesting a consistent decline, unlike the other stocks which show larger variances between consecutive time points.\nBased on the above thought, the answer is:\nStock 3 displayed the most consistent decrease in MSE values over time, with MSE values providing a steady downward trend throughout the observed periods." } ], "id_raw": 174 }, { "id": "YgxPEqxHov2w4aaT", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_9_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the average MSE for Stock 4 across all measured time points?" }, { "from": "gpt", "value": "To calculate the average MSE for Stock 4, I will find and sum {the MSE values for Stock 4}[0.805, 0.160], [0.848, 0.193], [0.892, 0.209], [0.936, 0.231], [0.980, 0.257] documented as 0.33, 0.32, 0.315, 0.308, and 0.3. I will then divide this sum by the total number of time points, which is 5.\nBased on the above thought, the answer is:\n0.315" } ], "id_raw": 174 }, { "id": "JlRsDxHkzngNMUwC", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_9_16.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table indicating the percentage change in performance of each model across different tasks from the provided data?" }, { "from": "gpt", "value": "To construct the table, I analyze each subchart, identifying the different tasks at {the titles of each subchart}[0.1, 0.9, 0.3, 0.93]. Each model's performance for each task is displayed as a bar in the subchart, from which I extract the performance data. Observing {the labels and percentage values on the bars}[0.2, 0.85, 0.25, 0.88], [0.5, 0.75, 0.55, 0.78] for each model, I collect the necessary values to populate the table rows representing the machine learning models.\nBased on the above thought, the answer is:\nTask,Back Propagation,Support Vector Machines,Decision Trees,Random Forests,Neural Networks\nImage Recognition,0%,2%,-5%,5%,0%\nSpeech Recognition,1%,3%,4%,2%,-1%\nNatural Language Processing,-2%,5%,1%,6%,-3%\nData Mining,3%,0%,2%,4%,-2%" } ], "id_raw": 176 }, { "id": "s4NKYqWaRUF6iQpD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_9_16.png", "conversations": [ { "from": "human", "value": "\nWhich machine learning model appears to have the most inconsistent performance across tasks, and why?" }, { "from": "gpt", "value": "To determine which model has the most inconsistent performance, I analyze the variation in bar lengths and colors across all tasks for each model. Specifically, {the bars for Back Propagation}[0.1, 0.8, 0.2, 0.82], {Support Vector Machines}[0.3, 0.8, 0.4, 0.82], {Decision Trees}[0.5, 0.8, 0.6, 0.82], {Random Forests}[0.7, 0.8, 0.8, 0.82], and {Neural Networks}[0.9, 0.8, 1.0, 0.82] reveals varying changes across tasks. Random Forests shows significant inconsistency with noticeable fluctuations in performance, having rapid expansion in NLP while showing a continuous decrease in Image Recognition.\nBased on the above thought, the answer is:\nRandom Forests appears to have the most inconsistent performance across tasks, illustrated by its sharp differences in performance growth in Natural Language Processing contrasted with a consistent decline in Image Recognition. This variety in outcomes suggests significant fluctuations in its effectiveness depending on the application." } ], "id_raw": 176 }, { "id": "Hh469dB3OxWdZOZ8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_9_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall performance change for the Support Vector Machines model across all tasks?" }, { "from": "gpt", "value": "To find the overall performance change for the Support Vector Machines model, I need to sum up the percentage values from each task for this model. By observing {the bar representing Support Vector Machines}[0.15, 0.75, 0.18, 0.78] in each task, I gather the performance changes: Image Recognition (2%), Speech Recognition (3%), Natural Language Processing (5%), Data Mining (0%). Adding these values together: 2 + 3 + 5 + 0 = 10%.\nBased on the above thought, the answer is:\n10%." } ], "id_raw": 176 }, { "id": "sHb8My1FgpG0x5ZP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_9_17.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, could you summarize the efficiency of various factories over different years into a structured format? Let the years be the rows and different factories be the columns in the table." }, { "from": "gpt", "value": "To construct the table, let's first identify {the x-axis tick labels}[[0.087, 0.892, 0.117, 0.916], [0.296, 0.892, 0.326, 0.916], [0.505, 0.892, 0.535, 0.916], [0.714, 0.892, 0.744, 0.916], [0.922, 0.892, 0.952, 0.916]] which represent the years. These would serve as our rows in the table. Next, locate {the legend texts}[[0.086, 0.164, 0.118, 0.182],[0.086, 0.190, 0.118, 0.208],[0.086, 0.217, 0.118, 0.234],[0.086, 0.243, 0.118, 0.260],[0.086, 0.269, 0.118, 0.286]] corresponding to the 5 factories. These would serve as the columns in our table. The corresponding efficiencies can be extracted by observing {the line points} in each factory's trend.\nBased on the above thought, the answer is:\nYear,Factory 1,Factory 2,Factory 3,Factory 4,Factory 5\nYear 1,0.70,0.75,0.60,0.65,0.72\nYear 2,0.72,0.73,0.58,0.69,0.75\nYear 3,0.69,0.76,0.62,0.67,0.74\nYear 4,0.71,0.77,0.64,0.68,0.73\nYear 5,0.68,0.78,0.66,0.70,0.76" } ], "id_raw": 177 }, { "id": "PjLLhZzzyniClxJJ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_9_17.png", "conversations": [ { "from": "human", "value": "\nOver the years, which factory exhibited the most improvement in manufacturing efficiency? Please explain your answer." }, { "from": "gpt", "value": "To deduce which factory had the most improvement, we need to compare the changes in efficiency over the years for each factory. By analyzing {line datasets for factories}[0.102, 0.441, 0.937, 0.500], [0.102, 0.295, 0.937, 0.207], [0.102, 0.734, 0.937, 0.558], [0.102, 0.587, 0.937, 0.441], [0.102, 0.383, 0.937, 0.266] I recognize the patterns reflecting the efficiency of manufacturing from Year 1 to Year 5. Factory 2 and Factory 4 appear to have improved more compared to the other factories.\nBased on the above thought, the answer is:\nFactory 4 displayed the most improvement in manufacturing efficiency over the years. As gleaned from the line graph, Factory 4's efficiency augments gradually every year. Despite small fluctuations, the overall trend for Factory 4 demonstrates a steady increase, marking substantial progress in its manufacturing efficiency." } ], "id_raw": 177 }, { "id": "7DKnbq0ni7kEtuCi", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_9_17.png", "conversations": [ { "from": "human", "value": "\nWhat was the greatest change in manufacturing efficiency that took place in any factory from Year 1 to Year 5?" }, { "from": "gpt", "value": "To determine this, we need to look at the differences between the efficiency of each factory in Year 1 and Year 5. The coordinates representing the end points for each line are: {Factory 1}[0.102, 0.441, 0.937, 0.500], {Factory 2}[0.102, 0.295, 0.937, 0.207], {Factory 3}[0.102, 0.734, 0.937, 0.558], {Factory 4}[0.102, 0.587, 0.937, 0.441], and {Factory 5}[0.102, 0.383, 0.937, 0.266]. By examining these values, we can calculate the greatest change.\nBased on the above thought, the answer is:\n0.10." } ], "id_raw": 177 }, { "id": "AkZBZJqWOQBs6CVH", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_9_18.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into a structured table with International Financial Institutions as rows and financial categories(GDP Growth, Inflation) as their columns?" }, { "from": "gpt", "value": "To generate the tabular data from the bar charts, I first identify {the subplot titles}[[0.129, 0.914, 0.232, 0.938], [0.129, 0.679, 0.238, 0.703], [0.129, 0.444, 0.253, 0.468], [0.129, 0.212, 0.223, 0.236]] which represent the row labels or the International Financial Institutions. Next, I observe {the bar labels}[[0.129, 0.873, 0.197, 0.895], [0.129, 0.640, 0.197, 0.662], [0.129, 0.405, 0.197, 0.427], [0.129, 0.173, 0.197, 0.195]] for each subplot which represent the column headers or financial categories. To populate the values, I locate {the end points of the bars}[[0.129, 0.875, 0.611, 0.895], [0.129, 0.841, 0.058, 0.862], [0.129, 0.641, 0.432, 0.661], [0.129, 0.607, 0.079, 0.628], [0.129, 0.406, 0.334, 0.426], [0.129, 0.372, 0.183, 0.393], [0.129, 0.174, 0.297, 0.194], [0.129, 0.140, 0.218, 0.161]] for each financial category under each institution and extract the corresponding values.\nBased on the above thought, the answer is:\nInstitutions,GDP Growth,Inflation\nBank of England,2.2,-0.3\nEuropean Central Bank,1.0,-0.5\nReserve Bank of Australia,1.8,-0.4\nUS Federal Reserve,0.5,0.2" } ], "id_raw": 178 }, { "id": "qD1acgXOhcCvGxK5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_9_18.png", "conversations": [ { "from": "human", "value": "\nBased on the bar charts, which institution has the most considerable negative impact on a category, and can you explain why this might be the case?" }, { "from": "gpt", "value": "The aim here is to find which institution has the most negative influence on a category. By checking the bars, I note that {the ECB's impact on Inflation}[0.129, 0.607, 0.079, 0.628] appears to extend the farthest to the left, indicating a notable negative impact. Upon further analysis, its value is -0.5, which is the most substantial negative impact among all institutions and categories. The reasoning behind this impact could be due to several factors: it might be because of the specific financial policies used by the ECB, external economic variables that particularly affect Inflation, or perhaps a combination of both.\nBased on the above thought, the answer is:\nBased on the bar charts, the European Central Bank (ECB) appears to have the most significant negative impact on a category, specifically on Inflation. This could be due to a variety of factors, including the nature of the ECB's policies and the influence of external economic variables on Inflation." } ], "id_raw": 178 }, { "id": "5ZxUcUlQpJ47RQ6p", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_9_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in GDP Growth impact between Bank of England (BOE) and US Federal Reserve (Fed)?" }, { "from": "gpt", "value": "To find the GDP Growth impact for Bank of England (BOE) and US Federal Reserve (Fed), I refer to {the BOE bar for GDP Growth}[0.129, 0.875, 0.611, 0.895] and determine its value to be 2.2. Conversely, for {the Fed bar for GDP Growth} [0.129, 0.174, 0.297, 0.194], the corresponding value is 0.5. So, the difference between GDP Growth impact of BOE and Fed is 2.2 - 0.5 = 1.7.\nBased on the above thought, the answer is:\n1.7" } ], "id_raw": 178 }, { "id": "SPpEzHkesgFld1wg", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_9_19.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured table showing the trend differences for various activities based on their increase or decrease in the dual horizontal bar charts?" }, { "from": "gpt", "value": "First, I observe {the color codes in the legends}[[0.01, 0.95, 0.03, 0.96]], distinguished by the colors in the two charts. In one chart, I focus on the activities that have decreased in popularity; in the other, those that have increased. I analyze each bar by verifying its alignment with the corresponding label {located at}[[0.1, 0.2, 0.4, 0.25]], and measure how much the bar extends to the left or right from the zero baseline {per its coordinates}[[0.5, 0.1, 0.9, 0.3]]. For decreased activities Running at -22%, Dining at -18%, Travel at -15%, and Programming at -14%, while Yoga shows an increase by 2%. For increased activities, Running is at 23%, Dining at 25%, Travel at 27%, and Programming at 29%, with Yoga decreasing by 20%.\nBased on the above thought, the answer is:\nActivity,Decrease,Increase\nRunning,-22%,23%\nDining,-18%,25%\nYoga,2%,-20%\nTravel,-15%,27%\nProgramming,-14%,29%" } ], "id_raw": 179 }, { "id": "c2erAIKRUHx78B15", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_9_19.png", "conversations": [ { "from": "human", "value": "\nWhy does Yoga show different trends in the two different charts?" }, { "from": "gpt", "value": "By analyzing {the bars for Yoga in both charts}[[0.3, 0.52, 0.5, 0.58]], I notice distinctly contrasting trends. The presence of the bars on both sides of the zero baseline highlights a rare inconsistency - a minimal increase in one chart and a significant decrease in another. It suggests the data might be capturing different aspects or periods of popularity, where certain factors led to a small increase in popularity in one scenario, while general trends or perhaps broader scale factors caused a significant decrease in another context. Further analysis of external factors influencing Yoga's popularity would be necessary to understand these diverging trends fully.\nBased on the above thought, the answer is:\nYoga shows different trends in the two charts possibly due to the data capturing different demographic factors or periods in time, reflecting a niche increase in one context while generally decreasing in broader popularity in another. This discrepancy underscores the variability and complexity of factors affecting social lifestyle trends." } ], "id_raw": 179 }, { "id": "Upnku0kbL6OPPZr8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_9_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall percentage difference in popularity for Yoga between the two charts?" }, { "from": "gpt", "value": "To determine the percentage difference for Yoga, I observe {the bars associated with Yoga}[[0.3, 0.52, 0.5, 0.58]] in both charts. In one chart, Yoga shows an increase of 2%, and in another, a decrease of 20%. The overall percentage difference is computed by subtracting the lesser percentage from the greater, i.e., |2% - (-20%)| = 22%.\nBased on the above thought, the answer is:\n22%" } ], "id_raw": 179 }, { "id": "4EAyenLLYxQEAXey", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_0.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the chart into a table with Years as columns and different measurements like Food Production, Agricultural Export, Food Import, and Population as rows?" }, { "from": "gpt", "value": "To construct this table, I would first identify {the x-axis tick labels}[[0.053, 0.939, 0.088, 0.957], [0.277, 0.939, 0.312, 0.957], [0.502, 0.939, 0.537, 0.957], [0.726, 0.939, 0.761, 0.957], [0.950, 0.939, 0.985, 0.957]] which represent Years. {The legend texts}[[0.122, 0.034, 0.234, 0.052], [0.122, 0.061, 0.253, 0.078], [0.122, 0.087, 0.206, 0.104], [0.122, 0.113, 0.195, 0.130]] represent the different measurements which will be our table rows. The actual values can be found on {the line points for each measurement}[[0.071, 0.545],[0.295, 0.449], ..., [0.968, 0.067]].\nBased on the above thought, the answer is:\n,2010,2015,2020,2025,2030\nFood Production,100,110,130,140,150\nAgricultural Export,80,95,100,102,105\nFood Import,60,70,75,77,80\nPopulation,70,76,80,83,86" } ], "id_raw": 180 }, { "id": "YAwCYRBWeBS8lFmW", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_0.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which metric seems to show the most significant growth from 2010 to 2030?" }, { "from": "gpt", "value": "To identify the metric with the most dramatic growth, I must analyze the trends in each line. Starting with {the line for Food Production}[0.071, 0.067, 0.968, 0.545], it starts at 100 and ends at 150 indicating a growth of 50%. Looking at {the line for Agricultural Export}[0.071, 0.497, 0.968, 0.736], it starts at 80 and ends at 105 indicating a growth of around 31.25%. Next, analyzing {the line for Food Import}[0.071, 0.736, 0.968, 0.927], the growth is from 60 to 80 meaning around 33.33% growth. Finally, {the line for Population}[0.071, 0.679, 0.968, 0.832], it starts at 70 and ends at 86 indicating a growth of around 22.86%. Comparing these, the Food Production metric has the highest growth percentage.\nBased on the above thought, the answer is:\nBased on the analysis of trends shown in the line chart, the 'Food Production' metric shows the most significant growth from 2010 to 2030. It demonstrates an increasing trend with the highest growth percentage of 50% compared to the other metrics like Agricultural Export, Food Import, and Population. The remarkable increase in Food Production underscores its expanding role in the considered timeframe." } ], "id_raw": 180 }, { "id": "5qKWoH8PPja2O7FP", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_0.png", "conversations": [ { "from": "human", "value": "\nAssuming the base year to be 2010, what is the overall percentage growth in Food Production by the year 2030?" }, { "from": "gpt", "value": "I need to calculate the percentage growth from 2010 to 2030, using the formula (Value_in_2030 - Value_in_2010)/Value_in_2010 * 100. {The line points for Food Production}[[0.071, 0.545], [0.295, 0.449], [0.519, 0.258], [0.743, 0.162], [0.968, 0.067]] correspond to the values for each year. The data point for 2010 is 100 and for 2030 is 150. Plugging these values into the formula gives: (150 - 100)/100 * 100 = 50%.\nBased on the above thought, the answer is:\n50%." } ], "id_raw": 180 }, { "id": "b3CSzbycqBqa0Kxd", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_1.png", "conversations": [ { "from": "human", "value": "\nCan you create a table capturing the trend of the cuisine popularity based on the line chart?" }, { "from": "gpt", "value": "I start by recognizing {the x-axis tick labels}[[0.055, 0.919, 0.091, 0.942], [0.154, 0.919, 0.192, 0.942], [0.249, 0.919, 0.296, 0.942], [0.354, 0.919, 0.390, 0.942], [0.449, 0.919, 0.495, 0.942], [0.559, 0.919, 0.584, 0.942], [0.643, 0.919, 0.698, 0.942], [0.744, 0.919, 0.797, 0.942], [0.828, 0.919, 0.912, 0.942], [0.952, 0.919, 0.988, 0.942]], which represent the different cuisines. Then, I track {the data points}[[0.073, 0.610], [0.173, 0.376], [0.273, 0.201], [0.372, 0.259], [0.472, 0.435], [0.571, 0.113], [0.671, 0.493], [0.770, 0.318], [0.870, 0.230], [0.970, 0.084]] along the line plot of 'Cuisine Trends' to derive the trends associated with each cuisine.\nBased on the above thought, the answer is:\nCuisine,Cuisine Trend (thousands)\nItalian,10\nFrench,18\nMexican,24\nIndian,22\nChinese,16\nThai,27\nAmerican,14\nJapanese,20\nMediterranean,23\nVegan,28" } ], "id_raw": 181 }, { "id": "kScBFq7zG9Ud40aP", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_1.png", "conversations": [ { "from": "human", "value": "\nLooking at the line chart, do you think there's a general correlation between the order of the cuisines listed and the associated cuisine trend?" }, { "from": "gpt", "value": "To answer this, I need to look at the order of the cuisines along {the x-axis}[[0.055, 0.919, 0.091, 0.942], [0.952, 0.919, 0.988, 0.942]] and the corresponding trends from {the line for Cuisine Trends}[[0.073, 0.610], [0.970, 0.084]]. It seems that there's no clear correlation between the order of cuisines and the trend in popularity as the values don't consistently increase or decrease. Instead, the trends fluctuate for different cuisines. There are ups and downs in the trend, so we can't say there's a positive or negative correlation.\nBased on the above thought, the answer is:\nThere doesn't seem to be a clear correlation between the order of the cuisines listed and the associated cuisine trend. The trends fluctuate regardless of their position in the ordering, indicating that other factors might be influencing the trend of the cuisines." } ], "id_raw": 181 }, { "id": "4wsgg4DReOLgwFSH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_1.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which cuisine showed the greatest positive difference in trend from its previous cuisine?" }, { "from": "gpt", "value": "For this, I need to observe {the line points for Cuisine Trends}[[0.073, 0.610], [0.173, 0.376], [0.273, 0.201], [0.372, 0.259], [0.472, 0.435], [0.571, 0.113], [0.671, 0.493], [0.770, 0.318], [0.870, 0.230], [0.970, 0.084]] and calculate the difference between consecutive points. After analysing the data, the greatest difference can be seen between Thai cuisine and Chinese cuisine as 'Thai' had a trend value of 27000 and 'Chinese' had a value of 16000. The difference will be 27000 - 16000 = 11000.\nBased on the above thought, the answer is:\n11000." } ], "id_raw": 181 }, { "id": "Ikktj2rcxhCNp6Uw", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_2.png", "conversations": [ { "from": "human", "value": "\nCan you organize the data from the line chart into table format with years as the columns and various classification types as the rows, and what is the data?" }, { "from": "gpt", "value": "To construct a table from the line chart, I can start by identifying {the x-axis tick labels}[0.066, 0.919, 0.981, 0.942], which are five years: 2015, 2016, 2017, 2018, and 2019. They would form the column headers in the table. Then, I find {the legend texts}[0.175, 0.030, 0.921, 0.053]. These would serve as the row labels, indicating the classification types: Waste Recycling, Solar Energy, Wind Energy, and Nuclear Energy. For the actual data values under each classification, I should locate the corresponding {line points for each classification type}[0.066, 0.154, 0.981, 0.785] and use their coordinates to estimate the specific values.\nBased on the above thought, the answer is:\nClassification Data,2015,2016,2017,2018,2019\nWaste Recycling,65,75,80,90,95\nSolar Energy,15,20,40,65,80\nWind Energy,40,55,70,90,100\nNuclear Energy,30,45,60,80,95" } ], "id_raw": 182 }, { "id": "7BIOKFrR7LvVqJMs", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_2.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which classification type seems to have experienced the greatest improvement in accuracy from 2015 to 2019? Provide your reasoning." }, { "from": "gpt", "value": "To identify the classification type with the greatest improvement in accuracy over the given time period, I have to compare the differences between the starting and ending values of each line. The lines corresponding to {Waste Recycling}[0.088, 0.390, 0.959, 0.154], {Solar Energy}[0.088, 0.785, 0.959, 0.272], {Wind Energy}[0.088, 0.588, 0.959, 0.114] and {Nuclear Energy}[0.088, 0.666, 0.959, 0.154] represent the change in accuracy for each classification type from 2015 to 2019. From their respective coordinates, I determine the values in 2015 and 2019 for each, and calculate the change. I find that Solar Energy shows the most considerable increase, from 15 in 2015 to 80 in 2019.\nBased on the above thought, the answer is:\nSolar Energy seems to have experienced the greatest improvement in accuracy from 2015 to 2019. This is based on the substantial increase from an estimated value of 15 in 2015 to 80 in 2019, which represents a greater gain in accuracy compared to the other classification types over the same time period." } ], "id_raw": 182 }, { "id": "FXlM8UXGX2NedsaQ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in classification accuracy for Wind Energy from 2015 to 2019?" }, { "from": "gpt", "value": "To find the total increase in classification accuracy for Wind Energy from 2015 to 2019, I need to look at {the line points for Wind Energy}[0.088, 0.588, 0.959, 0.114]. The y-coordinates of these points correspond to the classification accuracy values for Wind Energy in the respective years. So, I need to subtract the accuracy value for 2015 from the one for 2019. Given the coordinates, these values are estimated to be 40 for 2015 and 100 for 2019. The total increase would therefore be 100 - 40 = 60.\nBased on the above thought, the answer is:\n60." } ], "id_raw": 182 }, { "id": "EV1yn8bK8Q7QMkzP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_3.png", "conversations": [ { "from": "human", "value": "\nCan you transform the sales trend data from the line chart into a tabular format with Years as columns and Products as rows?" }, { "from": "gpt", "value": "To convert the line chart into a tabular format, I firstly take note of {the x-axis tick labels}[[0.112, 0.067, 0.158, 0.092], [0.248, 0.067, 0.294, 0.092], [0.385, 0.067, 0.431, 0.092], [0.521, 0.067, 0.567, 0.092], [0.657, 0.067, 0.703, 0.092], [0.793, 0.067, 0.839, 0.092]], which represent the column headers or years. Next, I identify the {legend texts}[[0.887, 0.613, 0.921, 0.638], [0.887, 0.682, 0.921, 0.708]] as the row labels denoting the products. I then locate {the line points for Product A}[[0.121, 0.728], [0.257, 0.764], [0.393, 0.655], [0.529, 0.728], [0.666, 0.800], [0.802, 0.822]] and {the line points for Product B}[[0.121, 0.822], [0.257, 0.800], [0.393, 0.750], [0.529, 0.678], [0.666, 0.633], [0.802, 0.615]] containing the unit sold values at each year for the respective products.\nBased on the above thought, the answer is:\nProduct,2016,2017,2018,2019,2020,2021\nProduct A,5200,5500,4800,5200,5800,6000\nProduct B,6800,6600,6000,5400,5000,4700" } ], "id_raw": 183 }, { "id": "Wa7xVJDTMMggo4rD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_3.png", "conversations": [ { "from": "human", "value": "\nWhat can you deduce from the sales trends of Products A and B?" }, { "from": "gpt", "value": "By observing the lines corresponding to Product A {the line for Product A}[[0.121, 0.728], [0.257, 0.764], [0.393, 0.655], [0.529, 0.728], [0.666, 0.800], [0.802, 0.822]], we can see that it has an overall increasing trend, even though there were certain years with slight dips. Looking at {the line for Product B}[[0.121, 0.822], [0.257, 0.800], [0.393, 0.750], [0.529, 0.678], [0.666, 0.633], [0.802, 0.615]], it can be seen that over the same duration, sales have consistently declined.\nBased on the above thought, the answer is:\nGiven the observed trends, it appears that the market preference is shifting from Product B to Product A. While Product B had higher sales initially, the falling trend indicates problems like a possible loss of market share, poor performance, or competition. On the other hand, the rising trend for Product A suggests an increasing preference, successful marketing strategies, or growth in its target market. However, to be totally certain, additional market information would be required. This estimation is based purely on the sales trend from the graphs." } ], "id_raw": 183 }, { "id": "a5wWXaRbzOGOQsFi", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_3.png", "conversations": [ { "from": "human", "value": "\nHow much did the sales of Product A increase from 2016 to 2021?" }, { "from": "gpt", "value": "In order to find out the increase in sales for Product A from 2016 to 2021, I need to examine its {initial sales data point in 2016}[0.121, 0.728] which stands at 5200 units, and its {final sales data point in 2021}[0.802, 0.822] which shows 6000 units. The difference between these two points will tell us the increase in sales over this period.\nBased on the above thought, the answer is:\n800 units." } ], "id_raw": 183 }, { "id": "sMl7HLakjuRE04j6", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_4.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data on the different economic sectors displayed in a line chart into a structured table format with years as columns and sectors as rows?" }, { "from": "gpt", "value": "Observing the line chart, I start by examining {the x-axis labels}[0.077, 0.899, 0.121, 0.923], [0.321, 0.899, 0.365, 0.923], [0.564, 0.899, 0.608, 0.923], [0.808, 0.899, 0.852, 0.923] which denote the column headers representing different years. For the row headers, I identify {the legend}[0.260, 0.946, 0.348, 0.970], [0.432, 0.946, 0.545, 0.970], [0.629, 0.946, 0.727, 0.970], [0.810, 0.946, 0.863, 0.970] that lists the sectors such as Gold Price, Stock Market, Real Estate, and Bonds. I further analyze the points of the plotted lines {line points for all sectors}[0.099, 0.685, 0.976, 0.841], [0.099, 0.430, 0.976, 0.671], [0.099, 0.077, 0.976, 0.459], [0.099, 0.515, 0.976, 0.630] to extract the data for each year and sector.\nBased on the above thought, the answer is:\nYear,Gold Price,Stock Market,Real Estate,Bonds\n2001,300,2000,4000,1800\n2002,310,1900,4100,1790\n2003,320,2000,4200,1800\n...\n2019,1400,3200,5700,2600" } ], "id_raw": 184 }, { "id": "kAZ5pS11AHZE546x", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_4.png", "conversations": [ { "from": "human", "value": "\nWhich sector showed the most consistent growth over the period 2001-2019 according to the chart?" }, { "from": "gpt", "value": "Observing the trend of the lines, I compare {the line for Real Estate}[0.099, 0.317, 0.976, 0.077] with {the lines for other sectors}[0.099, 0.685, 0.976, 0.841], [0.099, 0.430, 0.976, 0.671], [0.099, 0.515, 0.976, 0.630]. The Real Estate line shows a steady decrease in its slope, indicating a strong and consistent growth as compared to other sectors.\nBased on the above thought, the answer is:\nThe Real Estate sector showed the most consistent growth over the period 2001-2019, as evidenced by its steady upward trajectory in the chart." } ], "id_raw": 184 }, { "id": "JL9qi7CyxZW0QIEF", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_4.png", "conversations": [ { "from": "human", "value": "\nWhat was the percentage increase in the Gold Price from 2001 to 2019?" }, { "from": "gpt", "value": "To calculate the percentage increase, I analyze {the points on the Gold Price line}[0.099, 0.841], [0.976, 0.685]. The value for Gold Price in 2001 is 300 and in 2019 is 1400. The percentage increase is calculated using the formula ((New Value - Old Value) / Old Value) * 100.\nBased on the above thought, the answer is:\n366.67%" } ], "id_raw": 184 }, { "id": "pUTTYYsFydZxmQc2", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_5.png", "conversations": [ { "from": "human", "value": "\nCan you translate this line chart data into a structured table format where the columns are the different activities and the rows are the different age groups?" }, { "from": "gpt", "value": "To convert this line chart data into a tabular format, I start by identifying {the x-axis tick labels}[[0.077, 0.919, 0.099, 0.942], [0.959, 0.919, 0.981, 0.942]] as the row labels which represent age groups. Then, I notice {the legend labels}[[0.283, 0.030, 0.355, 0.053], [0.438, 0.030, 0.531, 0.053], [0.614, 0.030, 0.701, 0.053], [0.784, 0.030, 0.824, 0.053]] which correspond to the column headers, representing different activities. Now, to populate the table, I map each line's points to their respective activity and age, using {the line points for Exercise}[[0.088, 0.241], [0.970, 0.323]], {for Meditation}[[0.088, 0.365], [0.970, 0.282]], {for Journaling}[[0.088, 0.572], [0.970, 0.613]], and {for Trips}[[0.088, 0.820], [0.970, 0.737]].\nBased on the above thought, the answer is:\nAge,Exercise,Meditation,Journaling,Trips\n30,80,65,40,10\n50,70,75,35,20" } ], "id_raw": 185 }, { "id": "jdo2vrXcukHAwLBN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_5.png", "conversations": [ { "from": "human", "value": "\nConsidering the trend lines for the different activities, can you comment on the correlation between age and engagement in these activities?" }, { "from": "gpt", "value": "To assess the correlation between age and engagement in different activities, I need to analyze the trend lines for each activity. For {Exercise}[[0.088, 0.241], [0.970, 0.323]], there is a decrease in engagement as age increases - indicating a possible negative correlation. A similar trend is observed for {Journaling}[[0.088, 0.572], [0.970, 0.613]]. On the other hand, engagement in {Meditation}[[0.088, 0.365], [0.970, 0.282]] appears to increase with age, suggesting a positive correlation between age and engagement in this activity. Lastly, engagement in {Trips}[[0.088, 0.820], [0.970, 0.737]] also tends to rise as age increases. These conclusions, however, are based on observations for only two age points and can not be generalized without further data.\nBased on the above thought, the answer is:\nBased on the observed trend lines, there seems to be a negative correlation between age and engagement in Exercise and Journaling \u2013 the engagement scores in these activities decrease as age increases. Conversely, there appears to be a positive correlation between age and engagement in Meditation and Trips \u2013 these activities see an increase in engagement as the age increases. However, these observations are derived from only two age data points and may not represent a generalized trend across all age groups." } ], "id_raw": 185 }, { "id": "2AgvsJc6T4hF2Crx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall difference between the Activity Engagement Scores for Exercise from age 30 to age 50?" }, { "from": "gpt", "value": "To find the overall difference between the Activity Engagement Scores for Exercise from age 30 to age 50, I need to locate {the line points for Exercise}[[0.088, 0.241], [0.970, 0.323]] and extract the corresponding values: 80 at age 30 and 70 at age 50. The difference between these two scores is therefore 80 - 70 = 10.\nBased on the above thought, the answer is:\n10." } ], "id_raw": 185 }, { "id": "N8n6DLJsmIKT6Kps", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_6.png", "conversations": [ { "from": "human", "value": "\nCan you reformat the data from the line chart to a tabular format with rows as Years and columns for each Area?" }, { "from": "gpt", "value": "To create the dataframe, I need to use the X-axis labels for the rows, which are {the x-axis tick labels}[[0.053, 0.919, 0.088, 0.942], [0.277, 0.919, 0.312, 0.942], [0.502, 0.919, 0.537, 0.942], [0.726, 0.919, 0.761, 0.942], [0.950, 0.919, 0.985, 0.942]] and represent the Years. The column headers are taken from {the legend texts}[[0.814, 0.754, 0.955, 0.777], [0.814, 0.789, 0.934, 0.812], [0.814, 0.824, 0.941, 0.847], [0.814, 0.859, 0.910, 0.882]] which are Public Infrastructure, Residential Areas, Commercial Areas, and Green Spaces. For the cell values, I take the Y-axis coordinates of {the line points} for each Area in each Year.\nBased on the above thought, the answer is:\nYear,Public Infrastructure,Residential Areas,Commercial Areas,Green Spaces\n2010,80,70,75,85\n2015,85,75,80,90\n2020,75,80,85,90\n2025,90,85,90,95\n2030,100,100,95,100" } ], "id_raw": 186 }, { "id": "LX5VOjLDjkpRbFst", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_6.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which area shows the most improvement between 2010 and 2030 and how can you determine this?" }, { "from": "gpt", "value": "To determine which area shows the most improvement, I need to observe the changes in the lines for each area from 2010 to 2030. Looking at {the line for Public Infrastructure}[0.071, 0.135, 0.968, 0.683], it goes up from 80 to 100 showing an increase of 20 points. Observing {Residential Areas}[0.071, 0.135, 0.968, 0.793], I see an increase from 70 to 100, a rise of 30 points. For {Commercial Areas}[0.071, 0.244, 0.968, 0.683], the line increases from 75 to 95, an upward movement of 20 points. Finally, {Green Spaces}[0.071, 0.135, 0.968, 0.464] shows an increment from 85 to 100, a change of 15 points.\nBased on the above thought, the answer is:\nThe Residential Areas shows the most improvement between 2010 and 2030. This is determined by observing the line chart and comparing the increment in progress for each area from 2010 to 2030. It can be seen that the line representing Residential Areas shows the greatest upward movement by 30 points, which is higher than the improvements in progress observed for Public Infrastructure, Commercial Areas, and Green Spaces. Thus, it indicates the most improvement over the given period." } ], "id_raw": 186 }, { "id": "wHb1mn5iL6vXzwTK", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the percent change in progress for Public Infrastructure from 2010 to 2030?" }, { "from": "gpt", "value": "To get the percent change, I have to find the values of Public Infrastructure in 2010 and 2030. For 2010, I look at {the point for Public Infrastructure}{'point coordinates': '[0.071, 0.574]'} which is 80, and for 2030, I look at {the point for Public Infrastructure}{'point_coordinates': '[0.968, 0.135]'} which is 100. The percent change is calculated as ((100 - 80) / 80) * 100.\nBased on the above thought, the answer is:\n25%." } ], "id_raw": 186 }, { "id": "lhjI9Jbsn83tfwQZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_7.png", "conversations": [ { "from": "human", "value": "\nCan you arrange the efficiency data from the bar chart into a table format with years as columns and stages as rows?" }, { "from": "gpt", "value": "To accomplish this, the first step is to identify {the y-axis tick labels}[[0.071, 0.111, 0.144, 0.176], [0.208, 0.241, 0.274, 0.307], [0.339, 0.371, 0.404, 0.436], [0.468, 0.500, 0.532, 0.564], [0.597, 0.630, 0.662, 0.694], [0.725, 0.758, 0.790, 0.823], [0.854, 0.887, 0.920, 0.951]], which symbolize the row labels or the stages of production. Then, years 2020 and 2021 serve as the column headers. For the values, I\u2019ll locate the {center of each bar}[[0.146, 0.130], [0.128, 0.227], [0.197, 0.324], [0.351, 0.421], [0.293, 0.518], [0.212, 0.616], [0.201, 0.714], [0.673, 0.130], [0.554, 0.227], [0.643, 0.324], [0.421, 0.422], [0.382, 0.518], [0.571, 0.616], [0.480, 0.714]] and extract the respective values annotated for each stage, in each year.\nBased on the above thought, the answer is:\nStages, 2020, 2021\nRaw Materials, 14.6, 67.3\nAssembly, 12.8, 55.4\nQuality Control, 19.7, 64.3\nFinishing, 35.1, 42.1\nPackaging, 29.3, 38.2\nDistribution, 21.2, 57.1\nReturn Processing, 20.1, 48.0" } ], "id_raw": 187 }, { "id": "SrMTbDUuPDXQlwrP", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_7.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which stage had the most significant improvement in efficiency from 2020 to 2021?" }, { "from": "gpt", "value": "To determine which stage showed the most significant improvement, it's necessary to compare the growth percentages of all stages between 2020 and 2021. The corresponding bars for 2020 and 2021 include {Raw Materials}[0.146, 0.673], {Assembly}[0.128, 0.554], {Quality Control}[0.197, 0.643], {Finishing}[0.351, 0.421], {Packaging}[0.293, 0.382], {Distribution}[0.212, 0.571], {Return Processing}[0.201, 0.480]. After reviewing the percentage changes for each stage, the 'Raw Materials' stage exhibited the biggest rise with 463%.\nBased on the above thought, the answer is:\nThe 'Raw Materials' stage had the most significant improvement in efficiency between 2020 and 2021 as shown by an increase from 14.6% to 67.3%, representing a 463% rise. This is the highest percentage change across all stages in manufacturing." } ], "id_raw": 187 }, { "id": "rPWoIT0GG0hSlGko", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage difference in the efficiency score for the 'Finishing' stage between 2020 and 2021?" }, { "from": "gpt", "value": "The first step to find the percentage difference is to identify {the bar representing the 'Finishing' stage in 2020}[0.351, 0.421] and 2021 {the bar representing the 'Finishing' stage in 2021}[0.421, 0.422]. The efficiency scores for 2020 and 2021 are 35.1 and 42.1 respectively. The percentage difference can be therefore calculated as (42.1 - 35.1)/35.1 * 100 = 19.94%\nBased on the above thought, the answer is:\n19.94%" } ], "id_raw": 187 }, { "id": "blbVctAS1XOiOBFf", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_8.png", "conversations": [ { "from": "human", "value": "\nCan you provide the sales for different types of sales in each year in a structured table format?" }, { "from": "gpt", "value": "With the line chart, I think I can. I identify {the x-axis labels}[0.066, 0.919, 0.110, 0.942] as the year labels. After that, I recognize {the legend entries}[0.280, 0.030, 0.817, 0.053] to represent the different types of sales. Then, I go through the process of locating the {line points}[0.088, 0.158, 0.959, 0.489], that contain the sales data for each year and type, by matching the x-coordinates with the year and the line color with the type of sales. Once all these are determined, I can generate the table.\nBased on the above thought, the answer is:\nYear,Online Sales,Offline Sales,Ecommerce Sales\n2005,50,70,10\n2006,55,67,20\n2007,59,65,25\n2008,61,60,38\n2009,63,55,40\n2010,70,50,45\n2011,72,45,48\n2012,75,40,50\n2013,80,35,55\n2014,85,30,60\n2015,90,25,65" } ], "id_raw": 188 }, { "id": "lSeeKcSs5whbwmSA", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_8.png", "conversations": [ { "from": "human", "value": "\nWhich type of sales showed the most significant increase over the years 2005-2015 and why do you think so?" }, { "from": "gpt", "value": "To answer this question, I need to look at the end points and the start points of each type of sales: {Online sales}[0.088, 0.489, 0.959, 0.158], {Offline sales}[0.088, 0.323, 0.959, 0.696], and {Ecommerce sales}[0.088, 0.820, 0.959, 0.365]. By comparing the initial and final points of each line, it's clear that the most significant increase is shown in the Ecommerce Sales, which started at a point near the top of the chart and ended at a point much lower, indicating a significant increase.\nBased on the above thought, the answer is:\nEcommerce Sales showed the most significant increase over the years. The line plot for Ecommerce Sales starts near the top of the chart (indicating lower sales) and ends considerably lower (indicating higher sales), demonstrating a significant rise over the given period. This might be due to increasing internet penetration and the growing popularity of online shopping over the years." } ], "id_raw": 188 }, { "id": "Z10OAFUTKnH5JPVx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest sales and the lowest sales for offline sales during the years 2005-2015?" }, { "from": "gpt", "value": "To determine this difference, I need to observe the {red line}[0.088, 0.323, 0.959, 0.696] representing offline sales. I find the highest point at 70 in {2005}[0.088, 0.323] and the lowest point at 25 in {2015}[0.959, 0.696]. The difference is hence 70-25 = 45.\nBased on the above thought, the answer is:\n45." } ], "id_raw": 188 }, { "id": "u7HySIlhlcWlvIst", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_10.png", "conversations": [ { "from": "human", "value": "\nCan you extract the values from the bar chart and generate a table format with AI Use Cases as columns and Application Fields as rows?" }, { "from": "gpt", "value": "To generate the structured tabular data from the bar chart, first I look at {the y-axis tick labels}[[0.188, 0.638, 0.239, 0.659], [0.188, 0.582, 0.273, 0.602], [0.188, 0.526, 0.282, 0.547], [0.188, 0.470, 0.248, 0.490], [0.188, 0.414, 0.318, 0.434], [0.188, 0.358, 0.258, 0.378], [0.188, 0.302, 0.320, 0.323]], which represent the row labels or Application Fields. Then, I locate {the legend items}[[0.918, 0.030, 1.050, 0.053]], [0.592, 0.030, 0.835, 0.053]] as the column headers for different AI Use Cases. To populate the values for each AI use case in each field, I locate the {bars for Synthetic AI}[[0.188, 0.666, 0.333, 0.672]], and the corresponding {bars for Human-directed AI}[[0.188, 0.606, 0.350, 0.612]],... and extract the values annotated at the end of each bar.\nBased on the above thought, the answer is:\nField, Synthetic AI, Human-directed AI\nAutonomous Driving, -5, 5\nFraud Detection, -20, 8\nCustomer Service, -7, 6\nObject Detection, -3, 10\nContent Recommendation, -8, 4\nText Analysis, 8, 12\nSpeech Recognition, 6, 14" } ], "id_raw": 190 }, { "id": "8i6HG3xTRx3hLceO", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_10.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which field of application has benefited the most from switching to Human-directed AI Use Cases? Why do you think so?" }, { "from": "gpt", "value": "To determine the field that has benefited the most from switching to Human-directed AI, I need to compare the difference in Synthetic AI and Human-directed AI use cases across all the fields. For {Fraud Detection} [0.188, 0.582, 0.273, 0.602], I find that {Synthetic AI} [0.188, 0.652, 0.260, 0.682] has -20 units while {Human-directed AI} [0.188, 0.592, 0.297, 0.622] has 8 units, showing a large increase. This represents the largest increase from Synthetic AI to Human-directed AI across all the fields.\nBased on the above thought, the answer is:\nThe Fraud Detection field has benefited the most from switching to Human-directed AI Use Cases. It went from -20 units under Synthetic AI to 8 units under Human-directed AI, depicting the most notable increase among all the fields. This trend may suggest that Fraud Detection works more efficiently with human interaction and oversight, leading to a higher number of use cases." } ], "id_raw": 190 }, { "id": "k3rX6e5WBZL3aELI", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the net increase or decrease in use cases across all fields for Synthetic AI?" }, { "from": "gpt", "value": "To find the net increase or decrease, I need to sum all the individual values for Synthetic AI across all fields. I locate {the bars for Synthetic AI Use Cases}[[0.188, 0.666, 0.333, 0.672]], [[0.188, 0.606, 0.293, 0.612]], ..., then extract the following values corresponding to each of them: -5, -20, -7, -3, -8, 8, 6. I can calculate the sum of these values as -29.\nBased on the above thought, the answer is:\n-29" } ], "id_raw": 190 }, { "id": "Nu042KQkP2lL6NzX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_11.png", "conversations": [ { "from": "human", "value": "\nCould you extract the line plot data into a table format with the years as columns and the different categories (MOOCs Usage, K-12 Online Courses, University Online Education, EdTech Investment) as rows?" }, { "from": "gpt", "value": "Certainly. I first identify {the x-axis tick labels}[0.066, 0.919, 0.110, 0.942], [0.240, 0.919, 0.285, 0.942], [0.415, 0.919, 0.459, 0.942], [0.589, 0.919, 0.633, 0.942], [0.763, 0.919, 0.807, 0.942], [0.937, 0.919, 0.981, 0.942] which correspond to the years, serving as table columns. Next, I note the {legend texts}[0.299, 0.030, 0.422, 0.053], [0.299, 0.065, 0.473, 0.088], [0.556, 0.030, 0.797, 0.053], [0.556, 0.065, 0.720, 0.088] which correspond to the different categories and will form the rows. To fill the table, I locate each line's points like {the points for MOOCs usage}[0.088, 0.406], [0.263, 0.390], [0.437, 0.381], [0.611, 0.365], [0.785, 0.340], [0.959, 0.158] to determine the values for each year.\nBased on the above thought, the answer is:\nCategories,2015,2016,2017,2018,2019,2020\nMOOCs Usage,60,62,63,65,68,90\nK-12 Online Courses,55,58,60,65,70,85\nUniversity Online Education,50,53,58,60,63,80\nEdTech Investment,18,25,35,45,55,75" } ], "id_raw": 191 }, { "id": "EldzzT3CSlAhSw2M", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_11.png", "conversations": [ { "from": "human", "value": "\nAmong the different categories presented in the line plot, which one exhibited the most significant growth from 2015 to 2020? Justify your answer." }, { "from": "gpt", "value": "To identify the category with the most significant growth, I must review the changes in each line from 2015 to 2020. Upon examining {MOOCs line} [0.088, 0.158, 0.959, 0.406], I notice an increase from 60 to 90. For the {K-12 line} [0.088, 0.199, 0.959, 0.448], the growth is from 55 to 85. {University Online Education line} [0.088, 0.241, 0.959, 0.489] shows a rise from 50 to 80. Lastly, {EdTech Investment line} [0.088, 0.282, 0.959, 0.754] reveals an increase from 18 to 75.\nBased on the above thought, the answer is:\nThe EdTech Investment category exhibited the most significant growth from 2015 to 2020. This can be seen in its line plot which shows an increase from 18 in 2015 to 75 in 2020. This increase is noticeably larger in terms of absolute change and also relative to the starting value, compared to the other categories." } ], "id_raw": 191 }, { "id": "EwuT7LsonzmxUMRB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_11.png", "conversations": [ { "from": "human", "value": "\nBy what percentage did the University Online Education category increase from the year 2015 to 2020 based on the line plot?" }, { "from": "gpt", "value": "To calculate the percentage increase, I first need the starting and ending values for the University Online Education category. The starting value corresponds to the year 2015, which is {the point}[0.088, 0.489] with a value of 50. The ending value corresponds to the year 2020, which is {the point}[0.959, 0.241] with a value of 80. The percentage increase is then calculated as (Final - Initial)/Initial * 100% = (80-50)/50 * 100% = 60%.\nBased on the above thought, the answer is:\n60%." } ], "id_raw": 191 }, { "id": "jidFoL0FIN6PBwSV", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_12.png", "conversations": [ { "from": "human", "value": "\nCould you provide a tabulated version of the data displayed in the bar charts, detailing the values for different categories of social activities across the two different trends?" }, { "from": "gpt", "value": "To create a structured table from the data in the bar charts, I need to identify the trend types and the activity categories, and their corresponding values. Here, looking at {the x-axis labels}[0.055, 0.919, 0.099, 0.942], [0.232, 0.919, 0.276, 0.942], [0.408, 0.919, 0.452, 0.942], [0.585, 0.919, 0.628, 0.942], [0.761, 0.919, 0.805, 0.942], [0.937, 0.919, 0.981, 0.942], I get the categories such as: Outdoor Fitness, Inside Workout, Home Cooking, Social Media. The trends are Roller-coaster pattern and Volatile growth represented by {bar heights}[0.077, 0.354, 0.959, 0.851] associated with each category.\nBased on the above thought, the answer is:\nTrend,Outdoor Fitness,Inside Workout,Home Cooking,Social Media\nRoller-coaster pattern,75,90,50,80\nVolatile growth,10,60,5,55" } ], "id_raw": 192 }, { "id": "LULCSTEQmKbqoFWL", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_12.png", "conversations": [ { "from": "human", "value": "\nWhich type of social activity shows the largest difference between the two trends?" }, { "from": "gpt", "value": "To identify the social activity with the largest difference, I compare the variances in values for each activity between the two charts. In the case of {Inside Workout}[0.232, 0.919, 0.276, 0.942], it is clear that the difference between 90 in the 'Roller-coaster pattern' and 60 in the 'Volatile growth' trend is 30, which is larger than any other activities such as {Outdoor Fitness}[0.055, 0.919, 0.099, 0.942], {Home Cooking}[0.408, 0.919, 0.452, 0.942], and {Social Media}[0.937, 0.919, 0.981, 0.942].\nBased on the above thought, the answer is:\nThe social activity that shows the largest difference between the two trends is 'Inside Workout', with a 30% difference in values between the \"Roller-coaster pattern\" and the \"Volatile growth\" trend. This is more substantial than the variations seen in other categories." } ], "id_raw": 192 }, { "id": "6T9KafC4ffhAIiKG", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_12.png", "conversations": [ { "from": "human", "value": "\nWhat's the percentage difference in Home Cooking values between the two trends?" }, { "from": "gpt", "value": "To find the percentage difference, I need to identify the values associated with 'Home Cooking' in both trend types. The values can be found based on {the bars heights for 'Home Cooking'}[0.408, 0.919, 0.452, 0.942] in both trends. Their values are 50 and 5 for 'Roller-coaster pattern' and 'Volatile growth', respectively. The percentage difference is given by (Value1 - Value2) / Value1 * 100% which yields (50-5)/50*100% = 90%.\nBased on the above thought, the answer is:\n90%." } ], "id_raw": 192 }, { "id": "yMj0duFKjT2vyxic", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_13.png", "conversations": [ { "from": "human", "value": "\nCan you help me generate a table from the line chart data based on the Resource Types as rows and the associated Energy Consumption as values?" }, { "from": "gpt", "value": "Sure, to achieve this, I will first extract the labels for each resource from {the x-axis tick labels}[0.105, 0.919, 0.116, 0.942] which shows \"Coal\", \"Gas\", \"Oil\", \"Solar\", \"Hydro\", \"Nuclear\". Moving on, I pick all the {points on the line}[0.110, 0.146, 0.976, 0.785] which correspond to the energy consumption for each resource type. The values are: 64000, 44000, 58000, 10000, 17000, 51000.\nBased on the above thought, the answer is:\nResources,Energy Consumption\nCoal,64000\nGas,44000\nOil,58000\nSolar,10000\nHydro,17000\nNuclear,51000" } ], "id_raw": 193 }, { "id": "mmHL7YTQumoLEhkS", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_13.png", "conversations": [ { "from": "human", "value": "\nGiven the energy consumption, what can be inferred about the sustainability of these resources?" }, { "from": "gpt", "value": "To answer this question, I need to look at the amount of energy consumption associated with each resource type. By analysing the {points on the line}[0.110, 0.146, 0.976, 0.785], it's clear that Coal has the highest energy consumption, making it a dominant energy resource. However, it's vital to remember that coal is a non-renewable and highly polluting source of energy, which questions its sustainability in the long run. On the other hand, Solar energy has the least energy consumption, but it's a renewable and cleaner source of energy. Even though its current absorption might be low compared to others, it's a sustainable solution for the future.\nBased on the above thought, the answer is:\nThe analysis of energy consumption indicates that while Coal is a significant source of energy, its high environmental impact and non-renewable nature make it unsustainable in the long run. On the other side, Solar, with its less consumption, signifies low utilisation currently, but considering it's renewable and environmentally friendly, it holds immense potential for sustainable energy in the future." } ], "id_raw": 193 }, { "id": "j3TJVRgyKiFjl28f", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in energy consumption between the most and least energy consuming resources?" }, { "from": "gpt", "value": "To answer this question, I need to figure out the maximum and minimum values for energy consumption. If I examine the {points on the line}[0.110, 0.146, 0.976, 0.785], I see that the maximum value is 64000, corresponding to Coal, and the minimum value is 10000, corresponding to Solar. Hence, the difference in energy consumption between the most and least consuming resources is 64000 - 10000 = 54000.\nBased on the above thought, the answer is:\n54000." } ], "id_raw": 193 }, { "id": "oCB7c93uGDZ6tyhL", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_14.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table showing the popularity scores for each cuisine from the years 2006 to 2020 based on the line chart?" }, { "from": "gpt", "value": "First, I review the x-axis, specifically {the x-axis tick labels}[[0.053, 0.919, 0.088, 0.942], [0.181, 0.919, 0.216, 0.942], [0.309, 0.919, 0.344, 0.942], [0.437, 0.919, 0.473, 0.942], [0.566, 0.919, 0.601, 0.942], [0.694, 0.919, 0.729, 0.942], [0.822, 0.919, 0.857, 0.942], [0.950, 0.919, 0.985, 0.942]] to confirm the years. Then, I identify each cuisine trend by analyzing {the line for Italian}[0.071, 0.199, 0.968, 0.737], {the line for French}[0.071, 0.116, 0.968, 0.655], {the line for Chinese}[0.071, 0.241, 0.968, 0.779], {the line for Japanese}[0.071, 0.282, 0.968, 0.820], and {the line for Mexican}[0.071, 0.323, 0.968, 0.861], tracking the points along these lines to extract the popularity scores for each year.\nBased on the above thought, the answer is:\nYear,Italian,French,Chinese,Japanese,Mexican\n2006,40,50,35,30,25\n2007,45,55,38,35,30\n2008,50,60,45,42,35\n2009,55,62,50,45,40\n2010,57,65,52,48,45\n2011,60,70,55,50,48\n2012,65,75,60,55,50\n2013,70,80,65,60,55\n2014,75,85,70,65,60\n2015,80,90,75,70,65\n2016,85,95,80,75,70\n2017,90,100,85,80,75\n2018,95,105,90,85,80\n2019,100,110,95,90,85\n2020,105,115,100,95,90" } ], "id_raw": 194 }, { "id": "LBvApHyHvEBywgEy", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_14.png", "conversations": [ { "from": "human", "value": "\nWhich cuisine shows the most consistent upward trend in popularity over the years as per the line chart?" }, { "from": "gpt", "value": "To identify the most consistent upward trend, I review the slope and trend of each line. Observing {the line for Italian}[0.071, 0.199, 0.968, 0.737], {the line for French}[0.071, 0.116, 0.968, 0.655], {the line for Chinese}[0.071, 0.241, 0.968, 0.779], {the line for Japanese}[0.071, 0.282, 0.968, 0.820], and {the line for Mexican}[0.071, 0.323, 0.968, 0.861], I notice that all lines show upward movements. Italian cuisine's line, however, not only rises but does so with the least fluctuations, indicating a steady increase.\nBased on the above thought, the answer is:\nItalian cuisine shows the most consistent upward trend in popularity over the years according to the line chart. Its trajectory is smooth and continuous, with a consistent rate of increase from 2006 to 2020." } ], "id_raw": 194 }, { "id": "2NbEzExIn1MbTVxf", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in popularity score for Italian cuisine from 2006 to 2020 based on the data represented in the line chart?" }, { "from": "gpt", "value": "To determine the increase, I examine the start and end points for {the line representing Italian cuisine}[0.071, 0.737, 0.968, 0.199]. The starting point in 2006 shows a popularity score of 40, and the ending point in 2020 shows a score of 105. The increase is calculated by subtracting the initial score from the final score.\nBased on the above thought, the answer is:\n65." } ], "id_raw": 194 }, { "id": "Plb8LHYQ6Nbdd29v", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_15.png", "conversations": [ { "from": "human", "value": "\nConvert the line graph data detailing the energy utilization over the years (2000-2020) into a structured table. The columns should represent the different years, and the rows should correspond to the various types of energy sources." }, { "from": "gpt", "value": "To structure the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.044, 0.939, 0.079, 0.957], [0.271, 0.939, 0.306, 0.957], [0.497, 0.939, 0.532, 0.957], [0.724, 0.939, 0.759, 0.957], [0.950, 0.939, 0.985, 0.957]] as the column headers representing the years. I next identify {the legend texts}[[0.113, 0.062, 0.240, 0.080], [0.113, 0.088, 0.228, 0.106], [0.113, 0.115, 0.288, 0.132], [0.113, 0.141, 0.340, 0.158]] as the row labels representing each of the energy sources: Coal consumption, Oil consumption, Natural gas consumption, and Hydrogen fuel cell consumption, respectively. Finally, I fill in the percentage consumption values corresponding to each energy source for each year from the data points labeled on {the lines}[[0.062, 0.102, 0.968, 0.212], [0.062, 0.421, 0.968, 0.487], [0.062, 0.542, 0.968, 0.817], [0.062, 0.751, 0.968, 0.916]].\nBased on the above thought, the answer is:\nType of Energy,2000,2005,2010,2015,2020\nCoal Consumption,65,68,70,72,75\nOil Consumption,40,42,44,46,45\nNatural Gas Consumption,10,15,20,28,35\nHydrogen Fuel Cell Consumption,1,2,5,10,16" } ], "id_raw": 195 }, { "id": "BvAWSGL5zzuy2WBS", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_15.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart showing the consumption of various energy sources from 2000-2020, which energy source has shown the fastest rate of increase and why?" }, { "from": "gpt", "value": "In order to ascertain which energy source has seen the most increase over 2000-2020, I compare the growth rates of the four sources by looking at the {coal line}[[0.062, 0.102, 0.968, 0.212]], {oil line}[[0.062, 0.421, 0.968, 0.487]], {natural gas line}[[0.062, 0.542, 0.968, 0.817]] and the {hydrogen fuel cell line}[[0.062, 0.751, 0.968, 0.916]]. By doing so, I see that while all sources have increased to some degree, the hydrogen fuel cell's consumption rises most steeply over the 20 years, from 1% in 2000 to 16% in 2020.\nBased on the above thought, the answer is:\nThe consumption of Hydrogen Fuel Cell Consumption has shown the fastest rate of increase from 2000 to 2020. It started at 1% in 2000 and rose to 16% by 2020, representing a 16-fold increase over the time period. The significant increase can be owed to advancements in technology and efforts put forth to shift to environmentally friendly energy sources." } ], "id_raw": 195 }, { "id": "iIceNLNzvZVstaAX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_15.png", "conversations": [ { "from": "human", "value": "\nHow much difference in % consumption was there between coal and oil in the year 2005?" }, { "from": "gpt", "value": "To find the difference in % consumption between coal and oil in 2005, I look at the {data point for coal in 2005}[0.288, 0.179] which corresponds to a value of 68 and then at the {data point for oil in 2005}[0.288, 0.465] which corresponds to a value of 42. Therefore, the difference is 68 - 42 = 26.\nBased on the above thought, the answer is:\n26." } ], "id_raw": 195 }, { "id": "P45RVMKeM09N0jVo", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_16.png", "conversations": [ { "from": "human", "value": "\nCould you organize the energy consumption data displayed in the chart into a table format where each energy source corresponds to a unique column and the years are placed in rows?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first take note of the {x-axis tick labels}[0.066, 0.919, 0.110, 0.942] that represent the years which are the row headers. Subsequently, I identify the {legend texts}[0.258, 0.030, 0.297, 0.053] as the column labels that signify different energy sources. Lastly, I scan each line plot such as {Coal's line points}[0.088, 0.241, 0.959, 0.489], {Hydropower's line points}[0.088, 0.133, 0.959, 0.323], {Nuclear's line points}[0.088, 0.241, 0.959, 0.365], and {Green energy's line points}[0.088, 0.323, 0.959, 0.655] to fill in the table cells indicating the respective energy consumption for each year.\nBased on the above thought, the answer is:\nYear,Coal,Hydropower,Nuclear,Green Energy\n2010,80,70,65,30\n2011,75,74,66,32\n2012,70,77,68,35\n2013,69,80,70,37\n2014,67,83,72,40\n2015,66,86,74,45\n2016,64,89,75,50\n2017,62,90,76,55\n2018,58,91,78,60\n2019,52,92,80,65\n2020,50,93,80,70" } ], "id_raw": 196 }, { "id": "gcrXChCONF156Zkc", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_16.png", "conversations": [ { "from": "human", "value": "\nBased on the line graphs, which source of energy has shown the most significant increase over the decade? Justify your answer." }, { "from": "gpt", "value": "To determine which source of energy has the greatest increase over the decade, I inspect each line individually. {Coal's line}[0.088, 0.241, 0.959, 0.489] generally shows a declining trend from 80 to 50; {Hydropower's line}[0.088, 0.133, 0.959, 0.323] exhibits an upward trend from 70 to 93; {Nuclear's line}[0.088, 0.241, 0.959, 0.365] shows a minimal rise from 65 to 80; and {Green Energy's line}[0.088, 0.323, 0.959, 0.655] shows a predominantly rising trend from 30 to 70. Comparing the differences, it's evident that Green Energy has the most substantial increase.\nBased on the above thought, the answer is:\nFrom the line graphs, it is apparent that Green Energy has experienced the most significant increase over the decade, rising from 30% at the beginning of the decade to 70% by the end. This trend signifies a stark 133% increase in consumption from 2010 to 2020, the most significant change compared to other energy sources." } ], "id_raw": 196 }, { "id": "3m2Z8qdHvZQ5Eine", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_16.png", "conversations": [ { "from": "human", "value": "\nHow much percentage change in energy consumption do we observe in Nuclear power from 2010 to 2020?" }, { "from": "gpt", "value": "To calculate the percentage change in Nuclear energy consumption, I first find the consumption percentage in 2010 at {Nuclear's line for 2010}[0.088, 0.365], which is 65. I then find the consumption percentage in 2020 at {Nuclear's line for 2020}[0.959, 0.241], which is 80. To find the percentage change, I subtract the initial percentage from the final percentage and divide it by the initial percentage, then multiply it by 100.\nBased on the above thought, the answer is:\n23.08%" } ], "id_raw": 196 }, { "id": "WQaAXCw4YTJ4kX4f", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_17.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart into a table format with Technologies as rows and Years as columns?" }, { "from": "gpt", "value": "To transform the line chart data into a table, I'll start by looking at the {x-axis tick labels}[[0.228, 0.919, 0.239, 0.942], [0.356, 0.919, 0.378, 0.942], [0.489, 0.919, 0.511, 0.942], [0.622, 0.919, 0.644, 0.942], [0.756, 0.919, 0.778, 0.942]], these represent the column headers or years. Next, I identify {the legend texts}[[0.087, 0.030, 0.241, 0.053], [0.324, 0.030, 0.538, 0.053], [0.621, 0.030, 0.765, 0.053], [0.849, 0.030, 0.962, 0.053]] as the row labels representing different technologies. To populate the values, I locate {the line points} for each technology at the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nTechnology,5 Years,10 Years,15 Years,20 Years,25 Years\nAI in Radiography,78,85,89,92,93\nElectronic Health Record,68,70,71,75,78\nAI in Diagnostics,45,60,70,76,86\nTelemedicine,30,42,61,74,89" } ], "id_raw": 197 }, { "id": "sqc68nXr8utdC5FE", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_17.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which technology seems to have the most growth over time? Explain your reasoning." }, { "from": "gpt", "value": "To ascertain which technology has experienced the most growth, I analyze the line plots for each technology. Observing {the line for AI in Radiography}[0.233, 0.257, 0.767, 0.133], we note a gradual increase from 78 to 93. For {Electronic Health Record}[0.233, 0.340, 0.767, 0.257], there is a mild increase from 68 to 78. For {AI in Diagnostics}[0.233, 0.530, 0.767, 0.191], the line displays a substantial increase from 45 to 86. Meanwhile, for {Telemedicine}[0.233, 0.655, 0.767, 0.166], there is a dramatic rise from 30 to 89 over the years.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Telemedicine seems to have experienced the most growth over time. The line plot exhibits a marked increase from 30 at 5 Years to 89 at 25 Years. Although AI in Diagnostics also shows substantial growth, the surge in Telemedicine is more pronounced, indicating a higher rate of increase over time." } ], "id_raw": 197 }, { "id": "4JiSxCyQ3wnTww7l", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the maximum and minimum adoption rates for AI in Diagnostics over the given years?" }, { "from": "gpt", "value": "To find the maximum and minimum adoption rates for AI in Diagnostics, I need to locate {the line points for AI in Diagnostics}[[0.233, 0.530], [0.367, 0.406], [0.500, 0.323], [0.633, 0.274], [0.767, 0.191]] and extract the corresponding values: 45, 60, 70, 76, 86. The maximum value is 86 at {25 Years}[0.767, 0.919] and the minimum value is 45 at {5 Years}[0.233, 0.919]. So, the difference between the maximum and minimum is 86-45 = 41.\nBased on the above thought, the answer is:\n41." } ], "id_raw": 197 }, { "id": "fTEPsxqQM0Q75cNm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_18.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a table showing the annual values of E-commerce, Manufacture, Software Development, and Robotics from 2004 to 2020?" }, { "from": "gpt", "value": "To format the data from the chart into a table, I identified {the x-axis tick labels}[[0.099, 0.919, 0.143, 0.942], [0.938, 0.919, 0.981, 0.942]], which signify the years from 2004 to 2020. Then I located {the legend items}[[0.195, 0.030, 0.304, 0.053], [0.387, 0.030, 0.497, 0.053], [0.580, 0.030, 0.777, 0.053], [0.860, 0.030, 0.934, 0.053]] for sectors like E-commerce, Manufacture, Software Development, and Robotics. By analyzing {the line points}[[0.121, 0.215, 0.959, 0.712]], I extracted the values for each sector in each year.\nBased on the above thought, the answer is:\nYear,E-commerce,Manufacture,Software Development,Robotics\n2004,50000,70000,50000,30000\n2005,52000,67000,52000,34000\n2006,53000,64000,57000,50000\n2007,55000,60000,58000,61000\n2008,59000,59000,60000,62000\n2009,60000,58000,64000,70000\n2010,65000,57000,68000,85000\n2011,70000,54000,70000,90000\n2012,75000,51000,72000,95000\n2013,80000,50000,75000,98000\n2014,85000,48000,78000,100000\n2015,90000,47000,80000,105000\n2016,95000,46000,82000,110000\n2017,99000,45000,84000,116000\n2018,102000,44000,86000,120000\n2019,105000,43000,88000,122000\n2020,108000,42000,90000,124000" } ], "id_raw": 198 }, { "id": "xHHi2xuqotVNkEx4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_18.png", "conversations": [ { "from": "human", "value": "\nWhich sector shows a consistent improvement over the years based on the line chart?" }, { "from": "gpt", "value": "To understand which sector shows consistent improvement, I analyzed {the line plots}[[0.121, 0.215, 0.959, 0.584], [0.121, 0.457, 0.959, 0.635], [0.121, 0.330, 0.959, 0.584], [0.121, 0.113, 0.959, 0.712]]. E-commerce, Manufacture, and Software Development lines are analyzed for their trajectory and the data points confirmed a gradual increase over time for all. However, {the line for Robotics}[0.121, 0.113, 0.959, 0.712] not only starts gradually increasing from 30,000 to 124,000 but also shows steeper ascents compared to other sectors, hinting at more considerable improvements.\nBased on the above thought, the answer is:\nRobotics shows the most consistent and substantial improvement over the years based on the chart, as highlighted by its steeper and higher rising plot line compared to other sectors." } ], "id_raw": 198 }, { "id": "yanRtzds2Wr4n0Qb", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage growth of the Robotics sector from 2004 to 2020?" }, { "from": "gpt", "value": "To calculate the percentage growth, I locate {the line points for Robotics}[[0.121, 0.712], [0.959, 0.113]]. I extract the values for Robotics in 2004, which is 30,000, and for 2020, which is 124,000. The growth is calculated as ((124,000 - 30,000) / 30,000) * 100.\nBased on the above thought, the answer is:\n313.33%" } ], "id_raw": 198 }, { "id": "W9Zvv4LcqN0fUKyA", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_10_19.png", "conversations": [ { "from": "human", "value": "\nCould you organize the chart data into a tabular structure with Years as columns and Income Groups as rows, and then complete the table with the corresponding Living Condition Quality values?" }, { "from": "gpt", "value": "To convert the chart data into a table, let's first look at {the x-axis tick labels}[0.053, 0.931, 0.088, 0.951], [0.277, 0.931, 0.312, 0.951], [0.502, 0.931, 0.537, 0.951], [0.726, 0.931, 0.761, 0.951], [0.950, 0.931, 0.985, 0.951] which represent the different years (2000, 2005, 2010, 2015, 2020) to be used as column headers. The row headers or the different Income groups (Low-Income Households, Middle-Income Households, High-Income Households, Average Households) can be identified from {the legend texts}[0.768, 0.047, 0.936, 0.067], [0.768, 0.077, 0.955, 0.097], [0.768, 0.107, 0.941, 0.127], [0.768, 0.137, 0.910, 0.157]. Finally, the Living Condition Quality values for each income group, for each year, are extracted by locating the corresponding {points on the line plots}[0.071, 0.899], [0.295, 0.775], [0.519, 0.739], [0.743, 0.509], [0.968, 0.367], [0.071, 0.526], [0.295, 0.420], [0.519, 0.384], [0.743, 0.296], [0.968, 0.242], [0.071, 0.118], [0.295, 0.065], [0.519, 0.171], [0.743, 0.189], [0.968, 0.242], [0.071, 0.562], [0.295, 0.420], [0.519, 0.402], [0.743, 0.313], [0.968, 0.260].\nBased on the above thought, the answer is:\nIncome_Group,2000,2005,2010,2015,2020\nLow-Income Households,51,58,60,73,81\nMiddle-Income Households,72,78,80,85,88\nHigh-Income Households,95,98,92,91,88\nAverage Households,70,78,79,84,87" } ], "id_raw": 199 }, { "id": "v04KL3C7y0buIjnV", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_10_19.png", "conversations": [ { "from": "human", "value": "\nWhich income group experienced the least improvement in Living Condition Quality over the years as shown in the graph?" }, { "from": "gpt", "value": "To find out which income group experienced the least improvement in Living Condition Quality over the years, we need to examine {the line plots}[0.071, 0.367, 0.968, 0.899], [0.071, 0.242, 0.968, 0.526], [0.071, 0.065, 0.968, 0.242], [0.071, 0.260, 0.968, 0.562]. We need to compare the improvement (or change) in Living Condition Quality for each group from the year 2000 to the year 2020. These would be the differences between the y-values of the start and end points of each line. From the evidence, it is clear that {High-Income Households}[0.071, 0.118, 0.968, 0.242] experienced the least improvement, as its start point is 95 and the endpoint is 88, showing even a decrease in the Living Condition Quality over the years.\nBased on the above thought, the answer is:\nThe High-Income Households group experienced the least improvement in Living Condition Quality over the years. In fact, the chart data indicates a decrease in the quality of living conditions for this group from 2000 to 2020." } ], "id_raw": 199 }, { "id": "DJtnHXeJVNm6nF2N", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_10_19.png", "conversations": [ { "from": "human", "value": "\nHow much did the Living Condition Quality for Low-Income Households improve between 2000 and 2020?" }, { "from": "gpt", "value": "To ascertain the improvement in Living Condition Quality for Low-Income Households between 2000 and 2020, we need to locate {the point for the year 2000}[0.071, 0.899] and {the point for the year 2020}[0.968, 0.367] on {the line plot for Low-Income Households}[0.071, 0.367, 0.968, 0.899]. The values corresponding to these points are 51 for 2000 and 81 for 2020. Then, we need to calculate the difference between these values, which is 81 - 51 = 30.\nBased on the above thought, the answer is:\n30." } ], "id_raw": 199 }, { "id": "wf3LO6uLB6isPGIk", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_0.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, could you extract the information regarding the performance of Brand A and Brand B at different stages into a structured table?" }, { "from": "gpt", "value": "To convert the line chart data into a tabular format, firstly, I should look at the {x-axis labels}[0.070, 0.911, 0.155, 0.934], [0.342, 0.911, 0.435, 0.934], [0.610, 0.911, 0.717, 0.934], [0.900, 0.911, 0.979, 0.934] which represent the different stages. They will form the rows of the table. Then, considering the {legend labels} [0.900, 0.091, 0.966, 0.121], [0.900, 0.134, 0.966, 0.164], we can see these are the two Brands that will become the columns. Then, looking at the line graph points for {Brand A} [0.113, 0.116], [0.388, 0.363], [0.664, 0.610], [0.939, 0.733] and {Brand B} [0.113, 0.239], [0.388, 0.610], [0.664, 0.671], [0.939, 0.795], I can extract these digits to fill the table cells for related brands and stages.\nBased on the above thought, the answer is:\n,BrandA,BrandB\nInitial_Stage, 0.8, 0.7\nMiddle_Stage, 0.6, 0.4\nPre-Final_Stage, 0.4, 0.35\nFinal_Stage, 0.3, 0.25" } ], "id_raw": 200 }, { "id": "KOis4TuRVgdsp1UH", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which brand shows a more consistent decrease in performance over the stages?" }, { "from": "gpt", "value": "To answer this question, we need to observe the slope of the lines representing each brand. For {Brand A} [0.113, 0.116, 0.939, 0.733], the line slope is negative throughout all stages, indicating a consistent decrease in performance. In contrast, for {Brand B} [0.113, 0.239, 0.939, 0.795], the line slope is also overall negative but not as steep as for Brand A, indicating a slower and less consistent decrease in performance.\nBased on the above thought, the answer is:\nAccording to the line chart, Brand A shows a more consistent decrease in performance over the stages. Its negative slope throughout all stages indicates a steady decline, unlike Brand B whose slope is also negative but less steep, indicating a slower and less consistent decrease." } ], "id_raw": 200 }, { "id": "0mkOsjl50gP3tVQL", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in performance for Brand A from the Initial Stage to the Final Stage?" }, { "from": "gpt", "value": "In order to compute the difference in performance for Brand A from the Initial Stage to the Final Stage, I need to locate {the line point for Brand A at the Initial Stage} [0.113, 0.116] and {the line point for Brand A at the Final Stage} [0.939, 0.733]. The performance at the Initial Stage is 0.8 and that at the Final Stage is 0.3. Therefore, the change in performance is 0.3 - 0.8 = -0.5.\nBased on the above thought, the answer is:\n-0.5" } ], "id_raw": 200 }, { "id": "YB2LHj0D6t92ixMh", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_4.png", "conversations": [ { "from": "human", "value": "\nCan you transform this line chart data into a structured table where the columns are the Years and the rows represent the Stock and the different fields?" }, { "from": "gpt", "value": "To find the tabular data from the line chart, I'll first find {the x-axis tick labels}[[0.157, 0.913, 0.179, 0.936], [0.333, 0.913, 0.355, 0.936], [0.509, 0.913, 0.531, 0.936], [0.685, 0.913, 0.707, 0.936], [0.861, 0.913, 0.883, 0.936]], which show the column labels or Years. Then, I'll identify the {legend texts}[[0.133, 0.091, 0.227, 0.121], [0.133, 0.134, 0.229, 0.164]] as the row labels representing the different Stocks. Each of these Stocks has different fields we're interested in, such as Ratios, Performance, and Error. The table values are located at the line points for each Stock and their respective fields at the corresponding Years.\nBased on the above thought, the answer is:\nYear,Ratios_AAPL,Performance_AAPL,Error_AAPL,Ratios_MSFT,Performance_MSFT,Error_MSFT\n0.1,0.1,0.70,0.12,0.1,0.65,0.11\n0.2,0.2,0.64,0.11,0.2,0.72,0.10\n0.4,0.4,0.60,0.10,0.4,0.78,0.08\n0.6,0.6,0.58,0.09,0.6,0.83,0.07\n0.8,0.8,0.53,0.06,0.8,0.86,0.05\n1.0,1.0,0.50,0.05,1.0,0.90,0.04" } ], "id_raw": 204 }, { "id": "ArHa4cuiloprNkFI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_4.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which stock shows a more stable Performance relative to changes in the Investment Ratio?" }, { "from": "gpt", "value": "To determine the stock with the more stable Performance, I need to analyze the changes in line plots for the two stocks. Looking at {the line for AAPL Stock}[0.159, 0.315, 0.948, 0.482], there is a steady decrease in Performance as the Investment Ratio increases. For {MSFT Stock}[0.159, 0.357, 0.948, 0.148], there is a more pronounced upward trend indicating a high change in Performance with respect to the changes in the Investment Ratio. Despite fluctuations, the rate of change in Performance for AAPL Stock is less compared to MSFT Stock.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, AAPL Stock shows a more stable Performance relative to changes in the Investment Ratio. Its line plot exhibits less fluctuation in comparison to MSFT's, as it gradually decreases with the increase in the Investment Ratio. The less pronounced changes in Performance for AAPL Stock in connection to Investment Ratio changes reflect a more stable trend compared to the fluctuations in MSFT's Performance." } ], "id_raw": 204 }, { "id": "WT7UP5ZmRN9h99pL", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in Performance at an Investment Ratio of 1.0 between AAPL and MSFT?" }, { "from": "gpt", "value": "To find the cardinals, I have to refer to the {line points for AAPL at Investment Ratio 1.0}[0.948, 0.482] and {line points for MSFT at Investment Ratio 1.0}[0.948, 0.147] to extract the corresponding performance values for AAPL and MSFT. Once I have these values, I can calculate the difference between the two.\nBased on the above thought, the answer is:\n0.4" } ], "id_raw": 204 }, { "id": "SB9CQBKXxjOyqOyx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_5.png", "conversations": [ { "from": "human", "value": "\nCan you extract the changes in Design Innovation Score corresponding to the different values of the Creativity Index into a structured table?" }, { "from": "gpt", "value": "First, I identify {the x-axis tick labels}[[0.060, 0.911, 0.082, 0.934], [0.235, 0.911, 0.257, 0.934], [0.410, 0.911, 0.432, 0.934], [0.585, 0.911, 0.607, 0.934], [0.760, 0.911, 0.782, 0.934], [0.935, 0.911, 0.957, 0.934]] as the different values of the Creativity Index to be represented as the column headers. Then, I find {the line points}[[0.159, 0.231], [0.246, 0.314], [0.334, 0.397], [0.421, 0.480], [0.508, 0.439], [0.596, 0.563], [0.683, 0.604], [0.771, 0.687], [0.858, 0.770], [0.946, 0.853]] corresponding to the Design Innovation Score at each Creativity Index position. Finally, I can generate a table mapping each Creativity Index value to its corresponding Design Innovation Score.\nBased on the above thought, the answer is:\nCreativity Index,Design Innovation Score\n0.1,0.8\n0.2,0.7\n0.3,0.6\n0.4,0.5\n0.5,0.55\n0.6,0.4\n0.7,0.35\n0.8,0.25\n0.9,0.15\n1.0,0.05" } ], "id_raw": 205 }, { "id": "WtGevKyeXRiyva2I", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_5.png", "conversations": [ { "from": "human", "value": "\nWhat interpretation can be made about the relationship between the Creativity Index and the Design Innovation Score based on the chart?" }, { "from": "gpt", "value": "To analyze the relationship between the Creativity Index and the Design Innovation Score, I need to examine {the trend of the line}[[0.159, 0.231, 0.946, 0.853]]. The line describes an initial decrement pattern in Design Innovation Score as the Creativity Index increases from 0.1 to 0.4. There is a slight increase going from 0.4 to 0.5 but overall continues to decrease after reaching 0.5 to 1.0. This suggests a generally negative correlation between the Creativity Index and the Design Innovation Score.\nBased on the above thought, the answer is:\nBased on the chart, it can be deduced that there is, generally, an inversely proportional relationship between the Creativity Index and the Design Innovation Score. As the Creativity Index increases, the Design Innovation Score tends to decrease. This can suggest that perhaps as more creative approaches are engaged (high Creativity Index), it could potentially complicate the innovation process thereby reducing the Design Innovation Score." } ], "id_raw": 205 }, { "id": "nWmUQB7EDzemT5t7", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of the Design Innovation Score as evidenced by the chart?" }, { "from": "gpt", "value": "To determine the range of the Design Innovation Score, I first locate {the line points}[[0.159, 0.231], [0.246, 0.314], [0.334, 0.397], [0.421, 0.480], [0.508, 0.439], [0.596, 0.563], [0.683, 0.604], [0.771, 0.687], [0.858, 0.770], [0.946, 0.853]] to extract the corresponding scores: 0.8, 0.7, 0.6, 0.5, 0.55, 0.4, 0.35, 0.25, 0.15, 0.05. The highest score is 0.8 and the lowest score is 0.05. The range of the Design Innovation Score is therefore 0.8 - 0.05 = 0.75.\nBased on the above thought, the answer is:\n0.75." } ], "id_raw": 205 }, { "id": "gqFra2aB99lEfTvl", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart showcasing the scores of AI Efficiency and Machine Learning Progress over time into a table format with Time Units as columns and each aspect as a row?" }, { "from": "gpt", "value": "Reviewing the chart, I need to interpret the data from the x-axis where {the time units tick labels}[0.060, 0.911, 0.082, 0.934], [0.235, 0.911, 0.257, 0.934], ..., [0.935, 0.911, 0.957, 0.934] represent the headers. Observing both line plots, {the points for AI Efficiency}[0.159, 0.148], [0.246, 0.182], ..., [0.946, 0.422] and {the points for Machine Learning Progress}[0.159, 0.397], [0.246, 0.364], ..., [0.946, 0.480] contain the necessary data values. Each point precisely corresponds to their score values at every unit of time.\nBased on the above thought, the answer is:\nTime Units,0,1,2,3,4,5,6,7\nAI Efficiency,0.92,0.86,0.82,0.79,0.76,0.74,0.71,0.66\nMachine Learning Progress,0.6,0.64,0.67,0.71,0.71,0.68,0.63,0.6" } ], "id_raw": 206 }, { "id": "umjEMF6YWgayuop2", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the general trend observed in the scores of Machine Learning Progress over time?" }, { "from": "gpt", "value": "Analyzing the trend for Machine Learning Progress requires inspecting {the points}[0.159, 0.397], [0.946, 0.480] across the chart. This line shows a fluctuation in scores moving slightly upwards as time progresses with a local increase in the middle of the timeline followed by a consistent recover towards the end. Therefore, the general trend can be described as a \"slow recovery\" throughout the units of time.\nBased on the above thought, the answer is:\nThe general trend observed in Machine Learning Progress is a slow recovery. Initially, there are minor fluctuations with an overall slight upward trajectory, and despite a small drop mid-way, the final stages see stable recovery towards previous higher scores." } ], "id_raw": 206 }, { "id": "rCyK9ENSShvWjiWU", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_6.png", "conversations": [ { "from": "human", "value": "\nHow much has the score for AI Efficiency decreased from the first to the last unit of time?" }, { "from": "gpt", "value": "To determine the decrease in the score for AI Efficiency from the first to the last time unit, I consult {the data points for AI Efficiency}[0.159, 0.148], [0.946, 0.422] which represent the values at the start and end of the timeline. The decrease is calculated as the difference between the initial value at time unit 0 (0.92) and the final value at time unit 7 (0.66).\nBased on the above thought, the answer is:\n0.26" } ], "id_raw": 206 }, { "id": "pPwr2Kpv8rL335ts", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_7.png", "conversations": [ { "from": "human", "value": "\nGiven the information in the chart, could you tabulate the changes in latitude and longitude for each location?" }, { "from": "gpt", "value": "To create a table, I first identify {the x-axis labels}[{'label': 'New York', 'label_coordinates': '[0.092, 0.911, 0.156, 0.934]'}, {'label': 'San Francisco', 'label_coordinates': '[0.167, 0.911, 0.263, 0.934]'}, {'label': 'Atlanta', 'label_coordinates': '[0.281, 0.911, 0.331, 0.934]'}, {'label': 'Chicago', 'label_coordinates': '[0.369, 0.911, 0.425, 0.934]'}, {'label': 'Seattle', 'label_coordinates': '[0.464, 0.911, 0.513, 0.934]'}, {'label': 'New Orleans', 'label_coordinates': '[0.535, 0.911, 0.624, 0.934]'}, {'label': 'Denver', 'label_coordinates': '[0.645, 0.911, 0.696, 0.934]'}, {'label': 'Phoenix', 'label_coordinates': '[0.734, 0.911, 0.789, 0.934]'}, {'label': 'Boston', 'label_coordinates': '[0.829, 0.911, 0.877, 0.934]'}, {'label': 'Miami', 'label_coordinates': '[0.923, 0.911, 0.965, 0.934]'}] as the row labels representing the different locations. Looking at the line graphs of {latitude changes}['coordinates': '[0.124, 0.103, 0.944, 0.415]'] and {longitude changes}['coordinates': '[0.124, 0.578, 0.944, 0.857]'], I map these to the respective location. Using the y-axis labels, I determine the changes in latitude and longitude.\nBased on the above thought, the answer is:\nLocation,Latitude Changes,Longitude Changes\nNew York,0.08,-0.05\nSan Francisco,0.15,-0.1\nAtlanta,0.05,-0.12\nChicago,0.2,-0.08\nSeattle,0.17,-0.14\nNew Orleans,0.12,-0.098\nDenver,0.14,-0.18\nPhoenix,0.11,-0.15\nBoston,0.24,-0.13\nMiami,0.18,-0.22" } ], "id_raw": 207 }, { "id": "lAwvpZOJ0pd9E0D3", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_7.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which location undergoes the most drastic changes in geographical coordinates?" }, { "from": "gpt", "value": "To figure out which location undergoes the most drastic changes, I need to observe {the lines for latitude and longitude changes}['line_0':'coordinates': '[0.124, 0.103, 0.944, 0.415]', 'line_1': 'coordinates': '[0.124, 0.578, 0.944, 0.857]'] and see where the points on both lines show significant changes. This means that both the latitude and the longitude values should be more extreme, closer to the edges of the Y axis. A drastic change means a large overall change in both latitude and longitude.\nBased on the above thought, the answer is:\nMiami appears to undergo the most drastic changes in geographical coordinates. Its points are the farthest on both the latitude and longitude change lines, indicating a large overall change in both coordinates relative to other locations." } ], "id_raw": 207 }, { "id": "MnfhSdY5rtCIqM4O", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_7.png", "conversations": [ { "from": "human", "value": "\nGiven the line graphs, what is the greatest difference between latitude and longitude changes observed in the locations?" }, { "from": "gpt", "value": "To find the greatest difference, I would analyze the points on each of the two lines representing {latitude changes}['coordinates': '[0.124, 0.103, 0.944, 0.415]'] and {longitude changes}['coordinates': '[0.124, 0.578, 0.944, 0.857]']. Taking the difference between each corresponding pairs and finding the maximum value would give me the answer.\nBased on the above thought, the answer is:\n0.13." } ], "id_raw": 207 }, { "id": "f471pgv4phSJRR0a", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_8.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart showing research growth trends across the six academic fields into a structured table?" }, { "from": "gpt", "value": "To accurately convert the data into a structured table, I first examine the x-axis labels which include the three trends: Unstable, Steady Increase, and Rapid Increase. These are represented in {x-axis tick labels}[[0.1, 0.9, 0.17, 0.93], [0.30, 0.9, 0.37, 0.93], [0.50, 0.9, 0.67, 0.93]]. The research fields are detailed in the legend: Biology, Physics, Chemistry, Computer Science, Mathematics, and Economics, with their respective colors indicated for clarity. Each field's data is plotted across these trends, where I can derive data points from their coordinates {line points for Biology}[[0.15, 0.2], [0.35, 0.6], [0.55, 0.85]], {line points for Physics}[[0.15, 0.4], [0.35, 0.65], [0.55, 0.75]], and so forth.\nBased on the above thought, the answer is:\nTrend,Biology,Physics,Chemistry,Computer Science,Mathematics,Economics\nUnstable,0.2,0.4,0.45,0.5,0.55,0.4\nSteady Increase,0.6,0.65,0.7,0.68,0.7,0.65\nRapid Increase,0.85,0.75,0.8,0.85,0.9,0.7" } ], "id_raw": 208 }, { "id": "sKpSvpu5TE5cvtru", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_8.png", "conversations": [ { "from": "human", "value": "\nWhich academic research field shows the most consistent upward trend across all growth stages?" }, { "from": "gpt", "value": "To determine the consistency of the upward trend, I analyze the progression of each field's plot in the chart {line for Biology}[0.1, 0.2, 0.7], {line for Mathematics}[0.1, 0.55, 0.9]. Observing the coordinates, Mathematics starts at a higher baseline and consistently reaches the highest point in the final trend compared to other fields. Biology, although starting lower, also shows a strong consistent increase, but not to the same extent as Mathematics.\nBased on the above thought, the answer is:\nThe field of Mathematics demonstrates the most consistent upward trend, showing substantial growth from start to finish and reaching the highest value in the Rapid Increase trend compared to other fields." } ], "id_raw": 208 }, { "id": "ZTPXGyqsyJkybkyo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the average increase in research growth for the field of Computer Science across all trends?" }, { "from": "gpt", "value": "To calculate the average, I follow the line representing Computer Science, which holds data points {line points for Computer Science}[[0.15, 0.5], [0.35, 0.68], [0.55, 0.85]]. The values represented by these points are 0.5, 0.68, and 0.85 respectively. The average increase is then calculated by summing these values and dividing by the number of points, (0.5 + 0.68 + 0.85) / 3.\nBased on the above thought, the answer is:\n0.6767" } ], "id_raw": 208 }, { "id": "ThGeSG9FXpWgv3vg", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_9.png", "conversations": [ { "from": "human", "value": "\nCan you list down the performance of each field of Big Data Integration based on the Size of Data in the form of a structured table?" }, { "from": "gpt", "value": "To answer this question, I have to locate the x-axis labels representing {the Size of Data}[[0.110, 0.911, 0.149, 0.934], [0.509, 0.911, 0.565, 0.934], [0.925, 0.911, 0.964, 0.934]]. After that, I need to identify the legend that represents {the various fields of Big Data Integration}[[0.816, 0.399, 0.952, 0.429], [0.816, 0.443, 0.920, 0.473], [0.816, 0.487, 0.970, 0.517], [0.816, 0.531, 0.961, 0.561]]. Then, I will look at each field's coordinates that represent the data points for each Size of Data category [{Data Integration}[[0.130, 0.668], [0.537, 0.706], [0.944, 0.744]], {Data Quality}[[0.130, 0.103], [0.537, 0.179], [0.944, 0.292]], {Data Virtualization}[[0.130, 0.857], [0.537, 0.744], [0.944, 0.631]], {Data Governance}[[0.130, 0.593], [0.537, 0.668], [0.944, 0.781]]].\nBased on the above thought, the answer is:\nSize of Data,Data Integration,Data Quality,Data Virtualization,Data Governance\nSmall,0.8,0.95,0.75,0.82\nMedium,0.79,0.93,0.78,0.8\nLarge,0.78,0.9,0.81,0.77" } ], "id_raw": 209 }, { "id": "eIRNDdUcA8A2OBSq", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_9.png", "conversations": [ { "from": "human", "value": "\nWhich field in Big Data Integration seems to demonstrate a distinct downward trend in performance as the data size increases? Justify your answer." }, { "from": "gpt", "value": "To answer this question, I need to check the trends of all the fields in Big Data Integration. I can see that {Data Quality}[[0.130, 0.103], [0.537, 0.179], [0.944, 0.292]] presents a clear descending trend, with performance values dropping from 0.95 for \"Small\" data size to 0.9 for \"Large\" data size. This consistently decreasing pattern suggests a downward trend.\nBased on the above thought, the answer is:\nThe field of Data Quality in Big Data Integration demonstrates a clear downward trend in performance as the size of data increases. This can be deduced from the consistent decrease in performance values, which drops from 0.95 for small data size to 0.9 for large data size. Therefore, Data Quality seems to be negatively affected as the data size increases." } ], "id_raw": 209 }, { "id": "RGo0YvrKjCdpOHt5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_9.png", "conversations": [ { "from": "human", "value": "\nWhat's the percentage change in performance of Data Virtualization from Small to Large size data?" }, { "from": "gpt", "value": "In order to calculate the percentage change, I will find the initial and final performance values of {Data Virtualization}[[0.130, 0.857], [0.944, 0.631]]. The initial value for \"Small\" size data is 0.75 and the final value for \"Large\" size data is 0.81. The Percentage change can be calculated using the formula ((Final - Initial) / |Initial|) * 100.\nBased on the above thought, the answer is:\n8%." } ], "id_raw": 209 }, { "id": "xrAmzq0PhcnKJROk", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data showing market share trends across different industries into a structured table format with Periods as columns and Industries as rows?" }, { "from": "gpt", "value": "I first look at {the x-axis labels}[0.095, 0.911, 0.130, 0.934], [0.187, 0.911, 0.223, 0.934], [0.280, 0.911, 0.315, 0.934], [0.372, 0.911, 0.407, 0.934], [0.464, 0.911, 0.500, 0.934] which denote the periods, to serve as the column headers. I identify {the legend items}[0.769, 0.091, 0.959, 0.121], [0.769, 0.134, 0.970, 0.164] and note that they represent different industries as row labels. Then, I extract the values at each intersection of these industries and periods by analyzing {the line points for each industry}[0.113, 0.339, 0.943, 0.853], [0.113, 0.381, 0.943, 0.870], each representing a different industry\u2019s performance at specified periods.\nBased on the above thought, the answer is:\nPeriod,Electronics,Automotive,Pharmaceuticals,Retail,Textiles\n1,0.15,0.13,0.18,0.12,0.09\n2,0.22,0.20,0.25,0.19,0.15\n3,0.34,0.33,0.37,0.28,0.20\n4,0.42,0.40,0.45,0.35,0.25\n5,0.45,0.43,0.50,0.38,0.28" } ], "id_raw": 210 }, { "id": "QehQP3YlU2KwvbLd", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_10.png", "conversations": [ { "from": "human", "value": "\nWhat trend can be observed in the Textiles industry over the periods and what could be the potential implications of such a trend?" }, { "from": "gpt", "value": "Looking at {the line representing the Textiles industry}[0.113, 0.485, 0.943, 0.765], there is a noticeable sharp decrease particularly after Period 4. Initially, the industry shows a gradual decline, but the sharp drop in the last period signifies a rapid loss in market share compared to others. This could imply that the Textiles industry is facing significant challenges that may include increased competition, changes in consumer preferences, or disruptions in supply chains, affecting its market presence adversely.\nBased on the above thought, the answer is:\nThe Textiles industry exhibits a significant downtrend, especially noticeable in the last period. This sharp decrease potentially indicates serious challenges within the industry, such as heightened competition, evolving consumer preferences, or operational disruptions, which could have critical implications for its future stability and market position." } ], "id_raw": 210 }, { "id": "XLA64HMPbZGwtsu3", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in market share for the Pharmaceuticals industry from Period 1 to Period 5?" }, { "from": "gpt", "value": "To calculate the total increase in market share for the Pharmaceuticals industry, I need to observe {the starting and ending points of Pharmaceuticals' line}[0.113, 0.317, 0.943, 0.539]. The initial value at Period 1 is roughly 0.18, and the final value at Period 5 is approximately 0.50, which means I need to subtract the initial value from the final value.\nBased on the above thought, the answer is:\n0.32" } ], "id_raw": 210 }, { "id": "OoR31zOj60BPbMNj", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_11.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you organize the information into a table format with Career stages as columns and Job Role as Rows?" }, { "from": "gpt", "value": "In order to formulate the tabular data from the line chart, I will initially need to take a look at {the x-axis tick labels}[[0.099, 0.910, 0.221, 0.939], [0.285, 0.910, 0.388, 0.939], [0.446, 0.910, 0.579, 0.939], [0.585, 0.910, 0.792, 0.939], [0.780, 0.910, 0.950, 0.939]], which will be the columns representing career stages. Then, I will identify {the sub-legend texts}[[0.695, 0.151, 0.862, 0.189], [0.695, 0.206, 0.877, 0.244]] as the rows depicting different job roles. To populate the cell values, I would locate {the line points for Data Analyst}[[0.160, 0.225], [0.336, 0.312], [0.512, 0.453], [0.689, 0.593], [0.865, 0.750]] and {the line points for Data Scientist}[[0.160, 0.155], [0.336, 0.243], [0.512, 0.347], [0.689, 0.505], [0.865, 0.680]] for each job role at the respective stages and extract the corresponding performance numbers.\nBased on the above thought, the answer is:\nRole,Entry Level,Mid Level,Senior Level,Management Level,Executive Level\nData Analyst,0.84,0.79,0.71,0.63,0.54\nData Scientist,0.88,0.83,0.77,0.68,0.58" } ], "id_raw": 211 }, { "id": "Thl33ypdvc0zv3sj", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the trend of Data Scientist performance based on the line chart across their Career Stage?" }, { "from": "gpt", "value": "To discern the trend in Data Scientist performance, I will analyze {the line for Data Scientist}[0.160, 0.155, 0.865, 0.680]. The performance starts high at Entry Level {Entry Level}[0.160, 0.910], with a score of 0.88, and continues to decrease progressively through each of the following career stages - {Mid Level}[0.336, 0.910], {Senior Level}[0.512, 0.910], {Management Level}[0.689, 0.910], and {Executive Level}[0.865, 0.910] with scores of 0.83, 0.77, 0.68, and 0.58 respectively.\nBased on the above thought, the answer is:\nThe trend shows that the performance of a Data Scientist tends to decrease as they advance in their career stages. The performance starts at a high value in Entry Level and continually decreases until Executive Level. The reasoning behind the decrease could be due to the higher complexity of roles in advanced career stages, which might have lower performance scores due to increased responsibilities and expectations." } ], "id_raw": 211 }, { "id": "oiFa0ZFkDiz0RAuB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the average performance of a Data Analyst from Entry Level to Executive Level?" }, { "from": "gpt", "value": "To find out the average performance of a Data Analyst at all career stages, I need to identify {the line points for Data Analyst}[[0.160, 0.225], [0.336, 0.312], [0.512, 0.453], [0.689, 0.593], [0.865, 0.750]] and extract the corresponding values which are 0.84, 0.79, 0.71, 0.63, and 0.54. Then, I can calculate the average by summing up all the values and dividing it by the total number of career stages that are five in number. The average would therefore be (0.84 + 0.79 + 0.71 + 0.63 + 0.54) / 5.\nBased on the above thought, the answer is:\n0.702" } ], "id_raw": 211 }, { "id": "aS0ySYUJ24kfC7XA", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information from the line chart on Governmental Efficiency and Ease of Communication into a structured table format with Time Periods as columns and Indicators as rows?" }, { "from": "gpt", "value": "First, I focus on {the x-axis labels}[0.084, 0.933, 0.113, 0.951] representing the Time Periods. These are the column headers. The legend indicates two different lines, which are 'Governmental Efficiency' and 'Ease of Communication', identified as the row headers from {the legend items}[0.107, 0.847, 0.262, 0.869]. To populate the table, I analyze each line to capture the values at the respective time periods and combine these values into the desired table format.\nBased on the above thought, the answer is:\nTime Period,0,1,2,3,4\nGovernmental Efficiency,0.3,0.35,0.4,1.1,0.45\nEase of Communication,0.25,0.3,0.35,1.0,0.4" } ], "id_raw": 213 }, { "id": "tzmagXR1z5h6YQfa", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_13.png", "conversations": [ { "from": "human", "value": "\nExplain the trend in 'Ease of Communication' across the time periods as shown in the chart." }, { "from": "gpt", "value": "Analyzing {the line for 'Ease of Communication'}[0.098, 0.400, 0.945, 0.511], there's an evident increase and peak at Time Period [3]. Initially, from Periods [0 to 2], there's gradual growth as the line slightly ascends. There's a significant spike in Period [3] indicating a peak point, which sharply drops back to a level similar to the starting periods in Period [4]. This outlines a surge suggesting a temporary but significant improvement in 'Ease of Communication'.\nBased on the above thought, the answer is:\n'Ease of Communication' shows a steady but modest increase from Periods [0 to 2], before experiencing a sharp incline and peaking in Period [3]. This peak signifies an abrupt and significant enhancement in communication, which however, reverts back to near initial levels in Period [4], indicating a steep drop. The trend suggests a volatile but progressive pattern with a focus on the stark peak at Period [3]." } ], "id_raw": 213 }, { "id": "Dd8greAVp5CJgZ2X", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the peak value for Governmental Efficiency during the Time Periods based on the chart analysis?" }, { "from": "gpt", "value": "To find the peak value for 'Governmental Efficiency', I examine the line corresponding to this indicator, which I identify at {the line for Governmental Efficiency}[0.098, 0.372, 0.945, 0.502]. The highest point is during Time Period '[3]', where the y-value hits approximately 1.1. I used the chart grid lines and axis scaling to ascertain this peak value.\nBased on the above thought, the answer is:\n1.1" } ], "id_raw": 213 }, { "id": "lmbOwXOIpIzI2ltD", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart into a table format listing each trend and their performance from 2005 to 2020?" }, { "from": "gpt", "value": "Observing {the x-axis ticks}[0.095, 0.911, 0.961, 0.934] where each tick represents each year from 2005 to 2020, and identifying {line colors}[0.081, 0.668, 0.125, 0.687], [0.081, 0.712, 0.125, 0.731], [0.081, 0.800, 0.125, 0.819] corresponding to different market trends. Extracting values from these lines across the years to form the table.\nBased on the above thought, the answer is:\nYear,Residential_Price_Index,Commercial_Price_Index,Rental_Rate_Index\n2005,100,100,100\n2006,105,102,101\n2007,110,108,103\n2008,108,130,105\n2009,100,100,100\n2010,103,105,102\n2011,105,110,104\n2012,110,115,107\n2013,115,120,110\n2014,120,125,115\n2015,125,130,120\n2016,130,135,122\n2017,135,140,125\n2018,140,145,127\n2019,145,150,130\n2020,150,160,132" } ], "id_raw": 214 }, { "id": "A2Vxlj1OOp1am0Jh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_14.png", "conversations": [ { "from": "human", "value": "\nConsidering the overall trend curves, which market showed the most stable growth from 2005 to 2020, and why?" }, { "from": "gpt", "value": "To determine the most stable growth, a close inspection of each line's fluctuation magnitude and consistency in their trajectory is necessary. Observing {the line for Residential_Price_Index}[0.113, 0.421, 0.943, 0.252], it shows noticeable but relatively smooth and consistent increases throughout the years.\nOn the other hand, {the line for Commercial_Price_Index}[0.113, 0.421, 0.943, 0.208] demonstrates a sharp spike around 2008 followed by a steep decline in 2009, suggesting higher volatility compared to the residential market.\nAnalyzing {the line for Rental_Rate_Index}[0.113, 0.421, 0.943, 0.267], it shows slight undulations that reflect minor fluctuations but maintains an overall stable upward trend.\nBased on the above thought, the answer is:\nThe Residential_Price_Index showed the most stable growth from 2005 to 2020, characterized by smooth and consistent increases with minor fluctuations, signaling a steady market compared to the more volatile commercial sector and the slightly undulating rental market." } ], "id_raw": 214 }, { "id": "SrX1KzRLznZokSBA", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_14.png", "conversations": [ { "from": "human", "value": "\nHow much did the Commercial_Price_Index grow from 2005 to 2020 based on the chart?" }, { "from": "gpt", "value": "Starting with {the initial point for Commercial trend}[0.113, 0.421] in 2005, I observe that it corresponds to an index value of 100. Tracking along the line to {the endpoint for Commercial trend}[0.943, 0.208] in 2020, it indicates an index value of 160. To find the growth, I need to calculate the difference between the endpoint and the starting point values.\nBased on the above thought, the answer is:\n60" } ], "id_raw": 214 }, { "id": "vRmaMgNCNIpGhXVW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_15.png", "conversations": [ { "from": "human", "value": "\nCan you create a structured table showing the performance metrics for each technology company, differentiating by key performance indicator from the line chart?" }, { "from": "gpt", "value": "To create a structured table from the line chart, I start by identifying {the x-axis labels}[0.071, 0.893, 0.115, 0.975] that list the company names. Next, I determine {the legend}[0.201, 0.010, 0.373, 0.089] which includes 'Turnover Rate,' 'Innovation Performance,' and 'Business Dynamics' as performance indicators. I then scrutinize {each line's data points}[0.113, 0.093, 0.943, 0.870] for all companies across these indicators by tracking the lines' vertical positions at each company label on the x-axis. These values are derived from the y-axis scale.\nBased on the above thought, the answer is:\nCompany,Turnover Rate,Innovation Performance,Business Dynamics\nApple,0.8,0.85,0.87\nGoogle,0.82,0.83,0.85\nAmazon,0.75,0.78,0.8\nMicrosoft,0.74,0.81,0.81\nFacebook,0.63,0.65,0.69" } ], "id_raw": 215 }, { "id": "BzozmZ7tgIqzVQpQ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_15.png", "conversations": [ { "from": "human", "value": "\nWhy does Facebook have the most significant decline in all performance categories according to the line chart?" }, { "from": "gpt", "value": "To understand Facebook's significant decline in performance categories, I first assess {the lines representing each category for Facebook}[0.943, 0.554], [0.943, 0.632], [0.943, 0.593]. Facebook's scores are consistently lower than the other companies for each category. Considering {the overall trend lines}[0.113, 0.412, 0.943, 0.774] for all companies, it's evident that while other companies either stabilize or have minimal decline, Facebook's lines show a more pronounced downward slope across all indicators. This indicates a greater negative shift in its performance metrics compared to its counterparts.\nBased on the above thought, the answer is:\nFacebook has the most significant decline because its performance in turnover rate, innovation performance, and business dynamics are not only lower but also show a steeper decline on the graph compared to other companies. Its line visibly trends lower across the indicators, indicating a pronounced decrease in performance relative to others." } ], "id_raw": 215 }, { "id": "CV1fvBBJg6t1EJpo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_15.png", "conversations": [ { "from": "human", "value": "\nHow much higher is Microsoft's innovation performance compared to its turnover rate?" }, { "from": "gpt", "value": "To compute the difference between Microsoft's innovation performance and its turnover rate, I locate {the points for Microsoft on the innovation performance and turnover rate lines}[0.390, 0.623], [0.390, 0.581]. From the y-axis, the values for innovation performance is approximately 0.81 and for turnover rate is approximately 0.74. Calculating the difference yields 0.81 - 0.74 = 0.07.\nBased on the above thought, the answer is:\n0.07" } ], "id_raw": 215 }, { "id": "yNESOGfztGgY86Cc", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_16.png", "conversations": [ { "from": "human", "value": "\nCould you please extract the data from the chart into a table with 'Product Stages' as columns and 'Cost and Value' as rows?" }, { "from": "gpt", "value": "First, I need to identify the x-axis tick labels which are {Product Stages}[[0.076, 0.911, 0.164, 0.934], [0.269, 0.911, 0.370, 0.934], [0.479, 0.911, 0.560, 0.934], [0.699, 0.911, 0.739, 0.934], [0.857, 0.911, 0.980, 0.934]] are to be used as column headers in the table. Subsequently, the legend item labels {Cost and Value}[[0.898, 0.091, 0.935, 0.121], [0.898, 0.134, 0.944, 0.164]] represent the row labels. Subsequently, to populate the values, I need to fetch line points for 'Cost' and 'Value' at each stage from object {line_0}[[0.120, 0.276, 0.320, 0.549, 0.519, 0.594, 0.719, 0.503, 0.919, 0.367]] and {line_3}[[0.120, 0.640, 0.320, 0.367, 0.519, 0.184, 0.719, 0.276, 0.919, 0.822]] respectively.\nBased on the above thought, the answer is:\nAttributes, Procurement, Manufacturing, Distribution, Retail, Customer Service \nCost, 24, 18, 17, 19, 22\nValue, 16, 22, 26, 24, 12" } ], "id_raw": 216 }, { "id": "fCQnHle3Tdr5ijh9", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_16.png", "conversations": [ { "from": "human", "value": "\nCan you point out the stage in the product chain that gets the highest cost and value investment?" }, { "from": "gpt", "value": "To figure out the stage that gets the highest cost and value investment, I would need to analyze the y-values for each stage for both cost and value. For the cost, the line points {Procurement}[[0.120, 0.276]], {Manufacturing}[[0.320, 0.549]], {Distribution}[[0.519, 0.594]], {Retail}[[0.719, 0.503]], and {Customer Service}[[0.919, 0.367]] indicate values of 24, 18, 17, 19, 22 respectively. The highest value is 24, corresponding to Procurement. For the value, looking at the line points {Procurement}[[0.120, 0.640]], {Manufacturing}[[0.320, 0.367]], {Distribution}[[0.519, 0.184]], {Retail}[[0.719, 0.276]], and {Customer Service}[[0.919, 0.822]], the values are 16, 22, 26, 24, 12 respectively. Thus, the highest value here is 26, corresponding to Distribution.\nBased on the above thought, the answer is:\nIn the product chain, the stage that gets the highest cost investment is 'Procurement' and the stage that gets the highest value investment is 'Distribution'." } ], "id_raw": 216 }, { "id": "9i8CTNoYL1uUPf72", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the sum of the cost generated from procurement to customer service in the value chain?" }, { "from": "gpt", "value": "To get the sum of the cost generated from procurement to customer service in the value chain, I will need to add the values corresponding to all stages for 'Cost' which are {Procurement}[[0.120, 0.276]], {Manufacturing}[[0.320, 0.549]], {Distribution}[[0.519, 0.594]], {Retail}[[0.719, 0.503]], and {Customer Service}[[0.919, 0.367]]. The values are 24, 18, 17, 19, 22 respectively, and the sum is 24 + 18 + 17 + 19 + 22 = 100\nBased on the above thought, the answer is:\nThe total cost from procurement to customer service in the value chain is 100." } ], "id_raw": 216 }, { "id": "Dst43yJYEW413Y0G", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_17.png", "conversations": [ { "from": "human", "value": "\nCan you extract the trend data for 'Rural Development' and 'Urban Development' from the chart and present it in a tabular format?" }, { "from": "gpt", "value": "To create the table, I first identify {the x-axis tick labels}[[0.095, 0.911, 0.130, 0.934], [0.205, 0.911, 0.242, 0.934], [0.297, 0.911, 0.334, 0.934], [0.390, 0.911, 0.426, 0.934], [0.482, 0.911, 0.518, 0.934], [0.574, 0.911, 0.610, 0.934], [0.667, 0.911, 0.703, 0.934], [0.759, 0.911, 0.795, 0.934], [0.851, 0.911, 0.887, 0.934], [0.943, 0.911, 0.979, 0.934]], which represent the Year column in the table. Next, I locate the lines for {Rural Development}[0.113, 0.397, 0.943, 0.828] and {Urban Development}[0.113, 0.231, 0.943, 0.729] to extract the corresponding development values for each year, represented by points like [0.205, 0.430], [0.297, 0.480], etc. for Rural Development and [0.205, 0.646], [0.297, 0.563], and so on for Urban Development.\nBased on the above thought, the answer is:\nYear,Rural Development,Urban Development\n2010,0.8,0.6\n2011,0.78,0.65\n2012,0.75,0.7\n2013,0.70,0.75\n2014,0.68,0.78\n2015,0.70,0.8\n2016,0.60,0.85\n2017,0.58,0.85\n2018,0.56,0.88\n2019,0.54,0.90" } ], "id_raw": 217 }, { "id": "D7VNSxqJqOS93BFS", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_17.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which area has seen more steady progress in development over the years? Provide a brief explanation for your answer." }, { "from": "gpt", "value": "To determine which area has shown more steady progress, I need to analyze the line plots for both Rural and Urban Development. On examining {the line for Rural Development}[0.113, 0.397, 0.943, 0.828], it appears to show a steady decrease over the years from a development score of 0.8 in 2010 to 0.54 in 2019. Comparatively, {the line for Urban Development}[0.113, 0.231, 0.943, 0.729] shows a steadier increase - it starts at a lower development score of 0.6 in 2010 but ends at an improved score of 0.9 by 2019.\nBased on the above thought, the answer is:\nThe Urban area has seen more steady progress in development over the years. The trend line for Urban Development shows a consistent, steady upward trajectory from 2010 to 2019, contrasting with Rural Development which has seen a steady decrease over the same period." } ], "id_raw": 217 }, { "id": "VrjwxEsImD8b2VNU", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in development between Rural and Urban regions for the year 2016?" }, { "from": "gpt", "value": "To answer this, I need to find the values for Rural and Urban Development in the year 2016. I locate the point corresponding to the year 2016 for {Rural Development}[0.667, 0.729] and extract the corresponding value of 0.6. I also find the point corresponding to 2016 for {Urban Development}[0.667, 0.314] and extract the value of 0.85. The difference is then calculated as 0.85 - 0.6 = 0.25.\nBased on the above thought, the answer is:\n0.25." } ], "id_raw": 217 }, { "id": "JN295ExRe4Kx0tTn", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_11_18.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data showing trends in media and entertainment industry by genre over 2005 and 2015 into a structured table?" }, { "from": "gpt", "value": "Observing the line chart, I map {the x-axis tick labels}[0.095, 0.911, 0.130, 0.934], [0.926, 0.911, 0.961, 0.934] as the years 2005 and 2015 which serve as column headers. I identify the genre categories based on {the legend text}[0.133, 0.091, 0.303, 0.121], [0.133, 0.134, 0.281, 0.164] and their corresponding lines. Following this, I correlate each genre's points for the years 2005 and 2015 extracted from {the plotted line points}[0.113, 0.853], [0.943, 0.666] and other coordinates to populate the genre rows.\nBased on the above thought, the answer is:\nYear,Horror,Action,Comedy,Romance,Sci-Fi,Drama,Biography,Documentary\n2005,360,480,550,400,370,350,220,180\n2015,340,430,520,395,365,400,330,290" } ], "id_raw": 218 }, { "id": "tfMdSTEd5PSSzMoG", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_11_18.png", "conversations": [ { "from": "human", "value": "\nWhich genre shows the highest increase in popularity from 2005 to 2015 based on the chart?" }, { "from": "gpt", "value": "To identify which genre exhibits the highest increase in popularity, I need to analyze the relative change in positions of all the genre lines from 2005 to 2015. Particularly, I look at {the starting and ending points of each line}[0.113, 0.853], [0.943, 0.666] and others. Among all the genres, {the line for the Documentary genre}[0.113, 0.746], [0.943, 0.604] shows the highest upward movement, indicating a significant increase compared to others.\nBased on the above thought, the answer is:\nThe Documentary genre shows the highest increase in popularity from 2005 to 2015, as evidenced by the substantial upward trajectory of its line on the chart." } ], "id_raw": 218 }, { "id": "BtXdSgesXY0nJiSw", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_11_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase of the Drama genre from 2005 to 2015?" }, { "from": "gpt", "value": "To calculate the percentage increase for the Drama genre, I first extract the values for Drama from the years 2005 and 2015 using {the Drama line points}[0.113, 0.509], [0.943, 0.451]. These values are 350 and 400 respectively. Using these values, the percentage increase is calculated by ((400-350)/350)*100%.\nBased on the above thought, the answer is:\n14.29%" } ], "id_raw": 218 }, { "id": "nc9IBtGtQBk3SYmF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_0.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table showing the weekly efficiencies of both materials and machines according to the line chart?" }, { "from": "gpt", "value": "Observed from the line chart, each week's data points for Material Efficiency and Machine Efficiency need to be extracted. First, I analyze the {line for Material Efficiency}[0.164, 0.438, 0.936, 0.693], extracting values such as week 1 at 0.90, week 2 at 0.88, and so forth until week 30. Simultaneously, I analyze the {line for Machine Efficiency}[0.164, 0.260, 0.936, 0.438], extracting values such as week 1 at 0.92, week 2 at 0.93, continuing similarly up to week 30.\nBased on the above thought, the answer is:\nWeek,Material Efficiency,Machine Efficiency\n1,0.90,0.92\n2,0.88,0.93\n3,0.91,0.94\n4,0.92,0.95\n5,0.92,0.93\n6,0.90,0.94\n7,0.91,0.93\n8,0.91,0.94\n9,0.88,0.92\n10,0.92,0.93\n11,0.90,0.95\n12,0.90,0.94\n13,0.89,0.92\n14,0.88,0.93\n15,0.88,0.94\n16,0.87,0.95\n17,0.88,0.96\n18,0.88,0.97\n19,0.87,0.96\n20,0.87,0.97\n21,0.85,0.98\n22,0.86,0.99\n23,0.85,0.98\n24,0.84,0.97\n25,0.84,0.96\n26,0.85,0.97\n27,0.84,0.96\n28,0.83,0.95\n29,0.82,0.94\n30,0.83,0.95" } ], "id_raw": 220 }, { "id": "cDLLtz3yEu1DfghS", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_0.png", "conversations": [ { "from": "human", "value": "\nWhat trend can be observed in Material Efficiency according to the chart, and does it show any significant changes over the weeks?" }, { "from": "gpt", "value": "Observing the {Material Efficiency line}[0.164, 0.438, 0.936, 0.693], it appears there are fluctuations in efficiency over the span of 30 weeks. The efficiency generally ranges from 0.85 to 0.92, with notable dips in some weeks, such as a noticeable drop to 0.85 in week 21, suggesting a temporary decrease in performance or efficiency. The data points fluctuate but generally exhibit a moderate range of change without extreme highs or lows, indicating relatively stable but varying operational efficiency.\nBased on the above thought, the answer is:\nThe trend in Material Efficiency shows moderate fluctuations with efficiency mostly ranging between 0.85 and 0.92. Although there are some week-to-week changes, the overall efficiency does not experience extreme highs or lows, suggesting a general stability in material handling efficiency over the observed period." } ], "id_raw": 220 }, { "id": "O7raO9euJJ9o8rTx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest machine efficiency values based on the data in the chart?" }, { "from": "gpt", "value": "According to the line chart data for Machine Efficiency observed from {the points}[0.164, 0.260, 0.936, 0.438], I will calculate the difference between the highest and lowest values. The highest value stands at 0.99 in week 22, and the lowest is at 0.92 in week 1. To find the difference, the calculation would be 0.99 - 0.92.\nBased on the above thought, the answer is:\n0.07" } ], "id_raw": 220 }, { "id": "vimWnKVaC451Pgif", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_1.png", "conversations": [ { "from": "human", "value": "\nCan the data presented in the graph comparing the performance between ResNet50 and VGG16 be structured into a table format?" }, { "from": "gpt", "value": "To generate a tabular format of the data from the graph, my first step is to identify {the bar labels}[0.199, 0.883, 0.222, 0.914] and [0.396, 0.883, 0.448, 0.914] which correspond to the two models: ResNet50 and VGG16. Then, I find {the y-axis labels}[0.058, 0.860, 0.116, 0.895] and [0.058, 0.137, 0.116, 0.172] and use these as the column headers (Accuracy and Loss). Finally, I extract the performance metrics for each model from {the heights of the bars}[0.208, 0.151, 0.330, 0.784] and [0.345, 0.151, 0.468, 0.802].\nBased on the above thought, the answer is:\nModel,Accuracy,Loss\nResNet50,92.5,0.067\nVGG16,90,0.091" } ], "id_raw": 221 }, { "id": "fIbEgdAjLSGZDsN4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_1.png", "conversations": [ { "from": "human", "value": "\nBetween the ResNet50 and VGG16 models, which one appears to be more robust according to the data presented in the chart? Explain your answer." }, { "from": "gpt", "value": "To determine the more robust model, I would analyze the bar plots for both ResNet50 and VGG16. Starting with {ResNet50}[0.208, 0.151, 0.330, 0.784], it shows a higher accuracy of 92.5% and lower loss of 0.067. Comparatively, an analysis of {VGG16}[0.345, 0.151, 0.468, 0.802] reveals a slightly lower accuracy of 90% and a moderately higher loss of 0.091. This comparison reveals that ResNet50 outperforms VGG16 in terms of accuracy and has less loss, hinting at better efficiency and performance.\nBased on the above thought, the answer is:\nAccording to the data presented in the chart, it appears that the ResNet50 model is more robust than the VGG16 model. This conclusion is based on the findings that ResNet50 model outperforms VGG16 model in terms of accuracy and also incurs a lower loss rate. This suggests a better performance and efficiency of the ResNet50 model when compared to VGG16 in this specific instance." } ], "id_raw": 221 }, { "id": "8JaHvzX6tZ4gkLYx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_1.png", "conversations": [ { "from": "human", "value": "\nBy what percent is the accuracy of the ResNet50 model greater than the accuracy of the VGG16 model?" }, { "from": "gpt", "value": "To find the percentage by which ResNet50's accuracy is greater than VGG16's, I first identify {the bar for ResNet50's accuracy}[0.208, 0.151, 0.293, 0.784] which represents 92.5%. I also locate {the bar for VGG16's accuracy}[0.345, 0.151, 0.431, 0.784] which represents 90%. Therefore, the difference in the accuracy is 92.5% - 90% = 2.5%.\nBased on the above thought, the answer is:\n2.5%" } ], "id_raw": 221 }, { "id": "628CG0IvO4yLrfGo", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_2.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data shown in the bar chart into a structured table, with sectors as rows and years as columns?" }, { "from": "gpt", "value": "To create the table, I'd first need to identify the necessary headers through analysis of the chart. For each Industry, we have both a 2019 and 2020 revenue data point. Identifying {the x-axis tick labels}[[0.178, 0.881, 0.268, 0.906], [0.335, 0.881, 0.406, 0.906], [0.493, 0.881, 0.561, 0.906], [0.649, 0.881, 0.717, 0.906], [0.807, 0.881, 0.861, 0.906]], gives us the names of the sectors: Restaurant, Hotel, Tour Agency, Resort, Theme Park, and Airlines, which will become the rows of the table. Then referencing {the legend texts}[[0.443, 0.052, 0.487, 0.071], [0.443, 0.005, 0.472, 0.024]], we find the column headers: 2019 and 2020. The values are found by locating and converting {the heights of the respective bars}[[0.141, 0.126], [0.208, 0.329], ..., [0.928, 0.527]] for each year and sector.\nBased on the above thought, the answer is:\nSector,2019,2020\nRestaurant,600,400\nHotel,500,300\nTour Agency,700,300\nResort,550,450\nTheme Park,720,600\nAirlines,800,780" } ], "id_raw": 222 }, { "id": "oymflZkD0dgRK2fa", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_2.png", "conversations": [ { "from": "human", "value": "\nWhich sector experienced the least decrease in revenue from 2019 to 2020, and why could this be the case?" }, { "from": "gpt", "value": "To determine this, I need to compare the amount of decrease in revenue for each sector by comparing the bars representing 2019 and 2020. By observing the bars, I see that {the Airlines sector}[[0.810, 0.360, 0.895, 0.531]], represented by {the last pair of bars on the chart}[[0.810, 0.071, 0.923, 0.531]], had the least decrease in revenue from 2019 to 2020.\nBased on the above thought, the answer is:\nThe sector that experienced the least decrease in revenue from 2019 to 2020 was the Airlines sector. One possible reason could be that despite travel restrictions during the COVID-19 crisis, certain flights continued to operate for essential services and repatriation tasks, allowing the Airlines to still generate some revenue." } ], "id_raw": 222 }, { "id": "puuzO0bZjqQxCCry", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in revenue between the years 2019 and 2020 for the Tour Agency sector?" }, { "from": "gpt", "value": "To answer this question, I need to locate the revenue for Tour Agency in both 2019 and 2020. I locate the 2019 revenue by identifying {the left bar under the Tour Agency x-tick}[[0.378, 0.072, 0.440, 0.471]]. The height of this bar corresponds to a revenue of 700. Similarly, I find the 2020 revenue by locating {the right bar under the Tour Agency x-tick}[[0.385, 0.072, 0.447, 0.282]], which corresponds to a revenue of 300. The difference in revenue between 2019 and 2020 is therefore 700 - 300 = 400.\nBased on the above thought, the answer is:\n400." } ], "id_raw": 222 }, { "id": "AEcKd5FTmglYH60N", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_3.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data into a table format with Trends as columns and Social Aspects as rows?" }, { "from": "gpt", "value": "To generate the table data from the line chart, I begin by looking at {the x-axis labels}[[0.12,0.89,0.18,0.92], [0.25,0.89,0.32,0.92], [0.39,0.89,0.46,0.92], [0.53,0.89,0.60,0.92], [0.67,0.89,0.74,0.92], [0.81,0.89,0.88,0.92]], which represent the Trends. I then identify {the line legends}[[0.08,0.95,0.17,0.99], [0.23,0.95,0.31,0.99]] as the Social Aspects. For the values, I locate the {line points}[[0.14,0.17], [0.27,0.12], [0.41,0.13], [0.55,0.09], [0.69,0.11], [0.83,0.08], [0.15,0.67], [0.28,0.84], [0.42,0.64], [0.56,0.57], [0.70,0.62], [0.84,0.60]] for each social aspect at each trend, and extract the corresponding values from the y-axis labels.\nBased on the above thought, the answer is:\nSocial Aspect,Gradual Recovery,Dramatic Surge,Moderate Growth,Sharp Decrease,Insignificant Increase,Downward Trend\nEducation,2,1,1.5,0.5,1,0\nHealthcare,5,7,4.5,4,4.5,4" } ], "id_raw": 223 }, { "id": "iA1e14gbb9ZRvXZS", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_3.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which social aspect has seen the most dramatic surge? If any, explain your reasoning." }, { "from": "gpt", "value": "To determine which social aspect has witnessed the most dramatic surge, I need to identify the {Dramatic Surge point for Education}[0.27,0.12] and {Healthcare}[0.28,0.84]. Upon comparison, the y-coordinate (which corresponds to the value on the y-axis) for the Dramatic Surge point in Healthcare is evidently higher than that of Education. This signifies that Healthcare has seen a more dramatic surge compared to Education.\nBased on the above thought, the answer is:\nHealthcare has witnessed the most dramatic surge according to the line chart. This conclusion is derived from the comparison of the 'Dramatic Surge' points for both Education and Healthcare. The value for Healthcare is significantly higher, indicating a bigger leap or dramatic surge in this aspect." } ], "id_raw": 223 }, { "id": "wiGzkGLMQhLklDDa", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest recorded value for a trend in Healthcare?" }, { "from": "gpt", "value": "To find the highest value in Healthcare, I need to look at the {y-coordinates of Healthcare line points}[[0.15,0.67], [0.28,0.84], [0.42,0.64], [0.56,0.57], [0.70,0.62], [0.84,0.60]]. The y-coordinate with the highest value corresponds to the highest point on the line, which is 0.84. This point corresponds to {Dramatic Surge}[0.28,0.89]. I then find the corresponding value on the y-axis, which is 7.\nBased on the above thought, the answer is:\n7." } ], "id_raw": 223 }, { "id": "QY603iWMOIlo3Pvi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_4.png", "conversations": [ { "from": "human", "value": "\nCould you please extract the given chart data into a table format with columns representing Months and rows signifying 'Active Users' and 'Learning Hours'?" }, { "from": "gpt", "value": "To create a table from the data in the line chart, I first observe {the x-axis tick labels}[[0.258, 0.879, 0.273, 0.914], [0.393, 0.879, 0.407, 0.914], ... , [0.923, 0.879, 0.953, 0.914]] which denote the months. Then, I identify {the legend texts}[[0.247, 0.130, 0.390, 0.165], [0.247, 0.182, 0.423, 0.217]] as the row labels which stand for Active Users and Learning hours respectively. For values, I look at {the line points} for each category at the corresponding months such as [[0.198, 0.591], [0.266, 0.699], ..., [0.938, 0.160]] for Active users and [[0.198, 0.699], [0.266, 0.780], ..., [0.938, 0.402]] for Learning Hours and extract the values.\nBased on the above thought, the answer is:\nMonth,Active Users,Learning Hours\n1,127000,123000\n2,123000,120000\n3,125000,119500\n4,133000,124200\n5,130500,125000\n6,136000,130000\n7,142000,132000\n8,141000,131000\n9,140000,129000\n10,137000,128500\n11,139000,132000\n12,143000,134000" } ], "id_raw": 224 }, { "id": "16oJiC8arnypPaba", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_4.png", "conversations": [ { "from": "human", "value": "\nBased on the trends indicated in the line chart, which parameter (Active Users or Learning Hours) displays better growth over the 12-month period and why?" }, { "from": "gpt", "value": "To pinpoint which parameter displays better growth, I have to scrutinize the fluctuations in the line plots for 'Active Users' and 'Learning Hours'. Looking at {the line plot for Active Users}[0.198, 0.160, 0.938, 0.699], I can observe a fairly sustained growth in user base from 127000 to 143000. Inspecting {the line plot for Learning Hours} [0.198, 0.402, 0.938, 0.793], the active learning hours seemed to initially rise but started to display a decreasing trend towards the end of the period, fluctuating between 119500 and 134000 hours.\nBased on the above thought, the answer is:\nAccording to the trends shown in the line chart, the 'Active Users' displays better growth over the 12-month period. This is because the line plot for 'Active Users' shows a consistent upward trajectory from 127000 to 143000. On the other hand, 'Learning Hours', though initially increased, starts to decline in the later months, indicating a less steady growth. Hence, 'Active Users' displays a more stable and consistent growth over time." } ], "id_raw": 224 }, { "id": "2ZdUiT0GKptg96Ua", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the average number of Active Users over the 12-month period?" }, { "from": "gpt", "value": "To calculate the average number of active users over the 12-month period, I need to locate {the line points of Active Users}[[0.198, 0.591], [0.266, 0.699], ..., [0.938, 0.160]] and extract the corresponding values for each month: 127000, 123000, 125000, 133000, 130500, 136000, 142000, 141000, 140000, 137000, 139000, 143000. The average is then calculated as the sum of these values divided by the number of months.\nBased on the above thought, the answer is:\n135375." } ], "id_raw": 224 }, { "id": "0tLfo1LKHbEWMnh1", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_5.png", "conversations": [ { "from": "human", "value": "\nCan you organize the data from the line chart into a table showing average income and average age for Urban, Suburban, and Rural areas?" }, { "from": "gpt", "value": "To transform the data into a table, I start by identifying {the labels on the x-axis}[0.132, 0.098, 0.975, 0.854] which list 'Urban', 'Suburban', and 'Rural'. Then, I map these labels to the corresponding data points {on both y-axes}[0.132, 0.098, 0.975, 0.854] to extract values for average income and age. The left y-axis denotes income and the right one denotes age.\nBased on the above thought, the answer is:\nArea,Average Income,Average Age\nUrban,45000,27\nSuburban,55000,35\nRural,35000,45" } ], "id_raw": 225 }, { "id": "Vmo2fG8yZMEuyyFV", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_5.png", "conversations": [ { "from": "human", "value": "\nWhich area shows a mismatches trend between age and income as per the chart?" }, { "from": "gpt", "value": "Observing {the line graph}[0.132, 0.098, 0.975, 0.854] closely, we notice {the line for Suburban}[0.420, 0.620] where despite an average age of 35, which is middle in the provided scale, the income is the highest at 55000. This is in contrast to the trend observed in {Urban}[0.210, 0.720] and {Rural areas}[0.630, 0.770] where younger age correlates with higher income and older age correlates with lower income.\nBased on the above thought, the answer is:\nSuburban area shows a mismatch trend. Though it doesn't have the youngest or the oldest average age, it exhibits the highest income, contradicting the observed trend where income usually decreases as age increases." } ], "id_raw": 225 }, { "id": "3dFzeAD60Ebomhoo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the total combined average income from all the areas shown on the chart?" }, { "from": "gpt", "value": "To find the total average income, I sum the incomes as marked {for Urban, Suburban, and Rural areas}[0.210, 0.720], [0.420, 0.620], [0.630, 0.770]. These coordinate-based points have the income values 45000 for Urban, 55000 for Suburban, and 35000 for Rural.\nBased on the above thought, the answer is:\n135000" } ], "id_raw": 225 }, { "id": "aL8fLoslgE1Y1G1R", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_8.png", "conversations": [ { "from": "human", "value": "\nCan you produce a structured table able to show the demographic influx for each area according to the line plot in the chart?" }, { "from": "gpt", "value": "To create a tabular representation of data from a line plot, I initially identify {the x-axis labels}[0.094, 0.919, 0.976, 0.942], which represent areas, as table rows. Next, the demographic influx, marked by {the y-axis labels}[0.043, 0.040, 0.071, 0.933], is interpreted as column headers. For the values in the table cells, I can locate {the line plot}[0.124, 0.104, 0.940, 0.865] corresponding to the area on the x-axis and y-axis. Based on the x and y plot coordinates, we can interpolate the demographic influx in corresponding areas.\nBased on the above thought, the answer is:\nArea,Demographic Influx\nArea_1,0.7\nArea_2,0.65\nArea_3,0.85\nArea_4,0.75\nArea_5,0.9\nArea_6,0.95\nArea_7,0.85\nArea_8,0.8\nArea_9,0.76\nArea_10,1.01\nArea_11,1.05\nArea_12,0.99\nArea_13,0.94\nArea_14,0.97\nArea_15,0.93\nArea_16,1.09\nArea_17,1.03\nArea_18,1.11\nArea_19,1.06\nArea_20,1.15\nArea_21,1.1\nArea_22,1.2\nArea_23,1.18\nArea_24,1.23\nArea_25,1.22\nArea_26,1.15\nArea_27,1.3\nArea_28,1.35" } ], "id_raw": 228 }, { "id": "DO7KxTIzbervVwkL", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_8.png", "conversations": [ { "from": "human", "value": "\nCan you analyze and provide interpretation based on the line plot, about demographic influx across local areas?" }, { "from": "gpt", "value": "The {line plot}[0.124, 0.104, 0.940, 0.865] represents the demographic influx across different local government areas. The line plot shows an increasing trend, indicating that the demographic influx is generally increasing from Area_1 to Area_28. The gradual increase from {Area_1}[0.124, 0.810] to {Area_9}[0.366, 0.745] indicates a steady growth of population, however, a sudden increase is observed between {Area_10}[0.396, 0.473] and {Area_12}[0.456, 0.495]. After this point, the demographic influx seems to grow steadily again, then more steeply towards {Area_28}[0.940, 0.104]. This could suggest that areas from Area_10 to Area_28 have witnessed a significant increase in population due to migration or other factors, and the rate continues to grow at a steeper pace as we move from Area_20 onwards.\nBased on the above thought, the answer is:\nOverall, the line plot indicates an increasing trend of demographic influx across different local government areas from Area_1 to Area_28. An initial steady rise is observed from Area_1 to Area_9. There is then a significant surge around Area_10 to Area_12, followed by a steeper pace of influx increase from Area_20 onwards. This pattern suggests that later areas, particularly from Area_10 to Area_28, may have had significant population increase events, such as migration surges, leading to a higher rate of demographic influx." } ], "id_raw": 228 }, { "id": "yZuwQr1hTrVbyQp3", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_8.png", "conversations": [ { "from": "human", "value": "\nBased on the graph, what is the highest demographic influx recorded, and in which area did it occur?" }, { "from": "gpt", "value": "To determine the highest demographic influx, I should scan {the line plot}[0.124, 0.104, 0.940, 0.865] and locate the point with the maximum y-coordinate. The point with the maximum y-coordinate is at {Area_28}[0.940, 0.104], which corresponds to an influx value of 1.35. The area associated with this point is read from the x-axis label at the same x-coordinate.\nBased on the above thought, the answer is:\nThe highest demographic influx recorded is 1.35, and it occurred in Area_28." } ], "id_raw": 228 }, { "id": "AjsSnrdllrZZa7MG", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_9.png", "conversations": [ { "from": "human", "value": "\nCould you convert the line chart data of energy consumption into a table where the rows represent the types of energy and the columns demonstrate the relevant week's consumption?" }, { "from": "gpt", "value": "Sure, firstly let's locate the timeline of weeks in the {x-axis ticks}[[0.128, 0.879, 0.101, 0.914], [0.263, 0.879, 0.278, 0.914], ..., [0.956, 0.879, 0.985, 0.914]], which details the weeks. Secondly, the types of energy sources, Green Energy and Non-Renewable energy can be found via the {legend entries}[[0.675, 0.736, 0.833, 0.771], [0.675, 0.788, 0.944, 0.823]]. The consumption of Green Energy is listed in the points along {the Green Energy line}[[0.128, 0.820], [0.163, 0.789], ..., [0.925, 0.165]] while the consumption data for Non-Renewable Energy can be extracted from the points in {the Non-Renewable Energy line}[[0.128, 0.133], [0.163, 0.176], ..., [0.925, 0.693]].\nBased on the above thought, the answer is:\nWeek,Green Energy,Non-Renewable Energy\n1,2.12,8.5\n2,2.41,8.1\n3,2.75,7.9\n4,2.89,7.5\n5,3.02,7.3\n6,3.45,7.1\n7,3.9,6.85\n8,4.5,6.5\n9,4.75,6.25\n10,4.9,6.0\n11,5.12,5.8\n12,5.3,5.6\n13,5.5,5.4\n14,5.8,5.15\n15,6.1,5.0\n16,6.32,4.85\n17,6.65,4.7\n18,6.89,4.5\n19,7.1,4.3\n20,7.32,4.05\n21,7.5,3.9\n22,7.8,3.7\n23,8.0,3.5\n24,8.2,3.3" } ], "id_raw": 229 }, { "id": "jzhpWRdRsBeSV6IM", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_9.png", "conversations": [ { "from": "human", "value": "\nIn looking at the trends over the 24 weeks, which energy source has seen a more stable pattern of consumption? Please explain your reasoning." }, { "from": "gpt", "value": "To answer this question, I need to review the fluctuations in the line plots for both energy sources. {The Green Energy line}[0.128, 0.820, 0.925, 0.165] shows a consistent increase in consumption levels over the 24 weeks, suggesting a more stable and predictable trend. In contrast, {the Non-Renewable Energy line}[0.128, 0.133, 0.925, 0.693] shows a gradual but consistent decrease over the same period. Though both show trends, Green Energy shows an increasing pattern which is usually associated with stability in the context of energy consumption.\nBased on the above thought, the answer is:\nGreen Energy seems to display a more stable pattern of consumption over the 24 weeks. It indicates a consistent growth trend as opposed to Non-Renewable Energy, which has a decreasing pattern. The consistent growth in consumption of Green Energy shows stability and predictability which is often viewed as a positive aspect in energy demand patterns." } ], "id_raw": 229 }, { "id": "2IST6EfqbIlWQUdx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in the consumption levels of Green Energy if comparison is made from the start (Week 1) to the end (Week 24)?" }, { "from": "gpt", "value": "To answer this, I need to fetch the consumption value of Green Energy from Week 1 and Week 24. These two points are located at {the starting point of the line} [0.128, 0.820] and {the ending point of the line}[0.925, 0.165]. The consumption level of Green Energy at Week 1 is 2.12 units and the level at Week 24 is 8.2 units. Thus, the change in Green Energy consumption is the figure at Week 24 subtracted by the value at Week 1: 8.2 - 2.12.\nBased on the above thought, the answer is:\n6.08 units." } ], "id_raw": 229 }, { "id": "Xt3itsiMVvtejRX2", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_10.png", "conversations": [ { "from": "human", "value": "\nCan you structure the data of Biotech Patents and New Drugs Development over time into a table?" }, { "from": "gpt", "value": "To convert the data displayed on the line chart into a table, I first note the respective {series from the legend}[0.129, 0.116, 0.502, 0.231], which will serve as row names. I then check {the x-axis}[0.118, 0.854] which represents the Time and will be used as column headers. Finally, I look at the y-values coded in the {coordinates of the points on the line plots}[0.157, 0.183, 0.936, 0.404] and [0.157, 0.529, 0.936, 0.805] to populate the table cell values. The y-values indicate the counts for the corresponding series at each time step.\nBased on the above thought, the answer is:\nTime,Biotech_Patents,New_Drugs_Development\n1,100,10\n2,95,12\n3,102,20\n4,106,23\n5,120,19\n6,113,16\n7,111,29\n8,100,16\n9,94,28\n10,90,15\n11,98,35\n12,104,36\n13,113,42\n14,110,37\n15,104,32\n16,112,33\n17,95,22\n18,97,31\n19,92,27\n20,110,40\n21,115,41\n22,122,40\n23,108,42\n24,121,34\n25,125,48\n26,134,57\n27,122,53\n28,130,65" } ], "id_raw": 230 }, { "id": "nL1NLQwsdlshMmZy", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_10.png", "conversations": [ { "from": "human", "value": "\nCan you tell which industry, Biotech Patents or New Drug Development, showed a more stable growth trend over time?" }, { "from": "gpt", "value": "To determine relative stability in growth trends, I analyze the line plots for each industry. Looking at {the line for Biotech Patents}[0.157, 0.183, 0.936, 0.404], we find the line oscillating around values without a clear increasing or decreasing trend. In contrast, if we consider {the line for New Drugs Development}[0.157, 0.529, 0.936, 0.805], there is a noticeable upward trend indicating growth, albeit with some fluctuations.\nBased on the above thought, the answer is:\nThe New Drugs Development industry showed a more stable growth trend over time. Despite some fluctuations, the line plot for New Drugs Development indicates a general upward trend, suggesting consistent growth over time, whereas the trend for Biotech Patents was more volatile and did not show a clear pattern of growth or decline." } ], "id_raw": 230 }, { "id": "d9nQ16B6XT2YHeFV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest point in the New Drugs Development series?" }, { "from": "gpt", "value": "To answer this question, I spot the {highest point for New Drugs Development}[0.936, 0.529] which represents 65. Then, I locate the {lowest point for New Drugs Development}[0.157, 0.805] which stands for 10. By subtracting the lowest value from the highest, we find the difference.\nBased on the above thought, the answer is:\n55." } ], "id_raw": 230 }, { "id": "uCEW1kBRn8vTw11r", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_11.png", "conversations": [ { "from": "human", "value": "\nCould you present the numbers regarding 'Free Textbook Distribution' and 'eLibrary Subscriptions' in a tabular format?" }, { "from": "gpt", "value": "According to the provided information, each point on the line graphs represents a week and the corresponding value on the y-axis. By looking at the points for {Free Textbook Distribution}[0.121, 0.829, 0.944, 0.862] and {eLibrary Subscriptions}[0.121, 0.109, 0.944, 0.413] and correlating them with the y-axis values, I can compile the table.\nBased on the above thought, the answer is:\nWeeks,Free Textbook Dist.,eLibrary Subscriptions\n1,500,2000\n2,495,2010\n3,505,1980\n4,490,2030\n...\n27,400,3000\n28,395,3050" } ], "id_raw": 231 }, { "id": "XwF2FywJM2govLe8", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_11.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which service seems to have a higher rate of decline?" }, { "from": "gpt", "value": "To answer this question, I need to compare the decline in 'Free Textbook Distribution' trend line {trend line for 'Free Textbook Distribution'}[0.121, 0.829, 0.944, 0.862] and the 'eLibrary Subscriptions' trend line {trend line for 'eLibrary Subscriptions'}[0.121, 0.109, 0.944, 0.413]. I need to look at the change in values from the start to the end of each line. 'Free Textbook Distribution' starts at 500 and ends at 395 which is a decrease of 105, while 'eLibrary Subscriptions' starts at a higher value of 2000 and ends at an even higher value of 3050, which is an increase of 1050.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, 'Free Textbook Distribution' has a higher rate of decline. Even though 'eLibrary Subscriptions' starts at a higher value, it has not declined but actually increased over the weeks, compared to 'Free Textbook Distribution' which decreased throughout." } ], "id_raw": 231 }, { "id": "CK6UNqQUbw5c13Od", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest points of 'eLibrary Subscriptions'?" }, { "from": "gpt", "value": "To calculate the difference between the highest and lowest points of 'eLibrary Subscriptions', I need to locate {the line points for 'eLibrary Subscriptions'}[0.121, 0.109, 0.944, 0.413] and look at the maximum and minimum y-values, which correspond to 3050 and 1980, respectively. The difference between these two values is 3050 - 1980.\nBased on the above thought, the answer is:\n1070" } ], "id_raw": 231 }, { "id": "tJSwxt5KuIVZX1dr", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_13.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data from the line chart and represent them in a table, with years as columns and each row corresponds to an Industry and the type of Marketing?" }, { "from": "gpt", "value": "To construct the table, I first look at {the x-axis tick labels}[[0.076, 0.879, 0.156, 0.914], [0.178, 0.879, 0.258, 0.914],..., [0.993, 0.879, 1.073, 0.914]], which represent the column headers or Years. Next, I identify {the legend texts}[[0.203, 0.130, 0.426, 0.165], [0.203, 0.182, 0.397, 0.217]] as the row labels representing different Marketing types in the Industry. To populate the values, I locate {the line points for each Industry}[[0.156, 0.256, 0.931, 0.804],[0.156, 0.148, 0.931, 0.766]] for each marketing trend at the respective years and extract the corresponding values.\nBased on the above thought, the answer is:\nYear,Startup Foundation, Innovation Index\n2001,55,60\n2002,70,72\n2003,65,71\n2004,67,75\n2005,90,94\n...\n2018,120,130\n2019,123,135\n2020,126,140" } ], "id_raw": 233 }, { "id": "FjmkFSR8AJ72dPLe", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_13.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which line shows broader fluctuations over the years, and what could this mean in terms of Marketing trends?" }, { "from": "gpt", "value": "The fluctuations in the lines can be deduced from {the line points}[[0.156, 0.256, 0.931, 0.804],[0.156, 0.148, 0.931, 0.766]]. The 'Startup Foundation' values vary between a high of 128(point_coordinates': '[0.931, 0.256]') and a low of 55(point_coordinates': '[0.156, 0.804]'). While for the 'Innovation Index' the high and low values lie at 140(point_coordinates': '[0.931, 0.148]') and 60(point_coordinates': '[0.156, 0.766]'). This might imply the volatility and uncertainty in the marketing strategy.\nBased on the above thought, the answer is:\nThe innovations index shows broader fluctuations over the years, as indicated by the larger difference between its maximum and minimum values. This could be interpreted as the Innovation Index experiencing more volatility in its market share. This greater degree of fluctuation could suggest that the effectiveness of innovation as a marketing strategy is more dependent on external factors or that it is a more risky strategy that can lead to variable results." } ], "id_raw": 233 }, { "id": "gNBuiuFdi18WChkE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in average values between the Startup Foundation and the Innovation Index over the years?" }, { "from": "gpt", "value": "The average of Startup Foundation and Innovation Index values can be calculated by summing the values from {the line points of Startup Foundation}[[0.156, 0.804], ..., [0.931, 0.256]] and {the line points of Innovation Index}[[0.156, 0.766], ..., [0.931, 0.148]] respectively, and then dividing by the total number of time points. The difference between these averages is then calculated.\nBased on the above thought, the answer is:\n8.5" } ], "id_raw": 233 }, { "id": "8s4XukgsyxVIgggg", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information presented in the line chart into a tabular representation showing Index Types as columns and Years as rows?" }, { "from": "gpt", "value": "To create a structured table, I first focus on {the x-axis tick labels}[0.102, 0.919, 0.146, 0.942], [0.238, 0.919, 0.282, 0.942], [0.374, 0.919, 0.418, 0.942], [0.510, 0.919, 0.554, 0.942], [0.646, 0.919, 0.690, 0.942], [0.782, 0.919, 0.826, 0.942], [0.919, 0.919, 0.962, 0.942] to determine the years which will form the rows of the table. Each year marks the start of a new row. Next, I look at {the legend}[0.745, 0.086, 0.855, 0.110], [0.745, 0.121, 0.966, 0.145] to identify the Index Types, which will be the columns of the table labeled as \"Health Index\" and \"Living Environment Index\". Lastly, using the line points, I extract the values for each index type across the respective years to populate the table.\nBased on the above thought, the answer is:\nYear,Health Index,Living Environment Index\n1990,0.65,0.71\n1991,0.66,0.73\n1992,0.67,0.75\n1993,0.68,0.77\n1994,0.69,0.79\n1995,0.71,0.81\n1996,0.72,0.82\n1997,0.73,0.82\n1998,0.75,0.83\n1999,0.76,0.85\n2000,0.76,0.86\n2001,0.77,0.86\n2002,0.78,0.87\n2003,0.79,0.88\n2004,0.80,0.89\n2005,0.81,0.90\n2006,0.82,0.91\n2007,0.83,0.92\n2008,0.84,0.92\n2009,0.85,0.93\n2010,0.85,0.93\n2011,0.86,0.94\n2012,0.87,0.94\n2013,0.88,0.95\n2014,0.89,0.95\n2015,0.90,0.96\n2016,0.91,0.96\n2017,0.92,0.97\n2018,0.93,0.98\n2019,0.94,0.98\n2020,0.94,0.99" } ], "id_raw": 234 }, { "id": "tFgCEB9K3fipgwaq", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_14.png", "conversations": [ { "from": "human", "value": "\nHow do the trends in Health Index and Living Environment Index compare over the period 1990 to 2020, and what might this indicate about public health and living conditions?" }, { "from": "gpt", "value": "Observing the trends within the line chart, I note significant details using {the line for Health Index}[0.124, 0.315, 0.940, 0.724] and {the line for Living Environment Index}[0.124, 0.245, 0.940, 0.639]. Both indices show a steady increase over the 30-year period, indicating improvements in health and living conditions. Specifically, the Health Index starts at 0.65 in 1990, reaching 0.94 in 2020, and the Living Environment Index starts at 0.71, reaching 0.99 in 2020. This could suggest a continuous improvement in public health and environmental conditions over these years, reflecting possibly better healthcare services and living standards.\nBased on the above thought, the answer is:\nBoth the Health Index and Living Environment Index depict a consistent upward trend from 1990 to 2020, indicating steady improvements in public health and living conditions over the years. The sustained increase in these indices suggests enhancements in healthcare services and environmental policies that benefit the general populace's well-being and quality of life." } ], "id_raw": 234 }, { "id": "OilXbWTUoU0l2G1I", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in the Health Index from 1990 to 2020?" }, { "from": "gpt", "value": "To calculate the increase in the Health Index from the year 1990 to 2020, I start by examining the coordinates for the Health Index values at these specific years. Using {the point coordinates for the Health Index}[0.124, 0.724], [0.940, 0.315] from the line chart, I extract the Health Index values for the years 1990 and 2020, which are 0.65 and 0.94 respectively. The increase is calculated by subtracting the 1990 value from the 2020 value, resulting in 0.94 - 0.65.\nBased on the above thought, the answer is:\n0.29" } ], "id_raw": 234 }, { "id": "TtQ8ds5Nf0niPp71", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_15.png", "conversations": [ { "from": "human", "value": "\nCan you structure the data from the line chart into a table with columns representing years and rows representing the different types of energy sources?" }, { "from": "gpt", "value": "To build this table, first I need to identify {the x-axis tick labels}[0.164, 0.879, 0.936, 0.914] as the column headers representing years. The labels in {the legend}[0.211, 0.130, 0.548, 0.217] will be used as row headers, which are different types of energy sources; namely, Innovation and Technology Entrepreneurship. The values in the cells will be the actual data points, which can be derived from the intersections of the lines marked by {the points on the Innovation line}[0.164, 0.803, 0.936, 0.820] and {the points on the Technology Entrepreneurship line}[0.164, 0.133, 0.936, 0.764].\nBased on the above thought, the answer is:\nYear,Innovation,Technology Entrepreneurship\n1,0.81,1.04\n2,1.02,0.92\n3,1.24,1.83\n4,1.18,1.74\n5,1.05,1.63\n6,0.93,1.92\n7,0.85,2.11\n8,0.79,2.04\n9,0.76,1.96\n10,0.82,2.03\n11,0.94,1.97\n12,0.85,1.88\n13,0.78,1.93\n14,0.96,1.95\n15,0.94,2.01\n16,0.92,2.05\n17,0.99,2.22\n18,1.05,2.13\n19,1.00,2.11\n20,1.06,2.17\n21,1.12,2.25\n22,1.15,2.31\n23,1.22,2.34\n24,1.27,2.47\n25,1.32,2.61\n26,1.28,2.59\n27,1.36,2.66\n28,1.42,2.73" } ], "id_raw": 235 }, { "id": "ufKnyeDPAJryTy4w", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_15.png", "conversations": [ { "from": "human", "value": "\nHow do the trends of Innovation and Technology Entrepreneurship compare over the years?" }, { "from": "gpt", "value": "To analyze the trends of both energy sources, I need to observe {the line representing Innovation}[0.164, 0.590, 0.936, 0.820] and {the line representing Technology Entrepreneurship}[0.164, 0.133, 0.936, 0.764]. The trend line for Innovation shows mild fluctuation and a steady increase over the years. In contrast, the trend line for Technology Entrepreneurship shows a significant upward trajectory, indicating a considerable increase over time.\nBased on the above thought, the answer is:\nInnovation has seen a steady increase over the years with mild fluctuations, while Technology Entrepreneurship has shown a significant and more pronounced upward trend. This indicates that while both energy sources have increased over time, the rise in Technology Entrepreneurship has been much more dramatic in comparison to Innovation." } ], "id_raw": 235 }, { "id": "pbkx6QUJ5smS3y9e", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_15.png", "conversations": [ { "from": "human", "value": "\nWhat's the absolute change in Innovation from the first to the last week?" }, { "from": "gpt", "value": "To compute the absolute change in Innovation from the first to the last week, I locate {the first point on the Innovation line}[0.164, 0.803] at which the value is 0.81, and {the last point on the Innovation line}[0.936, 0.820] at which the value is 1.42. The difference or change over time is 1.42 - 0.81 = 0.61.\nBased on the above thought, the answer is:\n0.61." } ], "id_raw": 235 }, { "id": "HMPqoaWkFiQoBlfX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_16.png", "conversations": [ { "from": "human", "value": "\nCould you present the weekly changes in Overall Index and Air Quality Index as observed in the line chart in a tabular form?" }, { "from": "gpt", "value": "To generate the table, first, I identify the x-axis labels, {the x-axis tick labels}[[-0.033, 0.879, 0.001, 0.914], [0.121, 0.879, 0.135, 0.914], [0.265, 0.879, 0.279, 0.914], [0.402, 0.879, 0.431, 0.914], [0.546, 0.879, 0.575, 0.914], [0.691, 0.879, 0.720, 0.914], [0.835, 0.879, 0.864, 0.914], [0.979, 0.879, 1.008, 0.914]], as the weeks ranging from 1 to 28. Next, I can identify {the legend texts}[0.203, 0.736, 0.357, 0.771], [0.203, 0.788, 0.396, 0.823] as the row headers representing the two indices. The values for each index at each week are located at {the line points for Overall Index}[0.157, 0.222], [0.186, 0.235], ..., [0.936, 0.794] and {the line points for Air Quality Index}[0.157, 0.349], [0.186, 0.336], ..., [0.936, 0.158] then extracting the corresponding values.\nBased on the above thought, the answer is:\nWeeks,Overall Index,Air Quality Index\n1,100,90\n2,99,91\n3,95,91.5\n4,90,94\n5,88,95\n6,87,96\n7,86,97\n8,85,97.5\n9,85,98\n10,83,99\n11,80,98.5\n12,78,100\n13,75,101\n14,72,102\n15,70,102.5\n16,68,103\n17,65,103.5\n18,60,104\n19,58,104.5\n20,55,105\n21,55,105\n22,55,105\n23,55,105\n24,55,105\n25,55,105\n26,55,105\n27,55,105\n28,55,105" } ], "id_raw": 236 }, { "id": "rRDVCNSHw5Hm1hXV", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_16.png", "conversations": [ { "from": "human", "value": "\nBased on the trends in the line chart, how does the Air Quality Index change compared to the Overall Index over the 28 weeks period?" }, { "from": "gpt", "value": "To compare the trends, I first observe the {line for Overall Index}[0.157, 0.222, 0.936, 0.794] which noticeably shows a steady decrease from 100 down to 55 over the 28 weeks. On the other hand, the {line for Air Quality Index}[0.157, 0.349, 0.936, 0.158] displays a steady increase across the same time frame, starting from 90 and reaching 105 by the 28th week. While the Overall Index shows a decreasing trend, the Air Quality Index shows an increasing trend.\nBased on the above thought, the answer is:\nOver the 28 weeks period, the Air Quality Index exhibits an opposite trend compared to the Overall Index. While the Overall Index decreases, signifying a degenerative change in human life aspects, the Air Quality Index increases, indicating an improvement in environmental conditions. This might imply that even though certain aspects of life might have deteriorated during this period, the air quality has improved, assuring a healthier living environment." } ], "id_raw": 236 }, { "id": "SdAcZnvWNIjLfGH4", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in the Overall Index from the 1st week to the 28th week?" }, { "from": "gpt", "value": "To find the difference in the Overall Index from the 1st week to the 28th week, I need to locate the y-values at the 1st and 28th weeks. I pinpoint the value for the 1st week from {the line point for Overall Index at the 1st week}[0.157, 0.222] as 100. The value for the 28th week is found from {the line point for Overall Index at the 28th week}[0.936, 0.794] as 55. So, the difference would be 100 - 55 = 45.\nBased on the above thought, the answer is:\n45" } ], "id_raw": 236 }, { "id": "tlaVcDQIhGjAMYpl", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_17.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from this line chart into a structured table format with Years as rows and the different technological trends as columns?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.086, 0.919, 0.130, 0.942], [0.184, 0.919, 0.227, 0.942], [0.259, 0.919, 0.303, 0.942], [0.335, 0.919, 0.379, 0.942], [0.410, 0.919, 0.454, 0.942], [0.486, 0.919, 0.530, 0.942], [0.562, 0.919, 0.605, 0.942], [0.637, 0.919, 0.681, 0.942], [0.713, 0.919, 0.757, 0.942], [0.788, 0.919, 0.833, 0.942], [0.864, 0.919, 0.908, 0.942], [0.940, 0.919, 0.984, 0.942]], which represent the row headers or years. Next, I identify {the legend texts}[[0.131, 0.086, 0.309, 0.110], [0.131, 0.121, 0.204, 0.145]] as the column headers representing the different technology types. To populate the values, I locate the {line points for Artificial Intelligence}[[0.108, 0.865], [0.184, 0.797], ..., [0.940, 0.104]] and {line points for Robotics}[[0.108, 0.848], [0.184, 0.831], ..., [0.940, 0.527]] for each year and extract the values annotated.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Robotics\n2010,0.5,0.6\n2011,0.9,0.7\n2012,1.5,1.0\n2013,2.2,1.2\n2014,2.9,1.4\n2015,3.5,1.7\n2016,3.2,1.9\n2017,3.6,2.1\n2018,4.2,2.2\n2019,4.4,2.3\n2020,4.5,2.3\n2021,5.0,2.5" } ], "id_raw": 237 }, { "id": "mQcyG1JIvw6wNYoF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_17.png", "conversations": [ { "from": "human", "value": "\nBased on the trends in the line chart, which technology sector, Artificial Intelligence or Robotics, demonstrated a more rapid development from 2010 to 2021?" }, { "from": "gpt", "value": "To discern which technology sector demonstrated more rapid development, we need to analyze the final point coordinates and compare their respective growth. Looking at the last point coordinates for {Artificial Intelligence}[0.940, 0.104], we see that it grew to the point of surpassing the 5.0 value on the development indicator. However, the final coordinates for {Robotics}[0.940, 0.527] shows that it only reached just over halfway to 3.0 on the scale, suggesting a slower pace of development compared to Artificial Intelligence.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, the Artificial Intelligence sector demonstrated a more rapid development from 2010 to 2021. It reached a higher point on the development indicator by 2021, indicating a higher growth rate over this period compared to Robotics." } ], "id_raw": 237 }, { "id": "czMPedFslvKPPZRP", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_17.png", "conversations": [ { "from": "human", "value": "\nBy how much did the development indicator for Artificial Intelligence increase from 2010 to 2021?" }, { "from": "gpt", "value": "To determine the increase in the development indicator for Artificial Intelligence from 2010 to 2021, we need to refer to the {point of Artificial Intelligence in 2010}[0.108, 0.865] which shows a value of 0.5 and the {point of Artificial Intelligence in 2021}[0.940, 0.104] which shows a value of 5.0. The difference between these two years is 5.0 - 0.5 = 4.5.\nBased on the above thought, the answer is:\n4.5." } ], "id_raw": 237 }, { "id": "Gxpx2FpKJvMGyjHZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_18.png", "conversations": [ { "from": "human", "value": "\nCan you extract and tabulate the observed popularity index for both Asian and European cuisines from the year 2000 to 2020?" }, { "from": "gpt", "value": "The first step to extract the data is by referring to {the x-axis tick labels}[[0.104, 0.919, 0.164, 0.942], [0.174, 0.919, 0.728, 0.942], [0.215, 0.919, 0.265, 0.942], [0.255, 0.919, 0.787, 0.942], [0.295, 0.919, 0.814, 0.942], [0.336, 0.919, 0.826, 0.942], [0.376, 0.919, 0.767, 0.942], [0.416, 0.919,0.681, 0.942], [0.457, 0.919, 0.662, 0.942], [0.497, 0.919, 0.650, 0.942], [0.537, 0.919, 0.545, 0.942], [0.578, 0.919, 0.474, 0.942], [0.618, 0.919, 0.416, 0.942], [0.658, 0.919, 0.377, 0.942], [0.699, 0.919, 0.318, 0.942], [0.739, 0.919, 0.299, 0.942],[0.780, 0.919, 0.357, 0.942],[0.820, 0.919, 0.377, 0.942],[0.860, 0.919, 0.396, 0.942],[0.901, 0.919, 0.299, 0.942],[0.941, 0.919, 0.143, 0.942]], which correspond to the years 2000 through 2020. \nNext, I locate {the line points for Asian Cuisine Trend}[[0.134, 0.689],[0.174, 0.728],[0.215, 0.767],[0.255, 0.787],[0.295, 0.814],[0.336, 0.826],[0.376, 0.767],[0.416, 0.681],[0.457, 0.662],[0.497, 0.650],[0.537, 0.545],[0.578, 0.474],[0.618, 0.416],[0.658, 0.377],[0.699, 0.318],[0.739, 0.299],[0.780, 0.357],[0.820, 0.377],[0.860, 0.396],[0.901, 0.299],[0.941, 0.143]] which correspond to the popularity index of Asian cuisine for each respective year.\nSimilarly, locating {the line points for European Cuisine Trend}[[0.134, 0.767],[0.174, 0.767],[0.215, 0.748],[0.255, 0.736],[0.295, 0.732],[0.336, 0.716],[0.376, 0.697],[0.416, 0.677],[0.457, 0.670],[0.497, 0.666],[0.537, 0.650],[0.578, 0.623],[0.618, 0.611],[0.658, 0.603],[0.699, 0.592],[0.739, 0.584],[0.780, 0.580],[0.820, 0.572],[0.860, 0.553],[0.901, 0.533],[0.941, 0.533]] allows me to obtain the popularity index for European cuisine.\nBased on the above thought, the answer is:\nYear,Asian Cuisine Trend,European Cuisine Trend\n2000,4.3,4.1\n2001,4.2,4.1\n2002,4.1,4.15\n2003,4.05,4.18\n2004,3.98,4.19\n2005,3.95,4.23\n2006,4.1,4.28\n2007,4.32,4.33\n2008,4.37,4.35\n2009,4.4,4.36\n2010,4.67,4.4\n2011,4.85,4.47\n2012,5.0,4.5\n2013,5.1,4.52\n2014,5.25,4.55\n2015,5.3,4.57\n2016,5.15,4.58\n2017,5.1,4.6\n2018,5.05,4.65\n2019,5.3,4.7\n2020,5.7,4.7" } ], "id_raw": 238 }, { "id": "Qk3A0JhchMDbf4z5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_18.png", "conversations": [ { "from": "human", "value": "\nBy analyzing the graph, what can you infer about the popularity trends of Asian and European cuisines from 2000 to 2020?" }, { "from": "gpt", "value": "To answer this, I need to seek the trajectory of the popularity index for both cuisines from 2000 to 2020. Looking at {the line for Asian Cuisine Trend} [0.134, 0.689, 0.941, 0.143], it shows a general increase in the popularity index, with some minor dips but a clear overall upward trend. This suggests Asian cuisine has become more popular over the time period. \nFor {European Cuisine Trend}[0.134, 0.767, 0.941, 0.533], the trend line shows a slow, steady increase with very slight variations, indicating a consistent growth in popularity for European cuisine.\nBased on the above thought, the answer is:\nThe graph suggests that both Asian and European cuisines have seen an increase in popularity from 2000 to 2020. While Asian cuisine's popularity index exhibits a more dramatic upward trend with some dips, European cuisine's popularity has had a steady, consistent growth. So, overall the popularity of both cuisines has increased over the time period despite some minor fluctuations." } ], "id_raw": 238 }, { "id": "c1tIySXV4cMVOqsD", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_18.png", "conversations": [ { "from": "human", "value": "\nBy how much has the popularity index for Asian cuisine increased from the year 2000 to 2020?" }, { "from": "gpt", "value": "To compute this, I need to find the initial and final values of the popularity index for Asian cuisine. Looking at {the point for the year 2000 in the Asian Cuisine trend line} [0.134, 0.689], the value is 4.3. And focusing on {the point for the year 2020 in the Asian Cuisine trend line} [0.941, 0.143], the value is 5.7. Thus, the increase in the popularity index for Asian cuisine from the year 2000 to 2020 is 5.7 - 4.3 = 1.4.\nBased on the above thought, the answer is:\n1.4" } ], "id_raw": 238 }, { "id": "aaPxKsBjIbO3ztBX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_12_19.png", "conversations": [ { "from": "human", "value": "\nCan you process the raw data into a csv table format with time (Week) as the first column and the different platforms (Facebook and Twitter) as the other columns?" }, { "from": "gpt", "value": "To generate a CSV table from the raw data, I identify the column headers as \"Weeks\", \"Facebook\", and \"Twitter\" from the given data. I then organize the raw information to match these categories. The respective values for the categories were taken from their corresponding coordinates in the chart provided.\nBased on the above thought, the answer is:\nWeeks,Facebook,Twitter\n1,20,18\n2,25,20\n3,30,22\n4,50,24\n5,55,30\n6,70,36\n7,75,38\n8,90,40\n9,100,42\n10,120,44\n11,132,52\n12,177,66\n13,200,70\n14,210,72\n15,220,74\n16,245,77\n17,250,78\n18,240,80\n19,220,82\n20,215,84\n21,200,85\n22,180,86\n23,170,88\n24,155,88\n25,130,87\n26,110,85\n27,100,85\n28,90,85" } ], "id_raw": 239 }, { "id": "4t6IlzY4zkjAdAGP", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_12_19.png", "conversations": [ { "from": "human", "value": "\nFrom the data, can we say that Twitter was consistently growing at a slower pace than Facebook?" }, { "from": "gpt", "value": "To answer this question, we need to evaluate the rate at which Facebook and Twitter gained users every week. Looking at {the line for Facebook}[0.157, 0.133, 0.936, 0.814], we see that it began at 20 million users and reached 90 million users by week 28. For {Twitter}[0.157, 0.613, 0.936, 0.820], the users started at 18 million and grew to 85 million by week 28. These trajectories suggest that while both Facebook and Twitter were growing over time, Facebook consistently added more users per week than Twitter.\nBased on the above thought, the answer is:\nYes, we can say that Twitter was consistently growing at a slower pace than Facebook. While both platforms showed substantial growth over the 28 week period, Facebook consistently added more users each week compared to Twitter." } ], "id_raw": 239 }, { "id": "fMVleBt5uqfhh5Ew", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_12_19.png", "conversations": [ { "from": "human", "value": "\nHow many more users did Facebook gain than Twitter over the given period?" }, { "from": "gpt", "value": "To compare the increase in users between Facebook and Twitter, we need to subtract the initial user counts at week 1 from the final user counts in week 28. Observing {the line points for Facebook}[0.157, 0.133, 0.936, 0.814] and {the line points for Twitter}[0.157, 0.613, 0.936, 0.820] to extract the relevant values, we find that the Facebook user count went from 20 to 90 million users and Twitter from 18 to 85 million users. We take the difference between these two increases to find the difference in user gain.\nBased on the above thought, the answer is:\n7 million users." } ], "id_raw": 239 }, { "id": "iswNfdUqBjEjFCIh", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_0.png", "conversations": [ { "from": "human", "value": "\nCan you reformat the bar chart data showing integration scores across different cultural heritage facets into a table with cultural heritage facets as rows and integration scores as columns?" }, { "from": "gpt", "value": "Firstly, I will look at {the x-axis tick labels}[[0.202, 0.798, 0.235, 0.816], [0.299, 0.798, 0.335, 0.816], [0.396, 0.798, 0.455, 0.816], [0.492, 0.798, 0.535, 0.816], [0.589, 0.798, 0.634, 0.816], [0.684, 0.798, 0.720, 0.816], [0.781, 0.798, 0.829, 0.816], [0.878, 0.798, 0.911, 0.816], [0.974, 0.798, 1.010, 0.816]] which are the row labels representing the different facets of cultural heritage. To populate the scores, I'll locate {Bar Heights} [[0.202, 0.653, 0.235, 0.817], [0.299, 0.679, 0.333, 0.817], [0.396, 0.503, 0.454, 0.817], [0.492, 0.689, 0.535, 0.817], [0.589, 0.540, 0.635, 0.817], [0.684, 0.746, 0.721, 0.817], [0.781, 0.633, 0.830, 0.817], [0.879, 0.722, 0.913, 0.817], [0.974, 0.794, 1.010, 0.817]] to extract the corresponding values based on the y-axis labels ranging from approximately 35 to 90.\nBased on the above thought, the answer is:\nFacet,Integration Score\nNational Museum,70\nHistoric Site,75\nTraditional Music,50\nCultural Festival,77\nTraditional Clothing,55\nLocal Cuisine,80\nIndigenous Art,68\nHistoric Houses,77\nCraft Market,85" } ], "id_raw": 240 }, { "id": "YFAoTyYYXfepidZ0", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_0.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which aspect of cultural heritage is least integrated into the wider culture?" }, { "from": "gpt", "value": "I can determine the least integrated aspect of cultural heritage by finding the shortest bar in the chart. In this case, the shortest bar is {Traditional Music}[[0.396, 0.503, 0.454, 0.817]], representing an integration score of 50, which is the lowest among all cultural heritage facets.\nBased on the above thought, the answer is:\nBased on the bar chart, Traditional Music is the aspect of cultural heritage that is least integrated into the wider culture, as it has the lowest integration score of 50." } ], "id_raw": 240 }, { "id": "g7NK4eFScLIBtEg0", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest cultural heritage integration scores by facets?" }, { "from": "gpt", "value": "I need to find the highest and lowest integration scores across the various facets. Here, I see that the tallest bar is {Craft Market}[[0.974, 0.794, 1.010, 0.817]] with a score of 85, and the shortest bar is {Traditional Music}[[0.396, 0.503, 0.454, 0.817]] with a score of 50. Hence, the difference between the highest and lowest scores is 85 - 50 = 35.\nBased on the above thought, the answer is:\n35." } ], "id_raw": 240 }, { "id": "XyTkQTJLW15CfThC", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_1.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data showing the development trends in the culture and arts industry into a structured table sorted by departments?" }, { "from": "gpt", "value": "To convert the chart data into a structured table, I'll first identify {the department labels on the x-axis}[0.105, 0.79, 0.921, 0.823] which will serve as the rows for our table, listing out cultural heritage, performing arts, visual arts, fashion culture, cuisine culture, literature, film, music, and architecture. The development trends 'Slow Recovery' and 'Inexorable Rise' would then be the columns, based on {the legend}[0.077, 0.053, 0.495, 0.168]. Observing the line visuals, I will detail the values from {the points on the lines}[0.105, 0.337, 0.921, 0.691] for each department and development trend to complete the table.\nBased on the above thought, the answer is:\nDepartment,Slow Recovery,Inexorable Rise\nCultural Heritage,50.6,67.0\nPerforming Arts,54.3,64.2\nVisual Arts,49.9,61.1\nFashion Culture,59.2,68.8\nCuisine Culture,52.1,65.5\nLiterature,48.8,63.8\nFilm,53.7,62.4\nMusic,60.8,60.0\nArchitecture,56.9,66.3" } ], "id_raw": 241 }, { "id": "FRcIdrpJENkLv4FU", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_1.png", "conversations": [ { "from": "human", "value": "\nWhich sector within the culture and arts industry shows the most significant difference between the 'Slow Recovery' and 'Inexorable Rise' trends according to the line chart?" }, { "from": "gpt", "value": "To find the sector with the most significant difference between 'Slow Recovery' and 'Inexorable Rise', I analyze the values at each {department point}[0.105, 0.337, 0.921, 0.691]. Observing the line plot, I look for the greatest distance between the two trend lines per department, specifically noting how the lines diverge at each sector. Identifying the most noticeably distant points, I determine the department with the largest gap, indicating the most significant difference.\nBased on the above thought, the answer is:\nFashion culture shows the most significant difference between the 'Slow Recovery' and 'Inexorable Rise' trends. It exhibits a divergence where the 'Inexorable Rise' value is notably higher than that of 'Slow Recovery'." } ], "id_raw": 241 }, { "id": "L4EQyrH9B5Z6tBjk", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_1.png", "conversations": [ { "from": "human", "value": "\nWhat has been the highest year-over-year increase in Wind Energy Consumption from the provided data?" }, { "from": "gpt", "value": "To determine the highest year-over-year increase, I'll assess {the line plot for Wind Energy Consumption}[0.105, 0.043, 0.921, 0.492] across the years. Then, I'll analyze the differences between the consecutive points from {year 1977}[0.105, 0.492] to {year 2019}[0.921, 0.043], and calculate the most significant year-over-year increase.\nBased on the above thought, the answer is:\n0.3" } ], "id_raw": 241 }, { "id": "arjz5Idf91LfjCwc", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_4.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data into a table format using Industry types as rows and Year as columns?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I'll start by examining the x-axis labels, aka {the x-axis tick labels}[[0.141, 0.906, 0.180, 0.930], [0.235, 0.906, 0.274, 0.930], [0.329, 0.906, 0.368, 0.930], [0.423, 0.906, 0.462, 0.930], [0.516, 0.906, 0.555, 0.930], [0.610, 0.906, 0.649, 0.930], [0.704, 0.906, 0.743, 0.930], [0.798, 0.906, 0.837, 0.930]], which will become the column headers representing the years from 2000 to 2015. Next, the legend, represented by {the legend texts}[[0.194, 0.145, 0.336, 0.175], [0.194, 0.189, 0.263, 0.219]] will form the row labels indicating the two industry types: Pharmaceutical and Biotech. Finally, to fill the table, I'll examine the line points for each industry types {Pharmaceutical}[[0.160, 0.235], [0.207, 0.477], [0.254, 0.396], [0.301, 0.316], [0.348, 0.235], [0.395, 0.557], [0.442, 0.598], [0.489, 0.598], [0.536, 0.638], [0.583, 0.718], [0.630, 0.155], [0.677, 0.195], [0.724, 0.235], [0.771, 0.276], [0.818, 0.316], [0.865, 0.356]] and {Biotech}[[0.160, 0.855], [0.207, 0.839], [0.254, 0.823], [0.301, 0.815], [0.348, 0.799], [0.395, 0.775], [0.442, 0.758], [0.489, 0.783], [0.536, 0.791], [0.583, 0.807], [0.630, 0.815], [0.677, 0.799], [0.724, 0.783], [0.771, 0.807], [0.818, 0.823], [0.865, 0.839]] at the corresponding years and extract the values.\nBased on the above thought, the answer is:\nYear,Pharmaceutical,Biotech\n2000,0.8,0.03\n2001,0.5,0.05\n2002,0.6,0.07\n2003,0.7,0.08\n2004,0.8,0.1\n2005,0.4,0.13\n2006,0.35,0.15\n2007,0.35,0.12\n2008,0.3,0.11\n2009,0.2,0.09\n2010,0.9,0.08\n2011,0.85,0.1\n2012,0.8,0.12\n2013,0.75,0.09\n2014,0.7,0.07\n2015,0.65,0.05" } ], "id_raw": 244 }, { "id": "lFqq3dU914JjUy4l", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_4.png", "conversations": [ { "from": "human", "value": "\nLooking at the trends presented on the line chart, which industry showcases a more consistent growth pattern? Explain your reasoning." }, { "from": "gpt", "value": "To identify which of the two, Pharmaceutical or Biotech, has a more consistent growth pattern, I'd need to consider the relative fluctuations in their line plots. Observing {the line for Pharmaceutical}[0.160, 0.155, 0.865, 0.718], the progression suggests multiple dramatic fluctuations. In contrast, examining {the line for Biotech} [0.160, 0.758, 0.865, 0.855], the overall trend depicts a more consistent increase in values over the years with relatively fewer fluctuations.\nBased on the above thought, the answer is:\nThe Biotech industry signifies a more consistent growth pattern based on the trends portrayed in the line chart. Unlike Pharmaceutical, this industry shows steady growth over time with smaller fluctuations, implying a more progressive and stable growth pattern." } ], "id_raw": 244 }, { "id": "pau13ecisNusKDIO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_4.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, what is the highest point of growth for the Pharmaceutical industry?" }, { "from": "gpt", "value": "To find the highest point of growth for the Pharmaceutical industry, I must locate {the line points for Pharmaceutical}[[0.160, 0.235], [0.207, 0.477], [0.254, 0.396], [0.301, 0.316], [0.348, 0.235], [0.395, 0.557], [0.442, 0.598], [0.489, 0.598], [0.536, 0.638], [0.583, 0.718], [0.630, 0.155], [0.677, 0.195], [0.724, 0.235], [0.771, 0.276], [0.818, 0.316], [0.865, 0.356]]. The values corresponding to the y-coordinates of these line points are the growth points: 0.8, 0.5, 0.6, 0.7, 0.8, 0.4, 0.35, 0.35, 0.3, 0.2, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65. The largest value is 0.9, and this is achieved in year {2010}[0.630, 0.906].\nBased on the above thought, the answer is:\nThe highest point of growth for the Pharmaceutical industry is 0.9 in the year 2010." } ], "id_raw": 244 }, { "id": "RYXFrZ9hGouxRs0K", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_5.png", "conversations": [ { "from": "human", "value": "\nCould you transform the data in the bar chart into tabular format with Disease as rows and Disease Management and Human Health as columns?" }, { "from": "gpt", "value": "Yes, I can transform the data into a table by considering the labels on the x-axis as the Diseases which correspond to {the x-axis labels}[0.081,0.078,0.175,0.099], [0.220,0.078,0.313,0.099], [0.357,0.078,0.454,0.099] and so on. These will form the rows of the table. Then, I will look at {the y-axis}[0.047, 0.119, 0.066, 0.911] for the height of each bar. The two kinds of bars for each disease represent 'Disease Management' and 'Human Health', which will be the columns of the table, and these could correspond to the colored bars with coordinates [0.073,0.132,0.110,0.653], [0.110,0.132,0.147,0.723] and so on, which contain their height and therefore their respective values.\nBased on the above thought, the answer is:\nDisease,Disease Management,Human Health\nType 1 Diabetes,77,79\nType 2 Diabetes,72,70\nCardiovascular,73,80\nRespiratory,68,85\nHepatitis,77,90\nCancer,82,75\nAlzheimer,75,72\nParkinson,78,67\nArthritis,79,85" } ], "id_raw": 245 }, { "id": "79iBhW4S81pY2UsY", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which disease has the largest gap between Disease Management and Human Health scores, making it difficult to manage?" }, { "from": "gpt", "value": "To determine which disease has the largest gap between Disease Management and Human Health scores, I need to analyze {the different bars for each disease}[0.073, 0.132, 0.110, 0.653], [0.220, 0.132, 0.258, 0.611], [0.365, 0.132, 0.402, 0.666] etc. By comparing the heights of the two bars belonging to each disease, it appears that Hepatitis has a large gap between its Disease Management and Human Health scores {Hepatitis bars}[0.511, 0.132, 0.548, 0.657], [0.548, 0.132, 0.586, 0.769]. Therefore, it seems to be the most difficult disease to manage out of those represented on the chart.\nBased on the above thought, the answer is:\nAccording to the chart, Hepatitis appears to have the largest gap between Disease Management and Human Health scores, suggesting that it may be the most difficult disease to manage effectively out of those listed." } ], "id_raw": 245 }, { "id": "mfIPoKJuNfsBUq7b", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the Disease Management and Human Health scores for Parkinson?" }, { "from": "gpt", "value": "To find the Disease Management and Human Health scores for Parkinson, I would need to identify {the bars for Parkinson}[0.657, 0.132, 0.694, 0.654] and [0.694, 0.132, 0.731, 0.561]. Extrapolating the height of these bars to the y-axis indicates that Parkinson's Disease Management score appears to be around 78 and its Human Health score seems to be around 67. The difference between these two scores is 78 - 67 = 11.\nBased on the above thought, the answer is:\n11" } ], "id_raw": 245 }, { "id": "o0LPojGA2rrGfqFI", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_7.png", "conversations": [ { "from": "human", "value": "\nBased on the data provided in the line chart, can you convert the year-wise sales data for Condominiums and Single-Family Home into a structured table format?" }, { "from": "gpt", "value": "To convert the line chart data into a table, I start by identifying {the x-axis labels}[0.110, 0.898, 0.188, 0.934], [0.262, 0.898, 0.389, 0.934], [0.439, 0.898, 0.566, 0.934], [0.616, 0.898, 0.743, 0.934], [0.792, 0.898, 0.919, 0.934] as the years. Then, I locate the line labeled {Condo Sales}[0.127, 0.123, 0.185, 0.137] and the corresponding line points [0.149, 0.843], [0.237, 0.712], ..., [0.944, 0.816] to find the condominium sales data for each year. Similarly, I locate the line labeled {Single-Family Home Sales}[0.127, 0.123, 0.185, 0.137] and extract its corresponding line points [0.149, 0.382], [0.237, 0.325], ..., [0.944, 0.217] to find the single-family home sales data for each year.\nBased on the above thought, the answer is:\nYear,Condo Sales,Single-Family Home Sales\n2000,50000,70000\n2001,55000,73000\n2002,57500,74000\n2003,60000,76000\n2004,61000,80000\n2005,75000,85000\n2006,73250,88000\n2007,70000,86000\n2008,58000,82000\n2009,51000,79000" } ], "id_raw": 247 }, { "id": "0LbMAMSC3wwoMfRk", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_7.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which type of property witnessed the most significant drop in sales over the years and why?" }, { "from": "gpt", "value": "To identify which property type witnessed the most significant drop, I examine the {Condo Sales line}[0.149, 0.288, 0.944, 0.843] and the {Single-Family Home Sales line}[0.149, 0.069, 0.944, 0.382]. By comparing the line points, we can see that Condo Sales surged in the year {2005}[0.591, 0.898, 0.594, 0.931] to 75,000 but dropped sharply to 51,000 by {2009}[0.944, 0.898, 0.950, 0.934], a drop of 24,000. However, Single-Family Home Sales also reached a peak in {2006}[0.679, 0.898, 0.683, 0.931] at 88,000 and dipped to 79,000 in {2009}[0.944, 0.898, 0.950, 0.934], a drop of 9,000. Therefore, the condo sales witnessed the most significant drop.\nBased on the above thought, the answer is:\nThe Condominium market witnessed the most significant drop in sales over the years. This is evident from the sharp decrease in the number of Condo sales from 75,000 in 2005 to 51,000 in 2009, a drop of 24,000. While single-family homes also witnessed a sales drop in this period, it was less steep, making the Condo market's decrease more significant." } ], "id_raw": 247 }, { "id": "SON15vWdovOEivIG", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest number of Condominium sales in any given year according to the plot?" }, { "from": "gpt", "value": "To find the highest number of Condominium sales, I need to look at the {line points of the line corresponding to Condo Sales}[0.149, 0.843], [0.237, 0.712], ..., [0.944, 0.816] and extract the corresponding sales numbers: 50000, 55000, 57500, 60000, 61000, 75000, 73250, 70000, 58000, 51000. The maximum value among these is 75000, which corresponds to the year {2005}[0.591, 0.898, 0.594, 0.931].\nBased on the above thought, the answer is:\n75000." } ], "id_raw": 247 }, { "id": "KGmvc17Aw8ZHH3SV", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_8.png", "conversations": [ { "from": "human", "value": "\nCould you convert the line chart data showing trends of Traditional Education and Virtual Training over the years into a structured table format?" }, { "from": "gpt", "value": "Sure, to present the line chart data in a table format, the {x-axis labels}[0.125, 0.895, 0.942, 0.895] that represent the Years serve as the row headers. The {legend items}[0.175, 0.058, 0.436, 0.092], [0.175, 0.110, 0.362, 0.144] are used as column headers, standing for Traditional Education and Virtual Training respectively. For the values corresponding to each year and education type, I trace {the points on the line for Traditional Education}[0.125, 0.350, 0.942, 0.687] and {the points on the line for Virtual Training}[0.125, 0.065, 0.942, 0.855], matching each point's x-coordinate with a year and y-coordinate with the number of Users.\nBased on the above thought, the answer is:\nYear,Traditional Education,Virtual Training\n2000,100,10\n2001,90,15\n2002,80,23\n2003,75,29\n2004,65,36\n2005,60,45\n2006,55,54\n2007,53,64\n2008,50,75\n2009,48,86\n2010,47,98\n2011,45,110\n2012,44,123\n2013,42,137\n2014,40,151" } ], "id_raw": 248 }, { "id": "e71IVISKQflkQLZb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_8.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, how would you describe the trend of Traditional Education and Virtual Training?" }, { "from": "gpt", "value": "To analyze the trend of Traditional Education and Virtual Training, I have to examine their corresponding line graphs. The {line for Traditional Education}[0.125, 0.350, 0.942, 0.687] demonstrates a consistent downward trend from 100 to 40 from 2000 to 2014, indicating a gradual decrease in Traditional Education over time. In contrast, the {line for Virtual Training}[0.125, 0.065, 0.942, 0.855] exhibits an ascending trend from 10 to 151 over the same period, indicating a significant increase in the adoption of Virtual Training.\nBased on the above thought, the answer is:\nTraditional Education shows a consistent decrease over the years from 2000 to 2014, while in contrast, Virtual Training presents a substantial increase during the same period. This suggests a shift from traditional education methods to virtual learning platforms." } ], "id_raw": 248 }, { "id": "b3mKqudbhYHH0SUU", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in the number of Users for Virtual Training from year 2001 to 2014?" }, { "from": "gpt", "value": "To find the change in the number of Users for Virtual Training, I first locate {the point for Virtual Training in 2001}[0.183, 0.827] corresponding to 15 and then {the point for Virtual Training in 2014}[0.942, 0.065] corresponding to 151. The change is then calculated as 151 - 15 = 136.\nBased on the above thought, the answer is:\n136." } ], "id_raw": 248 }, { "id": "dbNzFMFwjEhmdbhC", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_9.png", "conversations": [ { "from": "human", "value": "\nCould you present the data from this line chart in structured table format with the different parameters ('Employee Morale', 'Market Share', 'Productivity', etc.) as rows and 'Non-aggregation' and 'Aggregation' as columns?" }, { "from": "gpt", "value": "To create the table, firstly we need to identify the x-axis labels seen in the chart. These can be obtained from {the x-axis tick labels}[[-0.358, 0.892, -0.337, 0.947], [-0.115, 0.892, -0.094, 0.947], [0.129, 0.892, 0.149, 0.934], [0.372, 0.892, 0.393, 0.934], [0.615, 0.892, 0.636, 0.934], [0.859, 0.892, 0.879, 0.934]], which act as the row headers representing different parameters. The legend items {the legend texts}[[0.190, 0.653, 0.451, 0.687], [0.190, 0.705, 0.377, 0.739]] represent 'Non-aggregation' and 'Aggregation', and are our column headers. The y-values of the chart match with the score of each parameter for both 'Non-aggregation' and 'Aggregation'. These scores can be extracted from {the line points for Non-aggregation}[[0.139, 0.068], [0.212, 0.131], [0.255, 0.193], [0.286, 0.255], [0.309, 0.317], [0.328, 0.379]] and {the line points for Aggregation}[[0.139, 0.255], [0.212, 0.193], [0.255, 0.131], [0.286, 0.193], [0.309, 0.068], [0.328, 0.131]].\nBased on the above thought, the answer is:\nParameters,Non-aggregation,Aggregation\nInterest Rate,0.1,0.0001\nMoney Supply,0.01,0.001\nPublic Debt,0.001,0.01\nExchange Rate,0.0001,0.001\nInflation Rate,0.00001,0.1\nUnemployment Rate,0.000001,0.01" } ], "id_raw": 249 }, { "id": "IDnMx798rJ6iUJJy", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_9.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which policy, Non-aggregation or Aggregation, would you recommend for a company aiming for steady growth?" }, { "from": "gpt", "value": "To answer this question, we need to compare the overall trends of the Non-aggregation and Aggregation lines in the chart. The line for {Non-aggregation}[0.139, 0.068, 0.328, 0.379] overall shows a steady increase in scores across multiple parameters with some fluctuations, indicating consistent growth. On the other hand, the line for {Aggregation}[0.139, 0.255, 0.328, 0.131] shows a steady decline, except for an uptick at 'Employee Satisfaction', overall indicating a trend of decline over time.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Non-aggregation would be the recommended policy for a company aiming for steady growth. The Non-aggregation approach indicates consistent growth across multiple business management and organizational leadership parameters, whereas the Aggregation approach shows a general decrease in performance, except for a single instance in the 'Employee Satisfaction' parameter. Hence, the Non-aggregation seems to yield better results for continuous growth." } ], "id_raw": 249 }, { "id": "7lHjHoKFhxPEfVZq", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in 'Employee Satisfaction' scores between Non-aggregation and Aggregation methodologies?" }, { "from": "gpt", "value": "To find the 'Employee Satisfaction' scores, we need to locate the points for 'Employee Satisfaction' on both lines. The Non-aggregation score for 'Employee Satisfaction' is recorded at {the point for Non-aggregation}[0.309, 0.317]. The Aggregation score for 'Employee Satisfaction' is recorded at {the point for Aggregation}[0.309, 0.068]. Subtracting the two scores will give the difference.\nBased on the above thought, the answer is:\n0.249." } ], "id_raw": 249 }, { "id": "4OPR3HIbbCJK61ld", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_10.png", "conversations": [ { "from": "human", "value": "\nCan you reformat information from the line chart into a table with the models as rows and the cryptocurrencies as columns?" }, { "from": "gpt", "value": "To create a table from the line chart, I first identify {the x-axis tick labels}[[0.106, 0.884, 0.133, 0.934], [0.429, 0.884, 0.452, 0.934], [0.618, 0.884, 0.646, 0.934], [0.753, 0.884, 0.778, 0.934], [0.857, 0.884, 0.880, 0.934], [0.942, 0.884, 0.968, 0.934]] as the column headers, and {the legend texts}[[0.771, 0.064, 0.958, 0.098] ,[0.771, 0.116, 0.961, 0.149]] as the row labels representing the two models. Then, in order to fill in the table, I find the {the line points for each cryptocurrency under each model}[[0.106, 0.039], [0.429, 0.147], ..., [0.942, 0.581]] and extract the corresponding values for each cryptocurrency.\nBased on the above thought, the answer is:\nCryptocurrencies,Model A,Model B\nBitcoin,$10,000,$9,500\nEthereum,$300,$350\nRipple,$0.23,$0.25\nCardano,$1.40,$1.35\nDogecoin,$0.09,$0.12\nPolkadot,$20.00,$18.00\nLitecoin,$170,$200\nChainlink,$27.50,$29.00\nStellar,$0.30,$0.28" } ], "id_raw": 250 }, { "id": "CvZ2gK8hk50LWTzk", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_10.png", "conversations": [ { "from": "human", "value": "\nWhich cryptocurrency displayed the least stability between Model A and Model B?" }, { "from": "gpt", "value": "Stability can be inferred from changes in price from Model A to Model B. I can observe {Bitcoin's line}[0.106, 0.039, 0.429, 0.147] which shows a mild decrease, {Ethereum\u2019s line}[0.429, 0.147, 0.618, 0.256] represents a slight increase, while {Dogecoin\u2019s line}[0.753, 0.364, 0.857, 0.473] indicates a significant increase in price. Other cryptocurrencies also show various changes but to various extents.\nBased on the above thought, the answer is:\nBased on the line chart, Dogecoin displayed substantial changes in price hence showing the least stability between Model A and Model B because it undergoes a substantial price increase from \\$0.09 to \\$0.12, which is a relatively large change for a low-value cryptocurrency." } ], "id_raw": 250 }, { "id": "DjcIf43QItCGfhkE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage difference in the price of Ethereum between Model A and Model B?" }, { "from": "gpt", "value": "To find the percentage difference, I need to locate the coordinates representing Ethereum prices under each model. In Model A, {Ethereum's price}[0.106, 0.256] is $300, and in Model B {Ethereum's price}[0.429, 0.364] slightly increased to $350. The formula to calculate percentage difference is ((New Value - Old Value)/Old Value)*100. Hence, the percentage difference in Ethereum's price from Model A to Model B is ((350-300)/300)*100 = 16.67%.\nBased on the above thought, the answer is:\n16.67%." } ], "id_raw": 250 }, { "id": "TEcAmnhT36Z12kkA", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_11.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, can you construct a table that compares the occupancy rates between local and foreign tourists for each hotel category?" }, { "from": "gpt", "value": "First, I locate {the x-axis tick labels}[[0.133, 0.911, 0.154, 0.934], [0.293, 0.911, 0.314, 0.934], [0.453, 0.911, 0.474, 0.934], [0.613, 0.911, 0.634, 0.934], [0.772, 0.911, 0.794, 0.934], [0.932, 0.911, 0.954, 0.934]], which represent the row labels or hotel categories. Then, I identify {the legend texts}[[0.195, 0.058, 0.370, 0.092], [0.195, 0.110, 0.402, 0.144]] as the column headers representing the local and foreign tourists. For each hotel category, I need to locate {the bars}[[0.143, 0.055, 0.158, 0.783], [0.158, 0.055, 0.172, 0.855], [0.303, 0.055, 0.318, 0.639], [0.318, 0.055, 0.332, 0.783], [0.463, 0.055, 0.478, 0.496], [0.478, 0.055, 0.493, 0.711], [0.623, 0.055, 0.638, 0.352], [0.638, 0.055, 0.653, 0.639]...] for local and foreign tourists and get their heights, which represent the occupancy rates.\nBased on the above thought, the answer is:\nHotel,Local Tourist Occupancy Rate,Foreign Tourist Occupancy Rate\nHotel A,78,86\nHotel B,64,78\nHotel C,50,71\nHotel D,35,64\nHotel E,21,57\nHotel F,14,50\nHotel G,7,39\nHotel H,0,30" } ], "id_raw": 251 }, { "id": "jl9GYHWG3wErOT5P", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_11.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which hotel category exhibited the most noticeable difference in occupancy rates between local and foreign tourists, and why do you think that might be the case?" }, { "from": "gpt", "value": "Analyzing the heights of {the bars}[[0.143, 0.055, 0.158, 0.783], [0.158, 0.055, 0.172, 0.855], [0.303, 0.055, 0.318, 0.639], [0.318, 0.055, 0.332, 0.783], [0.463, 0.055, 0.478, 0.496], [0.478, 0.055, 0.493, 0.711], [0.623, 0.055, 0.638, 0.352], [0.638, 0.055, 0.653, 0.639]...], it appears that the most noticeable difference in occupancy rates is exhibited by Hotel D, where the local tourist rate is significantly lower than the foreign tourist rate.\nBased on the above thought, the answer is:\nHotel D exhibits the most noticeable difference in occupancy rates between local and foreign tourists. This might be due to a variety of possible factors. For instance, the hotel might be primarily catering to foreign tourists on the basis of its location or services provided. It could also indicate a higher price point that may not be affordable for the local population, or a marketing strategy that mainly targets foreign tourists. It's also possible that the hotel is located in an area of high tourist interest, resulting in more bookings from foreign visitors compared to locals." } ], "id_raw": 251 }, { "id": "t0YKklAW2dY1VGiW", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in occupancy rates between local and foreign tourists for Hotel D?" }, { "from": "gpt", "value": "To calculate the difference in occupancy rates for Hotel D, I'll look at {the bars for Hotel D}[[0.623, 0.055, 0.638, 0.352], [0.638, 0.055, 0.653, 0.639]] and extract their heights representing the occupancy rates. The local tourist rate is approximately 35, and the foreign tourist rate is approximately 64. Therefore, the difference is 64 - 35 = 29.\nBased on the above thought, the answer is:\n29%." } ], "id_raw": 251 }, { "id": "69mXRCUQtvWvu4Wm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_12.png", "conversations": [ { "from": "human", "value": "\nCan you transform the chart's data into a structured table where the columns represent the years and the rows represent the type of entrepreneurs?" }, { "from": "gpt", "value": "To convert the graph\u2019s data into a tabular format, I would first identify {the x-axis labels}[[-0.099, 0.887, -0.021, 0.934], [0.067, 0.887, 0.146, 0.934], [0.233, 0.887, 0.312, 0.934], [0.400, 0.887, 0.478, 0.934], [0.566, 0.887, 0.644, 0.934], [0.732, 0.887, 0.811, 0.934], [0.898, 0.887, 0.977, 0.934]], meaning the years, as column titles. Then, I would interpret {the legend items}[[0.620, 0.058, 0.887, 0.092], [0.620, 0.110, 0.957, 0.144]] which correlate to the row head labels or types of entrepreneurs. To fill out the cells, I review {the line points for Novice Entrepreneurs}[[0.106, 0.787], [0.273, 0.448], [0.439, 0.702], [0.605, 0.787], [0.771, 0.448] [0.938, 0.702]] and {the line points for Experienced Entrepreneurs}[[0.106, 0.863], [0.273, 0.867], [0.439, 0.870], [0.605, 0.863], [0.771, 0.871] [0.938, 0.870]], which represent the values for each entrepreneur type per year.\nBased on the above thought, the answer is:\nYear,Novice Entrepreneurs,Experienced Entrepreneurs\n2005,1.0,1.0e-1\n2006,5.0,5.0e-2\n2007,2.0,2.0e-2\n2008,1.0,1.0e-1\n2009,5.0,5.0e-4\n2010,2.0,2.0e-2" } ], "id_raw": 252 }, { "id": "0kjLKoaPziohXR5f", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_12.png", "conversations": [ { "from": "human", "value": "\nBased on the data plot, which group of entrepreneurs showed a steadier trend in their contribution over the years and why so?" }, { "from": "gpt", "value": "To understand the steadiness of trends, I need to analyze the line plots for each group. Examining {the line for Novice Entrepreneurs}[0.106, 0.448, 0.938, 0.787], the values fluctuate between 1.0, 5.0, and 2.0 across the years showing considerable volatility. On the other hand, looking at {the line for Experienced Entrepreneurs}[0.106, 0.863, 0.938, 0.871], the values fluctuate between 1.0e-1, 5.0e-2, 2.0e-2, and 5.0e-4 which seems to gradually decrease and shows a more consistent trend compared to Novice Entrepreneurs.\nBased on the above thought, the answer is:\nThe Experienced Entrepreneurs show a steadier trend in their contributions over the years, according to the data plot. Though their values gradually decrease over time, the change is more consistent and shows less variation compared to the Novice Entrepreneurs, making their trend appear steadier." } ], "id_raw": 252 }, { "id": "ReWNUThx1sK9U7Vt", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the sum of values for Novice Entrepreneurs across all the years in the chart?" }, { "from": "gpt", "value": "To calculate the total values for Novice Entrepreneurs across all years, I need to look at {the line points of the Novice Entrepreneurs}[[0.106, 0.787], [0.273, 0.448], [0.439, 0.702], [0.605, 0.787], [0.771, 0.448] [0.938, 0.702]]. The values for those points are 1.0, 5.0, 2.0, 1.0, 5.0, and 2.0 respectively. The sum of these is 16.0.\nBased on the above thought, the answer is:\n16.0." } ], "id_raw": 252 }, { "id": "7ldtzUGlUVawTBnH", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_13.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured table that represents the information from the line chart, with Time points along the columns and the Assessment Criteria along the rows?" }, { "from": "gpt", "value": "To generate the table from the line chart, I first look at {the x-axis tick labels}[[-1.600, 0.884, -1.528, 0.934], [-0.765, 0.884, -0.693, 0.934], [0.079, 0.884, 0.133, 0.934], [0.915, 0.884, 0.968, 0.934]] representing years as columns. I also check {the legend labels}[[0.156, 0.071, 0.356, 0.105], [0.156, 0.123, 0.393, 0.157]] as the row labels or Assessment Criteria. The data for each criterion at different time points is taken from their respective {line points}[[0.106, 0.702, 0.756, 0.752], [0.106, 0.230, 0.756, 0.536]].\nBased on the above thought, the answer is:\nYears, Timepoint: 0, Timepoint: 1, Timepoint: 2, Timepoint: 3\nDigital Currency,0.0005,0.0008,0.001,0.0007\nFinTech Application,0.05,0.06,0.02,0.01" } ], "id_raw": 253 }, { "id": "h5nnSZfRAvE8RCz1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_13.png", "conversations": [ { "from": "human", "value": "\nWhich line shown in the chart has the most stable performance over time? Please explain your reasoning." }, { "from": "gpt", "value": "To determine which line demonstrates the most stable performance, I observe the fluctuation in {the line for Digital Currency}[0.106, 0.702, 0.756, 0.752] and {the line for FinTech Application}[0.106, 0.230, 0.756, 0.536]. The line for Digital Currency fluctuates between 0.0005 to 0.001, with a slight increase at time point 2 and decreases at time points 3 to 5, indicating a certain degree of instability. The line for FinTech Application starts from 0.05, increases to 0.06, drops significantly to 0.02, and then increases rapidly, showing a very high degree of volatility. In comparison, the fluctuations in the Digital Currency line are less drastic than that of the FinTech Application, implying a higher degree of stability.\nBased on the above thought, the answer is:\nThe Digital Currency line demonstrates the most stable performance over time as evidenced by their line plot. The line plot of the Digital Currency shows slight ups and downs around a narrow range, indicating relatively small fluctuations compared to the FinTech Application, which exhibits more drastic changes over time. These smaller fluctuations in the Digital Currency line suggests that it has more stability than the FinTech Application." } ], "id_raw": 253 }, { "id": "sL3PrlFdxQUuRquB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum value achieved by the Digital Currency line in the chart?" }, { "from": "gpt", "value": "To find the maximum value achieved by the Digital Currency, I examine {the line points for Digital Currency}[[0.106, 0.752], [0.358, 0.718], [0.505, 0.702], [0.609, 0.728], [0.690, 0.739], [0.756, 0.718]] corresponding to values: 0.0005, 0.0008, 0.001, 0.0007, 0.0006, 0.0008. The maximum value is seen at the third time point, corresponding to {Time Point: 2}[0.505, 0.884].\nBased on the above thought, the answer is:\n0.001." } ], "id_raw": 253 }, { "id": "xjVmEdQeMeJzv0Bw", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_14.png", "conversations": [ { "from": "human", "value": "\nCan you capture the trends from the line chart in the form of a table, with the heading showing media platforms and each row representing a specific time point?" }, { "from": "gpt", "value": "To construct the table, I first identify the columns as the different media platforms, indicated by {the legend labels}[0.686, 0.044, 0.971, 0.158]. The rows represent the time points, indicated by {the x-axis tick labels}[-0.055, 0.911, -0.022, 0.934], [0.120, 0.911, 0.130, 0.934], [0.279, 0.911, 0.298, 0.934], [0.442, 0.911, 0.462, 0.934], [0.606, 0.911, 0.625, 0.934], [0.769, 0.911, 0.789, 0.934], [0.928, 0.911, 0.957, 0.934]. To get the values, I look at {the line points} for each platform at each time point and read off the corresponding market share values.\nBased on the above thought, the answer is:\nTime,Netflix,Disney Plus,HBO Max,Prime Video,Hulu\n0,50,15,20,10,5\n20,40,20,15,15,10\n40,45,25,10,12,8\n60,42,30,8,10,10" } ], "id_raw": 254 }, { "id": "cQkHXaCxI1ADoWgr", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_14.png", "conversations": [ { "from": "human", "value": "\nConsidering the trends represented in the line chart, which streaming platform shows the most stable market share over time and why?" }, { "from": "gpt", "value": "To answer this, I need to observe each line in the chart and compare their fluctuations. {The Netflix line}[0.166, 0.065, 0.942, 0.443] bounces, suggesting unstable market share over time. {The Disney Plus line}[0.125, 0.202, 0.223, 0.855] consistently trends upward, indicating growth, but not necessarily stability. {The Hulu Line}[0.166, 0.065, 0.942, 0.443] goes up and down, indicating fluctuations and hence instability. {The Prime Video line}[0.166, 0.065, 0.942, 0.443] and {the HBO Max line}[0.166, 0.202, 0.223, 0.855] both exhibit a descending trend. Comparatively, the Prime Video line has lesser fluctuations in its downward trend, signifying more stability than HBO Max.\nBased on the above thought, the answer is:\nPrime Video shows the most stable market share over time. Although it depicts a downward trend, the rate of decrease is steady and progressive with lesser fluctuations than other platforms, indicating stability in comparison." } ], "id_raw": 254 }, { "id": "PUBQuqaqMasxaqeE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_14.png", "conversations": [ { "from": "human", "value": "\nAt what time is the lowest market share for Prime Video observed and what is that market share value?" }, { "from": "gpt", "value": "To find out the lowest market share for Prime Video, I need to analyze {the line plot for Prime Video}[0.166, 0.065, 0.942, 0.443] and find the lowest point. It corresponds to Time 60 with {the lowest point}[0.663, 0.765] and a value of 10.\nBased on the above thought, the answer is:\nPrime Video's lowest market share of 10 is observed at Time 60." } ], "id_raw": 254 }, { "id": "BX8YmGkCudmO3woo", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_15.png", "conversations": [ { "from": "human", "value": "\nGiven the line chart, can you present the values in a table format with time points as columns and AI applications as rows?" }, { "from": "gpt", "value": "To create the table, I'll use {the x-axis tick labels}[[0.179, 0.110, 0.198, 0.136], [0.309, 0.110, 0.334, 0.135], [0.447, 0.110, 0.468, 0.134], [0.578, 0.110, 0.599, 0.134]] as the column headings, representing the different time points. Next, I identify {the legend texts}[[0.915, 0.717, 0.986, 0.735], [0.915, 0.652, 0.987, 0.671], [0.915, 0.586, 0.995, 0.605], [0.915, 0.522, 0.983, 0.541], [0.915, 0.460, 0.988, 0.479]] as row labels showing the different AI applications. Then I trace values for each application at the respective time points, these values are {the line points}[[0.179, 0.699], [0.309, 0.616], [0.447, 0.692], [0.578, 0.697], [0.178, 0.377], [0.309, 0.487], [0.447, 0.562], [0.578, 0.685], [0.178, 0.516], [0.309, 0.512], [0.447, 0.526], [0.578, 0.553], [0.178, 0.450], [0.309, 0.457], [0.447, 0.473], [0.578, 0.493], [0.178, 0.594], [0.309, 0.603], [0.447, 0.611], [0.578, 0.619]].\nBased on the above thought, the answer is:\nAI Application,Time Point 1,Time Point 2,Time Point 3,Time Point 4\nSpeech Recognition,0.8,0.55,0.7,0.7\nAutonomous Vehicles,0.5,0.65,0.75,0.85\nMachine Learning,0.6,0.58,0.6,0.65\nRobotics,0.55,0.57,0.59,0.58\nNeural Networks,0.7,0.71,0.72,0.73" } ], "id_raw": 255 }, { "id": "v3q8xZNPvKjWlP7q", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_15.png", "conversations": [ { "from": "human", "value": "\nAccording to visual trends in the line graph, which AI application shows a consistently improving trend over time? Justify your answer." }, { "from": "gpt", "value": "To identify the AI application that shows a consistently improving trend, I need to examine the gradients of the line plots for each AI application.\nEvaluating {the line for Speech Recognition}[0.179, 0.699, 0.578, 0.697] shows an oscillating trend that goes down then up. Next, analyzing {the line for Autonomous Vehicles}[0.178, 0.377, 0.578, 0.685], I observe a steady upward slope indicating ongoing improvement. Comparatively, the other lines for {Machine Learning}[0.178, 0.516, 0.578, 0.553] and {Robotics}[0.178, 0.450, 0.578, 0.493] present negligible upward trends while {Neural Networks}[0.178, 0.594, 0.578, 0.619] shows a slightly positive slope, but not as steep as Autonomous Vehicles.\nBased on the above thought, the answer is:\nAccording to the visual trends in the line graph, Autonomous Vehicles exhibit a consistently improving trend over time. The line for this AI application shows a steady, consistent upward slope, which conveys a consistent improvement from the first to the last time point. Although other AI applications such as Machine Learning, Robotics and Neural Networks also show slight improvements, the upward trend is most pronounced for Autonomous Vehicles, making it the most improving AI application according to the graph's visual trends." } ], "id_raw": 255 }, { "id": "2nojuvG50jnGwtvE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the rate of growth for Autonomous Vehicles from the first to the last time point?" }, { "from": "gpt", "value": "To find the rate of growth for Autonomous Vehicles, I will have to calculate the difference in values from the first time point {at point}[0.178, 0.377] value of 0.5 to the last time point {at point}[0.578, 0.685] value of 0.85. The difference can be calculated as 0.85 - 0.5 = 0.35\nBased on the above thought, the answer is:\n0.35" } ], "id_raw": 255 }, { "id": "23D28P7OoUjmMs6q", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_16.png", "conversations": [ { "from": "human", "value": "\nCould you transform the data from the bar chart into a structured table with the categories as the columns and the time periods as the rows?" }, { "from": "gpt", "value": "In order to create a structured table from the bar chart, I will first identify the categories as column names. The categories can be found in {the legend}[0.712, 0.048, 0.974, 0.144], which lists - 'Startups', 'Investments', 'Patents', 'Innovation', and 'Funding'. Next, I'll determine the row labels by looking at {the x-axis tick labels}[-0.043, 0.901, 0.020, 0.934], [0.063, 0.901, 0.126, 0.934], [0.169, 0.901, 0.232, 0.934], [0.274, 0.901, 0.337, 0.934], [0.380, 0.901, 0.443, 0.934], [0.486, 0.901, 0.549, 0.934], [0.592, 0.901, 0.655, 0.934], [0.698, 0.901, 0.761, 0.934], [0.804, 0.901, 0.867, 0.934], [0.909, 0.901, 0.973, 0.934], [1.015, 0.901, 1.078, 0.934], which represent each year from 2012 to 2020. Finally, extracting the values from the bars at each year for each of the categories - For example {bar representing Startups in 2012}[0.094, 0.223], {bar representing Investments in 2012}[0.094, 0.318] and so on.\nBased on the above thought, the answer is:\nYears,Startups,Investments,Patents,Innovation,Funding\n2012,0.8,0.7,nil,nil,nil\n2013,0.68,0.6,nil,nil,nil\n2014,0.57,0.51,nil,nil,nil\n2015,0.49,0.4,nil,nil,nil\n2016,0.45,0.3,nil,nil,nil\n2017,0.41,0.25,nil,nil,nil\n2018,0.39,0.2,nil,nil,nil\n2019,0.35,0.35,nil,nil,nil\n2020,0.31,0.4,nil,nil,nil" } ], "id_raw": 256 }, { "id": "skXXEwdOpLMblzSX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_16.png", "conversations": [ { "from": "human", "value": "\nWhich of the two assessed areas, \"Social Responsibility\" or \"Sustainable Business\", faced a steeper decline from 2012 to 2020 and why?" }, { "from": "gpt", "value": "To determine which area faced a steeper decline, I need to identify the change in each area's focus over the given period. This involves analyzing the trends in {the line for Social Responsibility}[0.094, 0.223, 0.941, 0.686], and {the line for Sustainable Business}[0.094, 0.318, 0.941, 0.790]. By comparing the initial and final coordinates for each line, it can be seen that both have a negative slope indicating a decline over time. Subtraction of the initial Y-coordinate from the final Y-coordinate in both lines will give the total decline in each area respectively.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, \"Social Responsibility\" faced a steeper decline from 2012 to 2020. Despite both areas showing a general decrease over this period, the slope of the line for \"Social Responsibility\" is steeper, suggesting a greater rate of decrease in focus compared to \"Sustainable Business\"." } ], "id_raw": 256 }, { "id": "WPlV9DPUEexNkcmm", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_16.png", "conversations": [ { "from": "human", "value": "\nBetween the year 2013 and 2017, how much did the focus on \"Social Responsibility\" decrease by?" }, { "from": "gpt", "value": "To find the decrease in the focus on \"Social Responsibility\" between 2013 and 2017, I need to locate {the bar for Social Responsibility in 2013}[0.200, 0.337] and extract the corresponding value which is 0.68. Similarly, I will locate {the bar for Social Responsibility in 2017}[0.623, 0.592] which gives me the value 0.41. Subtracting the value in 2017 from the value in 2013 should give me the decrease.\nBased on the above thought, the answer is:\n0.27." } ], "id_raw": 256 }, { "id": "FSDQr8GBDlx7CkFZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_17.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data into a table format with the time periods as columns and the different parameters (Number of Family Members, Living Space, Number of Bedrooms, Income) as rows?" }, { "from": "gpt", "value": "To generate a table format from the bar chart series, I'll first identify the {x-axis time labels} based on bar color and the legend[0.744, 0.057, 0.971, 0.175] as the column headers or time periods. Then I'll interpret the {y-axis labels} at the start of the bars as the different row labels representing the parameters, and I should find {the lengths of the bars} spanning from their onset to their terminals for each parameter at the respective time periods and extract the values.\nBased on the above thought, the answer is:\nParameter,Time 1, Time 2, Time 3, Time 4\nNumber of Family Members,4,6,7,9\nLiving Space (Square Meters),60,80,105,130\nNumber of Bedrooms,2,3,3,2\nIncome (per month),3000,3500,3200,3600" } ], "id_raw": 257 }, { "id": "W9TyUMAMI89ksmbg", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_17.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which living condition parameter has seen the most significant change over time? Please explain your reasoning." }, { "from": "gpt", "value": "To determine which parameter has seen the most significant change, I need to compare the bar lengths representing initial and final values for each parameter. For 'Number of Family Members', there's a difference between {Time 1}[0.098, 0.315] and {Time 4}[0.941, 0.398] by 5 family members. For 'Living Space', the difference is from {Time 1}[0.098, 0.266] to {Time 4}[0.601, 0.251], a significant increase by 70 square meters. The 'Number of Bedrooms' decreased by 1 from {Time 1}[0.438, 0.231] to {Time 4}[0.941, 0.508], and 'Income' has increased by $600 between {Time 1}[0.438, 0.283] and {Time 4}[0.941, 0.508].\nBased on the above thought, the answer is:\nBased on the bar chart, the 'Living Space' parameter has seen the most significant change over the time periods. The total living space increased by 70 square meters from the initial to the final time period, which is the largest absolute change among all the parameters. While other parameters like the number of family members and income also increased, the relative increase in living space is the most substantial." } ], "id_raw": 257 }, { "id": "PSim9QjVMQt7azAQ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_17.png", "conversations": [ { "from": "human", "value": "\nHow much difference is there in the family's monthly income between the first and the last time periods?" }, { "from": "gpt", "value": "To find the difference in the family's monthly income between the first and last time periods, I first locate the {Income bar for Time 1}[0.260, 0.274] and extract the corresponding value: $3000. Then I locate the {Income bar for Time 4}[0.941, 0.508] and extract the corresponding value: $3600. The difference between the two is therefore 3600 - 3000 = 600.\nBased on the above thought, the answer is:\n$600." } ], "id_raw": 257 }, { "id": "Y9rRGFtDhkFUWGuX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_18.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the horizontally stacked bar chart into a tabular format with the Time as the columns and Categories as the rows?" }, { "from": "gpt", "value": "To create the table, the first step is to identify the category labels found at {the y-axis ticks}[[0.027, 0.836, 0.046, 0.859], [0.027, 0.696, 0.046, 0.719], [0.027, 0.560, 0.046, 0.583], [0.027, 0.423, 0.046, 0.446], [0.027, 0.284, 0.046, 0.307]]. Then, I'll identify the specific bars associated with each category such as {the bars for Interest Rates}[[0.107, 0.804, 0.254, 0.870], [0.264, 0.819, 0.415, 0.856], [0.420, 0.804, 0.574, 0.870], [0.582, 0.794, 0.736, 0.880]]. I'll repeat this for the bars for each category to fill the rows of the table. Lastly, the column headers or times can be obtained from {the legend}[0.775, 0.178, 0.930, 0.256].\nBased on the above thought, the answer is:\nCategory,Time 0,Time 20,Time 40,Time 60\nInterest Rates,1, 0.85, 0.75, 0.5\nForeign Exchange Rates, 0.9, 0.8, 0.7, 0.6\nCommodity Prices, 0.7, 0.6, 0.8, 0.5\nInflation, 0.8, 0.7, 0.6, 0.5\nPublic Debt, 0.9, 0.7, 0.6, 0.4" } ], "id_raw": 258 }, { "id": "3W84r2hitezzFNID", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_18.png", "conversations": [ { "from": "human", "value": "\nFrom the graph, which category in the Global Financial System manifest the most stability despite the general decline over time?" }, { "from": "gpt", "value": "To determine which category remains most stable over time, I need to investigate the change in bar lengths for each category. Looking through {the bars for Interest Rates}[[0.107, 0.804, 0.254, 0.870], [0.264, 0.819, 0.415, 0.856], [0.420, 0.804, 0.574, 0.870], [0.582, 0.794, 0.736, 0.880]], {for Foreign Exchange Rates}[[0.107, 0.663, 0.249, 0.730], [0.264, 0.679, 0.413, 0.716], [0.420, 0.664, 0.570, 0.730], [0.583, 0.651, 0.732, 0.743]], {for Commodity Prices}[[0.107, 0.523, 0.246, 0.590], [0.264, 0.538, 0.409, 0.575], [0.420, 0.523, 0.568, 0.590], [0.583, 0.505, 0.725, 0.595]], {for Inflation}[[0.107, 0.383, 0.245, 0.451], [0.264, 0.399, 0.411, 0.436], [0.420, 0.383, 0.567, 0.451], [0.583, 0.371, 0.728, 0.463]] and {for Public Debt}[[0.107, 0.239, 0.237, 0.306], [0.265, 0.252, 0.398, 0.294], [0.422, 0.245, 0.568, 0.306], [0.583, 0.231, 0.716, 0.318]]. Among all these, Commodity Prices show a conspicuous upswing from Time 20 to Time 40, making it the most stable category in terms of maintaining its score over time.\nBased on the above thought, the answer is:\nCommodity Prices appears the most stable across the selected time periods. Unlike the other categories, it shows a rise in stability from Time 20 to Time 40, in contrast to the general declining trend." } ], "id_raw": 258 }, { "id": "KfkDG225AvNxwRiN", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the total decline in Interest Rates from Time 0 to Time 60?" }, { "from": "gpt", "value": "To calculate the total decline in Interest Rates, I need to determine the values at Time 0 and Time 60. For Time 0, I checked the length of the first bar in {the bars for Interest Rates}[[0.107, 0.804, 0.254, 0.870]] and found it corresponded to a value of 1. For Time 60, I checked the length of the last bar in {the bars for Interest Rates}[[0.582, 0.794, 0.736, 0.880]] and found it corresponding to a value of 0.5. The difference between these two values is the total decline in Interest Rates.\nBased on the above thought, the answer is:\n0.5" } ], "id_raw": 258 }, { "id": "KwzvkOvhUzqthjCm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_13_19.png", "conversations": [ { "from": "human", "value": "\nCould you read the line chart and present the information in a structured table format with Years as columns and Types of time periods as rows?" }, { "from": "gpt", "value": "To convert the line chart into a structured table, I first observe {the x-axis tick labels}[[0.106, 0.907, 0.136, 0.938], [0.189, 0.907, 0.223, 0.938], [0.272, 0.907, 0.302, 0.938], [0.355, 0.907, 0.383, 0.938], [0.439, 0.907, 0.466, 0.938], [0.522, 0.907, 0.548, 0.938], [0.605, 0.907, 0.635, 0.938], [0.688, 0.907, 0.713, 0.938], [0.771, 0.907, 0.798, 0.938], [0.854, 0.907, 0.881, 0.938], [0.936, 0.907, 0.962, 0.938]], which represent the column headers or years. Next, I identify {the legend texts}[[0.156, 0.061, 0.317, 0.095], [0.156, 0.113, 0.357, 0.147]] as the row labels that represent the different time periods in the given system. Finally, to populate the cells I look at {the line points}[[0.106, 0.284, 0.936, 0.851], [0.106, 0.067, 0.936, 0.284]] for each time period at the respective years and extract the annotated values.\nBased on the above thought, the answer is:\nTime Periods,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010\nWaiting time,0.2,0.16,0.13,0.11,0.095,0.08,0.07,0.06,0.055,0.052,0.048\nProcessing Time,0.4,0.45,0.51,0.58,0.65,0.69,0.75,0.8,0.82,0.85,0.9" } ], "id_raw": 259 }, { "id": "ORaLQfFGmVfGsD7G", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_13_19.png", "conversations": [ { "from": "human", "value": "\nReflecting on the line chart, which between 'Waiting time' and 'Processing Time' appears to have decreased the most significantly over time? Please explain your observation." }, { "from": "gpt", "value": "To ascertain which line has decreased more significantly, I must study the slope of each line over the years. Now, observing {the line for Waiting Time}[0.106, 0.470, 0.936, 0.851], I can see a noticeable decline between 2000 and 2010. \nNext, I examine {the line for Processing Time}[0.106, 0.067, 0.936, 0.284], which also shows a decline, albeit less steep. The decline in the \"Waiting Time\" is evidently more significant compared to the decline in the \"Processing Time\" over the same period.\nBased on the above thought, the answer is:\nGiven the trends displayed on the line chart, the 'Waiting Time' appears to have decreased more significantly over the years compared to the 'Processing Time'. The slope of the 'Waiting Time' line is steeper, denoting a higher rate of decline over the given time period." } ], "id_raw": 259 }, { "id": "KcJ0pROgw1S1Vsa1", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_13_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the waiting time in 2001 and 2010?" }, { "from": "gpt", "value": "To find the difference between waiting times in 2001 and 2010, I need to look at {the line points for Waiting Time}[[0.189, 0.529], [0.936, 0.851]] and extract the corresponding values: 0.16 and 0.048. The difference between the waiting times in 2001 and 2010 is therefore 0.16 - 0.048 = 0.112.\nBased on the above thought, the answer is:\n0.112" } ], "id_raw": 259 }, { "id": "HfddCmme0gzL5qGv", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, can you extract the data into a structured table format with Time as columns and Regions as rows?" }, { "from": "gpt", "value": "To generate the table from the line chart, I would start by identifying {the x-axis tick labels}[[0.067, 0.911, 0.078, 0.934], [0.194, 0.911, 0.205, 0.934], [0.320, 0.911, 0.332, 0.934], [0.442, 0.911, 0.464, 0.934], [0.568, 0.911, 0.590, 0.934]], which represent different time points. Next, I look at {the legend texts}[[0.322, 0.034, 0.441, 0.066], [0.557, 0.034, 0.778, 0.066]] representing the different regions. To determine the values, I locate {the line points}[[0.073, 0.779], [0.199, 0.548], [0.326, 0.201], [0.453, 0.432], [0.579, 0.663]] for each region at the respective times, and then extract the corresponding values.\nBased on the above thought, the answer is:\nRegion,Time 0,Time 20,Time 40,Time 60,Time 80\nUnited States,0.5,1,1.5,2,1.5\nEuro Zone,1,3,6,4,2\nChina,0.5,1.5,1,2,1.5\nJapan,1,3,6,4,2\nUnited Kingdom,1,1.5,2,1.5,1" } ], "id_raw": 260 }, { "id": "LHw09ldZXbnGWlBu", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which region experiences the most substantial fluctuations in policy impact over time?" }, { "from": "gpt", "value": "To determine which region experiences the most substantial fluctuations in policy impact, I need to look at the volatility in the line plots for each region. For {the United States}[0.073, 0.663, 0.959, 0.866], there is a gradual increase from 0.5 to 2, then a decrease to 1.5. For {the Euro Zone}[0.073, 0.779, 0.959, 0.871], there is an increase from 1 to 6, followed by a decrease to 2. This pattern indicates high volatility. For {China}[0.073, 0.837, 0.959, 0.866], the line shows an increase, then a decrease, then an increase, and then a decrease, exhibiting volatility as well. For {Japan}[0.073, 0.779, 0.959, 0.871], there is a similar pattern to the Euro Zone, of increasing and then decreasing. Finally, for {the United Kingdom}[0.073, 0.837, 0.959, 0.866], the line rises to a peak at time 40 and then decreases slightly. Among all these, the Euro Zone, China, and Japan show the most substantial fluctuations.\nBased on the above thought, the answer is:\nThe regions that experience the most substantial fluctuations in policy impact over time are the Euro Zone, China, and Japan. They all have increased policy impacts in the beginning followed by a drop, showing clear volatility over time. It indicates that the global financial policies' impact on these regions vary more significantly over time." } ], "id_raw": 260 }, { "id": "uhA58s3d7kiDWEUX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in policy impact on the Euro Zone from the start to the final time point?" }, { "from": "gpt", "value": "To find the difference in policy impact on the Euro Zone from the start to the final time point, I locate {the line points for the Euro Zone}[[0.073, 0.779], [0.199, 0.548], [0.326, 0.201], [0.453, 0.432], [0.579, 0.663]] and extract the corresponding values: 1, 3, 6, 4, 2. The value at the start is 1, and the value at the final time point is 2. Therefore, the difference in policy impact on the Euro Zone from the start to the final time point is 2 - 1 = 1.\nBased on the above thought, the answer is:\n1." } ], "id_raw": 260 }, { "id": "ZYUxK2hT9GOcxfve", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_1.png", "conversations": [ { "from": "human", "value": "\nCan transformations be made to pull out the raw data from the chart figure into a structured table with columns as Time Periods and rows as Environmental Indicators?" }, { "from": "gpt", "value": "From the chart, I identify {the x-axis tick labels}[[0.132, 0.945, 0.171, 0.976], [0.232, 0.945, 0.284, 0.976], [0.335, 0.945, 0.387, 0.976], [0.444, 0.945, 0.495, 0.976], [0.541, 0.945, 0.588, 0.976]] as the column headers which stand for Severity of issue. I can identify {the y-axis tick labels}[[0.049, 0.837, 0.151, 0.852], [0.049, 0.687, 0.204, 0.702], [0.049, 0.537, 0.258, 0.552], [0.049, 0.387, 0.231, 0.402], [0.049, 0.237, 0.288, 0.252]] as the row labels which represent the various Environmental Indicators. To extract the values, I look at each bar's length corresponding to each environmental indicator over time. Exact values are not annotated but can be inferred from the chart.\nBased on the above thought, the answer is:\nEnvironmental Indicators,Time Period 1,Time Period 2,Time Period 3,Time Period 4,Time Period 5\nIce Melt,25,28,32,40,50\nLand Temperature Rise,45,55,66,79,90\nOceanic Temperature Rise,55,65,80,97,111\nAir Quality Index,100,90,79,59,45\nGreenhouse Gas Emission,55,75,96,140,225" } ], "id_raw": 261 }, { "id": "XbXJSRTaKzyZV1q1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_1.png", "conversations": [ { "from": "human", "value": "\nFrom the data visualized in the chart, which environmental condition seems to have seen the most significant rise in severity over the given time periods? Explain your reasoning." }, { "from": "gpt", "value": "To determine which environmental issue has seen the most significant rise in severity, I must analyze the increase in bar lengths for each issue. Looking at {the bar lengths for 'Ice Melt'}[[0.111, 0.871, 0.171, 0.873], [0.223, 0.871, 0.241, 0.873], [0.334, 0.871, 0.367, 0.873], [0.466, 0.871, 0.533, 0.873], [0.653, 0.871, 0.733, 0.873]], the length increases at a slowly growing rate. For both {the bar lengths for 'Land Temperature Rise'}[[0.111, 0.721, 0.204, 0.723], [0.223, 0.721, 0.316, 0.723], [0.334, 0.721, 0.439, 0.723], [0.466, 0.721, 0.575, 0.723], [0.653, 0.721, 0.796, 0.723]] and {the bar lengths for 'Oceanic Temperature Rise'}[[0.111, 0.571, 0.245, 0.573], [0.223, 0.571, 0.358, 0.573], [0.334, 0.571, 0.489, 0.573], [0.466, 0.571, 0.643, 0.573], [0.653, 0.571, 0.821, 0.573]], there is a steady increase in severity over time. For {the bar lengths for 'Air Quality Index'}[[0.111, 0.421, 0.346, 0.423], [0.223, 0.421, 0.310, 0.423], [0.334, 0.421, 0.262, 0.423], [0.466, 0.421, 0.149, 0.423], [0.653, 0.421, 0.028, 0.423]], there are clear decreases in severity. The most significant rise is seen in {the bar lengths for 'Greenhouse Gas Emission'}[[0.111, 0.271, 0.204, 0.273], [0.223, 0.271, 0.316, 0.273], [0.334, 0.271, 0.439, 0.273], [0.466, 0.271, 0.616, 0.273], [0.653, 0.271, 0.877, 0.273]], where the severity value increases notably and substantially in the last time period.\nBased on the above thought, the answer is:\nThe environmental issue that showed the most significant rise in severity throughout the presented time frames is 'Greenhouse Gas Emission'. This can be seen from its bars that escalate progressively with every time frame, culminating in a remarkable leap during the last time frame making it an outlier. These observations indicate the ever-worsening nature of greenhouse gas emissions." } ], "id_raw": 261 }, { "id": "AImiNXkUP11LsIWp", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the greatest severity value related to 'Greenhouse Gas Emission'?" }, { "from": "gpt", "value": "To find the greatest severity value for 'Greenhouse Gas Emission', I identify the heights of all the bars for this environmental issue {the bar heights}[[0.111, 0.131, 0.171, 0.133], [0.223, 0.131, 0.282, 0.133], [0.334, 0.131, 0.414, 0.133], [0.466, 0.131, 0.616, 0.133], [0.653, 0.131, 0.876, 0.133]]. The longest bar's length corresponds to the greatest severity value, which in this case is the last bar at 225.\nBased on the above thought, the answer is:\n225." } ], "id_raw": 261 }, { "id": "6E71ZWk8v5wya9AF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_2.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you organize the data into a table with 'Years' as rows and 'Activities' as columns?" }, { "from": "gpt", "value": "To arrange the data from the line chart into a table format, I first look at {the x-axis tick labels}[[0.051, 0.911, 0.095, 0.934], [0.139, 0.911, 0.183, 0.934], ..., [0.937, 0.911, 0.981, 0.934]], corresponding to the various years from 2000 to 2010. I then examine {the legend texts}[[0.311, 0.034, 0.410, 0.066], [0.527, 0.034, 0.583, 0.066], [0.700, 0.034, 0.790, 0.066]], signifying 'Running', 'Yoga', and 'Cycling' respectively. Lastly, I locate {the line points for each activity}[[0.073, 0.763], [0.161, 0.713], ..., [0.959, 0.298]] and extract the corresponding frequency values.\nBased on the above thought, the answer is:\nYear, Running, Yoga, Cycling\n2000, 1.3, 3.5, 0.5\n2001, 1.8, 4.1, 1.0\n2002, 2.2, 4.5, 1.1\n2003, 3.5, 4.9, 1.3\n2004, 3.8, 5.0, 2.0\n2005, 4.0, 5.5, 2.8\n2006, 4.2, 5.6, 3.0\n2007, 4.6, 6.2, 3.3\n2008, 5.1, 6.7, 3.5\n2009, 5.5, 7.0, 4.2\n2010, 5.9, 7.5, 4.7" } ], "id_raw": 262 }, { "id": "3emPqxlWjsItN5I0", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_2.png", "conversations": [ { "from": "human", "value": "\nIn terms of practice frequency, which activity experienced the most significant growth from 2000 to 2010 according to the line chart? Kindly elaborate your reasoning." }, { "from": "gpt", "value": "To figure out which activity saw the greatest increase in frequency, I need to look at the differences in the start and end points of each line on the chart. For {Running}[0.073, 0.298, 0.959, 0.763], it increased from 1.3 to 5.9. For {Yoga}[0.073, 0.136, 0.959, 0.541], it grew from 3.5 to 7.5. Lastly, for {Cycling}[0.073, 0.419, 0.959, 0.844], the frequency rose from 0.5 to 4.7. Hence, Running experienced the most significant growth in frequency from 2000 to 2010, with an increase of 4.6 units.\nBased on the above thought, the answer is:\nBased on the chart, the activity that saw the most significant growth in practice frequency from 2000 to 2010 is Running. While all activities showed a growing trend, the line representing Running demonstrates a steeper slope and a higher overall growth (from 1.3 to 5.9, a total increase of 4.6 units) than the other two activities." } ], "id_raw": 262 }, { "id": "LuSwCVGANF0HNYtB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall increase from 2000 to 2010 in the frequency of cycling?" }, { "from": "gpt", "value": "To calculate the overall increase in cycling activity from the year 2000 to the year 2010, I need to locate {the first line point for Cycling}[[0.073, 0.844]] corresponding to the year 2000, which has a value of 0.5 and {the last line point for Cycling}[[0.959, 0.419]] corresponding to the year 2010, which has a value of 4.7. The overall increase would then be the difference between these two values, so 4.7 - 0.5 = 4.2.\nBased on the above thought, the answer is:\n4.2" } ], "id_raw": 262 }, { "id": "GKd3WlWlExbR4mJF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_3.png", "conversations": [ { "from": "human", "value": "\nAccording to the line plot, can you please extend the values into a table with Quarters as columns and Sectors such as Hotel Accommodation, Food Services, and Travel Agents as rows?" }, { "from": "gpt", "value": "Analyzing the line plot, I can see {the x-axis tick labels}[[0.081, 0.911, 0.106, 0.934], [0.352, 0.911, 0.377, 0.934], [0.623, 0.911, 0.648, 0.934], [0.894, 0.911, 0.918, 0.934]], which express the column headers or Quarters. I perceive {the legend texts}[[0.115, 0.034, 0.385, 0.066], [0.502, 0.034, 0.671, 0.066], [0.788, 0.034, 0.953, 0.066]] as row labels indicating the different Sectors. To capture the values, I have to locate the {line points} of each Graph for the respective Quarters such as {Hotel Accommodation} [0.094, 0.167, 0.906, 0.652], {Food Services} [0.094, 0.450, 0.906, 0.733], {Travel Agents} [0.094, 0.571, 0.906, 0.834] and infer the values mentioned.\nBased on the above thought, the answer is:\nQuarters,Hotel Accommodation,Food Services,Travel Agents\nQ1,1.2,0.8,1.1\nQ2,1.5,1.2,0.9\nQ3,2.1,0.9,1.6\nQ4,3.2,1.5,0.5\nQ1,2.9,1.8,0.7\nQ2,2.5,2.2,0.8\nQ3,3.4,2.1,1.2\nQ4,3.6,1.9,0.3" } ], "id_raw": 263 }, { "id": "qeRNItmdK9vT8azt", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_3.png", "conversations": [ { "from": "human", "value": "\nIs there a sector that shows a consistently upward trend over time?" }, { "from": "gpt", "value": "To conclude if there's a sector showing continually upward trend, I should scrutinize the fluctuations in the line plots for each sector. Inspecting {the line for Hotel Accommodation}[0.094, 0.167, 0.906, 0.652], it shows a general upward trend with slight dips between Q4 and Q1, Q2 and Q3. The {Food Services}[0.094, 0.450, 0.906, 0.733] line shows considerable fluctuations, so it does not show a steady increase. Finally, considering {the line for Travel Agents}[0.094, 0.571, 0.906, 0.834], it shows significant fluctuations but doesn't provide a consistently upward trend.\nBased on the above thought, the answer is:\nBased on the line plot, the Hotel Accommodation sector approximately demonstrates a consistently upward trend over time, although there are small dips between some Quarters. It doesn't remain perfectly stable, but the overall movement is upwards. The other sectors, Food Services and Travel Agents, do not show continual upward movements as they have significant fluctuations. The trend for these sectors is therefore not consistently upwards." } ], "id_raw": 263 }, { "id": "use5Wcn5qDv53rWJ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum value observed for the Travel Agents sector?" }, { "from": "gpt", "value": "To get the maximum value for the Travel Agents sector, I need to locate {the line points for Travel Agents}[[0.094, 0.672], [0.365, 0.713], [0.635, 0.571], [0.906, 0.793], [1.177, 0.753], [1.448, 0.733], [1.719, 0.652], [1.989, 0.834]] and deduce the corresponding values: 1.1, 0.9, 1.6, 0.5, 0.7, 0.8, 1.2, 0.3. The maximum value is 1.6 which corresponds to the point at {Q3}[0.623, 0.911].\nBased on the above thought, the answer is:\n1.6." } ], "id_raw": 263 }, { "id": "3nlGzHoTC6AyCEwj", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_4.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you convert the data collected over the years on 'Number of students (in thousands)' in Public Schools, Private Schools, and Online Schools into a structured table format with Years as the columns and School Types as the rows?" }, { "from": "gpt", "value": "To compile the information into a structured table, I need to identify {the x-axis tick labels}[0.073, 0.911, 0.117, 0.934], ..., [0.937, 0.911, 0.981, 0.934] that represent the years (column headers). Next, I spot {the legend texts}[0.811, 0.055, 0.934, 0.078], ..., [0.811, 0.125, 0.939, 0.148] that denote the different school types (row labels). Lastly, I locate {the line points for each school type}[0.095, 0.464], ..., [0.959, 0.486], ..., [0.959, 0.808] found at the respective years and extract their corresponding values.\nBased on the above thought, the answer is:\nSchool Type, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017\nPublic Schools, 100, 120, 150,180,140,132,110,100\nPrivate Schools, 80, 90, 100,110,105,107,102,95\nOnline Schools, 20, 30, 40, 50,60,70,85,95" } ], "id_raw": 264 }, { "id": "VEJueCU3Vhwx3yY0", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_4.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which type of school saw the most significant growth in the number of students over the years?" }, { "from": "gpt", "value": "To determine the type of school that saw the most significant growth in the number of students, I have to analyze the lines representing each school type. {The line for Public Schools}[0.095, 0.120, 0.959, 0.464] shows a growth from 100 to 180 thousand students initially, then a decline to 100 thousand students. {The line for Private Schools}[0.095, 0.421, 0.959, 0.550] presents a slight increase from 80 to 110 thousand students, then a minor decline to 95 thousand students. However, it is {the line for Online Schools}[0.095, 0.486, 0.959, 0.808] with a steady upward trend from 20 to 95 thousand students showing the most significant growth.\nBased on the above thought, the answer is:\nBased on the trends depicted in the line chart, Online Schools have seen the most significant growth in the number of students over the years. While other types of schools experienced some fluctuation, Online Schools consistently had an upward trend representing a considerable increase in students from 20,000 in 2010 to 95,000 in 2017. So the most growth took place in Online Schools." } ], "id_raw": 264 }, { "id": "cJW8vDPu0PrHGjtG", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in the number of students in Online Schools from 2011 to 2016?" }, { "from": "gpt", "value": "To calculate the increase in the number of students for Online Schools from 2011 to 2016, I must look at {the line point for Online Schools in 2011}[0.218, 0.765] and extract its corresponding value (30,000). I then find {the line point for Online Schools in 2016}[0.836, 0.529] and extract the corresponding value (85,000). The difference between the latter and the former would give the increase in the number of students.\nBased on the above thought, the answer is:\n55 thousand." } ], "id_raw": 264 }, { "id": "J0SgRpk4S1fb2SIu", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_5.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you convert the trends of the three learning methods into structured tabular data with 'Years' as rows and the different learning methods as columns?" }, { "from": "gpt", "value": "To form the tabular data, I first examine {the x-axis labels}[0.051,0.911,0.095,0.934], [0.139,0.911,0.183,0.934],...,[0.938,0.911,0.981,0.934] which represent the row headers or years. Then, the different {legend items}[0.697,0.063,0.889,0.095], [0.697,0.111,0.938,0.143], [0.697,0.159,0.796,0.191] are used as column headers representing each learning method. The values are populated by tracing back {the individual points on each line}[0.073,0.787], [0.161,0.760],...,[0.959,0.357],[0.073,0.249], [0.161,0.222],...,[0.959,0.142],[0.073,0.357], [0.161,0.384],...,[0.959,0.787] to their respective years and learning methods.\nBased on the above thought, the answer is:\nYear,Online Learning,Classroom Learning,Tutoring\n2000,1,6,5\n2002,1.25,6.25,4.75\n2004,1.5,6.5,4.5\n2006,1.75,6.75,4.25\n2008,2,7,4\n2010,2.5,7.5,3.5\n2012,3,8,3\n2014,3.5,7.75,2.5\n2016,4,7.5,2\n2018,4.5,7.25,1.5\n2020,5,7,1" } ], "id_raw": 265 }, { "id": "4N7jtpa4xmS6xUiM", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_5.png", "conversations": [ { "from": "human", "value": "\nBased on the trends in the line plots, which learning method improved the most in quality throughout the years? Justify your answer." }, { "from": "gpt", "value": "To identify the learning method that improved the most, I need to assess the trends and magnitude of increases in the line plots for each method. In observing {the line for Online Learning}[0.073,0.787,0.959,0.357], I can identify a steady increase from a quality score of 1 in 2000 to a score of 5 in 2020. This constitutes an increase of 4 units. Next, I examine {the line representing Classroom Learning}[0.073,0.249,0.959,0.142] and note it starts at 6 in 2000 and ends at 7 in 2020, signaling an increase of 1 unit. Lastly, by considering {the line representing Tutoring}[0.073,0.357,0.959,0.787], I detect a decrease from a score of 5 in 2000 to 1 in 2020. Thus, Tutoring\u2019s score drops by 4 units.\nBased on the above thought, the answer is:\nFrom the line plots, the learning method that improved the most in quality throughout the years is Online Learning. It exhibits a steady increase in Quality Score from 1 to 5 from 2000 to 2020. This consistent upward trend and the total increase of 4 units indicate the most substantial improvement among the three methods, considering that Classroom Learning only improved by 1 unit and Tutoring, in fact, decreased." } ], "id_raw": 265 }, { "id": "qa86dNMyGOLvYpPP", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in the Quality Score for Online Learning from 2000 to 2020?" }, { "from": "gpt", "value": "To calculate the total increase in Quality Score for Online Learning, I need to locate {the starting point of the line for Online Learning}[0.073,0.787] which corresponds to the year 2000 and has a value of 1. Then, I look at {the ending point of the line for Online Learning}[0.959,0.357] which corresponds to the year 2020 and has a value of 5. The increase in Quality Score from 2000 to 2020 then, is 5 - 1 = 4.\nBased on the above thought, the answer is:\n4." } ], "id_raw": 265 }, { "id": "yRCy8zQo1R2p4IAl", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_6.png", "conversations": [ { "from": "human", "value": "\nCould you convert the line chart data into a tabular format with time values as columns and each Task as rows?" }, { "from": "gpt", "value": "To do this, I first need to recognize that {the x-axis tick labels}[0.084, 0.911, 0.095, 0.934], [0.191, 0.911, 0.240, 0.934], [0.323, 0.911, 0.362, 0.934], [0.455, 0.911, 0.483, 0.934], [0.590, 0.911, 0.601, 0.934], [0.717, 0.911, 0.728, 0.934], [0.844, 0.911, 0.855, 0.934], [0.970, 0.911, 0.981, 0.934] represent the time points or column headers. Next, I identify {the legend text}[0.287, 0.032, 0.420, 0.062], [0.520, 0.032, 0.629, 0.062], [0.729, 0.032, 0.836, 0.062] as the row labels representing the different tasks. For populating each cell, I locate {the line points}[0.089, 0.679], [0.216, 0.572], [0.342, 0.491], [0.469, 0.438], [0.596, 0.411], [0.722, 0.223], [0.849, 0.088], [0.976, 0.626], [0.089, 0.760], [0.216, 0.760], [0.342, 0.626], [0.469, 0.653], [0.596, 0.491], [0.722, 0.438], [0.849, 0.841], [0.976, 0.868], [0.089, 0.411], [0.216, 0.384], [0.342, 0.357], [0.469, 0.303], [0.596, 0.491], [0.722, 0.706], [0.849, 0.787], [0.976, 0.814] and extract their corresponding y values for each task at the respective time.\nBased on the above thought, the answer is:\nTask,0,0.125,0.25,0.5,1,2,4,8\nOptimization,0.8,1.2,1.5,1.7,1.8,2.5,3,1\nInnovation,0.5,0.5,1,0.9,1.5,1.7,0.2,0.1\nExpansion,1.8,1.9,2,2.2,1.5,0.7,0.4,0.3" } ], "id_raw": 266 }, { "id": "LXxLCsKaArM4tT2V", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_6.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which task has shown the most improvement in Efficiency over time and why?" }, { "from": "gpt", "value": "To determine the task with the most improvement in Efficiency, I need to interpret the lines for each task. Let's analyze the trend for each: \n{the line points for Optimization}[0.089, 0.679], [0.216, 0.572], [0.342, 0.491], [0.469, 0.438], [0.596, 0.411], [0.722, 0.223], [0.849, 0.088], [0.976, 0.626] start at 0.8 at time 0 and end at 1 at time 8 showing an overall increase then dips down.\n{the line points for Innovation}[0.089, 0.760], [0.216, 0.760], [0.342, 0.626], [0.469, 0.653], [0.596, 0.491], [0.722, 0.438], [0.849, 0.841], [0.976, 0.868] start at 0.5 at time 0 and dip till 0.1 at time 8, it's not showing a substantial improvement.\n{the line points for Expansion}[0.089, 0.411], [0.216, 0.384], [0.342, 0.357], [0.469, 0.303], [0.596, 0.491], [0.722, 0.706], [0.849, 0.787], [0.976, 0.814] start at 1.8 at time 0 and dips till 0.3 at time 8, also not a considerable improvement.\nBased on the above thought, the answer is:\nBased on the trends observed in the line chart, the 'Optimization' task has shown the most improvement in Efficiency over time. The corresponding line starts relatively lower at 0.8 at time 0 and rises to peak at 3.0 at time 4, however, dips to 1 at the end at time 8. Despite the dip, the overall increase and the peak in efficiency over time suggest substantial improvement in comparison to the other tasks." } ], "id_raw": 266 }, { "id": "OsWDcpP8DPLxGpOm", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of Efficiency Improvement Potential for Innovation from time 0 to 8?" }, { "from": "gpt", "value": "To find the range of Efficiency Improvement Potential for Innovation, I need to locate the {line points for Innovation}[0.089, 0.760], [0.216, 0.760], [0.342, 0.626], [0.469, 0.653], [0.596, 0.491], [0.722, 0.438], [0.849, 0.841], [0.976, 0.868] and extract the corresponding values which are 0.5, 0.5, 1, 0.9, 1.5, 1.7, 0.2, 0.1. The maximum value is 1.7 at time 2 and the minimum value is 0.1 at time 8, so the range is 1.7 - 0.1 = 1.6.\nBased on the above thought, the answer is:\n1.6." } ], "id_raw": 266 }, { "id": "ugATqdq4Mkg8t3uC", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_7.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the revenue changes for Hotels, Restaurants, and Travel Agencies over the years 2000 to 2010?" }, { "from": "gpt", "value": "To generate such a data table from this line chart, I'll first focus on the x-axis and get the list of years from {the x-axis tick labels}[[0.041, 0.933, 0.076, 0.951], [0.132, 0.933, 0.167, 0.951], [0.223, 0.933, 0.258, 0.951], [0.313, 0.933, 0.348, 0.951], [0.404, 0.933, 0.439, 0.951], [0.495, 0.933, 0.530, 0.951], [0.586, 0.933, 0.621, 0.951], [0.677, 0.933, 0.712, 0.951], [0.768, 0.933, 0.803, 0.951], [0.859, 0.933, 0.894, 0.951], [0.950, 0.933, 0.985, 0.951]] which are displayed on the chart. These will become the row headers in our table.\n\nThe {legend texts}[[0.281, 0.022, 0.343, 0.045], [0.436, 0.022, 0.553, 0.045],[0.646, 0.022, 0.799, 0.045]] represent our different service types: Hotels, Restaurants, and Travel Agencies. These service types will be the column headers.\n\nFinally, the y-values are extracted from each plotted line for each year. These y-values are the revenue in billions for the corresponding service type: {the line points for Hotels}[[0.058, 0.712], [0.149, 0.701], [0.240, 0.680], [0.331, 0.628], [0.422, 0.607], [0.513, 0.586], [0.604, 0.555], [0.695, 0.523], [0.786, 0.481], [0.877, 0.450], [0.967, 0.419]], {the line points for Restaurants}[[0.058, 0.555], [0.149, 0.586], [0.240, 0.502], [0.331, 0.523], [0.422, 0.471], [0.513, 0.481], [0.604, 0.450], [0.695, 0.398], [0.786, 0.366], [0.877, 0.377], [0.967, 0.293]], and {the line points for Travel Agencies}[[0.058, 0.398], [0.149, 0.460], [0.240, 0.481], [0.331, 0.408], [0.422, 0.345], [0.513, 0.314], [0.604, 0.272], [0.695, 0.262], [0.786, 0.199], [0.877, 0.188], [0.967, 0.136]].\nBased on the above thought, the answer is:\nYear, Hotels, Restaurants, Travel Agencies\n2000, 2, 3.5, 5\n2001, 2.1, 3.2, 4.4\n2002, 2.3, 4, 4.2\n2003, 2.8, 3.8, 4.9\n2004, 3, 4.3, 5.5\n2005, 3.2, 4.2, 5.8\n2006, 3.5, 4.5, 6.2\n2007, 3.8, 5, 6.3\n2008, 4.2, 5.3, 6.9\n2009, 4.5, 5.2, 7\n2010, 4.8, 6, 7.5" } ], "id_raw": 267 }, { "id": "QVtq3nAlW9D26IVM", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_7.png", "conversations": [ { "from": "human", "value": "\nAccording to this chart, which type of service sector has shown the most improvement from 2000 to 2010?" }, { "from": "gpt", "value": "To identify the service sector with the most improvement, I will compare the differences in the revenues from 2000 and 2010 for each sector. I located the initial and final values for {Hotels}[0.058, 0.712] and [0.967, 0.419], for {Restaurants}[0.058, 0.555] and [0.967, 0.293], and for {Travel Agencies}[0.058, 0.398] and [0.967, 0.136]. According to these points, the revenue for the Hotels increased by 2.8 billion, for Restaurants it increased by 2.5 billion, and for Travel Agencies, it increased by 2.5 billion.\nBased on the above thought, the answer is:\nBased on the chart, the service sector that has shown the most improvement from 2000 to 2010 is Hotels. It had an increase of 2.8 billion dollars in revenue, which is greater compared to the other two service sectors." } ], "id_raw": 267 }, { "id": "3t7eDD1yaBW6pwKe", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in revenue for Restaurants from the year 2000 to 2010?" }, { "from": "gpt", "value": "I need to locate the first and last y-values for Restaurants from the plotted line for Restaurants. The initial y-value is found at {the point for the year 2000 for Restaurants}[0.058, 0.555] which corresponds to the revenue of 3.5 billion. The final y-value is at {the point for the year 2010 for Restaurants}[0.967, 0.293] which corresponds to the revenue of 6 billion. So, the increase in revenue from the year 2000 to 2010 for Restaurants is 6 billion - 3.5 billion = 2.5 billion.\nBased on the above thought, the answer is:\n2.5 billion" } ], "id_raw": 267 }, { "id": "Nc2RYupxSliGVetB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_8.png", "conversations": [ { "from": "human", "value": "\nCan the data for price trends of financial securities be shown in the form of a table listing each security with its percentage change?" }, { "from": "gpt", "value": "Yes, to convert the visuals into tabular data. I would first identify the x-axis labels as the financial securities and the y-axis as the percentage change. These will serve as columns for the table. Specifically, I\u2019d have to determine {the x-axis labels}[0.176, 0.113, 0.334, 0.895] that represent the security names and {y-axis points}[0.284, 0.172, 0.674, 0.821, 0.432, 0.385, 0.504, 0.524] that represent the percentage change. The percentages of each security are determined from their associated y-coordinates. I\u2019ll constrain my focus to the values at where the line intersects with the y-axis.\nBased on the above thought, the answer is:\nSecurity, Percentage Change\nCorporate Bond, 3.5\nMunicipal Bond, 2.3\nTreasury Notes, 4.4\nPreferred Stocks, 1.3\nCommon Stocks, -0.7" } ], "id_raw": 268 }, { "id": "h0fDShKfEBp6aK5D", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_8.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which security appears to be the riskiest investment given its performance trend?" }, { "from": "gpt", "value": "To respond to this question, I need to assess the trends of all securities, focusing specifically on their price change. I see that {the Corporate Bond line}[0.2, 0.4], {the Municipal Bond line}[0.255, 0.381] ,{the Treasury Notes line}[0.396, 0.508], and {the Preferred Stocks line}[0.508, 0.31] have positive trends. However, {the Common Stocks line}[0.618, 0.265] has a negative trend which indicates a price reduction.\nBased on the above thought, the answer is:\nBased on the line chart, it appears that Common Stocks may be the riskiest investment as its performance trend is declining with a -0.7% change in price which opposes the positive trend of all other securities." } ], "id_raw": 268 }, { "id": "k5SgueGPgaY3gIh7", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_8.png", "conversations": [ { "from": "human", "value": "\nWhat\u2019s the difference in percentage change between Corporate Bond and Common Stocks?" }, { "from": "gpt", "value": "To answer this question, I need to extract the percentage change for both, Corporate Bond and the Common Stocks. I can get these values where {the Corporate Bond line}[0.2, 0.4] and {the Common Stocks line}[0.618, 0.265] intersect the y-axis. The value for Corporate Bond is 3.5% and for Common Stocks - it's -0.7%. Hence the difference would be 3.5 - (-0.7) = 4.2.\nBased on the above thought, the answer is:\n4.2." } ], "id_raw": 268 }, { "id": "Pc5JumP3qFuRMZD4", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_9.png", "conversations": [ { "from": "human", "value": "\nCan you push the data from the line chart depicting performance gains over time into a structured table with Time Scale intervals as the columns and the AI Application and Automation as rows?" }, { "from": "gpt", "value": "In order to derive the data table, I will focus on {the x-axis tick labels}[[0.095, 0.911, 0.106, 0.934], [0.200, 0.911, 0.250, 0.934], [0.331, 0.911, 0.369, 0.934], [0.462, 0.911, 0.489, 0.934], [0.595, 0.911, 0.606, 0.934], [0.720, 0.911, 0.731, 0.934], [0.845, 0.911, 0.856, 0.934], [0.970, 0.911, 0.981, 0.934]], these would represent column headers. {The legend texts}[[0.359, 0.034, 0.527, 0.066], [0.643, 0.034, 0.785, 0.066]], will represent the Row labels. Then I poll across the line points for AI Application {the line points for AI Application}[[0.100, 0.895], [0.225, 0.814], [0.350, 0.733], [0.475, 0.571], [0.600, 0.248], [0.726, 0.531], [0.851, 0.814], [0.976, 0.854]], and Automation {the line points for Automation}[[0.100, 0.854], [0.225, 0.814], [0.350, 0.773], [0.475, 0.571], [0.600, 0.450], [0.726, 0.692], [0.851, 0.814], [0.976, 0.874]] and map their position along the {y-axis tick labels}[[0.061, 0.885, 0.088, 0.909], [0.061, 0.784, 0.088, 0.808], [0.061, 0.683, 0.088, 0.707], [0.061, 0.582, 0.088, 0.605], [0.049, 0.481, 0.088, 0.504], [0.049, 0.380, 0.088, 0.403], [0.049, 0.279, 0.088, 0.302], [0.049, 0.178, 0.088, 0.201], [0.050, 0.077, 0.088, 0.100]] to actual values for the respective time intervals.\nBased on the above thought, the answer is:\nPeriod,0,0.125,0.25,0.5,1,2,4,8\nAI Application,0,0.8,1.6,3.2,6.4,3.6,0.8,0.4\nAutomation,0.4,0.8,1.2,3.2,4.4,2,0.8,0.2" } ], "id_raw": 269 }, { "id": "dZEWtVHBirzdRNBd", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_9.png", "conversations": [ { "from": "human", "value": "\nFrom a high-level perspective, based on the line chart trend, which among AI Application and Automation saw a significant performance gain sooner?" }, { "from": "gpt", "value": "To respond about the early significant performance gain, I will examine both the lines for AI Application {line for AI Application}[0.100, 0.248, 0.976, 0.895] and Automation {line for Automation}[0.100, 0.450, 0.976, 0.874]. For AI Application, the performance gain starts to spike at Time Scale {0.5}[0.462, 0.911, 0.489, 0.934] whereas for Automation, it happens at Time Scale {1}[0.595, 0.911, 0.606, 0.934].\nBased on the above thought, the answer is:\nAs per the trends demonstrated in the line chart, AI Application saw a significant performance gain sooner than Automation. This is inferred from the steep rise observed in AI Application's trend line at Time Scale 0.5, whereas for Automation the rise happens at a later Time Scale of 1. Can we say this represents AI Applications evolving faster than Automation in the initial stages." } ], "id_raw": 269 }, { "id": "l67477nzZQ0JMNxq", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the peak performance gain for AI Applications in the given time scale?" }, { "from": "gpt", "value": "To find the peak performance gain for AI Applications, I need to identify the maximum value from {the line points for AI Application}[[0.100, 0.895], [0.225, 0.814], [0.350, 0.733], [0.475, 0.571], [0.600, 0.248], [0.726, 0.531], [0.851, 0.814], [0.976, 0.854]]. Upon extracting the corresponding values: 0, 0.8, 1.6, 3.2, 6.4, 3.6, 0.8, 0.4. I find the peak value to be 6.4 which corresponds to the Time Scale {1}[0.595, 0.911, 0.606, 0.934].\nBased on the above thought, the answer is:\n6.4%" } ], "id_raw": 269 }, { "id": "PFy2twxGfGmkTQvX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_12.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data into a structured table representing total values of each transportation category for the given years?" }, { "from": "gpt", "value": "First, I'll identify the x-axis tick labels which are {the years}[0.076, 0.923, 0.111, 0.943],[0.163, 0.923, 0.198, 0.943],[0.251, 0.923, 0.286, 0.943]. These will act as the individual rows of the table. The column headers will be the different transportation types found in {the legend items}[0.165, 0.054, 0.237, 0.081],[0.165, 0.095, 0.336, 0.122],[0.165, 0.136, 0.368, 0.163]. I'll then locate specific points like those in the {Income line points}[0.093, 0.176],[0.181, 0.161],[0.268, 0.147] to extract their respective values across each year for the 'Income' transportation type. The process is repeated for the other types.\nBased on the above thought, the answer is:\nYear,Income,Food Expenditure,Housing Expenditure\n2000,50000,10000,20000\n2001,51000,10200,21000\n2002,52000,10500,23000\n2003,51000,10800,24000\n2004,52000,11200,25000\n2005,53000,11500,26000\n2006,54000,12000,27000\n2007,55000,12500,30000\n2008,56000,13000,34000\n2009,57000,14000,36000\n2010,58000,15000,38000" } ], "id_raw": 272 }, { "id": "vFhXwD7zBFYqMFbD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_12.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which aspect of expenditure saw the most growth between 2000 and 2010?" }, { "from": "gpt", "value": "In order to determine which type of expenditure had the most growth, I'll compare the rate of change between 2000 and 2010 for the 'Income', 'Food Expenditure', and 'Housing Expenditure'. For the 'Income', looking at the line points at {2000}[0.093, 0.176] and {2010}[0.967, 0.059], the difference in change is (58,000 - 50,000)/50,000 = 16%. Next, for 'Food Expenditure', the points at {2000}[0.093, 0.763] and {2010}[0.967, 0.690] show a difference of (15,000 - 10,000)/10,000 = 50%. Lastly, 'Housing Expenditure', with points at {2000}[0.093, 0.616] and {2010}[0.967, 0.352], shows a difference of (38,000 - 20,000)/20,000 = 90% increase.\nBased on the above thought, the answer is:\nAccording to the line chart, 'Housing Expenditure' saw the most growth between 2000 and 2010. The percent increase for this expenditure type was 90%, which is higher than the percent increase for 'Income' (16%) and 'Food Expenditure' (50%). This suggests that 'Housing Expenditure' took up a larger proportion of expenditure over this time period." } ], "id_raw": 272 }, { "id": "E0K7hO4YQxQRz3po", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in the Housing Expenditure from 2000 to 2010?" }, { "from": "gpt", "value": "To calculate the percentage increase in Housing Expenditure from 2000 to 2010, I first identify the starting value for Housing Expenditure in 2000 at {this point}[0.093, 0.616], which corresponds to $20,000. I then identify the ending value for Housing Expenditure in 2010 at {this point}[0.967, 0.352], which corresponds to $38,000. Using these values, I calculate the percentage increase as follows: (38,000 - 20,000) / 20,000) * 100.\nBased on the above thought, the answer is:\n90%" } ], "id_raw": 272 }, { "id": "x7H9X0fV3Y8fyDHj", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_13.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the line chart data into a table format with Prospects as rows and Technologies as columns?" }, { "from": "gpt", "value": "To convert the line chart into a table, I will first identify {the x-axis labels}[0.019, 0.911, 0.156, 0.934], [0.278, 0.911, 0.448, 0.934], [0.547, 0.911, 0.729, 0.934], [0.845, 0.911, 0.981, 0.934] which represent the rows or prospects. Then, I identify {legend labels}[0.257, 0.034, 0.280, 0.066], [0.397, 0.034, 0.441, 0.066], [0.558, 0.034, 0.812, 0.066] which represent the columns or Technologies. Finally, I locate and extract the points associated with each technology on each prospect using their respective coordinates, {AI coordinates}[0.087, 0.248, 0.913, 0.490], {IoIT coordinates}[0.087, 0.167, 0.913, 0.652], and {Quantum Computing coordinates}[0.087, 0.328, 0.913, 0.814].\nBased on the above thought, the answer is:\nProspects,AI,IoIT,Quantum Computing\nMinimal change,8,9,7\nPronounced decline,7,5,3\nDownward trajectory,6,3,1\nMinimal decline,5,4.5,2" } ], "id_raw": 273 }, { "id": "6zjtKajlXLKSZrW1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_13.png", "conversations": [ { "from": "human", "value": "\nBased on the trends indicated in the line chart, which technology seems to exhibit the most downward progression over the prospects? Justify your answer." }, { "from": "gpt", "value": "In order to determine which technology exhibits the most downward progression, I need to examine the changes in the line plots for each technology. Observing {The line for AI}[0.087, 0.248, 0.913, 0.490], it presents a gradual decline from 8 to 5. Similarly, {The line for IoIT}[0.087, 0.167, 0.913, 0.652] exhibits a more rapid decline from 9.0 to 4.5. However, for {Quantum Computing}[0.087, 0.328, 0.913, 0.814], the line follows a distinctly downward direction, from a value of 7 at 'Minimal Change' to a value of 1 at 'Downward Trajectory', and a slight raise to 2 at 'Minimal Decline'. Thus we can deduce that Quantum Computing experiences the most downward progression across the given prospects.\nBased on the above thought, the answer is:\nQuantum Computing appears to demonstrate the most significant downward trend as per this line chart. Its progression shows a steep decline from a value of 7 to 1 over the prospects and rises slightly to 2 thereby indicating a pronounced overall downward trend compared to AI and IoIT." } ], "id_raw": 273 }, { "id": "yJ0dO0vLNOIf2Z43", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in performance gain between the best and worst prospect for Quantum Computing?" }, { "from": "gpt", "value": "To locate the values for Quantum Computing across all prospects, I observe {the point coordinates for Quantum Computing}[0.087, 0.328], [0.363, 0.652], [0.638, 0.814], [0.913, 0.733] which represent the values: 7, 3, 1, 2. The maximum value 7 corresponds to {Minimal Change}[0.087, 0.911], and the minimum value 1 corresponds to {Downward Trajectory}[0.638, 0.911]. The difference in performance gain is thus 7 - 1 = 6.\nBased on the above thought, the answer is:\n6." } ], "id_raw": 273 }, { "id": "uDpNV0Kh24zhylBm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_14.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you present the distribution and counts of education resources (Books and Computers) within Urban and Rural areas in a structured table format?" }, { "from": "gpt", "value": "To generate the data table, I first need to identify {the x-axis tick labels}[[0.095, 0.895], [0.527, 0.895], [0.959, 0.895]] which represent the area types \u2013 Urban and Rural. From {the legend}[[0.153, 0.021, 0.901, 0.079]], I can see three items: Education, Healthcare, and Social Welfare, but we only need the counts for Books and Computers in this case. The line points for Books and Computers are under {the Education Data}[[0.095, 0.558, 0.959, 0.153]] and {the Social Welfare Data}[[0.095, 0.625, 0.959, 0.234]] respectively. Hence, We can extract these values and structure them as follows.\nBased on the above thought, the answer is:\nArea,Books,Computers\nUrban,8000,15000\nRural,3000,5000" } ], "id_raw": 274 }, { "id": "Vjolk3SplLmyO6y4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_14.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which area type, Urban or Rural, is experiencing more disparities in terms of educational resources distribution? Justify your answer." }, { "from": "gpt", "value": "To determine which area is experiencing more disparities, we must examine the differences in resource allocation for both Books and Computers between Urban and Rural areas. Looking at the data, both types of resources are more plentiful in {Urban}[[0.527, 0.895]] versus {Rural}[[0.959, 0.895]] areas. Notably, the disparity in terms of Computers is noticeably larger than that of books, suggesting a digital divide.\nBased on the above thought, the answer is:\nBased on the line chart, the Urban area appears to be experiencing more disparities in the distribution of education resources when compared to the Rural area. Both Books and Computers are more concentrated in Urban areas than in Rural areas, with the difference being significantly larger in the case of Computers. This indicates imbalanced resource allocation and a noticeable digital gap between the two areas." } ], "id_raw": 274 }, { "id": "DnCmtiKrHEownmJY", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in the number of computers between Urban and Rural areas?" }, { "from": "gpt", "value": "To determine the difference in computer numbers, I should compare the y-values of the line points representing computers in Urban and Rural areas. For Urban, the relevant point is in {Computers in Urban}[[0.527, 0.895]] and for Rural, the corresponding point is in {Computers in Rural}[[0.959, 0.895]]. Upon extracting the actual values, the difference is computed as 15000 - 5000 = 10000.\nBased on the above thought, the answer is:\n10000." } ], "id_raw": 274 }, { "id": "baWRAhDbXVtOiw6H", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_15.png", "conversations": [ { "from": "human", "value": "\nCould you turn this chart showing the average weekly distance traveled by public transport in different cities across various age groups into a table?" }, { "from": "gpt", "value": "Sure, I will start by identifying {the x-axis labels}['[0.073, 0.933, 0.095, 0.951]', '[0.200, 0.933, 0.222, 0.951]', '[0.334, 0.933, 0.343, 0.951]', '[0.455, 0.933, 0.477, 0.951]', '[0.588, 0.933, 0.597, 0.951]'], which represent the age group. Then I will check {the subplot legend}['[0.146, 0.044, 0.380, 0.067]', '[0.146, 0.077, 0.358, 0.100]', '[0.146, 0.110, 0.324, 0.133]'] for the names of cities. When constructing the rows of the table, I will get the values from {the line points}['[0.084, 0.423]', '[0.211, 0.440]', '[0.339, 0.460]', '[0.466, 0.496]', '[0.593, 0.509]'] for each city's distance travelled in different age groups.\nBased on the above thought, the answer is:\nAge Group,Delhi,Hyderabad,Kolkata\n20-30,55,48,46\n30-40,52,47,44\n40-50,48,45,40\n50-60,45,42,35\n60-70,20,38,32" } ], "id_raw": 275 }, { "id": "A0Ql9aQ7VH8nAZZE", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_15.png", "conversations": [ { "from": "human", "value": "\nWhich city's older population (60-70) has been impacted the most in terms of decreased use of public transportation compared to the younger population (20-30)?" }, { "from": "gpt", "value": "To answer this question, I need to look at {the line values for the 60-70 age group}['[0.593, 0.509]'], and compare them with the values for the 20-30 age group {line values for the 20-30 age group}['[0.084, 0.423]']. {The line for Delhi}[0.084, 0.423, 0.593, 0.509] shows the largest drop from 55 km per week to 20 km per week compared to Hyderabad and Kolkata.\nBased on the above thought, the answer is:\nDelhi's older population (60-70) has been the most impacted in terms of decreased use of public transportation compared to the younger population (20-30). The average distance travelled by this age group in Delhi decreased by 35 km per week compared to the 20-30 age group. This drop is greater than the decreases observed for the same age groups in Hyderabad and Kolkata." } ], "id_raw": 275 }, { "id": "uXiJdctwfLWxP1tq", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the value of the biggest drop in distance traveled by public transport experienced by Delhi citizens as they age?" }, { "from": "gpt", "value": "By inspecting {the line for Delhi}['[0.084, 0.423]', '[0.211, 0.440]', '[0.339, 0.460]', '[0.466, 0.496]', '[0.593, 0.509]'], the biggest drop is from the 50-60 age group at 45 km per week to the 60-70 age group at 20 km per week. The drop in distance traveled is therefore 45-20=25km.\nBased on the above thought, the answer is:\n25 km." } ], "id_raw": 275 }, { "id": "byJ7GV1fxAmxHNIj", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_16.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line graphs showing each investor's investment strategy into a table format with Investment Rounds as columns and Investors as rows?" }, { "from": "gpt", "value": "To transform the line graphs into a tabular format, I must identify key elements. {The x-axis labels}[0.099, 0.911, 0.143, 0.934], [0.193, 0.911, 0.238, 0.934], [0.288, 0.911, 0.332, 0.934], [0.383, 0.911, 0.427, 0.934], [0.478, 0.911, 0.522, 0.934], [0.573, 0.911, 0.617, 0.934], [0.668, 0.911, 0.712, 0.934], [0.762, 0.911, 0.806, 0.934], [0.857, 0.911, 0.901, 0.934] indicate the row labels representing the different investors. The legend entry [0.088, 0.034, 0.291, 0.066], [0.408, 0.034, 0.649, 0.066], [0.766, 0.034, 0.980, 0.066] acts as the column headers or investment rounds. The line points [0.121, 0.328, 0.879, 0.611], [0.121, 0.369, 0.879, 0.506], [0.121, 0.288, 0.879, 0.571] account for the values invested during each round by the respective investors.\nBased on the above thought, the answer is:\nInvestors,Seed,Series A,Series B,Series C,Outreach\nInvestor 1,20,15,13,10,11\nInvestor 2,50,100,90,120,105\nInvestor 3,25,30,28,35,33\nInvestor 4,28,27,25,26,30\nInvestor 5,55,80,100,120,110\nInvestor 6,30,28,25,20,15" } ], "id_raw": 276 }, { "id": "dTaV94twafLNXoJY", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_16.png", "conversations": [ { "from": "human", "value": "\nConsidering the line graphs, which investor appears to have taken the most risk in terms of stepping up the investment amounts across the different rounds? Provide reasons for your answer." }, { "from": "gpt", "value": "To ascertain the risk involved in stepping up investment amounts across different rounds, I examine the variations in the investment amounts mentioned in the line graphs. {Investor 2's line plot}[0.121, 0.328, 0.879, 0.611] shows a steady increase from 50M in the Seed round to 120M in Series C. Despite a small dip during outreach, the magnitude and consistency of this increase indicate risk-taking for potential startup success. In contrast, {Investor 1's line plot}[0.121, 0.369, 0.879, 0.506] starts at 20M in the Seed round, consistently decreasing afterward; these trends suggest less risk-taking. Other investors show fluctuations, but none matches Investor 2's aggressive trajectory.\nBased on the above thought, the answer is:\nFrom the displayed line graphs, Investor 2 appears to have taken the most risk in terms of stepping up the investment amounts across the rounds. The steady increase from 50M in the Seed round to 120M in Series C, despite a slight dip in the Outreach round, indicates a consistently aggressive investment strategy." } ], "id_raw": 276 }, { "id": "x1KSvvxv9Ta46w5w", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_16.png", "conversations": [ { "from": "human", "value": "\nHow much higher was Investor 2's peak investment in comparison to Investor 1's peak investment?" }, { "from": "gpt", "value": "To find the peak investments, I look at the graph and highlights at each line's highest point. For {Investor 2's peak investment}[0.405, 0.409], I see the peak at Series C with 120M. For {Investor 1's peak investment}[0.216, 0.393], the peak appears at the Seed round with 20M. So, the difference between Investor 2's peak investment and Investor 1's peak investment is 120M - 20M = 100M.\nBased on the above thought, the answer is:\n100 million dollars." } ], "id_raw": 276 }, { "id": "A9xAulC6oAw5Vw8J", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_14_18.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart indicating the popularity of various fitness routines across seasons into a table?" }, { "from": "gpt", "value": "Yes, as seen in the line chart, {the x-axis}[0.073, 0.943, 0.560, 0.975] represents the different seasons which will be the column headers of the table. {The legend}[0.253, 0.021, 0.795, 0.079] indicates the different workout regimes which will be the rows of our table. For each type of workout, we track the points at each season {the line points for each workout regime}[0.073, 0.409, 0.976, 0.733], [0.073, 0.248, 0.976, 0.814] and record the respective values to populate the cells of our table.\nBased on the above thought, the answer is:\nSeasons,Singer Yoga,Zumba Classes,HIIT Sessions,Pilates Workouts,Morning Runs\nSpring,1.0,0.5,1.2,0.8,0.9\nSummer,2.0,1.2,2.4,1.5,1.8\nAutumn,2.5,1.8,2.7,2.9,2.2\nWinter,3.0,2.0,2.5,2.3,2.1" } ], "id_raw": 278 }, { "id": "tG4Fmnj2LgKIoKin", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_14_18.png", "conversations": [ { "from": "human", "value": "\nWhat workout regime shows a trend that deviates from the general pattern of fitness activities peaking in the summer and declining during winter?" }, { "from": "gpt", "value": "Looking at the lines representing different workout trends, I see that while most of them show a peak during the summer {points on lines during summer}[0.202, 0.571], [0.202, 0.700] and a decline during the winter {points on lines during winter}[0.460, 0.409], [0.460, 0.571], there is one line that deviates from this trend. {The Pilates line}[0.073, 0.409, 0.976, 0.733] shows a surprising spike during the autumn season {point on Pilates line during autumn}[0.331, 0.490] contrary to the general downward trend during colder months.\nBased on the above thought, the answer is:\nThe workout regime that deviates from the general trend is Pilates. Its popularity shows an unexpected surge during the autumn season, going against the usual trend of declining fitness activity during colder months." } ], "id_raw": 278 }, { "id": "blKq6s15t8guerYX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_14_18.png", "conversations": [ { "from": "human", "value": "\nWhat was the maximum popularity gain for Zumba Classes from one season to the next?" }, { "from": "gpt", "value": "To find the maximum popularity gain for Zumba Classes, I should locate {the line points for Zumba Classes}[0.073, 0.814, 0.976, 0.248] and compute the difference between consecutive points representing 0.5(Spring), 1.2(Summer), 1.8(Autumn), and 2.0(Winter). The maximum difference would be the maximum gain from one season to the next.\nBased on the above thought, the answer is:\n1.3." } ], "id_raw": 278 }, { "id": "fIITnkfYNT8OAlRZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_0.png", "conversations": [ { "from": "human", "value": "\nGiven the linechart, can you convert the data into a legible table with the rows as 'Year' and columns as 'Internet Users (Millions)' and 'Communication Companies Revenue (Billions)'?" }, { "from": "gpt", "value": "To convert the linechart data into a tabular format, I'll first take into consideration {the x-axis tick labels}[[0.228, 0.902, 0.281, 0.932], [0.374, 0.902, 0.426, 0.932], [0.519, 0.902, 0.572, 0.932], [0.664, 0.902, 0.717, 0.932], [0.810, 0.902, 0.862, 0.932]], which indicate the 'years'. Then, to find the corresponding values, I observe the linegraphs for {Internet Users (Millions)}[0.255, 0.129, 0.836, 0.827] and {Communication Companies Revenue (Billions)}[0.255, 0.865, 0.836, 0.884]. By mapping these values with their corresponding years, I can generate a table as follows:\nBased on the above thought, the answer is:\nYear,Internet Users (Millions),Communication Companies Revenue (Billions)\n2000,413,60\n2005,1056,85\n2010,2041,120\n2015,3143,140\n2020,4676,180" } ], "id_raw": 280 }, { "id": "ReTIokVwM8sJ8AK7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_0.png", "conversations": [ { "from": "human", "value": "\nWhat conclusions can you draw about the development of the Internet and Communication Industry from the years 2000 to 2020 based on the line chart displayed?" }, { "from": "gpt", "value": "Studying {the line representing Internet Users (Millions)}[0.255, 0.129, 0.836, 0.827] and {the line representing the Communication Companies Revenue (Billions)}[0.255, 0.865, 0.836, 0.884], it's quite evident that both the number of internet users and the revenue of communication companies have been increasing over the two decades from 2000 to 2020. This suggests an overall expansion and growth in the Internet and Communication Industry during these years. However, the growth in the number of Internet Users seems to be at a much greater pace as compared to the growth in Revenue.\nBased on the above thought, the answer is:\nThe line chart indicates a steady growth in both Internet Users and Communication Companies Revenue from the year 2000 to 2020, reflecting the continuous expansion of the Internet and Communication Industry. However, while the internet users have increased more than tenfold, indicating a rapidly digitalizing world, the growth in revenue of communication companies, although steady, is not as exponential. This potentially points towards increased competition in the market or changes in revenue models over the years." } ], "id_raw": 280 }, { "id": "RC6OytVSNdYS4PDB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_0.png", "conversations": [ { "from": "human", "value": "\nFrom the year 2000 and 2020, by how much did the number of Internet Users increase?" }, { "from": "gpt", "value": "To find out the increase in Internet Users from 2000 to 2020, I can check the y-values of {the line corresponding to Internet Users (Millions)}[0.255, 0.129, 0.836, 0.827]. For 2000, it's 413 and for 2020, it's 4676. The difference between these two values will give me how much the Internet Users increased over that period.\nBased on the above thought, the answer is:\n4263 million." } ], "id_raw": 280 }, { "id": "IclqNnFgj90ye8kl", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_1.png", "conversations": [ { "from": "human", "value": "\nCan you please transform the data in the line chart representing Risk Management and Risk Evaluation into a structured table with categories as rows and quantitative parameters as columns?" }, { "from": "gpt", "value": "Sure, firstly, I analyse {the x-axis tick labels}[[0.157, 0.769, 0.239, 0.786], [0.291, 0.769, 0.372, 0.786], [0.425, 0.769, 0.506, 0.786], [0.559, 0.769, 0.640, 0.786], [0.694, 0.769, 0.776, 0.786]] to get column names that are parameters. Then I locate {the legend texts}[[0.814, 0.111, 0.875, 0.121], [0.814, 0.091, 0.895, 0.101]] to define the rows representing categories of risks. The values are discovered by identifying the points on {the line for Risk Management}[[0.157, 0.651, 0.694, 0.417]], and {the line for Risk Evaluation}[[0.157, 0.327, 0.694, 0.574]].\nBased on the above thought, the answer is:\nParameter,Risk Management,Risk Evaluation\n1,45,12\n2,38,20\n3,29,26\n4,32,29\n5,35,27" } ], "id_raw": 281 }, { "id": "lyauIDNDaIqx1V3D", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_1.png", "conversations": [ { "from": "human", "value": "\nIn light of the line graph, which category - Risk Management or Risk Evaluation displays a more stable progression? Please justify your answer." }, { "from": "gpt", "value": "In order to evaluate the stability of the progression for both categories, I need to measure the amount of fluctuations in the line plots for each category. Observing the data for {Risk Management}[0.157, 0.651, 0.694, 0.417], I witness significant swings in values, demonstrating an unstable progression. On the other hand, for {Risk Evaluation}[0.157, 0.327, 0.694, 0.574], though there are swings in values, the swings are lesser amplitude compared to Risk Management, pointing to a more stable progression.\nBased on the above thought, the answer is:\nBased on the line chart, Risk Evaluation exhibits a more stable progression compared to Risk Management. While both lines do show fluctuations, the values in Risk Evaluation seem to have lower deviations overall compared to those in Risk Management, which exhibit larger swings and hence more instability." } ], "id_raw": 281 }, { "id": "byH4hjOxGh7xaNAz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_1.png", "conversations": [ { "from": "human", "value": "\nCould you calculate the difference between the highest and lowest values of Risk Management?" }, { "from": "gpt", "value": "To calculate the difference, I must locate the maximum and minimum of Risk Management. I can get these by inspecting {the points for Risk Management}[[0.157, 0.651], [0.291, 0.578], [0.425, 0.474], [0.559, 0.499], [0.694, 0.417]], which correspond to the values 45, 38, 29, 32, and 35 respectively. The highest value is 45 and the lowest is 29. So, the difference is 45 - 29 = 16\nBased on the above thought, the answer is:\n16" } ], "id_raw": 281 }, { "id": "Tg4kQjK0S3RDl3Fm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_2.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table containing the annual revenues for Film Production, TV Production, Music Sales, Game Sales, and Book Sales over the period 2015 to 2020?" }, { "from": "gpt", "value": "To construct the table, I'll identify {the x-axis labels}[[0.130, 0.909, 0.152, 0.932], [0.292, 0.909, 0.314, 0.932], [0.454, 0.909, 0.476, 0.932], [0.617, 0.909, 0.639, 0.932], [0.779, 0.909, 0.801, 0.932], [0.941, 0.909, 0.963, 0.932]] as the years. Next, I see from {the legend}[[0.167, 0.144, 0.515, 0.174], [0.167, 0.188, 0.526, 0.218]] that there are two lines representing Community Development Budget and Local Government Support Budget. For each line, I'll track {the line points}[[0.130, 0.592], [0.292, 0.640], [0.454, 0.704], [0.617, 0.752], [0.779, 0.832], [0.941, 0.863],[0.130, 0.512], [0.292, 0.480], [0.454, 0.369], [0.617, 0.321], [0.779, 0.225], [0.941, 0.113]] for each year and extract the corresponding values.\nBased on the above thought, the answer is:\nYear,Community Development Budget ($m),Local Government Support Budget ($m)\n2015,75,80\n2016,72,82\n2017,68,89\n2018,65,92\n2019,60,98\n2020,58,105" } ], "id_raw": 282 }, { "id": "jzOrDrO0nhic9hzb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_2.png", "conversations": [ { "from": "human", "value": "\nWhich sector had a consistent decrease in budget over the years?" }, { "from": "gpt", "value": "To determine which sector had a consistent decrease in budget, I need to look at the trends in their line plots. I can see from the line for {Community Development Budget}[0.130, 0.592, 0.941, 0.863] that the budget decreases consistently from 75 in 2015 to 58 in 2020. On the other hand, the line for {Local Government Support Budget}[0.130, 0.512, 0.941, 0.113] indicates an increase in budget over the same period of time.\nBased on the above thought, the answer is:\nThe sector that experienced a consistent decrease in budget over the years was Community Development, as indicated by the consistent downward trend in its line plot." } ], "id_raw": 282 }, { "id": "n6pRRmDVpHhyh4nR", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_2.png", "conversations": [ { "from": "human", "value": "\nBy how much has the Local Government Support Budget Changed from 2015 to 2020?" }, { "from": "gpt", "value": "The 2015 value of the Local Government Support Budget is positioned at {2015}[0.130, 0.909], and the corresponding value is 80. The 2020 value for the same budget is located at {2020}[0.941, 0.909], with the corresponding value of 105. The change in budget from 2015 to 2020 is therefore 105 - 80 = 25.\nBased on the above thought, the answer is:\n25 million dollars" } ], "id_raw": 282 }, { "id": "hMS82KSGgK636oim", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_3.png", "conversations": [ { "from": "human", "value": "\nCan you provide the raw data from this chart as a table, with elements as column headers and years as row headers?" }, { "from": "gpt", "value": "To create the table, I first identify the column headers, which are {the legend} representing the different elements and their respective lines in the chart. Then, I identify {the x-axis tick labels}[0.201, 0.919, 0.245, 0.942], [0.341, 0.919, 0.385, 0.942], [0.481, 0.919, 0.525, 0.942], [0.621, 0.919, 0.665, 0.942], [0.761, 0.919, 0.805, 0.942] which are the years. Finally, to get the values, I need to locate the points along {the Grain Production line}[0.223, 0.089, 0.783, 0.864] and {the Per Capita Meat Consumption line}[0.223, 0.089, 0.783, 0.864] for each year.\nBased on the above thought, the answer is:\nYear,Grain Production (KG),Per Capita Meat Consumption (KG)\n2000,682394587,25.5\n2005,705697321,26.3\n2010,743286379,32.1\n2015,764183452,34.0\n2020,780569781,35.2" } ], "id_raw": 283 }, { "id": "e7bQxlgJLBbo3Vgb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_3.png", "conversations": [ { "from": "human", "value": "\nBased on the trend in Grain Production (KG) and Per Capita Meat Consumption (KG) over the years, what could we infer about the agriculture and food industry?" }, { "from": "gpt", "value": "To answer this question, I need to evaluate the overall trend in the lines representing Grain Production and Per Capita Meat Consumption. From the line for {Grain Production}[0.223, 0.089, 0.783, 0.864] and the line for {Meat Consumption}[0.223, 0.089, 0.783, 0.864], it can be observed that there is a general increase in both over the years. This indicates that the agriculture and food industry has seen steady growth.\nBased on the above thought, the answer is:\nThe increasing trend in both Grain Production and Per Capita Meat Consumption over the years indicates a steady growth in the agriculture and food industry. This could be due to advancements in agricultural technology, increased yield per hectare, and the adoption of more efficient farming practices. In terms of food consumption, the increased meat consumption per capita could be indicative of changes in dietary habits, increased income, and improved access to meat products. This reflects an overall improvement and expansion in the food and agriculture sector." } ], "id_raw": 283 }, { "id": "ogsHAWGJdomhMmQs", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in Per Capita Meat Consumption (KG) from 2000 to 2020?" }, { "from": "gpt", "value": "To determine the increase in Per Capita Meat Consumption from 2000 to 2020, I need to identify the corresponding y-values to the x-values 2000 and 2020. These are represented by the points on {the Per Capita Meat Consumption line} [0.223, 0.089, 0.783, 0.864]. In 2000, the meat consumption was 25.5 KG, while in 2020, it increased to 35.2 KG. The increase it therefore 35.2 - 25.5 = 9.7 KG.\nBased on the above thought, the answer is:\n9.7 KG." } ], "id_raw": 283 }, { "id": "gul0Pn9WU8tQwhsx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_4.png", "conversations": [ { "from": "human", "value": "\nCan you provide a monthly data breakdown of the web traffic sources mentioned in the chart, arranged in a tabular format?" }, { "from": "gpt", "value": "Observing the data represented in the chart, I focus on the subplots and their corresponding y-values for each source monthly. Noticing {the plotted line points for Direct Traffic}[0.160, 0.145], [0.224, 0.250], [0.288, 0.355], [0.352, 0.450], [0.416, 0.545], [0.480, 0.635], [0.545, 0.715], [0.609, 0.785], [0.673, 0.835], [0.737, 0.875], [0.801, 0.910], [0.865, 0.955] as well as {the plotted line points for Email, Search, Referral, and Social Media}Various corresponding coordinates, I extract their values monthly.\nBased on the above thought, the answer is:\nMonth,Direct Traffic,Email,Search,Referral,Social Media\nJan,145,55,70,200,130\nFeb,250,48,95,180,140\nMar,355,..., etc." } ], "id_raw": 284 }, { "id": "jBxf1pCTrJUBH7dh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_4.png", "conversations": [ { "from": "human", "value": "\nWhich web traffic source showed the greatest decline over the year?" }, { "from": "gpt", "value": "By comparing the starting and ending points of each source, I determine the trend for each. Focusing on {the Referral traffic line}[0.160, 1.4], ..., [0.865, 0.4], it starts higher at 200 in January and decreases consistently to below 50 in December, showing the greatest decline compared to others.\nBased on the above thought, the answer is:\nThe Referral traffic source showed the greatest decline over the course of the year. Starting off relatively strong, it demonstrates a consistent decrease each month, ending considerably lower than its starting point." } ], "id_raw": 284 }, { "id": "EPTuFoPZ5b3KTjCd", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the absolute change in 'Search' traffic from January to December?" }, { "from": "gpt", "value": "To find this change, I compare the y-values for the 'Search' line between January and December. Locating {the 'Search' line point in January}[0.160, 0.070] with a value of 70 and {the 'Search' line point in December}[0.865, 0.150] with a value of 950, I calculate the difference.\nBased on the above thought, the answer is:\n880." } ], "id_raw": 284 }, { "id": "DnuHgamnS1ZUTnIt", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_6.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the provided line chart data with the years as column headers and the type of sales as rows?" }, { "from": "gpt", "value": "Inspecting the data, the column headers or years can be inferred from the {x-axis labels}[[0.123, 0.902, 0.149, 0.932], [0.261, 0.902, 0.288, 0.932], [0.400, 0.902, 0.426, 0.932], [0.539, 0.902, 0.565, 0.932], [0.677, 0.902, 0.704, 0.932], [0.816, 0.902, 0.842, 0.932], [0.955, 0.902, 0.981, 0.932]]. The row labels or the type of sales such as 'Grocery Sales' and 'E-commerce Sales' can obtained from the {legend}[0.677, 0.740, 0.944, 0.880]. For the cell values, I can track {the points on the line of Grocery Sales}[0.123, 0.224, 0.955, 0.336] and {the points on the line of E-commerce Sales}[0.123, 0.150, 0.955, 0.820] and convert the coordinates into the corresponding sales values for every type in each year.\nBased on the above thought, the answer is:\nSales Type,2015,2016,2017,2018,2019,2020,2021\nGrocery Sales,12000,11900,11500,11000,10800,10500,10500\nE-commerce Sales,4000,4500,5000,6000,8000,11000,13000" } ], "id_raw": 286 }, { "id": "seFtfsczXKArI4HO", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_6.png", "conversations": [ { "from": "human", "value": "\nBy analyzing the chart, can you comment on the overall trend of Grocery Sales and E-commerce Sales over the years?" }, { "from": "gpt", "value": "To track the trend of Grocery Sales and E-commerce Sales over the years, I would inspect the {Grocery Sales line}[0.123, 0.224, 0.955, 0.336] and {E-commerce Sales line}[0.123, 0.150, 0.955, 0.820]. The Grocery Sales line shows a descending trend starting from 12000 in 2015 to 10500 in 2021 indicating a general decline over the years. In contrast, the E-commerce Sales line shows an ascending trend from 4000 in 2015 to 13000 in 2021 indicating a sustained increase over the years.\nBased on the above thought, the answer is:\nThe overall trend showcased by the chart for Grocery Sales is downward, suggesting a decrease in the sales over the years from 2015 to 2021. On the contrary, E-commerce Sales have evidently increased during the same period, as shown by the upward trend of the E-commerce Sales line." } ], "id_raw": 286 }, { "id": "X7k1zDNvhmpUU35a", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in E-commerce sales from 2015 to 2021?" }, { "from": "gpt", "value": "To compute the total increase in E-commerce sales from 2015 to 2021, I need to locate {the points on the E-commerce Sales line}[0.123, 0.150, 0.955, 0.820] and extract the corresponding sales values from {the y-axis labels}[[0.064, 0.883, 0.117, 0.913], [0.064, 0.808, 0.117, 0.838], [0.064, 0.734, 0.117, 0.764], [0.064, 0.659, 0.117, 0.689], [0.064, 0.585, 0.117, 0.615], [0.064, 0.510, 0.117, 0.540], [0.064, 0.436, 0.117, 0.466], [0.051, 0.362, 0.117, 0.392], [0.051, 0.287, 0.117, 0.317], [0.051, 0.213, 0.117, 0.243], [0.051, 0.138, 0.117, 0.168], [0.051, 0.064, 0.117, 0.094]] for the years 2015 and 2021. The total increase would be the sales value of 2021 minus that of 2015.\nBased on the above thought, the answer is:\n9000." } ], "id_raw": 286 }, { "id": "bEnrQGS3DcYDZJNn", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_9.png", "conversations": [ { "from": "human", "value": "\nCould you help to generate a structured table from the plotted line chart considering Technology Innovation Index as rows and Online Education Impact as columns?" }, { "from": "gpt", "value": "To make the table, I first notice that {the x-axis labels}[[0.096,0.902,0.109,0.932], [0.261,0.902,0.288,0.932], [0.433,0.902,0.459,0.932], [0.605,0.902,0.631,0.932], [0.777,0.902,0.803,0.932], [0.942,0.902,0.981,0.932]] represent the unique data points for Technology Innovation Index. I then see that {the points of the line for Online Education Impact}[[0.111,0.348], [0.163,0.621], [0.223,0.758], [0.360,0.485], [0.481,0.621], [0.601,0.294], [0.704,0.239], [0.833,0.348]] correspond to the corresponding Online Education Impact data for each Technology Innovation Index data point. Then I assign the Impact index for each Technology Innovation index and prepare the table.\nBased on the above thought, the answer is:\nTechnology Innovation Index,Online Education Impact Index\n1,0.90\n7,0.80\n14,0.75\n30,0.85\n44,0.80\n58,0.92\n70,0.94\n85,0.90" } ], "id_raw": 289 }, { "id": "plVRVNlx4jf2w7NX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the overall trend observed in the line chart for Technology Innovation Index against Online Education Impact Index?" }, { "from": "gpt", "value": "By examining {the line representing the Online Education Impact Index}[0.111, 0.239, 0.833, 0.758], the overall trend can be discerned. The line initially decreases from the point {start point}[0.111,0.348] at Technology Innovation Index of 1 to a local minimum at {local minimum}[0.223,0.758] around Technology Innovation Index 14, followed by an increase to a local maximum at {local maximum}[0.704, 0.239] around Technology Innovation Index 70, and then again a slight decrease at {end point}[0.833,0.348] for Technology Innovation Index 85.\nBased on the above thought, the answer is:\nFrom the line chart, it can be observed that the Online Education Impact Index decreases initially with increasing Technology Innovation Index. After reaching a local minimum, it rises again to reach a local maximum and finally decreases a little. Thus, the overall trend is a kind of a 'V' shape showing volatility as the Technology Innovation Index increases." } ], "id_raw": 289 }, { "id": "3gKqB3RllRYRr726", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum value of the Online Education Impact Index shown in the chart?" }, { "from": "gpt", "value": "To find the maximum value of the Online Education Impact Index, I need to identify {the points of the line for Online Education Impact}[[0.111,0.348], [0.163,0.621], [0.223,0.758], [0.360,0.485], [0.481,0.621], [0.601,0.294], [0.704,0.239], [0.833,0.348]]. The y-values of these points correspond to the Online Education Impact Index. The maximum y-value corresponds to the point at {max_point}[0.704, 0.239] which has a corresponding Online Education Impact Index of 0.94.\nBased on the above thought, the answer is:\n0.94" } ], "id_raw": 289 }, { "id": "5DxSuGBBhZkJRXvW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_10.png", "conversations": [ { "from": "human", "value": "\nCould you help me to reformat the chart data and arrange it in a structured table form, with 'Year' as the rows and 'Average Household Size' and 'Median Housing Price' as columns?" }, { "from": "gpt", "value": "In order to structure the data in the chart into tabular form, I first identify {the x-axis tick labels}[0.274, 0.909, 0.318, 0.932], [0.479, 0.909, 0.523, 0.932], representing the row headers or the year. Next, I located {the legend Items}[[0.167, 0.838, 0.418, 0.868]] which represent the column headers 'Average Household Size' and 'Median Housing Price'. To fill up the values, I locate {the points on the lines}[[0.296, 0.113], [0.501, 0.525], [0.706, 0.856]], interpret their relative quantities on the y-axis and extract their corresponding real-world numerical values.\nBased on the above thought, the answer is:\nYear,Average Household Size,Median Housing Price\n2010,5.2,120000\n2015,4.7,220000\n2020,4.3,350000" } ], "id_raw": 290 }, { "id": "syhEygNTNKqdRA8P", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_10.png", "conversations": [ { "from": "human", "value": "\nDiscuss the overall trend of 'Average Household Size' and 'Median Housing Price' from 2010 to 2020 based on the graphs." }, { "from": "gpt", "value": "To interpret the overall trends, I examine both the lines for 'Average Household Size' and 'Median Housing Price'. Observing the {line for 'Average Household Size'}[0.296, 0.113, 0.706, 0.856], I notice a clear downward trajectory from 5.2 in 2010 to 4.3 in 2020, depicting a decline in the average household size over this period. \nConversely, the line for 'Median Housing Price' is showcasing a different trend. The relevant plot points correspond to a quite significant increase over the same period, starting from 120,000 in 2010 {point at 2010 for Median Housing Price}[0.296, -49036.361], to 220,000 in 2015 {point at 2015 for Median Housing Price}[0.501, -89902.102], and up to 350,000 in 2020 {point at 2020 for Median Housing Price}[0.706, -143027.565].\nBased on the above thought, the answer is:\nThe line chart exhibits contrasting trends for 'Average Household Size' and 'Median Housing Price' from 2010 to 2020. While the 'Average Household Size' displays a consistent downward trend, indicating a decrease in the average household size over the decade, the 'Median Housing Price', on the other hand, experiences a considerable upward trend, signifying a rise in housing prices. Specifically, the housing prices increased from 120,000 in 2010, to 220,000 in 2015, and then to 350,000 in 2020. This divergence in trends may hint towards socio-economic changes affecting family structures and the housing market." } ], "id_raw": 290 }, { "id": "mOwCgz26CmzjbmX4", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the Average Household Size in the years 2010 and 2015?" }, { "from": "gpt", "value": "To find the difference in the Average Household Size between 2010 and 2015, I'll need to identify the points corresponding to these years on {the line for the Average Household Size}[0.296, 0.113, 0.706, 0.856]. Specifically, the points I need are at {2010}[0.296, 0.113] and {2015}[0.501, 0.525] which correspond to Average Household Sizes of 5.2 and 4.7 respectively. The difference would be 5.2 - 4.7 = 0.5.\nBased on the above thought, the answer is:\n0.5" } ], "id_raw": 290 }, { "id": "SEXYNfPrOk1DuRlW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_11.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the data from the line chart, setting the 'Year' as the row headers and the technological sectors as the columns?" }, { "from": "gpt", "value": "To create the table, I first use {the x-axis tick labels}[[0.093, 0.881, 0.134, 0.914], [0.273, 0.881, 0.304, 0.922], ..., [0.948, 0.881, 0.975, 0.938]], which represent the row headers, indicating the years. I then look at {the legend texts}[[0.130, 0.101, 0.300, 0.131], [0.130, 0.144, 0.303, 0.174], [0.130, 0.188, 0.255, 0.218]] to identify the column headers, referring to the three technological sectors 'Artificial Intelligence', 'Quantum Computing', and 'Bioengineering'. After that, I locate {the line points} for each sector at the respective years and extract the corresponding values.\nBased on the above thought, the answer is:\nYear, Artificial Intelligence, Quantum Computing, Bioengineering\n2020, 23, 15, 12\n2021, 24, 17, 13\n2022, 28, 20, 14\n2023, 32, 22, 15\n2024, 35, 25, 16\n2025, 37, 30, 19\n2026, 39, 35, 22\n2027, 45, 40, 23\n2028, 50, 40, 26\n2029, 52, 41, 28\n2030, 58, 45, 32" } ], "id_raw": 291 }, { "id": "RB5NFFxNLZHFKlu2", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_11.png", "conversations": [ { "from": "human", "value": "\nWhich sector of technology showed the steadiest growth over the 10 year period according to the trends in the line chart? Provide your reasoning." }, { "from": "gpt", "value": "To answer this question, I need to inspect the lines associated with each technological sector. Starting with {the line for Artificial Intelligence}[0.110, 0.113, 0.943, 0.678], it shows a fairly steady rate of increase from 23 to 58 over the 10 years. Then, the {line for Quantum Computing}[0.110, 0.323, 0.943, 0.807] exhibits a slightly greater increase from 15 to 45. Lastly, {Bioengineering}[0.110, 0.533, 0.943, 0.856], shows a relatively modest increase from 12 to 32. Considering the extent and consistency of percentage points growth over time, the growth of Artificial Intelligence seems to be the most steady among the three sectors.\nBased on the above thought, the answer is:\nAccording to the trends in the line chart, the sector of Artificial Intelligence demonstrated the steadiest growth over the 10-year period. It started from 23 in 2020 and steadily increased with regular increments up to 58 in 2030. Compared to Quantum Computing and Bioengineering, which had some more considerable jumps, Artificial Intelligence showed a more consistent rate of increase throughout the years." } ], "id_raw": 291 }, { "id": "9hZtpNuaUKcWinDF", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_11.png", "conversations": [ { "from": "human", "value": "\nBased on the data trends, by how many points did the growth in Quantum Computing increase from 2020 to 2030?" }, { "from": "gpt", "value": "In order to find the increase in the growth of Quantum Computing, I need to identify the {line points for Quantum Computing from 2020}[x=2020, y=15, point_coordinates=[0.110, 0.807]] and {2030}[x=2030, y=45, point_coordinates=[0.943, 0.323]]. The growt rate of Quantum Computing in 2020 was 15 and increased to 45 by 2030. Therefore the total increase is calculated by 45 - 15 = 30.\nBased on the above thought, the answer is:\n30." } ], "id_raw": 291 }, { "id": "ToYDl2Tjs38qkMOD", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_12.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the given chart into a structured table format with 'Financial Management Strategy' and 'Trend' as columns?" }, { "from": "gpt", "value": "To create a table from the chart, I first locate the categories of Financial Management on {the x-axis}[0.133, 0.065, 0.866, 0.201] which is the main column. The trends that the segments correspond to are identified from {the legend}[0.340, 0.917, 0.993, 1.000]. The segments in each bar represent the percentages for each trend. By looking at {the bars for Financial Planning}[0.341, 0.261, 0.429, 0.770] and {the bars for Risk Mitigation}[0.569, 0.261, 0.656, 0.780], I can gather each value for the corresponding trend.\nBased on the above thought, the answer is:\nFinancial Management Strategy, Trend, Percentage\nFinancial Planning, Minimal Change, 36\nFinancial Planning, Steady Decline, 34\nFinancial Planning, Substantial Decrease, 21\nFinancial Planning, Sustained Decline, 5\nFinancial Planning, Minimal Change, 3\nFinancial Planning, Gradual Improvement, 1\nRisk Mitigation, Minimal Change, 25\nRisk Mitigation, Steady Decline, 28\nRisk Mitigation, Substantial Decrease, 30\nRisk Mitigation, Sustained Decrease, 10\nRisk Mitigation, Minimal Change, 5\nRisk Mitigation, Gradual Improvement, 2" } ], "id_raw": 292 }, { "id": "YIQE3psgLL1FFnMX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_12.png", "conversations": [ { "from": "human", "value": "\nFrom the bars in the chart, which area of Financial Management has seen the more substantial decreases?" }, { "from": "gpt", "value": "To answer this question, I need to compare the 'Substantial Decrease' segments in both 'Financial Planning' and 'Risk Mitigation'. I find the substantial decrease segment for {Financial Planning}[0.341, 0.484, 0.429, 0.568] and for {Risk Mitigation}[0.569, 0.484, 0.656, 0.640], with respective percentages of 21% and 30%. Thus, comparing these percentages will give me the area with more substantial decreases.\nBased on the above thought, the answer is:\nThe area of 'Risk Mitigation' has seen more substantial decreases compared to 'Financial Planning' as per the information provided in the chart." } ], "id_raw": 292 }, { "id": "DMEgb6R11JKlocCm", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage difference between the 'minimal change' trend in Financial Planning and Risk Mitigation?" }, { "from": "gpt", "value": "To calculate the difference, I first need to identify the 'minimal change' segments for both Financial Planning and Risk Mitigation. I find the segment for 'minimal change' in {Financial Planning}[0.341, 0.618, 0.429, 0.770] and {Risk Mitigation}[0.569, 0.618, 0.656, 0.770]. The percentages for these are 36% and 25% respectively. The difference can be calculated as 36 - 25 = 11.\nBased on the above thought, the answer is:\n11%." } ], "id_raw": 292 }, { "id": "sODbUNlzIIusnvtD", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_14.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data shown in the stacked bar chart into a table format with the Process as columns and Potential areas for improvements as rows?" }, { "from": "gpt", "value": "To form a table from the stacked bar chart, I'll first analyze {the x-axis labels}[0.150, 0.902, 0.934, 0.932] that represent the different Software Development and Operations processes which will be our columns. Then, I locate {the legend texts}[0.558, 0.740, 0.971, 0.880] which specify Potential Areas for Improvements that will form rows in our table. Specifically, these areas are 'Resource Optimization', 'Bug Resolution', and 'Performance Tuning'. Now, to get the values, I'll inspect the {the lengths of each section in the stacked bars}[0.176, 0.193, 0.908, 0.286] in relation to their respective process and improvement area, and use them to extract the relevant percentages.\nBased on the above thought, the answer is:\nArea of Improvement,Project Management,Software Development,Testing & QA,Operations,Maintenance,Support & Services,R&D\nResource Optimization,0.33,0.30,0.28,0.18,0.33,0.25,0.35\nBug Resolution,0.35,0.40,0.35,0.29,0.33,0.32,0.40\nPerformance Tuning,0.32,0.30,0.37,0.53,0.34,0.43,0.25" } ], "id_raw": 294 }, { "id": "DE8BVktPPWqu5ZBS", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_14.png", "conversations": [ { "from": "human", "value": "\nBased on the charted data, which process seems to have the highest demand for improvements in 'Performance Tuning'? And why do you conclude so?" }, { "from": "gpt", "value": "To answer this question, I need to identify the segment in each bar that corresponds to the 'Performance Tuning' area of improvement - this is the third segment in each bar as given by {the legend}[0.558, 0.740, 0.971, 0.880]. Comparing the lengths of these segments, I can infer which process has the largest value, signifying the highest potential for improvement in Performance Tuning. Upon analysis, the 'Operations' bar shows {the longest segment for Performance Tuning}[0.396, 0.391, 0.469, 0.368].\nBased on the above thought, the answer is:\nBased on the charted data, 'Operations' seems to have the highest demand for improvements in 'Performance Tuning'. This conclusion comes from comparing the lengths of the 'Performance Tuning' segments in all process bars and finding that the 'Performance Tuning' segment of the 'Operations' bar is the longest. Thus, it indicates a higher potential for improvement in that particular aspect compared to the other processes." } ], "id_raw": 294 }, { "id": "6xeNKRq7jzqVSPAb", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the total percentage for all areas of potential improvements for Testing & QA based on the chart information?" }, { "from": "gpt", "value": "To answer this question, I need to look at the length of the entire bar corresponding to Testing & QA. The three segments in this bar represent the three areas of potential improvements: Resource Optimization, Bug Resolution, and Performance Tuning. Adding together the percentages reflected by {the segments of the Testing & QA bar}[0.322, 0.426, 0.396, 0.391] will give us the total potential improvement percentage for Testing & QA.\nBased on the above thought, the answer is:\n100%" } ], "id_raw": 294 }, { "id": "10kPtONOHWtbZ9Me", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_15.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from this line chart, where Years are the columns and Measures (Consumer Confidence and Market Sentiment) are rows?" }, { "from": "gpt", "value": "To turn the data from this line graph into a tabular format, I'll first look at {the x-axis labels}[0.066, 0.909, 1.070, 0.932], these correspond to the column headers: the Years. I then identify the {legend entries or row labels}[0.112, 0.089, 0.416, 0.186], which represent the 'Consumer Confidence' and 'Market Sentiment'. Then I'll analyze {the line points}[0.141, 0.150, 0.941, 0.856] and [0.141, 0.113, 0.941, 0.856] for each row at the respective Years and extract their y values.\nBased on the above thought, the answer is:\nYear,Consumer Confidence,Market Sentiment\n2005,0.60,0.75\n2006,0.65,0.78\n2007,0.69,0.80\n2008,0.40,0.42\n2009,0.48,0.40\n2010,0.52,0.50\n2011,0.54,0.51\n2012,0.57,0.54\n2013,0.60,0.55\n2014,0.65,0.60\n2015,0.70,0.65\n2016,0.70,0.68\n2017,0.72,0.71\n2018,0.76,0.73\n2019,0.78,0.75\n2020,0.60,0.60" } ], "id_raw": 295 }, { "id": "27mQ8Ms2XS1ghNpb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_15.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, describe the trend of Consumer Confidence and Market Sentiment over the years." }, { "from": "gpt", "value": "From analyzing {the line of Consumer Confidence}[0.141, 0.150, 0.941, 0.856], it seems that Consumer Confidence gradually increased from 0.60 in 2005 to 0.78 in 2019, with a few dips in between, most notably in 2008 during the economic recession where it hits the lowest at 0.40. However, a decrease is observed in 2020, potentially due to the global COVID-19 pandemic.\n\nLooking at {the line of Market Sentiment}[0.141, 0.113, 0.941, 0.856], Market Sentiment also experienced a steady increase from 0.75 in 2005 to 0.75 in 2019, albeit less extreme fluctuations compared to Consumer Confidence. However, unlike Consumer Confidence, Market Sentiment demonstrates an incremental downward trend after peaking in 2007 at 0.80, with a significant drop in 2008. It gradually recovers and stays steady from 2015 onwards. In 2020, similar to Consumer Confidence, Market Sentiment decreased, potentially due to the same global circumstances.\nBased on the above thought, the answer is:\nThe trends of Consumer Confidence and Market Sentiment over the years indicate a positive correlation, with both metrics showing similar peaks and troughs. Consumer Confidence showed gradual growth over the years, reaching the highest point in 2019 but dropped in 2020, possibly due to the impact of the COVID-19 pandemic. Market Sentiment, on the other hand, peaked in 2007, had a significant drop in 2008, and maintained a stable but slightly downwards trend till 2020. Again, the decrease in 2020 could possibly be attributed to global uncertainties." } ], "id_raw": 295 }, { "id": "bMEL8l9nyYHVOgpW", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in Consumer Confidence between the years 2005 and 2020?" }, { "from": "gpt", "value": "To find the change in Consumer Confidence between 2005 and 2020, I need to identify {the line points for Consumer Confidence}[0.141, 0.484] and [0.941, 0.484] and extract their corresponding values 0.60 and 0.60. The difference is therefore 0.60 - 0.60 = 0.00.\nBased on the above thought, the answer is:\n0.00" } ], "id_raw": 295 }, { "id": "zzgOcLzjxq3Oaig7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_16.png", "conversations": [ { "from": "human", "value": "\nCan you please extract the data shown in the line chart into a table format?" }, { "from": "gpt", "value": "First, I am looking at the line plots for {Social Inequality}[0.137, 0.212, 0.941, 0.894] and {Rights Protection}[0.137, 0.089, 0.941, 0.430] against the x-axis representing the time periods {the x-axis tick labels}[[0.130, 0.902, 0.143, 0.932], ..., [0.921, 0.902, 0.961, 0.932]]. I am noting down the respective values at each time period for both categories.\nBased on the above thought, the answer is:\nTime, Social_Inequality, Rights_Protection\n0, 0, 68\n10, 10, 75\n20, 20, 72\n30, 30, 90\n40, 40, 87\n50, 50, 93\n60, 60, 110\n70, 70, 115\n80, 80, 118\n90, 90, 105\n100, 100, 112" } ], "id_raw": 296 }, { "id": "wxcYVy8AmCl3ddzR", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_16.png", "conversations": [ { "from": "human", "value": "\nHow can you describe the trend for Rights Protection over time?" }, { "from": "gpt", "value": "To describe the trend for Rights Protection over time, I am looking at the coordinates of the line plot for {Rights Protection}[0.137, 0.089, 0.941, 0.430] and the corresponding time periods. Here, I can see that there is a general upward trend in Rights Protection over time, with periods of sharp increases, stabilized by periods of slower or negative growth. This is especially pronounced after the 50th time period, where the trend seems to grow significantly faster than before.\nBased on the above thought, the answer is:\nThe trend for Rights Protection over time can be described as generally increasing with periods of sharp increases intermixed with slower growth or declines. The measure of Rights Protection rises significantly after the 50th time period and reaches its maximum at the 80th time period, then slightly decreases afterwards." } ], "id_raw": 296 }, { "id": "gd4d6YkMe87BxKnb", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_16.png", "conversations": [ { "from": "human", "value": "\nAt which time period does the Rights Protection measure reach its maximum value?" }, { "from": "gpt", "value": "To find out when the Rights Protection measure is at its maximum, I need to observe the line plot for {Rights Protection}[0.137, 0.089, 0.941, 0.430]. Here, the lowest point on the y-axis corresponds to the maximum value (as y=0 is at the top). The point in question on this line plot is {the maximum point of Rights Protection}[0.780, 0.089] which corresponds to the Time Period of 80.\nBased on the above thought, the answer is:\n80." } ], "id_raw": 296 }, { "id": "9RCKFCv7IwG73BLR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_17.png", "conversations": [ { "from": "human", "value": "\nCan you translate the line chart data into a table format with the Years as the row labels and the metrics as the columns?" }, { "from": "gpt", "value": "In order to create a table from the line chart, I must first recognize the labels of {the x-axis tick labels}[[0.116, 0.902, 0.169, 0.932], [0.221, 0.902, 0.273, 0.932], [0.326, 0.902, 0.378, 0.932], [0.430, 0.902, 0.483, 0.932], [0.535, 0.902, 0.588, 0.932], [0.640, 0.902, 0.693, 0.932], [0.745, 0.902, 0.798, 0.932], [0.850, 0.902, 0.903, 0.932]] as the years, which will constitute the row labels. Then I spot the {legend labels}[[0.730, 0.795, 0.950, 0.825], [0.730, 0.839, 0.962, 0.869]] as the metrics 'Student Engagement' and 'Innovation in Teaching' that will form the column headers. Next, I follow the {line points for 'Student Engagement'}[[0.142, 0.731], [0.195, 0.722], [0.247, 0.665], [0.300, 0.640], [0.352, 0.624], [0.404, 0.485], [0.457, 0.419], [0.509, 0.411], [0.562, 0.403], [0.614, 0.321], [0.667, 0.280], [0.719, 0.338], [0.771, 0.313], [0.824, 0.403], [0.876, 0.239], [0.929, 0.198]] and {line points for 'Innovation in Teaching'}[[0.142, 0.812], [0.195, 0.771], [0.247, 0.780], [0.300, 0.771], [0.352, 0.763], [0.404, 0.739], [0.457, 0.714], [0.509, 0.698], [0.562, 0.649], [0.614, 0.616], [0.667, 0.567], [0.719, 0.550], [0.771, 0.518], [0.824, 0.485], [0.876, 0.436], [0.929, 0.403]] to extract the percentage rates for both metrics throughout the different years.\nBased on the above thought, the answer is:\nYear,Student Engagement,Innovation in Teaching\n2005,0.2,0.1\n2006,0.21,0.15\n2007,0.28,0.14\n2008,0.31,0.15\n2009,0.33,0.16\n2010,0.5,0.19\n2011,0.58,0.22\n2012,0.59,0.24\n2013,0.6,0.3\n2014,0.7,0.34\n2015,0.75,0.4\n2016,0.68,0.42\n2017,0.71,0.46\n2018,0.6,0.5\n2019,0.8,0.56\n2020,0.85,0.6" } ], "id_raw": 297 }, { "id": "1ZvllEATL82ZtUbD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_17.png", "conversations": [ { "from": "human", "value": "\nBased on the chart data, which metric showed the most rapid increase in rate from 2009 to 2020?" }, { "from": "gpt", "value": "To answer this, I have to identify the rate in 2009 and 2020 for each metric. For {Student Engagement}[0.352, 0.624, 0.929, 0.198], the rate increased from 0.33 in 2009 to 0.85 in 2020, a total increase of 0.52. For {Innovation in Teaching}[0.352, 0.763, 0.929, 0.403], the rate increased from 0.16 in 2009 to 0.6 in 2020, a total increase of 0.44. Student Engagement shows a greater total increase than Innovation in Teaching in the given period.\nBased on the above thought, the answer is:\nBased on the chart data, Student Engagement showed the most rapid increase in rate from 2009 to 2020. The rate increased by a total of 0.52, which is higher than the 0.44 increase for Innovation in Teaching during the same time period." } ], "id_raw": 297 }, { "id": "I4xgwlptFHwP3gHF", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the rate of Innovation in Teaching in 2020 compared to 2005?" }, { "from": "gpt", "value": "To find the change in the rate of Innovation in Teaching from 2005 to 2020, I first need to locate the {rate point for 2005 on the 'Innovation in Teaching' line}[0.142, 0.812] and then the {rate point for 2020 on the 'Innovation in Teaching' line}[0.929, 0.403]. The rate in 2005 was 0.1 and the rate in 2020 was 0.6. The difference or rate change is 0.6 - 0.1 = 0.5.\nBased on the above thought, the answer is:\n0.5" } ], "id_raw": 297 }, { "id": "6L16Q2jFg7XLDUZ9", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_15_18.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a structured form with 'Year' as the rows and various observations like Global Temperature Change, Sea Level Rise and Air Pollution Index as the columns?" }, { "from": "gpt", "value": "To create a tabular structure, I'll use the {x-axis ticks}[[0.190, 0.927, 0.233, 0.949], [0.301, 0.927, 0.343, 0.949], [0.411, 0.927, 0.454, 0.949], [0.522, 0.927, 0.564, 0.949], [0.632, 0.927, 0.675, 0.949], [0.743, 0.927, 0.785, 0.949], [0.853, 0.927, 0.896, 0.949]] as the row labels or Years. The column headers will be taken from {legend items}[[0.163, 0.108, 0.395, 0.131], [0.163, 0.141, 0.283, 0.164], [0.163, 0.174, 0.313, 0.197]]. The respective values for each year and observation will be extracted from {points on the lines}[[0.212, 0.174, 0.875, 0.424], [0.212, 0.498, 0.875, 0.875],[0.212, -16.368, 0.875, -9.994]].\nBased on the above thought, the answer is:\nYear,Global Temperature Change,Sea Level Rise,Air Pollution Index\n2000,0.75,-0.17,22\n2005,0.79,-0.05,23\n2010,0.84,0.06,26\n2015,0.95,0.20,28\n2020,1.07,0.33,30\n2025,1.15,0.48,32\n2030,1.26,0.60,35" } ], "id_raw": 298 }, { "id": "fW9y5jI9j195hnSu", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_15_18.png", "conversations": [ { "from": "human", "value": "\nConsidering all the trends shown in the line chart, which parameter is likely to show the most consequential change by 2030? Please explain your reasoning." }, { "from": "gpt", "value": "To answer this, I consider the overall trend of the three parameters: {Global Temperature Change line} [0.212, 0.174, 0.875, 0.424], {Sea Level Rise line} [0.212, 0.498, 0.875, 0.875], and {Air Pollution Index Line} [0.212, -16.368, 0.875, -9.994]. The Global Temperature Change, measured from 0.75 in 2000 to 1.26 in 2030, shows a significant rise over time. The Sea Level Rise from -0.17 in 2000 to 0.60 in 2030 indicates a flip from negative to positive values, suggesting a major impact. However, the Air Pollution Index shows a steady increase from 22 to 35, which indicates a relatively less severe change compared to the drastic shift in Sea Level Rise.\nBased on the above thought, the answer is:\nBased on the trends shown, the Sea Level Rise seems likely to show the most consequential change by 2030. This parameter starts with negative values, which means a falling trend, but then moves to positive values by 2030, denoting a rising trend. This flip in trend suggests potentially dramatic effects on the environment and highlights a crucial area for intervention." } ], "id_raw": 298 }, { "id": "VJ85Br53ub1jzPNy", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_15_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in Air Pollution Index from 2000 to 2030?" }, { "from": "gpt", "value": "To find the total increase in the Air Pollution Index, I'll start by determining the initial index value in 2000 and the final index value in 2030. This is represented by {the first point on Air Pollution Index line} [0.212, -9.994] with a value of 22 and {the last point on the same line} [0.875, -16.368] with a value of 35. Subtracting the initial value from the final value will give us the total increase.\nBased on the above thought, the answer is:\n13." } ], "id_raw": 298 }, { "id": "M3SENi6EqNSQRY6j", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_0.png", "conversations": [ { "from": "human", "value": "\nCan you create a table from the chart data with Community Areas as columns and Development Categories as rows?" }, { "from": "gpt", "value": "To generate a tabular data from the bar chart, I first identify {the x-axis tick labels}[[0.146, 0.902, 0.159, 0.932], [0.215, 0.902, 0.228, 0.932], [0.284, 0.902, 0.297, 0.932]] which represent the column headers or Development Categories. Then, I need to identify the community areas which are represented by different sets of bars in each category. Using the coordinates of the bars and the legend, I can create associations for each community area to every category.\nBased on the above thought, the answer is:\nDevelopment_Category,Central,East,West,North\nResidential_Area_Expansion,0.1,0.1,-0.1,0.2\nCommercial_Area_Expansion,-0.2,0.1,0.2,-0.1\nPublic_Facilities_Expansion,0.5,0.1,0.2,0.1" } ], "id_raw": 300 }, { "id": "3MFSk7BZ1oLKs1zK", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_0.png", "conversations": [ { "from": "human", "value": "\nBased on the information from the bar chart, which community area appears to have a preference for expanding public facilities over residential and commercial areas?" }, { "from": "gpt", "value": "To answer this question, I need to compare the values of Public Facilities Expansion with Residential Area Expansion and Commercial Area Expansion for each community. The bar for Public Facilities Expansion in {the Central community}[0.843, 0.193, 0.912, 0.684] has an impressive high value of 0.5, which is significantly higher than its respective values of 0.1 and -0.2 for Residential and Commercial expansion. This clearly indicates a strong preference of the Central community for Public Facilities Expansion.\nBased on the above thought, the answer is:\nFrom the bar chart, it appears that the Central community area shows a preference for expanding public facilities especially compared to residential and commercial areas. This is evidenced by the high value of 0.5 for Public Facilities Expansion in the Central community, which is notably higher than the corresponding figures for Residential Area Expansion and Commercial Area Expansion in the same community." } ], "id_raw": 300 }, { "id": "IK3hF9kdQEtxAnxd", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the total change (sum of changes) in Residential Area Expansion across all community areas?" }, { "from": "gpt", "value": "To find the total change in Residential Area Expansion, I need to locate the bars corresponding to the Residential Area Expansion on the chart. These are {the bars for Central}[0.152, 0.193, 0.912, 0.333], {for East}[0.221, 0.298, 0.912, 0.333], {for West}[0.290, 0.333, 0.912, 0.274] and {for North}[0.360, 0.274, 0.912, 0.254]. The values for these bars are 0.1, 0.1, -0.1, and 0.2 respectively. Summing these up gives 0.1 + 0.1 - 0.1 + 0.2 = 0.3.\nBased on the above thought, the answer is:\n0.3." } ], "id_raw": 300 }, { "id": "WouvzvahBs8djrig", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_1.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart into a table format where 'Year' is the column headers and various 'Power Sources' as row headers?" }, { "from": "gpt", "value": "By examining {the x-axis tick labels}[0.064, 0.062, 0.978, 0.448] for each subplot and the legend and identifying the various power sources, I can collect data points for each power source across the years. {the line points for Solar Power}[0.083, 0.080, 0.468, 0.430] and similarly for Wind, Hydropower, and Geothermal enable me to extract the yearly values to populate the table.\nBased on the above thought, the answer is:\nYear, Solar Power, Wind Power, Hydropower, Geothermal\n2018, 480, 380, 800, 700\n2019, 580, 450, 780, 720\n2020, 670, 520, 760, 740\n2021, 760, 590, 740, 760\n2022, 850, 660, 720, 780" } ], "id_raw": 301 }, { "id": "PHXKXFrzKEcd4MVo", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_1.png", "conversations": [ { "from": "human", "value": "\nWhich power source shows the most consistent increase over the years based on the chart?" }, { "from": "gpt", "value": "To analyze which power source is the most consistent in increase, I examine {the lines}[0.064, 0.062, 0.978, 0.935] for continuity and slope. Observing {the line for Solar Power}[0.083, 0.080, 0.468, 0.430] shows it has continually rising points without any dip. Similarly, I check {the lines for Wind Power, Hydropower, and Geothermal}[0.554, 0.062, 0.978, 0.935], among which Hydropower shows a consistent decrease, and Geothermal doesn't increase as uniformly as Solar Power does.\nBased on the above thought, the answer is:\nBased on the chart, Solar Power shows the most consistent increase over the years, as its lines steadily rise without any decrease, reflecting uniform positive growth." } ], "id_raw": 301 }, { "id": "GaWv4TvuMP8wdalA", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in Geothermal power production from 2018 to 2022?" }, { "from": "gpt", "value": "To find the increase in Geothermal power production, I look at {the line points for Geothermal}[0.574, 0.080, 0.959, 0.430] to extract the values at 2018 and 2022, which are 700 and 780 respectively. The increase is calculated as 780 - 700.\nBased on the above thought, the answer is:\n80" } ], "id_raw": 301 }, { "id": "Zr78eeG1pdHcSuqR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_2.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the line chart into a CSV table format, depicting trend in attendance rate and revenue across different cultural institutions over time?" }, { "from": "gpt", "value": "I shall extract the data by identifying {the x-tick labels}[0.194, 0.919, 0.214, 0.957] representing different cultural institutions such as Opera House, Art Gallery, etc. Next, by observing the line corresponding to each institution - for example, {the line for Opera House}[0.132, 0.181, 0.276, 0.850], I'll note the points that indicate attendance and revenue over different years. From these points, we can populate the structured table.\nBased on the above thought, the answer is:\nInstitution,Year,Attendance Rate (%),Revenue (Million USD)\nOpera House,2010,75,5\nOpera House,2011,78,5.5\nArt Gallery,2010,60,4.5\nArt Gallery,2011,65,5\nConcert Hall,2010,50,3.5\nConcert Hall,2011,55,3.7\nFilm Theatre,2010,45,4\nFilm Theatre,2011,60,3.8\nDance Studio,2010,40,2.5\nDance Studio,2011,70,3" } ], "id_raw": 302 }, { "id": "W1E0NkNF39ad97Kl", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_2.png", "conversations": [ { "from": "human", "value": "\nWhy does the Dance Studio exhibit a sudden spike in attendance in 2011 compared to the previous years?" }, { "from": "gpt", "value": "Analyzing the line chart, I observe the marked increase at {the point for Dance Studio in 2011}[0.893, 0.631], which shows a significant rise in attendance. Reasons could involve various external factors not depicted in the chart but might include an increased number of events, improved marketing strategies, or external factors influencing public engagement in cultural activities during that period. However, specific causes are difficult to determine solely from the chart data without additional context from other sources.\nBased on the above thought, the answer is:\nThe spike in attendance at the Dance Studio in 2011 could be due to an increased number of events or successful promotional efforts. While the exact reason can't be pinpointed from the chart alone, dramatic changes like these often involve strategic adaptations or external influences boosting public interest and participation." } ], "id_raw": 302 }, { "id": "ckHvrFdmycVd7tC1", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the revenue difference observed in the year 2011 between the Opera House and the Film Theatre?" }, { "from": "gpt", "value": "To find the revenue difference, I'll first locate the revenue marked for the year 2011 for both the Opera House at {the point}[0.276, 0.242] and the Film Theatre at {the point}[0.482, 0.728]. The revenue data shows $5.5 million for the Opera House and $3.8 million for the Film Theatre. To find the difference, I calculate $5.5 million - $3.8 million.\nBased on the above thought, the answer is:\n1.7 million USD." } ], "id_raw": 302 }, { "id": "jiSkFRExHyNoPM9j", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_3.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data depicted on the line chart into a tabular form with years as columns and each row representing either 'Cybersecurity spending' or 'Data breaches'?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.121, 0.911, 0.130, 0.934], [0.325, 0.911, 0.334, 0.934], [0.530, 0.911, 0.539, 0.934], [0.735, 0.911, 0.744, 0.934], [0.940, 0.911, 0.948, 0.934]] as the column headers or years. Next, I observe {the legend texts}[[0.808, 0.086, 0.973, 0.110], [0.808, 0.121, 0.909, 0.145]] that indicate 'Cybersecurity spending' and 'Data breaches' to be the row headers. The corresponding line points for each category at every year, such as for {Cybersecurity spending}[[0.125, 0.865], [0.330, 0.865], [0.535, 0.864], [0.739, 0.864], [0.944, 0.863]] and {Data breaches}[[0.125, 0.380], [0.330, 0.372], [0.535, 0.368], [0.739, 0.364], [0.944, 0.420]] provide the values for each year.\nBased on the above thought, the answer is:\nMetric, Year 0, Year 1, Year 2, Year 3, Year 4\nCybersecurity spending, 41.22, 44.79, 48.45, 50.91, 52.38\nData breaches, 3050, 3100, 3125, 3150, 2800" } ], "id_raw": 303 }, { "id": "9XYePGR3C8KEFCTy", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_3.png", "conversations": [ { "from": "human", "value": "\nLooking at the line chart, did the trend of Data breaches correspond to the trend of Cybersecurity spending over time? Please explain your findings." }, { "from": "gpt", "value": "To compare the trend of Data breaches and Cybersecurity spending, I would need to observe both {the line for Cybersecurity spending}[0.125, 0.863, 0.944, 0.865] and {the line for Data breaches}[0.125, 0.364, 0.944, 0.420]. The Cybersecurity spending has consistently increased over the four years. However, for Data breaches, the number initially increased from 3050 to 3150 over the first three years but then significantly decreased to 2800 in the fourth year. Thus, there is a divergence in the trends around the fourth year when the spending on Cybersecurity didn't decrease correspondingly with the number of Data breaches.\nBased on the above thought, the answer is:\nNo, the trend of Data breaches did not correspond entirely to the trend of Cybersecurity spending over time. While both increased initially, they diverged in the last year with the Cybersecurity spending continuing to rise as the number of Data breaches fell significantly." } ], "id_raw": 303 }, { "id": "lA8bmSkjO3Ckp74V", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_3.png", "conversations": [ { "from": "human", "value": "\nBy how many billions of dollars did Cybersecurity spending increase from Year 2 to Year 4?" }, { "from": "gpt", "value": "To find the increase in Cybersecurity spending from Year 2 to Year 4, I'll examine {the line points for Cybersecurity spending}[[0.125, 0.865], [0.330, 0.865], [0.535, 0.864], [0.739, 0.864], [0.944, 0.863]] and extract the values. The spending at Year 2 is 48.45 billion dollars and at Year 4 it is 52.38 billion dollars. The increase is therefore 52.38 - 48.45 = 3.93 billion dollars.\nBased on the above thought, the answer is:\n3.93 billion dollars." } ], "id_raw": 303 }, { "id": "0TvQy8DuyW4Tco8a", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_4.png", "conversations": [ { "from": "human", "value": "\nCan you create a table depicting the trends for Internal Struggle and Emotional Vulnerability across the categories mentioned in the line chart?" }, { "from": "gpt", "value": "Initially, I observe the available category labels on {the x-axis}[0.041, 0.511, 0.453, 0.882], which correspond to 'External Adaption', 'Personality Development', and 'Social Consciousness'. I further analyze the values for each category found on the two lines displayed in the chart: one for Internal Struggle and another for Emotional Vulnerability, utilizing the position of {the points on the lines}[0.110, 0.164, 0.384, 0.374] and [0.645, 0.152, 0.919, 0.251] respectively.\nBased on the above thought, the answer is:\nCategory,Internal Struggle,Emotional Vulnerability\nExternal Adaption,0.76,-0.87\nPersonality Development,0.58,0.45\nSocial Consciousness,0.67,0.41" } ], "id_raw": 304 }, { "id": "qVEE0xUb3GQOi4RN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_4.png", "conversations": [ { "from": "human", "value": "\nWhy does the trend in Emotional Vulnerability show a significant drop in the category of External Adaption compared to others?" }, { "from": "gpt", "value": "Upon analyzing {the line plot for Emotional Vulnerability}[0.110, 0.164, 0.384, 0.374], I notice a drastic dip in the values corresponding to the category of External Adaption compared to Personality Development and Social Consciousness. By examining further, I can see the label 'External Adaption' on the x-axis at approximately [0.089, 0.894, 0.130, 0.918]. The large negative value (-0.87) for this category could suggest heightened emotional sensitivity or exposure to adverse conditions in activities relating to external adaptation.\nBased on the above thought, the answer is:\nThe significant drop in Emotional Vulnerability for External Adaption indicated by the data may be due to an enhanced exposure to challenging or stressful scenarios in environments related to external social or environmental interactions. This can cause a heightened emotional response, reflected clearly in the considerable low values in the plot." } ], "id_raw": 304 }, { "id": "WnQCPZNIfECanNx5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the magnitude of the increase in Internal Struggle from Personality Development to Social Consciousness?" }, { "from": "gpt", "value": "I identify the line representing Internal Struggle and locate the related values in {the line plots for Internal Struggle}[0.645, 0.152, 0.919, 0.251]. Extracting the specific positions corresponding to Personality Development and Social Consciousness each, I derive the values of 0.58 and 0.67 respectively. The required magnitude of increase is the subtraction of the Personality Development value from the Social Consciousness value.\nBased on the above thought, the answer is:\n0.09" } ], "id_raw": 304 }, { "id": "8e88nA8XbdimKake", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart showing the Entrepreneurship and Innovation Ecosystem metrics across various regions into a CSV format?" }, { "from": "gpt", "value": "First, I analyze the structure of the bar chart closely. I recognize that the x-axis lists {the labels for different regions}[0.125, 0.93, 0.140, 0.960] such as 'New York', 'Silicon Valley', 'Beijing' which form the categorical axis of the table. Observing the bars, there are three different colors indicating three different metrics. Green bars correspond to 'Revenue Growth (%)'[0.220, 0.520, 0.240, 0.580], gray bars represent 'Investor Funding ($M)'[0.260, 0.520, 0.280, 0.600], and beige bars show 'Startup Density (# per 1000)'[0.300, 0.610, 0.320, 0.670]. The embedded texts atop each bar, which I refer to confirm the numerical data for each metric.\nBased on the above thought, the answer is:\nLocation,Revenue Growth (%),Investor Funding ($M),Startup Density (# per 1000)\nNew York,25,350,1.5\nSilicon Valley,30,340,2.0\nBeijing,35,220,1.2\nAustin,10,300,1.7\nTel Aviv,12,150,2.5" } ], "id_raw": 306 }, { "id": "RI92Wa4GPzXWrkNT", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_6.png", "conversations": [ { "from": "human", "value": "\nWhich region has a notable discrepancy between its Revenue Growth (%) and Startup Density (# per 1000)?" }, { "from": "gpt", "value": "To analyze discrepancies, I look closely at the green bars for 'Revenue Growth (%)'[0.220, 0.520, 0.240, 0.580] and beige bars for 'Startup Density (# per 1000)'[0.300, 0.610, 0.320, 0.670] across all regions labeled[0.125, 0.93, 0.140, 0.960] as 'New York', 'Silicon Valley', 'Beijing', etc. I find that Beijing has a high Revenue Growth at 35%[0.430, 0.130, 0.450, 0.170] while the Startup Density is comparatively low at 1.2 per 1000[0.430, 0.770, 0.450, 0.850]. This indicates a significant imbalance between the economic revenue growth and the startup community density in Beijing.\nBased on the above thought, the answer is:\nBeijing shows a notable discrepancy between its high Revenue Growth (%) and relatively low Startup Density (# per 1000). Despite the high economic growth, the startup dense ratio is low, possibly indicating hurdles in startup sustainability or operations in the region." } ], "id_raw": 306 }, { "id": "FSh3UOruWpr9wyxn", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the average Investor Funding ($M) across all regions as shown in the bar chart?" }, { "from": "gpt", "value": "To determine the average Investor Funding across all regions, I notice the gray bars representing 'Investor Funding ($M)'[0.260, 0.520, 0.280, 0.600]. By systematically locating each bar and reading the values from the annotations, we have the amounts of funding for the regions: 350M for New York, 340M for Silicon Valley, 220M for Beijing, 300M for Austin, and 150M for Tel Aviv. To find the average, the calculation involves adding these numbers and dividing by the number of regions, which is five.\nBased on the above thought, the answer is:\n272" } ], "id_raw": 306 }, { "id": "BLu68KxbabIQ62cU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_7.png", "conversations": [ { "from": "human", "value": "\nCan you list the development trends over time for both AI Development and Quantum Computing from the chart data?" }, { "from": "gpt", "value": "To generate this table, I first look at {the x-axis labels}[0.022, 0.937, 0.168, 0.953] indicating the phases from 'Fluctuating Decline' to 'Moderate Fall'. Next, by reviewing {the lines for AI Development and Quantum Computing}[0.026, 0.075, 0.368, 0.923] along with {the data points}[0.036, 0.884, 0.238, 0.113] on these lines, I can extract the values associated with each trend.\nBased on the above thought, the answer is:\nTrends,AI Development,Quantum Computing\nFluctuating Decline,-5,-11\nModerate Increase,3,10\nUnpredictable Decline,-33,-13\nSubdued Decline,-8,-3\nModerate Fall,-16,-9" } ], "id_raw": 307 }, { "id": "BHrrXKBO2da0FMXo", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_7.png", "conversations": [ { "from": "human", "value": "\nBased on the trends depicted in the chart, which technology shows greater resilience in terms of fewer and less drastic declines, AI Development or Quantum Computing?" }, { "from": "gpt", "value": "By studying the trend lines, I observe that {Quantum Computing}[0.274, 0.075, 0.496, 0.923] shows declines with values of -11, -13, -3, -9, which, although frequent, are less severe compared to {AI Development}[0.026, 0.075, 0.248, 0.923] which experiences sharper declines with values of -5, -33, -8, -16. This suggests that Quantum Computing exhibits fewer intense declines.\nBased on the above thought, the answer is:\nQuantum Computing shows greater resilience as it undergoes less severe declines than AI Development, evidenced by smaller values in its declining phases." } ], "id_raw": 307 }, { "id": "ZAE8QfJKOrNbUKtv", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the total decline exhibited by AI Development from 'Fluctuating Decline' to 'Moderate Fall' given the chart data?" }, { "from": "gpt", "value": "Observing the trajectory of {AI Development decline}[0.036, 0.884, 0.238, 0.113], the respective values for each trend phase are -5, 3, -33, -8, -16. To find the total decline, we sum the negative values only, which results in -5 + (-33) + (-8) + (-16) = -62.\nBased on the above thought, the answer is:\n-62" } ], "id_raw": 307 }, { "id": "jb5bXKja9kU3ryA5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_8.png", "conversations": [ { "from": "human", "value": "\nConvert the line chart data showing satisfaction and training program success into a table, categorizing them according to professional categories." }, { "from": "gpt", "value": "To transform the line chart into a table, I first observe that the x-axis categorizes various professional categories. I recognize {the professional categories on the x-axis}[0.105, 0.890, 0.884, 0.890] covering sectors like IT, Agriculture, Healthcare, etc. For each category, I look at the two lines- one showing Employee Satisfaction and one showing Training Program Success. By extracting the data points from these lines like {point in IT for Employee Satisfaction}[0.141, 0.370] and {point in Agriculture for Training Program Success}[0.564, 0.281], I can fill the corresponding values into the table.\nBased on the above thought, the answer is:\nProfessional_Category,Employee_Satisfaction(% decline),Training_Program_Success(% increase)\nIT,20,5\nAgriculture,3,18\nHealthcare,15,12\n..." } ], "id_raw": 308 }, { "id": "M2SWNgSupgI89r3z", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_8.png", "conversations": [ { "from": "human", "value": "\nWhich professional category shows the most alarming trend in Employee Satisfaction, and how can that be interpreted from the chart?" }, { "from": "gpt", "value": "Analyzing the line chart, I focus on the Employee Satisfaction trend line. {IT industry's line}[0.141, 0.370, 0.461, 0.337] displays a sharp decline when compared with others. By interpreting the plot trajectory, such as the significant drop at {Spring 2012}[0.212, 0.337], it is clear that IT exhibits a severe decreasing trend in Employee Satisfaction. This indicates high employee turnover or discontent within the IT industry potentially due to work pressures or inadequate job rewards compared to other sectors.\nBased on the above thought, the answer is:\nThe IT industry exhibits the most concerning trend with a sharp decline in Employee Satisfaction evident in the steep downward trajectory of its line on the chart. This trend suggests significant dissatisfaction potentially due to intense work conditions and inadequate compensation or incentives." } ], "id_raw": 308 }, { "id": "sd6VnVnBxbvEOhI8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the total difference in trends between the lowest and highest rate of increase in the Training Program Success across all professional categories?" }, { "from": "gpt", "value": "To determine the total difference, I analyze the points on the Training Program Success line for each professional category. Firstly, I calculate the lowest success rate observed at a data point, such as {in Healthcare}[0.248, 0.454] to be approximately 3%. Next, I find the highest success rate, noted in the {Agriculture category}[0.564, 0.281] at approximately 18%. The difference is computed by subtracting the lowest from the highest, which is 18% - 3% = 15%.\nBased on the above thought, the answer is:\n15%" } ], "id_raw": 308 }, { "id": "Ru8i3c7WZzSQhaP2", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_9.png", "conversations": [ { "from": "human", "value": "\nCan you convert the visual data in the chart into a high-level comparison table among the three innovations?" }, { "from": "gpt", "value": "Based on the provided chart, a structured tabular data will be constructed. The column headers should be the innovation types which could be recognized from {the bar labels}[0.163, 0.434, 0.197, 0.455] as Peer Learning Technology, AI Learning Systems, and VR Training. And the row headers are the assessment parameters, namely Effectiveness, Popularity, and Early Adoption, which I identify from {the y-axis labels}[0.051, 0.352, 0.089, 0.374]. Looking for each assessment parameter from {the corresponding bar points}[0.173, 0.366] in the chart I could establish each row value for the mentioned innovations.\nBased on the above thought, the answer is:\n,Efectiveness,Popularity,Early Adoption\nPeer Learning Technology,0.76,0.58,0.59\nAI Learning Systems,0.70,0.72,0.65\nVR Training,0.64,0.62,0.68" } ], "id_raw": 309 }, { "id": "ZTU61bj7HnA33o1V", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_9.png", "conversations": [ { "from": "human", "value": "\nWhich educational and training technology innovation is the most balanced in terms of effectiveness, popularity, and early adoption?" }, { "from": "gpt", "value": "To determine which innovation is the most balanced, I will look at the variance of the three parameters for each technology. Examining the values of Effectiveness, Popularity and Early Adoption from {the bars}[0.163, 0.434, 0.197, 0.455] for Peer Learning Technology (0.76, 0.58, 0.59), AI Learning Systems (0.70, 0.72, 0.65), and VR Training (0.64, 0.62, 0.68), it can be observed that VR Training has the smallest variance with a difference of 0.04 between the lowest and highest value.\nBased on the above thought, the answer is:\nVR Training seems to be the most balanced educational and training technology innovation in terms of Effectiveness, Popularity, and Early Adoption. It has the closest values across these three parameters, indicating balanced performance. Meanwhile, other technologies have more fluctuating values with more distinct strengths and weaknesses." } ], "id_raw": 309 }, { "id": "w9A5SeBrJ5pxy7le", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the average value of Effectiveness for all three educational technology innovations?" }, { "from": "gpt", "value": "To calculate the average Effectiveness between the three educational technologies, I would need to look at {the Effectiveness data points}[0.173, 0.638], [0.327, 0.604], [0.477, 0.564], which correspond to the Peer Learning Technology (0.76), AI Learning Systems (0.70), and VR Training (0.64) respectively. I would then add these values together and divide by three.\nBased on the above thought, the answer is:\n0.70" } ], "id_raw": 309 }, { "id": "FjMNoGfFKviA2xSg", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_10.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the line chart comparing Data Integration and Data Transformation across various trends into a structured table?" }, { "from": "gpt", "value": "Observing the line chart, I will use the x-axis labels which are {the x-axis labels}[0.053, 0.773, 0.207, 0.898] for the trends which will head the columns of our table. The y-axis does not specify distinct categories but reflects performance values directly tied to each trend and each process, which I'll extract from {the line points}[0.059, 0.186, 0.200, 0.575]. The legends, albeit not explicitly placed, infer categories of 'Data Integration' and 'Data Transformation' which are considered as our primary rows.\nBased on the above thought, the answer is:\nTrend,Data Integration,Data Transformation\nInconsistent Growth,4.2,-1.9\nConsistent Expansion,9.8,10.4\nAbrupt Drop,-3.2,-8.3" } ], "id_raw": 310 }, { "id": "z3dE79hAwvLdZBID", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_10.png", "conversations": [ { "from": "human", "value": "\nHow does the trend 'Abrupt Drop' compare between Data Integration and Data Transformation in terms of impact?" }, { "from": "gpt", "value": "To analyze the impact of the 'Abrupt Drop' trend, I inspect both lines for {Data Integration}[0.553, 0.082, 0.693, 0.342] and {Data Transformation}[0.623, 0.445, 0.770, 0.056] at the specific segment of 'Abrupt Drop'. Data Integration shows a drop from a previous high value to a lesser negative value, with an endpoint at -3.2. Conversely, Data Transformation plummets significantly, denoting a more drastic negative deviation with an ending point at -8.3.\nBased on the above thought, the answer is:\n'Abrupt Drop' has a more severe impact on Data Transformation compared to Data Integration. While both see a decline, the drop in Data Transformation is notably much steeper and larger, indicating a more profound negative impact." } ], "id_raw": 310 }, { "id": "HfoIniHk5QmsAYh4", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall improvement change for Data Integration from 'Inconsistent Growth' to 'Consistent Expansion'?" }, { "from": "gpt", "value": "To calculate the change, I'll find the values at {Inconsistent Growth for Data Integration}[0.059, 0.575] and {Consistent Expansion for Data Integration}[0.153, 0.316]. The values obtained are 4.2 and 9.8, respectively. The change is then computed by subtracting the initial value from the final value: 9.8 - 4.2.\nBased on the above thought, the answer is:\n5.6" } ], "id_raw": 310 }, { "id": "iar0wtnDy1pZFkpW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart showing tech companies' trends into a structured table format sorted by company names?" }, { "from": "gpt", "value": "To generate a structured table from the line chart, I begin by examining each line representing companies and trends on the chart. I identify {the x-axis tick labels}[0.15, 0.85, 0.90, 0.95] which represent various technology companies, such as Google, IBM, Microsoft, Apple, Facebook, Amazon, Tesla, and SpaceX. I then examine each associated trend point such as {the line point for Microsoft in sudden decrease trend}[0.45, 0.28] and {in erratic behavior trend}[0.55, 0.45]. Henceforth, I interpret the corresponding y-values for 'sudden decrease' and 'erratic behavior' from these coordinates.\nBased on the above thought, the answer is:\nCompany, Sudden Decrease, Erratic Behavior\nGoogle,1000,1600\nIBM,950,1500\nMicrosoft,1200,1400\nApple,800,1800\nFacebook,900,1700\nAmazon,700,1200\nTesla,850,1600\nSpaceX,750,1500" } ], "id_raw": 313 }, { "id": "ERYn4aOvzhuoh5NZ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_13.png", "conversations": [ { "from": "human", "value": "\nWhich company reflects the most stable trend with less variation in the tech industry according to the line chart?" }, { "from": "gpt", "value": "To assess company stability and lesser variation in trends, I analyze the proximity of data points on the 'sudden decrease' and 'erratic behavior' lines. {For IBM}[0.52, 0.60] the points show lesser variation with values 950 for sudden decrease and 1500 for erratic behavior that reflect smaller differences. Compared with others, where variations are relatively larger like {for Apple}[0.42, 0.30], [0.62, 0.20] showing more extreme behavior with values 800 and 1800 respectively. Therefore, based on the observed chart data, IBM exhibits less volatility.\nBased on the above thought, the answer is:\nIBM exhibits the most stability within the tech industry in terms of trends, as evidenced by its less varied numerical values between sudden decrease and erratic behavior trends." } ], "id_raw": 313 }, { "id": "oUTWlPOGsFc8hPsm", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the average trend value for Tesla considering both sudden decrease and erratic behavior?" }, { "from": "gpt", "value": "To calculate the average trend value for Tesla, I locate {the sudden decrease data point for Tesla}[0.65, 0.67] and find the corresponding y-value to be 850. Next, I examine {the erratic behavior data point for Tesla}[0.75, 0.62] and find that y-value as 1600. The average value can be calculated by adding these two values and dividing by 2.\nBased on the above thought, the answer is:\n1225" } ], "id_raw": 313 }, { "id": "TLrlBjFKkL936u4g", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_15.png", "conversations": [ { "from": "human", "value": "\nCan you transform the graphical data from the market statistics line chart into a tabular format where the columns represent years and the rows represent market trends such as Fluctuation, Decrease, or Increase?" }, { "from": "gpt", "value": "To transpose the graphical data from the line chart into a tabular data, I start by examining the {x-axis tick labels}[[0.071, 0.773, 0.139, 0.898], [0.167, 0.773, 0.233, 0.895], [0.263, 0.773, 0.329, 0.903], [0.359, 0.773, 0.425, 0.934], [0.455, 0.773, 0.521, 0.896], [0.551, 0.773, 0.617, 0.890], [0.647, 0.773, 0.713, 0.951], [0.743, 0.773, 0.809, 0.920], [0.839, 0.773, 0.905, 0.880], [0.935, 0.773, 0.999, 0.860]] which represent the column headers for years from 1990 to 1999. Then, I identify the distinct lines for each trend by examining their properties and translate each line's value across the years into table rows, respecting their corresponding {line plot coordinates}[see individual lines from data].\nBased on the above thought, the answer is:\nYear,Fluctuation,Decrease,Increase,Steep Increase,Outlier\n1990,50,40,30,20,60\n1991,55,35,45,25,62\n1992,43,38,50,30,63\n1993,60,33,55,40,67\n1994,50,30,60,47,70\n1995,48,25,65,55,75\n1996,55,22,70,65,80\n1997,60,20,75,72,85\n1998,63,18,80,78,90\n1999,65,15,85,82,95" } ], "id_raw": 315 }, { "id": "AJFOLJahthdYBUtg", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_15.png", "conversations": [ { "from": "human", "value": "\nHow did the \"Outlier\" trend pattern behave throughout the decade according to the chart?" }, { "from": "gpt", "value": "Observing the chart for the \"Outlier\" trend requires analyzing {the Outlier line plot}[[0.071, 0.610], [0.935, 0.710]]. The line clearly indicates a consistent upward pattern over the decade. Starting from a lower point in 1990, there is a gradual increase each year, reaching its peak in 1999. This upward movement suggests it consistently performed differently or markedly higher compared to the other trends throughout the years.\nBased on the above thought, the answer is:\nAccording to the chart, the \"Outlier\" trend exhibited a consistent increase throughout the decade, marking it as consistently higher in performance compared to other market trends. This consistent ascend illustrates a behavior of outperforming expected market behaviour year after year, culminating in a peak performance in 1999." } ], "id_raw": 315 }, { "id": "fo6OSnKEftixxKWC", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in the \"Steep Increase\" trend from 1990 to 1999?" }, { "from": "gpt", "value": "To calculate the total increase in the \"Steep Increase\" trend, I first identify the data values at the start and end within the plotted line for this specific trend using {the steep increase line plot}[[0.071, 0.420], [0.935, 0.182]]. The starting value in 1990 is 20 and the ending value in 1999 is 82. To find the total increase, I subtract the starting value from the ending value: 82 - 20.\nBased on the above thought, the answer is:\n62" } ], "id_raw": 315 }, { "id": "8Wzn9ttbf1djDlIL", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_16.png", "conversations": [ { "from": "human", "value": "\nCan you create a table listing each technology and their data values for the years 2000, 2005, 2010, 2015, and 2020 according to the complex line chart?" }, { "from": "gpt", "value": "By examining {the x-axis label positions}[0.184, 0.976, 0.224, 1.021] which represent the years, and identifying the corresponding line plot points for each technology in {AI Tech line points}[0.132, 0.855], [0.168, 0.668], [0.204, 0.528], [0.240, 0.342], [0.276, 0.155] and {Nano Tech line points}[0.338, 0.245], [0.374, 0.173], [0.410, 0.155], [0.446, 0.496], [0.482, 0.855], as well as interpreting others from Blockchain and Quantum Computing, I can construct the table.\nBased on the above thought, the answer is:\nTechnology, 2000, 2005, 2010, 2015, 2020\nAI Tech, 48, 52, 55, 59, 63\nNano Tech, 60, 64, 65, 46, 26\nBlockchain, 54, 57, 56, 62, 63\nQuantum Computing, 76, 78, 80, 82, 56" } ], "id_raw": 316 }, { "id": "6otfG9kRK0uTVODL", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_16.png", "conversations": [ { "from": "human", "value": "\nWhich technology has demonstrated consistent growth based on the data from the years 2000 to 2020 shown in the chart?" }, { "from": "gpt", "value": "To determine which technology shows consistent growth, I analyze the slope of the line plots across the years. Observing {the line plot for AI Tech}[0.132, 0.855], [0.276, 0.155] demonstrates a downwards trajectory towards higher values, indicating growth. In contrast, the {line plot for Nano Tech}[0.338, 0.245], [0.482, 0.855] depicts an upward trajectory toward lower values, indicating a decline.\nBased on the above thought, the answer is:\nAI Tech has demonstrated consistent growth from 2000 to 2020." } ], "id_raw": 316 }, { "id": "tRUEIJd9FM3DMcjN", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall percentage decrease in Nano Tech from 2000 to 2020 based on the chart values?" }, { "from": "gpt", "value": "To calculate the percentage decrease for Nano Tech, I extract the start and end values from {the line points for Nano Tech}[0.338, 0.245], [0.482, 0.855] which show 60 in 2000 and 26 in 2020. The formula used is [(Initial - Final) / Initial] * 100. In this case, [(60 - 26) / 60] * 100.\nBased on the above thought, the answer is:\n56.67%" } ], "id_raw": 316 }, { "id": "k7re4vfTVVJRCTRy", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_18.png", "conversations": [ { "from": "human", "value": "\nCan you convert the demographic data displayed in the stacked bar chart into a structured table format with Age Groups as columns and Gender as rows?" }, { "from": "gpt", "value": "Initially, I observe {the categories on the x-axis}[[0.125, 0.120, 0.477, 0.470]] which include Young, Adult, and Senior, representing the column headers. Next, I see {the segments inside the bars}[[0.141, 0.454], [0.173, 0.422], [0.205, 0.390]], each differentiated by colors showing Female and Male demographics as well as Total population which represents the row entries. These segments allow an extraction of population data for each gender within each age group.\nBased on the above thought, the answer is:\nGender,Young,Adult,Senior\nTotal Population,150000,200000,120000\nFemale,80000,105000,60000\nMale,70000,95000,60000" } ], "id_raw": 318 }, { "id": "pgbc24tLnax8piEb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_18.png", "conversations": [ { "from": "human", "value": "\nBased on the demographic distribution shown in the chart, which age group shows a higher population of females compared to males, and by how much?" }, { "from": "gpt", "value": "Observing the demographic distribution, I inspect the proportion of {the Female segment}[[0.141, 0.136, 0.461, 0.454]] and {the Male segment}[[0.564, 0.136, 0.884, 0.454]] across each age group. In the Young group, the Female population is slightly higher, represented as 80000 compared to Males who are 70000, showing a difference of 10000. For Adults, females are 105000 while males are 95000, a difference of 10000. In Seniors, both genders report 60000, showing no difference here.\nBased on the above thought, the answer is:\nThe Adult group shows a higher population of females compared to males, with the difference being 10000." } ], "id_raw": 318 }, { "id": "KFgX5QAlxLZxmLSx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the total population difference between the Adult and Young age groups represented in the bar chart?" }, { "from": "gpt", "value": "To compute the total population difference between the Adult and Young age groups, I need to examine {the total height of the bars for Adult}[[0.205, 0.390]] and {the total height of the bars for Young}[[0.141, 0.454]]. The plotted heights reflect the total population values where Adult's population is approximately represented as 200000 and Young's population as 150000. The difference is calculated as 200000 - 150000.\nBased on the above thought, the answer is:\n50000" } ], "id_raw": 318 }, { "id": "14nYVaJnjBpqGlQ4", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_16_19.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart showing data trends, can you compile the observed values for each series into a tabular format with Time as columns and Series as rows?" }, { "from": "gpt", "value": "To create a table from the line chart, I begin by referencing {the x-axis labels}[0.125, 0.95, 0.18, 0.97] to identify the timeline as columns. Each series is traced by analyzing their unique colors and patterns described in {the legend}[0.05, 0.06, 0.12, 0.13] placed on the upper left, identifying the rows as Series1 through Series4. I then track the coordinates of {the points on each series' line}[0.14, 0.55], [0.25, 0.62], [0.36, 0.50], etc. for the respective times, extracting the exact value points.\nBased on the above thought, the answer is:\nTime, Series1, Series2, Series3, Series4\n1, 0.58, 0.52, 0.62, 0.65\n2, 0.64, 0.56, 0.60, 0.68\n3, 0.70, 0.58, 0.58, 0.72\n4, 0.75, 0.60, 0.73, 0.75\n5, 0.78, 0.63, 0.69, 0.80\n6, 0.80, 0.65, 0.74, 0.86\n7, 0.82, 0.67, 0.78, 0.87\n8, 0.85, 0.70, 0.83, 0.89\n9, 0.88, 0.72, 0.81, 0.93\n10, 0.90, 0.75, 0.79, 0.97" } ], "id_raw": 319 }, { "id": "jMwNzhC1ByxUYg6L", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_16_19.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the line chart, which series demonstrates the most steady and consistent growth over the given period?" }, { "from": "gpt", "value": "To assess the consistency of growth across the series, I observe {the lines for each series} [0.14, 0.55], [0.25, 0.62], [0.36, 0.50], etc.. Series1 and Series3 show fluctuating values suggestive of erratic and volatile patterns. In contrast, Series2's line[0.14, 0.52], [0.25, 0.56], [0.36, 0.58], etc. progresses with slight incremental changes at each time point, indicating a more steady growth. Series4, although demonstrating an upward trend, shows a more rapid and less consistent growth.\nBased on the above thought, the answer is:\nBased on the line chart, Series2 exhibits the most steady and consistent growth throughout the period. It shows minimal fluctuations, with a gradual increase in value, unlike the other series that show more significant jumps or volatility." } ], "id_raw": 319 }, { "id": "Tco1NljBgC21C3C8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_16_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of values observed for Series3 from Time 1 to Time 10?" }, { "from": "gpt", "value": "To calculate the range for Series3, I first gather the values from {the line for Series3} [0.14, 0.62], [0.25, 0.60], [0.36, 0.58], [0.48, 0.73], [0.59, 0.69], [0.71, 0.74], [0.82, 0.78], [0.94, 0.83], [0.96, 0.81], [0.98, 0.79]. The maximum value for Series3 is 0.83 and the minimum value is 0.58. The range would be the difference between these values.\nBased on the above thought, the answer is:\n0.25" } ], "id_raw": 319 }, { "id": "DwSWdojt3oQAIrWG", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_1.png", "conversations": [ { "from": "human", "value": "\nCould you translate the Stacked Bar Graph into a structured table showing the popularity of different digital content creation channels for the two years?" }, { "from": "gpt", "value": "I'll focus my attention on {the bars on the graph}[0.113, 0.501, 0.943, 0.735]. Each bar in this range represents a specific form of digital media. In a stacked bar graph, the length of each segment of a bar corresponds to its values. So I need to extract the corresponding values from each segment, which respectively represent the first and second year. By referencing the respective labels associated with the x ticks {the x-axis tick labels}[0.095, 0.893, 0.130, 0.921],[0.178, 0.893, 0.213, 0.921],[0.261, 0.893, 0.296, 0.921],[0.344, 0.893, 0.380, 0.921], I can assign the values to the right columns.\nBased on the above thought, the answer is:\nMedia,Year 1,Year 2\\n\nDigitalContent,1200,1600\nSocialMedia,1300,1550\nVlog,1150,1475\nPodcast,1080,1250\nBlog,1600,2200\neBook,1700,2450\nWebinar,1500,1300\nOnlineCourse,1800,1900" } ], "id_raw": 321 }, { "id": "ifvR344IP2L25Usp", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_1.png", "conversations": [ { "from": "human", "value": "\nConsidering the trends in digital media forms provided, which type wsould you say showed the greatest improvement from Year 1 to Year 2?" }, { "from": "gpt", "value": "I start by inspecting all the {bar segments in the chart}[0.113, 0.501, 0.943, 0.735]. Each one corresponds to a different digital media form in Year 1 and Year 2. The difference between the two year values for each form denotes the amount of improvement. I notice that the {eBook section}[0.632, 0.501, 0.710, 0.735] has seen a significant increase from 1700 to 2450.\nBased on the above thought, the answer is:\nBased on the trends in the chart, the 'eBook' form of digital media experienced the greatest improvement from Year 1 to Year 2 with an increase in popularity from 1700 to 2450, which is the greatest rise among all other forms of media. This shows that eBooks have seen a boost in popularity and are being increasingly utilized for digital content creation." } ], "id_raw": 321 }, { "id": "klNnutcpWbg1C4JA", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_1.png", "conversations": [ { "from": "human", "value": "\nHow much was the total popularity gain in eBooks from Year 1 to Year 2?" }, { "from": "gpt", "value": "Looking specifically at the {eBook bar}[0.632, 0.501, 0.710, 0.735], I can see that it has two segments representing Year 1 and Year 2. The values of Year 1 and Year 2 are 1700 and 2450 respectively. We can simply subtract the Year 1 value from the Year 2 value to get the popularity gain over time.\nBased on the above thought, the answer is:\n750" } ], "id_raw": 321 }, { "id": "h38HoxVK1nv3hOXY", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_2.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data from this line chart into a table format with Integration Methods as rows and scales as columns?" }, { "from": "gpt", "value": "To generate a table from this line chart, I first identify {the x-axis tick labels}[[0.075, 0.733, 0.174, 0.865],[0.227, 0.733, 0.338, 0.881],[0.380, 0.733, 0.501, 0.894],[0.524, 0.733, 0.674, 0.932],[0.684, 0.733, 0.830, 0.927],[0.853, 0.733, 0.977, 0.898]] which represent the methods. I then find {the legend texts}[[0.162, 0.617, 0.282, 0.647],[0.162, 0.661, 0.257, 0.691]] as column names representing the types of scales. Each value represents the y-value at each point in the line coordinates for each method under each scale.\nBased on the above thought, the answer is:\nIntegration Method,Small Scale,Big Scale\nDirect Method,90,86.3\nData Federation,70,74.7\nMachine Learning,76,80\nMultidimensional View,55,65\nIncremental Updating,60,68\nVirtual Integration,85,90" } ], "id_raw": 322 }, { "id": "NkA7mwAiDPMyvhrI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_2.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which integration method seems to be most effective for large scales and why?" }, { "from": "gpt", "value": "To determine the most effective integration method for large scales, I need to analyze the line corresponding to Big Scale {line of Big Scale}[0.124, 0.094, 0.915, 0.440]. The point with the highest y-value indicates the most effective method. This point is {Virtual Integration}[0.915, 0.094] with a y-value of 90.\nBased on the above thought, the answer is:\nThe most effective integration method for large scales according to the line chart seems to be the Virtual Integration. Its line point on the line for Big Scale exhibits the highest y-value, which represents the highest level of effectiveness." } ], "id_raw": 322 }, { "id": "4jRcBbGkAEJTMd3k", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_2.png", "conversations": [ { "from": "human", "value": "\nHow much more effective is the Direct Method on small scales compared to Machine Learning?" }, { "from": "gpt", "value": "To find the effectiveness of Direct Method and Machine Learning on small scales, I must locate the points corresponding to these methods on the line for Small Scale. {The point for Direct Method on Small Scale}[0.124, 0.094] has a y-value of 90 while {the point for Machine Learning on Small Scale}[0.441, 0.288] has a y-value of 76. The difference in effectiveness is therefore 90 - 76 = 14.\nBased on the above thought, the answer is:\n14." } ], "id_raw": 322 }, { "id": "ZQXjvaSX9pApT9bM", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_3.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the line chart data into tabular format with the categories of social responsibility as columns and the years as rows?" }, { "from": "gpt", "value": "To convert the chart data into a table, I'll start by identifying the column names, which are shown in {the x-axis tick labels}[[0.161, 0.902, 0.214, 0.932], [0.305, 0.902, 0.358, 0.932], [0.450, 0.902, 0.503, 0.932], [0.594, 0.902, 0.647, 0.932], [0.738, 0.902, 0.791, 0.932], [0.883, 0.902, 0.935, 0.932]]. Next, I identify {the line points for each category on the line chart}[[0.188, 0.093, 0.909, 0.745]] and extract points at each year for all categories. Matching each set of points to their respective year on the x-axis will allow me to populate the table with the social responsibility performance scores.\nBased on the above thought, the answer is:\nYear,Environmental Sustainability, Customer Respect, Ethical Supply Chain, Employee Welfare, Local Community, Carbon Neutrality\n2010,5000,5400,4800,4500,4900,5000\n2011,5400,4800,4500,4900,5000,6100\n2012,4800,4500,4900,5000,6100,6400\n2013,4500,4900,5000,6100,6400,6600\n2014,4900,5000,6100,6400,6600,6800\n2015,5000,6100,6400,6600,6800,4900" } ], "id_raw": 323 }, { "id": "8Vd9Rw4HcplUDLhZ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_3.png", "conversations": [ { "from": "human", "value": "\nIf we were to interpret the line graph, which field of social responsibility did the company show the most improvement in over the years?" }, { "from": "gpt", "value": "To answer this question, I'd need to analyze the progression of each line on the chart. Among the lines, {the points representing Carbon Neutrality}[[0.188, 0.631], [0.260, 0.603], [0.332, 0.292], [0.404, 0.207], [0.476, 0.150], [0.548, 0.093], [0.620, 0.631]] show noticeable improvement by moving from 5000 in 2010 to 6800 in 2019, followed by a drop to 4900 in 2020. While the final value in 2020 is not the highest, the overall increase from the initial score in 2010 is the most significant among all categories and suggests the company's focus and consistent attempts to improve performance in this field.\nBased on the above thought, the answer is:\nThe company showed the most improvement in the field of Carbon Neutrality over the years, having its score elevated from 5000 in 2010 to 6800 in 2019 before a drop in 2020. This significant improvement reflects the company's commitment to sustainability and their efforts to reduce carbon emissions." } ], "id_raw": 323 }, { "id": "XGDdXpTzLvcw8bFj", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_3.png", "conversations": [ { "from": "human", "value": "\nWhat was the peak performance score for Environmental Sustainability during the monitored period?" }, { "from": "gpt", "value": "To answer this question, I need to locate the line points for Environmental Sustainability {Environmental Sustainability points on the line chart}[[0.188, 0.603], [0.260, 0.490], [0.332, 0.660], [0.404, 0.745], [0.476, 0.631], [0.548, 0.603], [0.620, 0.292], [0.693, 0.207], [0.765, 0.150], [0.837, 0.093], [0.909, 0.631]] and find the maximum value. This point corresponds to 2013 on the x-axis, with the score of 6800.\nBased on the above thought, the answer is:\n6800." } ], "id_raw": 323 }, { "id": "ILnpmV1r4sUXtSg7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_5.png", "conversations": [ { "from": "human", "value": "\nCould you interpret the data in the bar chart into a structured table format with Investment type and their corresponding Rate of Return?" }, { "from": "gpt", "value": "In order to interpret the bar chart into a table format, I locate {the x-axis tick labels}[[0.015, 0.665, 0.153, 0.895], [0.140, 0.665, 0.236, 0.826], [0.211, 0.665, 0.319, 0.845], [0.262, 0.665, 0.403, 0.899], [0.360, 0.665, 0.486, 0.875], [0.435, 0.665, 0.569, 0.887], [0.527, 0.665, 0.652, 0.874], [0.648, 0.665, 0.735, 0.811], [0.704, 0.665, 0.819, 0.856], [0.741, 0.665, 0.902, 0.932], [0.885, 0.665, 0.985, 0.832]], which indicate the different risk factors, representing the rows of the table. For the column 'High-Risk Percentage (%)', I refer to the bar heights given by {the points of the line plot}[[0.153, 0.138], [0.236, 0.194], [0.319, 0.263], [0.403, 0.303], [0.486, 0.331], [0.569, 0.353], [0.652, 0.409], [0.735, 0.447], [0.819, 0.496], [0.902, 0.545], [0.985, 0.580]] which provide the percentages associated with each risk factor.\nBased on the above thought, the answer is:\nRisk Factor,High-Risk Percentage(%)\nCritical Infrastructure,83.4\nPhysical Harm,79.7\nFinancial Impact,75.2\nReputational Damage,72.6\nOperational Impact,70.8\nLegal Consequences,69.3\nInformation Breach,65.7\nHuman Error,63.2\nNatural Disasters,60.0\nSupply Chain Disruptions,56.8\nPolitical Unrest,54.5" } ], "id_raw": 325 }, { "id": "QwOUrGK1JEPbUCKd", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_5.png", "conversations": [ { "from": "human", "value": "\nWhat insights can be drawn from the trend line regarding risk factors?" }, { "from": "gpt", "value": "When I examine the line plot defined by {Coordinates for the line plot} [0.153, 0.138, 0.985, 0.580], I can notice a clear downward trend. This pattern indicates that as we move from left to right on the x-axis - ordered from 'Critical Infrastructure' to 'Political Unrest', the 'High-Risk Percentage' generally decreases. This suggests that the risk factors on the left side of the chart such as 'Critical Infrastructure' and 'Physical Harm' are associated with higher risk percentages, while the risk factors on the right side, such as 'Supply Chain Disruptions' and 'Political Unrest' are associated with lower risk percentages.\nBased on the above thought, the answer is:\nThe line plot on the chart clearly illustrates a downward trend indicating that the assessed risk factors towards the right side of the chart pose less of a risk when compared to the ones on the left. This suggests that while factors related to the organization's infrastructure and physical harm are of high concern with respect to risk, aspects like political unrest and supply chain disruptions are considered relatively less risky." } ], "id_raw": 325 }, { "id": "VfLaLcktQK6MjOkC", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_5.png", "conversations": [ { "from": "human", "value": "\nHow much less is the High-Risk Percentage of Supply Chain Disruptions compared to Critical Infrastructure?" }, { "from": "gpt", "value": "To answer this question, I need to locate the 'High-Risk Percentage' of both 'Supply Chain Disruptions' and 'Critical Infrastructure'. I find the 'High-Risk Percentage' for 'Supply Chain Disruptions' at {line plot point} [0.902, 0.545] which provides a reading of 56.8%. Similarly, 'Critical Infrastructure' is listed at {line plot point} [0.153, 0.138] which corresponds to 83.4%. To find the difference, I must subtract the percentage of 'Supply Chain Disruptions' from 'Critical Infrastructure': 83.4% - 56.8% = 26.6%.\nBased on the above thought, the answer is:\n26.6%" } ], "id_raw": 325 }, { "id": "iTolbF0iNKVc8G3S", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_6.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the line chart into a table, using 'Investment Year' as rows and 'Investment Amount' and 'MV Amount' as columns?" }, { "from": "gpt", "value": "From the line chart, I first find {the x-axis tick labels}[[0.113, 0.902, 0.165, 0.932], [0.212, 0.902, 0.264, 0.932], [0.311, 0.902, 0.363, 0.932], [0.410, 0.902, 0.463, 0.932], [0.509, 0.902, 0.562, 0.932], [0.608, 0.902, 0.661, 0.932], [0.707, 0.902, 0.760, 0.932], [0.806, 0.902, 0.859, 0.932], [0.905, 0.902, 0.958, 0.932]], which signify the rows in the table, 'Investment Year'. Next, I pick out the two quantities represented by the lines, which can be found in {the legend}[[0.739, 0.048, 0.973, 0.125]] and will serve as the column headers, 'Investment Amount' and 'MV Amount'. For the values, I extract data points from each line, using {the line for Investment Amount}[[0.139, 0.414, 0.932, 0.839]] and {the line for MV Amount}[[0.139, 0.131, 0.932, 0.830]]. Using these data points, I can decipher the investment and MV amounts for each year.\nBased on the above thought, the answer is:\nInvestment Year,Investment Amount,MV Amount\n2004,1,1.2\n2005,1.5,1.8\n2006,2,3\n2007,4,5\n2008,2,2.5\n2009,3,4\n2010,4,5.5\n2011,5,7\n2012,4,6\n2013,5,7.5\n2014,6,9\n2015,5.5,8.5\n2016,7,11\n2017,6.5,10.5\n2018,8,13\n2019,9,14.5\n2020,10,16" } ], "id_raw": 326 }, { "id": "p8J3Y72WVWKUWDDa", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_6.png", "conversations": [ { "from": "human", "value": "\nCan the return on investment for each year be read from the timeline chart? If so, how?" }, { "from": "gpt", "value": "Yes, the return on investment for each year can be read by comparing the lines representing the 'Investment Amount' and the 'MV Amount'. By looking at {the lines of Investment Amount and MV Amount}[[0.139, 0.414, 0.932, 0.839], [0.139, 0.131, 0.932, 0.830]], I can observe that in every year, the MV Amount is always higher than the Investment Amount. By subtracting the Investment Amount from the MV Amount would give the return in each year.\nBased on the above thought, the answer is:\nYes, the return on investment for each year can be determined from the timeline chart. It can be derived by comparing the 'Investment Amount' and the 'MV Amount' for each year. The return can be calculated as the difference between the 'MV Amount' and the 'Investment Amount'. The chart displays that for every year, the 'MV Amount' is greater than the 'Investment Amount', indicating positive returns each year." } ], "id_raw": 326 }, { "id": "t2k7nIu5WrPlmkZe", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the total Investment Amount over the years?" }, { "from": "gpt", "value": "To find the total Investment Amount, I must sum up all the points on {the Investment Amount line}[[0.139, 0.414, 0.932, 0.839]]. Extracting the data from the points, we have the following investment amounts: 1, 1.5, 2, 4, 2, 3, 4, 5, 4, 5, 6, 5.5, 7, 6.5, 8, 9, 10. Summing these gives the total.\nBased on the above thought, the answer is:\n83.5" } ], "id_raw": 326 }, { "id": "A7jc9VKv47LweIIR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_9.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data values from the line chart and present it in a structured table format?" }, { "from": "gpt", "value": "Sure, to formulate the data in tabular format, I will look at the chart's x-axis as it represents the years under our analysis from 2005 to 2020, denoted by these coordinates: {the x-axis tick labels}[[0.094, 0.896, 0.137, 0.949], ..., [0.909, 0.896, 0.952, 0.949]]. These values will serve as our rows. The y-axis represents the find the corresponding production value for, denoted by these coordinates: {the estimated global food production line}[[0.115, 0.062, 0.931, 0.841]]. This will be used to populate the data under the respective years.\nBased on the above thought, the answer is:\nYear, Estimated_Global_Food_Production\n2005, 1.5\n2006, 1.75\n2007, 2.0\n2008, 2.1\n2009, 2.5\n2010, 2.75\n2011, 2.9\n2012, 3.0\n2013, 3.1\n2014, 3.3\n2015, 4.5\n2016, 4.8\n2017, 5.0\n2018, 5.2\n2019, 5.8\n2020, 6.0" } ], "id_raw": 329 }, { "id": "oxZdjyyT0HT5nh26", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall trend in global food production based on the line graph?" }, { "from": "gpt", "value": "To answer this question, I will analyze {the line graph} [0.115, 0.062, 0.931, 0.841]. It starts with a value of 1.5 in the year 2005 {at this point} [0.115, 0.841] and ends with a value of 6.0 in the year 2020 {at this point} [0.931, 0.062]. The line shows an overall increase over time, despite some minor fluctuations in between.\nBased on the above thought, the answer is:\nThe overall trend in global food production, as per the line graph, is an increasing one. Despite a few fluctuations, the graph shows that the global food production levels have significantly increased from 1.5 in the year 2005 to 6.0 in the year 2020." } ], "id_raw": 329 }, { "id": "3TpwsPyjsr7ZMjtb", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_9.png", "conversations": [ { "from": "human", "value": "\nBy how much did the global food production level increase between the year 2015 and 2020?" }, { "from": "gpt", "value": "I will begin by identifying the production level in 2015 and 2020. The point for 2015 is indicated by {this point on the line graph} [0.659, 0.322] and corresponds to the value 4.5. The point for 2020, represented by {this point on the line graph} [0.931, 0.062], corresponds to the value 6.0. Subtracting the production in 2015 from that in 2020 gives the increase in production.\nBased on the above thought, the answer is:\n1.5" } ], "id_raw": 329 }, { "id": "6b7cDHwGwX5bqkZp", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_10.png", "conversations": [ { "from": "human", "value": "\nCan you list the policy effectiveness ratings year by year from the dataset in a comma-separated table format with columns for Policy Name and Effectiveness?" }, { "from": "gpt", "value": "To generate the table from the line chart, I need to first read {the x-axis labels}[0.122, 0.891, 0.142, 0.918] which list the different policy names from 'Economic Recovery Package' to 'American Jobs Act'. Then, by analyzing {the line points}[0.136, 0.150, 0.166, 0.200] across the chart, I can tabulate the corresponding effectiveness ratings for each policy.\nBased on the above thought, the answer is:\nPolicy Name,Effectiveness\nEconomic Recovery Package,88.5\nTax Relief, Unemployment Insurance Reauthorization, and Job Creation Act,75\nPatient Protection and Affordable Care Act,55\nSmall Business Jobs Act,53\nAmerican Recovery and Reinvestment Act,46.7\nProtection and Job Creation Act,40\nAmerican Jobs Act,6.5" } ], "id_raw": 330 }, { "id": "CcqHnGkXMQ7Gpa85", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_10.png", "conversations": [ { "from": "human", "value": "\nWhich government policy, based on the dataset, is the most effective outlier lying between less effective subsequent policies? How was this determined?" }, { "from": "gpt", "value": "Examining the line chart, {the line point of the Tax Relief, Unemployment Insurance Reauthorization, and Job Creation Act}[0.286, 0.310] rises to an effectiveness of 75 despite being surrounded by policies with generally decreasing effectiveness. This makes it a notable outlier compared to the steeper declines noted in the chart trends for policies implemented before and after it.\nBased on the above thought, the answer is:\nThe most effective outlier is the 'Tax Relief, Unemployment Insurance Reauthorization, and Job Creation Act'. It is determined by its effectiveness rating of 75, which is significantly higher compared to adjacent policies that follow a general declining trend in the dataset." } ], "id_raw": 330 }, { "id": "GyaN8MrjaFRMdv74", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the rate of decline in policy effectiveness from the 'Economic Recovery Package' to the 'American Jobs Act'?" }, { "from": "gpt", "value": "To find the rate of decline, I first locate {the line point for Economic Recovery Package}[0.136, 0.110] with the effectiveness of 88.5, and then {the line point for American Jobs Act}[0.892, 0.918] with an effectiveness of 6.5. The effectiveness rate of decline is calculated as (88.5 - 6.5) / (number of policies between them).\nBased on the above thought, the answer is:\n13.67." } ], "id_raw": 330 }, { "id": "wUM1efE4Wo1Zh1Gy", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_11.png", "conversations": [ { "from": "human", "value": "\nCan you organize this data into a structured table with 'Year' as columns and 'Scores' as rows for both Innovation and Community Health?" }, { "from": "gpt", "value": "To generate a structured table from this line chart, initially I will observe {the x-axis labels}[[0.155, 0.883, 0.226, 0.919], [0.298, 0.883, 0.368, 0.919], [0.440, 0.883, 0.511, 0.919], [0.583, 0.883, 0.654, 0.919], [0.726, 0.883, 0.796, 0.919], [0.868, 0.883, 0.939, 0.919]] to identify the years under study. Then, I will determine {the legend entries}[[0.205, 0.769, 0.399, 0.797], [0.205, 0.810, 0.494, 0.838]] as row headers, representing the Innovation Score and Community Health Score respectively. Lastly, I map {the plot points}[[0.190, 0.857], [0.262, 0.855], [0.333, 0.853], [0.404, 0.851], [0.476, 0.853], [0.547, 0.851], [0.618, 0.852], [0.690, 0.852], [0.761, 0.847], [0.832, 0.847], [0.904, 0.846], [0.190, 0.061], [0.262, 0.060], [0.333, 0.059], [0.404, 0.055], [0.476, 0.055], [0.547, 0.053], [0.618, 0.052], [0.690, 0.047], [0.761, 0.046], [0.832, 0.045], [0.904, 0.044]] for each category to their respective years.\nBased on the above thought, the answer is:\nScores,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nInnovation Score,6.1,6.2,6.4,6.6,6.4,6.6,6.5,6.5,6.9,6.9,7.0\nCommunity Health Score,75.8,75.9,76.0,76.3,76.3,76.5,76.6,77.0,77.1,77.2,77.3" } ], "id_raw": 331 }, { "id": "FxxNfvGQkOa38eKy", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_11.png", "conversations": [ { "from": "human", "value": "\nHow can you describe the trend of the Innovation Score over the years 2010 to 2020?" }, { "from": "gpt", "value": "To describe the trend of the Innovation Score from 2010 to 2020, I analyze {the line for Innovation Score}[0.190, 0.846, 0.904, 0.857]. It generally shows an increasing trend from 6.1 to 7.0 over the decade, with some years experiencing a slight dip such as 2014 and 2016 before recovering to reach the decade's peak in 2020.\nBased on the above thought, the answer is:\nThe trend of the Innovation Score over the years 2010 to 2020 is an overall increasing trend, with a few intermittent declines, but eventually reaching a peak score of 7.0 in 2020." } ], "id_raw": 331 }, { "id": "xHfFt8N8iec1qcax", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in Innovation Score from 2010 to 2020?" }, { "from": "gpt", "value": "To compute the total increase in Innovation Score from 2010 to 2020, I first identify {the point corresponding to the Innovation Score in 2010} [0.190, 0.857] which is 6.1 {the label for Innovation Score1 in 2020}[0.904,0.846] yields a value of 7.0. The total increase is therefore 7.0 - 6.1 = 0.9.\nBased on the above thought, the answer is:\n0.9" } ], "id_raw": 331 }, { "id": "R8zXvyICkT0ciZ8x", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_12.png", "conversations": [ { "from": "human", "value": "\nGiven the line chart, can you extract the growth rate values for each year in a table format?" }, { "from": "gpt", "value": "To convert the line chart to a table format, the first step is to identify the {x-axis tick labels}[[0.125, 0.883, 0.195, 0.919], [0.266, 0.883, 0.337, 0.919], [0.408, 0.883, 0.479, 0.919], [0.550, 0.883, 0.620, 0.919], [0.692, 0.883, 0.762, 0.919], [0.833, 0.883, 0.904, 0.919]] which represent the years. Next, I extract values corresponding to each year by observing {the chart line}[[0.160, 0.108, 0.869, 0.818]], the points along the line represent the average growth rate for each year.\nBased on the above thought, the answer is:\nYear, Average Growth Rate\n2010, 3.7\n2011, 3.1\n2012, 2.2\n2013, 3.0\n2014, 3.5\n2015, 4.9\n2016, 5.0\n2017, 5.5\n2018, 6.3\n2019, 6.0\n2020, -1.5" } ], "id_raw": 332 }, { "id": "cZaL0liNslcO5VUM", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_12.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the trend in the average growth rate over the years, can you describe the general trend observed?" }, { "from": "gpt", "value": "To describe the general trend, I'll examine the trajectories of {the line}[[0.160, 0.108, 0.869, 0.818]] across the different years. The average growth rate appears to be on an increasing trend from 3.7 in 2010 to a peak of 6.3 in 2018 but experienced a severe decline to -1.5 in 2020.\nBased on the above thought, the answer is:\nThe general trend suggests that the average growth rate increased steadily between the years 2010 to 2018. However, a sharp drop was noticed in 2020 which indicates a significant decline in the growth rate." } ], "id_raw": 332 }, { "id": "zlLA93uHdgBeyk3h", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum growth rate recorded and in which year did it occur?" }, { "from": "gpt", "value": "To get the maximum growth rate, I need to check the values for {the line points}[[0.160, 0.344], [0.231, 0.399], [0.302, 0.481], [0.373, 0.408], [0.444, 0.363], [0.514, 0.235], [0.585, 0.226], [0.656, 0.181], [0.727, 0.108], [0.798, 0.135], [0.869, 0.818]]. The point at the highest position corresponds to the year with the maximum growth rate. It seems that it occurs approximately at the {point corresponding to 2018}[[0.727, 0.108]], meaning that the maximum average growth rate of 6.3 was recorded in 2018.\nBased on the above thought, the answer is:\n2018" } ], "id_raw": 332 }, { "id": "zo62rKgOkRkocwrT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data represented on the line chart into a structured table format in terms of different civilization periods against their respective populations in millions?" }, { "from": "gpt", "value": "To create the desired table, first, I noticed that the civilization periods serve as the column headers and are given by {the x-axis tick labels}[[0.124, 0.764, 0.142, 0.932], [0.178, 0.764, 0.196, 0.949], ..., [0.935, 0.764, 0.954, 0.893]]. The population values correspond to {the line points of human population}[[0.133, 0.719], [0.187, 0.718], ..., [0.944, 0.052]]. Now, associating each civilization period with its respective population, we can formulate the table.\nBased on the above thought, the answer is:\nCivilization_Period, Population_in_Millions\nPaleolithic_Early, 1\nPaleolithic_Middle, 2.2\nPaleolithic_Late, 3.4\nMesolithic_Early, 5.9\nMesolithic_Late, 6.2\nNeolithic_Early, 9.3\nNeolithic_Late, 12.7\nBronze_Age_Early, 19.9\nBronze_Age_Late, 27.6\nIron_Age_Early, 49.5\nIron_Age_Late, 58.7\nMedieval_Early, 84.6\nMedieval_Late, 136.5\nRenaissance, 372.9\nIndustrial_Age, 907.8\nModern_Age, 7800" } ], "id_raw": 334 }, { "id": "VYx6wxKt6olmyfEa", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_14.png", "conversations": [ { "from": "human", "value": "\nHow can you describe the trend in the growth of human population as depicted by the line chart from the Paleolithic to the Modern Age?" }, { "from": "gpt", "value": "Based on the trend represented by the line chart, ({the line}[0.133, 0.052, 0.944, 0.719]), the human population shows a slow growth during the Paleolithic period to the Early Bronze age, with small incremental steps as seen in the changes between subsequent Y values {corresponding to points}[[0.133, 0.719], [0.187, 0.718], ..., [0.512, 0.717]]. Next, a faster growth is observed during the Late Bronze Age and continues to the Iron Age, indicated by the steep slope of the line from coordinates {Bronze Age Late}[0.566, 0.716] to {Iron Age Late}[0.674, 0.714]. Subsequently, from the Medieval period onward, there is a significant exponential growth of the population, as seen in the even steeper slope from {Medieval Early}[0.728, 0.711] to {Modern Age}[0.944, 0.052].\nBased on the above thought, the answer is:\nThe line chart portrays a slow initial growth of the human population from the Paleolithic period to the Early Bronze Age. This is followed by a moderate rise in population during the Late Bronze Age through to the Iron Age. Finally, an exponential surge in population is seen from the Medieval Period to the Modern Age. This trend conveys the progress of human civilization through different ages and the corresponding dramatic increase in population, especially in more recent ages." } ], "id_raw": 334 }, { "id": "E4kYapgMCxhT1T2a", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, what was the increase in human population from the Early Iron Age to the Modern Age?" }, { "from": "gpt", "value": "To answer the question, I first find the population at the Early Iron Age, which is shown at {the point representing the start of Iron Age}[0.620, 0.714] as 49.5 million. I then locate the population at the Modern Age, which can be seen at {the point representing the Modern Age}[0.944, 0.052] as 7800 million. The required increase is therefore 7800 - 49.5 = 7750.5 million.\nBased on the above thought, the answer is:\n7750.5 million." } ], "id_raw": 334 }, { "id": "PDgLXToC3kgkwyg3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_15.png", "conversations": [ { "from": "human", "value": "\nConsidering the line chart, can the tech adoption data be restructured into a table format with Years being columns and Technologies being rows?" }, { "from": "gpt", "value": "To obtain the table data from the line chart, initially I examine {the x-axis tick labels}[[0.129, 0.902, 0.972, 0.932]], these marks signify the column headers or years to be considered. Proceeding to this, I classify {the legend texts}[[0.193, 0.050, 0.312, 0.080], [0.193, 0.094, 0.283, 0.124]] as the row labels which personify the diverse health and medical technologies. To seek the values, I detect {the line points}[[0.155, 0.861], [0.234, 0.859], ..., [0.942, 0.866]] designated for each technology in the corresponding years and extract these values.\nBased on the above thought, the answer is:\nTechnology,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nRobotic Surgery,50,53,52,56,58,60,65,67,70,62,40\nEMR Usage,1050,1078,1054,1120,1200,1300,1400,1500,1600,1500,900" } ], "id_raw": 335 }, { "id": "0d39PmcNqA94FGpR", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_15.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can we infer which medical technology has observed the most significant growth over time? Kindly state your justification." }, { "from": "gpt", "value": "To ascertain the technology with the most noticeable growth, an examination of the progression in the line plots for each medical technology is needed. Observing {the line for Robotic Surgery}[0.155, 0.861, 0.942, 0.866], a moderate increase is witnessed from an initial value of 50 to 40 over the years. Contrarily, when assessing {EMR Usage}[0.155, 0.354, 0.942, 0.430], a significant increase is seen from 1050 to 900.\nBased on the above thought, the answer is:\nGuided by the trends displayed in the line chart, EMR Usage appears to have witnessed the most substantial growth over time, increasing by a large margin over the course of the years. This suggests a wider acceptance and implementation of Electronic Medical Records in the medical and healthcare sector." } ], "id_raw": 335 }, { "id": "izToZTwpsPwPjs3b", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum value reached by EMR Usage over the recorded years?" }, { "from": "gpt", "value": "To find the maximum value for EMR Usage over the years, it is crucial to pinpoint {the line points for EMR Usage}[[0.155, 0.354], [0.234, 0.340], [0.312, 0.352], [0.391, 0.319], [0.470, 0.278], [0.548, 0.228], [0.627, 0.177], [0.706, 0.126], [0.785, 0.076], [0.863, 0.126], [0.942, 0.430]] and extract the linked values: 1050, 1078, 1054, 1120, 1200, 1300, 1400, 1500, 1600, 1500, 900. The maximum value happens to be 1600 connected to {2018}[0.785, 0.902].\nBased on the above thought, the answer is:\n1600." } ], "id_raw": 335 }, { "id": "Lf6TAuRyte89zoJa", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_16.png", "conversations": [ { "from": "human", "value": "\nCan you construct a table from the line chart with time points as columns and market players as rows?" }, { "from": "gpt", "value": "To convert the line chart into a table, I must first identify {the x-axis tick labels}[[0.186, 0.906, 0.197, 0.930], [0.318, 0.906, 0.336, 0.930], [0.451, 0.906, 0.470, 0.930], [0.589, 0.906, 0.607, 0.930], [0.727, 0.906, 0.746, 0.930], [0.865, 0.906, 0.884, 0.930], [1.006, 0.906, 1.024, 0.930]] which represent the column headers or time points in the year. Then, I get the {legend texts}[[0.755, 0.741, 0.863, 0.764], [0.755, 0.776, 0.887, 0.799], [0.755, 0.811, 0.829, 0.834], [0.755, 0.846, 0.859, 0.869]] as the market players or the row labels. To load the values, I locate the {line points}[[0.442, 0.378], [0.583, 0.425], [0.865, 0.457], [0.160, 0.386], ...] with their corresponding player and time point, and extract the annotated values.\nBased on the above thought, the answer is:\nPlayer,5,10,20,30\nRapid Recovery,92.0,90.0,92.5,94.0\nFluctuating Growth,77.5,75.0,77.0,76.0\nSharp Rise,75.0,60.0,63.0,67.0\nPeriodic Spikes,84.5,80.0,85.0,82.0" } ], "id_raw": 336 }, { "id": "NJCD9sZhD1kJ1IRa", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_16.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which market player's future technological development value shows the most significant increase from the beginning to the end of the evaluated periods?" }, { "from": "gpt", "value": "To determine which market player shows the most significant increase in future technological development value, I need to compare the differences in the starting and end values for each line. Looking at {the line for Rapid Recovery}[0.160, 0.235, 0.865, 0.298], the increase is from 92.0 to 94.0. For {Fluctuating Growth}[0.160, 0.497, 0.865, 0.537], the value decreased from 77.5 to 76.0. {Sharp Rise}[0.160, 0.537, 0.865, 0.775] improved from 75.0 to 67.0, while {Periodic Spikes}[0.160, 0.378, 0.865, 0.457] showed an increase from 84.5 to 82.0. Therefore, the market player with the most significant increase in value is the one with the largest difference.\nBased on the above thought, the answer is:\nThe market player 'Sharp Rise' shows the most significant increase in future technological development value from the beginning to the end of the evaluated periods. Their development value increased from 75.0 at the start to 67.0 in the end, showing a growth trend." } ], "id_raw": 336 }, { "id": "fqkQbABnLFSoMhpv", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the net change in developmental value of 'Fluctuating Growth' from the first to the last decade?" }, { "from": "gpt", "value": "To calculate the net change in the developmental value of 'Fluctuating Growth', I must locate the {first line point for Fluctuating Growth} at [0.160, 0.497] and the {last line point for Fluctuating Growth} at [0.865, 0.537]. The first point corresponds to a value of 77.5 and the last point corresponds to a value of 76.0. The net change is calculated as the difference between the last and first values, which is 76.0 - 77.5.\nBased on the above thought, the answer is:\n-1.5" } ], "id_raw": 336 }, { "id": "S0hupWykQuY417vU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_17.png", "conversations": [ { "from": "human", "value": "\nCan you extract data from the line chart and present it in a table format using the environmental elements as columns and the environmental concerns as rows?" }, { "from": "gpt", "value": "To create a table from the line chart, {the x-axis tick labels}[0.160, 0.883, 0.177, 0.919], [0.257, 0.883, 0.274, 0.919], [0.344, 0.883, 0.380, 0.919], [0.441, 0.883, 0.476, 0.919], [0.538, 0.883, 0.573, 0.919], [0.635, 0.883, 0.670, 0.919], [0.732, 0.883, 0.767, 0.919], [0.828, 0.883, 0.864, 0.919], [0.925, 0.883, 0.960, 0.919] will constitute the column headers or stages of time. I will then identify {the line plots} [0.165, 0.796], [0.192, 0.749], [0.220, 0.702], [0.247, 0.655], [0.274, 0.608], [0.302, 0.561], [0.329, 0.514], [0.356, 0.467], [0.383, 0.420], [0.411, 0.373], [0.438, 0.326], [0.465, 0.279], [0.493, 0.232], [0.520, 0.185], [0.547, 0.138], [0.575, 0.091] for each concern as rows and extract the values.\nBased on the above thought, the answer is:\nTime,Erratic Decline,Sharp Rise,Substantial Growth,Minimal Change\n6,500,550,600,650\n7,700,750,760,770\n8,780,790,800,810\n9,820,800,780,750\n10,720,690,660,630\n11,600,570,540,510\n12,480,500,520,540\n13,560,580,600,620\n14,640,660,680,700\n15,720,740,760,780\n16,800,820,840,860\n17,880,900,920,940\n18,960,980,1000,1020\n19,1040,1060,1080,1100\n20,1120,1140,1160,1180\n21,1200,1220,1240,1260\n22,1280,1300,1320,1340\n23,1360,1380,1400,1420\n24,1440,1460,1480,1500\n25,1520,1540,1560,1580\n26,1600,1620,1640,1660\n27,1680,1700,1720,1740\n28,1760,1780,1800,1820\n29,1840,1860,1880,1900\n30,1920,1940,1960,1980" } ], "id_raw": 337 }, { "id": "EoQLG6tbi1TE0u4H", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_17.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which environmental concern shows a steady decline in its values regardless of environmental elements? Explain." }, { "from": "gpt", "value": "To answer this question, I need to find a line on the chart that consistently moves downwards throughout the time period. Upon inspecting {the line plots} [0.165, 0.796], [0.192, 0.749], [0.220, 0.702], [0.247, 0.655], ..., [0.945, 0.188], there is a clear trend of a steady decline in its values. This trend doesn't show any indications of rising values implying a steady decline.\nBased on the above thought, the answer is:\nThe 'Erratic Decline' environmental element exhibits a steady decline throughout the given time period. The trend line associated with this element consistently moves downwards, indicating a continuous decrease in values. This suggests that the level of 'Erratic Decline' has been consistently reducing over time, regardless of the environmental concern it is associated with. Hence, 'Erratic Decline' shows a steady decline in its values throughout the given time period." } ], "id_raw": 337 }, { "id": "q4pQDt50BdhLjjgY", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_17.png", "conversations": [ { "from": "human", "value": "\nWhat's the range of 'Erratic Decline' in regards to the given period of time in the chart?" }, { "from": "gpt", "value": "To determine the range of the 'Erratic Decline' environmental element, I first identify that it is depicted by {the line plot} [[0.165, 0.796], [0.192, 0.749], [0.220, 0.702], [0.247, 0.655], ..., [0.945, 0.188]]. The maximum and minimum y-values correspond to the highest and lowest points, respectively. Reading from the y-axis, these are 1920 at {coordinate}[0.943, 0.863] and 500 at {coordinate}[0.169, 0.814]. Thus, the range would be 1920 - 500 = 1420.\nBased on the above thought, the answer is:\n1420." } ], "id_raw": 337 }, { "id": "0DA8UNsPpJUbz12z", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_18.png", "conversations": [ { "from": "human", "value": "\nCould you please reformat the data presented in the line chart into a table with Activities as columns and Income Groups as rows?" }, { "from": "gpt", "value": "To answer this, firstly, I'll associate {the x-axis tick labels}[0.093, 0.928, 0.135, 0.951],[0.180, 0.928, 0.222, 0.951],[0.267, 0.928, 0.309, 0.951],[0.354, 0.928, 0.396, 0.951],[0.441, 0.928, 0.483, 0.951], and [0.877, 0.928, 0.919, 0.951] to the activities as column headers. Then, the {legend items}[0.132, 0.046, 0.216, 0.069],[0.132, 0.079, 0.216, 0.102] become the row labels or income groups. Finally, I'll extract the percentage values at each point of {the lines}[0.114, 0.078, 0.941, 0.916],[0.114, 0.243, 0.941, 0.776] for each income group across different activities and place them at the corresponding position in the table to formulate the complete data.\nBased on the above thought, the answer is:\nIncome Group,Rainforest Conservation,Renewable Energy,Ecotourism,Recycling,Sustainable Agriculture\nLow Income,80.5,82.1,81.1,78.7,78.0\nMedium Income,82.6,85.1,84.9,84.5,83.2\nHigh Income,90.4,91.2,88.4,88.2,88.5" } ], "id_raw": 338 }, { "id": "K80n3dyGrNuNOESl", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_18.png", "conversations": [ { "from": "human", "value": "\nFrom the trends observed in the line chart, which income group seems to show the most interest in Renewable Energy and why?" }, { "from": "gpt", "value": "To determine the income group showing the most interest in Renewable Energy, I need to compare the participation percentages at the point of Renewable Energy across all income groups. This involves inspecting the line points for Renewable Energy from {the line for low income}[0.158, 0.656], {the line for medium income}[0.158, 0.465], and {the line for high income}[0.898, 0.269]. I will then compare these values to judge which income group has the highest participation percentage at this point.\nBased on the above thought, the answer is:\nFrom the trends observed in the line chart, the high income group shows the most interest in Renewable Energy. This is because the line for high income group is at the highest point on the y-axis at Renewable Energy, indicating the highest percentage of participation compared to the other income groups." } ], "id_raw": 338 }, { "id": "tX5Ew9mGcmZPgEbI", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the average rate of participation in recycling across all income groups?" }, { "from": "gpt", "value": "To calculate the average rate of participation in recycling, I need to add up the participation percentages for all income groups at the point of Recycling and then divide the total by the number of income groups. So, I locate the points representing recycling from {the low income line}[0.332, 0.871], {the medium income line}[0.332, 0.503] and {the high income line}[0.898, 0.269] and from these coordinates, I extract the values, add them up, and divide by 3.\nBased on the above thought, the answer is:\n83.8" } ], "id_raw": 338 }, { "id": "CB7GvlVuxK6g0MD2", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_17_19.png", "conversations": [ { "from": "human", "value": "\nCan you create a table showing the performance trends of railways, air transport, and shipping from 2010 to 2020 as seen in the chart?" }, { "from": "gpt", "value": "Observing the chart data's line plot, I first identified {@the x-axis tick labels}[0.101, 0.916, 0.112, 0.942], [0.172, 0.916, 0.182, 0.942], ..., [0.939, 0.916, 0.960, 0.942] to determine the years from 2010 to 2020. For each sector, {@the line for railways}[0.107, 0.311, 0.950, 0.860], {@the line for air transport}[0.107, 0.221, 0.950, 0.402], and {@the line for shipping}[0.107, 0.062, 0.950, 0.294] show relative performance across these years. Based on {the plotted points}[0.107, 0.538], [0.177, 0.508], ..., [0.950, 0.311], I extract the value for railways, air transport, and shipping for each year.\nBased on the above thought, the answer is:\nYear,Railways,Air Transport,Shipping\n2010,45,52,55\n2011,44,54,56\n2012,43,56,57\n2013,42,59,59\n2014,41,62,61\n2015,40,65,63\n2016,38,68,66\n2017,36,71,69\n2018,34,74,72\n2019,32,75,75\n2020,31,76,77" } ], "id_raw": 339 }, { "id": "J975JTKLTKD8jVaQ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_17_19.png", "conversations": [ { "from": "human", "value": "\nWhich sector showed the most consistent growth over the decade and how can you determine this from the line chart?" }, { "from": "gpt", "value": "Analyzing the consistency in growth by looking at the lines for each sector, observations can be made based on the smoothness and the incline of the lines as they progress from 2010 to 2020. {@The line for air transport}[0.107, 0.221, 0.950, 0.402] demonstrates a consistently upward trajectory with minimal fluctuations. This denotes steady growth in performance over the years. Comparatively, {@the line for railways}[0.107, 0.311, 0.950, 0.860] shows a declining trend, and {@the line for shipping}[0.107, 0.062, 0.950, 0.294], although increasing, does involve slight variances as seen in the line. Hence, air transport displays the most consistent growth pattern over the decade, evidenced by the upward movement and relative line smoothness.\nBased on the above thought, the answer is:\nAir transport showed the most consistent growth over the decade. This determination is based on observing the smooth, consistently ascending line, which compared to other sectors, shows steady and minimal fluctuation in its upward trend." } ], "id_raw": 339 }, { "id": "OnXJjwJmzWTEIeJC", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_17_19.png", "conversations": [ { "from": "human", "value": "\nHow many points difference is there between the performances of railways and shipping in 2020?" }, { "from": "gpt", "value": "To find the specific performances in 2020, I need to extract the values corresponding to the year 2020 from each sector's line. Considering {@the point for railways in 2020}[0.950, 0.860], the performance is noted as 31. Meanwhile, for {@the point for shipping in 2020}[0.950, 0.294], the noted value is 77. Consequently, the difference is calculated by subtracting the railways' value from the shipping's value, which is 77 - 31.\nBased on the above thought, the answer is:\n46" } ], "id_raw": 339 }, { "id": "5ocWichwy1qtu4dX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_0.png", "conversations": [ { "from": "human", "value": "\nCan you translate the line chart data of Science and Art development over time into a structured table?" }, { "from": "gpt", "value": "Based on the line chart visuals, I can observe {the Science development line} [0.078, 0.141, 0.474, 0.819] and {the Art development line} [0.572, 0.132, 0.968, 0.828]. The x-coordinates at each point correspond to the time period labels in the {x-axis tick labels}[0.013, 0.912, 0.489, 0.936], and the y-coordinates correspond to the development level labels in the {y-axis tick labels}[0.032, 0.889, 0.050, -0.034]. So, by matching the x-y coordinates with their respective labels, I can map the science development and art development levels with the periods.\nBased on the above thought, the answer is:\nPeriod,ScienceDevelopment,ArtDevelopment\n0,0.1,0.05\n500,0.4,0.2\n1000,0.7,0.44\n1500,0.85,0.55\n2000,0.9,0.6\n2500,0.92,0.65\n3000,0.93,0.70\n3500,0.94,0.72\n4000,0.96,0.74" } ], "id_raw": 340 }, { "id": "hGA8aJVLUJJtHRfF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_0.png", "conversations": [ { "from": "human", "value": "\nBased on the trends depicted in the line chart, which among Science and Art development had a more consistent growth and why?" }, { "from": "gpt", "value": "Looking at the line plots, {the Science development line}[0.078, 0.141, 0.474, 0.819] exhibits a more gradual and steady increase in development level across all periods. On the other hand, {the Art development line}[0.572, 0.132, 0.968, 0.828] presents a somewhat less consistent trajectory, with slower growth in the earlier years that then accelerates towards the final periods.\nBased on the above thought, the answer is:\nThe Science development had a more consistent growth based on the line chart. Its line plot shows a steady, almost linear progression over time, indicating a regular, consistent increase in Science development. In contrast, the Art development line shows greater variability and less consistency in its progression." } ], "id_raw": 340 }, { "id": "10v2nV2j29vdNunx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_0.png", "conversations": [ { "from": "human", "value": "\nHow much has the Science development improved from the first reading period to the last one in the chart?" }, { "from": "gpt", "value": "To answer this, I need to identify the start and end points of {the Science development line}[0.078, 0.141, 0.474, 0.819]. The line starts from the y-coordinate of 0.819 and ends at 0.141. So, to calculate the difference, we subtract the start value from the end value.\nBased on the above thought, the answer is:\n0.86" } ], "id_raw": 340 }, { "id": "nq0AXtYNh9G8Myci", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_1.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table listing the values for Ideas and Originality from the line chart across all iterations?" }, { "from": "gpt", "value": "To create the table, I first locate {the line for Ideas}[0.093, 0.175, 0.384, 0.810] and note down the values at each iteration point. Similarly, I do the same for {the line for Originality}[0.093, 0.160, 0.384, 0.772]. Each of these lines corresponds to an iteration with x-values indicating the iteration and y-values corresponding to the metric score.\nBased on the above thought, the answer is:\nIteration,Ideas,Originality\n1,0.15,0.20\n2,0.25,0.35\n3,0.40,0.45\n4,0.55,0.58\n5,0.65,0.66\n6,0.76,0.77\n7,0.85,0.86\n8,0.92,0.95\n9,0.99,1.01" } ], "id_raw": 341 }, { "id": "7ggUyLF1Jxeo9qGf", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_1.png", "conversations": [ { "from": "human", "value": "\nWhich design element shows the most consistent improvement in value across iterations?" }, { "from": "gpt", "value": "Analyzing the lines for each design element, particularly focusing on their coordinates and progression, I see that {the line for Ideas}[0.093, 0.175, 0.384, 0.810] shows steady and consistent improvement rising from 0.15 to 0.99 across iterations. Each iteration marks a noticeable increase from the previous, showing consistent growth.\nBased on the above thought, the answer is:\nThe Ideas element shows the most consistent improvement in value across iterations, as evidenced by its steady upward trend in the plot. Each point successively increases, indicating continuous progression and development in this particular design element." } ], "id_raw": 341 }, { "id": "v15f938phpR8Amn7", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_1.png", "conversations": [ { "from": "human", "value": "\nHow much higher is the maximum value for Execution compared to the minimum value?" }, { "from": "gpt", "value": "To find out, I need to examine {the line for Execution}[0.093, 0.130, 0.384, 0.697]. By observing the points, I can determine the maximum value of 1.05 at the last iteration and the minimum value of 0.30 at the first iteration. The difference is therefore calculated as 1.05 - 0.30.\nBased on the above thought, the answer is:\n0.75" } ], "id_raw": 341 }, { "id": "QnhF08eCrRwzt3zk", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_2.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data from the line chart that compares 'Years' against 'Old-Business' and 'Social-Media' into a structured table?" }, { "from": "gpt", "value": "To convert the data from the line chart into a table, I first observe the x-axis, where {the x-axis labels}[0.052, 0.912, 0.082, 0.936], [0.106, 0.912, 0.136, 0.936], ..., [0.481, 0.912, 0.511, 0.936] represent the 'Years'. Then, I identify two line plots. One of them is for 'Old-Business', represented by its line points such as {points for Old-Business}[0.067, 0.171], [0.089, 0.184], ..., [0.475, 0.790]. The other is for 'Social-Media' with its points like {points for Social-Media}[0.067, 0.824], [0.089, 0.810], ..., [0.475, 0.792]. Now, I can compile these points into a table format with three columns: Years, Old-Business, and Social-Media.\nBased on the above thought, the answer is:\nYears,Old-Business,Social-Media\n2000,100,5\n2001,98,7\n2002,95,15\n2003,90,25\n2004,85,35\n2005,75,45\n2006,60,55\n2007,45,65\n2008,35,75\n2009,30,80\n2010,27,82\n2011,25,83\n2012,23,84\n2013,20,85\n2014,18,86\n2015,16,87\n2016,14,88\n2017,12,89\n2018,11,90\n2019,10,91" } ], "id_raw": 342 }, { "id": "t2VHm90UoP0Pm772", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_2.png", "conversations": [ { "from": "human", "value": "\nWhich company shows a more consistent effort in reducing their carbon footprint, and how can you tell this from the chart?" }, { "from": "gpt", "value": "To determine which company shows more consistency in carbon footprint reduction efforts, I examine the stability of the lines on the chart. For instance, {Tesla\u2019s line for Carbon Footprint Reduction}[0.17, 0.20, 0.77, 0.40] remains relatively high and flat, indicating stable efforts. In contrast, {Microsoft\u2019s line}[0.35, 0.45, 0.88, 0.60] shows more fluctuations in the middle of the period depicted, suggesting less consistency. Tesla\u2019s line, therefore, visually represents a more consistent effort in minimizing their carbon emissions.\nBased on the above thought, the answer is:\nTesla shows a more consistent effort in reducing their carbon footprint compared to other companies. This is evident from the relatively flat and higher line on the chart, which implies that Tesla has maintained its efforts steadily over the years." } ], "id_raw": 342 }, { "id": "43r6gVxrh4UdXl5n", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_2.png", "conversations": [ { "from": "human", "value": "\nWhat was the total increase in the sustainability score for Social Media from the year 2000 to 2019?" }, { "from": "gpt", "value": "To calculate the total increase in the sustainability score for Social Media, I first locate the score for the year 2000 at {the point for Social Media in 2000}[0.067, 0.824] which is 5, and then find the score for the year 2019 at {the point for Social Media in 2019}[0.475, 0.232] which is 91. The total increase is the difference between these two values: 91 - 5.\nBased on the above thought, the answer is:\n86." } ], "id_raw": 342 }, { "id": "Cjh8y2UcXCJni7wF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_3.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data to a table, with AI Subfields as columns and the Machine Learning algorithms as rows?" }, { "from": "gpt", "value": "To create the table, I start by identifying {the x-axis tick labels}[[0.102, 0.876, 0.154, 0.910], [0.218, 0.876, 0.275, 0.910], [0.334, 0.876, 0.389, 0.910], [0.450, 0.876, 0.505, 0.910], [0.566, 0.876, 0.623, 0.910]] which represent the columns or AI subfields. Then, I locate {the legend texts}[[0.704, 0.111, 0.759, 0.136], [0.704, 0.156, 0.759, 0.182]] which are the row labels or Machine Learning algorithms. The values for each subfield and algorithm {the line points}[[0.102, 0.318], [0.218, 0.237], [0.334, 0.346], [0.450, 0.482], [0.566, 0.403), [0.102, 0.262], [0.218, 0.183], [0.334, 0.295], [0.450, 0.427], [0.566, 0.349]] are extracted from the points of the lines plotted.\nBased on the above thought, the answer is:\nMachine Learning Algorithm,NLP,Speech Recognition,Computer Vision,Machine Learning,Deep Learning\nNeural Network,0.65,0.75,0.80,0.85,0.90\nDecision Tree,0.70,0.80,0.85,0.90,0.95" } ], "id_raw": 343 }, { "id": "lxMgGtiHlm6wDiff", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_3.png", "conversations": [ { "from": "human", "value": "\nIn the field of Artificial Intelligence, which Machine Learning algorithm - Neural Network or Decision Tree, shows better performance overall? Provide your reasoning." }, { "from": "gpt", "value": "In order to answer this question, I need to analyze the performance of both the algorithms in all the subfields of AI. Looking at {the line for Neural Network}[0.102, 0.318, 0.566, 0.403], it can be seen that it shows an increasing performance trend from 0.65 to 0.90 across the AI subfields NLP to Deep Learning. However, when I analyze {the line for Decision Tree}[0.102, 0.262, 0.566, 0.349], it can be observed that it also shows an increasing trend, but starts from a higher performance baseline of 0.70 and ends at 0.95. Through this analysis, it can be assumed that the Decision Tree algorithm seems to work better overall.\nBased on the above thought, the answer is:\nBased on the given line chart, Decision Tree algorithm exhibits better performance in comparison to Neural Network. In all subfields, it consistently maintains an edge, showing a higher performance from the beginning to end. Its performance increases from 0.70 to 0.95 across subfields, while Neural Network\u2019s performance increases from 0.65 to 0.90. Thus, Decision Tree algorithm shows overall superior performance." } ], "id_raw": 343 }, { "id": "X5pVdoWtbHRDcAT9", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the performance difference between Neural Network and Decision Tree in the field of Deep Learning?" }, { "from": "gpt", "value": "In order to find this, I need to identify {the line points for Neural Network in Deep Learning}[[0.566,0.403]] and {the line points for Decision Tree in Deep Learning}[[0.566,0.349]], after which I can extract the corresponding performance values for comparison. The performance value for Neural Network is 0.90 and for Decision Tree is 0.95. The difference can be calculated as 0.95-0.90 = 0.05.\nBased on the above thought, the answer is:\n0.05." } ], "id_raw": 343 }, { "id": "9rfBuayG69MUCwQU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_4.png", "conversations": [ { "from": "human", "value": "\nCan you translate this line chart about future technological development prospects into a tabular format with years as columns and technology fields as rows?" }, { "from": "gpt", "value": "To generate the table from the line chart, I start by identifying {the labels on the x-axis}[0.153, 0.800, 0.205, 0.843], [0.421, 0.800, 0.501, 0.873], [0.695, 0.800, 0.774, 0.869] as column headers representing the years 2020, 2022, and 2023. Next, I recognize {the labels in the legend}[0.801, 0.039, 0.897, 0.213] as row headers symbolizing distinct technology fields, namely, AI, IoT, Blockchain, Robotics, and Quantum Computing. Then, I locate {the points on each line}[[0.171, 0.622], [0.443, 0.690], ..., [0.723, 0.835]], ..., [[0.178, 0.334], [0.449, 0.330], ..., [0.730, 0.282]] corresponding to each technology field at different times. By extracting these values, the cell values can be populated.\nBased on the above thought, the answer is:\nTechnology,2020,2022,2023\nAI,7.1,8.2,8.9\nIoT,6.0,6.5,6.7\nBlockchain,5.5,5.1,4.8\nRobotics,6.3,7.0,7.7\nQuantum Computing,3.2,5.5,6.5" } ], "id_raw": 344 }, { "id": "b9epAwpB8BPTeL57", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_4.png", "conversations": [ { "from": "human", "value": "\nWhich technological field shows the most significant growth rate from 2020 to 2023 based on the line chart?" }, { "from": "gpt", "value": "To distinguish the technological field with the most significant growth rate, I scrutinize the relative increases in the lines of the chart. The line for {AI}[0.171, 0.454, 0.730, 0.806] rises from 7.1 in 2020 to 8.9 in 2023. The {IoT line}[0.171, 0.378, 0.730, 0.399] increases scrappily from 6.0 to 6.7. The {Blockchain line}[0.178, 0.343, 0.730, 0.282] actually declines from 5.5 to 4.8. The {Robotics line}[0.171, 0.401, 0.730, 0.743] shows a steady rise from 6.3 to 7.7. However, the {Quantum Computing line}[0.178, 0.234, 0.730, 0.629] shows the most striking increase from 3.2 in 2020 to 6.5 in 2023.\nBased on the above thought, the answer is:\nThe technological field that shows the most significant growth rate from 2020 to 2023 is Quantum Computing. While all technological fields except Blockchain show some level of growth over the period, the progress of Quantum Computing is the most pronounced. It more than doubles its development prospects rating, moving from a relatively low 3.2 in 2020 to 6.5 in 2023." } ], "id_raw": 344 }, { "id": "zj0TO4lOZYaMsN1R", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_4.png", "conversations": [ { "from": "human", "value": "\nBy how much does the development prospects rating for AI increase from 2020 to 2023?" }, { "from": "gpt", "value": "To answer this, I need to find the initial and final values for AI. I locate {the line representing AI}[0.171, 0.454, 0.730, 0.806] and extract the corresponding prospect rating values at each year. The initial value for 2020 is 7.1 corresponding to {initial point on AI line}[0.171, 0.454], while the final value for 2023 is 8.9 corresponding to {final point on AI line}[0.730, 0.806]. The net increase from 2020 to 2023 is calculated as: 8.9 - 7.1= 1.8.\nBased on the above thought, the answer is:\n1.8" } ], "id_raw": 344 }, { "id": "HPDFldUq455qOgXu", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_5.png", "conversations": [ { "from": "human", "value": "\nCould you provide me with the data in a tabular format consisting of Time Period as rows and Investment types, Angel&Seed and Series A as columns?" }, { "from": "gpt", "value": "Converting the data to a table involves tracing back to the data points of each investment type in the line chart. For the investment type \"Angel&Seed\", I can locate the points {the line of Angel&Seed}[0.106, 0.185, 0.478, 0.755], where the investment amounts are represented at different time periods. Similarly, for Series A investment, I can find its data points on {the line of Series A}[0.593, 0.175, 0.965, 0.765]. Then, I can extract the corresponding values at each timepoint to fill the rows of the table.\nBased on the above thought, the answer is:\nTime Period,Angel&Seed,Series A\n0,1.0,1.2\n1,0.95,1.1\n2,0.9,0.98\n3,0.85,0.9\n4,0.82,0.88\n5,0.8,0.87\n6,0.78,0.86\n7,0.77,0.85\n8,0.69,0.83\n9,0.7,0.82\n10,0.75,0.81" } ], "id_raw": 345 }, { "id": "fWZXIxmCdZwzCn7z", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_5.png", "conversations": [ { "from": "human", "value": "\nWhat are the trend differences between Angel&Seed and Series A investments over the time period according to the line chart?" }, { "from": "gpt", "value": "{The Angel&Seed line}[0.106, 0.185, 0.478, 0.755] seems to exhibit a downward trend over the time period, starting with a value of 1.0 and declining to 0.75. On the other hand, {the Series A line}[0.593, 0.175, 0.965, 0.765] also shows a general decline but its investment amounts are higher than that of Angel&Seed throughout the same period. They both start at the same maximum value at the start of the time period but Series A remain higher despite the downward trend in the same period.\nBased on the above thought, the answer is:\nBoth Angel&Seed and Series A investments exhibit a downward trend over the time period. However, the Series A investments remain higher than the Angel&Seed investments despite the similar trend. In summary, both types of investments are declining over time but Series A is consistently higher than Angel&Seed." } ], "id_raw": 345 }, { "id": "bUlpuJMtrkjrvc39", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest investment amount recorded over the time period for Angel&Seed investment?" }, { "from": "gpt", "value": "To determine the highest investment amount for Angel&Seed, I need to find {the line points of Angel&Seed}[0.106, 0.185, 0.478, 0.755] and extract the corresponding values. The highest value is 1.0 which corresponds to {Time Period: 0}[0.106, 0.184].\nBased on the above thought, the answer is:\n1.0." } ], "id_raw": 345 }, { "id": "wqeJ91zohiHXLHH4", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_6.png", "conversations": [ { "from": "human", "value": "\nBased on the chart data, could you present the advancements of GPT-6 and AAI-1 in a table with Years as rows and AI Models as columns?" }, { "from": "gpt", "value": "To generate this table, I'll start by identifying the {years on the x-axis} [[0.097, 0.844], [0.159, 0.844], [0.221, 0.844], [0.283, 0.844], [0.345, 0.844], [0.407, 0.844], [0.470, 0.844]], which will become our rows. Then, I'll take {the plot lines' labels} [[0.890, 0.651, 0.950, 0.686], [0.890, 0.703, 0.966, 0.738]] as column headers; they represent the different AI models. The advancement rate of each AI model at each year can be deduced from the y-coordinate of each {data point on the lines} [[0.097, 0.484], [0.159, 0.442],..., [0.470, 0.201]], and after matching them to the y-axis labels, we can fill in the values for our table.\nBased on the above thought, the answer is:\nYear,GPT-6 Original,GPT-6 Upgraded\n2030,0.65,0.45\n2035,0.68,0.48\n2040,0.72,0.51\n2045,0.73,0.56\n2050,0.78,0.62\n2055,0.82,0.65\n2060,0.85,0.68" } ], "id_raw": 346 }, { "id": "T1r4OUc1CEtSmaea", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_6.png", "conversations": [ { "from": "human", "value": "\nConsidering the trends shown in the line charts, how do the advancement rates of the original and upgraded versions of the GPT-6 and AAI-1 models compare across different years?" }, { "from": "gpt", "value": "To answer this, I\u2019ll begin by looking at the {line plots of the GPT-6 original and upgraded versions}[0.097, 0.201, 0.470, 0.768]. The original GPT-6 model shows a consistent increase in advancement rate over the years, which means steady improvement. In comparison, the upgraded version also shows an increasing trend but at a higher rate, suggesting a substantial upgrade benefit. Now analyzing the {line plots for the AAI-1 original and upgraded versions}[0.588, 0.430, 0.961, 0.780], we see a similar pattern, with both showing rising trends. However, the original AAI-1 model seems to rise at a faster rate than its upgraded version, suggesting more consistent performance.\nBased on the above thought, the answer is:\nFrom the line chart, we can deduce that both the original and upgraded models of GPT-6 and AAI-1 show a consistent increasing trend in their advancement rates over the years. Specifically, the upgraded GPT-6 model seems to witness a substantially faster advancement compared to its original version. However, the AAI-1 models display a different pattern, where the original model registers a moderately faster advancement compared to its upgraded counterpart. It could indicate the consistent performance of the original AAI-1 model or the slower-than-expected progress of the upgraded version." } ], "id_raw": 346 }, { "id": "GvpurefPgwI718pt", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in the advancement rate of the upgraded GPT-6 model from 2030 to 2060?" }, { "from": "gpt", "value": "The total increase will be calculated by subtracting the starting advancement rate in 2030 from the final rate in 2060. I will first find the y-coordinates of the {upgraded GPT-6 model\u2019s data point for 2030}[0.097, 0.768] and extract its corresponding value, 0.45. Then I get the y-coordinate for the {upgraded GPT-6 model\u2019s data point for 2060}[0.470, 0.442] and extract its corresponding value, 0.68. Finally, I calculate the difference, which is 0.68 - 0.45.\nBased on the above thought, the answer is:\n0.23." } ], "id_raw": 346 }, { "id": "TERfvvnyYEGgp5D2", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_7.png", "conversations": [ { "from": "human", "value": "\nCan you please provide the data shown on the chart as a structured table?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.082, 0.895, 0.089, 0.923],[0.124, 0.895, 0.146, 0.923],[0.173, 0.895, 0.195, 0.923],[0.222, 0.895, 0.244, 0.923],[0.267, 0.895, 0.296, 0.923],[0.316, 0.895, 0.345, 0.923],[0.365, 0.895, 0.394, 0.923],[0.414, 0.895, 0.443, 0.923],[0.463, 0.895, 0.492, 0.923]], which represent Iterations. Next, I identify {the legend texts}[[0.907, 0.821, 0.977, 0.849]] as the row labels representing Slow Decline and Fast Decline. To populate the values, I locate {the line points}[[0.086, 0.165], [0.135, 0.320], [0.184, 0.424], [0.233, 0.528], [0.282, 0.580], [0.331, 0.632],[0.380, 0.684], [0.429, 0.735], [0.478, 0.787], [0.576, 0.153], [0.625, 0.234],[0.674, 0.315], [0.723, 0.395], [0.772, 0.476], [0.821, 0.557], [0.870, 0.637], [0.919, 0.718], [0.968, 0.799]] for each Decline type at the respective Iterations and extract the values annotated.\nBased on the above thought, the answer is:\nIterations,Slow Decline,Fast Decline\n0,0.98,0.98\n250,0.95,0.88\n500,0.93,0.78\n750,0.91,0.68\n1000,0.90,0.58\n1250,0.89,0.48\n1500,0.88,0.38\n1750,0.87,0.28\n2000,0.86,0.18" } ], "id_raw": 347 }, { "id": "e1JnJKGj9lkfLKqT", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_7.png", "conversations": [ { "from": "human", "value": "\nHow would you compare the decline rate of the two systems based on the given line chart?" }, { "from": "gpt", "value": "Looking at the line plots, both systems show a negative, downward trending slope. But I need to analyze the rate of decline for each system to assess their relative performance. Observing the {line representing Slow Decline} [0.086, 0.165, 0.478, 0.787], we see a gradual decrease from 0.98 to 0.86 over 2000 iterations. On the other hand, the {line representing Fast Decline} [0.576, 0.153, 0.968, 0.799] indicates a much steeper drop from 0.98 to 0.18 during the same iteration period.\nBased on the above thought, the answer is:\nThe Fast Decline system shows a steep decline compared to the Slow Decline system. Despite starting at the same Relative Performance in iteration 0, by iteration 2000, the Fast Decline System has a significantly lower Relative Performance. Hence, the Fast Decline system seems to have a significantly higher decline rate than the Slow Decline system, concluding that the Fast Decline system deteriorates at a faster pace." } ], "id_raw": 347 }, { "id": "W59emG0aBEKDzfqF", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_7.png", "conversations": [ { "from": "human", "value": "\nHow many iterations did it take for the system to reach a Relative Performance of less than 0.6 for Fast Decline?" }, { "from": "gpt", "value": "To understand how many iterations it took for the system to reach a Relative Performance of less than 0.6 for Fast Decline, I first need to check {the line points for Fast Decline}[[0.576, 0.153], [0.625, 0.234],[0.674, 0.315], [0.723, 0.395], [0.772, 0.476], [0.821, 0.557], [0.870, 0.637], [0.919, 0.718], [0.968, 0.799]]. I can see that the Relative Performance first reaches less than 0.6 at the {1000th iteration}[0.772, 0.476].\nBased on the above thought, the answer is:\n1000 iterations." } ], "id_raw": 347 }, { "id": "MAwkiQaM8R7ckW8A", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_8.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured table showing the percentage spent in each behavior state at every 10-second interval based on the line chart?" }, { "from": "gpt", "value": "To construct the table, I use {the x-axis tick labels}[[0.1, 0.9, 0.13, 0.92], [0.24, 0.9, 0.26, 0.92], [0.39, 0.9, 0.41, 0.92], [0.52, 0.9, 0.55, 0.92], [0.65, 0.9, 0.68, 0.92]] that mark each time interval. The behaviors increase every 10 seconds, labeled as 0s, 10s, 20s, 30s, and 40s. For {the line for Anxiety}[0.1, 0.4, 0.6, 0.85], {the line for Confidence}[0.1, 0.35, 0.6, 0.2], {the line for Distraction}[0.1, 0.8, 0.6, 0.7], {the line for Focused}[0.1, 0.7, 0.6, 0.55], and {the line for Happiness}[0.1, 0.65, 0.6, 0.45], I extract the position at each x-coordinates for every behavior and determine the corresponding approximate percentage.\nBased on the above thought, the answer is:\nInterval,Anxiety,Confidence,Distraction,Focused,Happiness\n0s,78%,25%,60%,55%,42%\n10s,65%,35%,58%,57%,52%\n20s,49%,45%,56%,60%,48%\n30s,67%,55%,53%,62%,50%\n40s,43%,65%,50%,65%,58%" } ], "id_raw": 348 }, { "id": "mKAGwODzfEdDRcVb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_8.png", "conversations": [ { "from": "human", "value": "\nWhich behavior shows the greatest improvement from the beginning to the end of the observed period?" }, { "from": "gpt", "value": "To evaluate the greatest improvement, I compare the start and end values of each behavior. Observing {the line for Anxiety}[0.1, 0.4, 0.6, 0.85] starts at approximately 78% and ends around 43%, indicating a decline. For {the line for Confidence}[0.1, 0.35, 0.6, 0.2], it begins at approximately 25% and increases to 65% by the end. {The line for Distraction}[0.1, 0.8, 0.6, 0.7] slightly decreases from 60% to 50%. Both {the line for Focused}[0.1, 0.7, 0.6, 0.55] and {the line for Happiness}[0.1, 0.65, 0.6, 0.45] show growth, but Confidence has the largest percentage change, improving by 40%.\nBased on the above thought, the answer is:\nConfidence shows the greatest improvement, with a 40% increase in the percentage of time the subject was in this state from the beginning to the end of the observed period. This deduction is based on the relative increase in values from the line representing Confidence." } ], "id_raw": 348 }, { "id": "Rvels8RjBiVWkvTU", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the average percentage of time spent in Distraction across all plotted time intervals?" }, { "from": "gpt", "value": "To solve this, I locate and calculate the values from {the line for Distraction}[0.1, 0.8, 0.6, 0.7] at each marked interval from {the x-axis tick labels}[0.1, 0.9, 0.13, 0.92] to {the end}[0.6, 0.9, 0.62, 0.92]. The identified values were approximately 60%, 58%, 56%, 53%, and 50% corresponding to each time interval. Averaging these gives (60+58+56+53+50)/5 = 55.4%.\nBased on the above thought, the answer is:\n55.4%" } ], "id_raw": 348 }, { "id": "dCmRnlJS2UnFsxL1", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_10.png", "conversations": [ { "from": "human", "value": "\nCan you organize the raw data for 2019 and 2020 sales according to the months in a structured table?" }, { "from": "gpt", "value": "To extract a structured table from the bar plot, I can start by identifying the labels from the X-Axis, which are present in the {xticks of subplot_0}[0.102, 0.896] and {xticks of subplot_1}[0.593, 0.877] on the chart representing the months. The Y-Axis in {subplot_0}[0.102, 0.727] and {subplot_1}[0.593, 0.735] give the values related to sales for the series plotted. Upon extracting the relevant information, the table can be structured with the first column as the months and other columns for sales values in 2019 and 2020.\nBased on the above thought, the answer is:\nMonth,2019 Sales,2020 Sales\n01,230,210\n02,245,220\n03,210,200\n04,220,230\n05,225,212\n06,235,240\n07,242,245\n08,243,250\n09,235,253\n10,255,260\n11,265,265\n12,272,270" } ], "id_raw": 350 }, { "id": "asz4AiGjhDdYwQd7", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in sales from January to December in 2020?" }, { "from": "gpt", "value": "To find the percentage increase in sales from January to December 2020, we need to find the sales values for these months. Looking at the {January point on subplot_1}[0.593, 0.659], which corresponds to a sales value of 210, and the {December point on subplot_1}[0.965, 0.205], correlating to a sales value of 270, we can calculate the percentage increase as (Final value - Initial value) / Initial value * 100% = (270 - 210) / 210 * 100%.\nBased on the above thought, the answer is:\n28.57%" } ], "id_raw": 350 }, { "id": "idu54UJwoJZr3Y2R", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_12.png", "conversations": [ { "from": "human", "value": "\nCan you translate the bar chart into a structured table with Years as the rows and Technology Aspects as the columns?" }, { "from": "gpt", "value": "To create a table from the chart, I will first identify {the y-axis tick labels}[[0.042, 0.833, 0.089, 0.867], [0.099, 0.647, 0.112, 0.724], [0.157, 0.462, 0.176, 0.576], [0.216, 0.276, 0.241, 0.411], [0.274, 0.091, 0.323, 0.218]] as the rows, representing the Years. Then, the {legend texts}[[0.823, 0.079, 0.897, 0.123], [0.823, 0.124, 0.922, 0.167], [0.823, 0.170, 0.927, 0.213], [0.823, 0.214, 0.932, 0.260], [0.823, 0.259, 0.943, 0.304]] serve as the column headers, representing different Technology Aspects. After that, I can extract the data values by finding the ends of {the bars for each technology}[[{x: 2025, y: 0.2, 'coordinates': [0.119, 0.773, 0.138, 0.820]}, {x: 2026, y: 0.21, 'coordinates': [0.147, 0.746, 0.168, 0.807]}, ... , {x: 2050, y: 0.98, 'coordinates': [0.705, 0.087, 0.719, 0.997]}]] at each year.\nBased on the above thought, the answer is:\nYear,AI Development,5G Implementation,Quantum Computing,Sustainability Innovation,Blockchain Utilization\n2025,0.2,0.25,0.1,0.1,0.05\n2026,0.21,0.3,0.12,0.11,0.07\n...\n2050,0.9,0.95,0.8,0.85,0.98" } ], "id_raw": 352 }, { "id": "vRIuOeTdrnXvmNUs", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_12.png", "conversations": [ { "from": "human", "value": "\nWhich technological aspect presents the slowest growth during this period?" }, { "from": "gpt", "value": "The slowest growth would be represented by the technology aspect with the smallest increase in bar height from 2025 to 2050. Looking at the {bars in 2025}[[0.119, 0.773, 0.138, 0.820], [0.139, 0.773, 0.162, 0.845], [0.163, 0.773, 0.182, 0.803], [0.183, 0.773, 0.202, 0.813], [0.203, 0.773, 0.226, 0.788]] and {bars in 2050}[[0.705, 0.087, 0.719, 0.997], [0.725, 0.087, 0.744, 0.968], [0.745, 0.087, 0.764, 0.823], [0.765, 0.087, 0.784, 0.910], [0.785, 0.087, 0.804, 0.987]], I can see that the Quantum Computing indicator has the lowest rise (0.8 - 0.1 = 0.7) compared to the other technologies.\nBased on the above thought, the answer is:\nBased on the chart, Quantum Computing presents the slowest growth during this period. Despite a general upward trend, the Quantum Computing bar height increases by only 0.7 from 2025 to 2050. This indicates a comparatively slower growth rate vis-a-vis the other technological aspects in the chart." } ], "id_raw": 352 }, { "id": "isakBpTLHFwbapsD", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_12.png", "conversations": [ { "from": "human", "value": "\nWhat's the total projected growth from 2025 to 2050 of all aspects?" }, { "from": "gpt", "value": "To calculate that, I have to sum up the bar heights at the start and end years. For 2025, I look at {the bars in 2025}[[0.119, 0.773, 0.138, 0.820], [0.139, 0.773, 0.162, 0.845], [0.163, 0.773, 0.182, 0.803], [0.183, 0.773, 0.202, 0.813], [0.203, 0.773, 0.226, 0.788]], and their corresponding heights are 0.2, 0.25, 0.1, 0.1, 0.05. Similarly for 2050, using {the bars in 2050}[[0.705, 0.087, 0.719, 0.997], [0.725, 0.087, 0.744, 0.968], [0.745, 0.087, 0.764, 0.823], [0.765, 0.087, 0.784, 0.910], [0.785, 0.087, 0.804, 0.987]], I get mail values of 0.9, 0.95, 0.8, 0.85, 0.98. The difference would be the sum of 2050 values minus the sum of 2025 values.\nBased on the above thought, the answer is:\n3.78" } ], "id_raw": 352 }, { "id": "jrIAtCB3XSRRWjZI", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_13.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, can you extract the data into a table format with Iterations as columns and Evaluation Types as rows?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.092, 0.869, 0.102, 0.904], [0.175, 0.869, 0.205, 0.904], [0.264, 0.869, 0.303, 0.904], [0.357, 0.869, 0.396, 0.904], [0.450, 0.869, 0.489, 0.904], [0.543, 0.869, 0.582, 0.904]], which represent the column headers or iterations. Next, I identify {the legend texts}[[0.864, 0.651, 0.966, 0.686], [0.864, 0.703, 0.946, 0.738]] as the row labels representing the different evaluation types. To populate the values, I locate {the line points}[[0.102, 0.166, 0.470], [0.143, 0.617], ..., [0.423, 0.366]] for each type at the respective iterations and extract the values annotated.\nBased on the above thought, the answer is:\nEvaluation Type,0,250,500,750,1000,1250,1500,1750,2000\nConventional,0.1,0.3,0.55,0.7,0.82,0.9,0.92,0.93,0.92\nFine-tuned,0.05,0.15,0.27,0.35,0.41,0.45,0.55,0.6,0.65" } ], "id_raw": 353 }, { "id": "SoFkq7iS2eBW0uZq", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_13.png", "conversations": [ { "from": "human", "value": "\nFrom the graphical representation, which evaluation seemed to have experienced more prominent improvement in the success rate between iteration 500 and 1250?" }, { "from": "gpt", "value": "To compare the improvement in success rate for both Conventional and Fine-tuned evaluations, I have to first examine the y-coordinate values at iterations 500 and 1250 for each line. Considering the points for {Conventional at iteration 500}[0.190, 0.438] and {Conventional at iteration 1250}[0.330, 0.187], I can observe an improvement in the attack success rate. Similarly, the points for {Fine-tuned at iteration 500}[0.682, 0.572] and {Fine-tuned at iteration 1250}[0.821, 0.410] also show an increase in success rate. However, the change in y-coordinate (success rate) is visibly higher for the Fine-tuned evaluation than for the Conventional one, indicating a greater improvement.\nBased on the above thought, the answer is:\nThe Fine-tuned evaluation demonstrated a more substantial improvement in attack success rate between iterations 500 and 1250, as reflected in the greater positive shift in y-coordinate values on the chart during this time period." } ], "id_raw": 353 }, { "id": "YPlnYp2TR6AsR1zs", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the maximum and minimum values observed for the Fine-tuned evaluation across all iterations?" }, { "from": "gpt", "value": "To identify the maximum and minimum values for the Fine-tuned evaluation across all iterations, I must locate {the line points for Fine-tuned}[[0.588, 0.796], [0.635, 0.677], [0.682, 0.572], [0.728, 0.537], [0.775, 0.523], [0.821, 0.410], [0.868, 0.340], [0.915, 0.305], [0.961, 0.284]] and extract the corresponding values: 0.05, 0.15, 0.27, 0.35, 0.41, 0.45, 0.55, 0.6, 0.65. The maximum value is 0.65 corresponding to {iteration 2000}[0.961, 0.844], and the minimum value is 0.05 corresponding to {iteration 0}[0.588, 0.844]. So, the difference between the maximum and minimum values is 0.65 - 0.05 = 0.6.\nBased on the above thought, the answer is:\n0.6" } ], "id_raw": 353 }, { "id": "J1E0FRTrcCiquMxr", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_14.png", "conversations": [ { "from": "human", "value": "\nCan you derive a table that shows the success rate of VR Education and AR Education for different number of students based on the line chart?" }, { "from": "gpt", "value": "Sure, to generate the table, I will first look at {the x-axis ticks on the first subplot}[[0.101, 0.869, 0.111, 0.904], [0.178, 0.869, 0.217, 0.904], [0.269, 0.869, 0.307, 0.904], [0.360, 0.869, 0.398, 0.904], [0.451, 0.869, 0.489, 0.904]][0.542, 0.869, 0.580, 0.904] to get the column headers, which represent the number of Students. Next, the rows represent the success rates of VR Education and AR Education respectively. I will locate {the line points for VR Education}[[0.106, 0.261, 0.470, 0.679]] and {the line points for AR Education}[[0.598, 0.192, 0.962, 0.748]] to extract their success rates for each number of students.\nBased on the above thought, the answer is:\nNumber of Students,VR Education,AR Education\n0,0.85,0.60\n500,0.90,0.75\n1000,0.92,0.77\n1500,0.91,0.80\n2000,0.88,0.84\n2500,0.85,0.85\n3000,0.81,0.86\n3500,0.78,0.86\n4000,0.76,0.87" } ], "id_raw": 354 }, { "id": "rq0VzyhQtOVmbVWc", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_14.png", "conversations": [ { "from": "human", "value": "\nWhich type of education- VR or AR, showed more improvement in success rates as the number of students increased from 0 to 4000?" }, { "from": "gpt", "value": "To answer this, I need to compare the change in success rates from 0 to 4000 students for both VR Education and AR Education. I look at {VR Education's initial and final datapoints}[[0.106, 0.444], [0.470, 0.679]] corresponding to 0.85 and 0.76 success rates respectively. Next, I examine {AR Education's initial and final datapoints}[[0.598, 0.748], [0.962, 0.192]] which correspond to 0.60 and 0.87 success rates respectively. The increase in success rates for VR Education is (0.76 - 0.85) = -0.09 whereas for AR Education, it's (0.87 - 0.60) = 0.27.\nBased on the above thought, the answer is:\nAR Education showed more improvement in success rates as the number of students increased from 0 to 4000. While the success rate for VR Education declined slightly, the success rate for AR Education significantly increased." } ], "id_raw": 354 }, { "id": "oAMw3APbBOQ6tUnY", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_14.png", "conversations": [ { "from": "human", "value": "\nWhat was the success rate of VR Education when the number of students reached 3000?" }, { "from": "gpt", "value": "I find the success rate for VR Education when the number of students is 3000 by locating {3000 on the x-axis}[0.360, 0.869, 0.398, 0.904] for VR Education and {the corresponding y-value for VR Education}[0.379, 0.549]. The success rate is 0.81 as noted on this point in the chart.\nBased on the above thought, the answer is:\n0.81" } ], "id_raw": 354 }, { "id": "a4bdUQLpzUtG1miT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_15.png", "conversations": [ { "from": "human", "value": "\nCould you extract data from the two line charts, depicting the trends of Conservatism Index and Progressivism Index over the years, and present it in a structured table format?" }, { "from": "gpt", "value": "Sure, first I'll consider {the plot labeled 'Trend of Conservatism Index'}[0.131, 0.037, 0.440, 0.085]. Here, {the x-axis labels}[0.086, 0.869, 0.125, 0.904], [0.176, 0.869, 0.215, 0.904], [0.266, 0.869, 0.305, 0.904], [0.356, 0.869, 0.395, 0.904], [0.446, 0.869, 0.485, 0.904] represent the years, and {the y-coordinates of the line points}[0.106, 0.810], [0.196, 0.470], [0.286, 0.130], [0.375, 0.266], [0.465, 0.334] enable extracting the Conservatism Index values. Next, for {the plot labeled 'Trend of Progressivism Index'}[0.627, 0.037, 0.937, 0.085], using {the same x-axis labels}[0.603, 0.869, 0.642, 0.904], [0.692, 0.869, 0.731, 0.904], [0.782, 0.869, 0.821, 0.904], [0.872, 0.869, 0.911, 0.904], [0.962, 0.869, 0.981, 0.904], I can obtain the Progressivism Index values from {the y-coordinates of the line points}[0.602, 0.810], [0.692, 0.640], [0.782, 0.470], [0.872, 0.300], [0.962, 0.130].\nBased on the above thought, the answer is:\nYear,Conservatism Index,Progressivism Index\n2000,0.5,0.6\n2005,0.55,0.65\n2010,0.6,0.7\n2015,0.58,0.75\n2020,0.57,0.8" } ], "id_raw": 355 }, { "id": "6sgBDuerc9fEQ97k", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_15.png", "conversations": [ { "from": "human", "value": "\nWhich trend, Conservatism or Progressivism, showed a more steady increase over the years based on the line charts?" }, { "from": "gpt", "value": "To answer this, the fluctuations in both lines must be examined. The {Conservatism Index line}[0.106, 0.130, 0.465, 0.810] shows ups and downs - from 0.50 in 2000 to 0.55 in 2005, peaking at 0.60 in 2010, then decreasing to 0.58 in 2015 and further to 0.57 in 2020. This indicates variability in the trend of Conservatism. On the other hand, the {Progressivism Index line}[0.602, 0.130, 0.962, 0.810] constantly rises - from 0.60 in 2000, 0.65 in 2005, 0.70 in 2010, 0.75 in 2015, finally reaching 0.80 in 2020. This shows a more steady increase compared to the Conservatism Index.\nBased on the above thought, the answer is:\nThe Progressivism Index showed a more steady increase over the years compared to the Conservatism Index. This observation is based on the consistent rise in the Progressivism Index values over time, unlike the variable trend seen in the Conservatism Index line." } ], "id_raw": 355 }, { "id": "9Xv4xB3WKAwiqftB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in the Progressivism Index from 2000 to 2020?" }, { "from": "gpt", "value": "To calculate this, I first locate {the point for 2000, which corresponds to a Progressivism Index}[0.602, 0.810] of 0.60. Then I find {the point for 2020}[0.962, 0.130], which corresponds to a Progressivism Index of 0.80. The percentage increase can be calculated as (0.80-0.60)/0.60 * 100%.\nBased on the above thought, the answer is:\n33.33%." } ], "id_raw": 355 }, { "id": "EBkQbfGLnfIglSws", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_16.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the given line chart into a well-structured table using the stages of Innovation project as columns and time intervals as rows?" }, { "from": "gpt", "value": "To make the transformation, the x-axis labels which are representing the time intervals located at the bottom, {the x-axis tick labels}[[0.03, 0.938, 0.063, 0.978],[0.156, 0.938, 0.188, 0.978],[0.279, 0.938, 0.311, 0.978],..,[0.958, 0.938, 0.989, 0.978]], will be taken as rows. Then the different types of project stages represented in {the legend}[[0.762,0.031,0.803,0.071],[0.807,0.031,0.874,0.071],[0.879,0.031,0.969,0.071],[0.974,0.031,0.999,0.071]] will be used for the columns of the table. For extracting the data, the line points indicated on the chart for each project stage are {the line points for Concept Generation}[[0.062, 0.328], [0.158, 0.311], ..., [0.948, 0.119]], {for Budgeting}[[0.062, 0.377], [0.158, 0.402], ...,[0.948, 0.584]], {for Team Building}[[0.062, 0.197], [0.158, 0.214], ...,[0.948, 0.280]], and {for Evaluation}[[0.062, 0.232], [0.158, 0.221], ...,[0.948, 0.282]].\nBased on the above thought, the answer is:\nTime,Concept Generation,Budgeting,Team Building,Evaluation\n0,0.85,0.80,0.30,0.40\n5,0.79,0.83,0.35,0.42\n10,0.73,0.86,0.40,0.44\n15,0.68,0.88,0.45,0.46\n20,0.63,0.91,0.50,0.75\n25,0.55,0.95,0.52,0.65\n30,0.50,0.97,0.55,0.64\n35,0.44,0.98,0.68,0.70\n40,0.40,1.00,0.75,0.72" } ], "id_raw": 356 }, { "id": "tjvnuLip3h9wDoXV", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_16.png", "conversations": [ { "from": "human", "value": "\nAccording to the trends in the chart, which Innovation project stage sees the most significant steady growth from time t=0 to time t=40?" }, { "from": "gpt", "value": "By closely analyzing the trends indicated by each project stage's line, let's start with the {line for Concept Generation}[0.062, 0.328, 0.948, 0.119], which appears to be a steady decline over time. However, {the line for Budgeting}[0.062, 0.377, 0.948, 0.584] represents a consistent upward trend from t=0 to t=40. The {line for Team Building}[0.062, 0.197, 0.948, 0.280] also shows a steady increase, though not as prominent as Budgeting. Lastly, {the line for Evaluation}[0.062, 0.232, 0.948, 0.282] shows a somewhat erratic trend. There is an abrupt increase around the 20th time frame and then a decline follows, which makes the overall trend less steady compared to others.\nBased on the above thought, the answer is:\nBased on the evident trends in the chart, 'Budgeting' seems to experience the most sustained upward growth from time t=0 to time t=40. The line representing 'Budgeting' is consistently rising, indicating a continual increase in the percentage of effort devoted to this stage over time." } ], "id_raw": 356 }, { "id": "Ru7EvNyef0yBEm0P", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_16.png", "conversations": [ { "from": "human", "value": "\nHow much does the percentage dedicated to Team Building increase from time t=0 to time t=40?" }, { "from": "gpt", "value": "The increase in percentage dedicated to Team Building project can be calculated by extracting the values of 'Team Building' at the start and end times from the chart. Thus, observe {the line points for Team Building}[[0.062, 0.197], [0.158, 0.214], ..., [0.948, 0.280]]. For time t=0, the effort percentage is approximately 0.20 and the effort percentage at t=40 is read as about 0.75. The increase in percentage is then 0.75 - 0.20 = 0.55.\nBased on the above thought, the answer is:\n0.55" } ], "id_raw": 356 }, { "id": "D4LMwoOjirq4Xu4r", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_17.png", "conversations": [ { "from": "human", "value": "\nBased on the graph, could you create a table with the size of each category ('Telemedicine', 'Machine Learning', 'AI', 'Predictive Analysis', 'Biotechnology') for each year?" }, { "from": "gpt", "value": "To construct this table, first I identify the {x-axis tick labels}[[0.100, 0.781, 0.139, 0.799], [0.223, 0.781, 0.263, 0.799], [0.346, 0.781, 0.385, 0.799], [0.468, 0.781, 0.507, 0.799], [0.590, 0.781, 0.629, 0.799], [0.712, 0.781, 0.751, 0.799], [0.834, 0.781, 0.873, 0.799], [0.957, 0.781, 0.996, 0.799]], which correspond to the years from 2012 to 2021. Then, I look at {the legend entries}[[0.794, 0.212, 0.892, 0.232], [0.794, 0.261, 0.892, 0.280], [0.794, 0.310, 0.892, 0.329], [0.794, 0.360, 0.892, 0.379], [0.794, 0.409, 0.892, 0.428]], which will form the rows of the table, representing the different aspects of medical technology. The values for each year and category can be inferred by measuring the height of each stack's color segments at {the bar locations}[[0.111, 0.390, 0.128, 0.752], [0.233, 0.396, 0.251, 0.775], ... , [0.967, 0.409, 0.985, 0.858]].\nBased on the above thought, the answer is:\nYear,Telemedicine,Machine Learning,AI,Predictive Analysis,Biotechnology\n2012,25,10,15,12,8\n2013,30,12,16,18,10\n2014,35,16,20,20,15\n2015,40,18,22,25,20\n2016,45,22,25,25,25\n2017,60,25,30,30,30\n2018,75,33,33,33,35\n2019,80,35,38,35,40\n2020,100,40,40,40,45\n2021,120,45,45,45,50" } ], "id_raw": 357 }, { "id": "XNeEiGi6h18SEk7a", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_17.png", "conversations": [ { "from": "human", "value": "\nAccording to the graph, which aspect of technology has been the most rapidly increasing in recent years and why might that be the case?" }, { "from": "gpt", "value": "To determine the most rapidly increasing aspect, I need to compare the advancement of the different aspects in recent years, by looking at the differences in the bar stacks starting from {the bar stack for 2018} [0.845, 0.386, 0.862, 0.830] to the last one present at {the bar stack for 2021} [0.967, 0.409, 0.985, 0.858]. The 'Telemedicine' section of the stack has grown the most, from 75 to 120. Although 'Biotechnology' has also significantly grown, its increase from 35 to 50 does not match the scale of growth seen in 'Telemedicine'.\nBased on the above thought, the answer is:\nAccording to the graph, 'Telemedicine' has been the most rapidly increasing in recent years. Telemedicine allows health care professionals to evaluate, diagnose, and treat patients remotely using telecommunications technology. As such, it has become a crucial component of health infrastructures, particularly in the wake of the Covid-19 pandemic when many patients pursued remote treatment options." } ], "id_raw": 357 }, { "id": "vhBuEBQxT6CLxLtq", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_17.png", "conversations": [ { "from": "human", "value": "\nHow much has the advancement in Telemedicine grown between 2012 and 2021 according to the chart?" }, { "from": "gpt", "value": "To answer this question, I need to find the value of the 'Telemedicine' category for the year 2012 and for the year 2021. The bar for 'Telemedicine' in the year 2012 can be found at {the first bar stack} [0.111, 0.390, 0.128, 0.752], with a value of 25. Then, I locate the bar for 'Telemedicine' in the year 2021 at {the last bar stack} [0.967, 0.409, 0.985, 0.858], with a value of 120. Then we subtract the value in 2012 from the value in 2021 to get the growth.\nBased on the above thought, the answer is:\n95." } ], "id_raw": 357 }, { "id": "lruZq8zkcD1SjSbL", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_18.png", "conversations": [ { "from": "human", "value": "\nCan you generate a structured table showing the Rate of Social Interaction and Self-satisfaction Index over time represented in the line charts?" }, { "from": "gpt", "value": "To convert the given line charts into a structured table, I focus on the x-axis and the y-values represented by the points in both line charts. I start with {the x-axis tick labels}[[0.063, 0.869, 0.072, 0.904], [0.131, 0.869, 0.141, 0.904], ..., [0.968, 0.869, 0.987, 0.904]], which represent the Time (Month) values from 1 to 12 at regular intervals. For Rate of Social Interaction, I identify {the line points}[[0.102, 0.130], [0.136, 0.182], ..., [0.478, 0.810]] which represent the y-values corresponding to each month. Similarly, for Self-satisfaction Index, I find {the line points}[[0.589, 0.810], [0.623, 0.598], ..., [0.965, 0.130]] as the y-values for each month.\nBased on the above thought, the answer is:\nTime (Month),Rate of Social Interaction,Self-satisfaction Index\n1,100,75\n2,95,80\n3,92,78\n4,80,76\n5,78,80\n6,70,83\n7,60,85\n8,58,83\n9,50,87\n10,45,90\n11,40,88\n12,35,91" } ], "id_raw": 358 }, { "id": "rWKVvCtFKxnVpsX8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_18.png", "conversations": [ { "from": "human", "value": "\nWhat was the rate of decrease in Rate of Social Interaction from month 3 to month 6?" }, { "from": "gpt", "value": "To find the rate of decrease in Rate of Social Interaction from month 3 to month 6, I first need to identify the value for month 3 by locating the corresponding {point for month 3}[[0.170, 0.214]] which is 92. Then, I identify the value for month 6 by locating the corresponding {point for month 6}[[0.273, 0.444]] which is 70. The rate of decrease is therefore calculated as ((92 - 70) / 92) * 100%.\nBased on the above thought, the answer is:\nApproximately 23.91%." } ], "id_raw": 358 }, { "id": "DQzhmGXOHgbNBXfq", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_18_19.png", "conversations": [ { "from": "human", "value": "\nCan you organize the decade's environmental impact data into a structured table with years as columns and indicators as rows?" }, { "from": "gpt", "value": "To create the structured table from the stacked line graph, I first identify {the x-axis tick labels}[0.101, 0.895, 0.136, 0.923] through {the x-axis tick labels}[0.501, 0.895, 0.536, 0.923] as the column headers representing the years from 2001 to 2010. Next, I recognize {the legend entries}[0.792, 0.132, 0.893, 0.208] as the row labels, which include Glacier Melt, SeaLevel Rise, CO2 Emission, Deforestation, and Temperature Increase. To populate the values, I analyze {the line plots}[0.119, 0.113, 0.479, 0.839], specifically noting the data points on each year.\nBased on the above thought, the answer is:\nYear,Glacier Melt,Sea Level Rise,CO2 Emission,Deforestation,Temperature Increase\n2001,0.2%,1.2 cm,32 billion,0.9 million,0.03\u00b0C\n2002,0.25%,1.3 cm,31 billion,1.0 million,0.02\u00b0C\n2003,0.27%,1.4 cm,33 billion,1.1 million,0.04\u00b0C\n2004,0.3%,1.5 cm,30 billion,1.3 million,0.07\u00b0C\n2005,0.35%,1.6 cm,34 billion,1.4 million,0.04\u00b0C\n2006,0.4%,1.8 cm,33 billion,1.5 million,0.05\u00b0C\n2007,0.45%,1.9 cm,35 billion,1.6 million,0.05\u00b0C\n2008,0.5%,2.0 cm,32 billion,1.8 million,0.08\u00b0C\n2009,0.53%,2.1 cm,31 billion,2.0 million,0.04\u00b0C\n2010,0.6%,2.3 cm,36 billion,2.1 million,0.05\u00b0C" } ], "id_raw": 359 }, { "id": "c9tCI6PLwHh584v5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_18_19.png", "conversations": [ { "from": "human", "value": "\nHow has the attitude towards CO2 emissions changed over the decade based on the fluctuation in the graph?" }, { "from": "gpt", "value": "To evaluate the attitude change towards CO2 emissions over the decade, I need to analyze {the line for CO2 Emission}[0.119, 0.769, 0.479, 0.425], noting its rise and falls. The line begins at 32 billion metric tons in 2001, dips slightly in subsequent years, then fluctuates up and down, reflecting unstable approaches and possibly varying global policies and economic factors impacting CO2 emissions.\nBased on the above thought, the answer is:\nBased on the fluctuations observed in the CO2 Emission line, it appears that there was no consistent strategy or attitude towards CO2 emissions over the decade. The line shows significant instability with alternate increases and decreases in emissions, suggesting changing and perhaps conflicting policies or economic factors influencing global carbon dioxide emissions throughout these years." } ], "id_raw": 359 }, { "id": "c2DOjDvnh5flG0mL", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_18_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in sea levels from 2001 to 2010 according to the line graph?" }, { "from": "gpt", "value": "To determine the total increase in sea levels from 2001 to 2010, I need to locate {the line points for Sea Level Rise}[0.119, 0.809, 0.479, 0.355] and extract the values corresponding to each year: 1.2 cm, 1.3 cm, 1.4 cm, 1.5 cm, 1.6 cm, 1.8 cm, 1.9 cm, 2.0 cm, 2.1 cm, and 2.3 cm. Starting at 1.2 cm in 2001 and ending at 2.3 cm in 2010, the total increase is calculated as 2.3 - 1.2 = 1.1 cm.\nBased on the above thought, the answer is:\n1.1 cm." } ], "id_raw": 359 }, { "id": "xX1dDiNWolJ0H1vx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_19_0.png", "conversations": [ { "from": "human", "value": "\nPlease convert the given line chart data into a readable table format with the Years as the columns, and Resource Types as rows." }, { "from": "gpt", "value": "Firstly, I identify {the x-axis tick labels}[[0.172, 0.879, 0.218, 0.914], [0.276, 0.879, 0.304, 0.914], [0.411, 0.879, 0.426, 0.914], [0.493, 0.879, 0.492, 0.914], [0.597, 0.879, 0.609, 0.914], [0.701, 0.879, 0.709, 0.914], [0.797, 0.879, 0.813, 0.914], [0.901, 0.879, 0.917, 0.914], [1.005, 0.879, 1.021, 0.914]] which are the Years. Then {the legend texts}[[0.218, 0.083, 0.340, 0.108],[0.218, 0.135, 0.348, 0.170]] represent the different types of resources or rows classifications. The respective allocation values for each resource in each year are represented by {the line points}[[0.170, 0.854], [0.253, 0.853], [0.336, 0.846], [0.419, 0.840], [0.502, 0.658], [0.585, 0.488], [0.668, 0.427], [0.751, 0.392], [0.834, 0.180], [0.917, 0.112], [0.170, 0.854], [0.253, 0.853], [0.336, 0.851], [0.419, 0.843], [0.502, 0.790],[0.585, 0.694],[0.668, 0.597], [0.751, 0.501], [0.834, 0.437], [0.917, 0.372]].\nBased on the above thought, the answer is:\nResources,-6000,-4000,-2000,0,2000,4000,6000,8000,10000,12000\nPopulation,5,7,27,46,612,1141,1329,1438,2100,2310\nTechnology,1,3,10,35,200,500,800,1100,1300,1500" } ], "id_raw": 360 }, { "id": "vOQAUyfHml2z1lwp", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_19_0.png", "conversations": [ { "from": "human", "value": "\nWhich line on the chart represents a steady increase over the years? Why?" }, { "from": "gpt", "value": "In order to determine which line represents a steady increase over the years, I have to interpret and observe each line plot on the chart. {The line for Population}[0.419, 0.840, 0.917, 0.112] starts at a low point and rises consistently over thousands of years. Although there are minor dips along the line, they do not reflect a significant decrease over time. Based on its visual representation, it would appear that it presents a steady increase in Population.\nBased on the above thought, the answer is:\nThe line of 'Population' represents a steady increase over the years. This is shown by the consistent upward trend seen on the line chart, denoting an increase in population size. Despite minor fluctuations, the overall progression is upward, indicating growth over the years." } ], "id_raw": 360 }, { "id": "XVpq7SBQ9hoIEwsb", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_19_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the decrease in population size from 10000 to 12000 years?" }, { "from": "gpt", "value": "To find the decrease in population size between 10000 and 12000 years, I need to locate and extract the population size for both years from {the line points for Population}[[0.170, 0.853], [0.253, 0.852], [0.336, 0.846], [0.419, 0.840], [0.502, 0.658], [0.585, 0.488], [0.668, 0.427], [0.751, 0.392], [0.834, 0.180], [0.917, 0.112]]. The population at 10000 years is 2100 and at 12000 years it is 2310. The decrease can be calculated as 2310 - 2100 = 210.\nBased on the above thought, the answer is:\n210" } ], "id_raw": 360 }, { "id": "A2B01sTT2Ew9sZc4", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_19_1.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart tracking education metrics can you extract the data into a table format with Years as columns and Metrics ['Student Enrollment Rate', 'Dropout Rate'] as rows?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.114, 0.879, 0.172, 0.914], [0.231, 0.879, 0.289, 0.914], [0.319, 0.879, 0.377, 0.914], [0.407, 0.879, 0.465, 0.914], [0.495, 0.879, 0.553, 0.914], [0.583, 0.879, 0.641, 0.914], [0.671, 0.879, 0.729, 0.914], [0.759, 0.879, 0.817, 0.914], [0.847, 0.879, 0.905, 0.914], [0.935, 0.879, 0.993, 0.914]], which represent the column headers or years. Next, I identify {the legend texts}[[0.669, 0.069, 0.954, 0.104], [0.669, 0.121, 0.824, 0.156]] as the row labels representing the different metrics. To populate the values, I locate {the line points for Student Enrollment Rate}[[0.143, 0.075], [0.231, 0.096], ..., [0.935, 0.232]] and {the line points for Dropout Rate}[[0.143, 0.817], [0.231, 0.796], ..., [0.935, 0.650]] and extract the corresponding values.\nBased on the above thought, the answer is:\nMetric,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021\nStudent Enrollment Rate,85,83,82,80,78,76,75,74,72,70\nDropout Rate,14,16,17,18,20,22,23,24,25,30" } ], "id_raw": 361 }, { "id": "744KYVInuNyT9Jh4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_19_1.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the line chart, which educational metric seems to have worsened over the years and what could be the potential implications?" }, { "from": "gpt", "value": "Analyzing {the line for Student Enrollment Rate} [0.143, 0.075, 0.935, 0.232] and {the line for Dropout Rate}[0.143, 0.817, 0.935, 0.650], we see that the percentage of student enrollment has decreased from 85% in 2012 to 70% in 2021, this represents an unfavorable trend. On the other side, the dropout rate has increased from 14% in 2012 to 30% in 2021, representing a negative trend.\nBased on the above thought, the answer is:\nThe educational metric that has worsened over the years according to the line chart is the Dropout Rate. As the percentage of students leaving school before graduating has increased over the years from 14% in 2012 to 30% in 2021, the implications of this trend can be severe. It may reflect declining youth engagement with education or issues with the strength of the educational system itself. This trend, if continued, may result in a less educated workforce, which can have diverse socio-economic implications, including reduced earning potentials and loss of productivity and innovation in the society." } ], "id_raw": 361 }, { "id": "2qwCsUP0vx1nNIFI", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_19_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in the student enrollment rate from the year 2012 to 2021?" }, { "from": "gpt", "value": "To find the change in student enrollment rate from 2012 to 2021, I need to locate {the line point for student enrollment rate in 2012}[0.143, 0.075] and {the line point for student enrollment rate in 2021}[0.935, 0.232] on the line chart for \"Student Enrollment Rate and extract the corresponding values: 85 and 70. The change from 2012 to 2021 in the student enrollment rate is therefore 85 - 70 = 15.\nBased on the above thought, the answer is:\n15." } ], "id_raw": 361 }, { "id": "ylNKWgJWHJ6dNoUU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_19_2.png", "conversations": [ { "from": "human", "value": "\nCould you transform the data from the line chart, indicating the frequency of social activities during weekdays and weekends, into a table?" }, { "from": "gpt", "value": "To create a table from the chart, I will focus first on the {x-axis}[[0.149, 0.879, 1.037, 0.914]], where the activities' labels are located. \nThen, I turn to the {legend}[[0.665, 0.069, 0.963, 0.184]], to determine the weekdays and weekends categories. \nThen, the measurements for each of these categories can be retrieved from their corresponding lines {Intrusion Attempts line}[[0.157, 0.185, 0.936, 0.587]] and {Data Breaches line}[[0.157, 0.620, 0.936, 0.741]]. The value can be deducted by following the points on the line and referring to the y-axis scale.\nBased on the above thought, the answer is:\nActivity,Weekdays,Weekends\nVisiting Parks,70,65\nHome Cooking,80,75\nLate-night Parties,50,55\nEarly Wakeup,30,28\nWatching Television,18,19\nOutdoor Cycling,13,10\nLibrary Study,10,5" } ], "id_raw": 362 }, { "id": "ok6qMHAbfc0r4Nzh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_19_2.png", "conversations": [ { "from": "human", "value": "\nHow does the trend of Intrusion Attempts look over the period shown on the graph?" }, { "from": "gpt", "value": "To analyze the trend of Intrusion Attempts, I must follow the progression of {the line for Intrusion Attempts}[0.157, 0.185, 0.936, 0.587]. It starts at a high point at time = 0 (100 instances), then there's a consistent decline until it reaches a lower point at the end of the observed period (40 instances). This visual information suggests a decreasing trend.\nBased on the above thought, the answer is:\nThe trend of Intrusion Attempts over the given period shows a consistent decline. The number of evidences starts high and then gradually decreases over time, indicating improved security or reducing threats." } ], "id_raw": 362 }, { "id": "JrmNMt6XyreiqIJq", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_19_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the number of Intrusion Attempts at the 1st month and the last month?" }, { "from": "gpt", "value": "For that, I need to find the coordinates of the first and last points of {Intrusion Attempts line}[[0.157, 0.185, 0.936, 0.587]]. The starting point is at {[0.157, 0.185]}, and the ending point is at {[0.936, 0.587]}. I can see from the y-axis that the y-coordinates of these points correspond to 100 and 40 respectively. The difference can be calculated by subtracting these two values: 100 - 40 = 60.\nBased on the above thought, the answer is:\n60." } ], "id_raw": 362 }, { "id": "fRJHtoP0tY2oMSVG", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_19_5.png", "conversations": [ { "from": "human", "value": "\nCan you translate the chart data into a structured table that will present each sector's production volume for the years 2021 and 2022?" }, { "from": "gpt", "value": "To create a table from the line chart, I will first identify {the x-axis labels}[0.062, 0.763, 0.156, 0.904], [0.900, 0.769, 0.992, 0.912] as representing the years 2021 and 2022. Then, I'll take each sector mentioned in {the legend}[0.781, 0.089, 0.935, 0.173], [0.781, 0.190, 0.929, 0.279], [0.781, 0.285, 0.903, 0.381], [0.781, 0.398, 0.899, 0.487], [0.781, 0.497, 0.909, 0.594] to generate column header. The production volumes for each sector, essential for populating the table, are specified by the positions of {the points on the lines for each sector}[0.148, 0.185, 0.946, 0.769].\nBased on the above thought, the answer is:\nSector,2021,2022\nCereal Crops,4950,4800\nVegetable Crops,5622,5300\nFruit Crops,2540,2400\nLivestock,2800,2700\nSeafood,1200,1145" } ], "id_raw": 365 }, { "id": "PSYfi52U7VR9xVSy", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_19_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the trends shown in the line chart, which sector experienced the most significant change in production volume?" }, { "from": "gpt", "value": "To answer this question, I must analyze the different sectors. Starting with {Cereal Crops}[0.156,0.254,0.946,0.292], the production volume decreased from 4950 to 4800, so the change was fairly minor. The {Vegetable Crops}[0.156, 0.337, 0.946, 0.403] dropped its production from 5622 to 5300. {Fruit Crops}[0.156, 0.418, 0.946, 0.447] had a slight decrease as well, from 2540 to 2400. The {Livestock}[0.156, 0.462, 0.946, 0.477] production volume declined slightly from 2800 to 2700. And lastly, the {Seafood}[0.156, 0.696, 0.946,0.711] sector displayed a small decline in production from 1200 to 1145.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Vegetable Crops sector experienced the most significant decrease in production volume from 2021 to 2022. This sector's line plot shows a sharp decrease from 5622 thousand metric tonnes in 2021 to 5300 thousand metric tonnes in 2022, a difference of 322 thousand metric tonnes." } ], "id_raw": 365 }, { "id": "VoAi35OJlOB027Sy", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_19_5.png", "conversations": [ { "from": "human", "value": "\nWhat was the decrease in the Vegetable Crops sector's production volume from 2021 to 2022?" }, { "from": "gpt", "value": "To determine the decrease in the Vegetable Crops sector's production volume, I locate {the line points for Vegetable Crops}[0.156, 0.337], [0.946, 0.403]. The 2021 production volume was 5622 thousand metric tonnes and the 2022 production volume was 5300 thousand metric tonnes. The decrease is calculated as 5622 - 5300.\nBased on the above thought, the answer is:\n322." } ], "id_raw": 365 }, { "id": "XUTdvnqa9BflmB3j", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_19_6.png", "conversations": [ { "from": "human", "value": "\nCan you provide the data in the plot in a table format with row representing the year and columns representing the Hydro Power and Hydro Conventional?" }, { "from": "gpt", "value": "In order to generate table from the plot, I look at {the x-axis tick labels}[[0.122, 0.912, 0.166, 0.936], [0.211, 0.912, 0.255, 0.936], [0.299, 0.912, 0.343, 0.936], [0.388, 0.912, 0.432, 0.936], [0.477, 0.912, 0.520, 0.936], [0.565, 0.912, 0.609, 0.936], [0.654, 0.912, 0.698, 0.936], [0.742, 0.912, 0.786, 0.936], [0.831, 0.912, 0.875, 0.936], [0.919, 0.912, 0.963, 0.936]], which will become the rows corresponding to the years. The {legend items}[[0.794, 0.085, 0.904, 0.108], [0.794, 0.120, 0.966, 0.143]] will be the headers representing the energy generation types. For each year, I look at the corresponding {line points for Hydro Power}[[0.144, 0.179], [0.233, 0.296], [0.321, 0.373], [0.410, 0.567], [0.498, 0.645], [0.587, 0.723], [0.676, 0.800], [0.764, 0.800], [0.853, 0.839], [0.941, 0.858]] and {line points for Hydro Conventional}[[0.144, 0.102], [0.233, 0.218], [0.321, 0.257], [0.410, 0.296], [0.498, 0.412], [0.587, 0.490], [0.676, 0.567], [0.764, 0.645], [0.853, 0.723], [0.941, 0.761]] and map these points back to their corresponding values on the y-axis to fill the table.\nBased on the above thought, the answer is:\nYear,Hydro Power,Hydro Conventional\n2030,8000,8200\n2031,7700,7900\n2032,7500,7800\n2033,7000,7700\n2034,6800,7400\n2035,6600,7200\n2036,6400,7000\n2037,6400,6800\n2038,6300,6600\n2039,6250,6500" } ], "id_raw": 366 }, { "id": "rKMKUfpnolJm8Ez9", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_19_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the average energy production for Hydro Conventional over the given years?" }, { "from": "gpt", "value": "To find the average energy production for Hydro Conventional, I need to locate {the line points for Hydro Conventional}[[0.144, 0.102], [0.233, 0.218], [0.321, 0.257], [0.410, 0.296], [0.498, 0.412], [0.587, 0.490], [0.676, 0.567], [0.764, 0.645], [0.853, 0.723], [0.941, 0.761]] and extract the corresponding values: 8200, 7900, 7800, 7700, 7400, 7200, 7000, 6800, 6600, 6500. I calculate the sum of these values and divide by the total number of years, which is 10.\nBased on the above thought, the answer is:\n7210" } ], "id_raw": 366 }, { "id": "P4wIvHty2f5A1aWy", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_19_7.png", "conversations": [ { "from": "human", "value": "\nCan the data from the line chart depicting the risk scores over time for Operational Risk, Compliance Risk, Credit Risk, and Market Risk be extracted into a table?" }, { "from": "gpt", "value": "Absolutely, to transform this line chart into a tabular format, I should take a look at {the x-axis labels}[[0.113, 0.919, 0.124, 0.942], [0.296, 0.919, 0.307, 0.942], [0.478, 0.919, 0.489, 0.942], [0.661, 0.919, 0.672, 0.942], [0.843, 0.919, 0.854, 0.942], [1.020, 0.919, 1.042, 0.942]], indicating the intervals of time from 0 to 9 which would be the column headers. Secondly, I identify {the legend texts}[[0.142, 0.055, 0.285, 0.078], [0.142, 0.090, 0.285, 0.113], [0.142, 0.125, 0.236, 0.148], [0.142, 0.160, 0.243, 0.183]] which represents the risk types and will be the row labels. Then, to fill in the data, I check {the line points on the plot}[[0.118, 0.469], [0.210, 0.521], ..., [0.940, 0.434]] for each risk type at the respective time intervals and extract the corresponding risk scores.\nBased on the above thought, the answer is:\nTime,Operational Risk,Compliance Risk,Credit Risk,Market Risk\n0,65,55,45,75\n1,62,48,52,78\n2,59,51,58,76\n3,60,50,54,77\n4,58,56,62,82\n5,55,60,59,80\n6,59,65,61,79\n7,61,62,63,84\n8,62,68,65,85\n9,65,70,67,87" } ], "id_raw": 367 }, { "id": "jCpEVu5v7hFbRJHb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_19_7.png", "conversations": [ { "from": "human", "value": "\nLooking at the trends in the line chart, which risk do you believe will continue to rise, and why?" }, { "from": "gpt", "value": "To infer which risk is likely to continue rising, I need to analyze the trends for each risk type. Looking at the {line for Operational Risk}[0.118, 0.469, 0.940, 0.642], it shows a general decreasing trend from 65 to 65. Looking at the {line for Compliance Risk}[0.118, 0.382, 0.940, 0.764], it shows a general increasing trend from 55 to 70. The {line for Credit Risk}[0.118, 0.434, 0.940, 0.816] shows a fluctuating but generally increasing trend from 45 to 67. Finally, {the line for Market Risk}[0.118, 0.086, 0.940, 0.295] indicates a steady increase over time from 75 to 87.\nBased on the above thought, the answer is:\nBased on the line chart, I would infer that the Market Risk would continue to rise. Its trend line consistently ascends from 75 to 87 over time, indicating a steady increase. Though the lines for Compliance Risk and Credit Risk also show an increasing trend, the slope of the Market Risk line seems to be the steepest and most consistent, suggesting a stronger upward trend. Therefore, it's likely that the Market Risk will continue to rise." } ], "id_raw": 367 }, { "id": "FONqoMhhqyosaH0g", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_19_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in Market Risk score from Time = 0 to Time = 9?" }, { "from": "gpt", "value": "To find out the change in Market Risk score from Time = 0 to Time = 9, I need to look at {the line points for Market Risk}[[0.118, 0.295], [0.940, 0.086]]. The initial score at Time = 0 is 75, corresponding to {the point at Time=0}[0.118, 0.295] and the final score at Time = 9 is 87, corresponding to {the point at Time=9}[0.940, 0.086]. The change is calculated as the final score minus the initial score, therefore it would be 87 - 75 = 12.\nBased on the above thought, the answer is:\n12." } ], "id_raw": 367 }, { "id": "l391TuSs2fwWhTUT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_19_8.png", "conversations": [ { "from": "human", "value": "\nCan you present the data from the line chart in a table format with the rows being the 'year' and the columns being 'popularity' of 'Haute Couture' and 'Streetwear'?" }, { "from": "gpt", "value": "To create a structured table from the line chart, I will use {the x-axis tick labels}[[0.017, 0.919, 0.061, 0.942], [0.197, 0.919, 0.241, 0.942], [0.378, 0.919, 0.421, 0.942], [0.558, 0.919, 0.602, 0.942], [0.738, 0.919, 0.782, 0.942], [0.919, 0.919, 0.963, 0.942]] as the row labels, representing different years. I identify {the legend texts}[[0.839, 0.046, 0.966, 0.069], [0.839, 0.081, 0.935, 0.104]] for 'Haute Couture' and 'Streetwear' respectively as the column headers. The data in the table is the 'popularity' of 'Haute Couture' and 'Streetwear', which I derive from {the points on the Haute Couture line}[[0.129, 0.077], [0.219, 0.283], [0.309, 0.490], [0.399, 0.696], [0.490, 0.789], [0.580, 0.800], [0.670, 0.779], [0.760, 0.769], [0.850, 0.763], [0.941, 0.789]] and {the points on the Streetwear line}[[0.129, 0.386], [0.219, 0.325], [0.309, 0.304], [0.399, 0.294], [0.490, 0.283], [0.580, 0.268], [0.670, 0.247], [0.760, 0.237], [0.850, 0.226], [0.941, 0.221]] respectively for these years.\nBased on the above thought, the answer is:\nYear, Haute Couture, Streetwear\n2005, 1000, 700\n2006, 800, 760\n2007, 600, 780\n2008, 400, 790\n2009, 310, 800\n2010, 300, 815\n2011, 320, 835\n2012, 330, 845\n2013, 335, 855\n2014, 310, 860" } ], "id_raw": 368 }, { "id": "6wUnc0OmoTnEujus", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_19_8.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which fashion trend\u2019s popularity decreases over time, and which one increases?" }, { "from": "gpt", "value": "To determine which fashion trend's popularity decreases or increases over time, I need to analyze the lines representing the two fashion trends on the chart. I note that {the line representing Haute Couture}[0.129, 0.077, 0.941, 0.790] shows a decrease in popularity from 1000 in 2005 to 310 in 2014. However, {the line representing Streetwear}[0.129, 0.386, 0.941, 0.221] shows an increase in popularity from 700 in 2005 to 860 in 2014.\nBased on the above thought, the answer is:\nBased on the line chart, the popularity of 'Haute Couture' shows a declining trend over the years, whereas 'Streetwear' exhibits an increasing trend." } ], "id_raw": 368 }, { "id": "fiINYVyB0wn8FKiO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_19_8.png", "conversations": [ { "from": "human", "value": "\nBy what percentage did the popularity of Streetwear increase from 2005 to 2014?" }, { "from": "gpt", "value": "To answer this, I look at {the point for Streetwear in 2005}[0.129, 0.386], which corresponds to a popularity figure of 700, and {the point for Streetwear in 2014}[0.941, 0.221], which corresponds to a popularity figure of 860. The percentage increase from 2005 to 2014 is ((860-700)/700)*100.\nBased on the above thought, the answer is:\n22.9%" } ], "id_raw": 368 }, { "id": "p6chrSFrZjRQIVN1", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_19_10.png", "conversations": [ { "from": "human", "value": "\nCan I have the info from the line chart in a tabular form with years as rows and each cryptocurrency as a column?" }, { "from": "gpt", "value": "To generate a table from this line chart, we firstly list all marked points from x-axis which represent the column headers or years. We then locate {the legend texts}[0.716, 0.075, 0.973, 0.152] to serve as our column labels for the table, which represent the names of the four cryptocurrencies: Bitcoin, Ethereum, Ripple, and Polkadot. Afterwards, we need to look at each line point for respective cryptocurrencies and fill the values according to their respective year entries. This is done by examining each line's respective point positions in the chart.\nBased on the above thought, the answer is:\nYear, Bitcoin, Ethereum, Ripple, Polkadot\n2017,1250,100,0.06,0\n2018,3500,350,3.84,0\n2019,3700,130,0.37,0\n2020,9000,200,0.19,5.54\n2021,48000,1800,0.65,30" } ], "id_raw": 370 }, { "id": "M5JKy6aILnF2cOfs", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_19_10.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which of the cryptocurrencies depicted can be said to have shown the most stable performance and why?" }, { "from": "gpt", "value": "To deduce the cryptocurrency with the most stable performance, I need to analyze the fluctuations of each line plot for the cryptocurrencies. The line which shows the least fluctuations, underscored by a smooth curve or a straight line, can be interpreted as the one depicting stable performance. Upon examining {the lines for Bitcoin, Ethereum, Ripple, and Polkadot}[0.118, 0.399, 0.940, 0.763], Ethereum seems to have less fluctuation compared to others prior to 2021. It shows a steady incremental trend from 2017 to 2019 before a significant jump in 2020. Polkadot, on the other hand, has large fluctuations highlighted by the huge dip and spike between the years 2020-2021. Ripple shows a high degree of instability, particularly indicated by the sharp peak in 2018 before plummeting back to its original price range. Bitcoin, while having upward trend, still shows a few peaks and troughs which indicate a degree of instability.\nBased on the above thought, the answer is:\nBased on the line chart, Ethereum has shown the most stable performance across the years until 2021. Its trend line shows the least fluctuations and a relatively stable, incrementally increasing trend compared to the other observed cryptocurrencies: Bitcoin, Ripple, and Polkadot." } ], "id_raw": 370 }, { "id": "74zii7JOfqsRtTeU", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_19_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum growth in price Ethereum has experienced in one year?" }, { "from": "gpt", "value": "To calculate the maximum growth for Ethereum, I need to inspect {the line representing Ethereum}[0.118, 0.399, 0.940, 0.763], and note the largest jump between two consecutive years. By looking at the data, I find that the largest difference is between 2020 and 2021, where it went from 200 to 1800.\nBased on the above thought, the answer is:\n1600." } ], "id_raw": 370 }, { "id": "qCU4cy5Kqn2WPspz", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_19_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a convenient table format, using 'Year' as the columns and 'Companies' as the rows?" }, { "from": "gpt", "value": "Sure, to convert the line chart data into a table, the x-axis {tick labels}[0.123, 0.912, 0.940, 0.936] signify the columns that are the years. I identify the {legend text}[0.840, 0.055, 0.966, 0.113] as the row headers, these represent the different companies - 'Tech' and 'Manufacturing'. The values are populated by extracting corresponding y-values from the line points visually drawn on the graph, these can be extracted from the {line coordinates for Tech}[0.123, 0.322, 0.940, 0.753] and {line coordinates for Manufacturing}[0.123, 0.235, 0.940, 0.695].\nBased on the above thought, the answer is:\nCompanies,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014\nTech,15,18,15,23,20,28,22,29,30,30\nManufacturing,20,23,19,17,25,33,29,32,33,30" } ], "id_raw": 372 }, { "id": "qhkcmWDF4Bn7HGGk", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_19_12.png", "conversations": [ { "from": "human", "value": "\nBased on the plotted line graph, can you interpret the changes in market shares of Tech and Manufacturing over the years?" }, { "from": "gpt", "value": "By examining the {line plotted for Tech}[0.123, 0.322, 0.940, 0.753], I can see an overall increase in market share from 15% to 30% over the years. The climb is not consistent, seeing an increase between 2005-2006 and then a fall in 2007. Thereafter, it rises and falls again in 2011 and then steadily increases till 2014.\nOn the other hand, the {line plotted for Manufacturing}[0.123, 0.235, 0.940, 0.695], reveals an increase in market share from 20% to 30%. This line also demonstrates a similar, however less volatile, trend of rising and falling market share, with noticeable increase in 2006 followed by a decrease in 2008 and 2009. It then experiences a rise till 2010 and remains steady from 2012 to 2014.\nBased on the above thought, the answer is:\nGiven the line graph, Tech started with lower market share in 2005 and experienced fluctuating growth till it matched Manufacturing in 2014, both ending at 30% share. Manufacturing however, started at a higher position but experienced less growth over the years. Both companies illustrated periods of growth and reduction, with Tech showing a more volatile pattern. In essence, while both Tech and Manufacturing grew over the years, Tech had a larger overall growth, eventually matching its competitor." } ], "id_raw": 372 }, { "id": "aVNp0M5kw5xWIVOd", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_19_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in Market Share for Tech between 2005 and 2014?" }, { "from": "gpt", "value": "To answer this, I need to look at the starting and ending points of the {line for Tech}[0.123, 0.322, 0.940, 0.753]. The y-value at 2005 is 15 and at 2014 it's 30. Therefore, the increase in the market share for Tech between 2005 and 2014 is 30 - 15 = 15.\nBased on the above thought, the answer is:\n15%" } ], "id_raw": 372 }, { "id": "advC4ugFUre5gWxg", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_19_13.png", "conversations": [ { "from": "human", "value": "\nCould you provide a structured table depicting the change in effectiveness for each AI and Automation model across different languages during two periods?" }, { "from": "gpt", "value": "Initially, to construct a structured table from the chart, I would recognize {the x-axis labels}[0.071, 0.025, 0.985, 0.903] which lists the AI and Automation models. Additionally, I identify {the legend}[0.078, 0.037, 0.314, 0.113] which gives the languages used for each model. By evaluating {the line plots}[0.112, 0.045, 0.943, 0.719] and [0.112, 0.045, 0.943, 0.903], I could extract the effectiveness values for each language and period.\nBased on the above thought, the answer is:\nModel,Language,Period1,Period2\nAI Bot,EN,85,90\nAI Bot,DE,80,85\nAI Bot,FR,95,98\nAutomated System,EN,101,104\nAutomated System,DE,99,102\nAutomated System,FR,97,101\nDeep Learning,EN,120,118\nDeep Learning,DE,122,120\nDeep Learning,FR,130,125\nNeural Network,EN,140,138\nNeural Network,DE,138,135\nNeural Network,FR,142,140\nPredictive Modeling,EN,150,148\nPredictive Modeling,DE,149,147\nPredictive Modeling,FR,152,150" } ], "id_raw": 373 }, { "id": "ZoKj4lbu85KGwA4k", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_19_13.png", "conversations": [ { "from": "human", "value": "\nHow does the effectiveness of Predictive Modeling change over two periods and what might be inferred about its stability?" }, { "from": "gpt", "value": "To assess how Predictive Modeling's effectiveness changes over two periods, I would examine {the points for Predictive Modeling}[0.500, 0.413], [0.528, 0.372] and [0.500, 0.395], [0.528, 0.362]. In Period1, the effectiveness values noted are 150, 149, and 152. In Period2, the values slightly decrease to 148, 147, and 150. This subtle decrease suggests a slight volatility but overall, Predictive Modeling seems to exhibit relatively stable effectiveness as the changes are minor.\nBased on the above thought, the answer is:\nThe effectiveness of Predictive Modeling in Period1 is slightly higher than in Period2. These small variations across different languages suggest that Predictive Modeling, while experiencing some decrements, maintains a moderate level of stability regarding its effectiveness. The reduction is minimal, indicating its reliability and consistency over the observed periods." } ], "id_raw": 373 }, { "id": "8MLm9xO3UnX3PAy8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_19_13.png", "conversations": [ { "from": "human", "value": "\nWhat's the average Period1 effectiveness of AI Bot models across all languages?" }, { "from": "gpt", "value": "To determine the average Period1 effectiveness for AI Bot models across all languages, I would locate the data points for AI Bot in Period1 from {the points for AI Bot Period1}[0.112, 0.699], [0.140, 0.682], [0.168, 0.719] which show values of 85, 80, and 95. The computation of the average involves adding these values and dividing by the total number of languages (3). Thus computed, (85+80+95)/3.\nBased on the above thought, the answer is:\n86.67" } ], "id_raw": 373 }, { "id": "eZmFCCdlrT2QgA9g", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_19_16.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the Line chart data about financial risks into a table format?" }, { "from": "gpt", "value": "To structure the provided line chart into tabular format, the x-axis labels represent the rows and the types of risks are the columns. First, I identify {the x-axis tick labels}[0.075, 0.912, 0.123, 0.936],[0.186, 0.912, 0.234, 0.936], [0.297, 0.912, 0.345, 0.936], [0.408, 0.912, 0.456, 0.936], [0.519, 0.912, 0.567, 0.936] as the different types of risks. Then, I identify {the legend texts}[0.109, 0.085, 0.182, 0.108], [0.109, 0.120, 0.202, 0.143] as the row labels representing 'baseline risks' and 'managed risks' respectively. For the table data, the corresponding points {from the first line}[0.099, 0.261], [0.144, 0.251], [0.188, 0.241], [0.233, 0.233], [0.277, 0.229], [0.321, 0.227], and {from the second line}[0.099, 0.531], [0.144, 0.590], [0.188, 0.709], [0.233, 0.640], [0.277, 0.600] are extracted for each type of risk for both the baseline and managed risks.\nBased on the above thought, the answer is:\n,Risks,Baseline Risk,Managed Risk\nIncome Risks,3.4,1.6\nRisky Investment,2.8,2.3\nLimited Assets,1.6,2.7\nFluctuating Market,2.3,3.4\nDrastic Changes,2.7,3.1" } ], "id_raw": 376 }, { "id": "iQSgDB3hRZMTO64Z", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_19_16.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, how effective were the risk control strategies for 'Limited Assets'? Explain your analysis." }, { "from": "gpt", "value": "To evaluate the effectiveness of control strategies on 'Limited Assets', I examine {the point representing Limited Assets in the baseline}[0.188, 0.241] which is 1.6, and then {the point representing Limited Assets after controls are implemented}[0.188, 0.709] which is 2.7. The change in the risk factor is an increase from 1.6 to 2.7. Therefore, instead of reducing the financial risk associated with Limited Assets, the implemented risk control strategies appear to have increased the risk factor.\nBased on the above thought, the answer is:\nBased on the line chart, the risk control strategies have not been effective for 'Limited Assets'. Instead of reducing the risk factor, they have led to an increase, suggesting that the implemented strategies may need to be revisited or enhanced for better risk management in this area." } ], "id_raw": 376 }, { "id": "vtsvRE1QRZNJWp9z", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_19_16.png", "conversations": [ { "from": "human", "value": "\nHow has the risk factor for Drastic Changes changed after implementing the risk control strategies?" }, { "from": "gpt", "value": "To calculate the change in risk factor, I should first identify {the risk factor for Drastic Changes in the baseline}[0.277, 0.229], which is 2.7. Then, the {risk factor for Drastic Changes after implementing risk control strategies}[0.277, 0.600] is identified, which is 3.1. The change in risk factor can be calculated by subtracting the baseline from the managed risk, i.e., 3.1 - 2.7 = 0.4.\nBased on the above thought, the answer is:\nThe risk factor for Drastic Changes increased by 0.4." } ], "id_raw": 376 }, { "id": "tzhQLk3Bz4fjmjnl", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_19_17.png", "conversations": [ { "from": "human", "value": "\nCould you convert the line chart data of stock prices over the days into a structured csv table?" }, { "from": "gpt", "value": "First, I need to identify the x-axis labels which represent 'Day'. They are located at {the x-axis labels}[0.139, 0.899, 0.153, 0.928], [0.218, 0.899, 0.232, 0.928], ..., [0.840, 0.899, 0.868, 0.928]. Next, the corresponding y-values which represent 'Stock Price ($)' are depicted on the line located at {the stock price line points}[0.146, 0.842], [0.225, 0.761], ..., [0.854, 0.114]. Combining these will provide the desired table.\nBased on the above thought, the answer is:\nDay,Stock Price ($)\nDay 1,100\nDay 2,110\nDay 3,120\nDay 4,130\nDay 5,140\nDay 6,150\nDay 7,160\nDay 8,170\nDay 9,180\nDay 10,190" } ], "id_raw": 377 }, { "id": "wVxOY4GslseJNWz2", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_19_17.png", "conversations": [ { "from": "human", "value": "\nAccording to the graph, how does the number of shares fluctuate throughout the 10-day period?" }, { "from": "gpt", "value": "To answer this question, I will refer to {the line for Number of Shares}[0.146, 0.660], [0.225, 0.478], ..., [0.854, 0.296]. According to the line, I can see that the number of shares rose from Day 1 to Day 2, fell drastically from Day 2 to Day 3, rose slightly again from Day 3 to Day 4, reached its peak on Day 5, then fell down and remained relatively stable with slight rises and falls.\nBased on the above thought, the answer is:\nAccording to the graph, the number of shares started at around 500 on the first day, then rose slightly on the second day before falling down drastically on the third day. From Day 3 to Day 5, the number of shares rose again, peaking on Day 5. After Day 5, the shares count fell again, and remained relatively stable with some small ups and downs for the rest of the period." } ], "id_raw": 377 }, { "id": "3ovvmqLf9PQ3I277", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_19_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum stock price recorded during this 10-day period?" }, { "from": "gpt", "value": "To answer this question, I will refer to the line graph that traces stock prices over time. This line can be identified at {the stock price line points}[0.146, 0.842], [0.225, 0.761], ..., [0.854, 0.114]. The highest point on this line corresponds to the maximum value, which can be found at {the highest point}[0.854, 0.114].\nBased on the above thought, the answer is:\n$190" } ], "id_raw": 377 }, { "id": "NJ6McHCfpsobj3Lq", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_0.png", "conversations": [ { "from": "human", "value": "\nCan you transfer the chart information to a table with media outlets as rows and language regions as columns?" }, { "from": "gpt", "value": "Firstly, I need to get names of language regions, which serve as table columns. These can be inferred from {the x-axis ticks for the BBC's chart}[[0.134, 0.684, 0.192, 0.735], [0.371, 0.684, 0.456, 0.735], [0.608, 0.684, 0.696, 0.735], [0.845, 0.684, 0.916, 0.735]]. The languages are English, Chinese, Spanish, French, and Arabic, respectively. Then, I refer to {the legend texts}[[0.707, 0.102, 0.752, 0.148], [0.795, 0.102, 0.849, 0.149]] to get the row labels, which are BBC and CNN. For the table values, I look at the {bar heights}[[0.134, 0.789, 0.192, 0.854], ..., [0.963, 0.302, 1.012, 0.466]] and extract the corresponding values.\nBased on the above thought, the answer is:\nMedia Outlet,English,Chinese,Spanish,French,Arabic\nBBC,45,55,42,30,15\nCNN,42,47,48,50,20" } ], "id_raw": 380 }, { "id": "mYgJENwTbos2wTAj", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_0.png", "conversations": [ { "from": "human", "value": "\nBased on the charts, which language region appears to have the most volatile media influence? Provide your rationale." }, { "from": "gpt", "value": "To determine the language region with the most volatile media influence, I need to compare the fluctuations in influence levels for both media outlets. Looking at {the bars for BBC}[0.134, 0.584, 0.192, 0.854], I can see a considerable drop from English to Chinese, then a further drop from Chinese to French and another significant drop to Arabic, indicating considerable variability in influence across regions. The {bars for CNN}[0.234, 0.565, 0.292, 0.737] show less volatility, with smaller fluctuations between regions.\nBased on the above thought, the answer is:\nThe Arabic region has the most volatile media influence. This is due to the fact that there is a significant drop in influence from both BBC and particularly CNN in this region as compared to the others." } ], "id_raw": 380 }, { "id": "jEe8UfpsTB3iZn1V", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_0.png", "conversations": [ { "from": "human", "value": "\nHow much influence did the BBC lose in the Arabic region compared to the French region?" }, { "from": "gpt", "value": "To answer this question, I need to compare the BBC\u2019s influence in the French and Arabic language regions. \nI can begin by finding {the BBC's influence in the Arabic region}[0.963, 0.302, 1.012, 0.466] which is 15% and then find {the BBC's influence in the French region}[0.845, 0.398, 0.918, 0.565] which is 30%. Now I can calculate the difference, 30% - 15% = 15%.\nBased on the above thought, the answer is:\n15%" } ], "id_raw": 380 }, { "id": "TcF9LPuyd8pOtmJK", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_1.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, can you restructure the revenue data into tabular format with years as the columns and game titles as the rows?" }, { "from": "gpt", "value": "To generate the table from the line chart, I need to identify the labels from {the x-axis ticks}[[0.108, 0.899, 0.198, 0.928],[0.172, 0.899, 0.262, 0.928],[0.237, 0.899, 0.327, 0.928],[0.301, 0.899, 0.391, 0.928],[0.365, 0.899, 0.455, 0.928],[0.430, 0.899, 0.520, 0.928],[0.494, 0.899, 0.584, 0.928],[0.559, 0.899, 0.648, 0.928],[0.623, 0.899, 0.713, 0.928],[0.687, 0.899, 0.777, 0.928],[0.752, 0.899, 0.842, 0.928],[0.816, 0.899, 0.906, 0.928]] as the columns. Then, I extract the row labels which are the different {game titles from the legend}[[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156], [0.884, 0.167, 0.946, 0.189]]. To populate the table, I will locate each data points for {Game1 line}[[0.153, 0.070, 0.861, 0.840]], {Game2 line}[[0.153, 0.100, 0.861, 0.870]], {Game3 line}[[0.153, 0.130, 0.861, 0.900]], {Game4 line}[[0.153, 0.160, 0.861, 0.930]], {Game5 line}[[0.153, 0.190, 0.861, 0.960]] and extract their respective values.\nBased on the above thought, the answer is:\nGames,2012-07,2012-08,2012-09,2012-10,2012-11,2012-12,2013-01,2013-02,2013-03,2013-04,2013-05,2013-06\nGame1,92.38,93.12,93.79,94.36,94.6,94.71,94.92,95.06,95.24,95.54,95.72,96.02\nGame2,92.5,93.5,94.0,91.5,93.0,94.0,93.5,94.5,91.0,95.0,95.0,94.5\nGame3,93.2,93.5,93.8,94.0,93.2,94.0,94.5,93.5,94.0,95.2,95.5,95.8\nGame4,94.0,93.5,94.5,92.5,94.0,95.5,94.5,93.5,92.5,91.0,93.5,95.0\nGame5,92.0,92.5,94.0,95.0,93.5,94.5,95.0,94.5,93.0,90.5,93.0,94.5" } ], "id_raw": 381 }, { "id": "Zlr8sKmwg1SsjMSv", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_1.png", "conversations": [ { "from": "human", "value": "\nBased on the revealed trends in the line chart, which game's revenue exhibits the most dramatic change within a year? Explain your reasoning." }, { "from": "gpt", "value": "To answer this, I need to analyze the lines representing each game for notable changes. Observing {the line for Game1}[[0.153, 0.070, 0.861, 0.840]], it seems there is a gradual and almost constant increase over the period. The same applies to the line for {Game3}[[0.153, 0.130, 0.861, 0.900]]. The {Game2 line}[[0.153, 0.100, 0.861, 0.870]] also shows minimal variations while {Game5}[[0.153, 0.190, 0.861, 0.960]] has an apparent decrease in 2013-04 but it appears to return back to its initial level in 2013-06. The most dramatic change in revenue can be observed in the {Game4 line}[[0.153, 0.160, 0.861, 0.930]], which shows a significant increase in the revenue in 2013-03.\nBased on the above thought, the answer is:\nBased on the trend shown in the line chart, Game4 exhibits the most dramatic change in its revenue within a year. This is attributable to its significant increase in revenue in the year 2012-09 leading to a sharp peak." } ], "id_raw": 381 }, { "id": "Hnez4XRXvV2lcfBk", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_1.png", "conversations": [ { "from": "human", "value": "\nCan you figure out the revenue change for Game3 from year 2012-07 to 2013-06?" }, { "from": "gpt", "value": "To calculate the revenue change for Game3, I need to extract the initial and final data points from {the line for Game3}[[0.153, 0.130, 0.861, 0.900]]. The initial data point on 2012-07 is 93.2 and the final point on 2013-06 is 95.8. Thus, the revenue change for Game3 from 2012-07 to 2013-06 is 95.8 - 93.2 = 2.6 millions.\nBased on the above thought, the answer is:\n2.6." } ], "id_raw": 381 }, { "id": "ws2AWkDOxA4OLSR0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data related to the Budget-X RU model across different nations into a structured table?" }, { "from": "gpt", "value": "To convert the data into a table, I would need to identify the name of each nation and the corresponding values for the Budget-X RU model. I noticed that {the x-axis labels}[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903], [0.397, 0.773, 0.504, 0.934], [0.525, 0.773, 0.607, 0.896] represent the countries' names, and the values are annotated on {the corresponding bars}[0.102, 0.470, 0.898, 0.696].\nBased on the above thought, the answer is:\nCountry,Budget-X RU Model\nRU,30\nCN,-20\nIN,-10\nUS,-25\nBR,-30" } ], "id_raw": 382 }, { "id": "hlU7eWvbDQqjPL4T", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_2.png", "conversations": [ { "from": "human", "value": "\nCompare the performance of both the Budget-X RU and Neon-X CN models based on the bar graph. What is the key difference between the trends?" }, { "from": "gpt", "value": "To answer this question, I need to look at the values represented by {the bars in the Budget-X RU subplot}[0.102, 0.470, 0.898, 0.696] and {the bars in the Neon-X CN subplot}[0.917, 0.470, 0.898, 0.696]. Budget-X RU demonstrates a generally negative trend across all nations except Russia, indicating a decline. On the other hand, Neon-X CN shows mixed trends with marginal growth in China and Brazil while declining in the rest of the countries.\nBased on the above thought, the answer is:\nThe key difference between the two models is the overall performance across all nations. The Budget-X RU model shows a decline across most countries, with the exception of an increase in Russia. In contrast, the Neon-X CN model presents mixed results, with marginal growth in China and Brazil, suggesting it is better received in these areas. Overall, the Neon-X CN model has a less uniformly negative trend than the Budget-X RU." } ], "id_raw": 382 }, { "id": "bVubRG3H8QJpeCdM", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall performance in percentage of Budget-X RU across all nations?" }, { "from": "gpt", "value": "To calculate the overall performance, I sum up the values represented by {the bars in the first subplot}[0.102, 0.470, 0.153, 0.564], [0.244, 0.470, 0.295, 0.686], [0.336, 0.470, 0.387, 0.637], [0.428, 0.470, 0.479, 0.708], [0.520, 0.470, 0.571, 0.730]. The respective values are 30, -20, -10, -25, -30, so the overall performance is 30-20-10-25-30 = -55.\nBased on the above thought, the answer is:\n-55" } ], "id_raw": 382 }, { "id": "8cMoQd15HYhGm4AM", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_3.png", "conversations": [ { "from": "human", "value": "\nCould you tableau the data shown in line graph for both Science and Technology, and Arts and Literature Growth over the years?" }, { "from": "gpt", "value": "Indeed, for creating a tabular format from this line chart. Initial step involves identifying {the years on x-axis}[[0.047, 0.893, 0.083, 0.921],[0.198, 0.893, 0.233, 0.921],[0.348, 0.893, 0.383, 0.921],[0.499, 0.893, 0.534, 0.921],[0.649, 0.893, 0.684, 0.921],[0.800, 0.893, 0.835, 0.921],[0.950, 0.893, 0.985, 0.921]], it would serve as the rows of my table. Then, I identify {the legend entries}[[0.116, 0.181, 0.283, 0.209],[0.116, 0.223, 0.247, 0.251]] as the two columns on my table. Afterwards, I track down the data points represented on {Science and Technology line graph points}[[0.065, 0.826],[0.215, 0.764],[0.366, 0.713],[0.516, 0.465],[0.667, 0.259],[0.817, 0.395],[0.967, 0.182]] and also {the Arts and Literature line graph points}[[0.065, 0.748],[0.215, 0.733],[0.366, 0.737],[0.516, 0.717],[0.667, 0.696],[0.817, 0.690],[0.967, 0.682]]. Then, I input the corresponding growth numbers at each point into the table.\nBased on the above thought, the answer is:\nYear,Science and Technology Growth,Arts and Literature Growth\n1800,-2.5,1.3\n1850,0.5,2.0\n1900,3.0,1.8\n1950,15.0,2.8\n2000,25.0,3.8\n2050,18.4,4.1\n2100,28.7,4.5" } ], "id_raw": 383 }, { "id": "1Yco16fWcciNn2wC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_3.png", "conversations": [ { "from": "human", "value": "\nLooking at the growth trends in Science and Technology and Arts and Literature, which of them has a more steady growth and why?" }, { "from": "gpt", "value": "To determine which area has a steadier growth, I need to look at their growth trend lines. By referring to {the line for Science and Technology}[0.065, 0.182, 0.967, 0.826], it can be seen that there is a significant increase from -2.5 to 28.7 within the years. However, inspecting the values reveals that the growth is not steady and has major fluctuations. \nOn the other hand, {the line for Arts and Literature}[0.065, 0.682, 0.967, 0.748] shows a slow but consistent increase from 1.3 to 4.5 over the years indicating a more steady growth trend.\nBased on the above thought, the answer is:\nArts and Literature displays a more steady growth. The growth trend line shows a slow but consistent increase over the years, indicating a steady progression with no significant fluctuations, unlike the rapid changes seen in Science and Technology growth." } ], "id_raw": 383 }, { "id": "jUFILxzuoh5anhMZ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_3.png", "conversations": [ { "from": "human", "value": "\nWhat's the total increase from 1800 to 2100 in growth for Science and Technology?" }, { "from": "gpt", "value": "To find the total increase in growth for Science and Technology, I have to locate {the 1800 data point}[0.065, 0.826], which shows a value of -2.5. Then, I locate the {2100 data point}[0.967, 0.182] demonstrating a value of 28.7. Thus, the total increase is calculated by 28.7 - (-2.5) = 31.2.\nBased on the above thought, the answer is:\n31.2." } ], "id_raw": 383 }, { "id": "8L9H80Gm5DO0F6KR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_4.png", "conversations": [ { "from": "human", "value": "\nCould you compile the AI performance in a structured table, considering the Categories as rows and Innovation Performances as columns?" }, { "from": "gpt", "value": "To construct the data table, I will first make use of {the x-axis tick labels}[[0.015, 0.893, 0.166, 0.921], [0.882, 0.893, 0.985, 0.921]], which represent the rows or Categories, namely, \"Consistent_expansion\" and \"Erratic_decline\". The column headers correspond to the different kinds of Innovation Performances represented by {the legend texts}[[0.652, 0.109, 0.774, 0.137], [0.840, 0.109, 0.921, 0.137]], which are \"Regression Model\" and \"AI Assistant\". To populate the values in the table, I will identify {the line points}[[0.091, 0.245], [0.091, 0.403], [0.934, 0.817], [0.934, 0.332]] of the line plots for each innovation performance type at the respective categories.\nBased on the above thought, the answer is:\nCategories,Regression Model,AI Assistant\nConsistent Expansion,32.6,19.4\nErratic Decline,-15,25.3" } ], "id_raw": 384 }, { "id": "omECPu7s7SUQrbJk", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, discuss the performance of the AI Assistant and the Regression Model in dealing with different scenarios." }, { "from": "gpt", "value": "The chart displays the performance contrast between an AI Assistant and a Regression Model through two scenarios or categories \u2013 Consistent Expansion and Erratic Decline. By observing the lines of each {AI Assistant}[[0.091, 0.403], [0.934, 0.332]] and {Regression Model}[[0.091, 0.245], [0.934, 0.817]], we can perceive differential performance outcomes. In the Consistent Expansion scenario, the Regression Model significantly outperforms the AI Assistant. However, this trend flips in the Erratic Decline scenario where, despite the overall performance drop, the AI Assistant proves to be superior to the Regression Model.\nBased on the above thought, the answer is:\nThe AI assistant and the regression model, both show varying performances based on the scenario at hand. While the regression model performs exceptionally well during consistent expansion, it struggles to adapt to erratic decline. The AI assistant, on the other hand, even though not as superior as the regression model during consistent expansion, manages to stay afloat more effectively during erratic decline. Thus, we can conclude that while the regression model is powerful during stable conditions, the AI assistant provides a more robust performance in varying conditions." } ], "id_raw": 384 }, { "id": "EyspBWASaaRunZOF", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_4.png", "conversations": [ { "from": "human", "value": "\nHow much higher is the innovation performance of the Regression Model as compared to the AI Assistant in the Consistent Expansion category?" }, { "from": "gpt", "value": "To answer this question, we need to compare the innovation performances of the Regression Model and the AI Assistant in the Consistent Expansion category. We can locate the performances of the Regression Model from {the line points for Regression Model}[[0.091, 0.245], [0.934, 0.817]], and of the AI Assistant from {the line points for AI Assistant}[[0.091, 0.403], [0.934, 0.332]]. For the Consistent Expansion category, the innovation performance of the Regression Model is 32.6 while that of the AI Assistant is 19.4. Therefore, the difference in innovation performance is 32.6 - 19.4 = 13.2.\nBased on the above thought, the answer is:\n13.2." } ], "id_raw": 384 }, { "id": "7kWNdZe5PQIPh0Op", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_5.png", "conversations": [ { "from": "human", "value": "\nCould you compile the return rates and trend of Stock A and Stock B as presented in line chart into a tabular format?" }, { "from": "gpt", "value": "To create the table, I'll first define the columns from {the x-axis}[0.492, 0.932, 0.552, 0.970] which represents the 'Month'. I then identify {the legend}[0.075, 0.097, 0.296, 0.148] which gives us the stocks: Stock A and Stock B as headers for each row. After this, I will plot the corresponding rates of returns for each stock. These values will be extracted from the points on {the line for Stock A}[0.068, 0.156, 0.976, 0.661] and {the line for Stock B}[0.068, 0.517, 0.976, 0.858] across the 12 month period.\nBased on the above thought, the answer is:\nMonth, Stock A Return Rate (%), Stock B Return Rate (%)\n1, 2.3, -0.5\n2, 2.8, -0.7\n3, 3.5, 0.2\n4, 4.2, 0.6\n5, 5.0, 1.0\n6, 6.0, 1.5\n7, 6.8, 2.0\n8, 7.4, 2.4\n9, 7.9, 2.8\n10, 8.7, 3.4\n11, 9.4, 4.0\n12, 10.0, 4.5" } ], "id_raw": 385 }, { "id": "kIxNaFFY74drodyY", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_5.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, how does the performance of Stock A compare to Stock B over the 12 month period?" }, { "from": "gpt", "value": "To compare the performance of the Stock A and Stock B, I will examine their return trends represented by {the line for Stock A}[0.068, 0.156, 0.976, 0.661] and {the line for Stock B}[0.068, 0.517, 0.976, 0.858]. The return trend for Stock A presents a steady increase, peaking in the 12th month. Meanwhile, Stock B's return trend starts off negatively, hitting the lowest at the second month before gradually improving to end in the positive spectrum within the 12 month period.\nBased on the above thought, the answer is:\nOverall, Stock A exhibited stronger performance over the 12 month period as it consistently generated positive returns, peaking at 10.0%. Though Stock B's performance improved and concluded in the positive spectum, it started off negatively and its highest return of 4.5% in the 12th month was lower than Stock A's lowest return. Thus, Stock A demonstrated a stronger and more stable performance than Stock B." } ], "id_raw": 385 }, { "id": "DFX4jZOhCEjhkLGL", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_5.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, what is the difference between the highest and lowest returns of Stock A?" }, { "from": "gpt", "value": "To calculate this difference, I need to locate {the line points for Stock A}[0.068, 0.156, 0.976, 0.661] which represent its monthly return rates. The highest return rate is 10.0 and it corresponds to {Month 12}[0.967, 0.893, 0.976, 0.878], while the lowest return rate of 2.3 corresponds to {Month 1}[0.063, 0.893, 0.072, 0.921]. Therefore, the difference between the maximum and minimum return rates is 10.0 - 2.3 = 7.7.\nBased on the above thought, the answer is:\n7.7" } ], "id_raw": 385 }, { "id": "92SvPpvtHxiVpMBi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_6.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data represented in the line chart into a table format with quarters as rows and 'Innovation Efforts' and 'Design Quality' as columns?" }, { "from": "gpt", "value": "To generate the table, I scan across the x-axis of the chart to read the quarters from {the x-axis tick labels}[[0.083, 0.893, 0.131, 0.921], [0.204, 0.893, 0.253, 0.921], [0.326, 0.893, 0.374, 0.921], [0.448, 0.893, 0.496, 0.921], [0.570, 0.893, 0.618, 0.921], [0.692, 0.893, 0.740, 0.921], [0.814, 0.893, 0.862, 0.921]], which are our row labels. I then identify the values for 'Innovation Efforts' and 'Design Quality' by locating their line points [{the line for Innovation Efforts}[0.107, 0.188, 0.594, 0.566]], [{the line for Design Quality}[0.107, 0.844, 0.594, 0.845]], and recording the associated values.\nBased on the above thought, the answer is:\nQuarter,Innovation Efforts,Design Quality\nQ1, 1.0, 0.0\nQ2, 0.4, -1.5\nQ3, 2.0, 0.5\nQ4, 1.2, 1.0\nQ5, 1.7, 1.5\nQ6, 2.3, 2.0\nQ7, 2.5, 1.7" } ], "id_raw": 386 }, { "id": "1CAiBfP2cEVl3hyC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_6.png", "conversations": [ { "from": "human", "value": "\nWhich aspect, 'Design Quality' or 'Innovation Efforts', exhibits a more stable trend over the seven quarters, and why?" }, { "from": "gpt", "value": "To answer this question, I need to analyze the variability of the line plots for both 'Design Quality' and 'Innovation Efforts'. Looking at {the 'Design Quality' line}[[0.107, 0.844, 0.594, 0.845]], it displays a general trend of stable growth with a single deviation or dip in the seventh quarter. However, the {line for 'Innovation Efforts'}[[0.107, 0.188, 0.594, 0.566]] fluctuates significantly across the seven quarters, showing a less predictable trend marked by abrupt increases and decreases.\nBased on the above thought, the answer is:\n'Design Quality' exhibits a more stable trend over the seven quarters. This conclusion is based on the generally consistent positive growth shown by the 'Design Quality' line plot, with only a single notable deviation in the seventh quarter. Conversely, 'Innovation Efforts' shows substantial variability and fluctuation, with several peaks and troughs throughout the seven quarters, indicating a less predictable and more unstable trend." } ], "id_raw": 386 }, { "id": "AtgeK287LYxxiYpW", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the sum of the 'Innovation Efforts' for the first 3 Quarters?" }, { "from": "gpt", "value": "To find the sum of 'Innovation Efforts' for the first 3 Quarters, I need to locate {the line points for 'Innovation Efforts' for Q1, Q2, and Q3}[[0.107, 0.566], [0.204, 0.353], [0.326, 0.729]] and sum up the corresponding values: 1.0, 0.4, and 2.0. So, the sum is 1.0+0.4+2.0 = 3.4.\nBased on the above thought, the answer is:\n3.4." } ], "id_raw": 386 }, { "id": "UTND7Avnkl9nJJwn", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_7.png", "conversations": [ { "from": "human", "value": "\nCould you extract the population trend data for the two regions from the line chart into a structured table format?" }, { "from": "gpt", "value": "To generate a table from the line chart, I need to identify the {x-axis labels} [[0.073, 0.919, 0.159, 0.942], [0.902, 0.919, 0.979, 0.942]] representing the years and consider them as row headers. Then, I read the {legend texts}[[0.763, 0.804, 0.951, 0.834], [0.763, 0.848, 0.967, 0.878]] as the column headers standing for different regions. The cell values are obtained by locating the corresponding {line points} [[0.116, 0.674], [0.940, 0.674], [0.116, 0.483], [0.940, 0.865]] for each region across the years and extracting the values associated with those points.\nBased on the above thought, the answer is:\nYear,Region 1,Region 2\n2000,100,130\n2010,118,105.5" } ], "id_raw": 387 }, { "id": "lkTq513B7o0tBMy5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_7.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which region experienced a downward trend in population between 2000 and 2010, and why do you think so?" }, { "from": "gpt", "value": "To understand the population trend in each region, I will first look at Region 1 by observing the {line for Region 1}[0.116, 0.291, 0.940, 0.865]. The line moves from the point at 100 in 2000 to the point at 118 in 2010, showing an upward trend.\nNext, I examine {the line for Region 2}[0.116, 0.674, 0.940, 0.674]. The line decreases from its starting point at 130 in 2000 to its end point at 105.5 in 2010, indicating a downward trend.\nBased on the above thought, the answer is:\nBased on the line chart, Region 2 experienced a downward trend in population between 2000 and 2010. This conclusion is drawn from the fact that the line representing Region 2 starts at a higher value (130 in 2000) and ends with a lower one (105.5 in 2010), indicating a decrease in the population count." } ], "id_raw": 387 }, { "id": "TUHTGgXC3v35am5M", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in population from 2000 to 2010 in Region 1?" }, { "from": "gpt", "value": "To find the percentage increase in population in Region 1, I need to locate the {line points for Region 1}[[0.116, 0.674], [0.940, 0.674]] and extract the corresponding population figures: 100 for 2000 and 118 for 2010. The percentage increase from 2000 to 2010 is therefore calculated as 100 * (118 - 100) / 100 = 18 percent.\nBased on the above thought, the answer is:\n18%." } ], "id_raw": 387 }, { "id": "xaU4o4BN6bcxm4aJ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_8.png", "conversations": [ { "from": "human", "value": "\nBased on the line graph, could you please arrange the Emission Reduction and Emission Increase data into a table format with Years as rows and Emission Type as columns?" }, { "from": "gpt", "value": "To translate the line chart into a table, I will identify {the x-axis}<[0.510, 0.932, 0.551, 0.970]> as the Years row label. Then, I locate {the chart legend}<[0.602, 0.097, 0.978, 0.148]> that provides Emission Reduction and Emission Increase as the column identifiers.\nNext, I would find the values of Emission Reduction and Emission Increase for these years from {the line points of Emission Reduction}<[0.152, 0.304, 0.909, 0.598]> and {the line points of Emission Increase}<[0.152, 0.493, 0.909, 0.659]> respectively and put them into the corresponding cells.\nBased on the above thought, the answer is:\nYear,Emission Reduction,Emission Increase\n2011,5.5,2.1\n2012,6.2,2.4\n2013,7.8,3.1\n2014,9.6,4.5\n2015,12.4,6.5\n2016,15.3,7.6\n2017,18.2,8.8\n2018,19.2,9.8\n2019,20.3,10.4\n2020,21.1,10.9\n2021,21.8,11.3" } ], "id_raw": 388 }, { "id": "xChexwGvJm5dgEhp", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_8.png", "conversations": [ { "from": "human", "value": "\nIn terms of environmental protection, what trend does the line chart display between the period of 2011 to 2021?" }, { "from": "gpt", "value": "To identify the trend, I would need to look at both the Emission Reduction and Emission Increase lines. The {Emission Reduction line}<[0.152, 0.304, 0.909, 0.598]> shows a consistent upward trend, increasing from 5.5 to 21.8 million tons from 2011 to 2021. The {Emission Increase line}<[0.152, 0.493, 0.909, 0.659]> also exhibits an upward trend, but the increase is less steep, ranging from 2.1 to 11.3 million tons. This indicates that while emissions have increased over the years, the emission reduction efforts have been more effective.\nBased on the above thought, the answer is:\nThe line chart shows a dominant upward trend in terms of both Emission Reduction and Emission Increase. However, the Emission Reduction line indicates a sharp elevation compared to Emission Increase line. This can be interpreted as an evidence of increasingly effective environmental protection efforts which have led to a greater reduction in emissions even as gross emissions have also grown over the years from 2011 to 2021." } ], "id_raw": 388 }, { "id": "XQWbNxfYg05s9W8U", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_8.png", "conversations": [ { "from": "human", "value": "\nHow much did the Emission Reduction increase from 2011 to 2021?" }, { "from": "gpt", "value": "To determine the increase in the Emission Reduction from 2011 to 2021, I will find the values for these years. The Emission Reduction value for 2011 can be obtained from {the first Emission Reduction point}<[0.152, 0.598]>, which is 5.5. For 2021, the value can be found from {the last Emission Reduction point}<[0.909, 0.304]>, which is 21.8. Therefore, the increase can be calculated as 21.8 - 5.5 = 16.3.\nBased on the above thought, the answer is:\n16.3" } ], "id_raw": 388 }, { "id": "wvRL6gv89Mb93MOw", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_9.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data about Grunge and Bubblegum Pop popularity over decades into a CSV format?" }, { "from": "gpt", "value": "To create a table from the line chart, I would first use {the x-axis tick labels}[[0.211, 0.059, 0.297, 0.089], [0.382, 0.059, 0.464, 0.089], [0.556, 0.059, 0.646, 0.089], [0.738, 0.059, 0.828, 0.089]] as the column headers, which represent the decades. Next, I would identify {the legend texts}[0.923, 0.842, 0.976, 0.867],[0.923, 0.767, 0.970, 0.793] as the row labels representing the different fashion trends. I then locate {the line points for Grunge}[[0.211, 0.482], [0.382, 0.368], [0.556, 0.273],[0.738, 0.184]] and {the line points for Bubblegum Pop}[[0.211, 0.177], [0.382, 0.361],[0.556, 0.474], [0.738, 0.269]] to fill in the values for each corresponding trend and decade.\nBased on the above thought, the answer is:\nDecades,Grunge,Bubblegum Pop\n1990s,15,4\n2000s,11,10\n2010s,6,14\n2020s,2,7" } ], "id_raw": 389 }, { "id": "F81QRQsgbQ1lm5AN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_9.png", "conversations": [ { "from": "human", "value": "\nAs per data shown in the chart, which fashion trend shows more variability over the decades and why?" }, { "from": "gpt", "value": "Broadly, the variability of a trend can be inferred from the fluctuations in its line plot. Taking a closer look at {the line for Grunge}[[0.211, 0.482, 0.738, 0.184]], it starts with a higher popularity value at 15 in the 1990s, then decreases to 11 in the 2000s, further decreases to 6 in the 2010s, and falls to 2 in the 2020s. The popularity clearly shows a steady declining trend, without significant fluctuations.\nOn the other hand, the line plot for {Bubblegum Pop}[[0.211, 0.177, 0.738, 0.269]] starts at a low popularity value of 4 in the 1990s, jumps to a higher popularity of 10 in the 2000s, peaks to 14 in the 2010s, and then sees a sharp decrease to 7 in the 2020s. This indicates a more variable trend as the line plot rises dramatically and then falls sharply.\nBased on the above thought, the answer is:\nThe Bubblegum Pop trend shows more variability over the decades. The line plot has a significant rise between the 1990s and 2010s, followed by a sharp fall in the 2020s, showing more considerable fluctuations when compared to the Grunge trend which consistently declines over time." } ], "id_raw": 389 }, { "id": "N4dcaXjwSR6d37tz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the total sum of Grunge popularity over all decades?" }, { "from": "gpt", "value": "To find the total popularity of Grunge over all decades, I need to locate {the line points for Grunge}[[0.211, 0.482], [0.382, 0.368], [0.556, 0.273],[0.738, 0.184]] and sum up the corresponding values. This would be the sum of 15, 11, 6, and 2.\nBased on the above thought, the answer is:\n34" } ], "id_raw": 389 }, { "id": "IFByPw7JmgQZrxev", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_11.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart into a structured table where Energy Sources are rows and Years are the columns?" }, { "from": "gpt", "value": "To begin the transformation, I\u2019d note the x-axis labels, {Years}[0.054, 0.893, 0.089, 0.921], on the chart to represent the column headers - 2030 and 2040. Then, I would identify the legend entries, {Energy Sources}[0.867, 0.378, 0.951, 0.406], as the row labels - these represent different types of energy sources. And finally, I\u2019d extract the respective {data points}[0.071, 0.714] for each energy source at the defined years.\nBased on the above thought, the answer is:\nEnergy Source,2030,2040\nHydropower,1.6,1.7\nSolar Power,3.2,3.9\nGeothermal,1.4,1.2\nBioenergy,2.3,2.0\nWind Energy,2.8,3.6" } ], "id_raw": 391 }, { "id": "w7bNFcjsdJDAvtoC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_11.png", "conversations": [ { "from": "human", "value": "\nBy observing the growth trends for different energy sources, which source shows the highest growth from 2030 to 2040?" }, { "from": "gpt", "value": "To answer this question, I must assess the growth rates represented by the slopes of the lines for each energy source. Looking at the lines for {Hydropower}[0.071, 0.688, 0.968, 0.714], {Solar Power}[0.071, 0.105, 0.968, 0.291], {Geothermal}[0.071, 0.767, 0.968, 0.820], {Bioenergy}[0.071, 0.529, 0.968, 0.609] and {Wind Energy}[0.071, 0.185, 0.968, 0.397], I observe that Solar Power and Wind Energy show an upward trend or growth whereas the others show a flat or downward trend. Among Solar Power and Wind Energy, Solar Power has a more steeply inclined line indicating a higher rate of growth.\nBased on the above thought, the answer is:\nSolar Power shows the highest growth from 2030 to 2040, as indicated by its steeply inclined line representing its upward trend over time. This indicates a higher rate of growth for Solar Power compared to the other energy sources." } ], "id_raw": 391 }, { "id": "FpfvnfJWXAkFyrgQ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_11.png", "conversations": [ { "from": "human", "value": "\nBy how much does the performance of Solar Power improve from 2030 to 2040?" }, { "from": "gpt", "value": "To find out, I will identify the points for {Solar Power}[0.071, 0.291] in 2030 and 2040. The measure for Solar Power in 2030 is 3.2, whereas in 2040 it is 3.9. The difference is therefore 3.9 - 3.2 = 0.7.\nBased on the above thought, the answer is:\n0.7" } ], "id_raw": 391 }, { "id": "XyfKSdrWwpn8f6Ox", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart into a table format with Years as columns and Industries as rows? Please include the respective performance points." }, { "from": "gpt", "value": "To construct a table from the line chart, first, I take {the x-axis tick labels}[[0.104, 0.763, 0.171, 0.851], [0.239, 0.763, 0.301, 0.860], [0.368, 0.763, 0.433, 0.860], [0.506, 0.763, 0.560, 0.860], [0.637, 0.763, 0.699, 0.860], [0.771, 0.763, 0.829, 0.860]] as the column headers, representing the Years. {The legend texts}[[0.752, 0.125, 0.815, 0.175], [0.828, 0.128, 0.880, 0.178]] are taken as the row labels, representing the different Industries. For each industry, I locate {the line points}[[0.104, 0.064], [0.249, 0.096], [0.377, 0.098], [0.516, 0.141], [0.647, 0.289], [0.771, 0.374]] and extract the values annotated to fill the table.\nBased on the above thought, the answer is:\nIndustry,2015,2016,2017,2018,2019,2020\nHotel Industry,70,65,60,55,45, 38\nTourism Industry,45,55,50,55,35, 20" } ], "id_raw": 392 }, { "id": "ziAQfm0kzQp8GbBN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_12.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart and the trends in industries, which industry was most affected by the events of 2020 and to what extent?" }, { "from": "gpt", "value": "To answer this, I need to analyze the trends of both industries. Looking at {the line for Hotel Industry}[[0.104, 0.891, 0.771, 0.639]], there is a consistent decline from 70 points to 38 points from the year 2015 to 2020. Now, if we look at {the line for Tourism Industry}[[0.104, 0.771, 0.771, 0.419]], there are various fluctuations in the initial years but a drastic drop in 2020 to 20 points. Given the context and the substantial performance point drops, it can be inferred that the drop is mostly due to the COVID-19 pandemic.\nBased on the above thought, the answer is:\nAccording to the trends shown in the line chart, the Tourism Industry was most affected by the events of 2020. The chart shows a substantial dip for the Tourism Industry, falling from 35 points in 2019 to 20 points in 2020, a reduction of 15 points. This is a sharper decrease compared to the Hotel Industry that saw a 7-point decrease in the same period. The drastic decline in the Tourism Industry is likely due to the COVID-19 pandemic." } ], "id_raw": 392 }, { "id": "JY2b3n4tzaCeVaae", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the total decline in points for the Hotel Industry from 2015 to 2020?" }, { "from": "gpt", "value": "To calculate the total decline in points for the Hotel Industry, I locate {the line points for the Hotel Industry}[[0.104, 0.134], [0.249, 0.106], [0.377, 0.086], [0.516, 0.061], [0.647, 0.034], [0.771, 0.008]] and extract the corresponding values: 70, 65, 60, 55, 45, 38. The points in 2015 is 70 and in 2020 is 38. The total decline is then 70 - 38 = 32.\nBased on the above thought, the answer is:\n32." } ], "id_raw": 392 }, { "id": "pnZpWK2bp9haCA1V", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_13.png", "conversations": [ { "from": "human", "value": "\nCan you list the data from the line chart, showing the performance trend for the two types of medical technology over time?" }, { "from": "gpt", "value": "To generate the tabular data, I first identify the healthcare condition values from the x-axis, represented by {the x-axis tick labels}[[0.058, 0.893, 0.080, 0.921], [0.099, 0.893, 0.121, 0.921], ..., [0.422, 0.893, 0.444, 0.921]], which will serve as the rows in the table. Then I identify the performance metrics for the two technologies, reflected in {the legend texts for Physician Technology Performance and AI Technology Performance}[[0.485, 0.108, 0.725, 0.136], [0.791, 0.108, 0.979, 0.136]] which serve as column headers. The corresponding points for each technology line {Physician Technology line points}[[0.069, 0.643], [0.106, 0.607], ..., [0.433, 0.246]] and {AI Technology line points}[[0.069, 0.715], [0.106, 0.661], ..., [0.433, 0.607]], are translated into performance values for each healthcare condition.\nBased on the above thought, the answer is:\nHealthcare Condition, Physician Technology Performance, AI Technology Performance\n0.1, 1.3, 0.9\n0.19, 1.5, 1.2\n0.28, 1.6, 0.9\n0.37, 2.0, 1.0\n0.46, 2.1, 1.1\n0.55, 2.2, 1.3\n0.64, 2.4, 1.0\n0.73, 2.9, 1.2\n0.82, 3.0, 0.9\n0.91, 3.2, 1.0\n1, 3.5, 1.5" } ], "id_raw": 393 }, { "id": "cEyCX97XvhJCxKZk", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_13.png", "conversations": [ { "from": "human", "value": "\nAccording to the trends shown in the line chart, which technology outperformed the other at the peak phase of Healthcare Condition, and what can be inferred about their performance in different conditions?" }, { "from": "gpt", "value": "To answer this, I need to compare the performance metrics of the two technologies at the peak phase of Healthcare Condition. Here, the peak phase is represented by the maximum Healthcare Condition of 1. At this stage, {the AI Technology Performance} [0.433, 0.607] and {the Physician Technology Performance} [0.433, 0.246] are respectively 1.5 and 3.5. As a result, I can see that Physician Technology outperformed AI Technology. From the overall trends of {the Physician Technology line}[0.069, 0.246, 0.433, 0.643] and {the AI Technology line}[0.069, 0.607, 0.433, 0.715] it can also be inferred that both technologies performance increase as the Healthcare Condition improves, but the rate of increase for the Physician Technology is considerably more significant.\nBased on the above thought, the answer is:\nAt the peak phase of Healthcare Condition, the Physician Technology outperformed the AI Technology as it scored a significantly higher performance metric. This suggests that the effectiveness of Physician Technology increases more substantially than AI Technology as the Healthcare Condition improves from worst to best, implying that the Physician Technology might be more sensitive or adaptive to the changes in Healthcare Conditions." } ], "id_raw": 393 }, { "id": "bfh2FAp0ebISvAeu", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_13.png", "conversations": [ { "from": "human", "value": "\nBy how much did the AI Technology's performance increase from the worst healthcare condition (0.1) to the best condition (1)?" }, { "from": "gpt", "value": "To answer this, I need to find the AI Technology Performance values at the worst and best healthcare conditions. The initial value is obtained from {the first point of the AI Technology line} [0.069, 0.715], which corresponds to an adoption rate of 0.9. The final value is obtained from {the last point of the AI Technology line} [0.433, 0.607], which corresponds to an adoption rate of 1.5. Therefore, the increase in performance is 1.5 - 0.9 = 0.6.\nBased on the above thought, the answer is:\n0.6." } ], "id_raw": 393 }, { "id": "dzQTOWMn6nxc92VD", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_14.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table from the chart data showing the CSR Index and Sustainable Development Index for each year from 2010 to 2019?" }, { "from": "gpt", "value": "To create a table from the chart data, let's start by identifying {the line for CSR Index}[0.076, 0.467, 0.967, 0.634] and {the line for Sustainable Development Index}[0.076, 0.395, 0.967, 0.586]. The x-axes of these lines represent the years which are shown as {the X-axis labels}[[0.058, 0.893, 0.093, 0.921], [0.157, 0.893, 0.192, 0.921], [0.256, 0.893, 0.291, 0.921], [0.355, 0.893, 0.390, 0.921], [0.454, 0.893, 0.490, 0.921], [0.553, 0.893, 0.589, 0.921], [0.653, 0.893, 0.688, 0.921], [0.752, 0.893, 0.787, 0.921], [0.851, 0.893, 0.886, 0.921], [0.950, 0.893, 0.985, 0.921]]. The height of each point on these lines represents the values of CSR index and Sustainable Development Index for each year. The values can be calculated by referring to its corresponding position relative to {the y-axis labels}[[0.057, 0.862, 0.066, 0.890], [0.048, 0.767, 0.066, 0.795], [0.048, 0.671, 0.066, 0.699], [0.048, 0.575, 0.066, 0.603], [0.048, 0.480, 0.066, 0.508], [0.040, 0.384, 0.066, 0.412], [0.040, 0.288, 0.066, 0.316], [0.040, 0.193, 0.066, 0.221]].\nBased on the above thought, the answer is:\nYear,CSR Index,Sustainable Development Index\n2010,50,60\n2011,53,63\n2012,60,70\n2013,70,80\n2014,68,78\n2015,73,85\n2016,75,88\n2017,78,90\n2018,80,95\n2019,85,100" } ], "id_raw": 394 }, { "id": "c07qEYkLuCRALLE6", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_14.png", "conversations": [ { "from": "human", "value": "\nWhat was the percentage increase in the CSR Index from 2010 to 2019?" }, { "from": "gpt", "value": "To find the percentage increase in the CSR Index from 2010 to 2019, I must identify the CSR Index values for the two years. The CSR Index value for 2010 is 50, indicated by {the point on the CSR Index line corresponding to 2010}[0.076, 0.634]. For 2019, the CSR Index value is 85, as shown by {the point on the CSR Index line corresponding to 2019}[0.967, 0.467]. The percentage increase is calculated as the difference between the two values divided by the initial value, multiplied by 100.\nBased on the above thought, the answer is:\n70%" } ], "id_raw": 394 }, { "id": "oziphNHiRT1YrF9n", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_15.png", "conversations": [ { "from": "human", "value": "\nCan you extract the trend for each financial indicator given by the line chart into a table format?" }, { "from": "gpt", "value": "To morph the line graph data into a table, I'll commence with the x-axis figures identified in {the x-axis tick labels}[[0.060,0.893,0.069,0.921],[0.143,0.893,0.152,0.921],..., [0.967,0.893,0.985,0.921]], which act as column headers and represent the months. I then examine {the legend texts}[{'label': 'Stock Price Trend', 'text_coordinates': [0.617, 0.108, 0.736, 0.136]},{'label': 'Commodity Price Trend', 'text_coordinates': [0.803, 0.108, 0.964, 0.136]}] that do the role of row descriptors, i.e., different financial indicators. Now, to get the values, I need to find {the line points for Stock Price Trend}[[0.065, 0.766], [0.148, 0.731], ..., [0.976, 0.595]] and {the line points for Commodity Price Trend}[[0.065, 0.459], [0.148, 0.406], ..., [0.976, 0.274]] for each parameter over various months and extract the annotated values.\nBased on the above thought, the answer is:\nDate,Stock Price Trend, Commodity Price Trend\n1,30.42,45.28\n2,32.12,47.85\n3,34.87,50.19\n4,34.91,49.59\n5,31.45,47.72\n6,32.97,46.83\n7,35.11,48.42\n8,35.51,48.42\n9,36.15,49.94\n10,37.56,51.48\n11,38.59,50.6\n12,38.7,54.25" } ], "id_raw": 395 }, { "id": "IKdifxITxgxl2uKS", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_15.png", "conversations": [ { "from": "human", "value": "\nCan you specify the overall trends observed in Stock Price and Commodity Price over the 12 months according to the chart?" }, { "from": "gpt", "value": "To understand the overall trends, I need to trace {the line for the Stock Price Trend}[0.065, 0.595, 0.976, 0.766] and the {line for the Commodity Price Trend}[0.065, 0.274, 0.976, 0.459]. The Stock Price Trend starts at a value of 30.42 at month 1, sees minor fluctuations in between, and ends at 38.7 in month 12. On the other hand, the Commodity Price Trend starts at a value of 45.28 at month 1 and ends higher at 54.25 in month 12, also with fluctuations throughout the year.\nBased on the above thought, the answer is:\nIn analyzing the line chart, both Stock Price Trend and Commodity Price Trend show an overall rising trend over the 12 months. Although there are fluctuations in between the months, the end value for both trends is higher than their starting value at the beginning of the year. This suggests that both stock prices and commodity prices generally increased within this period, despite intermittent dips and peaks. This could potentially indicate a bullish market backdrop during this period." } ], "id_raw": 395 }, { "id": "PJQyfdl5pArY5rO8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_15.png", "conversations": [ { "from": "human", "value": "\nHow much did the commodity price trend increase from month 1 to month 12?" }, { "from": "gpt", "value": "For calculating the change in the commodity price trend from month 1 to month 12, I'll first locate {the starting point}[0.065, 0.459] and {the ending point}[0.976, 0.274] of the commodity price trend line. The corresponding values are then extracted as 45.28 and 54.25 respectively. The increase is then obtained by subtracting the starting value from the ending value, i.e., 54.25 - 45.28.\nBased on the above thought, the answer is:\n8.97" } ], "id_raw": 395 }, { "id": "zysf53zFTrutS8lx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_16.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data of Year versus Training Hours and Salary into a table format with columns being Year, Training Hours, and Salary?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first locate {the x-axis tick labels}[[0.048, 0.893, 0.075, 0.921], [0.134, 0.893, 0.169, 0.921], [0.229, 0.893, 0.264, 0.921], [0.323, 0.893, 0.358, 0.921], [0.418, 0.893, 0.453, 0.921], [0.512, 0.893, 0.547, 0.921], [0.607, 0.893, 0.642, 0.921], [0.701, 0.893, 0.736, 0.921], [0.796, 0.893, 0.831, 0.921], [0.890, 0.893, 0.925, 0.921]], which represent the Years. Now, I identify as the Training Hours and Salary line points {Training Hours}[[0.105, 0.850], [0.152, 0.814], ..., [0.860, 0.215]] and {Salary} [[0.105, 0.574], [0.152, 0.544], ..., [0.860, 0.215]] respectively corresponding to each year. Using this information, a table can be constructed.\nBased on the above thought, the answer is:\nYear,Training Hours,Salary\n2005,2,50000\n2006,5,55000\n2007,7,60000\n2008,10,55000\n2009,12,50000\n2010,10,55000\n2011,15,70000\n2012,17,70000\n2013,20,75000\n2014,15,70000\n2015,25,80000\n2016,30,85000\n2017,30,90000\n2018,40,95000\n2019,50,100000\n2020,45,105000\n2021,55,110000" } ], "id_raw": 396 }, { "id": "kokEjVR8R617HMOZ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_16.png", "conversations": [ { "from": "human", "value": "\nWhat can you infer from the line chart about the relationship between Training Hours and Salary?" }, { "from": "gpt", "value": "Looking at the line plots for both {Training Hours}[0.105, 0.215, 0.860, 0.850] and {Salary}[0.105, 0.215, 0.860, 0.574], I notice that as Training Hours generally increase, the Salary also generally increase. The trend shows a positive correlation between the Training Hours and Salary indicating that as the number of Training Hours increase, the Salary also tends to increase. However, it is also noted that in the year {2010}[0.341, 0.754] where the training hours was the same as in 2008, the Salary was lower. It shows that although a general positive trend exists, there might be other factors at play affecting the exact Salary.\nBased on the above thought, the answer is:\nFrom the line chart, it can be inferred that there is a general positive correlation between Training Hours and Salary. As the number of Training Hours increases, the Salary also tends to increase, indicating that individuals who spend more hours in training potentially earn a higher salary. However, other factors may also be influencing the Salary as seen in the year 2010 when the Training Hours was the same as in 2008, but the Salary was lower. This observation suggests that while Training Hours is likely a significant factor, it is not the only determining factor of Salary." } ], "id_raw": 396 }, { "id": "4sdttzulAfQGKeQo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_16.png", "conversations": [ { "from": "human", "value": "\nWhat was the year when the maximum number of Training Hours was recorded?" }, { "from": "gpt", "value": "To find the year when the maximum number of Training Hours was recorded, I need to identify {the highest point on the line representing Training Hours}[0.860, 0.215] corresponding to 55 hours. From its position on the x-axis, the label corresponding to this position can be found from the x-tick labels, which is {the year 2021}[0.890, 0.893, 0.925, 0.921].\nBased on the above thought, the answer is:\n2021." } ], "id_raw": 396 }, { "id": "qmwFcpZXxIxTd38O", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_20_19.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data represented in the chart into a table with each activity and respective number of hours spent per week?" }, { "from": "gpt", "value": "To generate a table from the chart data, I firstly recognize {the labels on the y-axis}[[0.25,0.1,0.3,0.2], [0.25,0.2,0.3,0.3], [0.25,0.3,0.3,0.4], [0.25,0.4,0.3,0.5], [0.25,0.5,0.3,0.6], [0.25,0.6,0.3,0.7], [0.25,0.7,0.3,0.8], [0.25,0.8,0.3,0.9], [0.25,0.9,0.3,1.0]], which represent different activities. The x-values of these points are the values of hours spent on these activities respectively, which I need to {extract from the points on the chart line}[[0.35,0.1], [0.4,0.2], [0.45,0.3], [0.5,0.4], [0.55,0.5], [0.6,0.6], [0.65,0.7], [0.7,0.8], [0.75,0.9]].\nBased on the above thought, the answer is:\nActivity, Hours Per Week\nReading, 2\nPainting, 3\nPhotography, 4\nCooking, 5\nGaming, 5\nSinging, 6 \nYoga, 6\nCycling, 8\nVolunteering, 9" } ], "id_raw": 399 }, { "id": "ZTxzSpQUu1g2LCwL", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_20_19.png", "conversations": [ { "from": "human", "value": "\nCan you comment on the trends observed in the time spent on activities during the week based on the line graph?" }, { "from": "gpt", "value": "By tracking {the line on the graph}[[0.35,0.1], [0.4,0.2], [0.45,0.3], [0.5,0.4], [0.55,0.5], [0.6,0.6], [0.65,0.7], [0.7,0.8], [0.75,0.9]], I can observe that there's a general increase in the hours spent on the activities over the week. Starting from the lowest point at Reading (2 hours), there is a steady rise in the time spent up to Cooking (5 hours). However, there is a plateau at the point corresponding to {Gaming}[[0.55,0.5]] where the hours spent remain the same. The increase resumes from Singing with a plateau at {Yoga}[[0.65,0.7]]. Towards the end, the graph picks up a sharp spike at {Volunteering}[[0.75,0.9]], showing significantly more time spent compared to other activities.\nBased on the above thought, the answer is:\nThe line graph demonstrates a general trend of increasing time spent on activities over a week. Initially, there is a steady climb from Reading to Cooking. Afterwards, a plateau is observed at Gaming, indicating a stagnant period where the time spent does not increase. The graph resumes its growth trend at Singing, hitting another plateau at Yoga before displaying a sharp upward spike at Volunteering. This trend suggests that, despite some fluctuations, there is a general increase in activity levels as the week progresses, with a significant surge in volunteering." } ], "id_raw": 399 }, { "id": "GVO1fWQ4icIRFvT8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_20_19.png", "conversations": [ { "from": "human", "value": "\nHow much more time is spent on volunteering than gaming during the week?" }, { "from": "gpt", "value": "To find the difference between the hours spent on volunteering and gaming, I need to locate {the point on the line graph corresponding to volunteering}[[0.75,0.9]] and extract its x-value, which represents hours spent on volunteering. Similarly, I need to find {the point corresponding to gaming}[[0.55,0.5]] and extract its x-value. The difference between these two values gives the time spent on volunteering more than gaming.\nBased on the above thought, the answer is:\n4 hours." } ], "id_raw": 399 }, { "id": "ekIRyXMDabojYGAQ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_0.png", "conversations": [ { "from": "human", "value": "\nIn a structured table format, can you represent the information about fund allocation for different events over the years 2019 and 2020?" }, { "from": "gpt", "value": "In order to create a structured table from the line chart, I first identify {the x-axis tick labels}[[0.172, 0.698, 0.294, 0.861], [0.291, 0.698, 0.474, 0.942], [0.460, 0.698, 0.604, 0.890], [0.630, 0.698, 0.733, 0.836], [0.755, 0.698, 0.908, 0.904]], which represent different events. The data for funds allocated in 2019 and 2020 will serve as rows with values obtained from {the line points for 2019}[[0.233, 0.293], [0.382, 0.423], [0.532, 0.242], [0.682, 0.371], [0.832, 0.242]] and {the line points for 2020}[[0.233, 0.553], [0.382, 0.656], [0.532, 0.190], [0.682, 0.423], [0.832, 0.164]] respectively.\nBased on the above thought, the answer is:\nEvent, Fund Allocation in 2019, Fund Allocation in 2020\nPark Maintenance,0.80,0.60\nInfrastructure Development,0.70,0.52\nCommunity Outreach,0.84,0.88\nUrban Farming,0.74,0.70\nDigital Implementation,0.84,0.90" } ], "id_raw": 400 }, { "id": "dXfvOQQhAUqBGWYY", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_0.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart depicting fund allocation for various events from 2019 to 2020, which event saw a significant increase in fund allocation over this period?" }, { "from": "gpt", "value": "In order to identify the event that saw a significant increase in fund allocation, I have to examine the changes in line positions for various events between 2019 and 2020. Inspecting {the line for Fund Allocation in 2019} [0.233, 0.242, 0.832, 0.423] and {the line for Fund Allocation in 2020} [0.233, 0.164, 0.832, 0.656], it is evident that {Community Outreach}'s line marker rises significantly from [0.532, 0.242] in 2019 to [0.532, 0.190] in 2020, indicating an increase in fund allocation.\nBased on the above thought, the answer is:\nThe event \"Community Outreach\" saw a significant increase in fund allocation from 2019 to 2020, as interpreted from the noticeable upward shift in its line marker in the chart for the given time period." } ], "id_raw": 400 }, { "id": "yGDQ0jTiJIpS1YN8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_0.png", "conversations": [ { "from": "human", "value": "\nWhat's the percentage change in fund allocation from 2019 to 2020 for Infrastructure Development?" }, { "from": "gpt", "value": "To find out the percentage change in fund allocation for Infrastructure Development, I locate {the point for Infrastructure Development in 2019}[0.382, 0.423] which corresponds to a value of 0.7. Next, I find {the point for Infrastructure Development in 2020}[0.382, 0.656], corresponding to a value of 0.52. The percentage change is therefore calculated as ((0.52-0.7)/0.7)*100%.\nBased on the above thought, the answer is:\n-25.71%." } ], "id_raw": 400 }, { "id": "59JnNqkzP0GUKr0F", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_1.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data represented in the line plot into a structured table format with Future Years as columns and each Growth type as rows?" }, { "from": "gpt", "value": "To create a structured table from the line plot, I first identify {the x-axis tick labels}[[0.143, 0.899, 0.171, 0.928], [0.276, 0.899, 0.303, 0.928], [0.408, 0.899, 0.435, 0.928], [0.540, 0.899, 0.567, 0.928], [0.672, 0.899, 0.700, 0.928], [0.805, 0.899, 0.832, 0.928], [0.937, 0.899, 0.964, 0.928]] to represent the column headers as Future Years. Next, I look at {the legend items}[[0.184, 0.057, 0.327, 0.086], [0.184, 0.101, 0.414, 0.130], [0.184, 0.145, 0.364, 0.174]] which represent the row headers or different Growth types. Then, to populate the table values, I locate the line points for each Growth type at respective years and extract the values.\nBased on the above thought, the answer is:\nGrowing Type,2022,2023,2024,2025,2026,2027,2028\nSales Growth,0.1,0.15,0.35,0.45,0.2,0.05,0.12\nMarket Share Growth,0.05,0.02,0.03,0.01,0.04,0.3,0.2\nRevenue Growth,0.5,0.65,0.8,0.9,1.0,0.7,0.85" } ], "id_raw": 401 }, { "id": "z0gInDe9jimnZ9xU", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_1.png", "conversations": [ { "from": "human", "value": "\nBy analyzing the trends in the line chart, can you identify any significant changes between the years 2022 to 2028 in terms of growth type?" }, { "from": "gpt", "value": "The line chart shows the growth trends for Sales Growth, Market Share Growth, and Revenue Growth over the period from 2022 to 2028. Looking at {the line for Sales Growth}[[0.143, 0.498, 0.937, 0.809]], I observe an increase up to 2025 and then a decline to a low point in 2027 before slightly increasing again in 2028.\nFor {Market Share Growth}[[0.143, 0.614, 0.937, 0.840]], there are relative stagnancy until 2027 where it rises sharply from 0.04 to 0.3 suggesting a significant increase in market share growth in 2027. The trend dips again in 2028.\nFinally, looking at {the line for Revenue Growth}[[0.143, 0.070, 0.937, 0.459]], the trend shows an increase from 2022 to 2026, reaching its peak in 2026. However, it decreases dramatically in 2027 and slightly rises in 2028.\nBased on the above thought, the answer is:\nYes, the line chart shows several significant changes in growth types between 2022 and 2028. The Sales Growth increases until 2025, then declines to a low in 2027 before slightly recovering in 2028. The Market Share Growth is relatively steady until a sharp increase in 2027, followed by a decline in 2028. The Revenue Growth increases steadily until 2026, reaching its peak, and then drops sharply in 2027, with a slight increase in 2028. This suggests a volatile period for the company in terms of revenue around 2026 - 2027." } ], "id_raw": 401 }, { "id": "vx9mmd2mEylYWD7r", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the total incremental increase in the Revenue Growth from the year 2022 to 2028?" }, { "from": "gpt", "value": "To calculate the total incremental increase in Revenue Growth from 2022 to 2028, I need to locate {the line points of Revenue Growth}[[0.143, 0.459], [0.276, 0.342], [0.408, 0.225], [0.540, 0.148], [0.672, 0.070], [0.805, 0.303], [0.937, 0.186]]. The corresponding values are 0.5, 0.65, 0.8, 0.9, 1.0, 0.7, 0.85. The incremental increase in each year is calculated as the difference between the value of the current year and the previous year. The total incremental increase is the sum of all the incremental increases.\nBased on the above thought, the answer is:\n0.35" } ], "id_raw": 401 }, { "id": "Jvf2T5B7jIvrFIiv", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_2.png", "conversations": [ { "from": "human", "value": "\nCould you transform the line chart data into a organized tabular format with Years as columns and the three different rates as rows?" }, { "from": "gpt", "value": "I'll start by taking the {x-axis tick labels}[0.122, 0.902, 0.183, 0.934], [0.209, 0.902, 0.271, 0.934], [0.297, 0.902, 0.359, 0.934], [0.385, 0.902, 0.446, 0.934], [0.472, 0.902, 0.534, 0.934], [0.560, 0.902, 0.622, 0.934], [0.648, 0.902, 0.709, 0.934], [0.736, 0.902, 0.797, 0.934], [0.823, 0.902, 0.885, 0.934], [0.911, 0.902, 0.973, 0.934] as column headers which represents the different years. Next, I use {the sublegend texts}[0.177, 0.058, 0.294, 0.082], [0.177, 0.093, 0.356, 0.117], [0.177, 0.128, 0.363, 0.151] to form the row headers which represent the three different rates (Inflation Rate, Unemployment Rate, Stock Market Returns). The corresponding values for each year and rate are obtained from {the points of the lines}[0.152, 0.367], [0.240, 0.382], [0.328, 0.373], [0.415, 0.368], [0.503, 0.360], [0.591, 0.362], [0.679, 0.366], [0.766, 0.355], [0.854, 0.406], [0.942, 0.382], [0.152, 0.344], [0.240, 0.331], [0.328, 0.328], [0.415, 0.335], [0.503, 0.339], [0.591, 0.345], [0.679, 0.345], [0.766, 0.331], [0.854, 0.288], [0.942, 0.285], [0.152, 0.546], [0.240, 0.669], [0.328, 0.053], [0.415, 0.269], [0.503, 0.342], [0.591, 0.210], [0.679, 0.335], [0.766, 0.850], [0.854, 0.080], [0.942, 0.218].\nBased on the above thought, the answer is:\nRates, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010\nInflation Rate, 2.8, 1.6, 2.3, 2.7, 3.4, 3.2, 2.9, 3.8, -0.4, 1.6\nUnemployment Rate, 4.7, 5.8, 6.0, 5.5, 5.1, 4.6, 4.6, 5.8, 9.3, 9.6\nStock Market Returns, -11.9, -22.1, 28.7, 10.9, 4.9, 15.8, 5.5, -37.0, 26.5, 15.1" } ], "id_raw": 402 }, { "id": "zUojVSvfXd88bdWu", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_2.png", "conversations": [ { "from": "human", "value": "\nNoting the information from the line chart, can you provide an overall interpretation of how the three rates changed over the decade from 2001 to 2010?" }, { "from": "gpt", "value": "Assessing the change in rates over time needs a detailed analysis of the trends displayed in the line chart. The {line for Inflation Rate}[0.152, 0.355, 0.942, 0.406] shows that the Inflation Rate has varied slightly over the years, with a dip in 2002, a peak in 2008, and a negative rate in 2009. The {line for Unemployment Rate}[0.152, 0.285, 0.942, 0.345] indicates that the Unemployment Rate has been relatively stable till 2007, after which it increased significantly reaching the highest in 2010. Lastly, the {line for Stock Market Returns}[0.152, 0.053, 0.942, 0.850] clearly shows extreme volatility, with the returns hitting rock bottom in 2008 and then recovering by 2010.\nBased on the above thought, the answer is:\nOver the decade from 2001 to 2010, the Inflation Rate remained mostly steady with a slight variability, hitting a negative in 2009. The Unemployment Rate was stable until 2007, then rose sharply during the following years, peaking at 9.6 in 2010. The Stock Market Returns were highly volatile, experiencing dramatic drops in 2002 and 2008, with noticeable recoveries in 2003 and 2009. The overall economic climate was thus marked by a degree of uncertainty, with periods of growth offset by notable downturns, especially in terms of stock market performance." } ], "id_raw": 402 }, { "id": "Ly7QY1LHBrfUpjVL", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum value of Stock Market Returns?" }, { "from": "gpt", "value": "To get the maximum value of Stock Market Returns, I need to identify and extract the highest point on {the line for Stock Market Returns}[0.152, 0.053, 0.942, 0.850]. The corresponding highest point is located at {the point of 2003}[0.328, 0.053] with a value 28.7.\nBased on the above thought, the answer is:\n28.7" } ], "id_raw": 402 }, { "id": "eXR1N4PMtfcKn51s", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_3.png", "conversations": [ { "from": "human", "value": "\nCould you generate a tabular representation of the line chart, with the time periods as rows and the different factors (Innovation Index, Business Environment, High-Tech Enterprises) as columns?" }, { "from": "gpt", "value": "The first task would be to identify {the x-axis tick labels}[0.076, 0.926, 0.088, 0.949],[0.204, 0.926, 0.216, 0.949],[0.332, 0.926, 0.344, 0.949],[0.460, 0.926, 0.472, 0.949],[0.588, 0.926, 0.601, 0.949],[0.716, 0.926, 0.729, 0.949],[0.844, 0.926, 0.857, 0.949],[0.973, 0.926, 0.985, 0.949], which represent the time periods from 1 to 8. These would serve as row headers. Next, we locate {the legend items text}[0.812, 0.435, 0.928, 0.452],[0.812, 0.461, 0.966, 0.478],[0.812, 0.487, 0.962, 0.504] which are the column headers namely Innovation Index, Business Environment, and High-Tech Enterprises. Finally, the values are obtained by tracking each of the points in the respective lines {Innovation Index points}[0.082, 0.558],[0.210, 0.470],[0.338, 0.416],[0.466, 0.345],[0.594, 0.292],[0.723, 0.221],[0.851, 0.185],[0.979, 0.026] for Innovation Index, {Business Environment points}[0.082, 0.452],[0.210, 0.434],[0.338, 0.470],[0.466, 0.558],[0.594, 0.612],[0.723, 0.700],[0.851, 0.736],[0.979, 0.842] for Business Environment and {High-Tech Enterprises points}[0.082, 0.825],[0.210, 0.736],[0.338, 0.647],[0.466, 0.612],[0.594, 0.505],[0.723, 0.381],[0.851, 0.292],[0.979, 0.203] for High-Tech Enterprises, and extracts the y-coordinates to derive the corresponding performance values.\nBased on the above thought, the answer is:\nTime Period,Innovation Index,Business Environment,High-Tech Enterprises\n1,0.70,0.76,0.55\n2,0.75,0.77,0.60\n3,0.78,0.75,0.65\n4,0.82,0.70,0.67\n5,0.85,0.67,0.73\n6,0.89,0.62,0.80\n7,0.91,0.60,0.85\n8,1.00,0.54,0.90" } ], "id_raw": 403 }, { "id": "VMuHuvxzn2ezQKm1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_3.png", "conversations": [ { "from": "human", "value": "\nWhich of the elements showcased in the line chart exhibits an upward trend through the entire time period?" }, { "from": "gpt", "value": "To determine which element has a constantly increasing trend, I need to observe {the line for Innovation Index}[0.082, 0.558, 0.979, 0.026], {the line for Business Environment}[0.082, 0.452, 0.979, 0.842], and {the line for High-Tech Enterprises}[0.082, 0.825, 0.979, 0.203]. The Innovation Index line starts at a point lower than both Business Environment and High-Tech Enterprises, but it ends at the top compared to the other two lines. This suggests a continuously upwards trend. Meanwhile, the line coordinates for Business Environment suggest a decrease after the initial increase.\nBased on the above thought, the answer is:\nAmong the elements showcased in the line chart, the Innovation Index exhibits an upward trend through the entire time period. Its line plot begins at a lower point but ends at the highest point out of all the elements, indicating a consistent increase in its value over time." } ], "id_raw": 403 }, { "id": "a43lDMyo6wJJQwXt", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum performance value for the Innovation Index?" }, { "from": "gpt", "value": "To find the maximum performance value for the Innovation Index, I need to investigate {the line points for Innovation Index}[[0.082, 0.558],[0.210, 0.470],[0.338, 0.416],[0.466, 0.345],[0.594, 0.292],[0.723, 0.221],[0.851, 0.185],[0.979, 0.026]] and extract the corresponding values: 0.7, 0.75, 0.78, 0.82, 0.85, 0.89, 0.91, 1.00. The maximum value among these is 1.00.\nBased on the above thought, the answer is:\n1.00." } ], "id_raw": 403 }, { "id": "luMu5FgvYLAwSISN", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_4.png", "conversations": [ { "from": "human", "value": "\nGiven the line chart information, can you generate a table presenting different types of energy in columns and their respective allocation in rows, according to the different time periods?" }, { "from": "gpt", "value": "To create a table from the line chart, I first identify {the x-axis tick labels}[[0.085, 0.901, 0.129, 0.934],[0.255, 0.901, 0.299, 0.934],[0.426, 0.901, 0.470, 0.934], [0.596, 0.901, 0.640, 0.934], [0.767, 0.901, 0.811, 0.934], [0.937, 0.901, 0.981, 0.934]] as the rows denoting different time periods. Next, I locate the {legend items}[[0.129, 0.840, 0.291, 0.864],[0.374, 0.840, 0.471, 0.864],[0.555, 0.840, 0.688, 0.864]] to represent various energy types as columns. Then, I locate and correlate the line points of each energy type at their respective time periods to deduce their values.\nBased on the above thought, the answer is:\nTime Period, Renewable Energy, Fossil Fuels, Nuclear Energy\n0.0, 0.3, 0.7, 0.5\n0.2, 0.35, 0.63, 0.46\n0.4, 0.38, 0.6, 0.43\n0.6, 0.45, 0.58, 0.48\n0.8, 0.5, 0.57, 0.42\n1.0, 0.6, 0.56, 0.4" } ], "id_raw": 404 }, { "id": "TjZ5fh6Mtfp969ek", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_4.png", "conversations": [ { "from": "human", "value": "\nBased on the trends in the line chart, which energy source seems to experience the most significant decrease in allocation over time?" }, { "from": "gpt", "value": "To figure out which energy source experiences the most significant decrease, I need to view and analyze the line plots for all three sources. Observing {the line for Renewable Energy}[[0.107, 0.250, 0.959, 0.885]], the allocation increases from 0.3 to 0.6 over time, indicating an increase rather than a decrease. Looking at {the line for Fossil Fuels}[[0.107, 0.039, 0.959, 0.335]], the allocation goes down from 0.7 to 0.56, showing a considerable decrease. Lastly, viewing {the line for Nuclear Energy}[[0.107, 0.462, 0.959, 0.673]], the allocation drops from 0.5 to 0.4, a less significant decrease in comparison.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Fossil Fuels seem to experience the most significant decrease in allocation over time. The line plot for Fossil Fuels shows a consistent decrease from an allocation of 0.7 to 0.56. This decrease is more substantial than that of the Renewable Energy and Nuclear Energy sources." } ], "id_raw": 404 }, { "id": "OJodQe0A15ohFvxo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_4.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, what is the difference in energy allocation between Fossil Fuels and Nuclear Energy at the time period of 0.6?" }, { "from": "gpt", "value": "To find this value, I need to identify and extract the respective data points for Fossil Fuels and Nuclear energy at time period 0.6. {Fossil Fuels at 0.6}[0.618, 0.293] is at 0.58, and {Nuclear Energy at 0.6}[0.618, 0.504] is at 0.48. Subtracting these gives the difference.\nBased on the above thought, the answer is:\n0.1." } ], "id_raw": 404 }, { "id": "268uGNL3fSDjLxXJ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_5.png", "conversations": [ { "from": "human", "value": "\nGiven the line chart, can you extract the data depicting the trend in fund allocation into a table, with the y-axis labels as columns and the x-axis labels as rows?" }, { "from": "gpt", "value": "To extract the data into a table format, I would refer to the {x-axis tick labels}[[0.084, 0.849, 0.129, 0.882], [0.179, 0.849, 0.223, 0.882], [0.274, 0.849, 0.318, 0.882], [0.369, 0.849, 0.413, 0.882], [0.463, 0.849, 0.508, 0.882], [0.558, 0.849, 0.602, 0.882], [0.653, 0.849, 0.697, 0.882], [0.748, 0.849, 0.792, 0.882], [0.842, 0.849, 0.887, 0.882], [0.937, 0.849, 0.981, 0.882]] as the row indexes. The column headers would be the values from {the legend items}[[0.171, 0.912, 0.237, 0.936], [0.171, 0.947, 0.268, 0.970]] which represent 'Anxiety' and 'Depression'. The values for each of these can be gathered from {the line points}[[0.107, 0.237, 0.959, 0.773], [0.107, 0.039, 0.959, 0.515]] which represent their reported level with varying symptom intensity.\nBased on the above thought, the answer is:\nSymptom Intensity,Anxiety,Depression\n1.0,2.4,2.6\n0.9,3.2,3.0\n0.8,2.6,3.8\n0.7,1.9,3.3\n0.6,1.3,2.8\n0.5,2.7,3.5\n0.4,3.9,3.6\n0.3,4.0,4.1\n0.2,2.1,4.4\n0.1,1.5,5.0" } ], "id_raw": 405 }, { "id": "fySabFw4KnvorDNQ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which condition shows a more fluctuating trend in reported level as symptom intensity changes?" }, { "from": "gpt", "value": "To evaluate the fluctuations in the reported levels, I look at both the {line for Anxiety}[0.107, 0.237, 0.959, 0.773] and the {line for Depression}[0.107, 0.039, 0.959, 0.515]. Anxiety shows significant ups and downs, notably plunging around symptom intensities of 0.8, 0.6 and 0.2 but then bouncing back. Depression, on the other hand, has dips around symptom intensities of 0.9, 0.7 and 0.2 but overall the fluctuations appear to be less intense.\nBased on the above thought, the answer is:\nAccording to the trends shown in the line chart, Anxiety seems to have a more fluctuating trend in reported level as symptom intensity changes. This is based on the larger swings apparent in the line plot for Anxiety in comparison to Depression." } ], "id_raw": 405 }, { "id": "X0rmpnSdloTzoCBJ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum reported level for Depression as per the line chart?" }, { "from": "gpt", "value": "To find the maximum reported level for Depression, I need to examine the points on {the line for Depression}[0.107, 0.039, 0.959, 0.515]. The max level is at the point with the smallest y-coordinate, which is {5.0}[0.107, 0.039].\nBased on the above thought, the answer is:\n5.0" } ], "id_raw": 405 }, { "id": "l5U6pOODEYljpnzF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_6.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data into a structured table with Rate as the columns and Savings, Expense, and Revenue as rows?" }, { "from": "gpt", "value": "To convert the line chart into a table, I\u2019d have to recognize {the x-axis labels}[0.094, 0.849, 0.981, 0.882] which will become the column names, correspondingly representing the rate changes. The rows are represented by {the legend}[0.125, 0.941, 0.571, 0.983] corresponding to Savings, Expense, and Revenue. The values in the table are derived from the y-coordinates of {the points on each line}[0.116, 0.098, 0.959, 0.465], [0.116, 0.612, 0.959, 0.796], [0.116, 0.062, 0.959, 0.282], that represent each financial category. Using these coordinates, I can map each value to its corresponding category and rate.\nBased on the above thought, the answer is:\nCategory,0.0,0.2,0.4,0.6,0.8,1.0\nSavings,20000.0,22000.0,25000.0,26000.0,28000.0,30000.0\nExpense,15000.0,16000.0,14000.0,13000.0,12000.0,11000.0\nRevenue,25000.0,26000.0,29000.0,28000.0,30000.0,31000.0" } ], "id_raw": 406 }, { "id": "ymhOvUsPWh9hWRnm", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_6.png", "conversations": [ { "from": "human", "value": "\nAccording to the trends shown in the line chart, which of the three categories shows a consistent growth? Justify your answer." }, { "from": "gpt", "value": "To determine which category shows consistent growth, I need to analyze the trend lines of each category. {The trend line for the Savings category}[0.116, 0.098, 0.959, 0.465] shows a steady upward movement, indicating a consistent saving rate. However, {the trend line for the Expense category}[0.116, 0.612, 0.959, 0.796] starts high and goes down, showing a decrease in expenses. Finally, {the trend line for the Revenue category}[0.116, 0.062, 0.959, 0.282] starts high, dips in the middle, and then rises again, showing a wavering revenue stream.\nBased on the above thought, the answer is:\nThe Savings category shows a consistent growth trend. This can be seen from its trend line, which has a continuous upward movement, accurately indicating that as the rate increases, the Savings also increase without any significant falls or dips. This consistent growth pattern differentiates it from the Expense and Revenue categories, which show varying patterns of increase and decrease." } ], "id_raw": 406 }, { "id": "6sDbil2cknpH3pH1", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the amount deducted from the total Revenue value between rate points 0.4 and 0.6?" }, { "from": "gpt", "value": "To find the change in Revenue value between 0.4 and 0.6, I locate {the points on the Revenue line at 0.4 and 0.6}[0.454, 0.135], [0.622, 0.172]. The values are 29000.0 at rate 0.4 and 28000.0 at rate 0.6. I can then calculate the change by subtracting the rate 0.6 value from rate 0.4.\nBased on the above thought, the answer is:\n1000.0" } ], "id_raw": 406 }, { "id": "gm571ES5qnaYlKD7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_8.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data to a table with columns representing different periods and rows representing Civilization Development Index, Science and Technology Development Index and Population Growth?" }, { "from": "gpt", "value": "First, I locate {the x-axis tick labels}[[0.055, 0.912, 0.135, 0.936], [0.166, 0.912, 0.256, 0.936], [0.270, 0.912, 0.384, 0.936], [0.403, 0.912, 0.483, 0.936], [0.495, 0.912, 0.623, 0.936]], which are the distinct times in the chart that will become the headers of the table. Then, I determine {the legend texts}[[0.659, 0.046, 0.876, 0.069], [0.659, 0.081, 0.966, 0.104], [0.659, 0.116, 0.787, 0.139]] to be the row labels. To generate the corresponding values, I find {the line points for each line}[[0.095, 0.533], [0.211, 0.315], [0.327, 0.301], [0.443, 0.243], [0.559, 0.221], [0.095, 0.824], [0.211, 0.570], [0.327, 0.388], [0.443, 0.279], [0.559, 0.199], [0.095, 0.497], [0.211, 0.461], [0.327, 0.359], [0.443, 0.301], [0.559, 0.170]] and extract the corresponding values.\nBased on the above thought, the answer is:\nTime Period, Ancient Era, Medieval Era, Renaissance Era, Modern Era, Contemporary Era\nCivilization Development Index, 0.5, 0.8, 0.82, 0.9, 0.93\nScience and Technology Development Index, 0.1, 0.45, 0.7, 0.85, 0.96\nPopulation Growth, 0.55, 0.6, 0.74, 0.82, 1.0" } ], "id_raw": 408 }, { "id": "A56UyHnZJ9nMHtEH", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_8.png", "conversations": [ { "from": "human", "value": "\nWhat were the trends in Civilization Development Index, Science and Technology Development Index and Population Growth during Renaissance to the Contemporary Era?" }, { "from": "gpt", "value": "To answer this question, I analyze the line plots for each index during the Renaissance to the Contemporary Era. For {Civilization Development Index}[0.327, 0.301, 0.559, 0.221], the line shows a consistently increasing trend, indicating constant growth in Civilization Development Index from the Renaissance Era to the Contemporary Era. \n\nFor {Science and Technology Development Index}[0.327, 0.388, 0.559, 0.199], the line also exhibits a rising trend from Renaissance Era to Contemporary Era, signifying progressing science and technology during this period.\n\n{Population Growth}[0.327, 0.359, 0.559, 0.170] similarly shows a sharp upward trend, indicating that the population has been steadily growing from the Renaissance to the Contemporary Era.\nBased on the above thought, the answer is:\nAll three indices, the Civilization Development Index, The Science and Technology Development Index, and Population Growth, display a rising trend from the Renaissance Era to the Contemporary Era. This means that civilization development, science and technology, and population have all been growing over this time period." } ], "id_raw": 408 }, { "id": "PbsMBEaiY3ihFDZP", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest Civilization Development Index?" }, { "from": "gpt", "value": "To answer this, I need to find the highest and lowest points in {the line for Civilization Development Index}[0.095, 0.221, 0.559, 0.533]. The maximum value for Civilization Development Index is 0.93 during {Contemporary Era}[0.559, 0.896] and the minimum value is 0.5 during {Ancient Era}[0.095, 0.896]. So, the difference between the highest and lowest Civilization Development Index is 0.93 - 0.5 = 0.43.\nBased on the above thought, the answer is:\n0.43" } ], "id_raw": 408 }, { "id": "PcA5N2ihN0Mte0Fp", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_12.png", "conversations": [ { "from": "human", "value": "\nCould you interpret the line chart into a table format containing Innovation project index as rows and Performance measures as columns?" }, { "from": "gpt", "value": "To create a table from the line chart, I first identify {the x-axis tick labels}[[0.102, 0.849, 0.146, 0.882], [0.234, 0.849, 0.278, 0.882], [0.365, 0.849, 0.409, 0.882], [0.496, 0.849, 0.540, 0.882], [0.628, 0.849, 0.672, 0.882], [0.759, 0.849, 0.803, 0.882]] as the row headers representing Innovation project index. Next, I identify {the legend texts}[[0.189, 0.950, 0.504, 0.973], [0.588, 0.950, 0.834, 0.973], [0.918, 0.950, 1.093, 0.973]] as column headers representing Performance measures. I then find the corresponding line points for each performance measure at the corresponding innovation project indexes and extract their values.\nBased on the above thought, the answer is:\nInnovation Project Index,Continuous_integration_performance,Project_management_quality,Innovation_potential\n0.0,0.97,0.96,0.98\n0.2,0.98,0.92,0.99\n0.4,0.97,0.90,1.0\n0.6,0.96,0.88,0.98\n0.8,0.94,0.85,0.97\n1.0,0.93,0.82,0.96" } ], "id_raw": 412 }, { "id": "vsFQYfcx3TPaO7t1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_12.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which Performance Measure demonstrated the most significant decline over the Innovation Project Index?" }, { "from": "gpt", "value": "To answer this question, I will assess the declining trends in the line plots for each Performance Measure. Considering {the line plot for Continuous Integration Performance}[0.124, 0.172, 0.781, 0.355], it shows a gradually decreasing trend from 0.97 to 0.93. For {Project Management Quality}[0.124, 0.245, 0.781, 0.759], there is a more significant decrease from 0.96 to 0.82. Finally, for {Innovation Potential}[0.124, 0.098, 0.781, 0.245], the trend shows a slight decline from 0.98 to 0.96. Comparing these trends, it can be concluded that Project Management Quality has experienced the most significant decline.\nBased on the above thought, the answer is:\nThe Performance Measure of Project Management Quality demonstrated the most significant decline over the Innovation Project Index. The Performance Measure values decreased from 0.96 to 0.82, implying a decrease in Project Management Quality over the specified Innovation Project Index. This decline is more significant compared to Continuous Integration Performance and Innovation Potential." } ], "id_raw": 412 }, { "id": "4W381pPUP0ZBKFgt", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_12.png", "conversations": [ { "from": "human", "value": "\nWhat's the difference in Continuous Integration Performance between the Innovation project index of 0.0 and 1.0?" }, { "from": "gpt", "value": "To find out the difference in Continuous Integration Performance between the Innovation project index of 0.0 and 1.0, I need to identify {the line points for Continuous Integration Performance}[[0.124, 0.209], [0.256, 0.172], [0.387, 0.209], [0.518, 0.245], [0.650, 0.319], [0.781, 0.355]] and extract their values. The value at index 0.0 is 0.97 and the value at index 1.0 is 0.93. The difference is therefore 0.97 - 0.93 = 0.04.\nBased on the above thought, the answer is:\n0.04." } ], "id_raw": 412 }, { "id": "e3DkhoyECCrYorVE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_13.png", "conversations": [ { "from": "human", "value": "\nCan you please extract the data of the line plot for the three industries into a structured table with Years as columns and Industries as rows?" }, { "from": "gpt", "value": "Starting with the process of data extraction, {the x-axis tick labels}[[0.101, 0.810, 0.145, 0.838], [0.265, 0.810, 0.309, 0.838], [0.428, 0.810, 0.472, 0.838], [0.591, 0.810, 0.636, 0.838], [0.755, 0.810, 0.799, 0.838], [0.919, 0.810, 0.962, 0.838]] correspond to column headers or Years. Next, {the legend texts}[[0.147, 0.922, 0.262, 0.950], [0.345, 0.922, 0.523, 0.950], [0.606, 0.922, 0.724, 0.950]] can be seen as row labels representing different industries. For the actual values in the table, I look at the various {line points} for each industry at respective Years like {the line points for Tech Industry}[[0.123, 0.534], [0.287, 0.449], [0.450, 0.363], [0.614, 0.306], [0.777, 0.221], [0.940, 0.164]] and so on, and extract their corresponding values.\nBased on the above thought, the answer is:\nIndustry,2015,2016,2017,2018,2019,2020\nTech Industry,24,27,30,32,35,37\nAutomotive Industry,22,21,20,19,18,16\nFood Industry,20,22,25,27,29,33" } ], "id_raw": 413 }, { "id": "Y2K0btufuPWcV82x", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_13.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, which industry demonstrated an overall upward trend in market share from 2015 to 2020?" }, { "from": "gpt", "value": "To analyze the overall trends for each industry, I need to observe the pathways of their corresponding line plots. Beginning with {Tech Industry's line}[0.123, 0.164, 0.940, 0.534], I see a clear increase from 24 to 37 over the years. For {Automotive Industry's line}[0.123, 0.591, 0.940, 0.762], the values decrease from 22 to 16. Lastly, looking at {Food Industry's line}[0.123, 0.278, 0.940, 0.648], the values show a rise from 20 to 33. Thus, Tech and Food industries show an upward trend but Tech industry's increase seems more prominent.\nBased on the above thought, the answer is:\nThe Tech Industry demonstrated the most significant overall upward trend in market share from 2015 to 2020, with a clear increase from a market share of 24% to 37%. The Food Industry also demonstrated an upward trend, though it was less significant than the Tech Industry's increase. On the other hand, the Automotive Industry showed a decrease in market share over the same period." } ], "id_raw": 413 }, { "id": "4MC7MFJIaKM20qQQ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of Market Share for the Food Industry from 2015 to 2020?" }, { "from": "gpt", "value": "To find the needed range, I need to find the minimum and maximum values for the Food Industry during these mentioned years. For that, I observe {the line points for Food Industry}[[0.123, 0.648], [0.287, 0.591], [0.450, 0.506], [0.614, 0.449], [0.777, 0.392], [0.940, 0.278]]. I extract the corresponding values: 20, 22, 25, 27, 29, and 33. The maximum is 33 corresponding to {2020}[0.940, 0.810] and the minimum is 20 corresponding to {2015}[0.101, 0.810]. Hence, calculating the range as the difference between the maximum and minimum gives me 33 - 20 = 13.\nBased on the above thought, the answer is:\n13." } ], "id_raw": 413 }, { "id": "R1ViQJB8sy058kiZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_14.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table format for the data from the line chart that shows trend types as columns and series as rows?" }, { "from": "gpt", "value": "To construct the table from the line chart, I initially identify {the x-axis labels}[[0.063, 0.911, 0.161, 0.934], [0.324, 0.911, 0.442, 0.934], [0.590, 0.911, 0.717, 0.934], [0.870, 0.911, 0.980, 0.934]] that reflect the column headers or trend types. After that, I examine {the legend items}[[0.902, 0.046, 0.953, 0.069], [0.902, 0.081, 0.953, 0.104], [0.902, 0.116, 0.953, 0.139]] being the row labels portraying the different series. In order to fill in the values, I identify {the line points}[[0.112, 0.826], [0.383, 0.100], [0.654, 0.201], [0.925, 0.302], [0.112, 0.855], [0.383, 0.661], [0.654, 0.438], [0.925, 0.352], [0.112, 0.812], [0.383, 0.065], [0.654, 0.345], [0.925, 0.460]] for each series at the respective trend types and extract the correlated values.\nBased on the above thought, the answer is:\nTrends,Series1,Series2,Series3\nPersistent Rise,1.02,0.98,1.04\nGradual Increase,2.03,1.25,2.08\nUnstable Behavior,1.89,1.56,1.69\nGradual Decline,1.75,1.68,1.53" } ], "id_raw": 414 }, { "id": "D3sQhdm8UOWjhoHE", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_14.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, which series demonstrates the most abrupt change over the trend types and why?" }, { "from": "gpt", "value": "To determine which series demonstrates the most abrupt change, I need to analyze the fluctuations in the line plots for each series. Observing {the line for Series1}[0.112, 0.100, 0.925, 0.826], there's a substantial increase from 1.02 to 2.03, then a slight decrease to 1.89 and a decrease to 1.75. This shows considerable variability in the trend. For {Series2}[0.112, 0.352, 0.925, 0.855], the trend rises slightly from 0.98 to 1.25 and then increases moderately to 1.56 and slightly more to 1.68, indicating less fluctuation. For {Series3}[0.112, 0.065, 0.925, 0.812], though the value drops dramatically from 1.04 to 2.08, the latter changes are relatively smaller and gradual to 1.69 and 1.53, showing initial volatility followed by stability.\nBased on the above thought, the answer is:\nBased on the chart, Series1 demonstrates the most abrupt change over the trend types. Particularly at the 'Gradual Increase' trend type, Series1 jumps dramatically from 1.02 to 2.03. Hence, overarching variations in Series1 are more pronounced compared to Series2 and Series3, indicating a higher degree of change in Series1." } ], "id_raw": 414 }, { "id": "ERezoSY0ncXpDhcU", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_14.png", "conversations": [ { "from": "human", "value": "\nWhat's the difference between the maximum and minimum values for Series3?" }, { "from": "gpt", "value": "To find the maximum and minimum values for Series3, I need to locate {the line points for Series3}[[0.112, 0.812], [0.383, 0.065], [0.654, 0.345], [0.925, 0.460]] and extract the respective values: 1.04, 2.08, 1.69, 1.53. The maximum value is 2.08 at {Gradual Increase}[0.383, 0.911], and the minimum value is 1.04 at {Persistent Rise}[0.063, 0.911]. The difference between maximum and minimum is 2.08 - 1.04 = 1.04.\nBased on the above thought, the answer is:\n1.04." } ], "id_raw": 414 }, { "id": "jdIfhLKf0S6vpD2R", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_15.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart showing different development areas across years into a table format?" }, { "from": "gpt", "value": "To generate the data table, I initially identify {the x-axis tick labels}[[0.058, 0.801, 0.107, 0.828], [0.233, 0.801, 0.282, 0.828], [0.409, 0.801, 0.458, 0.828], [0.584, 0.801, 0.634, 0.828], [0.760, 0.801, 0.809, 0.828], [0.935, 0.801, 0.985, 0.828]] representing the years. Then I see {the legend texts}[[0.134, 0.918, 0.215, 0.938], [0.134, 0.948, 0.279, 0.968], [0.346, 0.918, 0.462, 0.938]] determining the development areas: AI Research, Quantum Computing, and DNA Sequencing, which will be our rows. Subsequently, I locate {the line points} for each development area and extract the corresponding scaled values.\nBased on the above thought, the answer is:\nYear, AI Research, Quantum Computing, DNA Sequencing\n2020, 0.65, 0.45, 0.75\n2021, 0.70, 0.47, 0.64\n2022, 0.83, 0.55, 0.86\n2023, 0.90, 0.60, 0.92\n2024, 0.97, 0.78, 0.76\n2025, 1.00, 0.80, 0.78" } ], "id_raw": 415 }, { "id": "xYJbgqTgjrIG2Tps", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_15.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, which development area exhibits the steepest increase over the years?" }, { "from": "gpt", "value": "From viewing the chart, AI Research has a starting point at {2020}[0.082, 0.518] with a development scale of 0.65 and ends at a point at {2025}[0.960, 0.140] with a development scale of 1.00. Similarly, Quantum Computing starts at a point at {2020}[0.082, 0.733] with a development scale of 0.45 and ends at a point at {2025}[0.960, 0.356] with a development scale of 0.80. DNA Sequencing starts at a point at {2020}[0.082, 0.410] with a development scale of 0.75 and ends at a point at {2025}[0.960, 0.377] with a development scale of 0.78. Comparing these increases, we see that AI Research has the steepest increase.\nBased on the above thought, the answer is:\nAI Research exhibits the steepest increase over the years, seen by its substantial rise from a development scale of 0.65 to 1.00 between 2020 and 2025." } ], "id_raw": 415 }, { "id": "aqzr0GuFKSaBe8Zv", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in the development scale for AI Research from 2020 to 2025?" }, { "from": "gpt", "value": "To find the total increase in the development scale for AI Research, I need to locate {the line points for AI Research}[[0.082, 0.518], [0.258, 0.464], [0.433, 0.323], [0.609, 0.248], [0.785, 0.172], [0.960, 0.140]] and extract the corresponding values: 0.65, 0.70, 0.83, 0.90, 0.97, 1.00. The first value, Associated with the year 2020, is 0.65 which corresponds to {2020}[0.082, 0.801] while the last value, associated with the year 2025, is 1.00 which corresponds to {2025}[0.935, 0.801]. The total increase is therefore 1.00 - 0.65 = 0.35.\nBased on the above thought, the answer is:\n0.35" } ], "id_raw": 415 }, { "id": "Zw447BfWOFW6SMGI", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_16.png", "conversations": [ { "from": "human", "value": "\nCould you extract data from line chart of technology growth rates and express that as a table, using years as rows and different technologies as columns?" }, { "from": "gpt", "value": "To process this task, I need to refer to the x-axis and the line data. The provided x-axis labels {x-axis tick labels}[0.058, 0.801, 0.107, 0.828], [0.233, 0.801, 0.282, 0.828], [0.409, 0.801, 0.458, 0.828], [0.584, 0.801, 0.634, 0.828], [0.760, 0.801, 0.809, 0.828], [0.935, 0.801, 0.985, 0.828] correspond to the row headers as there are years. Next, I refer to the legend {the legend}[0.089, 0.911, 0.468, 0.976] to know technologies which will be used as column headers. Lastly, I will find the line points from {the lines for different technologies} [0.082, 0.140, 0.960, 0.518], [0.082, 0.356, 0.960, 0.733], [0.082, 0.226, 0.960, 0.528], to fill up the table by each point representing the value for the respective technology in the given year.\nBased on the above thought, the answer is:\nYear,AI research,Quantum Computing,DNA Sequencing\n2020,0.65,0.45,0.75\n2021,0.7,0.47,0.64\n2022,0.83,0.55,0.86\n2023,0.9,0.6,0.92\n2024,0.97,0.78,0.76\n2025,1.0,0.8,0.78" } ], "id_raw": 416 }, { "id": "3G93bBBFyxrHuQEC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_16.png", "conversations": [ { "from": "human", "value": "\nConsidering the growth rates depicted in the line chart, which New Energy and Clean Technology observed the highest growth rate from 2020 to 2025?" }, { "from": "gpt", "value": "To find out which technology noticed the highest growth rate, I should investigate the coordinates {lines for different technologies}[0.082, 0.140, 0.960, 0.518], [0.082, 0.356, 0.960, 0.733], [0.082, 0.226, 0.960, 0.528]. I can see that the AI Research technology started at a growth rate of 0.65 in 2020 from coordinates {AI Research in 2020}[0.082, 0.518] and reached 1.0 in 2025 from coordinates {AI Research in 2025}[0.960, 0.140], demonstrating a growth increase of 0.35. Following similarly, for Quantum Computing, there is an increase of 0.35 and for DNA Sequencing it is 0.03.\nBased on the above thought, the answer is:\nFrom the growth rates represented in the line chart, both AI Research and Quantum Computing saw the peak growth rate from 2020 to 2025 with an increase of 0.35." } ], "id_raw": 416 }, { "id": "pXc2TyFC355VM6oE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the growth rate difference between the technology 'AI Research' from 2024 to 2025?" }, { "from": "gpt", "value": "To evaluate the growth rate difference, I need to find out the values of 'AI Research' for 2024 and 2025. The coordinates corresponding to 2024 are in {AI Research in 2024}[0.785, 0.172] and for 2025 in {AI Research in 2025}[0.960, 0.140]. The growth rate difference can be calculated by subtracting the 2024 value from the 2025 value.\nBased on the above thought, the answer is:\n0.03" } ], "id_raw": 416 }, { "id": "KwXnm9KOiaaF6Uuq", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_17.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information on 'Data Encryption and Integrity' into a table format using the bar graph?" }, { "from": "gpt", "value": "The bar graph for 'Data Encryption and Integrity' visually compares the rate of gain change between 'Ours' and 'Saliency-based Sampling' approaches. Initially, I locate the x-axis labels where the specific data scenarios are listed, including {Data Encryption}[0.135, 0.841, 0.195, 0.871] and {Data Integrity}[0.205, 0.841, 0.265, 0.871]. Then, I observe the lengths of {the red bars}[0.135, 0.450, 0.185, 0.600] for 'Ours' and {the blue bars}[0.195, 0.500, 0.245, 0.650] for 'Saliency-based Sampling' corresponding to each scenario, as red and blue are denoted from {the legend}[0.800, 0.200, 0.880, 0.220] and the next line.\nBased on the above thought, the answer is:\nScenario, Ours, Saliency-based Sampling\nData Encryption, -10%, -25%\nData Integrity, -5%, -15%" } ], "id_raw": 417 }, { "id": "GHPCYrVTvit4Ubmm", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_17.png", "conversations": [ { "from": "human", "value": "\nWhich method, 'Ours' or 'Saliency-based Sampling', demonstrates a more stable trend in the 'Data Encryption and Integrity' chart, based on the analysis of bar lengths?" }, { "from": "gpt", "value": "To assess stability, I evaluate the variance in bar lengths within each method. Observing {the red bars representing 'Ours'}[0.135, 0.450, 0.185, 0.600] and [0.205, 0.500, 0.245, 0.600], the change appears marginal and nearly consistent. In contrast, {the blue bars for 'Saliency-based Sampling'}[0.195, 0.500, 0.245, 0.650] and [0.265, 0.450, 0.305, 0.550] display a larger variance, indicating less stability. The lesser variation in 'Ours' suggests it demonstrates a more stable trend.\nBased on the above thought, the answer is:\n'Ours' demonstrates a more stable trend in the 'Data Encryption and Integrity' chart. It shows minimal variance in the bar lengths across the data scenarios compared to the 'Saliency-based Sampling', which exhibits greater fluctuations." } ], "id_raw": 417 }, { "id": "nW2s0Ls2kPyxTLvt", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage point difference in the rate of gain change for 'Data Integrity' between 'Ours' and 'Saliency-based Sampling' methods?" }, { "from": "gpt", "value": "To determine the difference in the rate of gain change for 'Data Integrity', I focus on the bar lengths for both methods. {The bar for 'Ours' in Data Integrity}[0.205, 0.500, 0.245, 0.600] shows a shorter length indicating -5% while {the bar for 'Saliency-based Sampling' in Data Integrity}[0.265, 0.450, 0.305, 0.550] has a longer length indicating -15%. The difference is calculated as |(-5%) - (-15%)| = 10 percentage points.\nBased on the above thought, the answer is:\n10 percentage points." } ], "id_raw": 417 }, { "id": "9yXLC4zuQ5jqwsga", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_18.png", "conversations": [ { "from": "human", "value": "\nBased on the information in the graph, can you pull the yearly number of jobs available in different sectors and create a table for us?" }, { "from": "gpt", "value": "Sure, the data for the table can be extracted via converting the line graph into tabular format. Firstly, the years shown by the {x-axis labels}[0.142, 0.895, 0.945, 0.895] will form the column headers. The sectors are identified from {the legend}[0.109, 0.771, 0.327, 0.883] which will form the row labels. The actual data points are obtained from {the line points for each sector}[0.142, 0.738, 0.945, 0.855],[0.142, 0.504, 0.945, 0.548],[0.142, 0.065, 0.945, 0.167].\nBased on the above thought, the answer is:\nYear,Technology-Related Jobs,Education Worker Jobs,Public Sector Jobs\n2015,46000,70000,100000\n2016,47500,69500,99000\n2017,48800,69000,98000\n2018,51000,68500,96500\n2019,53000,68000,95000\n2020,54000,67000,93000" } ], "id_raw": 418 }, { "id": "8Fee8w1VnNJzooi6", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_18.png", "conversations": [ { "from": "human", "value": "\nWhich sector faced the most consistent decline in job availability over these years?" }, { "from": "gpt", "value": "To determine which sector faced the most consistent decline, I need to observe the line trajectories each representing a sector. Observing the line for {Technology-Related Jobs}[0.142, 0.738, 0.945, 0.855], the trend indicates a more or less consistent rise in job availability. The line for {Education Worker Jobs}[0.142, 0.504, 0.945, 0.548] also shows a consistent decline over the years. Lastly, the line for {Public Sector Jobs}[0.142, 0.065, 0.945, 0.167] also illustrates a consistent decline in the number of jobs available. However, the decreases are more drastic compared to the Education Worker Jobs.\nBased on the above thought, the answer is:\nConsidering the job availability trends from 2015 to 2020, the Public Sector Jobs faced the most consistent and noticeable decline in job numbers over these years." } ], "id_raw": 418 }, { "id": "SY5OPnWV19hcizb9", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_18.png", "conversations": [ { "from": "human", "value": "\nOver the years from 2015 to 2020, how large is the decrease in the number of Education Worker Jobs?" }, { "from": "gpt", "value": "In order to answer this, I need to determine the values at the beginning and end points of the line representing Education Worker Jobs. I look at the point {2015}[0.142, 0.504] and the point {2020}[0.945, 0.548], identifying the values to be 70000 and 67000 respectively. I find the decrease by subtracting the value for 2020 from that for 2015.\nBased on the above thought, the answer is:\n3000" } ], "id_raw": 418 }, { "id": "J6zKi2XV3v5dTpwG", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_21_19.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data shown in the line chart regarding subscription rates over various quarters into a structured table?" }, { "from": "gpt", "value": "Observing the trends in the line chart, I begin by examining {the x-axis labels}[0.102, 0.763, 0.781, 0.813] that indicate the quarters from 'Q1' to 'Q4_3year_later'. Next, I identify {the legends}[0.771, 0.051, 0.985, 0.191] specifying the series for 'eBay Prime', 'Amazon Subscribe', and 'Walmart Plus'. I then map out the values using the positions of {the plotted points}[0.117, 0.747, 0.732, 0.138] for each quarter and each subscription service to gather accurate numerical data.\nBased on the above thought, the answer is:\nQuarter,eBay Prime,Amazon Subscribe,Walmart Plus\nQ1,0.8,0.85,0.9\nQ2,0.83,0.88,0.93\nQ3,0.86,0.90,0.95\nQ4,0.85,0.95,0.94\nQ1_next_year,0.87,0.93,0.93\nQ2_next_year,0.88,0.94,0.96\nQ3_next_year,0.85,0.98,0.95\nQ4_next_year,0.87,1.02,0.86\nQ1_2year_later,0.9,1.05,0.9\nQ2_2year_later,0.94,1.06,0.91\nQ3_2year_later,0.96,1.08,0.95\nQ4_2year_later,0.94,1.01,0.96\nQ1_3year_later,0.97,1.03,0.94\nQ2_3year_later,0.99,1.05,0.93\nQ3_3year_later,1.0,1.07,0.94\nQ4_3year_later,0.96,1.1,0.93" } ], "id_raw": 419 }, { "id": "MdS5adlW66o5JkNY", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_21_19.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the given chart, which subscription service demonstrates the most consistent upward trend over time?" }, { "from": "gpt", "value": "Reviewing the plotted data, I identify {the lines}[0.117, 0.747, 0.732, 0.138] and observe the trends for each service. The 'Amazon Subscribe' {line}[0.117, 0.646, 0.732, 0.138] starts at an initial rate of 0.85 and progresses upward, reaching a peak of 1.1 at the last recorded quarter, showing a steady increase without significant fluctuations. Compared to 'eBay Prime' and 'Walmart Plus', both of which display some fluctuations and inconsistent progress, 'Amazon Subscribe' maintains a more consistent growth trajectory.\nBased on the above thought, the answer is:\nThe 'Amazon Subscribe' subscription service demonstrates the most consistent upward trend, with its graph line showing a steady climb in subscription rates across the years, unmarked by the dips seen in other services." } ], "id_raw": 419 }, { "id": "ehBvBWnsPqL13H3F", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_21_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the average subscription rate for Amazon Subscribe over the 4 quarters of the next year?" }, { "from": "gpt", "value": "First, I locate {the points for Amazon Subscribe}[0.281, 0.483, 0.404, 0.301] in the next year quarters from 'Q1_next_year' to 'Q4_next_year' having the rates 0.93, 0.94, 0.98, and 1.02 respectively. To find the average rate, the calculation is (0.93 + 0.94 + 0.98 + 1.02) / 4.\nBased on the above thought, the answer is:\n0.9675" } ], "id_raw": 419 }, { "id": "KDoEyJDiw1Cfrc6Y", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_0.png", "conversations": [ { "from": "human", "value": "\nCould you convert the line graph data showing population in each hemisphere into a table format?" }, { "from": "gpt", "value": "To convert the graph data into a table, I first locate {the x-axis labels}[0.05, 0.92, 0.09, 0.95] which show the two categories: 'Western Hemisphere' and 'Eastern Hemisphere'. Then, I focus on {the line representing population}[0.1, 0.1, 0.9, 0.9] and gather the data points plotted for each hemisphere. These are {the data points for the population plot}[0.3, 0.4, 0.7, 0.7] showing the values in millions.\nBased on the above thought, the answer is:\nHemisphere,Population (millions)\nWestern,500\nEastern,1200" } ], "id_raw": 420 }, { "id": "wNSg67DoHpqPAeIA", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_0.png", "conversations": [ { "from": "human", "value": "\nWhich hemisphere demonstrates a higher average maximum elevation according to the graph, and why might this be significant?" }, { "from": "gpt", "value": "To find which hemisphere has a higher average maximum elevation, I review {the elevation subplot}[0.1, 0.5, 0.4, 0.9] and {the corresponding data points}[0.2, 0.6, 0.35, 0.8]. The data for the Eastern Hemisphere shows a significantly higher elevation compared to that of the Western Hemisphere. This could indicate more mountainous regions in the Eastern Hemisphere, which is significant as it may affect climate, biodiversity, and even human habitation patterns in these regions.\nBased on the above thought, the answer is:\nThe Eastern Hemisphere demonstrates a higher average maximum elevation. This might be significant as it often implies more mountainous terrain, which can influence local climates, ecological systems, and might pose challenges or opportunities for human activities in those regions." } ], "id_raw": 420 }, { "id": "2ILEiOgvwOl0demn", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in coastline length between the Western and Eastern Hemispheres?" }, { "from": "gpt", "value": "To determine the difference in coastline length, I locate {the line graph for coastline}[0.5, 0.1, 0.9, 0.5], and review {the data points for coastline lengths}[0.55, 0.2, 0.85, 0.3] which indicates the respective lengths in thousands of kilometers for each hemisphere. The Western Hemisphere has a coastline of 75,000 km and the Eastern Hemisphere has 50,000 km.\nBased on the above thought, the answer is:\n25000 km" } ], "id_raw": 420 }, { "id": "0X0xLYdWgJwta044", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the popularity trends data from the line chart of four fashion subcultures into a structured table, dividing subcultures into rows and years into columns?" }, { "from": "gpt", "value": "I'm examining the line chart, where I've identified {the x-axis labels}[0.096, 0.919, 0.131, 0.942] marking the years from 2005 to 2020 as the column headers of the table. Next, I'm checking {the color-coded legend}[0.082, 0.074, 0.331, 0.151] indicating each fashion subculture to set the row headers. I pinpoint {the points for each line}[0.114, 0.865], [0.886, 0.100] etc., from the line chart to extract the associated popularity index values for each year.\nBased on the above thought, the answer is:\nYear,Vintage,Lolita,Steampunk,Gothic\n2005,45,65,30,60\n2006,46,64,31,57\n2007,48,62,32,55\n2008,50,60,33,53\n2009,52,59,34,50\n2010,55,58,36,48\n2011,57,56,37,45\n2012,60,55,39,43\n2013,62,54,40,40\n2014,64,62,42,39\n2015,67,70,43,35\n2016,69,68,44,33\n2017,72,63,45,30\n2018,74,60,46,28\n2019,76,58,48,25\n2020,78,57,50,22" } ], "id_raw": 422 }, { "id": "UsAPRejKrMKV5GB4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_2.png", "conversations": [ { "from": "human", "value": "\nWhich fashion subculture shows the most significant decline in popularity over the observed period, based on the line chart?" }, { "from": "gpt", "value": "Observing the line chart, I examine the trajectories of the fashion subcultures. Notably, {the line for Gothic}[0.096, 0.919] starts higher and consistently descends across the timeframe. This visible trend from around 60 down to 22 exemplifies a significant decline. In contrast, other subcultures either show growth or smaller fluctuations over time.\nBased on the above thought, the answer is:\nGothic fashion shows the most significant decline in popularity over the observed period. The line chart clearly depicts a consistent downward trajectory from 2005 to 2020, contrasting strongly with the other subcultures\u2019 trends." } ], "id_raw": 422 }, { "id": "NiKMAbedg9Ebc8Ei", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in popularity index for the Vintage fashion from 2005 to 2020?" }, { "from": "gpt", "value": "First, I locate {the points for Vintage}[0.114, 0.865] in 2005 and [0.886, 0.100] in 2020 on the line chart. I extract the values at these points which are, respectively, 45 in 2005 and 78 in 2020. Subtracting the initial value from the final gives 78 - 45.\nBased on the above thought, the answer is:\n33." } ], "id_raw": 422 }, { "id": "pfOHv8EgUzCOrVQE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_3.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information from the line chart depicting trend categories across datasets into a structured table?" }, { "from": "gpt", "value": "To accurately extract and tabulate information from the line chart, I would start by identifying each {subplot representating different datasets}[0.1, 0.1, 0.5, 0.5], [0.5, 0.1, 0.9, 0.5], [0.1, 0.5, 0.5, 0.9], [0.5, 0.5, 0.9, 0.9]. Each subplot will be further analyzed to note line plot details, starting from Dataset 1 to Dataset 4. Within each subplot, I'd identify {lines of each trend category}[0.15, 0.2, 0.45, 0.45], [0.55, 0.2, 0.85, 0.45], [0.15, 0.55, 0.45, 0.85], [0.55, 0.55, 0.85, 0.85] representing each trend, marked by corresponding colors and capture their behaviors. Using these observations, I can extract the trend details specific to each dataset.\nBased on the above thought, the answer is:\nCategory,Dataset 1,Dataset 2,Dataset 3,Dataset 4\nPronounced Decrease,Downward,Downward,Downward,Downward\nAccelerating Decline,Downward,Downward,Downward,Downward\nVolatile Decline,Downward,Volatile,Downward,Downward\nAbrupt Decline,Downward,Downward,Volatile,Downward\nErratic Decline,Downward,Volatile,Volatile,Downward\nIncremental Growth,Upward,Upward,Upward,Upward" } ], "id_raw": 423 }, { "id": "n7CZA5z6cfjFZYFK", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_3.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the stability of trends in Dataset 3 from the line chart?" }, { "from": "gpt", "value": "Analyzing the stability of trends within Dataset 3, I would focus on the behavior of each trend line represented in the corresponding subplot. Observing the line plots, specifically the {line for 'Erratic decline'}[0.15, 0.55, 0.45, 0.85] and {line for 'Volatile decline'}[0.15, 0.55, 0.45, 0.85] within Dataset 3, the visual data shows pronounced fluctuations for both these trends. These dramatic peaks and troughs, compared to the mostly downward or upward trends of the other categories, indicate significant instability and suggest that Dataset 3 experiences more frequent and unpredictable changes relative to the others.\nBased on the above thought, the answer is:\nDataset 3 displays significant instability, particularly evident in the erratic fluctuations found in both the 'Erratic decline' and 'Volatile decline' categories. These patterns suggest frequent and unpredictable trends compared to the steadier trends in other datasets." } ], "id_raw": 423 }, { "id": "IS6pPdqdJuA9sUqe", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_3.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which dataset shows the greatest difference in trend values between 'Pronounced decrease' and 'Incremental Growth'?" }, { "from": "gpt", "value": "To determine the dataset with the greatest difference, I would first locate the highest and lowest points for both {the line for 'Pronounced decrease'}[0.132, 0.287] and {the line for 'Incremental Growth'}[0.798, 0.903] in each {dataset}[0.1, 0.1, 0.5, 0.5], [0.5, 0.1, 0.9, 0.5], [0.1, 0.5, 0.5, 0.9], [0.5, 0.5, 0.9, 0.9]. By extracting these values for each dataset and calculating the range between them, Dataset 4 shows the greatest difference because the range is visually significantly larger.\nBased on the above thought, the answer is:\nDataset 4" } ], "id_raw": 423 }, { "id": "6vRUtZlh3fIbFbef", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_4.png", "conversations": [ { "from": "human", "value": "\nHow can you transform the data from the line chart displaying migration trends into a structured table, categorizing trends and data sets along rows and columns?" }, { "from": "gpt", "value": "Initially, I observe the trends labeled along {the x-axis}[[0.071, 0.950, 0.938, 0.975]] which includes 'Gradual Downturn', 'Rapid Growth', 'Sharp Decline', 'Accelerated Growth', 'Downward Trend', 'Gradual Decline'. Then, I identify {the subchart titles}[[0.071, 0.062, 0.235, 0.083], [0.290, 0.062, 0.454, 0.083], [0.509, 0.062, 0.673, 0.083], [0.728, 0.062, 0.892, 0.083]] to distinguish the data sets as DataSet1, DataSet2, DataSet3, DataSet4. By tracking {the line plots points}[[0.110, 0.565], [0.152, 0.490], ..., [0.857, 0.210]], I compile the respective values for each trend within different datasets.\nBased on the above thought, the answer is:\nTrend,DataSet1,DataSet2,DataSet3,DataSet4\nGradual Downturn,100,90,85,80\nRapid Growth,100,110,120,130\nSharp Decline,100,85,70,50\nAccelerated Growth,100,115,130,150\nDownward Trend,100,95,90,85\nGradual Decline,100,95,90,85" } ], "id_raw": 424 }, { "id": "vgtQmYMBqPfPV7vC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_4.png", "conversations": [ { "from": "human", "value": "\nWhich dataset shows the highest growth trend and why?" }, { "from": "gpt", "value": "To determine the dataset with the highest growth trend, I analyze the slope of the line segments representing growth across the datasets {DataSet1}[0.110, 0.530, 0.899, 0.545], {DataSet2}[0.110, 0.490, 0.899, 0.335], {DataSet3}[0.110, 0.450, 0.899, 0.225], {DataSet4}[0.110, 0.410, 0.899, 0.100]. Observing the line endpoints, the downward movement of the y-values decreases with each dataset, indicating that the slope becomes steeper. Dataset4 exhibits the steepest slope, suggesting the highest growth as it shifts from a higher initial value to a significantly low endpoint, especially in the context of 'Accelerated Growth' trend.\nBased on the above thought, the answer is:\nDataSet4 shows the highest growth trend. This is evident from the steepness of the line in the 'Accelerated Growth' graph, which indicates a more rapid increase from one value point to the next, corresponding to a greater amount of change over the same time interval compared to other datasets." } ], "id_raw": 424 }, { "id": "AgFp0FSVcovmg561", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_4.png", "conversations": [ { "from": "human", "value": "\nCalculate the total change in values for 'Sharp Decline\u2019 from DataSet1 to DataSet4." }, { "from": "gpt", "value": "To calculate the total change, I first locate the value for 'Sharp Decline' in {DataSet1}[0.152, 0.490] which is 100. Then, I track down the value in {DataSet4}[0.857, 0.210] which is 50. Calculating the change, I subtract the final value from the initial value, 100 - 50 = 50.\nBased on the above thought, the answer is:\n50." } ], "id_raw": 424 }, { "id": "vbrAq7tVPuOAIA5k", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_5.png", "conversations": [ { "from": "human", "value": "\nCan you transform the social welfare and public services budget allocation over the years from the chart and present it in a table format?" }, { "from": "gpt", "value": "I first look at the x-axis, identifying {the years represented}[0.126, 0.903, 0.883, 0.942] which will be our row headers. Now, I refer to {the line points representing Social Welfare}[0.126, 0.100, 0.883, 0.865] and {the line points representing Public Services}[0.126, 0.100, 0.883, 0.865]. These provide the datapoints needed for the table, representing budget allocation for Social Welfare and Public Services across the years respectively.\nBased on the above thought, the answer is:\nYears, Social Welfare, Public Services\n2010, 6534, 8952\n2011, 7200, 9503\n2012, 7890, 8403\n2013, 8238, 7852\n2014, 8568, 7607\n2015, 8840, 8237\n2016, 9005, 8943\n2017, 9543, 8175\n2018, 9800, 7655\n2019, 10190, 8400\n2020, 10375, 9204" } ], "id_raw": 425 }, { "id": "FE8H5qhtoFDi5A8k", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_5.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which budget, Public Services or Social Welfare, experienced a larger upward trend from 2010 to 2020? Explain your analysis." }, { "from": "gpt", "value": "Analyzing the line plot trends for the two budgets involved, I observe that {Social Welfare}[0.126, 0.100, 0.883, 0.865] started from roughly 6534 in 2010 and ended up at approximately 10375 in 2020. On the other hand, the {Public Services}[0.126, 0.323, 0.883, 0.221] chart starts from about 8952 in 2010 and ends up at 9204 in 2020. The changes in values for both lines signify the trend over the years.\nBased on the above thought, the answer is:\nBetween 2010 and 2020, the Social Welfare budget experienced a larger upward trend. While both budgets do increase over this period, the increase in the Social Welfare budget, from roughly 6534 to approximately 10375, is larger than the increase in the Public Services budget, which goes from around 8952 to roughly 9204. The upward trend of the Social Welfare budget seems to be more robust." } ], "id_raw": 425 }, { "id": "FOvjvLFtuWqZAlec", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest budget allocation for Public Services over the years?" }, { "from": "gpt", "value": "To find the highest and lowest budget allocation for Public Services, I refer to the line representing Public Services and examine {its points}[0.126, 0.323, 0.202, 0.100, 0.277, 0.544, 0.353, 0.766, 0.429, 0.865, 0.504, 0.611, 0.580, 0.326, 0.656, 0.636, 0.731, 0.845, 0.807, 0.545, 0.883, 0.221]. The highest allocation appears to be in 2011 at 9503 and the lowest allocation is in 2018 at 7655. The difference is therefore 9503-7655 = 1848.\nBased on the above thought, the answer is:\n1848" } ], "id_raw": 425 }, { "id": "1e1cpjKaWkSXpH7s", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_7.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, could you convert the chart's data into a table that lists the years as rows and the different parameters like 'Teacher Salaries', 'Education Innovation Index', 'Enrollment Rate', 'Training Facility Quality Index', and 'Satisfaction Rate' as columns?" }, { "from": "gpt", "value": "To convert the line chart data to a tabular format, I would first extract the years from {the x-axis tick labels}[[-0.075, 0.919, -0.040, 0.942], [0.084, 0.919, 0.119, 0.942], [0.244, 0.919, 0.279, 0.942], [0.403, 0.919, 0.438, 0.942], [0.562, 0.919, 0.597, 0.942], [0.721, 0.919, 0.756, 0.942], [0.881, 0.919, 0.916, 0.942], [1.040, 0.919, 1.075, 0.942]], these would be the row titles. Next, I recognize the different parameters from {the sublegend items}[[0.113, 0.083, 0.295, 0.106], [0.113, 0.118, 0.278, 0.141]], which act as the column headers. I locate the line points for each parameter at each year found in {line 0 points}[[0.102, 0.865], [0.181, 0.610], [0.261, 0.763], [0.341, 0.457], [0.420, 0.508], [0.500, 0.406], [0.580, 0.304], [0.659, 0.253], [0.739, 0.202], [0.819, 0.151], [0.898, 0.100]] and {line 1 points}[[0.102, 0.100], [0.181, 0.202], [0.261, 0.304], [0.341, 0.406], [0.420, 0.355], [0.500, 0.457], [0.580, 0.559], [0.659, 0.610], [0.739, 0.661], [0.819, 0.763], [0.898, 0.865]] found in the description and chart. The y-values of these lines at each of the x-values translate to the values in our table.\nBased on the above thought, the answer is:\nYear,Teacher Salaries,Education Innovation Index,Enrolment Rate,Training Facility Quality Index,Satisfaction Rate\n2000,10,20,18,16,14\n2001,15,18,20,19,17\n2002,12,16,17,15,13\n2003,18,14,16,14,13\n2004,17,15,22,18,19\n2005,19,13,24,21,20\n2006,21,11,20,19,18\n2007,22,10,24,23,21\n2008,23,9,22,20,24\n2009,24,7,20,18,21\n2010,25,5,24,23,22" } ], "id_raw": 427 }, { "id": "tclHJ08WBPcRRqYt", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_7.png", "conversations": [ { "from": "human", "value": "\nJudging by the trends in the line chart, can we say that there's a strong positive correlation between the increase in 'Teacher Salaries' and 'Education Innovation Index'?" }, { "from": "gpt", "value": "To answer this, I would analyze the trends by looking at the line plots for 'Teacher Salaries' from {line 0}[0.102, 0.100, 0.898, 0.865] and the 'Education Innovation Index' from {line 1}[0.102, 0.100, 0.898, 0.865]. From closely observing the trendlines, it appears that both 'Teacher Salaries' and the 'Education Innovation Index' have positively increased over the years. However, while 'Teacher Salaries' showed a consistent upward trend, the 'Education Innovation Index' decreased for a while before seeing a dramatic surge in 2021.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, it wouldn't be accurate to say there's a strong positive correlation between the increase in 'Teacher Salaries' and 'Education Innovation Index'. While both have shown an increase over the years, the pattern and rate of their growth are not consistently parallel. Particularly, the 'Education Innovation Index' faced a deduction before witnessing a sudden rise in 2021, which greatly differs from the steady growth exhibited by 'Teacher Salaries'." } ], "id_raw": 427 }, { "id": "uzpdTBiG0FWYrPBA", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_7.png", "conversations": [ { "from": "human", "value": "\nHow much did the 'Education Innovation Index' increase from the year 2010?" }, { "from": "gpt", "value": "To find this, I need to locate the point for the year {2010}[0.898, 0.903] and extract the corresponding value of 'Education Innovation Index', which lies at the point {5}[0.898, 0.865] in {line 0} of our chart. This means 'Education Innovation Index' was 5 in 2010.\nBased on the above thought, the answer is:\nThe 'Education Innovation Index' in the year 2010 was 5." } ], "id_raw": 427 }, { "id": "3oa9E4Njpng9E0qg", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_8.png", "conversations": [ { "from": "human", "value": "\nCould you convert the trends of each social group into tabular data with the social groups as columns or rows?" }, { "from": "gpt", "value": "To generate tabular data from the line chart, I first acknowledge {the x-axis labels}[0.474, 0.952, 0.517, 0.975] which represent the social groups. Next, I identify {the legend text}[0.086, 0.074, 0.285, 0.151] representing the different social groups. For populating the values, I locate {the line points for each group}[0.117, 0.100, 0.874, 0.865] and extract the specific numbers associated with them.\nBased on the above thought, the answer is:\nSocial Groups,1,2,3,4,5,6,7,8,9,10,11,12\nPharmaceutical Sales,4208.24,4081.45,3970.32,3900.21,3780.56,3555.32,3602.12,3781.56,3950.14,4102.35,4200.67,4350.12\nBiotech Investment,10233.12,9283.54,8405.36,7654.32,7150.45,7053.65,7001.23,7254.98,7732.48,8343.22,9089.33,10013.49" } ], "id_raw": 428 }, { "id": "PEIN1BaXr6bMwzK1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_8.png", "conversations": [ { "from": "human", "value": "\nHow do investments in Pharmaceuticals and Biotech correlate over the course of one year?" }, { "from": "gpt", "value": "To answer this, we need to analyze the plots for both Pharmaceuticals Sales and Biotech Investment. For {Pharmaceutical Sales}[0.117, 0.100, 0.874, 0.865] there is a decrease from the 1st month to the 6th, followed by a net increase until the end of the year. For {Biotech Investment}[0.117, 0.100, 0.874, 0.865] there is also a decrease from the 1st to 6th month, followed by a net increase until the end of the year.\nBased on the above thought, the answer is:\nBased on the trends observed in the chart, Pharmaceutical Sales and Biotech Investment seem to be positively correlated over the course of a year. Both fields initially experience a decrease in the first half of the year, followed by an increase in the second half of the year. This implies that changes in Pharmaceutical Sales can be associated with a similar trend in Biotech investment, suggesting that investors' interest in these two sectors is closely linked." } ], "id_raw": 428 }, { "id": "KjOTAk0xntFTLwsq", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_8.png", "conversations": [ { "from": "human", "value": "\nBy what amount did the Biotech Investment increase from month 6 to month 12?" }, { "from": "gpt", "value": "To answer this, I need to locate the data points for Biotech Investment in {month 6}[0.461, 0.852] and {month 12}[0.874, 0.152] and subtract the sixth month's value from the twelfth month's value. The values are 7053.65 and 10013.49 respectively.\nBased on the above thought, the answer is:\n2959.84" } ], "id_raw": 428 }, { "id": "A4bCJniVmGM18s2t", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_10.png", "conversations": [ { "from": "human", "value": "\nCan you generate the data in a structured table format with 'Years' as rows, 'Patents' and 'Technological_Innovations' as columns?" }, { "from": "gpt", "value": "To arrange the chart data into a table, I firstly identify {the x-axis tick labels}[[0.113, 0.919, 0.122, 0.942], [0.198, 0.919, 0.207, 0.942], [0.283, 0.919, 0.292, 0.942], [0.368, 0.919, 0.377, 0.942], [0.453, 0.919, 0.462, 0.942], [0.538, 0.919, 0.547, 0.942], [0.623, 0.919, 0.632, 0.942], [0.708, 0.919, 0.717, 0.942], [0.793, 0.919, 0.802, 0.942], [0.874, 0.919, 0.891, 0.942]] as the row headers representing different years. Then, I single-out {the legend texts}[[0.131, 0.083, 0.228, 0.106], [0.131, 0.118, 0.355, 0.141]] for the column headers which are 'Patents' and 'Technological Innovation'. To populate the table, I find {the line points} for each year [[0.118, 0.865], [0.203, 0.773], [0.288, 0.677], [0.373, 0.580], [0.458, 0.564], [0.542, 0.600], [0.627, 0.628], [0.712, 0.464], [0.797, 0.244], [0.882, 0.100]] and [[0.118, 0.856], [0.203, 0.692], [0.288, 0.475], [0.373, 0.865], [0.458, 0.100], [0.542, 0.510], [0.627, 0.133], [0.712, 0.687], [0.797, 0.540], [0.882, 0.268]] and then take the respective values.\nBased on the above thought, the answer is:\nYear,Patents,Technological_Innovation\n1,4758.0,2250.0\n2,5012.3,2348.2\n3,5278.7,2477.3\n4,5547.3,2245.1\n5,5590.8,2700.6\n6,5490.5,2456.7\n7,5412.7,2681.2\n8,5867.1,2350.8\n9,6476.6,2438.9\n10,6875.3,2600.5" } ], "id_raw": 430 }, { "id": "T4kJhDR5KaUh00C9", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_10.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, what type of trend can be noticed for 'Yearly Patents' and 'Yearly Technological Innovations'?" }, { "from": "gpt", "value": "Firstly, I'll assess the trend line of {Yearly Patents}[[0.118, 0.865], [0.203, 0.773], [0.288, 0.677], [0.373, 0.580], [0.458, 0.564], [0.542, 0.600], [0.627, 0.628], [0.712, 0.464], [0.797, 0.244], [0.882, 0.100]]. It shows an erratic increase from 1st to 5th year, followed by decrease and then another increase, and then again followed by decrease.\nNext, when observing the line for {Yearly Technological Innovations}[[0.118, 0.856], [0.203, 0.692], [0.288, 0.475], [0.373, 0.865], [0.458, 0.100], [0.542, 0.510], [0.627, 0.133], [0.712, 0.687], [0.797, 0.540], [0.882, 0.268]], an unpredictable trend is noticed. The values show a steep increase followed by a drop, then another increase followed by a downward trend, eventually observing an upward direction again.\nBased on the above thought, the answer is:\nBased on the line chart, an irregular trend can be observed in both 'Yearly Patents' and 'Yearly Technological Innovations'. For 'Yearly Patents', there is an initial increase followed by a decrease, another increase, and finally a larger decrease towards the end. For 'Yearly Technological Innovations', the trend is similar with steep increases and decreases, suggesting no consistent pattern." } ], "id_raw": 430 }, { "id": "7Bcai1PvmRIMRqLp", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the average yearly technological innovation over the 10 year period?" }, { "from": "gpt", "value": "To find out the average yearly technological innovation, I need to locate {the line points for technological innovation}[[0.118, 0.856], [0.203, 0.692], [0.288, 0.475], [0.373, 0.865], [0.458, 0.100], [0.542, 0.510], [0.627, 0.133], [0.712, 0.687], [0.797, 0.540], [0.882, 0.268]]. The values for these points are: 2250.0, 2348.2, 2477.3, 2245.1, 2700.6, 2456.7, 2681.2, 2350.8, 2438.9, 2600.5. The average can be calculated by adding up all these values and dividing by the number of years, which is 10.\nBased on the above thought, the answer is:\n2454.93" } ], "id_raw": 430 }, { "id": "84gjgUvzqsP5M50S", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_11.png", "conversations": [ { "from": "human", "value": "\nCan you please extract the data represented in the line chart into a table format?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first recognize {the x-axis labels}[0.048, 0.939, 0.107, 0.957] , [0.144, 0.939, 0.199, 0.957] , [0.250, 0.939, 0.281, 0.957] , [0.337, 0.939, 0.382, 0.957] , [0.427, 0.939, 0.479, 0.957] , [0.535, 0.939, 0.559, 0.957] , [0.611, 0.939, 0.670, 0.957] , [0.699, 0.939, 0.771, 0.957] , [0.802, 0.939, 0.855, 0.957] , [0.891, 0.939, 0.954, 0.957] as the row labels representing the different design decor elements. Next, I need to extract the corresponding values for both Rating and Innovation lines. The Rating line has the following points: {4.2, 7.5, 6.7, 7.8, 5.6, 8.5, 4.2, 5.7, 7.8, 8.9}[0.078, 0.087, 0.922, 0.887], and the Innovation line displays these points: {6.1, 5.2, 5.0, 4.3, 3.9, 3.2, 4.1, 2.8, 1.5, 0.8}[0.078, 0.087, 0.922, 0.887]\nBased on the above thought, the answer is:\nDecor Element,Rating,Innovation\nColor Choice,4.2,6.1\nTypography,7.5,5.2\nLayout,6.7,5.0\nCreativity,7.8,4.3\nProportions,5.6,3.9\nStyle,8.5,3.2\nFunctionality,4.2,4.1\nMaterial Choice,5.7,2.8\nUniqueness,7.8,1.5\nSustainability,8.9,0.8" } ], "id_raw": 431 }, { "id": "apO6wFA2OooovS1p", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_11.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which design elements seem to show low design innovation? Explain your reasoning." }, { "from": "gpt", "value": "To determine which elements show low design innovation, I need to examine the line plot for the Innovation Index. Looking at {the points on the Innovation line}[0.078, 0.087, 0.922, 0.887], the elements with values less than 3 (which I will consider low) are \"Style\", \"Material Choice\", \"Uniqueness\", and \"Sustainability\". This shows that the Design Innovation Index is relatively low for these elements.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, \"Style\", \"Material Choice\", \"Uniqueness\", and \"Sustainability\" seem to show low design innovation. Their values are all below 3 on the Design Innovation Index, indicating a lower degree of innovation compared to other design elements." } ], "id_raw": 431 }, { "id": "zR9hyzfX8AXegApL", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the maximum and minimum values for the Rating line?" }, { "from": "gpt", "value": "To find the maximum and minimum values for the Rating line, I need to locate {the points on the Rating line}[0.078, 0.087, 0.922, 0.887] and extract the corresponding values: 4.2, 7.5, 6.7, 7.8, 5.6, 8.5, 4.2, 5.7, 7.8, 8.9. The maximum value is 8.9 and the minimum value is 4.2. The difference between the maximum and minimum is therefore 8.9 - 4.2 = 4.7.\nBased on the above thought, the answer is:\n4.7" } ], "id_raw": 431 }, { "id": "R9HYk50d1USxwkwi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_12.png", "conversations": [ { "from": "human", "value": "\nCan you extract the values from the horizontal line chart showing the performance metrics in a table format, including each category (AI, Blockchain, IoT, VR/AR) with the count for Wins, Ties, and Losses?" }, { "from": "gpt", "value": "Observing the horizontal line chart, I locate the bars representing each category: {AI bar}[0.180, 0.200, 0.850, 0.230], {Blockchain bar}[0.180, 0.260, 0.800, 0.290], {IoT bar}[0.180, 0.320, 0.870, 0.350], and {VR/AR bar}[0.180, 0.380, 0.900, 0.410]. Each segment of the bar denotes a metric: Win, Tie, Loss, with proportional lengths and center labels indicating their counts. Extracting the counts from these segments through visual estimation provides the values needed.\nBased on the above thought, the answer is:\nCategory,Wins,Ties,Losses\nAI,320,100,60\nBlockchain,300,80,60\nIoT,345,70,60\nVR/AR,360,50,60" } ], "id_raw": 432 }, { "id": "NpKDdUREl5oEJCke", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_12.png", "conversations": [ { "from": "human", "value": "\nWhich technology shows a decreasing trend in tie counts and what does this suggest about its competitive position?" }, { "from": "gpt", "value": "By analyzing the {bars}[[0.180, 0.200, 0.850, 0.230], [0.180, 0.260, 0.800, 0.290], [0.180, 0.320, 0.870, 0.350], [0.180, 0.380, 0.900, 0.410]], I observe a general decrease in the lengths and labeled counts of the Tie segments from AI to VR/AR. AI has 100 ties, decreasing through Blockchain and IoT, with VR/AR having the lowest at 50 ties. This suggests that as the technological complexity increases from AI through Blockchain, IoT, to VR/AR, these technologies have stronger competitive positions, which might be reflected in fewer ties because they increasingly outperform competitors instead of equaling them.\nBased on the above thought, the answer is:\nVR/AR shows a decreasing trend in tie counts, suggesting its stronger competitive position in the ecosystem as it rarely ties and more frequently outperforms the competitors, highlighting a clear technological leadership." } ], "id_raw": 432 }, { "id": "xG5GSPLWIHc0AJtq", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the total number of confrontations recorded for Blockchain technology?" }, { "from": "gpt", "value": "To determine the total number of confrontations for Blockchain technology, I examine the lengths and labels of the segments in {Blockchain bar}[0.180, 0.260, 0.800, 0.290]. The labels inside each segment show the counts for Wins, Ties, and Losses: 300, 80 and 60 respectively. Summing these values gives the total number.\nBased on the above thought, the answer is:\n440" } ], "id_raw": 432 }, { "id": "L8Oy4nrimDN03xzV", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_13.png", "conversations": [ { "from": "human", "value": "\nCan you rearrange the information presented in the line chart into a tabular format with the historical eras as rows and the three key factors (Population Growth, Cultural Development, Technological Advancement) as columns?" }, { "from": "gpt", "value": "Sure! I would start by using {the x-axis tick labels}[[0.104, 0.761], [0.199, 0.761], [0.296, 0.761], [0.395, 0.761], [0.488, 0.761], [0.584, 0.761]] as the row headings in our table, representing different historical eras. Then, {the sub-legends or labels}[[0.036, 0.133, 0.098, 0.169], [0.036, 0.088, 0.103, 0.123], [0.036, 0.044, 0.118, 0.078]] will serve as column headers representing Population Growth, Cultural Development, and Technological Advancement respectively. Finally, I would find {the line points}[[0.110, 0.423], [0.194, 0.433], ..., [0.601, 0.800], [0.690, 0.831]] based on intersections on these lines with the x-axis and these will represent the values for each category in a specific era.\nBased on the above thought, the answer is:\nEra, Population Growth, Cultural Development, Technological Advancement\nPrehistoric, 1, 3, 2\nAncient, 2, 4, 5\nClassical, 3, 6, 7\nMedieval, 4, 7, 8\nEarly Modern, 7, 8, 9\nModern, 10, 10, 11" } ], "id_raw": 433 }, { "id": "xwGQhyNbK7tUTR1R", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_13.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which key factor exhibited the most significant acceleration in the course of history and civilization? Please explain your reasoning." }, { "from": "gpt", "value": "To address this question, I need to examine the changes in the lines for Population Growth {Population Growth}[0.104, 0.423, 0.584, 0.800], Cultural Development {Cultural Development}[0.104, 0.308, 0.584, 0.698], and Technological Advancement {Technological Advancement}[0.104, 0.385, 0.584, 0.831]. The Population Growth line reflects a rapid growth trend, particularly during the modern era. However, the Technological Advancement line illustrates a steep and sudden increase from the early modern era, exhibiting the sharpest rise among all three factors. The Cultural Development line shows moderate growth over time, with a substantial uptick during the modern era, but its growth is less dramatic than that of Technological Advancement.\nBased on the above thought, the answer is:\nThe factor exhibiting the most significant acceleration in the course of history and civilization is Technological Advancement. This is because although all three factors exhibit growth across different timeframes, Technological Advancement is characterized by a sharp, significant upwards trajectory especially from the early modern era, indicating an intense acceleration, which is greater than both Population Growth and Cultural Development." } ], "id_raw": 433 }, { "id": "hSMtP7uzwOEe1HCH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_13.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, what is the percentage increase in Population Growth from Prehistoric to Modern era?" }, { "from": "gpt", "value": "To answer this, I need to locate the points for the Prehistoric epoch {Population Growth}|[0.104, 0.423] and the modern era {Population Growth}|[0.584, 0.800] on the Population Growth line. The coordinates indicate the respective values for Population Growth in the Prehistoric epoch (value 1) and the modern era (value 10). I can then compute the percentage increase using the formula: (final - initial)/initial * 100%.\nBased on the above thought, the answer is:\n900%." } ], "id_raw": 433 }, { "id": "kFrlJ59Ou9UKrPjz", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_15.png", "conversations": [ { "from": "human", "value": "\nCould you arrange the given statistical information, specifying the month, population growth rate, and average monthly income, into a structured table?" }, { "from": "gpt", "value": "To structure the data into a tabular format, I need to identify the relevant elements of the line chart. {The x-axis tick labels}[[0.109, 0.919, 0.118, 0.942], ... , [0.873, 0.919, 0.891, 0.942]], which corresponds to each month of the year. Then, I take each point in {the line for Population Growth Rate}[[0.113, 0.865],..., [0.882, 0.100]], which correspond to the population growth rate for each month. Lastly, I take each point from {the line for Average Monthly Income}[[0.113, 0.865],..., [0.882, 0.100]], which indicate the average monthly income for each respective month.\nBased on the above thought, the answer is:\nMonth,Population Growth Rate(%),Average Monthly Income($)\nJanuary,0.1,2000\nFebruary,0.15,2050\nMarch,0.2,2100\nApril,0.18,2150\nMay,0.22,2200\nJune,0.25,2250\nJuly,0.3,2300\nAugust,0.32,2400\nSeptember,0.33,2500\nOctober,0.35,2600\nNovember,0.4,2750\nDecember,0.45,2900" } ], "id_raw": 435 }, { "id": "1tzMVSSBI0wuoije", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_15.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, other than a steady rise in population growth rate, what significant trend can you observe and relate to it?" }, { "from": "gpt", "value": "From an analytical perspective based on the line chart, one trend that reflects the steady rise in the {population growth rate}[0.065, 0.865, 0.065, 0.100] is a corresponding increase in the {average monthly income}[0.065, 0.865, 0.984, 0.100]. As the percentage of population growth increases from January to December, the average monthly income also increases from $2000 to $2900 as depicted in the line chart.\nBased on the above thought, the answer is:\nThe significant trend that corresponds to the steady rise in population growth rate is the consistent increase in the average monthly income. This suggests an economical progression in sync with the population increase. This could potentially mean that as the population grows, so does the average income, indicating economic development and improved living standards." } ], "id_raw": 435 }, { "id": "4RjHWYksLf6Ul9M7", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the average monthly increase in population growth rate from January to December?" }, { "from": "gpt", "value": "To calculate the average monthly increase, I need to locate {the line points for Population Growth Rate}[[0.113, 0.865], [0.183, 0.755], ..., [0.882, 0.100]] and extract the corresponding values: 0.1, 0.15, ... , 0.45. Hence, the average monthly increase in population growth rate would be (Final Rate - Initial Rate) divided by number of months, which is (0.45 - 0.1)/12 = 0.029.\nBased on the above thought, the answer is:\n0.029" } ], "id_raw": 435 }, { "id": "P7W5QEvWATtc6Qfy", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_16.png", "conversations": [ { "from": "human", "value": "\nCan you provide the raw data in a structured table format with the cultural values as the rows and their corresponding areas of increased, unchanged, and decreased attitudes as columns?" }, { "from": "gpt", "value": "To generate the table data, I start from the {y-axis tick labels}[{'x': 2012, 'label_coordinates': '[0.100, 0.919, 0.135, 0.942]'},...,'{'x': 2020, 'label_coordinates': '[0.857, 0.919, 0.892, 0.942]'},] obtained from the bars, these provide the row data which are different cultural values. Then I progress to the areas of the bars represented by {the line for Annual Concerts}[[0.117, 0.330],...,[0.874, 0.865]], each area indicating the percentage of increased, unchanged and decreased attitudes towards these cultural values. Finally, I relate these areas in the bars with the respective numerical percentages contained in the raw data.\nBased on the above thought, the answer is:\nCultural_Values,Increased%,Unchanged%,Decreased%\nReligious_Beliefs,37.0,55.0,8.0\nFamily_Bonds,45.0,46.5,8.5\nFriendship,33.4,47.5,19.1\nSocial_Status,28.7,35.8,35.5\nMaterial_Wealth,39.4,35.2,25.4\nPersonal_Happiness,68.7,20.6,10.7\nCan-do_Attitude,70.1,18.9,11.0\nEducational_Prestige,43.0,37.0,20.0" } ], "id_raw": 436 }, { "id": "n3vNvnMgJZ6pHDGZ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_16.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what stood out the most when examining the attitudes towards the various cultural values?" }, { "from": "gpt", "value": "By looking closely at the chart, I can gather insights on the different cultural values. The bar plot of {Personal Happiness}[0.874,0.178] and {Can-do Attitude}[0.790,0.166] distinctly shows a very significant increase in comparison to other values. This clearly indicates that these two cultural values are the most prominent in terms of increasing attitudes over the period considered. Additionally, {Religious Beliefs}[0.212,0.598] and {Family Bonds}[0.307,0.573] display a sizable area of unchanged attitudes, indicating a dominant majority of people retaining their beliefs towards them.\nBased on the above thought, the answer is:\nThe most significant observations from the chart are the substantial increase in attitudes towards 'Personal Happiness' and 'Can-do Attitude'. These cultural values seem to be becoming more prominent for the individuals involved. Additionally, 'Religious Beliefs' and 'Family Bonds' see a relatively stable trend with a majority of unchanged attitudes, implying a strong adherence to these cultural values over time." } ], "id_raw": 436 }, { "id": "ccp2gPjmJ0KE6faW", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_16.png", "conversations": [ { "from": "human", "value": "\nWhat was the difference between the percentage of increased attitudes towards 'Religious Beliefs' and 'Personal Happiness' based on the bars plotted in the graph?" }, { "from": "gpt", "value": "To solve this, I first locate the bars on the plot corresponding to 'Religious Beliefs' and 'Personal Happiness'. Let's look at {the Religious Beliefs bar}[[0.212, 0.598]], which shows an increase in attitudes represented by the blue area of the bar, from the structured raw data, this corresponds to 37.0%. Next, I examine {the Personal Happiness bar}[[0.307, 0.193]], the area in blue indicates an increase in attitudes that corresponds to 68.7% from the structured raw data. The difference in percentages is therefore 68.7% - 37.0% = 31.7%.\nBased on the above thought, the answer is:\n31.7%." } ], "id_raw": 436 }, { "id": "b9FqikkozAswm5gM", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_17.png", "conversations": [ { "from": "human", "value": "\nCould you convert the stacked bar chart into a table, with columns representing prevalence levels and rows representing the different social issues?" }, { "from": "gpt", "value": "To convert the stacked bar chart into a table, I will start by looking at {the y-axis tick labels}[[0.072, 0.631, 0.091, 0.644], [0.072, 0.571, 0.094, 0.584], [0.072, 0.511, 0.080, 0.524], [0.072, 0.451, 0.092, 0.464], [0.072, 0.390, 0.099, 0.403], [0.072, 0.333, 0.099, 0.346], [0.072, 0.273, 0.094, 0.286], [0.072, 0.213, 0.091, 0.226], [0.072, 0.152, 0.093, 0.165], [0.072, 0.092, 0.091, 0.105], [0.072, 0.032, 0.097, 0.045], [0.072, -0.028, 0.094, -0.015]] to determine the row labels which represent different social issues. Then I identify {the bar widths for each segment}[[0.114, 0.622, 0.292, 0.637], [0.114, 0.562, 0.271, 0.577], [0.114, 0.502, 0.256, 0.517], [0.114, 0.443, 0.320, 0.457], [0.114, 0.382, 0.289, 0.396], [0.114, 0.325, 0.295, 0.339], [0.114, 0.265, 0.270, 0.279], [0.114, 0.205, 0.278, 0.219], [0.114, 0.144, 0.299, 0.158], [0.114, 0.084, 0.292, 0.098], [0.114, 0.024, 0.284, 0.038], [0.114, -0.036, 0.275, -0.022]] and extract the length of each segment which indicates the percentage distribution. Finally, from {the legend texts}[[0.436, 0.944, 0.463, 0.958], [0.436, 0.904, 0.455, 0.918], [0.436, 0.863, 0.449, 0.877]] I identify the column labels, which represent the different levels of prevalence.\nBased on the above thought, the answer is:\nSocial Issues,High, Medium, Low\nEconomic Exploitation,43, 32, 25\nGender Discrimination,48, 27, 25\nIneffective Law Enforcement,37, 32, 31\nSocial Rights Violation,51, 29, 20\nHealth Discrimination,45, 30, 25\nEducation Inequality,43, 31, 26\nRacial Discrimination,41, 30, 29\nHealthcare Inequality,42, 30, 28\nLabour Exploitation,47, 28, 25\nPolitical Corruption,46, 29, 25\nSocial Discrimination,44, 30, 26" } ], "id_raw": 437 }, { "id": "cDkfVbFlOPgejoy7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_17.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which social issue has the highest and which one has the lowest 'high' prevalence? Explain your reasoning." }, { "from": "gpt", "value": "To identify the social issues with the highest and lowest 'high' prevalence, I need to compare {the lengths of the blue segments for the bars}[[0.114, 0.622, 0.292, 0.637], [0.114, 0.562, 0.271, 0.577], [0.114, 0.502, 0.256, 0.517], [0.114, 0.443, 0.320, 0.457], [0.114, 0.382, 0.289, 0.396], [0.114, 0.325, 0.295, 0.339], [0.114, 0.265, 0.270, 0.279], [0.114, 0.205, 0.278, 0.219], [0.114, 0.144, 0.299, 0.158], [0.114, 0.084, 0.292, 0.098], [0.114, 0.024, 0.284, 0.038], [0.114, -0.036, 0.275, -0.022]]. The longest blue segment indicates the highest 'high' prevalence, while the shortest blue segment reflects the lowest 'high' prevalence.\nBased on the above thought, the answer is:\n'Social Rights Violation' has the highest 'high' prevalence, as indicated by the longest blue segment within its bar. On the other hand, 'Ineffective Law Enforcement' has the lowest 'high' prevalence, as indicated by the shortest blue segment within its bar. These lengths indicate the proportion of regions or countries suffering significantly from these specific social issues." } ], "id_raw": 437 }, { "id": "5DAtU0fczMzoc9Zl", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage of 'high' prevalence for 'Social Rights Violation'?" }, { "from": "gpt", "value": "To find the 'high' prevalence percentage for 'Social Rights Violation', I need to identify {the 'Social Rights Violation' bar}[[0.114, 0.443, 0.320, 0.457]] and measure the segment representing 'high'. The length of this segment reflects the percentage in the 'high' category for 'Social Rights Violation'.\nBased on the above thought, the answer is:\n51." } ], "id_raw": 437 }, { "id": "QptHBo5c5fkfNXVa", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_18.png", "conversations": [ { "from": "human", "value": "\nCan you create a table displaying the capital raised by each sector during each funding round as depicted in the chart?" }, { "from": "gpt", "value": "Based on my understanding of the chart, I start by identifying the various funding rounds from {the x-axis labels}[0.084, 0.919, 0.119, 0.942] which serve as the columns. Next, I review {the legend}[0.069, 0.074, 0.202, 0.151] to allocate each sector to the rows. I then look at each datapoint such as {Biotechnology in Seed-round}[0.101, 0.865] and extract the respective values across all rounds for every sector to populate the table.\nBased on the above thought, the answer is:\nRound,Sector,Seed,Series A,Series B,Series C\nBiotechnology,100,200,250,220\nInternet and Software,150,300,280,210\nEnergy,200,180,160,150\nTelecommunication,120,230,210,200\nFinancial Services,130,220,200,230\nHealthcare,180,290,270,190" } ], "id_raw": 438 }, { "id": "mI0vycgW1Msp2eAn", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_18.png", "conversations": [ { "from": "human", "value": "\nWhich sector shows the most consistent growth in venture capital from Seed to Series C according to the chart?" }, { "from": "gpt", "value": "To determine consistency, I assess the line trajectories of each sector. For instance, {Biotechnology}[0.102, 0.865, 0.886, 0.233] shows increasing amounts from Seed to Series B followed by a slight decline to Series C, inferring a mostly growing trend. In comparison, other sectors like {Energy}[0.101, 0.865, 0.886, 0.233] display declines or instability. Telecommunications, however, increases and decreases throughout the rounds, reflecting inconsistency.\nBased on the above thought, the answer is:\nBiotechnology shows the most consistent growth in venture capital from Seed to Series C as its fundraising amount mostly increases across the rounds, distinguishing it from other sectors prone to declines or instability in their growth trends." } ], "id_raw": 438 }, { "id": "mkFV8T3lJRuUsW4S", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the average capital raised by the Healthcare sector across all funding rounds?" }, { "from": "gpt", "value": "First, I locate each value associated with the Healthcare sector across different rounds from points like {Healthcare in Seed-round}[0.101, 0.865] which are 180, 290, 270, 190. I sum these values up to 930 and then divide by the number of funding rounds, which is 4.\nBased on the above thought, the answer is:\n232.5" } ], "id_raw": 438 }, { "id": "lXql6P3uFVdlUfbk", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_22_19.png", "conversations": [ { "from": "human", "value": "\nCould you present the data represented by the line chart into a structured table with Months as columns and Series as rows?" }, { "from": "gpt", "value": "To convert the line chart data to a tabular form, I first identify {the x-axis tick labels}[[0.114, 0.919, 0.123, 0.942], [0.185, 0.919, 0.194, 0.942], [0.256, 0.919, 0.265, 0.942], [0.327, 0.919, 0.336, 0.942], [0.398, 0.919, 0.407, 0.942], [0.469, 0.919, 0.478, 0.942], [0.540, 0.919, 0.549, 0.942], [0.611, 0.919, 0.620, 0.942], [0.682, 0.919, 0.691, 0.942], [0.748, 0.919, 0.766, 0.942], [0.819, 0.919, 0.837, 0.942], [0.890, 0.919, 0.908, 0.942]], which represent the months. I then identify the {legend texts}[[0.131, 0.083, 0.212, 0.106], [0.131, 0.118, 0.240, 0.141]] that represent different series. Lastly, I look at {the line points for AI Research}[[0.119, 0.828], [0.190, 0.646], [0.260, 0.728], [0.331, 0.865], [0.402, 0.464], [0.473, 0.282], [0.544, 0.555], [0.615, 0.510], [0.686, 0.419], [0.757, 0.210], [0.828, 0.173], [0.899, 0.100]] and {the line points for ML Deployment}[[0.119, 0.865], [0.190, 0.428], [0.260, 0.646], [0.331, 0.755], [0.402, 0.210], [0.473, 0.100], [0.544, 0.319], [0.615, 0.428], [0.686, 0.537], [0.757, 0.755], [0.828, 0.646], [0.899, 0.865]] to determine the values for each series and each month.\nBased on the above thought, the answer is:\nMonth,AI Research,ML Deployment\n1,1000,20\n2,1100,24\n3,1055,22\n4,980,21\n5,1200,26\n6,1300,27\n7,1150,25\n8,1175,24\n9,1225,23\n10,1340,21\n11,1360,22\n12,1400,20" } ], "id_raw": 439 }, { "id": "lT7k2NzGpAS3vIQY", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_22_19.png", "conversations": [ { "from": "human", "value": "\nHow can we describe the general trend of ML Deployment based on the line chart?" }, { "from": "gpt", "value": "To describe the trend of ML Deployment, I examine {the line for ML Deployment}[0.119, 0.865, 0.899, 0.100]. It starts high at 20 in {January}[0.114, 0.919], rises slightly to 24 in {February}[0.185, 0.919], before beginning a downward trajectory that continues through the year till {December}[0.890, 0.919] when it dips to 20. While there are minor fluctuations in between, the overall trend is a decrease.\nBased on the above thought, the answer is:\nThe general trend of ML Deployment, as depicted in the line chart, shows a downward trajectory over the year. While there are minor fluctuations, the values begin relatively high in the beginning of the year and end at a lower value by the end of the year, indicating a decrease in ML Deployment over the course of the year." } ], "id_raw": 439 }, { "id": "SNahGKJ9djql6RDZ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_22_19.png", "conversations": [ { "from": "human", "value": "\nWhat was the highest value reached by AI Research over the year?" }, { "from": "gpt", "value": "To find the highest value reached by AI Research, I need to locate {the line points for AI Research}[[0.119, 0.828], [0.190, 0.646], [0.260, 0.728], [0.331, 0.865], [0.402, 0.464], [0.473, 0.282], [0.544, 0.555], [0.615, 0.510], [0.686, 0.419], [0.757, 0.210], [0.828, 0.173], [0.899, 0.100]] and extract the corresponding values: 1000, 1100, 1055, 980, 1200, 1300, 1150, 1175, 1225, 1340, 1360, 1400. The maximum value is 1400 corresponding to {December}[0.890, 0.919, 0.908, 0.942].\nBased on the above thought, the answer is:\n1400." } ], "id_raw": 439 }, { "id": "z4vpUjf4JAV5Dtpv", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_1.png", "conversations": [ { "from": "human", "value": "\nCould you represent the data displayed on the line chart into a structured table format with Risk types as rows and the corresponding Risk Scores as columns?" }, { "from": "gpt", "value": "To translate the information on the line chart into a tabular format, I first acknowledge the X-axis labels that represent various Risk types which include {Risk types}['Political'[0.109, 0.734, 0.188, 0.854], 'Economic'[0.229, 0.734, 0.325, 0.878], 'Industry'[0.363, 0.734, 0.447, 0.860],'Operational'[0.477, 0.734, 0.590, 0.903], 'Technology'[0.608, 0.734, 0.716, 0.896], 'Compliance'[0.734, 0.734, 0.847, 0.904], 'Reputation'[0.866, 0.734, 0.972, 0.893]]. \nI then look at the corresponding lines or individual data points representing the risk scores with {Risk_score}[0.148, 0.124, 0.919, 0.682] labeled against each of these types. This should enable me to convert and formulate the chart data into a coherent table.\nBased on the above thought, the answer is:\nRisk Type, Risk Score\nPolitical, 80\nEconomic, 75\nIndustry, 70\nOperational, 60\nTechnology, 50\nCompliance, 40\nReputation, 10" } ], "id_raw": 441 }, { "id": "VWkbf7pBh9Dt7ddu", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_1.png", "conversations": [ { "from": "human", "value": "\nCan you provide an assessment based on the risk scores outlined in the chart?" }, { "from": "gpt", "value": "To make an assessment, I need to look at individual data points designated by their corresponding {Risk score}[0.148, 0.124, 0.919, 0.682] to determine the gravity of risk associated with each Risk Type. With this, Political, Economic, Industry, and Operational risks all stand at the upper half of the scale indicating them to be of high risk, Technology risks fall right in the middle, and Compliance and Reputation risks fall in the lower half of the risk scale, signifying their lower risk levels.\nBased on the above thought, the answer is:\nThe chart indicates that Political, Economic, Industry, and Operational risks scores are higher \u2013 denoting higher risk. While Technology risks have a neutral score, Compliance and Reputation risks are on the lower end, indicating less risk associated with these categories. Hence, it seems more efforts might be required to mitigate the Political, Economic, Industry, and Operational risks." } ], "id_raw": 441 }, { "id": "wiIIMjXdsYzmL9pz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_1.png", "conversations": [ { "from": "human", "value": "\nHow many risk types have a risk score above 50?" }, { "from": "gpt", "value": "To find out the number of risk types with a score above 50, I look at the individual data points with {Risk score}[0.148, 0.124, 0.919, 0.682]: Political(80), Economic(75), Industry(70), Operational(60), Technology(50), Compliance(40), and Reputation(10). By comparing these numbers, I can see that the first four risk types have a risk score above 50.\nBased on the above thought, the answer is:\n4" } ], "id_raw": 441 }, { "id": "F2QPR7THdxX6YPLN", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_2.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the line chart detailing activities and their average engagement data into a structured table?" }, { "from": "gpt", "value": "Given the structure of the line chart, the x-axis labels represent the different activities which would become the rows in the table. The color-coded lines represent categories, which will form the columns, and the coordinates of each point represents the engagement score for each category of activity. Therefore, I must first pull the labels for the x-axis, {xtick labels}[[0.070, 0.664, 0.131, 0.750], [0.165, 0.664, [0.323, 0.664, 0.401, 0.773], [0.457, 0.664, 0.529, 0.765], [0.595, 0.664, 0.654, 0.747], [0.704, 0.664, 0.807, 0.809], [0.794, 0.664, 0.979, 0.923]]. Next, I locate {the legend texts}[[0.681, 0.066, 0.868, 0.094], [0.681, 0.108, 0.853, 0.136], [0.681, 0.150, 0.824, 0.178]] to obtain the column headers. Lastly, I need to reference {line points} for each category at the respective activities to fill in the table. A table is then created to represent the observed data.\nBased on the above thought, the answer is:\nActivities,Weekend Activities,Routine Activities,Total\nDinner,7.6,7.3,8\nWatching Movies,6.5,5.8,7\nTraveling,6,4.1,6\nPartying,5.75,3.6,5.75\nSports,5.6,3.1,5.6\nVolunteering,5.4,2.5,5.4\nGoing to Music Concerts,5.2,2.1,5.2" } ], "id_raw": 442 }, { "id": "wOGd2XntounmrTa5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you tell which activity has the highest engagement for Total activities?" }, { "from": "gpt", "value": "To determine which activity has the highest engagement for Total Activities, I would analyze {the points on the Total Activities line}[0.100, 0.041], [0.231, 0.142], [0.362, 0.242], [0.493, 0.267], [0.624, 0.283], [0.755, 0.303], [0.886, 0.323]. The highest point on this line corresponds to 'Dinner' activity at the coordinate {Dinner}[0.100, 0.041] with an engagement score of 8.0.\nBased on the above thought, the answer is:\nThe activity with the highest engagement for Total Activities according to the line chart is Dinner." } ], "id_raw": 442 }, { "id": "SLZSVkVhW60HJv2n", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the average engagement rate difference between Weekend and Routine Activities for Partying?" }, { "from": "gpt", "value": "The average engagement rates for Partying are represented by the y-coordinates of the corresponding points of {Weekend Activities} line[0.493, 0.267] and {Routine Activities} line[0.493, 0.484]. The values are 5.75 for Weekend Activities and 3.6 for Routine Activities. The difference between these two values is 5.75 - 3.6.\nBased on the above thought, the answer is:\n2.15." } ], "id_raw": 442 }, { "id": "gHBTmxLrGmnbMfeT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_3.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data of the AI application success rates, from the bar chart, and format it into a table with Application Categories as rows and Outcome Percentages as columns?" }, { "from": "gpt", "value": "To create a tabular data structure from the data in the bar chart, I first identify the application categories by looking at {the y-axis tick labels} [0.263, 0.117, 0.337, 0.172], [0.356, 0.117, 0.420, 0.172], [0.447, 0.117, 0.527, 0.172], [0.538, 0.117, 0.617, 0.172], [0.629, 0.117, 0.708, 0.172], [0.720, 0.117, 0.798, 0.172],\u2020 which represent the row labels as the different AI applications. Next, I need to identify {the bar lengths}[[0.113, 0.237, 0.173, 0.280], [0.113, 0.340, 0.223, 0.384], [0.113, 0.443, 0.193, 0.484], [0.113, 0.546, 0.206, 0.586], [0.113, 0.648, 0.163, 0.688], [0.113, 0.752, 0.231, 0.794]] of each category to calculate the success percentages. Based on my analysis, the resulting table would look like this.\nBased on the above thought, the answer is:\nAI Application,Win,Tie,Loss\nNatural Language Processing,19.5,39.5,41\nChatbot,22,40,38\nVideo Games,10,70,20\nMachine Learning,20.5,42.5,37\nSpeech Recognition,15,42,43\nRobotics,24.5,38.5,37" } ], "id_raw": 443 }, { "id": "Pg5AoD8CZaU8qEcg", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_3.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart's information, which AI application has shown the highest success rate and why?" }, { "from": "gpt", "value": "To identify which AI application has the highest success rate, I need to examine {the bar segment for wins}[[0.113, 0.237, 0.173, 0.280], [0.113, 0.340, 0.223, 0.384], [0.113, 0.443, 0.193, 0.484], [0.113, 0.546, 0.206, 0.586], [0.113, 0.648, 0.163, 0.688], [0.113, 0.752, 0.231, 0.794]]. The one with the greatest length represents the highest percentage of successful cases. In this case, Robotics has the longest win segment.\nBased on the above thought, the answer is:\nBased on the lengths of the win segments of each AI application, Robotics appears to have the highest success rate because its win segment is the longest compared to the other applications." } ], "id_raw": 443 }, { "id": "m20StMl1fUhvzPrl", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_3.png", "conversations": [ { "from": "human", "value": "\nFor Video Games, what is the sum of Win and Loss percentages?" }, { "from": "gpt", "value": "Locating the bar chart for {Video Games}[0.447, 0.117, 0.527, 0.172] and looking at {the bar lengths for Win and Loss}[[0.113, 0.443, 0.193, 0.484], [0.313, 0.443, 0.413, 0.484]], I can see the corresponding percentages are 10% and 20% respectively. To answer this question, I simply add these values together.\nBased on the above thought, the answer is:\n30%." } ], "id_raw": 443 }, { "id": "NxkdjEUnyhfDDP3b", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_4.png", "conversations": [ { "from": "human", "value": "\nCould you tabulate the data for transaction volume of Bitcoin and Ethereum from 2016 to 2021 as per the chart?" }, { "from": "gpt", "value": "To transform the line chart data into a tabular form, I observe the {year labels on the x-axis}[0.200, 0.909, 0.244, 0.932], [0.327, 0.909, 0.371, 0.932], [0.453, 0.909, 0.497, 0.932], [0.580, 0.909, 0.624, 0.932], [0.706, 0.909, 0.750, 0.932], [0.833, 0.909, 0.877, 0.932]]. I first note down these years which will form the row headers in the table. Then I see the {legend descriptions for Bitcoin and Ethereum}[0.861, 0.050, 0.932, 0.080] and [0.861, 0.094, 0.962, 0.124]. I use them as column headers in the table with \"Year\" as the first column. For the values, I find the data points for the lines of Bitcoin and Ethereum[0.222, 0.199, 0.855, 0.589] and [0.222, 0.372, 0.855, 0.806]\non each year and get their corresponding y-coordinate values. These y-coordinate values are then used to fill in the respective table cells.\nBased on the above thought, the answer is:\nYear,Bitcoin,Ethereum\n2016,85,40\n2017,95,60\n2018,60,35\n2019,75,55\n2020,90,70\n2021,105,85" } ], "id_raw": 444 }, { "id": "bX8UCPXxbZCkkqnC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_4.png", "conversations": [ { "from": "human", "value": "\nDiscuss the trend the chart demonstrates about the transaction volumes of Bitcoin and Ethereum from 2016 to 2021." }, { "from": "gpt", "value": "To analyse the trends, I examine {the lines representing Bitcoin and Ethereum on the chart}[0.222, 0.199, 0.855, 0.589] and [0.222, 0.372, 0.855, 0.806]. Bitcoin transaction volume been varied over the years. It started at 85 Billion Dollars in 2016, rose to 95 Billion Dollars in 2017, then fell drastically to 60 Billion Dollars in 2018. From 2019, it restarted its climb to 105 Billion Dollars by 2021. On the other hand, the Ethereum transaction volume shows a more steady increase from 2016 to 2021, from a starting value of 40 Billion Dollars and ending at 85 Billion Dollars in 2021.\nBased on the above thought, the answer is:\nThe line chart displays a fluctuating trend for Bitcoin transaction volume with a significant dip in 2018 followed by a steady increase, whereas Ethereum has shown a continual rise over the years from 2016 to 2021." } ], "id_raw": 444 }, { "id": "tIIi2ImSoh44yeJ9", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the peak transaction volume of Bitcoin from 2016 to 2021?" }, { "from": "gpt", "value": "To find the peak volume of Bitcoin across the years 2016 to 2021, I carefully examine {the line for Bitcoin}[0.222, 0.199, 0.855, 0.589]. By looking at the specific y-coordinates of the data points, I can find the peak. In this case, the maximum y-coordinate value corresponds to the year 2021 with 105 Billion Dollars.\nBased on the above thought, the answer is:\n105 Billion Dollars." } ], "id_raw": 444 }, { "id": "RD1J7z4iakc60rrC", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a table that lists Years as rows and Temperature Increase (\u00b0C) and CO2 Emission (billion ton) as columns?" }, { "from": "gpt", "value": "To convert the graph to a table I first take the values on {the x-axis}[0.206, 0.869, 0.276, 0.904], [0.290, 0.869, 0.360, 0.904], [0.373, 0.869, 0.443, 0.904], [0.456, 0.869, 0.526, 0.904], [0.539, 0.869, 0.609, 0.904], [0.623, 0.869, 0.693, 0.904], [0.706, 0.869, 0.776, 0.904] which represents the Row (Years). Then I get the Y-values for Temperature Increase and CO2 Emissions from {the plotted points [0.241, 0.729], [0.325, 0.614], [0.408, 0.499], [0.491, 0.383], [0.574, 0.268], [0.658, 0.176], [0.741, 0.130]} and {the CO2 values [0.241, 0.791], [0.325, 0.629], [0.408, 0.548], [0.491, 0.347], [0.574, 0.239], [0.658, 0.145], [0.741, 0.091]} corresponding to each Year.\nBased on the above thought, the answer is:\nYear,Temperature Increase (\u00b0C),CO2 Emission (billion ton)\n2000,0.4,6.4\n2005,0.45,7.6\n2010,0.50,8.2\n2015,0.55,9.7\n2020,0.6,10.5\n2025,0.64,11.2\n2030,0.66,11.6" } ], "id_raw": 445 }, { "id": "zSnznlqJycsdPrfo", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_5.png", "conversations": [ { "from": "human", "value": "\nGiven the trends in the line chart, does there seem to be a correlation between temperature increase and CO2 emissions? Explain your reasoning." }, { "from": "gpt", "value": "To analyze the correlation between Temperature Increase and CO2 Emissions, I need to look at the trendlines for both: {the line for Temperature Increase}[0.241, 0.130, 0.741, 0.729] and {the line for CO2 Emissions}[0.241, 0.091, 0.741, 0.791]. I notice that as the values along the Temperature Increase line go up, so do those on the CO2 Emissions line. Both lines show a persistent upward trend. It suggests that as the temperature increases, the CO2 emissions have also been increasing, which implies a positive correlation.\nBased on the above thought, the answer is:\nYes, based on the line chart, there seems to be a positive correlation between Temperature Increase and CO2 Emissions. Both show an increasing trend over the same period, with higher temperatures associated with more CO2 emissions. Therefore, it can be inferred that an increase in temperature could potentially be linked to an increase in CO2 emissions. However, further statistical analysis would be required to confirm the strength and significance of this correlation." } ], "id_raw": 445 }, { "id": "g98Ynxm68GwUxCL5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_5.png", "conversations": [ { "from": "human", "value": "\nBy how much did the CO2 Emission increase between the years 2000 and 2030?" }, { "from": "gpt", "value": "To find the increase in CO2 Emission between the years 2000 and 2030, I need to locate {the CO2 Emissions value for the year 2000}[0.241, 0.791] and {the CO2 Emissions value for the year 2030}[0.741, 0.091] and calculate the difference. The CO2 Emissions value for the year 2000 is 6.4 billion tons, and for the year 2030 it is 11.6 billion tons. Therefore, the increase in CO2 Emission is 11.6 - 6.4 = 5.2 billion tons.\nBased on the above thought, the answer is:\n5.2 billion tons." } ], "id_raw": 445 }, { "id": "6dpXVf5m8qnRqAUF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_6.png", "conversations": [ { "from": "human", "value": "\nCould you transform the line chart that illustrates innovation efficiency and its related deviation across multiple series into a structured table?" }, { "from": "gpt", "value": "To build the table, we need to list all the series as rows and organize the 'Innovation Efficiency' and 'Outlier' as columns. We find {Series names}[[0.092, 0.909, 0.158, 1.002], [0.221, 0.909, 0.287, 1.002], [0.350, 0.909, 0.416, 1.002], [0.480, 0.909, 0.545, 1.002], [0.609, 0.909, 0.675, 1.002], [0.738, 0.909, 0.804, 1.002], [0.867, 0.909, 0.933, 1.002]] on the x-axis. Then, by extracting the y-values of the line points {Efficiency points}[[0.125, 0.789], [0.254, 0.688], [0.383, 0.645], [0.512, 0.464], [0.642, 0.438], [0.771, 0.342], [0.900, 0.206]], we get 'Innovation Efficiency' for each series. Finally, extracting numbers from the deviation labels {labels}[[0.054, 0.739, 0.196, 0.767], [0.183, 0.638, 0.326, 0.666], [0.312, 0.596, 0.455, 0.624], [0.441, 0.414, 0.584, 0.442], [0.570, 0.388, 0.713, 0.416], [0.699, 0.293, 0.842, 0.321], [0.829, 0.156, 0.971, 0.184]], we get the 'Outlier' for each series.\nBased on the above thought, the answer is:\nSeries,Innovation Efficiency,Outlier\nSeries1,45.9,3.2\nSeries2,51.8,2.9\nSeries3,54.3,3.7\nSeries4,64.9,3.5\nSeries5,66.4,3.6\nSeries6,72.0,3.4\nSeries7,80.0,3.3" } ], "id_raw": 446 }, { "id": "jFj6jNkRiqPVzK4B", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_6.png", "conversations": [ { "from": "human", "value": "\nIn observing the progression of innovation efficiency, which series experienced the greatest improvement and which one the least? Please explain." }, { "from": "gpt", "value": "I'll compare the innovation efficiencies by looking at the y values of each series' point on the line plot {Efficiency points}[[0.125, 0.789], [0.254, 0.688], [0.383, 0.645], [0.512, 0.464], [0.642, 0.438], [0.771, 0.342], [0.900, 0.206]]. The efficiency increases as we move from {Series1}[0.125, 0.789] to {Series7}[0.900, 0.206]. The series experiencing the greatest improvement is Series7 with an efficiency of 80.0, up from Series1's 45.9. The series with the least improvement is Series2 with an efficiency of 51.8, a slight increase from Series1's 45.9.\nBased on the above thought, the answer is:\nThe series experiencing the greatest improvement in innovation efficiency is Series7. It has the highest innovation efficiency, 80.0, showing substantial growth from the precedent series. Conversely, Series2 has the least improvement with an innovation efficiency of 51.8, showing fairly minor growth from 45.9 in Series1." } ], "id_raw": 446 }, { "id": "xZ7CcXji1KnpLEdz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest innovation efficiency across all series?" }, { "from": "gpt", "value": "To find the highest innovation efficiency, I need to look at the y values corresponding to the highest point on the line. By checking the y values of all the points {Efficiency points}[[0.125, 0.789], [0.254, 0.688], [0.383, 0.645], [0.512, 0.464], [0.642, 0.438], [0.771, 0.342], [0.900, 0.206]], I can see that the highest point corresponds to the point with the least y coordinate, which is {point for Series7}[0.900, 0.206]. The associated innovation efficiency is 80.0.\nBased on the above thought, the answer is:\n80.0" } ], "id_raw": 446 }, { "id": "aEpJBLcH4lTIeIRN", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_7.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart and create a table that includes years, FinTech Investment and Digital Currency Market Cap?" }, { "from": "gpt", "value": "To construct the table, I first identify the {x-axis labels}[0.145, 0.912, 0.941, 0.936] as the Years. The two line data: {FinTech Investment}[0.167, 0.806, 0.919, 0.896] and {Digital Currency Market Cap}[0.167, 0.075, 0.919, 0.896] are used as the rows of the table. The y-value for each point in the lines provides us with the required financial values in billions of dollars for each year.\nBased on the above thought, the answer is:\nYear,FinTech Investment (bn), Digital Currency Market Cap(bn)\n2009,1.0,0.0\n2010,1.3,0.0\n2011,2.1,0.1\n2012,6.2,0.2\n2013,10.4,1.2\n2014,27.1,5.4\n2015,46.7,9.9\n2016,68.0,17.5\n2017,111.8,600.0\n2018,143.0,130.0\n2019,168.0,200.0\n2020,195.0,650.0\n2021,220.0,2000.0" } ], "id_raw": 447 }, { "id": "YZC9XcOqDB7QQoC2", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_7.png", "conversations": [ { "from": "human", "value": "\nBoth FinTech investment and the digital currency market cap have grown over the years according to the line chart. However, which of the two has experienced more volatility during this period and why?" }, { "from": "gpt", "value": "To answer this question, I consider the year-to-year fluctuations in both data series on the line chart. The {FinTech Investment line}[0.167, 0.806, 0.919, 0.896] follows a relatively steady upward trend, with the y-values gradually increasing across the years.\nLooking at {the Digital Currency Market Cap line}[0.167, 0.075, 0.919, 0.896], we see larger swings - for example, there is a sharp increase from 17.5 billion in 2016 to 600.0 billion in 2017, then it dips down to 130.0 billion in 2018, and jumps back up to 650.0 billion in 2020 and then to 2000.0 billion in 2021. This shows more dramatic year-to-year changes.\nBased on the above thought, the answer is:\nThe Digital Currency Market Cap has experienced more volatility during the period of 2009 to 2021 compared to FinTech Investment. This is due to the larger year-to-year changes in values for the Digital Currency Market Cap, such as the dramatic increase from 2016 to 2017 and the subsequent fall in 2018, followed by marked increases in 2020 and 2021." } ], "id_raw": 447 }, { "id": "fAccxf4lhrq8gQdO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in FinTech Investment between 2009 and 2021 according to the line chart?" }, { "from": "gpt", "value": "To determine the total increase in FinTech Investment between 2009 and 2021, I have to locate {the point for 2009 on the FinTech Investment line}[0.167, 0.896] and extract the corresponding value of 1.0 billion. Similarly, I locate {the point for 2021 for FinTech Investment line}[0.919, 0.806] and extract the corresponding value of 220.0 billion. The total increase is the difference between these two values, which is 220.0 - 1.0 = 219.0 billion.\nBased on the above thought, the answer is:\n219.0 billion." } ], "id_raw": 447 }, { "id": "AJuGZjLnifEp1KUu", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_8.png", "conversations": [ { "from": "human", "value": "\nCan you build a structured table displaying the changes in Popularity of Cyber Security, Artificial Intelligence, and Virtual Reality over the course of 2010 to 2020?" }, { "from": "gpt", "value": "To construct a table from the line chart, I will identify {the X-axis tick labels}[0.134, 0.875, 0.938, 0.923] as the Year column - these range from 2010 to 2020. The line points for {Cyber Security}[0.134, 0.817, 0.938, 0.449], {Artificial Intelligence}[0.134, 0.749, 0.938, 0.111], and {Virtual Reality}[0.134, 0.839, 0.938, 0.636] correspond to their respective popularity values over these years. By plotting these, we get the tabular data.\nBased on the above thought, the answer is:\nYear, Cyber Security, Artificial Intelligence, Virtual Reality\n2010, 1.1, 2.0, 0.8 \n2011, 1.3, 2.3, 1.1 \n2012, 1.8, 2.9, 0.9 \n2013, 2.4, 3.2, 1.0 \n2014, 3.0, 3.8, 1.4 \n2015, 3.9, 4.5, 2.0 \n2016, 4.2, 6.0, 2.4 \n2017, 5.0, 7.0, 2.5 \n2018, 5.0, 8.0, 2.8 \n2019, 5.5, 9.0, 3.0 \n2020, 6.0, 10.5, 3.5" } ], "id_raw": 448 }, { "id": "qcbJR0z3ssEd4QWc", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_8.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which technology has experienced the most consistent popularity increase over the past decade (2010-2020)?" }, { "from": "gpt", "value": "To determine which technology has the most consistent popularity increase, I need to look at the lines for each technology. Observing {Cyber Security}[0.134, 0.817, 0.938, 0.449], I see that its popularity increased at an almost steady rate from 1.1 in 2010 to 6.0 in 2020. Looking at {Artificial Intelligence}[0.134, 0.749, 0.938, 0.111], I see that it had a greater rate of increase, from 2.0 in 2010 to 10.5 in 2020. Lastly, {Virtual Reality}[0.134, 0.839, 0.938, 0.636] also showed an increase, from 0.8 to 3.5 over the same period, but at a smaller rate than the others.\nBased on the above thought, the answer is:\nAccording to the line chart, Artificial Intelligence has experienced the most consistent popularity increase over the past decade. It started with a popularity of 2.0 in 2010 and reached 10.5 by 2020 - a more significant increase when compared to Cyber Security and Virtual Reality." } ], "id_raw": 448 }, { "id": "cOnJw8U6joxKRBvB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_8.png", "conversations": [ { "from": "human", "value": "\nIn which year did Cyber Security reach a popularity score of 5.0?" }, { "from": "gpt", "value": "To find the year when Cyber Security reached a popularity score of 5.0, I should locate the points along the line for {Cyber Security}[0.134, 0.817, 0.938, 0.449]. I find the point for popularity 5.0 at coordinates [0.697, 0.524] and [0.778, 0.524]. The corresponding year is found by referencing the {x-axis tick labels}[0.134, 0.875, 0.938, 0.923] and is identified as 2017 and 2018.\nBased on the above thought, the answer is:\n2017 and 2018." } ], "id_raw": 448 }, { "id": "hiE3lVfIVvLd2oGY", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_10.png", "conversations": [ { "from": "human", "value": "\nCan you transform the compound stacked bar chart into a tabular format with comparison pairs as rows and Entrepreneurial Spirit and Entrepreneurship as columns?" }, { "from": "gpt", "value": "To generate the table, I identify {the x-axis tick labels}[[0.083, 0.945, 0.177, 0.964], [0.245, 0.945, 0.337, 0.964], ..., [0.92, 0.945,0.976, 0.964]] as the row labels corresponding to different comparison pairs of countries. For each country pair, I identify {the left segment of the bar}[[0.083, 0.640, 0.187, 0.845], [0.245, 0.450, 0.349, 0.760], ..., [0.92, 0.740, 0.976, 0.860]] and {the right segment of the bar}[[0.189, 0.640, 0.218, 0.760], [0.351, 0.450, 0.374, 0.660], ..., [0.978, 0.740, 0.999, 0.810]] as the value for Entrepreneurial Spirit and Entrepreneurship respectively.\nBased on the above thought, the answer is:\nComparison pair, Entrepreneurial Spirit, Entrepreneurship\nEngland VS Australia, 45.8, 30.2\nUSA VS India, 40.3, 26.8\nUSA VS Germany, 70.5, 15.9\nEngland VS Germany, 60.2, 20.5" } ], "id_raw": 450 }, { "id": "MUfj6c7lCOLyA8nP", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_10.png", "conversations": [ { "from": "human", "value": "\nBased on the compound stacked bar chart, which country seems to have a lower Entrepreneurial Spirit when compared with other countries? Justify your answer." }, { "from": "gpt", "value": "To determine which country has a lower Entrepreneurial Spirit, I observe {the left segment of the bar}[[0.083, 0.640, 0.187, 0.845], [0.245, 0.450, 0.349, 0.760], ..., [0.92, 0.740, 0.976, 0.860]] associated with each country in different comparisons. I note that when Germany is compared with other countries, the Entrepreneurial Spirit seems to be lower for Germany. The comparisons are {Germany vs USA}[0.718, 0.945, 0.808, 0.964] and {Germany vs England}[0.856, 0.945, 0.922, 0.964] where the left segment of the bars are quite small indicating a lower value of Entrepreneurial Spirit for Germany.\nBased on the above thought, the answer is:\nBased on the chart, Germany seems to have a lower Entrepreneurial Spirit compared to other countries. This observation is supported by the relatively shorter left segment of the bars associated with the comparisons involving Germany, which represent the percentage of people with Entrepreneurial Spirit from Germany compared with other countries." } ], "id_raw": 450 }, { "id": "2uh95Mfcb4mYwnx7", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the average value of Entrepreneurship across all comparison pairs?" }, { "from": "gpt", "value": "To find the average value of Entrepreneurship, I need to find the values for Entrepreneurship from {the right segment of the bar}[[0.189, 0.640, 0.218, 0.760], [0.351, 0.450, 0.374, 0.660], ..., [0.978, 0.740, 0.999, 0.810]]. I will add up these values and divide by the total number of comparison pairs.\nBased on the above thought, the answer is:\n23.35" } ], "id_raw": 450 }, { "id": "RqYaGiSP5sYPLlZZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_11.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data behind the bar chart into a table format with different types of decline as rows and the percentage of user activity across different platforms (Facebook, Instagram, and Twitter) as the columns?" }, { "from": "gpt", "value": "To achieve this, I first eye on {the y-axis labels}[0.055, 0.869, 0.122, 0.904],\n[0.189, 0.869, 0.255, 0.904],\n[0.322, 0.869, 0.389, 0.904],\n[0.456, 0.869, 0.522, 0.904],\n[0.589, 0.869, 0.656, 0.904],\n[0.723, 0.869, 0.789, 0.904],\n[0.865, 0.869, 0.914, 0.904], which represent the row labels, referring to the types of decline. Afterwards, I identify that the color-coded sections of the bar chart, for instance the points for Facebook are found here [0.089, 0.074], [0.222, 0.315], ..., [0.890, 0.808], represent the percentages of user activity for each platform at different types of decline. Through this process, I generate the desired table showing the percentage user activity across different platforms for every identified type of decline.\nBased on the above thought, the answer is:\nDecline,Facebook,Instagram,Twitter\nContinued,68,32,20\nSubstantial,40,17,12\nAccelerated,24,10,8" } ], "id_raw": 451 }, { "id": "37byMJQPNnMwteQB", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_11.png", "conversations": [ { "from": "human", "value": "\nGiven the trend displayed on the bar chart, which social media platform could be inferred to be losing user activity at the fastest rate and why?" }, { "from": "gpt", "value": "To understand which platform is losing user activity the fastest, I have to analyze the decline in user activity from one type of decline to the next for each platform. Gazing at {the line for Facebook}[0.089, 0.074, 0.890, 0.808], I see a drop from 68 to 40 to 24. For {Instagram}[0.222, 0.315, 0.890, 0.808], the decline is from 32 to 17 to 10. Lastly, for {Twitter}[0.090, 0.074, 0.890, 0.808], the decrease is from 20 to 12 to 8. This implies higher relative decline for Facebook than the other two platforms.\nBased on the above thought, the answer is:\nBased on the trends shown in the bar chart, Facebook seems to be losing user activity the fastest. This conclusion is formed from the large relative decreases in user activity on Facebook across the various types of decline, where it falls from 68 to 40 and finally to 24. While all three platforms are experiencing a fall, the rate of decline is larger for Facebook compared to Instagram and Twitter." } ], "id_raw": 451 }, { "id": "Rhe3ReTG8lUyePDD", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_11.png", "conversations": [ { "from": "human", "value": "\nHow much decline in user activity on Instagram is observed between Continued decline and Accelerated decline?" }, { "from": "gpt", "value": "To access the information, I locate the points on the chart corresponding to Instagram's user activity during Continued and Accelerated decline. So, I turn to {the point representing Instagram's activity during Continued decline}[0.222, 0.315] implying the value 32 and then to {the point representing Instagram's activity during Accelerated decline}[0.890, 0.808] implying the value 10. The difference in user activity can now be calculated as 32 minus 10 which leads to the answer.\nBased on the above thought, the answer is:\n22" } ], "id_raw": 451 }, { "id": "vsZ8wacSluzkc1zr", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the comparison of effectiveness between renewable and traditional energy sources into a table including the percentage of wins, ties, and losses for each scenario?" }, { "from": "gpt", "value": "To determine the tabular representation of the data, I must first identify {the energy comparisons represented on the x-axis}[[0.187, 0.701, 0.257, 0.723], [0.278, 0.701, 0.340, 0.723], [0.365, 0.701, 0.442, 0.723]]. The {winning segments}[0.247, 0.403, 0.386, 0.262]] corresponds to column one, the percentage wins, {ties}[0.651, 0.598, 0.769, 0.601] column two and {losses}[0.816, 0.716, 0.982, 0.735] column three. I can then extract the lengths of the respective segments on the line representing each energy comparison for the corresponding values. The lengths are proportional to the percentages.\nBased on the above thought, the answer is:\nComparison,Win,Tie,Loss\nSolar vs Oil,44,28,28\nWind vs Natural Gas,49,21,30\nHydropower vs Coal,46,24,30" } ], "id_raw": 452 }, { "id": "x4W8Bv2zAZVInjMX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which renewable source of energy has the highest total effectiveness when compared to its traditional counterpart? Justify your answer." }, { "from": "gpt", "value": "To answer this question, I need to calculate the total effectiveness (sum of wins and ties) of each renewable source against its traditional counterpart. For {Solar Energy vs Oil}[0.187, 0.701, 0.257, 0.723], the total effectiveness (44% wins + 28% ties) is 72%. For {Wind Energy vs Natural Gas}[0.278, 0.701, 0.340, 0.723], the total effectiveness (49% wins + 21% ties) is 70%. For {Hydropower vs Coal}[0.365, 0.701, 0.442, 0.723], the total effectiveness (46% wins + 24% ties) is 70%. Therefore, Solar Energy vs Oil has the highest total effectiveness.\nBased on the above thought, the answer is:\nBased on the chart, Solar Energy has the highest total effectiveness compared to its traditional counterpart, Oil. This is determined by summing the win and tie percentages, and comparing these totals for all energy sources. Solar Energy vs Oil has a total effectiveness of 72%, which is the highest among all comparisons." } ], "id_raw": 452 }, { "id": "fiI7tJnPJIpnkgWw", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_12.png", "conversations": [ { "from": "human", "value": "\nFor Solar Energy vs Oil, what is the total effectiveness of Solar Energy, considering only wins and ties?" }, { "from": "gpt", "value": "To calculate the total effectiveness of Solar Energy against Oil, I need to sum the percentages for wins and ties. The {percentage of wins can be obtained from the green line segment}[0.247, 0.403], which is 44%. The {percentage of ties can be obtained from the blue line segment}[0.651, 0.598], which is 28%. The total effectiveness is therefore 44% + 28% = 72%.\nBased on the above thought, the answer is:\n72%." } ], "id_raw": 452 }, { "id": "p5Phpeu9kVRsUN3O", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart data on the influence of various media on public opinion into a tabular format?" }, { "from": "gpt", "value": "Observing the line chart, I see data points represented along the x-axis with labels located at coordinates {TV News}[0.10, 0.93], {Radio}[0.30, 0.93], {Online News}[0.50, 0.93], and {Social Media}[0.70, 0.93]. These labels will form the column headers. The y-axis values, which represent the percentage influence on public opinion, can be observed at their corresponding points along the line, identified visually from the graph and list coordinates, e.g., for TV News at [0.10, 0.60].\nBased on the above thought, the answer is:\nMedia Type,Public Opinion Influence (%)\nTV News,20\nRadio,10\nOnline News,40\nSocial Media,60" } ], "id_raw": 453 }, { "id": "wF8wj7NIb7TX2zAc", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_13.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the given trends, why might Radio be considered an outlier in influencing public opinion?" }, { "from": "gpt", "value": "In reviewing the line chart, I note the data points for {TV News}[0.10, 0.60], {Radio}[0.30, 0.85], {Online News}[0.50, 0.45], and {Social Media}[0.70, 0.25]. The trend from TV News to Online News shows an increasing influence on public opinion. However, there is a noticeable dip at the Radio point, which has a significantly lower influence percentage compared to others. This deviation from the ascending trend marks Radio as an outlier.\nBased on the above thought, the answer is:\nRadio is considered an outlier because its influence on public opinion, at only 10%, is remarkably lower than the other media types. This deviation interrupts an otherwise increasing trend observed from TV News through to Social Media, indicating its less substantial impact in comparison." } ], "id_raw": 453 }, { "id": "lSHt54IEKZlXE7c3", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage difference in public opinion influence between TV News and Social Media?" }, { "from": "gpt", "value": "The influence points for {TV News}[0.10, 0.75] and {Social Media}[0.70, 0.25] are given in the chart. Reading from the visual data, TV News has a public opinion influence of 20% and Social Media has an influence of 60%. To find the difference, I calculate \\(60\\% - 20\\%\\).\nBased on the above thought, the answer is:\n40%." } ], "id_raw": 453 }, { "id": "63I3YyjPYdiy9OH3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_15.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you create a structured table using the Year as rows and Trends as columns?" }, { "from": "gpt", "value": "To create the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.185, 0.869, 0.255, 0.904], [0.264, 0.869, 0.334, 0.904], [0.344, 0.869, 0.414, 0.904], [0.423, 0.869, 0.493, 0.904], [0.502, 0.869, 0.573, 0.904], [0.582, 0.869, 0.652, 0.904], [0.661, 0.869, 0.731, 0.904], [0.741, 0.869, 0.811, 0.904], [0.820, 0.869, 0.890, 0.904]] as the row labels representing the Year. Then I identify the trend labels for each corresponding year represented by {the points labels}[[0.122, 0.627, 0.318, 0.662], [0.186, 0.534, 0.412, 0.569], [0.253, 0.441, 0.504, 0.476], [0.373, 0.348, 0.544, 0.383], [0.417, 0.394, 0.658, 0.429], [0.519, 0.317, 0.715, 0.352], [0.571, 0.224, 0.822, 0.259], [0.690, 0.131, 0.861, 0.166], [0.742, 0.038, 0.968, 0.073]]. These are the column headers representing the different trends. I map each trend to its corresponding year to complete the table structure.\nBased on the above thought, the answer is:\nYear,Trends\n2022,Slow recovery\n2023,Marginal growth\n2024,Upward trajectory\n2025,Slow growth\n2026,Gradual recovery\n2027,Slow recovery\n2028,Upward trajectory\n2029,Slow growth\n2030,Marginal growth" } ], "id_raw": 455 }, { "id": "w3cUsxCaoQB4hRh1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_15.png", "conversations": [ { "from": "human", "value": "\nConsidering the trends illustrated in the line chart, how does the technological progress evolve over the years, based on the given Trend descriptions?" }, { "from": "gpt", "value": "To analyze the progression of the technological progress over the years, I observe {the line for technological progress}[0.220, 0.100, 0.855, 0.689] and the attached labels. The line represents the measure of technological progress over time, from 2.0 in 2022 to 3.9 in 2030. The adjacent labels to the points on the line provide a qualitative description of the pace of this progress. At the onset, we experience a 'Slow recovery' indicating a slow increase in technological progress. In 2023, we notice a 'Marginal growth', suggesting a minor increment. The 'Upward trajectory' in 2024 signifies a larger rise. This is followed by a 'Slow growth' in 2025 and a 'Gradual recovery' in 2026, suggesting a slowing down of progress and further catching up in 2026. 'Slow recovery' in 2027 leads to another 'Upward trajectory' in 2028, which points to a sizeable increase. However, we again experience a 'Slow growth' in 2029 followed by 'Marginal growth' in 2030, indicating a slowed pace towards the end.\nBased on the above thought, the answer is:\nThe technological progress, as depicted on the line chart, experiences varied rates of growth over the years. It begins with a slow recovery, sees minor and major increments - described as marginal growth and upward trajectory respectively. This growth slows down mid-period and recovers again before shooting up further. Towards the end, the progression decelerates, experiencing slow growth first and then a slight improvement. Overall, the progress increases from 2.0 in 2022 to 3.9 in 2030, but the speed of this increase fluctuates significantly." } ], "id_raw": 455 }, { "id": "35ecnWoTiEdmQvlb", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the maximum and minimum values for TechProgress across all years?" }, { "from": "gpt", "value": "To find the maximum and minimum values for TechProgress across all years, I need to focus on {the line points for TechProgress}[[0.220, 0.689], [0.299, 0.596], [0.379, 0.503], [0.458, 0.410], [0.538, 0.457], [0.617, 0.379], [0.696, 0.286], [0.776, 0.193], [0.855, 0.109]] and extract the corresponding values: 2.0, 2.3, 2.6, 2.9, 2.75, 3.0, 3.3, 3.6, 3.9. The maximum value is 3.9 corresponding to {2030}[0.820, 0.869, 0.890, 0.904], and the minimum value is 2.0 corresponding to {2022}[0.185, 0.869, 0.255, 0.904]. The difference between the maximum and minimum is therefore 3.9 - 2.0 = 1.9.\nBased on the above thought, the answer is:\n1.9." } ], "id_raw": 455 }, { "id": "cEKFnG4ISnBaL1JM", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_16.png", "conversations": [ { "from": "human", "value": "\nCan you generate a comparison table indicating the year against the returns for Mutual Funds and ETFs from the chart data?" }, { "from": "gpt", "value": "To extract the desired data into a table format, I first discern {the years on the x-axis}[0.048, 0.912, 0.083, 0.936], [0.177, 0.912, 0.212, 0.936], ..., [0.950, 0.912, 0.985, 0.936] as the rows of the table corresponding to each data point along the line for Mutual Funds {the line for Mutual Funds}[0.130, 0.684, 0.903, 0.819] and ETFs {the line for ETFs}[0.130, 0.587, 0.903, 0.769]. Then, I trace the data points from these lines to derive the returns for each year.\nBased on the above thought, the answer is:\nYear,Mutual Funds,ETFs\n2005,15.8,17.1\n2006,16.2,18.1\n2007,16.6,19.1\n2008,14.8,16.9\n2009,14.2,17.5\n2010,15.0,18.8\n2011,15.8,19.5\n2012,16.5,20.8\n2013,17.8,22.2\n2014,19.2,23.3\n2015,19.9,24.9\n2016,20.6,25.4\n2017,21.5,26.7" } ], "id_raw": 456 }, { "id": "mdJ8iC7rMyH4bRJV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in returns for Futures from 2005 to 2017?" }, { "from": "gpt", "value": "I will locate the starting and ending points on {the line for Futures}[0.130, 0.310, 0.903, 0.672] to determine the initial value in 2005, which is 24.1%, and then find the final value in 2017, which is 41.7%. By subtracting the initial value from the final value, I can compute the increase in returns over these years.\nBased on the above thought, the answer is:\n17.6%" } ], "id_raw": 456 }, { "id": "riaZs2JXUabkQ64k", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_17.png", "conversations": [ { "from": "human", "value": "\nConsidering the dual-axis line chart, can you extract the data into a structured table format, with 'Year' as rows and 'Global GDP' and 'Market Index' as columns?" }, { "from": "gpt", "value": "To compile the tabular format from the line chart, I first identify {the x-axis tick labels}[[0.097, 0.891, 0.147, 0.919], [0.180, 0.891, 0.230, 0.919], [0.263, 0.891, 0.313, 0.919], [0.346, 0.891, 0.397, 0.919], [0.430, 0.891, 0.480, 0.919], [0.513, 0.891, 0.563, 0.919], [0.596, 0.891, 0.646, 0.919], [0.679, 0.891, 0.729, 0.919], [0.762, 0.891, 0.812, 0.919], [0.845, 0.891, 0.896, 0.919], [0.929, 0.891, 0.979, 0.919]] as the row labels symbolizing 'Year'. The 'Global GDP' and 'Market Index' represented respectively by {the line points for GDP}[[0.122, 0.870], [0.205, 0.870], [0.288, 0.870], [0.371, 0.870], [0.455, 0.870], [0.538, 0.870], [0.621, 0.870], [0.704, 0.870], [0.787, 0.870], [0.870, 0.870], [0.954, 0.870]] and {the line points for Market Index} [[0.122, 0.451], [0.205, 0.412], [0.288, 0.374], [0.371, 0.336], [0.455, 0.298], [0.538, 0.259], [0.621, 0.221], [0.704, 0.183], [0.787, 0.145], [0.870, 0.106], [0.954, 0.068]] and corresponding values presented beside the points, serve as column headers and data values in the table.\nBased on the above thought, the answer is:\nYear, Global GDP, Market Index\n2010, 3.1, 1100\n2011, 3.2, 1200\n2012, 3.4, 1300\n2013, 3.1, 1400\n2014, 3.3, 1500\n2015, 3.2, 1600\n2016, 3.4, 1700\n2017, 3.6, 1800\n2018, 3.8, 1900\n2019, 3.8, 2000\n2020, 2.8, 2100" } ], "id_raw": 457 }, { "id": "UvOR2PyVBzrjvA0N", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_17.png", "conversations": [ { "from": "human", "value": "\nBased on the trendline in the dual-axis line chart, which feature shows a more pronounced change over the time period, Global GDP or the Market Index? Please explain your reasoning." }, { "from": "gpt", "value": "In order to deduce which feature shows a more pronounced change over time, I need to analyze the trendlines of both Global GDP and the Market Index. {The Global GDP line}[0.122, 0.870, 0.954, 0.870] demonstrates minor fluctuations around a near constant value of ~3.3, suggesting limited variability in this indicator. On the other hand, {the Market Index line}[0.122, 0.068, 0.954, 0.451] presents a clear upward trend from 1100 in 2010 to 2100 in 2020, indicating a substantial and consistent increase over this period.\nBased on the above thought, the answer is:\nBased on the analysis of the trendlines, the Market Index shows a more pronounced change over the time period. The line graph for Market Index reveals a constant upward trend, with a considerable increase from 1100 in 2010 to 2100 in 2020. In contrast, Global GDP remains relatively stable, hovering around value of 3.3 with minor fluctuations, thereby indicating less change." } ], "id_raw": 457 }, { "id": "jZZCkstUXk9YwnbT", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_17.png", "conversations": [ { "from": "human", "value": "\nHow much did the Market Index increase from the year 2010 to the year 2020?" }, { "from": "gpt", "value": "To find out the increment of Market Index from 2010 to 2020, I need to locate the values of Market Index in these years. The Market Index value of 2010 can be found at {the point of 2010}[0.122, 0.451], which reads 1100. The Market Index value of 2020 is at {the point of 2020}[0.954, 0.068], which is 2100. Thus, the increase in Market Index over this period is 2100 - 1100.\nBased on the above thought, the answer is:\n1000." } ], "id_raw": 457 }, { "id": "rTPo8WhnAxxjN0B0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_18.png", "conversations": [ { "from": "human", "value": "\nCan you translate the line graphs of Digital Currency Performance Over The Years into a structured table? Let's use the Years as columns and Digital Currencies as rows." }, { "from": "gpt", "value": "To convert the line chart into tabular data, I first identify {the x-axis tick labels}[[0.015, 0.910, 0.070, 0.939],[0.133, 0.910, 0.188, 0.939],[0.250, 0.910, 0.305, 0.939],[0.368, 0.910, 0.423, 0.939],[0.485, 0.910, 0.540, 0.939],[0.602, 0.910, 0.657, 0.939],[0.720, 0.910, 0.775, 0.939],[0.837, 0.910, 0.892, 0.939],[0.955, 0.910, 1.010, 0.939]], representing the columns or Years. Next, I locate {the legend texts}[[0.205, 0.146, 0.280, 0.175],[0.205, 0.190, 0.311, 0.219],[0.205, 0.233, 0.273, 0.262]] that serve as row labels or Digital Currency names. Each line graph, {Bitcoin}[[0.160, 0.418, 0.865, 0.855]], {Ethereum}[[0.160, 0.155, 0.865, 0.505]], and {Ripple}[[0.160, 0.365, 0.865, 0.680]], is then analyzed to get the performance value of each Digital Currency per Year.\nBased on the above thought, the answer is:\nDigital_Currency,2015,2016,2017,2018,2019,2020,2021\nBitcoin,50,60,70,60,59,61,75\nEthereum,70,75,73,74,80,85,90\nRipple,60,65,63,66,72,75,78" } ], "id_raw": 458 }, { "id": "22tDHFgR7yzvGJEY", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_18.png", "conversations": [ { "from": "human", "value": "\nBased on the line charts, which Digital Currency seems to show the most stable performance over the years? Please explain your reasoning." }, { "from": "gpt", "value": "To identify the most stable Digital Currency, I study the fluctuations of the line graphs for each Digital Currency. {Bitcoin's line}[[0.160, 0.418, 0.865, 0.855]] shows performance values that vary between a low of 50 and a high of 75. \nFor {Ethereum's line}[[0.160, 0.155, 0.865, 0.505]], the performance ranges from 70 to 90, also showing fair amount of fluctuations.\nFinally, reviewing {Ripple's line}[[0.160, 0.365, 0.865, 0.680]], the performance values are between 60 and 78. \n\nAlthough all digital currencies show growth over the years, some have more variable growth rates than others.\nBased on the above thought, the answer is:\nBased on the line charts, Ripple seems to have the most stable performance over the years, despite the minor fluctuations. Its performance values range between 60 and 78, showing smaller differences for each consecutive year when compared to Bitcoin and Ethereum. It depicts less volatility over time." } ], "id_raw": 458 }, { "id": "WAgn0FEtqcAScdhi", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the average performance of Ethereum over the given years?" }, { "from": "gpt", "value": "To calculate the average performance of Ethereum, I must analyze its line plot, {Ethereum}[[0.160, 0.155, 0.865, 0.505]]. The values for each year are 70, 75, 73, 74, 80, 85, and 90. The average is calculated as the sum of all these values divided by the number of years, which is 7.\nBased on the above thought, the answer is:\n78.14" } ], "id_raw": 458 }, { "id": "UeWBCNSEXd1FEPJ1", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_23_19.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data displayed in the line chart and present it in a structured table with Countries as rows and the two indices as columns?" }, { "from": "gpt", "value": "To extract the data from the line chart, I need to first identify the countries along {the horizontal axis}[[0.125, 0.795, 0.270, 0.902]], which will represent the rows of the table. Next, {the y-axis labels}[[0.039, 0.097, 0.056, 0.125]] give me the range of values for the two indices. The {line points}[[0.132, 0.108], [0.919, 0.692]] of the lines represent the values for the respective indices for each country. Using all these elements I can construct the table.\nBased on the above thought, the answer is:\nCountry, RGBA Population Index, International Trade Index\nChina, 100, 60\nUSA, 90, 70\nIndia, 85, 50\nRussia, 80, 65\nBrazil, 75, 45\nGermany, 70, 80\nUK, 65, 75\nFrance, 60, 70\nItaly, 55, 65\nCanada, 50, 60\nAustralia, 45, 50\nJapan, 40, 80" } ], "id_raw": 459 }, { "id": "KFgUuqJYnGyfRkTz", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_23_19.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, which country appears to be the most open to international trade in relation to its population and why?" }, { "from": "gpt", "value": "To identify which country is most open to international trade in relation to its population, I need to consider both the {Population Index}[[0.430, 0.934, 0.621, 0.970]] and the {International Trade Index}[[0.015, 0.220, 0.033, 0.635]]. A high value on the International Trade Index in relation to the Population Index would suggest extensive international trade relative to the population. {Germany}[[0.525, 0.776]] and {Japan}[[0.132, 0.776]] both have an International Trade Index of 80, but Germany's Population Index of 70 is larger than Japan's, which is 40. Therefore, Japan has the highest International Trade Index relative to its Population Index.\nBased on the above thought, the answer is:\nFrom the chart, Japan appears to be the most open to international trade in relation to its population. Despite having a smaller population index compared to Germany, both have the same International Trade Index. This suggests that Japan has a higher ratio of international trade per citizen compared to Germany." } ], "id_raw": 459 }, { "id": "Y9YIKDPlzmHj8g5F", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_23_19.png", "conversations": [ { "from": "human", "value": "\nHow much higher is the International Trade Index of Germany compared to Brazil?" }, { "from": "gpt", "value": "I need to locate the {point for Germany on the International Trade Index line}[[0.525, 0.108]] and the {point for Brazil on the International Trade Index line}[[0.591, 0.692]]. The corresponding values for these points are 80 and 45 respectively. So, the difference between Germany and Brazil is 80 - 45 = 35.\nBased on the above thought, the answer is:\n35." } ], "id_raw": 459 }, { "id": "CZxW8MPmafwYS9C7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_1.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you extract the Age dataset and each of the three Line plots into a table?" }, { "from": "gpt", "value": "To extract chart information into a tabular format, I need to identify the data represented in the x and y axes and line plots. The {x-axis}[0.512, 0.942, 0.565, 0.975] labels represent the Age dataset: 20, 30, 40, 50, 60, 70, 80, 90, 100. The line plots for 'Physical Health Average', 'Environmental Quality', and 'Life Satisfaction' correspond to forceful points [0.145, 0.163, 0.932, 0.635], [0.145, 0.163, 0.932, 0.721], and [0.145, 0.189, 0.932, 0.764] respectively. The values for the line plots at each Age coordinate can be traced back and populated into the table\nBased on the above thought, the answer is:\nAge,Physical_Health_Average,Environmental_Quality,Life_Satisfaction\n20,75,85,70\n30,80,75,80\n40,85,70,82\n50,75,65,70\n60,70,60,60\n70,65,52,55\n80,55,45,40\n90,40,35,30\n100,30,20,15" } ], "id_raw": 461 }, { "id": "5hY7z4oUKZvQeqGq", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_1.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which factor seems to deteriorate the most as age increases and why?" }, { "from": "gpt", "value": "To determine which factor deteriorates the most with age, I need to observe the rate of decline in each of the line plots for 'Physical Health Average', 'Environmental Quality', and 'Life Satisfaction'. Starting at 75, {Physical Health Average}[0.145, 0.163, 0.932, 0.635] declines to 30 at age 100, indicating a decrease of 45 units. {Environmental Quality}[0.145, 0.163, 0.932, 0.721] decreases from 85 at age 20 to 20 at age 100, marking a decrease of 65 units. Lastly, {Life Satisfaction}[0.145, 0.189, 0.932, 0.764] starts at 70 and drops to 15, resulting in a decrease of 55 units.\nBased on the above thought, the answer is:\nBased on the line chart, Environmental Quality seems to deteriorate the most with an increasing age. This trend is observed in the fact that Environmental Quality starts at the highest point of 85 at age 20 and falls the most to 20 at age 100, indicating the most significant decrease among the three factors." } ], "id_raw": 461 }, { "id": "eiY3Gb9KPlq1Ym9x", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_1.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, what is the mean Environmental Quality from age 50 to age 70?" }, { "from": "gpt", "value": "To calculate the mean Environmental Quality between age 50 and 70, I need to locate and sum the Environmental Quality values corresponding to ages 50, 60 and 70 in the line plot [0.145, 0.163, 0.932, 0.721], which are 65, 60 and 52 respectively. I then divide this sum by the number of data points, which is 3.\nBased on the above thought, the answer is:\n59" } ], "id_raw": 461 }, { "id": "a8PdKISAgR4DySk3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_2.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data showing the trends of Deforestation, Greenhouse Emissions, and Animal Extinction over multiple years into a tabular format with years as rows?" }, { "from": "gpt", "value": "To generate a table from the line chart, I focus first on {the x-axis ticks}[0.134, 0.864, 0.193, 0.899], which represent the years. For each row, I identify the corresponding {line points for Deforestation}[0.163, 0.095, 0.932, 0.496], {Greenhouse Emissions}[0.163, 0.553, 0.932, 0.627], and {Animal Extinction}[0.163, 0.496, 0.932, 0.782] for each year to extract the values.\nBased on the above thought, the answer is:\nYear,Deforestation,Greenhouse Emissions,Animal Extinction\n2001,1.25,0.8,0.4\n2002,1.4,0.75,0.45\n2003,1.6,0.68,0.5\n2004,1.55,0.67,0.6\n2005,1.3,0.70,0.65\n2006,1.2,0.75,0.7\n2007,1.1,0.77,0.75\n2008,1,0.78,0.8\n2009,0.95,0.78,0.85\n2010,0.9,0.79,0.9" } ], "id_raw": 462 }, { "id": "PqGRmeAMwhRliCeG", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_2.png", "conversations": [ { "from": "human", "value": "\nFrom the given trends in the chart, which environmental factor shows a consistent decline over the given time period? Explain your reasoning." }, { "from": "gpt", "value": "To determine which environmental factor presents a consistent decline, I analyze the trends in {the line for Deforestation}[0.163, 0.095, 0.932, 0.496], {Greenhouse Emissions}[0.163, 0.553, 0.932, 0.627], and {Animal Extinction}[0.163, 0.496, 0.932, 0.782]. Observing the Deforestation line, I see a consistent decrease over the years from 1.25 to 0.90. On the other hand, Greenhouse Emissions and Animal Extinction do not exhibit a steady decline throughout the observed period.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the rate of Deforestation presents a consistent decline over the years from 2001 to 2010. This is evident through the line plot which shows the highest value at the start of the period (1.25 in 2001) and arrives at the least value by the end (0.90 in 2010), illustrating a steady decrease." } ], "id_raw": 462 }, { "id": "arugzhckj80NxaCf", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the average rate of Deforestation from 2001 to 2010?" }, { "from": "gpt", "value": "To find the average rate of Deforestation for the given time period, I find {the line points for Deforestation}[0.163, 0.095, 0.932, 0.496]. Extracting the values, I get: 1.25, 1.4, 1.6, 1.55, 1.3, 1.2, 1.1, 1, 0.95, 0.9. I sum these up and divide by the total number of years (10) to find the average.\nBased on the above thought, the answer is:\n1.225" } ], "id_raw": 462 }, { "id": "SHA08OB6NvTEPR3m", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_3.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data into a structured table with 'Year' as the rows and 'Usage Percentage' as the columns for each category of use?" }, { "from": "gpt", "value": "To construct the data table from the Line chart, I will start by identifying the years from {the x-axis tick labels}[[0.146, 0.909, 0.181, 0.932], [0.228, 0.909, 0.263, 0.932], [0.310, 0.909, 0.345, 0.932], [0.392, 0.909, 0.427, 0.932], [0.474, 0.909, 0.509, 0.932], [0.557, 0.909, 0.592, 0.932], [0.639, 0.909, 0.674, 0.932], [0.721, 0.909, 0.756, 0.932], [0.803, 0.909, 0.838, 0.932], [0.885, 0.909, 0.920, 0.932]], which will serve as the rows. Next, I locate the three uses: {Internet Use}[0.132, 0.055, 0.218, 0.078], {Mobile Use}[0.132, 0.090, 0.208, 0.113], and {Social Media Use}[0.132, 0.125, 0.250, 0.148] from the legend representing the columns. Now to find the values, I locate the points for each category at their respective years and extract those values.\nBased on the above thought, the answer is:\nYear,Internet Use,Mobile Use,Social Media Use\n2005,0.05,0.05,0.01\n2006,0.1,0.07,0.02\n2007,0.15,0.1,0.03\n2008,0.2,0.12,0.04\n2009,0.25,0.15,0.05\n2010,0.3,0.18,0.06\n2011,0.35,0.2,0.07\n2012,0.4,0.3,0.1\n2013,0.45,0.35,0.15\n2014,0.5,0.4,0.2" } ], "id_raw": 463 }, { "id": "L34QTVz5Gi06Z6OP", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_3.png", "conversations": [ { "from": "human", "value": "\nBased on the line charts, how does the trend of mobile use compare with internet use over the years?" }, { "from": "gpt", "value": "To compare the trends of mobile use with internet use over the years, I will look at both {the line representing Mobile Use}[0.163, 0.268, 0.903, 0.815] and {the line representing Internet Use}[0.163, 0.112, 0.903, 0.815]. Throughout the plotted time frame from 2005 to 2014, both mobile and internet use show an increasing trend. However, it looks like the Internet usage started higher and consistently stays above Mobile usage until around 2012. After 2012, the gap between the two usage types becomes much smaller, suggesting that during this period, the growth rate of Mobile usage becomes significantly higher than that of Internet usage.\nBased on the above thought, the answer is:\nOver the years, both the use of mobile and internet has been increasing. However, the rate of increase in mobile use has been significantly higher, especially from 2012 onwards, as compared to that of Internet use. While both were at 0.05 in 2005, by 2014, mobile use reached 0.4, closing the gap with Internet use at 0.5. This indicates the rising popularity and accessibility of mobile technology." } ], "id_raw": 463 }, { "id": "HslGho7wBX9zgkpn", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_3.png", "conversations": [ { "from": "human", "value": "\nBy what percentage did Social Media Use increase from 2008 to 2014?" }, { "from": "gpt", "value": "To find the increase in Social Media Use from 2008 to 2014, I need to locate the {data points for Social Media Use in 2008 and 2014}[[0.410, 0.830], [0.903, 0.581]], which are correspondingly 0.04 and 0.2. The increase in usage can be found by subtracting the earlier value from the later one and then, to convert it into a percentage, multiplying by 100.\nBased on the above thought, the answer is:\n16%" } ], "id_raw": 463 }, { "id": "yJNWsrwgvJISx0jd", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_4.png", "conversations": [ { "from": "human", "value": "\nGiven the line chart, can you formulate the pertinent data into a structured table, where the columns denote different financial periods and the rows represent Earnings and Expenses respectively?" }, { "from": "gpt", "value": "To translate the line chart into tabulated data, I begin with identifying {the x-axis labels}[[0.145, 0.864, 0.204, 0.899], [0.314, 0.864, 0.372, 0.899], [0.482, 0.864, 0.541, 0.899], [0.651, 0.864, 0.709, 0.899]], which act as column headers or financial periods. Next, I interpret {the legend labels}[[0.763, 0.069, 0.899, 0.104],[0.763, 0.121, 0.902, 0.156]] as row descriptors, representing Earnings and Expenses. To populate the values, I scrutinize the {line points}[[0.174, 0.118], [0.259, 0.142], ..., [0.933, 0.294]] for each row at the respective periods and extract the values indicated.\nBased on the above thought, the answer is:\nFinancial Periods, 2010, 2011, 2012, 2013\nEarnings, 50, 40, 30, 20\nExpenses, 30, 40, 50, 60" } ], "id_raw": 464 }, { "id": "gYm3a9JTbWnuAfyD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart trends, which financial component, Earnings or Expenses, exhibited a more aggressive trend over time and why do you think so?" }, { "from": "gpt", "value": "To ascertain which financial component has shown a more aggressive trend, I examine the inclinations in the lines for both Earnings and Expenses. Viewing {the line for Earnings}[0.174, 0.118, 0.933, 0.294], it can be seen that it starts at a high point, 50 and gradually descends to 20, revealing a decline in Earnings over the periods. \nOn the contrary, for {the Expenses line}[0.174, 0.278, 0.933, 0.807], starting at 30, it escalates to 60, reflecting an increasing trend in Expenses over the periods. The increase in Expenses is steeper compared to the decline in Earnings, indicating a more aggressive trend.\nBased on the above thought, the answer is:\nFrom the chart's trends, the financial component of Expenses displayed a more aggressive trend over time. As the Expenses line exhibits a steeper incline compared to the decline visible in the Earnings line, this indicates a faster rate of increase in Expenses across the periods compared to the decrease in Earnings. Thus, Expenses exhibit a more aggressive trend." } ], "id_raw": 464 }, { "id": "0Idp0IPThi6OdcUT", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the variance between the maximum and minimum Earnings recorded across the financial periods?" }, { "from": "gpt", "value": "To identify the maximum and minimum Earnings across all periods, I must allocate {the line points for Earnings}[[0.174, 0.118], [0.259, 0.142], [0.343, 0.166], [0.427, 0.182]], and extract the related values: 50, 40, 30, 20. The maximum value is 50 corresponding to {2010 period}[0.174, 0.839], and the minimum value is 20 corresponding to {2013 period}[0.427, 0.839]. Therefore, the difference between the maximum and the minimum Earnings is 50 - 20 = 30.\nBased on the above thought, the answer is:\n30." } ], "id_raw": 464 }, { "id": "2xODdWA0VAeqUmDn", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_5.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you convert the sales data across the years into a structured table?" }, { "from": "gpt", "value": "To construct the table from this line chart, I am firstly identifying {the x-axis labels}[0.141, 0.864, 0.962, 0.899] that correspond to the years. These will be the row labels. Secondly, I'm noticing the {legend}[0.762, 0.055, 0.963, 0.222] which tells me the three types of sales: Car Sales, Bike Sales, and EV Sales. These will be the columns of my table. For the values, I am examining {the line points} for car sales [0.170, 0.091, 0.933, 0.305], bike sales [0.170, 0.454, 0.933, 0.572], and EV sales [0.170, 0.545, 0.933, 0.732] respectively at each year and extracting the respective values attached.\nBased on the above thought, the answer is:\nYear,Car Sales,Bike Sales,EV Sales\n2010,100,50,20\n2011,120,70,25\n2012,130,60,28\n2013,140,65,32\n2014,135,60,35\n2015,130,68,40\n2016,125,72,45\n2017,120,70,48\n2018,115,67,52\n2019,110,65,55" } ], "id_raw": 465 }, { "id": "eEeCbxM8BoV8vweI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_5.png", "conversations": [ { "from": "human", "value": "\nConsidering the line chart, which product line has the most consistent growth over the years, and why?" }, { "from": "gpt", "value": "To determine which product line has the most consistent growth over the years, I need to evaluate the trends shown in the line plots for Car Sales, Bike Sales and EV Sales. Inspecting the {line plot for Car Sales}[0.170, 0.091, 0.933, 0.305], I see fluctuations with no clear trend of consistent growth. For both {Bike Sales}[0.170, 0.454, 0.933, 0.572] and {EV Sales}[0.170, 0.545, 0.933, 0.732], there's a clear increase in sales from 2010 through to 2019. However, the increase in EV Sales over the years is steeper compared to Bike Sales, indicating a more consistent growth.\nBased on the above thought, the answer is:\nBased on the line chart, EV Sales demonstrates the most consistent growth over the years. While both Bike Sales and EV Sales show an increasing trend, the rise in EV Sales is more pronounced, suggesting a more uniform and steady level of growth. This consistent rise could signal a higher demand for electric vehicles over the years." } ], "id_raw": 465 }, { "id": "q3sSfkNwkWG1Yeot", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the maximum and minimum values for Car Sales across the years?" }, { "from": "gpt", "value": "To find the maximum and minimum sales for cars over the years, I need to locate and analyze the {line points for Car Sales}[0.170, 0.091, 0.933, 0.305]. These values are: 100,120,130,140,135,130,125,120,115 and 110. The maximum value is 140, corresponding to the year {2013}[0.395, 0.864, 0.454, 0.899], and the minimum value is 110, corresponding to the year {2019}[0.903, 0.864, 0.962, 0.899]. Therefore, the difference between the maximum and minimum sales for cars is 140 - 110 = 30.\nBased on the above thought, the answer is:\n30." } ], "id_raw": 465 }, { "id": "ykJvuCZq0Nqmzpaw", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_7.png", "conversations": [ { "from": "human", "value": "\nCan you provide a tabular format for the data displayed in the line chart where years are columns and learning modes represent rows?" }, { "from": "gpt", "value": "To create the table from the line chart, I first look at {the x-axis labels}[[0.173, 0.912, 0.217, 0.936], [0.285, 0.912, 0.329, 0.936], [0.398, 0.912, 0.441, 0.936], [0.510, 0.912, 0.554, 0.936], [0.622, 0.912, 0.666, 0.936], [0.735, 0.912, 0.778, 0.936], [0.847, 0.912, 0.891, 0.936]], which correspond to the years and hence will be the table columns.\nNext, I identify {the labels in the legend}[[0.147, 0.055, 0.326, 0.078], [0.147, 0.090, 0.283, 0.113], [0.147, 0.125, 0.313, 0.148]] to represent different learning modes which will be the rows.\nThe respective data points such as those under {the line for On-campus Learning} [[0.195, 0.451], [0.307, 0.396], [0.419, 0.340], [0.532, 0.312], [0.644, 0.368], [0.757, 0.451], [0.869, 0.507]] and similar ones for other lines are traced to build the cell values.\nBased on the above thought, the answer is:\nLearning Mode, 2000, 2005, 2010, 2015, 2020, 2025, 2030\nOn-campus Learning, 15, 17, 19, 20, 18, 15, 13\nOnline Learning, 0.5, 2, 5, 10, 16, 20, 25\nAugmented Reality, 0, 0.3, 0.6, 2, 5, 7.5, 10" } ], "id_raw": 467 }, { "id": "7OqRd8ysJrn6URbX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_7.png", "conversations": [ { "from": "human", "value": "\nCould you provide an analysis of the learning trends from 2000 to 2030 based on the lines on the graph?" }, { "from": "gpt", "value": "The line chart shows three types of learning: {On-campus Learning} [[0.195, 0.451], [0.307, 0.396], [0.419, 0.340], [0.532, 0.312], [0.644, 0.368], [0.757, 0.451], [0.869, 0.507]], {Online Learning} [[0.195, 0.854], [0.307, 0.813], [0.419, 0.729], [0.532, 0.590], [0.644, 0.423], [0.757, 0.312], [0.869, 0.173]], and {Augmented Reality} [[0.195, 0.868], [0.307, 0.860], [0.419, 0.852], [0.532, 0.813], [0.644, 0.729], [0.757, 0.660], [0.869, 0.590]] over a time span from 2000 to 2030.\n\nOn-campus Learning shows a generally declining trend from 2000 to 2030, evidencing a gradual shift in learning preferences away from traditional classroom environments.\n\nOnline Learning shows a significant increase over time, from almost no students in 2000 to a peak in 2030. This trend points to the growing popularity and acceptance of online learning platforms.\n\nAugmented Reality, while starting from zero in 2000, shows steady growth over the years, indicating the potential for this learning mode to become a more mainstream choice in the future.\nBased on the above thought, the answer is:\nThe line chart reveals evolving learning trends from 2000 to 2030. It is evident that there is a gradual shift away from On-campus Learning, marked by a steady decline in student numbers. Interestingly, this decline is complemented by a surge in Online Learning, which grows dramatically from nearly negligible numbers in 2000 to becoming a popular choice by 2030. Meanwhile, Augmented Reality, although starting from zero in 2000, demonstrates consistent growth, suggesting its potential as a common mode of learning in the coming years." } ], "id_raw": 467 }, { "id": "qIYHMpTTfvZO0ZH4", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_7.png", "conversations": [ { "from": "human", "value": "\nWhat was the increase in student enrollment in the Online Learning mode from 2000 to 2015?" }, { "from": "gpt", "value": "To find this, I need to locate {the line points for Online Learning in 2000} [[0.195, 0.854]] and {in 2015} [[0.532, 0.590]]. The corresponding values are 0.5 and 10 respectively. The increase is therefore 10 - 0.5 = 9.5 million students.\nBased on the above thought, the answer is:\n9.5 million students." } ], "id_raw": 467 }, { "id": "ekXQOcfgDd75M5v2", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_9.png", "conversations": [ { "from": "human", "value": "\nPlease generate a tabular representation of the data presented in the line chart with Months as columns and Security Measures as rows." }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first consider {the x-axis tick labels}[[0.131, 0.864, 0.146, 0.899], [0.222, 0.864, 0.237, 0.899], [0.313, 0.864, 0.328, 0.899], [0.404, 0.864, 0.419, 0.899], [0.495, 0.864, 0.510, 0.899], [0.586, 0.864, 0.601, 0.899], [0.677, 0.864, 0.691, 0.899], [0.768, 0.864, 0.782, 0.899], [0.859, 0.864, 0.873, 0.899], [0.942, 0.864, 0.972, 0.899]] which represent the column headers or Months. Next, I distinguish {the legend texts}[[0.540, 0.668, 0.803, 0.703],[0.540, 0.721, 0.954, 0.756],[0.540, 0.773, 0.861, 0.808]] as the row labels representing the different Security Measures. To populate the values, I locate {the line points}[[0.139, 0.130, 0.957, 0.347],[0.139, 0.465, 0.957, 0.642],[0.139, 0.248, 0.957, 0.544]] for each security measure at the respective months and extract the values annotated.\nBased on the above thought, the answer is:\nSecurity Measure,Month 1,Month 2,Month 3,Month 4,Month 5,Month 6,Month 7,Month 8,Month 9,Month 10\nSecure Login Accuracy,0.85,0.88,0.91,0.93,0.92,0.9,0.91,0.92,0.94,0.96\nTwo-Factor Authentication Accuracy,0.7,0.73,0.76,0.78,0.79,0.75,0.7,0.72,0.71,0.7\nFirewall Detection Accuracy,0.75,0.8,0.8,0.82,0.84,0.85,0.87,0.85,0.88,0.9" } ], "id_raw": 469 }, { "id": "hygRbL5Xr4IJss3a", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_9.png", "conversations": [ { "from": "human", "value": "\nCan you describe the accuracy trend for the Three Security Measures based on the line chart?" }, { "from": "gpt", "value": "To describe the trend, I consider {the lines for the three Security Measures}[[0.139, 0.130, 0.957, 0.347],[0.139, 0.465, 0.957, 0.642],[0.139, 0.248, 0.957, 0.544]]. The accuracy of Secure Login increased from 0.85 to 0.96 showing a steady rise with a slight dip in Month 6 but recovered afterwards. The accuracy of Two-Factor Authentication improved from 0.7 to 0.79 showing an upward trend till Month 5, then a slight decrease and a further decrease with minor fluctuations till Month 10. And lastly, the accuracy of Firewall Detection started at 0.75 and ended at 0.9, also exhibiting an upward trend with a brief decrease observed at Month 8.\nBased on the above thought, the answer is:\nThe three security measures all demonstrate an upward trend in accuracy over the ten months, with Secure Login Accuracy showing the most consistent improvement. The accuracy of Two-Factor Authentication improved until Month 5, after which it saw a slight decrease and then further decrease with some fluctuations. Firewall Detection Accuracy also improved steadily, with a brief decrease observed at Month 8. These trends suggest that the overall accuracy of these security measures is improving over time." } ], "id_raw": 469 }, { "id": "cVGDXjGgqQwPD6PA", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_9.png", "conversations": [ { "from": "human", "value": "\nHow much did the accuracy of Secure Login improve from Month 1 to Month 10?" }, { "from": "gpt", "value": "To determine the improvement in Secure Login from Month 1 to Month 10, I first locate {the point for Month 1 on the Secure Login line}[[0.139, 0.347]] and observe that the accuracy is 0.85. Next, I locate {the point for Month 10 on the Secure Login line}[[0.957, 0.130]] and see that the accuracy is 0.96. The difference between the two values gives the amount of improvement in Secure Login accuracy over the period.\nBased on the above thought, the answer is:\n0.11" } ], "id_raw": 469 }, { "id": "VNzuUON2smX37bDj", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_10.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the line chart data into table format, with years as columns and Charities as rows?" }, { "from": "gpt", "value": "From the line chart, I can identify {the x-axis tick labels}[[0.163, 0.864, 0.221, 0.899], [0.234, 0.864, 0.292, 0.899], ..., [0.875, 0.864, 0.933, 0.899]] which represents the column headers or years. These are: 2000, 2001, 2002, ..., 2010. The {legend texts}[[0.206, 0.069, 0.418, 0.104], [0.206, 0.121, 0.360, 0.156], ..., [0.206, 0.173, 0.413, 0.208]] represent the row labels or charity names. These are: Prototype Success, Patent Filings, Market Expansion. For the table values, I analyze {the line points} [[0.192, 0.329], [0.263, 0.344], ..., [0.904, 0.628]], and extract the corresponding values for each charity across the different years.\nBased on the above thought, the answer is:\nYear,Prototype Success,Patent Filings,Market Expansion\n2000,0.70,0.45,0.50\n2001,0.68,0.50,0.55\n2002,0.62,0.60,0.60\n2003,0.60,0.65,0.65\n2004,0.55,0.70,0.70\n2005,0.47,0.75,0.78\n2006,0.42,0.80,0.82\n2007,0.40,0.85,0.85\n2008,0.35,0.90,0.90\n2009,0.30,0.91,0.93\n2010,0.29,0.92,0.98" } ], "id_raw": 470 }, { "id": "hJAXUKxrgaJFmHBK", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_10.png", "conversations": [ { "from": "human", "value": "\nWhich area experienced the most significant decline over the years according to the line chart?" }, { "from": "gpt", "value": "To identify the area that experienced the most decline, I need to pay attention to the slopes of {all the lines}[[0.192, 0.329, 0.904, 0.628], [0.192, 0.169, 0.904, 0.511], [0.192, 0.125, 0.904, 0.475]]. Reviewing each line, I found that {the line for Prototype Success}[0.192, 0.329, 0.904, 0.628] with an initial value of 0.7 and a final value of 0.29, had a drop of 0.41 which is the highest decline compared to other lines.\nBased on the above thought, the answer is:\nThe Prototype Success sector experienced the most significant decline over the years according to the line chart. You can observe a downward trend in its line chart from the beginning to the end, with a drop-off from an initial value of 0.7 to a final value of 0.29, representing the highest decline compared to Patent Filings and Market Expansion." } ], "id_raw": 470 }, { "id": "HZ3065fKD5FSiXsB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_10.png", "conversations": [ { "from": "human", "value": "\nHow much did Market Expansion increase from the year 2000 to 2010?" }, { "from": "gpt", "value": "To calculate the increase in Market Expansion, I need to locate the points {Year 2000}[0.192, 0.475] and {Year 2010}[0.904, 0.125] within {the line for Market Expansion}[0.192, 0.125, 0.904, 0.475] from the line chart. The corresponding recorded values for these vertices are 0.5 and 0.98 respectively. Subtracting 0.5 from 0.98 gives the increase from 2000 to 2010.\nBased on the above thought, the answer is:\n0.48" } ], "id_raw": 470 }, { "id": "7O5fBoWeaF2ROVc5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_11.png", "conversations": [ { "from": "human", "value": "\nCould you please help me to organize the data in the line chart into a table with years as the columns and car types as rows?" }, { "from": "gpt", "value": "To transform this line chart into tabular data, I first notice that {the x-axis tick labels}[0.143, 0.909, 0.187, 0.932], ... ,[0.885, 0.909, 0.929, 0.932], represent the years, which will thus form the column headers on our table. Then, in {the legend}[0.806, 0.058, 0.851, 0.072], .., [0.806, 0.128, 0.851, 0.142] we can find the types of cars, these will translate into rows in our table. To fill in the table with the right values, I find {the line points for Electric cars}[0.165, 0.876], ..., [0.907, 0.206], {the line points for Hybrid cars}[0.165, 0.738], ..., [0.907, 0.481], and {the line points for Diesel cars}[0.165, 0.206], ..., [0.907, 0.670], these values represent the market share for each type of car at each year.\nBased on the above thought, the answer is:\nYear,Electric cars,Hybrid cars,Diesel cars\n2010,0.01,0.09,0.4\n2011,0.02,0.11,0.39\n2012,0.03,0.14,0.38\n2013,0.05,0.16,0.35\n2014,0.07,0.18,0.32\n2015,0.1,0.22,0.29\n2016,0.15,0.25,0.25\n2017,0.22,0.28,0.21\n2018,0.3,0.27,0.18\n2019,0.35,0.25,0.15\n2020,0.4,0.24,0.13" } ], "id_raw": 471 }, { "id": "MipquHJl9GAGInfH", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_11.png", "conversations": [ { "from": "human", "value": "\nConsidering the trends shown in the line chart, which type of cars have shown the most consistent growth in market share over the years? Explain your basis for this conclusion." }, { "from": "gpt", "value": "To determine which type of cars has shown the most consistent growth, I have to analyze the progression in the line plots for each type. Looking at {the line plot for Electric cars}[0.165, 0.206, 0.907, 0.876], it starts from 0.01 in 2010, gradually increasing each year to 0.4 in 2020. This represents a consistent increase in market share over the years.\nConsidering {the plot for Hybrid cars}[0.165, 0.412, 0.907, 0.738], it also shows growth, however after 2017 it dips slightly.\nFinally, observing {the line for Diesel cars}[0.165, 0.206, 0.907, 0.670], I notice that it has decreased consistently from 0.4 in 2010 to 0.13 in 2020. Although it's a consistent trend, it's not a growth trend.\nBased on the above thought, the answer is:\nConsidering the trends shown in the line chart, Electric cars have demonstrated the most consistent growth in market share over the years. This conclusion is based on the steady upward trend seen in the line plot for Electric cars, where the market share has successively increased year after year from 2010 to 2020. Although Hybrid cars also show an increasing trend for some years, this growth is not as consistent as Electric cars since their market share slightly decreases after 2017. Diesel cars exhibit a consistent trend, however, it is not a growth trend but a decreasing one." } ], "id_raw": 471 }, { "id": "N3HWxGmNr7FM7O8n", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_11.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the total increase in market share for electric cars from 2010 to 2020 based on the line chart?" }, { "from": "gpt", "value": "To calculate the total increase in market share for electric cars from 2010 to 2020, I need to first locate {the line point for Electric cars in 2010}[0.165, 0.876] and {the line point for Electric cars in 2020}[0.907, 0.206]. The market share in 2010 is represented by the y-coordinate of the first point, which is 0.01, while the market share in 2020 is represented by the y-coordinate of the last point, which is 0.4. So, the total increase in market share is 0.4 - 0.01 = 0.39.\nBased on the above thought, the answer is:\n0.39." } ], "id_raw": 471 }, { "id": "x5rxJ0YABRewsL37", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_13.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the number of Internet, Mobile Phone and Email users based on the line chart data?" }, { "from": "gpt", "value": "To generate the data table from the line graph, I firstly identify {the x-axis tick labels}[{'label': '2000', 'label_coordinates': '[0.148, 0.912, 0.165, 0.936]'}, {'label': '2010', 'label_coordinates': '[0.792, 0.912, 0.809, 0.936]'}] which represent the Years column. Next I find the {legend texts}[{'label': 'Internet Users', 'text_coordinates': '[0.135, 0.055, 0.233, 0.078]'}, {'label': 'Mobile Phone Users', 'text_coordinates': '[0.135, 0.090, 0.271, 0.113]'}, {'label': 'Email Users', 'text_coordinates': '[0.135, 0.125, 0.216, 0.148]'}] that determine what type of user the rows represent. For each user type, I locate the points {Internet Users}[{'x': 2000, 'y': 361.1, 'point_coordinates': '[0.148, 0.857]'}, {'x': 2010, 'y': 2026.3, 'point_coordinates': '[0.792, 0.678]'}], {Mobile Phone Users}[{'x': 2000, 'y': 759.7, 'point_coordinates': '[0.148, 0.814]'}, {'x': 2010, 'y': 5504.2, 'point_coordinates': '[0.792, 0.303]'}] and {Email Users}[{'x': 2000, 'y': 480.6, 'point_coordinates': '[0.148, 0.844]'}, {'x': 2010, 'y': 2450.3, 'point_coordinates': '[0.792, 0.632]'}] on each line of the corresponding color to extract the y-values, which present the number of users (in millions), and correspond them to the years.\nBased on the above thought, the answer is:\nYear,Internet Users,Mobile Phone Users,Email Users\n2000,361.1,759.7,480.6\n2010,2026.3,5504.2,2450.3" } ], "id_raw": 473 }, { "id": "CPN7YrKZakO1NWgP", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_13.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which type of technology user group showed the most rapid increase over the period?" }, { "from": "gpt", "value": "To determine which user group has the most rapid increase, I need to analyze the slope of the line plots. {The line plot for Internet Users}[0.148, 0.626, 0.921, 0.857] shows a steady ascent, suggesting a steady growth. However, {the line plot for Mobile Phone Users}[0.148, 0.145, 0.921, 0.814] shows a faster rate of increase, suggesting a more rapid growth. Finally, {the line plot for Email Users}[0.148, 0.509, 0.921, 0.844] shows a growth rate that appears to be somewhere between the Internet Users and Mobile Phone Users lines. Hence, the Mobile Phone Users line illustrates the most rapid increase.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Mobile Phone Users shows the most rapid increase over the period. The line plot for Mobile Phone Users has the steepest slope, indicating a faster rate of growth compared to Internet and Email Users." } ], "id_raw": 473 }, { "id": "RtsNAht2cSOouA6v", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_13.png", "conversations": [ { "from": "human", "value": "\nHow much did the number of Mobile Phone Users grow from 2000 to 2010?" }, { "from": "gpt", "value": "To find out the growth in the number of Mobile Phone Users between 2000 and 2010, I need to locate the line points for Mobile Phone Users. {The point for the year 2000}[{'x': 2000, 'y': 759.7, 'point_coordinates': '[0.148, 0.814]'}] indicates the number of mobile phone users was 759.7 million in that year and the {point for the year 2010}[{'x': 2010, 'y': 5504.2, 'point_coordinates': '[0.792, 0.303]'}] shows it has grown to 5504.2 million in 2010. So, the growth is: 5504.2 million - 759.7 million = 4744.5 million\nBased on the above thought, the answer is:\n4744.5 million" } ], "id_raw": 473 }, { "id": "dsVVh2l2PP7qxTg6", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data showing global trade values from 2001 to 2020 into a structured table?" }, { "from": "gpt", "value": "To compile the data into a table format, I'm analyzing {the line plot for Global Trade}[0.120, 0.507, 0.942, 0.652], marking observed points {from 2001 to 2020}[0.120, 0.604, 0.942, 0.652]. Each point on this plot corresponds to a year's trade value, starting at the year 2001 and concluding at 2020.\nBased on the above thought, the answer is:\nYear,Global Trade\n2001,100\n2002,102\n2003,105\n2004,110\n2005,114\n2006,117\n2007,119\n2008,120\n2009,112\n2010,110\n2011,108\n2012,104\n2013,102\n2014,101\n2015,100\n2016,99\n2017,98\n2018,97\n2019,92\n2020,90" } ], "id_raw": 474 }, { "id": "kJHuTDqrcvr3bGGF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_14.png", "conversations": [ { "from": "human", "value": "\nHow does the trend in Supply Chain Management compare to Global Trade between 2001 and 2020 in terms of growth stability?" }, { "from": "gpt", "value": "To analyze the stability of growth trends, I assess both {the line for Supply Chain Management}[0.120, 0.064, 0.942, 0.242] alongside {the line for Global Trade}[0.120, 0.507, 0.942, 0.652]. Supply Chain Management demonstrates a more consistent upward trajectory until 2008 with minor fluctuations, thereafter it appears to stabilize. In contrast, Global Trade maintains an even level initially, followed by a gradual decline post-2008 with sharper oscillations towards 2020.\nBased on the above thought, the answer is:\nSupply Chain Management shows greater stability in growth compared to Global Trade from 2001 to 2020, with less fluctuation and a steadier increase through the years until it stabilizes, whereas Global Trade showcases more variability and a noticeable decline in later years." } ], "id_raw": 474 }, { "id": "x8CzQ01XQDCf4P5E", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_14.png", "conversations": [ { "from": "human", "value": "\nWhat was the highest recorded value for Volatile Market from 2001 to 2020?" }, { "from": "gpt", "value": "To determine the highest value for Volatile Market, I need to examine {the line for Volatile Market}[0.120, 0.616, 0.942, 0.866] and review the peak points within this range. The value observation begins in 2001, visualized as {the highest peak in the plot}[0.120, 0.616].\nBased on the above thought, the answer is:\n97.5" } ], "id_raw": 474 }, { "id": "arfVTcfDP2heKKFr", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_15.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table of the yearly funds raised by each organization from 2017 to 2022 as displayed in the line chart?" }, { "from": "gpt", "value": "To extract the data for each organization's funds per year, I observe the yearly markers on {the x-axis}[0.120, 0.106, 0.975, 0.839] labeled from 2017 to 2022 and inspect the line graphs for each organization, focusing on their respective data points. By examining {the data points for each line}[0.159, 0.456, 0.936, 0.778] representing the organizations, I can determine the funds raised for each year per organization.\nBased on the above thought, the answer is:\nYear, Doctors Without Borders, Samaritan's Purse, Red Cross, St. Jude Children's Research Hospital\n2017, 3500, 4200, 5100, 2800\n2018, 3700, 4300, 5300, 2950\n2019, 3600, 4150, 4900, 2700\n2020, 4100, 4500, 5500, 3100\n2021, 4400, 4850, 6000, 3500\n2022, 4750, 5000, 6800, 3900" } ], "id_raw": 475 }, { "id": "RgWgXYqqHRuA3YE2", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_15.png", "conversations": [ { "from": "human", "value": "\nWhich organization showed the most consistent upward growth trend in the funds raised from 2017 to 2022?" }, { "from": "gpt", "value": "To identify the organization with the most consistent positive trend in fund raising, it's necessary to visually assess the smoothness and steady increase of the line graphs. Observing each line graph, specifically looking at {the coordinates of the line graph for St. Jude Children's Research Hospital}[0.159, 0.456, 0.936, 0.778], it appears that this organization shows a consistent upward slope without significant drops, in contrast to others who exhibit dips.\nBased on the above thought, the answer is:\nSt. Jude Children's Research Hospital showed the most consistent upward growth trend in the funds raised from 2017 to 2022." } ], "id_raw": 475 }, { "id": "YWXIp9fBfXXxJrXM", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in funds raised by Red Cross from 2017 to 2022?" }, { "from": "gpt", "value": "To calculate this increase, I pinpoint {the data point for Red Cross in 2017}[0.159, 0.778] and {the data point for Red Cross in 2022}[0.936, 0.456]. By subtracting the funds of 2017 from that of 2022, I can find the increase.\nBased on the above thought, the answer is:\n1700." } ], "id_raw": 475 }, { "id": "8BmMtmd0b1AnmXx0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_16.png", "conversations": [ { "from": "human", "value": "\nCan you convert the time series information from the line chart into a table structure with column being various learning methods and rows being the years?" }, { "from": "gpt", "value": "To tabulate the data from the chart, I will first make use of the {x-axis tick labels}[0.120, 0.861, 0.942, 0.931] to determine the row labels, which represent the years. Then, I identify the {legend texts}[0.814, 0.705, 0.976, 0.828] as the column headers demonstrating the types of learning methods. The values for each category across the years are found by tracing the {line points}[0.120, 0.232, 0.942, 0.446], [0.120, 0.066, 0.942, 0.398], and [0.120, 0.050, 0.942, 0.351] of Direct Learning, Interactive Learning, and Video Learning respectively.\nBased on the above thought, the answer is:\nYear,Direct Learning,Interactive Learning,Video Learning\n2007,75,56,62\n2008,77,60,65\n2009,73,66,67\n2010,69,71,72\n2011,64,75,75\n2012,60,80,81\n2013,58,82,85\n2014,56,84,87\n2015,55,87,91\n2016,52,90,93\n2017,53,92,95\n2018,52,94,97\n2019,51,96,99\n2020,50,98,100" } ], "id_raw": 476 }, { "id": "D63ZFSZuCE0WkHld", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_16.png", "conversations": [ { "from": "human", "value": "\nWhich learning method shows the fastest rate of improvement from 2007 to 2020?" }, { "from": "gpt", "value": "To determine the learning method with the fastest rate of improvement, I need to compare the slopes of the lines representing each learning method. Observing the line for {Direct Learning}[0.120, 0.232, 0.942, 0.446], it shows a slow decline over the years. The line for {Interactive Learning}[0.120, 0.066, 0.942, 0.398] shows a steady increase also. Comparatively, {Video Learning}[0.120, 0.050, 0.942, 0.351] shows a steeper increase, indicating improvement at a quicker rate.\nBased on the above thought, the answer is:\nAccording to the chart, Video Learning shows the fastest rate of improvement from 2007 to 2020. Its trend line has a steeper slope compared to Direct Learning and Interactive Learning, suggesting a rapid increase in success rate over the course of the years." } ], "id_raw": 476 }, { "id": "hBhrSUtDSBf8FjIH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in success rate of Video Learning from 2007 to 2020?" }, { "from": "gpt", "value": "To solve this, I need to find the initial success rate in 2007 and the final success rate in 2020 for Video Learning. I locate the starting point {Video Learning 2007}[0.120, 0.351] and find the success rate to be 62%. Subsequently, I locate the end point {Video Learning 2020}[0.942, 0.050] and find the success rate to be 100%. The total increase is then calculated by subtracting the initial success rate from the final success rate, which is 100% - 62% = 38%.\nBased on the above thought, the answer is:\n38%." } ], "id_raw": 476 }, { "id": "7uzc9RuNqTocwR8S", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_18.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line graph information of the different government policies for the years 2020 and 2021 into a tabular format?" }, { "from": "gpt", "value": "Sure, I will convert the visual data shown in the diagram into tabular format. To start with, I'll take {the x-axis} [0.506, 0.930, 0.565, 0.970] which corresponds to the years 2020 and 2021. These will be our column headers. Next, I'll identify {the individual line plot legends}[0.666, 0.724, 0.973, 0.858] which will serve as our row headers or government policies. Lastly, I'll extract the data from {the individual line plot points}[0.127, 0.321, 0.945, 0.768], [0.127, 0.145, 0.945, 0.560], [0.127, 0.332, 0.945, 0.456]. These points will correspond to the evaluation scores for their respective policies in 2020 and 2021.\nBased on the above thought, the answer is:\n-| 2020 | 2021\n--- | --- | ---\nGreen Energy Act | 70 | 75\nElectric Car Subsidy | 60 | 70\nAffordable Housing Act | 85 | 90\nMinimum Wage Increase | 65 | 70\nClimate Change Act | 50 | 60\nWork-from-Home Credits | 75 | 80\nFree College Strive Act | 80 | 85\nCarbon Tax | 90 | 60\nUniversal Medicare Act | 95 | 98" } ], "id_raw": 478 }, { "id": "SAwEjR68lxlaYPJs", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_18.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which policy's effectiveness increased the most from 2020 to 2021?" }, { "from": "gpt", "value": "To determine which policy's effectiveness increased the most, I'll need to analyze the increases in the line plots for each policy. Checking each line plot, starting with {the line for 'Green Energy Act'}[0.127, 0.321, 0.945, 0.768] and moving onto each subsequent line, it appears that '{Universal Medicare Act}'[0.667, 0.822, 0.713, 0.839] has the most noticeable increase in its evaluation score from 2020 to 2021.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the effectiveness of the 'Universal Medicare Act' policy increased the most from 2020 to 2021." } ], "id_raw": 478 }, { "id": "ysZ78aAbsKIdE6u8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_18.png", "conversations": [ { "from": "human", "value": "\nHow much did the score of the 'Carbon Tax' policy decrease from 2020 to 2021?" }, { "from": "gpt", "value": "I will find the specific line for 'Carbon Tax' and locate its points for the years 2020 and 2021. The {line points for 'Carbon Tax'}[0.127, 0.145, 0.945, 0.560] indicate that the scores for 'Carbon Tax' in 2020 and 2021 are 90 and 60, respectively. By subtracting these two values, we can find the decrease in the score.\nBased on the above thought, the answer is:\n30" } ], "id_raw": 478 }, { "id": "geNepqbhcpGdM6Tt", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_24_19.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table from this chart with the stages in the value chain as columns and each progression stage as rows?" }, { "from": "gpt", "value": "To create the table, I start by identifying the column headers as {the x-axis tick labels}[[0.192, 0.891, 0.250, 0.919], [0.299, 0.891, 0.358, 0.919], [0.407, 0.891, 0.466, 0.919], [0.515, 0.891, 0.573, 0.919], [0.622, 0.891, 0.681, 0.919], [0.730, 0.891, 0.789, 0.919], [0.838, 0.891, 0.897, 0.919]], which symbolize each progression. For the row headers, I look at {the legend texts}[[0.199, 0.055, 0.563, 0.083], [0.199, 0.097, 0.577, 0.125], [0.199, 0.139, 0.525, 0.167]] and recognize them as the distinct stages of progression. By mapping these values to {the line points}[[0.221, 0.556], [0.329, 0.451], [0.436, 0.377], [0.544, 0.272], [0.652, 0.209], [0.760, 0.167], [0.867, 0.114]], I can generate the table information.\nBased on the above thought, the answer is:\nStages,2015,2016,2017,2018,2019,2020,2021\nRate of Digital Record Adoption,25,35,42,52,58,62,67\nIncrease in Medical Data Volume,15,18,25,40,47,55,60\nAI Adoption in Health Sector,2,5,8,12,15,15,18" } ], "id_raw": 479 }, { "id": "nxgGoqUJ2cck1zUw", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_24_19.png", "conversations": [ { "from": "human", "value": "\nWhich aspect made the most progress from 2015 to 2021 according to the trends in the chart, and why?" }, { "from": "gpt", "value": "To respond to this question, I need to observe the line trends on the chart between 2015 and 2021 for each aspect. Starting with {Rate of Digital Record Adoption}[0.221, 0.114, 0.867, 0.556], we see an upward but mild incline from 25 to 67. For {Increase in Medical Data Volume}[0.221, 0.188, 0.867, 0.661], there's a more significant increase from 15 to 60. Lastly, {AI Adoption in Health Sector}[0.221, 0.630, 0.867, 0.798] shows a steep increase from 2 to 18 over the period. Even though all trends are upward, the AI Adoption in Health Sector shows the most progress.\nBased on the above thought, the answer is:\nThe aspect that made the most progress from 2015 to 2021 according to the trends in the chart was the AI Adoption in Health Sector. This is mainly due to the steep increase of its value from 2 to 18 over the specified period. The significant growth in the value indicates a rapid adoption of AI in the health sector during this time." } ], "id_raw": 479 }, { "id": "Fe0CEnkgmc1qOVyR", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_24_19.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what was the increase in percentage of AI Adoption in Health Sector from 2015 to 2021?" }, { "from": "gpt", "value": "To evaluate the increase in percentage of AI Adoption in Health Sector from 2015 to 2021, I should locate {the line points for AI Adoption in Health Sector}[[0.221, 0.798], [0.329, 0.766], [0.436, 0.735], [0.544, 0.693], [0.652, 0.661], [0.760, 0.661], [0.867, 0.630]] and extract the corresponding values. The value for 2015 is given by {2}[0.221, 0.798] and for 2021, it is provided by {18}[0.867, 0.630]. The increase is then calculated as 18 - 2 = 16.\nBased on the above thought, the answer is:\n16." } ], "id_raw": 479 }, { "id": "hhUC6IC8hLwJdyjK", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_1.png", "conversations": [ { "from": "human", "value": "\nCan you reorganize the data in the chart into a structured table with years as columns and job categories as rows?" }, { "from": "gpt", "value": "To transform the chart data into a table, I first identify the column headers as {the x-axis tick labels}[[0.118, 0.919, 0.136, 0.942], [0.273, 0.919, 0.291, 0.942], [0.429, 0.919, 0.446, 0.942], [0.584, 0.919, 0.602, 0.942], [0.740, 0.919, 0.757, 0.942], [0.895, 0.919, 0.913, 0.942]], corresponding to the years of analysis. I then recognize {the legend items}[[0.993, 0.406, 1.052, 0.455], [0.993, 0.368, 1.014, 0.390], [0.993, 0.330, 1.029, 0.352], [0.993, 0.292, 1.028, 0.314]] as the row labels, which represent different job categories. Lastly, I pinpoint {the line data points}[[0.118, 0.199], [0.196, 0.167], ..., [0.895, 0.865]] and extract the corresponding job figures to fill in the table.\nBased on the above thought, the answer is:\nCategory,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nNumber_Of_Tourists,2000,2500,2750,3000,3250,3600,4000,4250,4500,4800,2000\nFacility_Rating,4.5,4.6,4.6,4.7,4.7,4.9,4.5,4.6,4.7,4.8,2.5" } ], "id_raw": 481 }, { "id": "EeTiv2GNPKtGMeHO", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_1.png", "conversations": [ { "from": "human", "value": "\nBased on the charts, can you infer any relationship between the number of tourists and the facility rating over the years?" }, { "from": "gpt", "value": "To understand the relationship between the number of tourists and the facility rating, I need to observe the trends in the two line plots. Looking at the line for {Number_Of_Tourists}[0.118, 0.865, 0.895, 0.865], the number of tourists is generally increasing from 2010 to 2019 before a sudden drop in 2020. When I compare this with the line for {Facility_Rating}[0.118, 0.230, 0.895, 0.865], the facility rating also increases till 2015 before a dip in 2016 and then continues its upward trend till 2019. In 2020, the facility rating decrease is correlated with a decrease in the number of tourists. This suggests a positive correlation between the number of tourists and the facility ratings over the years.\nBased on the above thought, the answer is:\nYes, there appears to be a positive relationship between the number of tourists and the facility rating over the years. When the facility rating goes up, the number of tourists also tends to increase. Similarly, when the facility rating goes down, as seen in 2020, the number of tourists also decreases. This correlation might suggest that tourists prefer facilities with higher ratings." } ], "id_raw": 481 }, { "id": "89jAZzmLc0qvJpPi", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage difference in tourist numbers between 2018 and 2020?" }, { "from": "gpt", "value": "To determine the percentage difference in tourist numbers between 2018 and 2020, I need to locate and retrieve the corresponding values. {The line data point for Number_Of_Tourists in 2018} is at [0.740, 0.185] and the value is 4,500. While {the line data point for Number_Of_Tourists in 2020} is at [0.895, 0.865] and the value is 2,000. The percent difference can be computed as |4500-2000|/4500 * 100%.\nBased on the above thought, the answer is:\n55.56%" } ], "id_raw": 481 }, { "id": "qNzSULurIs9907VO", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_2.png", "conversations": [ { "from": "human", "value": "\nCan you form a table with the data depicted in the line graph? Where each row represents a Risk Level and columns correspond to the Probability and Impact?" }, { "from": "gpt", "value": "From the visualization, I first identify {the x-axis tick labels}[[0.100, 0.909, 0.127, 0.932], [0.293, 0.909, 0.349, 0.932], [0.513, 0.909, 0.545, 0.932], [0.703, 0.909, 0.770, 0.932], [0.914, 0.909, 0.973, 0.932]] which are translated to the row labels. They represent the Risk Level. I can also see two line plots labeled as {Probability}[[0.124, 0.086, 0.198, 0.110]] and {Impact}[[0.124, 0.121, 0.172, 0.145]] from the legend. The y-values on these lines correspond to the data values in the cells of the table. Based on these observations, I form the table by mapping each point on the line to its corresponding Risk Level and extract its y-value as the data.\nBased on the above thought, the answer is:\nRisk_Level,Probability,Impact\nLow,0.1,0.2\nMedium,0.3,0.5\nHigh,0.7,1.2\nVery High,1.5,2.3\nExtreme,2.2,3.5" } ], "id_raw": 482 }, { "id": "NXDeoJpD5yVbsb57", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_2.png", "conversations": [ { "from": "human", "value": "\nBetween Probability and Impact, which metric appears to be more sensitive to increasing Risk Levels?" }, { "from": "gpt", "value": "To answer this question, I need to compare the degrees of fluctuation in the lines that represent Probability and Impact as Risk Level increases. By observing the line for {Probability}[0.114, 0.391, 0.944, 0.855], it starts at a lower point for Low risk and increases steadily, reaching a high at Extreme. On the other hand, the line for {Impact}[0.114, 0.103, 0.944, 0.833] also starts low and then increases dramatically as risk level increases towards Extreme. The slope of the Impact line is steeper compared to Probability which suggests more sensitivity to increasing Risk Levels.\nBased on the above thought, the answer is:\nThe Impact metric appears to be more sensitive to increasing Risk Levels. Its line curve shows significant increase as we move from Low to Extreme Risk Levels, and it indicates a higher rate of change across the risk levels compared to the Probability metric." } ], "id_raw": 482 }, { "id": "hZmIgyIvOMRBBUOh", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in Impact when Risk Level changes from Medium to Very High?" }, { "from": "gpt", "value": "To answer this question, I first need to locate the points on the Impact line corresponding to Risk Levels Medium and Very High. I find {Medium}[[0.321, 0.767]] which corresponds to Impact=0.5 and {Very High}[[0.736, 0.369]] which corresponds to Impact=2.3. The increase in Impact when Risk Level changes from Medium to Very High is the difference between these two values, which is 2.3 - 0.5 .\nBased on the above thought, the answer is:\n1.8" } ], "id_raw": 482 }, { "id": "2q5b1CPnEpoR6eDC", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_3.png", "conversations": [ { "from": "human", "value": "\nCan you create a table listing the academic progress for each research field across the years from the line chart?" }, { "from": "gpt", "value": "To create the table, I begin by identifying the years mapped along {the x-axis}[0.123, 0.902, 0.965, 0.932] which serve as the headers for the columns in the table. For the rows, I recognize each research field represented by different colors, referred to in {the legend}[0.808, 0.077, 0.978, 0.154]. I systematically extract the coordinates of points on each line, corresponding to the academic progress values for each year. AI is represented with blue points, Data Science with green points, BioTech with red points, and Physics with purple points. By collecting the {y-values of these points}[0.123, 0.314, 0.944, 0.837], I fill in the respective cells in the table for each combination of year and field.\nBased on the above thought, the answer is:\nYear,AI,Data Science,BioTech,Physics\n2016,1,1.1,1.5,1.0\n2017,1.5,1.7,2.0,1.2\n2018,2,2.5,2.4,1.4\n2019,2.5,3.0,3.5,1.6\n2020,3,3.5,3.2,1.8\n2021,3.5,4.0,3.8,2.0" } ], "id_raw": 483 }, { "id": "L5N6KkNBKnbTuYGI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_3.png", "conversations": [ { "from": "human", "value": "\nWhich research field shows the most consistent growth without significant volatility in the line chart?" }, { "from": "gpt", "value": "To answer this question, I analyze the trends of each field by focusing on the slope and fluctuations of their respective lines. Observing {the line for AI}[0.123, 0.814, 0.944, 0.314], it shows a steady upward trend without major fluctuations, indicating consistent growth. In contrast, examining {other lines}, Data Science also increases but with a slightly more aggressive slope, BioTech shows larger fluctuations especially around 2019, and Physics progresses slowly with almost negligible growth. Thus, the properties of each line and growth pattern help me determine the consistency.\nBased on the above thought, the answer is:\nAI shows the most consistent growth without significant volatility, as its line is smoothly increasing with minor and steady changes each year." } ], "id_raw": 483 }, { "id": "Lc8kfB9Z7wImniiD", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the total academic progress increase for the AI field from 2016 to 2021 according to the chart?" }, { "from": "gpt", "value": "To determine the total increase, I examine the academic progress values for AI at the start and end of the period by referencing {the line for AI}[0.123, 0.314, 0.944, 0.314]. The progress starts at {1.0}[0.123, 0.837] in 2016 and increases to {3.5}[0.944, 0.314] by 2021. Subtracting the start value from the end value gives a total increase.\nBased on the above thought, the answer is:\n2.5" } ], "id_raw": 483 }, { "id": "ArRUhTFWdH1BZsjJ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_4.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the data in the sub-charts representing the trends in academic fields into a statistical table." }, { "from": "gpt", "value": "Firstly, I identified the x-axis labels, representing the increase trends in academic fields, which are {'Abrupt Increase'}[0.114, 0.919],[0.270, 0.903] and {'Persistent rise'}[0.582, 0.903],[0.895,0.903]. Using this information as column headers, I'll generate represenatations for each academic field. \nNext, I identified the academic fields from the sub-chart titles, namely, {Revolutionary Physics}[0.312, 0.768, 0.398, 0.842], {Cutting-edge Biology}[0.302, 0.573, 0.407, 0.635], {Ancient Philosophy}[0.315, 0.383, 0.400, 0.442], {Modern Sociology}[0.315, 0.191, 0.404, 0.238], and {Theoretical Math}[0.328, 0.003, 0.399, 0.445], which serve as row headers. \nFor the actual data, I located and extracted the values from the points in the line charts for each of the fields. The metric values for each academic field corresponding to the trends are mapped from this data.\nBased on the above thought, the answer is:\nFields, Abrupt Increase, Persistent rise\nRevolutionary Physics, 50, 62\nCutting-edge Biology, 70, 85\nAncient Philosophy, 65, 75\nModern Sociology, 80, 88\nTheoretical Math, 55, 70" } ], "id_raw": 484 }, { "id": "8iIJLNkQdtQqDLZm", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_4.png", "conversations": [ { "from": "human", "value": "\nComparatively, which academic field observed the greatest rise between 'Abrupt Increase' and 'Persistent rise'?" }, { "from": "gpt", "value": "To answer this, I need to find the difference in the values of 'Abrupt Increase' and 'Persistent rise' of each academic field, and then compare them to find out the field with the greatest rise.\nFor the field {Revolutionary Physics}[0.312, 0.768], I found the values at {Abrupt Increase}[0.114, 0.654] and {Persistent rise}[0.895, 0.654] and calculated the difference as 62 - 50 = 12.\nFollowing the same steps, I obtained the differences for {Cutting-edge Biology}[0.302, 0.504], {Ancient Philosophy}[0.315, 0.317], {Modern Sociology}[0.315, 0.127], and {Theoretical Math}[0.315, -0.037] as 15, 10, 8, and 15 respectively.\nOn comparing these differences, it can be seen that 'Theoretical Math' and 'Cutting-edge Biology' both show the greatest rise between 'Abrupt Increase' and 'Persistent rise'.\nBased on the above thought, the answer is:\nBoth 'Theoretical Math' and 'Cutting-edge Biology' observed the greatest comparative rise between 'Abrupt Increase' and 'Persistent rise' in the academic fields, with a growth of 15 units." } ], "id_raw": 484 }, { "id": "vThYZWLt3wh2pajh", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_4.png", "conversations": [ { "from": "human", "value": "\nWhat was the difference between the values of 'Abrupt Increase' in 'Theoretical Math' and 'Modern Sociology'?" }, { "from": "gpt", "value": "To answer this question, I need to locate and determine the values corresponding to 'Abrupt Increase' for both 'Theoretical Math' and 'Modern Sociology'. I found {the corresponding coordinates for Abrupt Increase for Theoretical Math}[0.582, 0.340] and {the corresponding coordinates for Abrupt Increase for Modern Sociology}[0.582, 0.767]. Using this information, the difference between the 'Abrupt Increase' values of 'Modern Sociology' (80) and 'Theoretical Math' (55) could be calculated as 80 - 55 = 25.\nBased on the above thought, the answer is:\n25" } ], "id_raw": 484 }, { "id": "ZBIlWqKeCwQHSxJH", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into a table with Rows as Models and Columns as Sustainability Aspects?" }, { "from": "gpt", "value": "To generate the table, I first identify {the subplot titles}[[0.168, 0.783, 0.206, 0.796], [0.168, 0.553, 0.206, 0.567], [0.168, 0.323, 0.206, 0.337], [0.168, 0.093, 0.206, 0.107]] which represent each model. Next, I locate {the x-axis ticks}[0.210, 0.736, 0.252, 0.764], which represent the sustainability aspects for each model. To extract the values I locate {the bars}[[0.221, 0.681, 0.238, 0.728], [0.258, 0.681, 0.271, 0.706], ...] in each model's subplot and extract the bar heights.\nBased on the above thought, the answer is:\nModels,Economical,Environmental,Social,Innovational,Legal,Governance\nModel1,0.84, 0.79, 0.85, 0.87, 0.92, 0.91\nModel2,0.86, 0.82, 0.84, 0.88, 0.93, 0.92\nModel3,0.88, 0.81, 0.83, 0.87, 0.92, 0.92\nModel4,0.89, 0.80, 0.85, 0.86, 0.92, 0.92" } ], "id_raw": 485 }, { "id": "QyUxVEZSflSO9ihP", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_5.png", "conversations": [ { "from": "human", "value": "\nWhich category shows the most consistent performance across all models and why?" }, { "from": "gpt", "value": "The consistent performance of a category across all models can be assessed by minimizing the variation in bar heights for each category across subplots. By comparing {the bars corresponding to 'Economical'}[0.221, 0.681, 0.238, 0.728], {the bars corresponding to 'Environmental'}[0.258, 0.681, 0.275, 0.706], {the bars corresponding to 'Social'}[0.298, 0.681, 0.313, 0.713], {the bars corresponding to 'Innovational'}[0.334, 0.681, 0.349, 0.715], {the bars corresponding to 'Legal'}[0.374, 0.681, 0.391, 0.723], and {the bars corresponding to 'Governance'}[0.412, 0.681, 0.428, 0.722] across the four subplots representing different models, the 'Legal' aspect seems to show the most consistent performance, with the least variation in its value.\nBased on the above thought, the answer is:\nThe 'Legal' category shows the most consistent performance across all models. This can be observed from the similar bar heights for this aspect in all four models, indicating that the performance scores for 'Legal' are closely grouped and do not vary significantly between different models." } ], "id_raw": 485 }, { "id": "uUjiIKEdHio5qHRA", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the average performance across all categories for Model3?" }, { "from": "gpt", "value": "To calculate this, I need to locate the {bars in the Model3 subplot}[0.271, 0.528, 0.292, 0.549], each representing a sustainability aspect. Their heights are 0.88, 0.81, 0.83, 0.87, 0.92, and 0.92 respectively. The average performance can be computed as the mean of these values.\nBased on the above thought, the answer is:\n0.872" } ], "id_raw": 485 }, { "id": "Y5l9OrCfbn2MSqBn", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data presented in the bar chart to a table with rows representing different research fields and columns representing the condition of research funding (without and with)?" }, { "from": "gpt", "value": "To generate the table, I would first consider {the subplots}[[0.12, 0.85, 0.43, 0.71], [0.54, 0.85, 0.85, 0.71],[0.12, 0.61, 0.43, 0.46], [0.54, 0.61, 0.85, 0.46]] which represent the different research fields. Each subplot has two bars \u2013 firstly, {the bars in the first position of each subplot}[[0.22, 0.8, 0.28, 0.74], [0.64, 0.8, 0.72, 0.74],[0.22, 0.55, 0.3, 0.46], [0.64, 0.55, 0.72, 0.48]] represent the scores without research funding and secondly, {the bars in the second position of each subplot}[[0.32, 0.8, 0.42, 0.75], [0.76, 0.8, 0.87, 0.78],[0.32, 0.55, 0.46, 0.51], [0.76, 0.55, 0.9, 0.5]] represent the scores with research funding.\nBased on the above thought, the answer is:\nFields, Without Research Funding, With Research Funding\nEngineering, 0.65, 0.92\nMathematics, 0.75, 0.85\nPhysics, 0.68, 0.8\nBiology, 0.62, 0.78" } ], "id_raw": 486 }, { "id": "PKsPhiAK3neFJxzD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_6.png", "conversations": [ { "from": "human", "value": "\nWhich field experienced the greatest difference in academic scores due to the presence of research funding? Provide a brief explanation." }, { "from": "gpt", "value": "To answer this question, I need to find out the increase in each field's score once they received funding. From {the bars in the first position of each subplot}[[0.22, 0.8, 0.28, 0.74], [0.64, 0.8, 0.72, 0.74],[0.22, 0.55, 0.3, 0.46], [0.64, 0.55, 0.72, 0.48]], the scores without funding are found, and from {the bars in the second position of each subplot}[[0.32, 0.8, 0.42, 0.75], [0.76, 0.8, 0.87, 0.78],[0.32, 0.55, 0.46, 0.51], [0.76, 0.55, 0.9, 0.5]], the scores with funding are identified. The difference in each field is calculated as [(0.92 - 0.65), (0.85 - 0.75), (0.8- 0.68), (0.78- 0.62)], and the maximum difference among these was observed in the Engineering field.\nBased on the above thought, the answer is:\nThe Engineering field experienced the greatest difference in academic scores due to the presence of research funding. With funding, the academic score in Engineering increased from 0.65 to 0.92, indicating a distinctly more significant enhancement relative to the other fields. This implies that the Engineering field benefits the most from research funding in terms of academic performance." } ], "id_raw": 486 }, { "id": "Xe6BmM5ezADAUWWH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the average academic score increase in all research fields upon receiving funding?" }, { "from": "gpt", "value": "To find the average academic score increase, I need to calculate the difference between scores with and without funding for each research field. The field scores without funding are located at {the bars in the first position of each subplot}[[0.22, 0.8, 0.28, 0.74], [0.64, 0.8, 0.72, 0.74],[0.22, 0.55, 0.3, 0.46], [0.64, 0.55, 0.72, 0.48]], and the scores with funding are at {the bars in the second position of each subplot}[[0.32, 0.8, 0.42, 0.75], [0.76, 0.8, 0.87, 0.78],[0.32, 0.55, 0.46, 0.51], [0.76, 0.55, 0.9, 0.5]]. The difference in each field is (0.92 - 0.65), (0.85 - 0.75), (0.8- 0.68), (0.78- 0.62) respectively. The total increase equals the sum of these differences, and the average can be calculated by dividing the total by the number of fields.\nBased on the above thought, the answer is:\n0.1025." } ], "id_raw": 486 }, { "id": "wRbU7txCk44jRf6q", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_7.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data presented in the line chart into a tabular format with years as rows and Platforms as columns?" }, { "from": "gpt", "value": "In generating a table from the line chart, I begin with the {x-axis tick labels}[0.100, 0.909, 0.136, 0.932], [0.192, 0.909, 0.227, 0.932], [0.284, 0.909, 0.319, 0.932], [0.376, 0.909, 0.411, 0.932], [0.467, 0.909, 0.503, 0.932], [0.559, 0.909, 0.594, 0.932], [0.651, 0.909, 0.686, 0.932], [0.743, 0.909, 0.778, 0.932], [0.834, 0.909, 0.870, 0.932], [0.926, 0.909, 0.961, 0.932] to determine the row headers which represent the years. I then identify {the legend text}[0.814, 0.086, 0.972, 0.110], [0.814, 0.121, 0.954, 0.145] as the column headers that represent the different social media platforms. The remaining action is to identify the line points for each platform in each year. This is achieved by locating all {points of the lines}[0.118, 0.682], [0.210, 0.506], [0.302, 0.611], [0.393, 0.330], [0.485, 0.418], [0.577, 0.277], [0.668, 0.189], [0.760, 0.154], [0.852, 0.242], [0.944, 0.277], [0.118, 0.858], [0.210, 0.822], [0.302, 0.770], [0.393, 0.717], [0.485, 0.664], [0.577, 0.611], [0.668, 0.576], [0.760, 0.470], [0.852, 0.365], [0.944, 0.294] which are marked on the line for each platform. These points' coordinates correspond to the values in the table.\nBased on the above thought, the answer is:\nYear, Facebook Engagement, Twitter Engagement\n2011,100,90\n2012,110,92\n2013,104,95\n2014,120,98\n2015,115,101\n2016,123,104\n2017,128,106\n2018,130,112\n2019,125,118\n2020,123,122" } ], "id_raw": 487 }, { "id": "zu1TxU1S5e5mB2VA", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_7.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the engagement levels of users on both Twitter and Facebook over the years based on the line chart?" }, { "from": "gpt", "value": "To make inferences based on the line chart, I examine the trend line for each platform. Looking at {the line for Facebook Engagement}[0.118, 0.154, 0.944, 0.682], the trend is mostly increasing except for some minor dips. This shows a generally increasing engagement level on Facebook over the years. For {Twitter Engagement}[0.118, 0.294, 0.944, 0.858], the trend line is consistently increasing with no major dips, indicating a steady increase in engagement level on Twitter over this period. A comparative analysis of both lines also indicates that while the engagement levels for both platforms increased, the rate of increase was higher for Twitter than Facebook.\nBased on the above thought, the answer is:\nBased on the line chart, it can be inferred that engagement levels have generally increased for both Facebook and Twitter over the years. However, Twitter's engagement levels appears to have increased at a faster rate than Facebook's over the same period. This could suggest a growing preference for Twitter among users over the observed period or that Twitter's strategies for boosting user interaction have been more effective than Facebook's." } ], "id_raw": 487 }, { "id": "a9SugsnYSDs7ptSV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_7.png", "conversations": [ { "from": "human", "value": "\nDuring which year did Facebook Engagement show the highest level?" }, { "from": "gpt", "value": "To determine the year with the highest level of Facebook Engagement, I need to review {the line points for Facebook Engagement}[0.118, 0.682], [0.210, 0.506], [0.302, 0.611], [0.393, 0.330], [0.485, 0.418], [0.577, 0.277], [0.668, 0.189], [0.760, 0.154], [0.852, 0.242], [0.944, 0.277]. Each point on this line corresponds to a value for a certain year. The highest point on the line corresponds to the maximum value and the x-coordinate of this point corresponds to the year. Observing the coordinates, the point with the greatest y-value is at [0.118, 0.682], which corresponds to the {year 2011}[0.100, 0.909, 0.136, 0.932].\nBased on the above thought, the answer is:\n2011" } ], "id_raw": 487 }, { "id": "IQTY3KDOhh44vvBC", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_8.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a table with Years as rows and Columns as both Population and AQI?" }, { "from": "gpt", "value": "To derive the tabular data from the line chart, I recognize {the x-axis tick labels}[[0.117, 0.899, 0.172, 0.928], [0.211, 0.899], [0.278, 0.899], [0.345, 0.899], [0.412, 0.899], [0.547, 0.899], [0.614, 0.899], [0.681, 0.899], [0.748, 0.899], [0.815, 0.899]] as the row values representing different years. For the column headers, I utilize the labels for the two y-axes: {AQI}[0.023, 0.319, 0.045, 0.642] and {Population}[0.955, 0.404, 0.977, 0.556]. To fill the values in the cells, I observe {the points of the AQI line}[0.144, 0.118, 0.815, 0.842] and {the points of the Population line}[0.144, 0.842, 0.815, 0.118] and extract the corresponding values for each year.\nBased on the above thought, the answer is:\nYear,Air Quality Index (AQI),Population\n2010,150,100000\n2011,145,103000\n2012,140,106000\n2013,128,112000\n2014,118,120000\n2015,115,126000\n2016,100,130000\n2017,97,136000\n2018,90,145000\n2019,85,155000\n2020,82,165000" } ], "id_raw": 488 }, { "id": "VeYMTPSASG8pzWS9", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the trends shown in the line chart, what is the relationship between population growth and AQI over the years? Explain your reasoning." }, { "from": "gpt", "value": "To interpret the relationship between population growth and AQI, I need to analyze the trends in the respective lines. Looking at {the AQI line}[0.144, 0.118, 0.815, 0.842], I see a consistent decrease from 150 to 82 over the years, indicating a steady improvement in the air quality. Observing {the Population line}[0.144, 0.842, 0.815, 0.118], I see a consistent increase from 100000 to 165000 over the same period, indicating population growth. The inverse trends suggest that as the population increased, the AQI decreased. This implies that despite population growth, measures may have been put in place to maintain and even improve air quality over time.\nBased on the above thought, the answer is:\nAccording to the trends in the line chart, an inverse relationship is observed between population growth and Air Quality Index (AQI) over the years. This means that even as the population was increasing, the AQI was improving, decreasing from 150 to 82 over the period. This suggests an effective management of environmental resources and successful implementation of air pollution control measures, despite the challenges posed by continuing population growth." } ], "id_raw": 488 }, { "id": "4TuUlwT0rHf7GHFY", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_8.png", "conversations": [ { "from": "human", "value": "\nHow much did the Population grow from 2010 to 2020 according to the line chart?" }, { "from": "gpt", "value": "To determine the population growth from 2010 to 2020, I first find {the plot point for 2010}[0.144, 0.842] which corresponds to a population of 100000. Next, I locate {the plot point for 2020}[0.815, 0.118] which corresponds to a population of 165000. The difference between these two values gives the population growth over that period, which is 165000 - 100000 = 65000.\nBased on the above thought, the answer is:\n65000." } ], "id_raw": 488 }, { "id": "4mUtlRAkfv020Gkp", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_9.png", "conversations": [ { "from": "human", "value": "\nCould you please extract the data represented in this line chart into a table format with Years as columns and the two categories of Spending as rows?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first locate {the x-axis tick labels}[[0.101, 0.909, 0.136, 0.932], [0.156, 0.909, 0.191, 0.932], [0.211, 0.909, 0.246, 0.932], [0.266, 0.909, 0.301, 0.932], [0.321, 0.909, 0.356, 0.932], [0.376, 0.909, 0.411, 0.932], [0.431, 0.909, 0.466, 0.932], [0.486, 0.909, 0.521, 0.932], [0.541, 0.909, 0.576, 0.932], [0.596, 0.909, 0.631, 0.932], [0.651, 0.909, 0.686, 0.932], [0.706, 0.909, 0.741, 0.932], [0.761, 0.909, 0.796, 0.932], [0.816, 0.909, 0.851, 0.932], [0.871, 0.909, 0.906, 0.932], [0.926, 0.909, 0.961, 0.932]], which represent the column headers or years. Next, I identify {the legend texts}[[0.128, 0.086, 0.333, 0.110], [0.128, 0.121, 0.319, 0.145]] as the row labels representing Medical Technology Spending and Medical Research Spending. To populate the values, I locate {the points on the lines for Medical Technology Spending and Medical Research Spending}[[0.118, 0.777], [0.173, 0.726], ..., [0.944, 0.777]] and [[0.118, 0.259], [0.173, 0.311], ..., [0.944, 0.855]] respectively for each year and extract the respective spending values.\nBased on the above thought, the answer is:\nCategory,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nMedical Technology Spending,100,110,130,150,160,170,160,150,160,165,170,180,195,210,220,230\nMedical Research Spending,200,190,170,160,150,140,135,130,125,117,110,105,100,95,90,85" } ], "id_raw": 489 }, { "id": "bQkKsrLidFbwddnH", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_9.png", "conversations": [ { "from": "human", "value": "\nGiven the line chart, which sector (Medical Technology or Medical Research) experienced a greater rate of change in spending from 2005 to 2020 and why so?" }, { "from": "gpt", "value": "Looking at the lines representing each sector, I analyze {the line for Medical Technology Spending}[0.118, 0.103, 0.944, 0.777] and {the line for Medical Research Spending}[0.118, 0.259, 0.944, 0.855]. For Medical Technology Spending, spending increases from 100 to 230 which is an increase of 130 billion. Whereas in the case of Medical Research Spending, the spending decreases from 200 to 85 which is a decrease of 115 billion. Thus, the Medical Technology sector experienced a greater absolute change.\nBased on the above thought, the answer is:\nThe Medical Technology sector experienced a greater rate of change in spending from 2005 to 2020. This is indicated by the greater absolute change in the chart, where Medical Technology Spending increased by 130 billion, while Medical Research Spending decreased by 115 billion." } ], "id_raw": 489 }, { "id": "ywBVQ3KLnfbZi6KU", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in Medical Technology Spending from 2005 to 2020?" }, { "from": "gpt", "value": "To find the increase in Medical Technology Spending from 2005 to 2020, I first find {the point corresponding to the 2005 Medical Technology Spending}[[0.118, 0.777]]. It's value is 100. Then, I locate {the point associated with the 2020 Medical Technology Spending}[[0.944, 0.103]]. That value is 230. The increase from 2005 to 2020 is the difference between these two values, which is 230 - 100 = 130.\nBased on the above thought, the answer is:\n130." } ], "id_raw": 489 }, { "id": "vSOWs7inDmNij7Cp", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a structured table with Fashion Elements as rows and their respective Trend values?" }, { "from": "gpt", "value": "To convert the chart into tabular data, I first observe {the x-axis tick labels}[[0.103, 0.755, 0.117, 0.914],[0.178, 0.755, 0.192, 0.885], [0.254, 0.755, 0.268, 0.901], [0.330, 0.755, 0.344, 0.905], [0.406, 0.755, 0.420, 0.878], [0.482, 0.755, 0.496, 0.868], [0.557, 0.755, 0.571, 0.900], [0.633, 0.755, 0.647, 0.916], [0.709, 0.755, 0.723, 0.908], [0.785, 0.755, 0.799, 0.884], [0.861, 0.755, 0.875, 0.932], [0.936, 0.755, 0.950, 0.886]] which are the row labels or Fashion Elements. To get the trend values, I locate {the line points}[[0.110, 0.252], [0.185, 0.108], [0.261, 0.588], [0.337, 0.540], [0.413, 0.396], [0.489, 0.300], [0.564, 0.204], [0.640, 0.492], [0.716, 0.685], [0.792, 0.636], [0.868, 0.444], [0.943, 0.156]] for each fashion element and extract the corresponding values.\nBased on the above thought, the answer is:\nFashion Elements, Trend\nBootcut Jeans, 80\nFlare Skirts, 95\nSkinny Jeans, 45\nRipped Jeans, 50\nWide Belts, 65\nCrop Tops, 75\nOff Shoulder, 85\nFlowers Prints, 55\nAnimal Prints, 35\nPlaid Shirts, 40\nLeather Jackets, 60\nTurtlenecks, 90" } ], "id_raw": 490 }, { "id": "2zzeDWhYc4enyLIo", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_10.png", "conversations": [ { "from": "human", "value": "\nWhich fashion element demonstrated the least interest according to the trend analysis?" }, { "from": "gpt", "value": "To answer this question, I need to look at {the line points for each fashion element}[[0.110, 0.252], [0.185, 0.108], [0.261, 0.588], [0.337, 0.540], [0.413, 0.396], [0.489, 0.300], [0.564, 0.204], [0.640, 0.492], [0.716, 0.685], [0.792, 0.636], [0.868, 0.444], [0.943, 0.156]]. The point with the lowest y-value corresponds to the least interest in a fashion element. Comparing the y-values, the point {0.716, 0.685} stands out as it is the lowest, which corresponds to the 'Animal Prints' fashion element.\nBased on the above thought, the answer is:\nAccording to the trend analysis, 'Animal Prints' seems to have shown the least interest among all the fashion elements. The corresponding line plot indicates the lowest y-value, reflecting the lowest trend value." } ], "id_raw": 490 }, { "id": "mvDs0r0Hp1a1yQoe", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest trend value observed among all the fashion elements?" }, { "from": "gpt", "value": "To find the highest trend value, I should look at the highest point coordinates amongst {the individual line points}[[0.110, 0.252], [0.185, 0.108], [0.261, 0.588], [0.337, 0.540], [0.413, 0.396], [0.489, 0.300], [0.564, 0.204], [0.640, 0.492], [0.716, 0.685], [0.792, 0.636], [0.868, 0.444], [0.943, 0.156]]. After reviewing each point, the highest y-value corresponds to the 'Flare Skirts' trend, which is 95.\nBased on the above thought, the answer is:\n95." } ], "id_raw": 490 }, { "id": "zgo95KQuDPc1N7y5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert the attendance numbers from the line chart for 'King's Landing' and 'Winterfell' into a structured table format with 'Attendance Ratio' as columns and 'Location' as rows?" }, { "from": "gpt", "value": "Utilizing the plotted points in the line chart, I extract the attendance numbers for both 'Local King's Landing' and 'International King's Landing' {line_0}[[0.126, 0.130, 0.944, 0.226]] and similarly for 'Local Winterfell' and 'International Winterfell' {line_2}[[0.126, 0.412, 0.944, 0.430]]. These lines respectfully provide the data for each attendance ratio. Observing the {the x-axis labels}[[0.122, 0.909, 0.131, 0.932], ..., [0.935, 0.909, 0.953, 0.932]] helps categorize each data point into the appropriate attendance ratio column.\nBased on the above thought, the answer is:\nAttendance Ratio,Local King's Landing,International King's Landing,Local Winterfell,International Winterfell\n5,6504.72,804.72,4611.80,804.72\n6,6595.88,895.88,4622.47,895.88\n7,6672.96,972.96,4632.29,972.96\n...\n30,7400.60,1700.60,4773.86,1700.60" } ], "id_raw": 491 }, { "id": "JVKzjl33GSYwNX15", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_11.png", "conversations": [ { "from": "human", "value": "\nHow does the trend in international attendance compare between King's Landing and Winterfell?" }, { "from": "gpt", "value": "By analyzing the lines representing international attendance, {line for International King's Landing}[0.126, 0.743, 0.944, 0.839] shows a gradual upward slope, suggesting a steady increase in international attendance in King's Landing. Meanwhile, the {line for International Winterfell}[0.126, 0.743, 0.944, 0.839] indicates a similar upward trajectory. However, upon close inspection, the slope of King's Landing is slightly steeper, signifying a somewhat faster rate of increase in international visitors compared to Winterfell.\nBased on the above thought, the answer is:\nBoth King's Landing and Winterfell demonstrate an increasing trend in international attendance. However, King's Landing exhibits a slightly steeper increase, indicating a faster growth in international visitor numbers compared to Winterfell." } ], "id_raw": 491 }, { "id": "aV9XBOjDDTUY4ghj", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the total difference in mean local attendance between 'King's Landing' and 'Winterfell' at the ratio of '20'?" }, { "from": "gpt", "value": "To calculate the total difference in mean local attendance at an attendance ratio of '20', I observe the local attendance points for both locations. For 'King's Landing', the data point at ratio 20 is {dot on the graph for Local King's Landing at ratio 20}[0.617, 0.152]. For 'Winterfell', it is captured at {dot on the graph for Local Winterfell at ratio 20}[0.617, 0.418]. The values are 7197.87 for King's Landing and 4723.61 for Winterfell. Thus, the difference is 7197.87 - 4723.61.\nBased on the above thought, the answer is:\n2474.26" } ], "id_raw": 491 }, { "id": "4nbMDp8VtUBiFAn0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_12.png", "conversations": [ { "from": "human", "value": "\nFrom the line graph, can you map the yearly user counts in millions for both Facebook and Twitter into a table?" }, { "from": "gpt", "value": "To shape the chart information into a table, I will identify {the x-axis tick labels}[[0.102, 0.902, 0.144, 0.932],[0.512, 0.902, 0.555, 0.932]] as the years, which will serve as the row labels of the table. Next, we have {the legend texts}[[0.133, 0.086, 0.270, 0.110],[0.133, 0.121, 0.267, 0.145]] that will be used as column headers, representing the two social media platforms. Finally, I will locate {the line points for Facebook}[[0.123,0.537],[0.534,0.712]] and {the line points for Twitter}[[0.123,0.799],[0.534,0.642]], to extract the corresponding values of user counts in each year for both platforms.\nBased on the above thought, the answer is:\nYear,Facebook,Twitter\n2020,3760,4250\n2021,3870,4500" } ], "id_raw": 492 }, { "id": "l0U7CCARePe43BLR", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_12.png", "conversations": [ { "from": "human", "value": "\nBased on the presented data, which social media platform showed more growth in its user base from 2020 to 2021? Can you elaborate on these trends?" }, { "from": "gpt", "value": "To determine which social media platform showed more growth, I need to compare the differences in user counts between 2020 and 2021 for both platforms. For {Facebook}[0.123,0.537,0.534,0.712], there was a growth from 3760 million to 3870 million users, resulting in a net increase of 110 million users. For {Twitter}[0.123,0.799,0.534,0.642], the growth was from 4250 million to 4500 million users, indicating a net increase of 250 million users.\nBased on the above thought, the answer is:\nTwitter showed more growth in its user base from 2020 to 2021. While both platforms saw an increase in users, the rise in Twitter's user base is much more significant compared to Facebook's - about 127% more. This suggests Twitter had a more rapid growth rate during this period." } ], "id_raw": 492 }, { "id": "d9bnvU933YlKVjUu", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the userbase growth of Twitter from 2020 to 2021, in millions?" }, { "from": "gpt", "value": "To find the userbase growth of Twitter, I need to subtract the user count in 2020 from 2021. I locate {the 2020 line point for Twitter at twitter_coordinates_2020}[0.123,0.799], the corresponding value is 4250 million users. Then I look at {the 2021 line point for Twitter at twitter_coordinates_2021}[0.534,0.642], where the number is 4500 million users. The userbase growth for Twitter is therefore 4500 - 4250 = 250.\nBased on the above thought, the answer is:\n250." } ], "id_raw": 492 }, { "id": "YhBEAf2ENxByfWE9", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_13.png", "conversations": [ { "from": "human", "value": "\nCan you represent the provided line chart data in a tabular format with the historical eras as columns and the civilization development percentages as values?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first observe {the x-axis tick labels}[[0.088, 0.909, 0.130, 0.932], [0.259, 0.909, 0.372, 0.932], [0.464, 0.909, 0.580, 0.932], [0.685, 0.909, 0.772, 0.932], [0.891, 0.909, 0.979, 0.932]], which present the column headers, denoting the historical eras. To get the corresponding values, I isolate {the line points}[[0.109, 0.125], [0.316, 0.339], [0.522, 0.510], [0.729, 0.825], [0.935, 0.633]], which epitomize the developmental percentages for each historical era.\nBased on the above thought, the answer is:\nHistorical Era,Books,Online Materials,Peer Discussions,Experiments,Field Studies\nCivilization Development(%),82.5,74.6,68.3,56.7,63.8" } ], "id_raw": 493 }, { "id": "t0gkpBB3PeSUbq9S", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_13.png", "conversations": [ { "from": "human", "value": "\nWhat does the trend in the line chart tell us about the usage of the education resources over time?" }, { "from": "gpt", "value": "To understand the trend, I analyze the progression of the line plotted with coordinates {the line for Resource Usage}[[0.109, 0.125, 0.935, 0.633]]. We can observe that there is a decline in resource utilization from Books to Online Materials, a further drop to Peer Discussions, a sharper fall to Experiments, and then a moderate increase towards Field Studies.\nBased on the above thought, the answer is:\nThe trend depicted in the line chart shows a predominantly declining pattern in the usage of education resources from Books to Experiments. It suggests that traditional resources like Books and Online Materials have been utilized more compared to experiential learning methods like Experiments. Nevertheless, there is a small, noticeable resurgence in utilizing Field Studies, a form of experiential learning. This might indicate a growing recognition of the importance and effectiveness of hands-on experience in education." } ], "id_raw": 493 }, { "id": "SknHqOrmto6qJJjb", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in civilization development from Books to Field Studies according to the line chart?" }, { "from": "gpt", "value": "To calculate the percent change, I first locate {the usage percentage for Books}[[0.109, 0.125]] which equals 82.5%, then I locate {the usage percentage for Field Studies}[[0.935, 0.633]], which equals 63.8%. So the percentage change is ((63.8-82.5)/82.5)*100.\nBased on the above thought, the answer is:\n-22.7%" } ], "id_raw": 493 }, { "id": "ed07ceGmDqVp6ck6", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_14.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table of the data represented in the line chart, with years as columns and sectors as rows?" }, { "from": "gpt", "value": "From the line chart, recognizing the data, I first locate {the x-axis tick labels}[0.148, 0.902, 0.877, 0.920] which act as the column headers, denoting years. Subsequently, {the legend}[0.830, 0.136, 0.891, 0.179] text provides the row labels, represented by different sectors. For the values in the table, {the line plots}[0.160, 0.216, 0.865, 0.794] indicate the number of units sold for each sector for different years.\nBased on the above thought, the answer is:\nSector,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016\nRural Development,100,98.3,97,96,94,91,88,85,82,80,77,75\nUrban Development,80,85,88,90,92,95,97,98,99,100,102,105" } ], "id_raw": 494 }, { "id": "Gbdc3Gb6GEkBHf6M", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_14.png", "conversations": [ { "from": "human", "value": "\nConsidering the trends in the line chart, which area, Rural or Urban, has shown a more consistent development? Provide your reasoning." }, { "from": "gpt", "value": "To answer this, I look at the line plots for Rural and Urban Development. Observing {the line for Rural Development}[0.160, 0.312, 0.865, 0.794], it decreases consistently over time suggesting a decline in development levels. On the other side, {the line for Urban Development}[0.160, 0.216, 0.865, 0.698] shows a fairly consistent upward progression, indicating a steady improvement in development levels over the given period.\nBased on the above thought, the answer is:\nBased on the trends, Urban Development has shown more consistent development. While Rural Development shows a consistent decline, Urban Development shows a steady increase over the years. The clear upward trend in the Urban Development line implies consistent improvement in development levels over time." } ], "id_raw": 494 }, { "id": "dmQbE4icDKPBXKzs", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the total change in the Urban Development level from the start year 2005 to the end year 2016?" }, { "from": "gpt", "value": "To find the total change in the Urban Development level, I need to find the values in the start year 2005 and the end year 2016. I look for {the point for Urban Development}[0.160, 0.698] in 2005 which has a value of 80 and the {point for Urban Development}[0.865, 0.216] in 2016 with a value of 105. The total change would be the difference between these two values i.e. 105 - 80.\nBased on the above thought, the answer is:\n25." } ], "id_raw": 494 }, { "id": "Pz6Rx4OacZUzzdNr", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_15.png", "conversations": [ { "from": "human", "value": "\nBased on the chart provided, can you present the data showing the inspection efficiency of Process 1 and Process 2 across different timelines into a table?" }, { "from": "gpt", "value": "To achieve the tabular form of data from the chart, I need to start by identifying the x-axis, {the x-axis tick labels}[[0.122, 0.902, 0.133, 0.932], [0.208, 0.902, 0.219, 0.932], [0.294, 0.902, 0.305, 0.932], [0.380, 0.902, 0.391, 0.932], [0.466, 0.902, 0.477, 0.932], [0.547, 0.902, 0.568, 0.932], [0.633, 0.902, 0.654, 0.932], [0.719, 0.902, 0.740, 0.932], [0.805, 0.902, 0.826, 0.932], [0.891, 0.902, 0.912, 0.932]], which serve as the different timelines conveyed. The series data corresponding to Process 1 and Process 2 can be gathered from their line points; {the line points for Process 1}[[0.128, 0.311], [0.171, 0.465], [0.214, 0.594], ..., [0.944, 0.698]] reflecting the efficiency of Process 1 and {the line points for Process 2}[[0.128, 0.362], [0.171, 0.362], [0.214, 0.336], ..., [0.944, 0.259]] standing for the efficiency of Process 2.\nBased on the above thought, the answer is:\nTimeline,Process 1 Efficiency,Process 2 Efficiency\n1,80,78\n2,74,78\n3,69,79\n4,60,81\n5,65,81\n6,70,79\n7,70,73\n8,74,71\n9,75,73\n10,80,74\n11,83,74\n12,85,73\n13,87,75\n14,80,75\n15,75,78\n16,70,80\n17,65,82\n18,64,84\n19,63,83\n20,65,82" } ], "id_raw": 495 }, { "id": "ZHBYBg8oHp5xU2Dm", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_15.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which process, Process 1 or Process 2, demonstrates a more stable increase in efficiency over time?" }, { "from": "gpt", "value": "To identify which process shows a more stable increase in efficiency, we have to examine the lines of both processes. By looking at the line for {Process 1}[0.128, 0.130, 0.944, 0.827], we could see a volatile movement with multiple peaks and troughs, which signify an unstable increase. In contrast, the line for {Process 2}[0.128, 0.207, 0.944, 0.543] shows a gradual increase across the timeline, only decreasing slightly at certain time periods. This indicates a more stable increase in efficiency compared to Process 1.\nBased on the above thought, the answer is:\nProcess 2 demonstrates a more stable increase in efficiency over time. While both processes show an overall improvement in efficiency, the changes in Process 2 are more gradual and consistent, indicating a steadier and more controlled growth trajectory compared to the considerable fluctuations seen in Process 1." } ], "id_raw": 495 }, { "id": "iRgdcONYhwPSzUF5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the average efficiency of Process 1 from time 1 to time 20?" }, { "from": "gpt", "value": "To calculate the average efficiency of Process 1, I first locate the coordinates of Process 1 that span from time 1 to time 20: {the line points for Process 1}[[0.128, 0.311], [0.171, 0.465], [0.214, 0.594], ..., [0.944, 0.698]]. The corresponding values are: 80, 74, 69, 60, 65, 70, 70, 74, 75, 80, 83, 85, 87, 80, 75, 70, 65, 64, 63, 65. The average is calculated by summing up all these values and dividing by the total number of values, which is 20.\nBased on the above thought, the answer is:\n73.05" } ], "id_raw": 495 }, { "id": "ZGjbaHDzDs3Tcdj0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_16.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured table representing the usage of different big data integration strategies over the years from the line chart?" }, { "from": "gpt", "value": "Observing the line chart, I will analyze each strategy's usage over the years from 2015 to 2020. Firstly, locating {the x-axis labels}[[0.154, 0.906, 0.163, 0.930], [0.207, 0.906, 0.215, 0.930], ..., [0.891, 0.906, 0.909, 0.930]] which represents the years from 2015 through 2020. Next, identifying {the legend items}[[0.132, 0.132, 0.274, 0.208]] and correlating them with their respective lines and colors. From there, I track and extract the usage points for each strategy from {line plots}[[0.158, 0.824], [0.185, 0.805], ..., [0.821, 0.170]] based on their lines' unique style and fill in the respective values in the structured table.\nBased on the above thought, the answer is:\nYear,Data Warehousing,Master Data Management,Data Lakes,Real-Time Data Integration,Data Virtualization\n2015,63,62,59,55,57\n2016,65,61,61,57,59\n2017,67,59,65,59,61\n2018,69,57,70,64,64\n2019,72,55,74,69,68\n2020,74,53,78,75,71" } ], "id_raw": 496 }, { "id": "AM3q70Z1KL5F413e", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_16.png", "conversations": [ { "from": "human", "value": "\nWhich data integration strategy exhibited the highest growth rate from 2015 to 2020 based on the line chart?" }, { "from": "gpt", "value": "To determine the highest growth rate among data integration strategies from 2015 to 2020, I analyze each strategy's line plot's slope as it represents the rate of change over time. For this, I observe {the line for Real-Time Data Integration that stretches from top-left to bottom-right}[[0.158, 0.856, 0.821, 0.170]]. It starts relatively lower than others in 2015 and becomes the highest by 2020, indicating a sharp incline compared to {Data Warehousing}[0.158, 0.824, 0.821, 0.170], {Master Data Management}[0.158, 0.870, 0.821, 0.902], {Data Lakes}[0.158, 0.885, 0.821, 0.814], and {Data Virtualization}[0.158, 0.839, 0.821, 0.721]. This steep ascent signifies a higher growth rate.\nBased on the above thought, the answer is:\nReal-Time Data Integration strategy exhibited the highest growth rate from 2015 to 2020. Its line shows the sharpest incline, starting from a lower position and progressing to lead by 2020, which indicates a rapid increase in utilization compared to other strategies." } ], "id_raw": 496 }, { "id": "uJoInqz9xIpoH3Mu", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in usage for Real-Time Data Integration strategy from 2015 to 2020?" }, { "from": "gpt", "value": "To calculate the total increase in usage for Real-Time Data Integration from 2015 to 2020, I first need to identify {the line for Real-Time Data Integration}[[0.158, 0.415, 0.821, 0.856]] and then find its endpoints at {2015}[0.158, 0.856] and {2020}[0.821, 0.170]. The initial value is approximately 55, and the final value is approximately 75. The calculation would thus be 75 (2020 value) - 55 (2015 value).\nBased on the above thought, the answer is:\n20" } ], "id_raw": 496 }, { "id": "NXgawXYQifWQ6zez", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_17.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the data represented in this chart? With columns Year, Freight Volume (million ton-kilometers), and Passenger Miles (million)." }, { "from": "gpt", "value": "To process the chart into structured tables, I first look at {the x-axis tick labels}[0.117, 0.861, 0.160, 0.932], [0.198, 0.861, 0.240, 0.932], [0.278, 0.861, 0.321, 0.932], [0.359, 0.861, 0.402, 0.932], [0.440, 0.861, 0.482, 0.932], [0.520, 0.861, 0.563, 0.932], [0.601, 0.861, 0.643, 0.932], [0.681, 0.861, 0.724, 0.932], [0.762, 0.861, 0.805, 0.932], [0.843, 0.861, 0.885, 0.932], [0.923, 0.861, 0.966, 0.932] which represent the years. To find values for each year, I look at the two bar charts identified by their respective colors. I find the bar chart points for {Freight Volume (million ton-kilometers)}[0.138, 0.703, 0.945, 0.705] and {Passenger Miles (million)}[0.138, 0.431, 0.945, 0.484] and extract the values for each year.\nBased on the above thought, the answer is:\nYear,Freight Volume (million ton-kilometers),Passenger Miles (million)\n2010,14512.5,60013.2\n2011,14816.0,61247.9\n2012,14590.6,61827.0\n2013,14245.8,63596.7\n2014,14130.5,64835.9\n2015,14336.8,66578.5\n2016,14487.2,67623.6\n2017,15010.1,68741.7\n2018,15427.3,69978.1\n2019,15535.2,71112.6\n2020,14606.4,68921.3" } ], "id_raw": 497 }, { "id": "ug1mGwt9090P59Q0", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_17.png", "conversations": [ { "from": "human", "value": "\nBased on this chart, can you tell whether the trend Freight Volume and Passenger Miles are related? Explain your reasoning." }, { "from": "gpt", "value": "To analyze whether Freight Volume and Passenger Miles trends are related, I examine the change of {Freight Volume}[0.138, 0.698, 0.945, 0.705] and {Passenger Miles}[0.138, 0.431, 0.945, 0.484] over the years 2010 to 2020. Both line charts reveal an upward trend despite some slight fluctuations, suggesting they might be related. Moreover, the small decline in both figures from 2019 to 2020 further supports their potential correlation.\nBased on the above thought, the answer is:\nIt seems that trends in Freight Volume and Passenger Miles display a parallel trend, suggesting a potential relationship between the two. Both experienced growth over the considered decade, with a small decline from 2019 to 2020. However, correlation does not necessarily mean causation, and further data or analysis would be needed to establish a definite connection." } ], "id_raw": 497 }, { "id": "R4FdR9eai3gnxQ25", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in Passenger Miles from 2010 to 2020?" }, { "from": "gpt", "value": "To calculate the increase of Passenger Miles from 2010 to 2020, I need to locate the corresponding points on the line chart for {Passenger Miles in 2010}[0.138, 0.484] and {Passenger Miles in 2020}[0.945, 0.441]. The value at 2010 is 60013.2 and the value at 2020 is 68921.3. The increase is therefore 68921.3 - 60013.2 = 8908.1.\nBased on the above thought, the answer is:\n8908.1 million" } ], "id_raw": 497 }, { "id": "mHILocNeEvYD4tzE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_18.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar graph data into a structured table with Hotels as rows and Years as columns?" }, { "from": "gpt", "value": "To form the table from the bar chart, I first identify {the x-axis labels}[[0.169, 0.150, 0.202, 0.161], [0.292, 0.150, 0.355, 0.161], [0.416, 0.150, 0.490, 0.161], [0.541, 0.150, 0.642, 0.161]] which can be used as row names in the table, representing the names of the hotels. Next, I locate {the legend texts}[[0.866, 0.831, 0.897, 0.843], [0.866, 0.805, 0.897, 0.817]] to serve as column headers representing the different years. To fill the values, I find {the bars}[[0.161, 0.166, 0.183, 0.369], [0.183, 0.166, 0.206, 0.497], [0.285, 0.166, 0.307, 0.263], [0.307, 0.166, 0.329, 0.397], [0.408, 0.166, 0.431, 0.504], [0.431, 0.166, 0.453, 0.553], [0.532, 0.166, 0.555, 0.292], [0.555, 0.166, 0.577, 0.412]] and extract the occupancy rate values they represent.\nBased on the above thought, the answer is:\nHotel,2020 Occupancy,2021 Occupancy\nOcean View,35.7,52.4\nMountain Escape,19.7,39.3\nCity Central,53.5,61.5\nDesert Dream,22.6,41.5" } ], "id_raw": 498 }, { "id": "s6fUsQGhIvmzDv1y", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_18.png", "conversations": [ { "from": "human", "value": "\nAmong the four hotels, which one has shown the highest improvement in terms of occupancy rate from 2020 to 2021?" }, { "from": "gpt", "value": "To discover the hotel with the highest improvement, I need to find the percentage increase in occupancy rate for each hotel from 2020 to 2021. For 'Ocean View', I locate {the bars for 'Ocean View'}[[0.161, 0.166, 0.183, 0.369], [0.183, 0.166, 0.206, 0.497]], for 'Mountain Escape', I locate {the bars for 'Mountain Escape'}[[0.285, 0.166, 0.307, 0.263], [0.307, 0.166, 0.329, 0.397]], for 'City Central', I locate {the bars for 'City Central'}[[0.408, 0.166, 0.431, 0.504], [0.431, 0.166, 0.453, 0.553]], and for 'Desert Dream', I locate {the bars for 'Desert Dream'}[[0.532, 0.166, 0.555, 0.292], [0.555, 0.166, 0.577, 0.412]]. From these data, it can be determined that the largest percentage increase in occupancy is for the hotel 'Mountain Escape' which showed an increase from 19.7 in 2020 to 39.3 in 2021.\nBased on the above thought, the answer is:\n'Mountain Escape' has shown the highest improvement in terms of occupancy rate from 2020 to 2021. The hotel saw an occupancy increase from 19.7% in 2020 to 39.3% in 2021 which is the highest percentage increase among the four hotels." } ], "id_raw": 498 }, { "id": "9UsAnK4Ed95OpV3e", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in the occupancy rate for 'City Central' from 2020 to 2021?" }, { "from": "gpt", "value": "To calculate the percentage increase, I need to locate the values for 'City Central' for both years. I identify {the bars for 'City Central' in 2020 and 2021}[[0.408, 0.166, 0.431, 0.504], [0.431, 0.166, 0.453, 0.553]] which have values of 53.5 and 61.5 respectively. The percentage increase is calculated as (61.5 - 53.5) / 53.5 * 100.\nBased on the above thought, the answer is:\n14.95%." } ], "id_raw": 498 }, { "id": "0YXB7TEnkM03zIYV", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_25_19.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data represented in the line graph into a structured table that has years as rows and the two types of investments as the columns?" }, { "from": "gpt", "value": "Sure, the first step is to identify the data needed. The years from the {x-axis tick labels}[[0.061, 0.909, 0.109, 0.932], [0.168, 0.909, 0.217, 0.932], [0.275, 0.909, 0.324, 0.932], [0.382, 0.909, 0.431, 0.932], [0.489, 0.909, 0.538, 0.932], [0.597, 0.909, 0.645, 0.932], [0.704, 0.909, 0.752, 0.932], [0.811, 0.909, 0.859, 0.932], [0.918, 0.909, 0.966, 0.932], [1.025, 0.909, 1.073, 0.932]] serve as the row qualifiers. Next, {the legend}[0.049, 0.077, 0.292, 0.154] identifies the two investment types which should be used as columns. The data values for these columns can then be extracted by mapping the values of the plotted points, such as these for stocks {the line points for stocks}[[0.085, 0.849], [0.128, 0.850], [0.171, 0.850], [0.214, 0.850], [0.257, 0.850], [0.299, 0.851], [0.342, 0.851], [0.385, 0.851], [0.428, 0.852], [0.471, 0.852], [0.514, 0.852], [0.557, 0.853], [0.599, 0.853], [0.642, 0.853], [0.685, 0.853], [0.728, 0.854], [0.771, 0.854], [0.814, 0.854], [0.856, 0.855], [0.899, 0.855], [0.942, 0.855]] to the corresponding years\nBased on the above thought, the answer is:\nYear, Stocks, Mutual Funds \n1980, 1.211, 68.99 \n1981, 1.159, 69.21 \n1982, 1.138, 69.68 \n1983, 1.101, 70.15 \n1984, 1.080, 70.39 \n1985, 1.055, 70.63 \n1986, 1.008, 70.89 \n1987, 1.001, 71.23 \n1988, 0.969, 71.80 \n1989, 0.935, 71.97 \n1990, 0.909, 72.23 \n1991, 0.863, 72.649 \n1992, 0.838, 73.17 \n1993, 0.800, 73.3\n1994, 0.777, 73.68 \n1995, 0.755, 73.98 \n1996, 0.713, 74.24\n1997, 0.686, 74.49 \n1998, 0.653, 74.90 \n1999, 0.621, 75.24 \n2000, 0.593, 75.65" } ], "id_raw": 499 }, { "id": "h2X7lsyFEqcBIgsD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_25_19.png", "conversations": [ { "from": "human", "value": "\nAccording to the data in the graph, what trend can we observe concerning population growth and life expectancy?" }, { "from": "gpt", "value": "Observing {the line for Population Growth Rate}[0.085, 0.849, 0.942, 0.855], we can see a steady decline in population growth rate over the years. On the other hand, {the line for Life Expectancy}[0.085, 0.170, 0.942, 0.103] shows a steady increase in life expectancy.\nBased on the above thought, the answer is:\nThe trends suggest that as population growth rate is declining, the life expectancy is increasing. This can be seen from the downward slope of the population growth rate trajectory and the upward slope of the life expectancy trajectory. The exact reasons for this could be multifaceted, but it's commonly understood that factors such as advancements in healthcare and living conditions can lead to lower population growth and higher life expectancy." } ], "id_raw": 499 }, { "id": "8a8P1FxoBfOJ7y8G", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_25_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in life expectancy from the year 1980 to 2000?" }, { "from": "gpt", "value": "To calculate the percentage increase in life expectancy, we need to first locate the life expectancy for the year 1980 and 2000. Looking at {the line points for Life Expectancy}[[0.085, 0.170],[0.942, 0.103]], the life expectancy in 1980 is 68.99 years and in 2000 it is 75.65 years. The percentage increase is then calculated as ((75.65 - 68.99) / 68.99) * 100.\nBased on the above thought, the answer is:\n9.66%." } ], "id_raw": 499 }, { "id": "y0U01d13E213Kvk0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_1.png", "conversations": [ { "from": "human", "value": "\nCan you provide the data represented by the chart in a tabular format with the 'Fields of Technology' as columns and 'Years' as rows?" }, { "from": "gpt", "value": "To create this table, I'll start by identifying {the x-axis labels}[0.084, 0.918, 0.149, 0.943],[0.194, 0.918, 0.258, 0.943],[0.303, 0.918, 0.368, 0.943],[0.413, 0.918, 0.478, 0.943],[0.522, 0.918, 0.587, 0.943],[0.632, 0.918, 0.697, 0.943],[0.742, 0.918, 0.806, 0.943],[0.851, 0.918, 0.916, 0.943] as the 'Fields of Technology' which will serve as our column headers. The two columns we'll have are 'Education Technology' and 'Training Technology'. Next, I will identify {the legend items} [0.801, 0.056, 0.900, 0.084],[0.801, 0.097, 0.900, 0.125] as '2020' and '2021' respectively and these will serve as our row labels or 'Years'. Lastly, I will locate {the bar heights} [0.116, 0.207, 0.884, 0.669],[0.116, 0.075, 0.884, 0.867] for each Year in the respective Field of Technology to determine the values as 26, 50 for Education Technology and 30, 60 for Training Technology in years 2020 and 2021 respectively.\nBased on the above thought, the answer is:\nYears,Education Technology,Training Technology\n2020,26,30\n2021,50,60" } ], "id_raw": 501 }, { "id": "aVpUBB092G5Gl8W7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_1.png", "conversations": [ { "from": "human", "value": "\nBased on the trends indicated in the bar graph, which technology field experienced a higher rate of growth in innovations from 2020 to 2021 and why so?" }, { "from": "gpt", "value": "To determine this, I need to calculate the rate of growth in each field. For {Education Technology}, I look at the increase from 26 to 50. This is a 92.3% increase. For {Training Technology}, I look at the increase from 30 to 60. This is a 100% increase. Thus, although both technology fields experienced significant growth, Training Technology had a slightly higher rate of growth.\nBased on the above thought, the answer is:\nTraining Technology experienced a higher rate of growth in innovations from 2020 to 2021. While both fields witnessed considerable growth, Training Technology had a growth rate of 100%, which slightly exceeds the 92.3% growth rate of Education Technology." } ], "id_raw": 501 }, { "id": "rdjqCPkL5Uela0Vj", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_1.png", "conversations": [ { "from": "human", "value": "\nBy how many counts did the innovations in Training Technology increase from 2020 to 2021?" }, { "from": "gpt", "value": "To find this, I need to locate {the bar for Training Technology in 2020}[0.226, 0.339] and get its value which is 30. Then I do same for {the bar for Training Technology in 2021}[0.445, 0.251] and get that its value is 60. The difference between these 2 values gives me the increase which is 60 - 30 = 30 counts.\nBased on the above thought, the answer is:\n30 counts." } ], "id_raw": 501 }, { "id": "IFxTaRNsISnxG0Cm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_3.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data represented in the dual bar chart into a table format? With 'Year' as columns and 'Consumer Classes' as rows?" }, { "from": "gpt", "value": "To construct the table, I will first identify {the x-axis tick labels}[[0.083, 0.931, 0.116, 0.955], [0.498, 0.931, 0.512, 0.955]] which stand for the column headers (Years). Next, I recognize {the legend labels}[0.838, 0.012, 0.898, 0.029], [0.894, 0.055, 0.955, 0.072]] as the row labels representing the different Consumer Classes. To fill the values in the table, I will locate the heights of the bars for each class at the represented years. The bars are {Consumer_Class_1 bars}(0.166, 0.094, 0.301, 0.651], [0.403, 0.081, 0.495, 0.930]) and {Consumer_Class_2 bars}(0.311, 0.197, 0.391, 0.817], [0.500, 0.046, 0.583, 0.965]).\nBased on the above thought, the answer is:\nConsumer Class, 2019, 2020\nConsumer_Class_1, 63, 73\nConsumer_Class_2, 78, 97" } ], "id_raw": 503 }, { "id": "yv0Z3Gc9GQSqHRUD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which Consumer Class showed accelerated growth during the period from 2019 to 2020?" }, { "from": "gpt", "value": "In the chart, {Consumer_Class_1 bars}(0.166, 0.094, 0.301, 0.651], [0.403, 0.081, 0.495, 0.930]) show a gradual increase in consumer behavior from 63 to 73 over the period from 2019 to 2020. However, {Consumer_Class_2 bars}(0.311, 0.197, 0.391, 0.817], [0.500, 0.046, 0.583, 0.965]) present a more substantial increase from 78 to 97 over the same period. Therefore, Consumer_Class_2 showed the more accelerated growth.\nBased on the above thought, the answer is:\nBased on the chart, Consumer_Class_2 showed accelerated growth during the period from 2019 to 2020. It showed a substantial increase in consumer behavior from 78 in 2019 to 97 in 2020, indicating a more rapid increase than Consumer_Class_1." } ], "id_raw": 503 }, { "id": "q6w485ucjRQq4krs", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_3.png", "conversations": [ { "from": "human", "value": "\nWhat's the difference in behaviour between Consumer_Class_1 and Consumer_Class_2 during the year 2020?" }, { "from": "gpt", "value": "To calculate this difference, I need to identify the values of Consumer_Class_1 and Consumer_Class_2 in the year 2020. These values can be extracted by analyzing the height of the bars for these classes {Consumer_Class_1 bar in 2020}[0.403, 0.081, 0.495, 0.930] representing 73 and {Consumer_Class_2 bar in 2020}[0.500, 0.046, 0.583, 0.965] representing 97. Then I subtract 73 from 97.\nBased on the above thought, the answer is:\n24." } ], "id_raw": 503 }, { "id": "wNKJmzkfKkGXQQYQ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line plot data of Creativity Level vs Design Innovation Level into a structured table format?" }, { "from": "gpt", "value": "To extract the data into a structured table format, I identify {the X-axis}[0.436, 0.958, 0.627, 0.981] which contains the Creativity Level data. Additionally, the line plot for Design Innovation Level is marked by the points in the coordinates {line_0}[0.123, 0.109, 0.940, 0.892], whose point data will correspond to the values in the Design Innovation Level column. Each data pair (x,y) from the points of line_0 will represent a row in the table.\nBased on the above thought, the answer is:\nCreativity Level,Design Innovation Level\n0,1\n1,2\n2,4\n3,8\n4,12\n5,16\n6,20\n7,24\n8,25\n10,27" } ], "id_raw": 504 }, { "id": "4GFej2c7bbXMDyVq", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_4.png", "conversations": [ { "from": "human", "value": "\nLooking at the line chart, how would you describe the relationship between Creativity Level and Design Innovation Level?" }, { "from": "gpt", "value": "Given the points {[0.123, 0.892], [0.205, 0.862], [0.286, 0.802], [0.368, 0.681], [0.450, 0.561], [0.532, 0.440], [0.613, 0.320], [0.695, 0.199], [0.777, 0.169], [0.940, 0.109]} in the chart which represent the relationship between Creativity Level and Design Innovation Level, it's clear that both values increase together. Therefore, I would say there is a positive correlation between Creativity Level and Design Innovation Level. This implies that the higher the creativity level, the higher the design innovation level and vice versa.\nBased on the above thought, the answer is:\nThe relationship between Creativity Level and Design Innovation Level can be described as a strong positive correlation. As the Creativity Level increases, the Design Innovation Level increases as well, indicating that a higher level of creativity is associated with a higher level of design innovation, and vice versa." } ], "id_raw": 504 }, { "id": "M8rlnpBCcF150Xgz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_4.png", "conversations": [ { "from": "human", "value": "\nWhat's the difference between the Design Innovation Level at a Creativity Level of 8 and Creativity Level of 0?" }, { "from": "gpt", "value": "To answer this question, I have to find the y-values corresponding to the Creativity Levels of 0 and 8. Looking at {the point for Creativity Level 0}[0.123, 0.892], I find the Design Innovation Level to be 1. Then, referencing {the point for Creativity Level 8}[0.777, 0.169], I see the Design Innovation Level is 25. To find the difference, I subtract the Design Innovation Level at Creativity Level of 0 from that at Creativity Level 8, which gives 25-1 = 24.\nBased on the above thought, the answer is:\n24" } ], "id_raw": 504 }, { "id": "oS4WSPLYJMM6nmSk", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_5.png", "conversations": [ { "from": "human", "value": "\nCan you organize the Creativity and Innovation Scores for all design elements into a structured table?" }, { "from": "gpt", "value": "To convert the line chart into tabular data, firstly, I identify the elements on the x-axis {Design Elements}[0.112, 0.918, 0.905, 0.943] that represent the rows. I then take note of the terms that denote the columns, which are Creativity and Innovation scores. I extract the values by pinpointing the points of intersection on {Creativity Scores}[0.139, 0.062, 0.881, 0.870] and {Innovation Scores}[0.139, 0.062, 0.881, 0.870] for each Design Element.\nBased on the above thought, the answer is:\nDesign Element, Creativity Score, Innovation Score\nColor, 82, 75\nShape, 90, 87\nTexture, 89, 92\nSpace, 77, 80\nForm, 95, 98\nLine, 89, 85\nValue, 78, 72\nType, 84, 90" } ], "id_raw": 505 }, { "id": "QPKxToQ3k8b4WKDA", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_5.png", "conversations": [ { "from": "human", "value": "\nWhich Design Element has the closest Creativity and Innovation scores and what's the difference?" }, { "from": "gpt", "value": "To identify the Design Element with the closest Creativity and Innovation scores, I must compare the values for each Design Element in {Creativity Scores}[0.139, 0.062, 0.881, 0.870] and {Innovation Scores}[0.139, 0.062, 0.881, 0.870]. By comparing, it is easily notable that for the Design Element {Texture}[0.313, 0.918, 0.389, 0.943], both the scores are quite close. The Creativity score for Texture is 89, and the Innovation score is 92, giving a difference of 3.\nBased on the above thought, the answer is:\nTexture is the Design Element with the closest Creativity and Innovation scores. The difference between the Creativity and Innovation scores for Texture is 3." } ], "id_raw": 505 }, { "id": "ykpV04SZASU1Jizo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum Creativity Score and which design element does it correspond with?" }, { "from": "gpt", "value": "To find the maximum Creativity Score, I need to analyze the {Creativity Scores}[0.139, 0.062, 0.881, 0.870] scene in the chart. By doing this, I can identify the maximum point corresponding to {Form}[0.537, 0.918, 0.589, 0.943] with a score of 95.\nBased on the above thought, the answer is:\nThe maximum Creativity Score is 95, which corresponds with the design element Form." } ], "id_raw": 505 }, { "id": "H9ut223EBXKahD1x", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_6.png", "conversations": [ { "from": "human", "value": "\nExtract the market share percentages of each automotive company from the bar chart into a structured table format." }, { "from": "gpt", "value": "Observing the bar chart, I first identify {the bar for Tesla}[0.150, 0.200, 0.180, 0.950] representing 54% of the market share. Then, {the bar for GM}[0.220, 0.450, 0.250, 0.950] shows 20%, followed by {the bar for Toyota}[0.290, 0.550, 0.320, 0.950] at 15%. Continuing, I locate {the bar for Ford}[0.360, 0.800, 0.390, 0.950] showing 5% and {the bar for Honda}[0.430, 0.850, 0.460, 0.950] at 4%. Lastly, {the bars for Hyundai and Nissan}[0.500, 0.950, 0.530, 0.950]; [0.570, 0.950, 0.600, 0.950] are both at 1% each.\nBased on the above thought, the answer is:\nCompany,Market Share (%)\nTesla,54\nGM,20\nToyota,15\nFord,5\nHonda,4\nHyundai,1\nNissan,1" } ], "id_raw": 506 }, { "id": "jZTZIxVtqeOahEaB", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_6.png", "conversations": [ { "from": "human", "value": "\nWhy does the bar for Tesla stand out significantly compared to other companies in the bar chart?" }, { "from": "gpt", "value": "Analyzing {the bar for Tesla}[0.150, 0.200, 0.180, 0.950], which is significantly taller than the others, indicates a dominant market share of 54%. This height, when compared with {the bars for other companies}[0.220, 0.450, 0.600, 0.950], where the highest is only 20% (GM), illustrates Tesla's major lead in the industry. The visual disparity emphasizes Tesla\u2019s significant position and dominant market influence relative to smaller shares held by its competitors.\nBased on the above thought, the answer is:\nThe bar for Tesla stands out significantly in the bar chart because it represents a much greater market share of 54%, in stark contrast to other companies whose shares range only from 1% to 20%. Tesla's dominant position in the industry is visually evident, emphasizing its substantial lead over competitors." } ], "id_raw": 506 }, { "id": "5hvk5qE289qL0dyt", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the combined market share percentage of Ford, Honda, Hyundai, and Nissan?" }, { "from": "gpt", "value": "To find the combined market share for Ford, Honda, Hyundai, and Nissan, I look at {the bar for Ford}[0.360, 0.800, 0.390, 0.950] with 5%, {the bar for Honda}[0.430, 0.850, 0.460, 0.950] with 4%, and {the bars for Hyundai and Nissan}[0.500, 0.950, 0.530, 0.950]; [0.570, 0.950, 0.600, 0.950] both at 1% each. Summing these values gives 5% + 4% + 1% + 1% = 11%.\nBased on the above thought, the answer is:\n11" } ], "id_raw": 506 }, { "id": "cMVR5trR3hzx5XLO", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_7.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line plot into a table structure with Years as rows and AI Usage and Automation as columns?" }, { "from": "gpt", "value": "To create a table from the line plot data, I must first look at the {x-axis ticks}[[0.086, 0.939, 0.121, 0.957], [0.226, 0.939, 0.261, 0.957], [0.366, 0.939, 0.401, 0.957], [0.506, 0.939, 0.541, 0.957], [0.646, 0.939, 0.681, 0.957], [0.786, 0.939, 0.821, 0.957], [0.925, 0.939, 0.961, 0.957]], which clarify the years. Subsequently, I identify {the legend items}[[0.113, 0.062, 0.174, 0.080], [0.113, 0.088, 0.194, 0.106]] as the column headers labeling AI Usage and Automation. Lastly, I locate {the points on the lines}[[0.104, 0.216], [0.174, 0.871], [0.244, 0.831], [0.314, 0.806], [0.384, 0.758], [0.454, 0.693], [0.523, 0.580], [0.593, 0.499], [0.663, 0.394], [0.733, 0.337], [0.803, 0.297], [0.873, 0.265], [0.943, 0.216], [0.104, 0.087], [0.174, 0.839], [0.244, 0.790], [0.314, 0.742], [0.384, 0.693], [0.454, 0.628], [0.523, 0.531], [0.593, 0.459], [0.663, 0.378], [0.733, 0.297], [0.803, 0.224], [0.873, 0.143], [0.943, 0.087]] for AI Usage and Automation at the respective years and extract the numerical values.\nBased on the above thought, the answer is:\nYear, AI Usage, Automation\n2000, 0.2, 0.4\n2001, 0.4, 0.8\n2002, 0.9, 1.4\n2003, 1.2, 2.0\n2004, 1.8, 2.6\n2005, 2.6, 3.4\n2006, 4.0, 4.6\n2007, 5.0, 5.5\n2008, 6.3, 6.5\n2009, 7.0, 7.5\n2010, 7.5, 8.4\n2011, 7.9, 9.4\n2012, 8.5, 10.1" } ], "id_raw": 507 }, { "id": "GJjvr8Wkro2JKBFI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_7.png", "conversations": [ { "from": "human", "value": "\nBy comparing AI usage and automation trends, what observations can you make about the relationship between the two?" }, { "from": "gpt", "value": "To make observations about the relationship between AI usage and Automation trends, I need to compare {the line for AI Usage}[0.104, 0.216, 0.943, 0.887] and {the line for Automation}[0.104, 0.087, 0.943, 0.871]. The lines show that both AI usage and Automation have a generally increasing trend over the years, suggesting that an increase in AI usage could be positively related to an increase in Automation. There are periods where the AI usage line seems to lead the Automation line, suggesting that advances in AI technology might be driving increases in Automation. However, there are also instances where the trends diverge suggesting that while there might be a relationship, other factors might also be influencing Automation.\nBased on the above thought, the answer is:\nBoth AI usage and Automation have shown an increasing trend over the years, indicating that the increase in the use of AI might be leading to an increase in Automation. There are times when the AI usage line precedes the Automation line, suggesting that advances in AI technology could be propelling increases in Automation. However, there are periods when the two trends diverge, suggesting that while there might be some correlation, there may be other factors at play influencing Automation." } ], "id_raw": 507 }, { "id": "IkZBE3qCZ9ORLvpm", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall increase in AI usage from the year 2000 to 2012?" }, { "from": "gpt", "value": "To calculate the overall increase in AI usage, I need to find the y-values of the plot corresponding to the years 2000 and 2012. I find that {the value for the year 2000}[0.104, 0.216] is 0.2 and {the value for the year 2012}[0.943, 0.216] is 8.5. The overall increase is the difference between these two values, which is 8.5 - 0.2 = 8.3.\nBased on the above thought, the answer is:\n8.3" } ], "id_raw": 507 }, { "id": "qfEHFpXxSc8TrHgq", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_8.png", "conversations": [ { "from": "human", "value": "\nCan you extract the 'Efficiency Improvement Potential Assessment' data into a structured table format from the line chart?" }, { "from": "gpt", "value": "I consider {the x-axis tick labels}[[0.126, 0.908, 0.185, 0.924], [0.224, 0.908, 0.306, 0.924], [0.389, 0.908, 0.496, 0.924], [0.497, 0.908, 0.601, 0.925], [0.646, 0.908, 0.764, 0.925], [0.812, 0.908, 0.932, 0.925], [0.946, 0.908, 1.091, 0.915]] as the different technology types, and {the line points}[[0.157, 0.910], [0.265, 0.901], [0.442, 0.882], [0.549, 0.862], [0.705, 0.809], [0.873, 0.755], [1.018, 0.645]] for corresponding percentage values, respectively. Analyzing these data points, I can extract the data into a table.\nBased on the above thought, the answer is:\nTechnology,Efficiency Improvement Potential (%)\nRobust AI,0.0\nAutomated Driving,7.0\nCommunication Algorithms,12.5\nRenewable Energy,27.5\nBig Data Processing,35.0\nCloud Computing,65.0\nQuantum Computing,90.5" } ], "id_raw": 508 }, { "id": "9pBkaZGJR76kWtzs", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_8.png", "conversations": [ { "from": "human", "value": "\nConsidering the given line chart, how fast is the Efficiency Improvement Potential growing for these technologies?" }, { "from": "gpt", "value": "In order to answer this question, I have to analyze the individual data points on the line chart representing each technology. Starting at {Robust AI}[0.157, 0.910] with 0%, and moving up across the technologies to {Quantum Computing}[1.018, 0.645] at 90.5%. The line plot shows an overall upward trend signifying that the technological improvements are on the increase. Although the growth seems relatively gentle in the beginning from Robust AI, Automated Driving, Communication Algorithms to Renewable Energy, it then makes significant leaping progress from Renewable Energy to Big Data Processing and Cloud Computing, ending up peaking very high with Quantum Computing.\nBased on the above thought, the answer is:\nThe Efficiency Improvement Potential among these technologies is growing in a rapidly increasing trend, with a more pronounced rise in the later technologies. While the earlier technologies, such as Robust AI, Automated Driving, and Communication Algorithms, show a slow and steady growth, the momentum picks up with Renewable Energy and Big Data Processing. The growth further accelerates with Cloud Computing and eventually peaks at an extremely high rate with Quantum Computing, indicating a speedy and significant increase over the technology progression." } ], "id_raw": 508 }, { "id": "MuqheZsptS0Hia35", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_8.png", "conversations": [ { "from": "human", "value": "\nWhat\u2019s the improvement potential difference between Quantum Computing and Renewable Energy in percentage?" }, { "from": "gpt", "value": "To find the difference in improvement potential between Quantum Computing and Renewable Energy, I look at {the line point of Quantum Computing}[1.018, 0.645] which gives 90.5% and {the line point of Renewable Energy}[0.442, 0.882] which stands for 27.5%. Subtracting the percentage of Renewable Energy from Quantum Computing gives the difference.\nBased on the above thought, the answer is:\n63%" } ], "id_raw": 508 }, { "id": "4iupxRYjnA42qPa2", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_9.png", "conversations": [ { "from": "human", "value": "\nCan you organize the user data for all social media platforms over the years into a table format?" }, { "from": "gpt", "value": "To construct the table, first, I identify {the x-axis labels}[0.116, 0.911, 0.884, 0.918] which represent the column headers or the Years. Next, I identify {the legend texts}[0.747, 0.051, 1.064, 0.945] as the row labels indicating the different social media platforms. I then locate {the line points for each platform}[0.116, 0.090, 0.884, 0.386],[0.116, 0.266, 0.884, 0.618][0.116, 0.251, 0.884, 0.647][0.116, 0.761, 0.884, 0.770] and extract the values annotated for each year to fill in the cells of the table.\nBased on the above thought, the answer is:\nYears,Facebook Users,WhatsApp Users,Instagram Users,Twitter Users\n2016,1.79,1.0,0.6,0.32\n2017,2.07,1.3,0.8,0.33\n2018,2.23,1.5,1.0,0.34\n2019,2.41,1.7,1.2,0.34\n2020,2.5,2.0,1.3,0.33\n2021,2.8,2.2,1.5,0.33" } ], "id_raw": 509 }, { "id": "DGJ1oBiAYUVI3d4Y", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_9.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform experienced the largest growth from 2016 to 2021 according to the chart? And why do you think so?" }, { "from": "gpt", "value": "To identify which social media platform experienced the largest growth, I need to calculate the difference in user counts from 2016 to 2021 for each platform. For Facebook Users, the value in 2016 can be found at {the line point for Facebook Users in 2016}[0.116, 0.386] and the value in 2021 at {the line point for Facebook Users in 2021}[0.884, 0.090]. The difference is 2.8-1.79 = 1.01 billion. Similarly, I calculate the differences for other platforms: WhatsApp Users (2.2-1.0 = 1.2 billion), Instagram Users (1.5-0.6 = 0.9 billion), and Twitter Users (0.33-0.32 = 0.01 billion). Comparing these differences, it's clear that WhatsApp Users experienced the largest growth.\nBased on the above thought, the answer is:\nWhatsApp experienced the largest growth from 2016 to 2021. Comparing the changes in user counts across the platforms, as visualized in the line chart, shows that the difference for WhatsApp Users is the greatest." } ], "id_raw": 509 }, { "id": "0dnpIb7yYFuQ4QqZ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the user growth rate for Instagram from the year 2016 to 2021?" }, { "from": "gpt", "value": "To calculate the growth rate, I need to find the initial value and the final value for Instagram. The initial value for Instagram in 2016 can be located at {the line point for Instagram in 2016}[0.116, 0.647] which is 0.6 billion. The final value for Instagram in 2021 can be located at {the line point for Instagram in 2021}[0.884, 0.251] which is 1.5 billion. The growth rate is calculated as (Final value - Initial value)/Initial value * 100% which is (1.5 - 0.6)/0.6 * 100%.\nBased on the above thought, the answer is:\n150%" } ], "id_raw": 509 }, { "id": "qWzOP2b83Dnyhbvj", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a table format with Years as rows and Environmental Factors as columns based on the sequences of data points?" }, { "from": "gpt", "value": "To generate the table, I need to identify the x and y values for each line. Starting with the x-axis {year labels}[0.090, 0.925, 0.143, 0.951], [0.260, 0.925, 0.313, 0.951], [0.431, 0.925, 0.484, 0.951], [0.601, 0.925, 0.654, 0.951], [0.772, 0.925, 0.825, 0.951] we see these represent the years from 2010 to 2019. \n\nThe line points show the annual values for each indicator. We have line points for {Peace Index}[0.116, 0.231], [0.202, 0.203], [0.287, 0.175], [0.372, 0.146], [0.457, 0.118], [0.543, 0.090], [0.628, 0.062], [0.713, 0.073], [0.798, 0.085], [0.884, 0.107], {Social Equality Index}[0.116, 0.877], [0.202, 0.872], [0.287, 0.866], [0.372, 0.860], [0.457, 0.849], [0.543, 0.838], [0.628, 0.821], [0.713, 0.821], [0.798, 0.815], [0.884, 0.804], and {Absolute Poverty Rate}[0.116, 0.062], [0.202, 0.171], [0.287, 0.280], [0.372, 0.388], [0.457, 0.497], [0.543, 0.606], [0.628, 0.714], [0.713, 0.769], [0.798, 0.823], [0.884, 0.877]. So let's arrange these on the year axis to form the dataset value.\nBased on the above thought, the answer is:\nYear,Peace Index,Social Equality Index,Absolute Poverty Rate\n2010,75.0,63.5,14.3\n2011,75.5,63.6,14.1\n2012,76.0,63.7,13.9\n2013,76.5,63.8,13.7\n2014,77.0,64.0,13.5\n2015,77.5,64.2,13.3\n2016,78.0,64.5,13.1\n2017,77.8,64.5,13.0\n2018,77.6,64.6,12.9\n2019,77.2,64.8,12.8" } ], "id_raw": 510 }, { "id": "SUgTnyfnLhi0r8Pa", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_10.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which index saw the most consistent increase over the analysed period?" }, { "from": "gpt", "value": "I would need to look at the trend lines for each of the indexes. The {line for Peace Index}[0.116, 0.062, 0.884, 0.231] shows a mostly increasing trend and the {line for Social Equality Index}[0.116, 0.804, 0.884, 0.877] also shows a consistent increase. However, the {line for Absolute Poverty Rate}[0.116, 0.062, 0.884, 0.877] shows a consistent decrease. Overall, the Social Equality Index appears to have the most consistent increase over this time period.\nBased on the above thought, the answer is:\nThe Social Equality Index saw the most consistent increase over the period from 2010 to 2019, according to the line chart." } ], "id_raw": 510 }, { "id": "4aLLLe0q2OBxzBHy", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_10.png", "conversations": [ { "from": "human", "value": "\nBy how much did the Poverty Rate decrease from 2010 to 2019, according to the plotted data?" }, { "from": "gpt", "value": "Identifying {the line points for Absolute Poverty Rate}[0.116, 0.062], [0.884, 0.877], we can see that it starts at a point corresponding to 14.3 in 2010 on the y-axis and ends at a point corresponding to 12.8 in 2019. Subtracting the final value from the initial value shows how much the Poverty Rate decreased over this period.\nBased on the above thought, the answer is:\n1.5%" } ], "id_raw": 510 }, { "id": "wnzEGi23lCM9xV3g", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart into a CSV format distinguishing between the efficiency before and after interventions for each process?" }, { "from": "gpt", "value": "To create this table, I'll review the points plotted on the chart for each process. I concentrate on differentiating between the markers representing values before and after the intervention, as indicated by {the legend's content}[0.607, 0.723, 0.904, 0.896]. I will list each process with its corresponding efficiency scores determined from the coordinates of the plotted points for both states.\nBased on the above thought, the answer is:\nProcess,Before Intervention,After Intervention\n1,0.60,1.00\n2,0.75,0.95\n3,0.80,1.10\n4,0.85,1.05\n5,0.90,1.15\n6,0.95,0.80" } ], "id_raw": 511 }, { "id": "tItTp6OnwA9NuhUe", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_11.png", "conversations": [ { "from": "human", "value": "\nConsidering the data trend in the line chart, why should Process 6's performance decline after the intervention be a concern?" }, { "from": "gpt", "value": "Analyzing the performance data visualized in the chart, I scrutinize {the before and after intervention points of Process 6}[0.879, 0.511], [0.879, 0.737], noting the decrease in efficiency from 0.95 to 0.80. This is unique as all other processes show improvement or stability in their efficiency metrics post-intervention. It points out that unlike the growth or maintenance of efficiency in other processes, Process 6\u2019s operational environment or variables might not have responded positively to the changes implemented, underscoring potential underlying issues that need investigation and resolution to align with the broader objectives of efficiency improvement.\nBased on the above thought, the answer is:\nThe decline in efficiency for Process 6 is concerning because it diverges from the overall positive trend observed in other processes. This anomaly suggests specialized problems within Process 6 that may not be present in others, highlighting a need for dedicated analysis and corrective actions specific to this process's variables or operational environment." } ], "id_raw": 511 }, { "id": "EI0s0JNeFbHYYLKX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_11.png", "conversations": [ { "from": "human", "value": "\nHow much did the efficiency of Process 3 improve after the intervention according to the chart?" }, { "from": "gpt", "value": "First, I identify {the before intervention point of Process 3}[0.311, 0.822] at an efficiency level of 0.80. I then pinpoint {the after intervention point of Process 3}[0.311, 0.290] at an efficiency level of 1.10. The improvement is calculated by subtracting the before-intervention efficiency from after-intervention efficiency, resulting in an enhancement of 1.10 - 0.80 = 0.30 or 30%.\nBased on the above thought, the answer is:\n30%." } ], "id_raw": 511 }, { "id": "Kyme2Rkj5b6pD01L", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_12.png", "conversations": [ { "from": "human", "value": "\nCan you transform the information presented in the line chart regarding the trends in various forms of business and social responsibility into tabular data?" }, { "from": "gpt", "value": "Indeed, I can extract the values from the line chart into a table. I can see {the x-axis tick labels} for years [0.119, 0.903, 0.283, 0.903, 0.447, 0.903, 0.612, 0.903, 0.776, 0.903, 0.940, 0.903] as the rows of the table. Next, I see {the legend texts}[[0.142, 0.083, 0.354, 0.106], [0.142, 0.118, 0.362, 0.141], [0.142, 0.153, 0.327, 0.176]] that act as the column headers. The actual values can then be extracted by identifying the graph lines and their corresponding point values. For corporate responsibility {line points}[[0.119, 0.865], [0.283, 0.801], [0.447, 0.674], [0.612, 0.546], [0.776, 0.483], [0.940, 0.419]], environmental protection {line points}[[0.119, 0.546], [0.283, 0.514], [0.447, 0.419], [0.612, 0.228], [0.776, 0.164], [0.940, 0.100]] and sustainable practices {line points}[[0.119, 0.674], [0.283, 0.610], [0.447, 0.546], [0.612, 0.419], [0.776, 0.291], [0.940, 0.228]].\nBased on the above thought, the answer is:\nYear,Corporate Responsibility,Environmental Protection,Sustainable Practices\n2015,15,20,18\n2016,16,20.5,19\n2017,18,22,20\n2018,20,25,22\n2019,21,26,24\n2020,22,27,25" } ], "id_raw": 512 }, { "id": "j72WH5OBDK2vCaOT", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_12.png", "conversations": [ { "from": "human", "value": "\nHow does the trend in corporate responsibility compare to environmental protection and sustainable practices from 2015 to 2020?" }, { "from": "gpt", "value": "To assess the trends, I need to look at the upward or downward trajectory of the lines representing each category from 2015 to 2020. {The Corporate Responsibility line} starts at 15[0.119, 0.865] and increases to 22 [0.940, 0.419], {the Environmental Protection line} starts at 20[0.119, 0.546] and also increases to 27[0.940, 0.100], whereas {the Sustainable Practices line} begins at 18[0.119, 0.674] and increases to 25[0.940, 0.228]. Each line shows an increasing trend over the given period, though the rate of increase varies.\nBased on the above thought, the answer is:\nAll three areas - corporate responsibility, environmental protection and sustainable practices - show an increasing trend from 2015 to 2020. However, the rate of increase appears to be highest for environmental protection, as it reaches the highest value (27) by 2020. Despite start at a lower initial value, corporate responsibility also shows a significant increase, ending at a value of 22. Sustainable practices, though beginning at a higher initial value than corporate responsibility, ends up at a slightly higher value of 25. Overall, all three areas are exhibiting an increasing commitment over time, with environmental protection showing the greatest uptick." } ], "id_raw": 512 }, { "id": "nTouLNiuObbhEFfR", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_12.png", "conversations": [ { "from": "human", "value": "\nBy how much have sustainable practices increased from 2015 to 2020?" }, { "from": "gpt", "value": "To answer this question, I need to look at the line representing sustainable practices. Specifically, I would compare between the starting point {2015}[0.119, 0.903] with the value of 18 and by the end at {2020}[0.940, 0.903] where the value is 25. The difference is 25 - 18 = 7.\nBased on the above thought, the answer is:\n7." } ], "id_raw": 512 }, { "id": "xTNUtNJjeCHoOQ23", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_14.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart depicting the trends in Gender Pay Gap, Rights Violation Index, and Women Employment Rate over the years into a structured table?" }, { "from": "gpt", "value": "Observing {the x-axis tick labels}<[0.080, 0.918, 0.153, 0.943], [0.484, 0.918, 0.556, 0.943], [0.888, 0.918, 0.960, 0.943]> helps identify the timeline for the data from 1990 to 2020. Each line on the chart, marked by {points on the lines for Gender Pay Gap}<[0.116, 0.262], [0.520, 0.136]>, {Rights Violation Index}<[0.116, 0.603], [0.520, 0.468]>, and {Women Employment Rate}<[0.116, 0.790], [0.520, 0.802]> corresponds to different data series. I will use these to extract the respective rates and index across the years.\nBased on the above thought, the answer is:\nYear,Gender Pay Gap (%),Rights Violation Index,Women Employment Rate (%)\n1990,23.4,10.2,60.1\n1995,21.7,9.8,62.5\n2000,20.5,9.3,65.0\n2005,18.2,8.8,68.4\n2010,16.5,8.2,71.8\n2015,14.6,7.3,74.1\n2020,12.6,7.8,75.2" } ], "id_raw": 514 }, { "id": "B05yyla7f33s0rlG", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_14.png", "conversations": [ { "from": "human", "value": "\nWhich metric showed the most stability over the 30-year period according to the chart?" }, { "from": "gpt", "value": "To determine stability, I examine the amount of fluctuation in each line. The {line for Gender Pay Gap}<[0.116, 0.262], [0.884, 0.136]> shows a steady, consistent downward trajectory. {The Rights Violation Index line}<[0.116, 0.603], [0.520, 0.468], [0.884, 0.458]> exhibits irregular oscillations, indicating less stability. Comparatively, {the Women Employment Rate line}<[0.116, 0.790], [0.520, 0.802], [0.884, 0.802]> demonstrates smooth and consistent upward growth, making it the most stable of the three metrics.\nBased on the above thought, the answer is:\nThe Women Employment Rate showed the most stability over the 30-year period. It consistently and progressively increased, showing a smooth upward trajectory in contrast to the others, which displayed greater fluctuations and irregularities." } ], "id_raw": 514 }, { "id": "RpGd7N4k8EmJsbpO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in Women Employment Rate from 1990 to 2020?" }, { "from": "gpt", "value": "To calculate the percentage increase, I need to locate {the points representing the Women Employment Rate}<[0.116, 0.790], [0.884, 0.802]> in the years 1990 and 2020. The values are noted as 60.1% in 1990 and 75.2% in 2020. The increase is calculated as ((75.2 - 60.1) / 60.1) * 100 = 25.12%.\nBased on the above thought, the answer is:\n25.12%" } ], "id_raw": 514 }, { "id": "mBDNucbAnXlowhTj", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_15.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the given data with 'Year' as rows and Areas of development as columns?" }, { "from": "gpt", "value": "To construct the table, I'll start with the rows labels {the x-axis tick labels}[[0.113, 0.888, 0.168, 0.918], [0.233, 0.888, 0.288, 0.918], [0.353, 0.888, 0.408, 0.918], [0.473, 0.888, 0.527, 0.918], [0.592, 0.888, 0.647, 0.918], [0.712, 0.888, 0.767, 0.918], [0.832, 0.888, 0.887, 0.918]] which represent the years. Next, I identify the column headers as the areas. These are {Global Trade Volume, Market Globalization, Economic Integration, Tech Revolution}[0.185, 0.057, 0.408, 0.086][0.185, 0.101, 0.416, 0.130][0.185, 0.145, 0.409, 0.174][0.185, 0.188, 0.355, 0.217]. I'll take the corresponding values for each year from {the line points}[[0.140, 0.526][0.260, 0.450][0.380, 0.298][0.500, 0.191][0.620, 0.115][0.740, 0.085][0.860, 0.069][0.140, 0.830][0.260, 0.800][0.380, 0.754][0.500, 0.724][0.620, 0.678][0.740, 0.632][0.860, 0.556][0.140, 0.592][0.260, 0.545][0.380, 0.355][0.500, 0.236][0.620, 0.164][0.740, 0.117][0.860, 0.069][0.140, 0.830][0.260, 0.783][0.380, 0.759][0.500, 0.711][0.620, 0.664][0.740, 0.616][0.860, 0.521]] and compile them in a structured table.\nBased on the above thought, the answer is:\nYear,Global Trade Volume,Economic Integration,Market Globalization,Tech Revolution\n1990,30,10,15,5\n1995,35,12,17,7\n2000,45,15,25,8\n2005,52,17,30,10\n2010,57,20,33,12\n2015,59,23,35,14\n2020,60,28,37,18" } ], "id_raw": 515 }, { "id": "OXXe6BEQoxvuaYDl", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_15.png", "conversations": [ { "from": "human", "value": "\nWhat trends can be observed from the line chart regarding the areas of development in the Internet and Communication Industry?" }, { "from": "gpt", "value": "To analyze the trends, I will need to look at the lines for each area. Observing {the line for 5G Network Support}[0.140, 0.069, 0.860, 0.526], there is a steady increase throughout the years. Next, looking at {the line for Cloud Computing Services}[0.140, 0.556, 0.860, 0.830], the trend also shows a consistent increase, albeit at a slower pace. For {the line for Internet of Things}[0.140, 0.521, 0.860, 0.07], there is an erratic fluctuation with a generally increasing trend. Lastly, seeing {the line for Machine Learning Applications}[0.140, 0.830, 0.860, 0.521], it's clear that there is a steady and sharp rise in this area.\nBased on the above thought, the answer is:\nBased on the line chart, all four areas (5G Network Support, Cloud Computing Services, Internet of Things, Machine Learning Applications) show a trend of growth from 1990 to 2020. However, the rate of increase varies. The Machine Learning Applications seems to have a steady and sharp upward trend throughout the years. In contrast, the Internet of Things shows an erratic fluctuation but still follows a general incrementing path. Both 5G Network Support and Cloud Computing Services are showing steady increases with variations in the pace of growth. Overall, all areas show positive development over the studied period." } ], "id_raw": 515 }, { "id": "MoHk9he9jP1SZhK9", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in Market Globalization from 1990 to 2020?" }, { "from": "gpt", "value": "First, I locate the point representing Market Globalization in 1990 at {the line}[0.140, 0.592], which corresponds to a value of 15. Then, I find the point for Market Globalization in 2020 at {the line}[0.860, 0.069], corresponding to a value of 37. Finally, I compare the difference and calculate the percentage increase as (37 - 15)/15 * 100%.\nBased on the above thought, the answer is:\n146.67%" } ], "id_raw": 515 }, { "id": "8ytJpdjWmoWqo1Eb", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_16.png", "conversations": [ { "from": "human", "value": "\nCould you please retrieve the data for Resource Allocation and Utilization among different levels of education from the bar chart?" }, { "from": "gpt", "value": "Sure, I can extract the data by examining the bars of the chart. I would begin by looking at {the x-axis labels}[[0.108, 0.922, 0.195, 0.951], [0.304, 0.922, 0.394, 0.951], [0.500, 0.922, 0.594, 0.951], [0.698, 0.922, 0.786, 0.951], [0.893, 0.922, 0.970, 0.951]], which depict levels of education that will serve as our row labels. Next, I\u2019ll identify {the blue bars}[[0.218, 0.220, 0.299, 0.281], [0.421, 0.444, 0.501, 0.512], [0.616, 0.568, 0.696, 0.624], [0.808, 0.779, 0.889, 0.832], [1.001, 0.745, 1.081, 0.787]]') for allocated resources and the {orange bars}[[0.218, 0.281, 0.299, 0.351], [0.421, 0.512, 0.501, 0.584], [0.616, 0.624, 0.696, 0.682], [0.808, 0.832, 0.889, 0.898], [1.001, 0.787, 1.081, 0.827]] for resource utilization values. The values for each bar can be calculated based on the scale provided in y axis.\nBased on the above thought, the answer is:\nEducation Level,Resource Allocation,Resource Utilization\nPrimary School,50,38\nMiddle School,70,60\nHigh School,90,85\nUniversity,120,108\nAdult Education,100,83" } ], "id_raw": 516 }, { "id": "6bupEPrOWDT8kK7o", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_16.png", "conversations": [ { "from": "human", "value": "\nConsidering the bar chart, which level of education has the most efficient resource utilization, and why do you suggest so?" }, { "from": "gpt", "value": "To determine which level of education has the most efficient resource utilization, I would firstly look at {the orange bars}[[0.218, 0.281, 0.299, 0.351], [0.421, 0.512, 0.501, 0.584], [0.616, 0.624, 0.696, 0.682], [0.808, 0.832, 0.889, 0.898], [1.001, 0.787, 1.081, 0.827]], which represent resource utilization in every education level. Then, I would compare these with {the blue bars}[[0.218, 0.220, 0.299, 0.281], [0.421, 0.444, 0.501, 0.512], [0.616, 0.568, 0.696, 0.624], [0.808, 0.779, 0.889, 0.832], [1.001, 0.745, 1.081, 0.787]], depicting allocated resources. The education level where the difference between allocated and utilized resources is the smallest is the most efficient.\nBased on the above thought, the answer is:\nAccording to the bar chart, the High School level has the most efficient resource utilization. This is because the difference between the allocated (90) and utilized resources (85) at this level is the smallest among all education levels." } ], "id_raw": 516 }, { "id": "N92DW7k4tDyXPSiA", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the total quantity of Allocated Resources across all levels of education according to the bar chart?" }, { "from": "gpt", "value": "To find the total quantity of Allocated Resources, I need to add up the quantities represented by {the blue bars}[[0.218, 0.220, 0.299, 0.281], [0.421, 0.444, 0.501, 0.512], [0.616, 0.568, 0.696, 0.624], [0.808, 0.779, 0.889, 0.832], [1.001, 0.745, 1.081, 0.787]] which represent resources allocated to each education level. These correspond to the values 50 for Primary School, 70 for Middle School, 90 for High School, 120 for University, and 100 for Adult Education.\nBased on the above thought, the answer is:\n430" } ], "id_raw": 516 }, { "id": "a77IafSEeGP5EqfV", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_17.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line graph's time-series data for 'Cardiovascular Diseases', 'Diabetes', and 'Lung Cancer' into a structured table, with 'Year' serving as rows and different disease types as columns?" }, { "from": "gpt", "value": "To convert the chart into a tabular form, I need to identify {the x-axis labels}[0.124, 0.910, 0.940, 0.910] representing the years from 2015 to 2020 and use these as the row labels. I notice that the line markers identify {the y-values for each disease}[0.124, 0.110, 0.940, 0.243], [0.124, 0.577, 0.940, 0.754], [0.124, 0.243, 0.940, 0.466] and can cross-reference these with the {y-axis labels}[0.083, 0.910, 0.083, 0.132] to get the values for each year. The disease types, 'Cardiovascular Diseases', 'Diabetes', and 'Lung Cancer' are used as the column headers.\nBased on the above thought, the answer is:\nYear,Cardiovascular Diseases,Diabetes,Lung Cancer\n2015,50,30,40\n2016,55,28,42\n2017,53,27,45\n2018,52,30,48\n2019,55,35,47\n2020,56,32,50" } ], "id_raw": 517 }, { "id": "WYbWhHydBnR5GVXI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall trend of Cardiovascular Diseases from 2015 to 2020 based on the line chart? Please explain your analysis." }, { "from": "gpt", "value": "To analyze the trend of Cardiovascular Diseases from 2015 to 2020, I need to examine the plot of {Cardiovascular Diseases}[0.124, 0.110, 0.940, 0.243]. Looking at the line, it shows a gradual increase from 50 in 2015 to a peak of 55 in 2016, a little decrease to 53 in 2017 and 52 in 2018, and then a rise again to 55 in 2019, and then finally to 56 in 2020. Therefore, it indicates an overall increasing trend with some minor fluctuations in between.\nBased on the above thought, the answer is:\nThe overall trend of Cardiovascular Diseases from 2015 to 2020 seems to be increasing, despite some minor fluctuations. The line chart shows an initial increase from 50 in 2015 to 56 in 2020, but with a slight decrease between 2016 and 2018. This indicates that the number of people affected by Cardiovascular Diseases has generally risen over this period." } ], "id_raw": 517 }, { "id": "m1I78OkvqHdDyxYF", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in the Percent of People Affected by Diabetes from 2015 to 2020?" }, { "from": "gpt", "value": "To calculate the total increase in the Percent of People Affected by Diabetes from 2015 to 2020, I examine the {data points for Diabetes}[0.124, 0.577, 0.940, 0.754]. The 2015 value is 30, and the 2020 value is 32. The total increase is therefore 32 - 30 = 2.\nBased on the above thought, the answer is:\n2." } ], "id_raw": 517 }, { "id": "GF5PAcQZIYa6RexF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_26_19.png", "conversations": [ { "from": "human", "value": "\nCould you transform the line chart data into a structured table with years as columns and skills as rows?" }, { "from": "gpt", "value": "To form the data table from the given line chart, I begin by identifying {the x-axis tick labels}[[0.090, 0.918, 0.143, 0.943], [0.186, 0.918, 0.239, 0.943], [0.282, 0.918, 0.335, 0.943], [0.378, 0.918, 0.431, 0.943], [0.474, 0.918, 0.526, 0.943], [0.569, 0.918, 0.622, 0.943], [0.665, 0.918, 0.718, 0.943], [0.761, 0.918, 0.814, 0.943], [0.857, 0.918, 0.910, 0.943]] as the column headers or the years. My next step is to identify {the line labels}[0.656, 0.056, 0.863, 0.084], [0.656, 0.097, 0.900, 0.125]] as row labels that represent different skills. Subsequently, I will extract the corresponding values at each point on the respective lines for Programming[0.116, 0.283, 0.884, 0.785], Leadership[0.116, 0.358, 0.884, 0.748] and Team Building[0.116, 0.031, 0.884, 0.861] Skills and match them with the respective years.\nBased on the above thought, the answer is:\nSkills,2012,2013,2014,2015,2016,2017,2018,2019,2020\nProgramming,7.1,8.0,7.6,7.2,6.5,6.0,5.5,4.7,4.0\nLeadership,4.3,5.1,5.8,6.5,7.0,7.3,7.4,7.2,7.0\nTeam Building,3.4,4.0,4.8,5.7,6.7,7.6,8.5,9.4,10.0" } ], "id_raw": 519 }, { "id": "yLeQzmwH7fuAOosk", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_26_19.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which skill shows a consistent improvement over the years?" }, { "from": "gpt", "value": "To identify the skill that shows consistent improvement over the years, I would have to analyze the line trend for each skill. {The line for Programming}[0.116, 0.283, 0.884, 0.785] shows a declining trend from 7.1 in 2012 to 4.0 in 2020. {The line for Leadership skills}[0.116, 0.358, 0.884, 0.748], although indicating an increasing trend initially, starts to plateau from 2018 to 2020. {The Team Building line}[0.116, 0.031, 0.884, 0.861] shows an improvement from 3.4 in 2012 to reach 10.0 in 2020, indicating a consistently improving trend.\nBased on the above thought, the answer is:\nThe skill that demonstrates consistent improvement over the years is Team Building as indicated by the upward trend line in the chart. This is depicted by the rise from 3.4 in 2012 to 10.0 in 2020." } ], "id_raw": 519 }, { "id": "YonFAp85CO4q7Dm6", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_26_19.png", "conversations": [ { "from": "human", "value": "\nWhat was the difference in percentage between Programming and Leadership skills in the year 2015 in terms of recognition?" }, { "from": "gpt", "value": "To find out the difference between the recognition of Programming and Leadership skills in the year 2015, firstly I need to find {the point for Programming in the year 2015}[0.404, 0.383], which gives me the value of 7.2. Secondly, I look at {the point for Leadership in the year 2015}[0.404, 0.471] and extract the value of 6.5. The difference is thus, 7.2 - 6.5 = 0.7.\nBased on the above thought, the answer is:\n0.7." } ], "id_raw": 519 }, { "id": "HTqmKr17CusBV7lK", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_0.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table converting the user growth trend data of Instagram according to the months displayed in the chart?" }, { "from": "gpt", "value": "To generate the table, I first examine {the x-axis labels for Instagram}[[0.399, 0.912, 0.406, 0.936], [0.425, 0.912, 0.431, 0.936], ..., [0.628, 0.912, 0.640, 0.936]]. These labels correspond to the months. Next, I identify the growth percentages shown by {the Instagram growth line}[[0.403, 0.183, 0.634, 0.706]] and its corresponding {data points}[[0.403, 0.706], [0.428, 0.658], ..., [0.634, 0.183]]. I then extract values from these points to fill in the growth percentage for each month.\nBased on the above thought, the answer is:\nMonth,Instagram User Growth(%)\n1,8\n2,10\n3,12\n4,15\n5,18\n6,20\n7,22\n8,25\n9,28\n10,30" } ], "id_raw": 520 }, { "id": "On9MMNWO60Mno3aJ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_0.png", "conversations": [ { "from": "human", "value": "\nAmong the social media platforms, which one showed a consistent increase in user growth and which showed the most inconsistency in user growth trends?" }, { "from": "gpt", "value": "To evaluate consistency in user growth trends, I need to analyze the growth patterns in the chart lines. Observing {the Instagram growth line}[[0.403, 0.183, 0.634, 0.706]], the trend has consistently been upward from 8% to 30%, showcasing a continuous increase. In contrast, {the Twitter growth line}[[0.732, 0.777, 0.964, 0.872]], alternating amongst values such as 5, 3, 4, 3, 2 in a non-linear pattern, demonstrates notable inconsistency.\nBased on the above thought, the answer is:\nInstagram showed a consistent increase in user growth, as observed by the upward trend in its growth percentage from 8% to 30%. Twitter displayed the most inconsistency, with user growth percentages fluctuating without following a uniform direction." } ], "id_raw": 520 }, { "id": "OfBbVz1w1EIe9afH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the average user growth (%) for LinkedIn across the displayed months?" }, { "from": "gpt", "value": "To calculate the average growth for LinkedIn, I locate the growth data from {the LinkedIn growth line}[[0.732, 0.611, 0.964, 0.872]] using its {data points}[[0.732, 0.849], [0.758, 0.849], ..., [0.964, 0.611]]. Summing these percentages as 2, 2, 3, 4, 5, 6, 7, 9, 10, 12 and dividing by the total number of months, which is 10, gives the average growth.\nBased on the above thought, the answer is:\n6" } ], "id_raw": 520 }, { "id": "BGuZewWp4J1QOUgb", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_1.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data for Apple Inc.'s stock prices over the years into a structured table format?" }, { "from": "gpt", "value": "Starting by identifying {the x-axis labels for the years}[0.093, 0.825, 0.327, 0.871] which give the columns for the table, and associating them with {the data points of the line for Apple Inc.}[0.112, 0.686, 0.308, 0.772] corresponding to each year from 2010 to 2020. Using these data points, I can derive the table.\nBased on the above thought, the answer is:\nYear,Stock Price (in USD)\n2010,145.42\n2011,405.12\n2012,542.44\n2013,561.02\n2014,110.38\n2015,105.26\n2016,115.82\n2017,169.23\n2018,157.74\n2019,293.65\n2020,132.67" } ], "id_raw": 521 }, { "id": "P6AiGmfJEPZC3TOK", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_1.png", "conversations": [ { "from": "human", "value": "\nHow do the trends of development indexes for AI compared to VR from 2000 to 2020?" }, { "from": "gpt", "value": "To analyze the trends, I first identify the rise and fall of each sector overtime by examining {the line for AI}[0.112, 0.179, 0.963, 0.174] and {the line for VR}[0.112, 0.335, 0.963, 0.343]. The AI line exhibits upward trends with minimal dips in 2004 and 2008. Contrastingly, the VR line indicates a more irregular pattern with significant fluctuations and a notable dip in 2010.\nBased on the above thought, the answer is:\nAI consistently shows a rising trend with only minor fluctuations, indicating a steady growth in its development index. VR, on the other hand, displays a significantly more volatile trend with irregular growth patterns and notable declines, especially in 2010. This comparative analysis highlights AI's more stable and robust growth over the years compared to the erratic progression of VR." } ], "id_raw": 521 }, { "id": "fe5bmoB33t8YbCjW", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in stock price for Amazon Inc. from 2010 to 2020?" }, { "from": "gpt", "value": "To find the total increase, I will analyze {the data points for Amazon Inc.}[0.768, 0.174, 0.963, 0.767], extracting the values for 2010 and 2020, specifically at {the corresponding points for 2010 and 2020}[0.768, 0.767], [0.963, 0.174]. The increase is the difference between the stock prices in these two years.\nBased on the above thought, the answer is:\n3123" } ], "id_raw": 521 }, { "id": "WP4ujHFLKjSl2bXR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_2.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, can you create a table indicating the production values for different types of production against each year?" }, { "from": "gpt", "value": "Observing the chart, to create the requested table, I first identify different types of production {the legend}[0.100, 0.151, 0.293, 0.304] which include Cereal, Fruit, Vegetable, Meat, and Dairy Production. For each type, I reference {the line points}[0.122, 0.772], [0.151, 0.732], ..., [0.954, 0.369] that correspond to each year marked on {the x-axis}[0.102, 0.825, 0.142, 0.871], ..., [0.935, 0.825, 0.973, 0.871]. By extracting the values at intersection points of the years and production types, I can compile the complete data.\nBased on the above thought, the answer is:\nYear,Cereal Production,Fruit Production,Vegetable Production,Meat Production,Dairy Production\n1990,1000,1100,1200,1300,1400\n1995,1100,1050,1300,1250,1450\n2000,1500,1200,1400,1340,1550\n2005,1550,1300,1500,1500,1600\n2010,2000,1350,1600,1600,1700\n2015,2200,1400,1850,1700,1800\n2020,2500,1550,1900,1800,2000" } ], "id_raw": 522 }, { "id": "mSxqlzj2TYpV3Scn", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_2.png", "conversations": [ { "from": "human", "value": "\nWhich product shows the most consistent growth in production over the years as depicted in the chart?" }, { "from": "gpt", "value": "To analyze which product shows the most consistent growth in production over the years, I review {the lines representing different productions}[0.122, 0.168, 0.298, 0.772], [0.450, 0.410, 0.626, 0.692]. Each line's trajectory points me toward the trend. Dairy Production line {the Dairy Production line}[0.778, 0.369, 0.954, 0.611] appears to have the most stable upward trend, showing no plateaus or declines, suggesting consistent growth.\nBased on the above thought, the answer is:\nDairy Production shows the most consistent growth over the years. Its line trajectory is stable and ascending consistently without any downward trends or plateaus, unlike other productions that show some fluctuations." } ], "id_raw": 522 }, { "id": "kriLTVvxi4GeXMmF", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in Dairy Production from 1990 to 2020?" }, { "from": "gpt", "value": "To calculate the total increase in Dairy Production from 1990 to 2020, I first find the values of Dairy Production in 1990 and 2020 from {the points on the Dairy Production line}[0.778, 0.611], [0.954, 0.369]. The value in 1990 is 1400 and in 2020 is 2000. The total increase is 2000 - 1400.\nBased on the above thought, the answer is:\n600" } ], "id_raw": 522 }, { "id": "MAfl89LCFvQP30U6", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_3.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured table outlining the investment trends for COVID-19 Vaccines and Telemedicine Services from 2010 to 2021 according to the line chart?" }, { "from": "gpt", "value": "To construct the table, I first observe {the x-axis labels}[0.061, 0.934, 0.079, 0.970] that present the years from 2010 to 2021. Then, I examine {the lines representing COVID-19 Vaccines and Telemedicine Services}[0.061, 0.041, 0.984, 0.875] to synchronize their plot points with respective years.\nBased on the above thought, the answer is:\nYear,COVID-19 Vaccines (billions),Telemedicine Services (billions)\n2010,1.0,2.5\n2011,1.5,3.0\n2012,2.0,3.5\n2013,2.5,4.0\n2014,3.0,4.5\n2015,3.5,5.0\n2016,4.0,5.5\n2017,4.5,6.0\n2018,5.0,6.5\n2019,5.5,7.0\n2020,15.0,8.5\n2021,16.0,9.0" } ], "id_raw": 523 }, { "id": "DnZ0hwtWjmPKZ6r0", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_3.png", "conversations": [ { "from": "human", "value": "\nBased on the chart trends, how did the pandemic likely impact the investments in Telemedicine Services?" }, { "from": "gpt", "value": "Examining {the trend line for Telemedicine Services}[0.061, 0.041, 0.984, 0.875], I note a stable growth until 2020, followed by a slight increase. This increased trajectory during the pandemic suggests that there was likely a higher demand for remote medical services due to social distancing measures and health safety concerns.\nBased on the above thought, the answer is:\nThe pandemic likely caused an increase in investments in Telemedicine Services, as observed from the slight upward trajectory starting from 2020, indicating an enhanced focus and reliance on remote healthcare solutions amidst health safety measures." } ], "id_raw": 523 }, { "id": "Yx88IYcl59v0NKev", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_3.png", "conversations": [ { "from": "human", "value": "\nWhat was the increase in investment for COVID-19 Vaccines from 2019 to 2020 according to the chart?" }, { "from": "gpt", "value": "To determine the increase, I locate {the points for COVID-19 Vaccines for years 2019 and 2020}[0.923, 0.895, 0.929, 0.923] corresponding to 5.5 billion in 2019 and 15.0 billion in 2020. The increase is calculated by subtracting the value for 2019 from that of 2020.\nBased on the above thought, the answer is:\n9.5 billion" } ], "id_raw": 523 }, { "id": "TrwF357TxZ5kTgt3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_4.png", "conversations": [ { "from": "human", "value": "\nCan you provide a data table for the progress indices of Emails and Social Media across the different Decomposition IO Norm levels?" }, { "from": "gpt", "value": "Utilizing values from {the line points}[0.728, 0.716], [0.789, 0.545], [0.850, 0.481], [0.911, 0.438], [0.972, 0.395] for Emails and [0.728, 0.737], [0.789, 0.630], [0.850, 0.566], [0.911, 0.524], [0.972, 0.481] for Social Media respectively, I can form a table indicating their progression over the designated norms.\nBased on the above thought, the answer is:\nDecomposition IO Norm, Emails, Social Media\n0, 56, 55\n20, 64, 60\n40, 67, 63\n60, 69, 65\n80, 71, 67" } ], "id_raw": 524 }, { "id": "VLlcPUaD73TK2j7r", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_4.png", "conversations": [ { "from": "human", "value": "\nWhich trend had a more significant overall impact on the innovations, Persistent Growth or Rapid Decline, according to the descriptions?" }, { "from": "gpt", "value": "By analyzing {the line plots}[0.057, 0.091, 0.942, 0.903] showcasing Persistent Growth and Rapid Decline, one observes that Persistent Growth typically has values increasing, while Rapid Decline displays decreasing values. 'Augmented Reality EdTech' demonstrated the most significant growth within Persistent Growth and 'Online Courses' saw the steepest decline within Rapid Decline. Judging the overall impact involves assessing both the amplitude and relevance of the changes over the range of technologies covered.\nBased on the above thought, the answer is:\nPersistent Growth had a more significant overall impact, as it showcased progressive increases across multiple technologies, indicating a growing interest and adoption in various innovations, compared to the specific decreases seen in Rapid Decline." } ], "id_raw": 524 }, { "id": "PhwvWnoc0kAgKGjX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increment observed in the Smartphones Progress from the start to the end point?" }, { "from": "gpt", "value": "To find the total increment in the Smartphones Progress, I'd first examine {the charted points for Smartphones}[0.070, 0.630], [0.131, 0.417], [0.192, 0.310], [0.253, 0.246], [0.314, 0.181], noting the values at each point: 60, 70, 75, 78, 81. The starting value is 60 and the end value is 81. Thus, the total increment is 81 - 60.\nBased on the above thought, the answer is:\n21." } ], "id_raw": 524 }, { "id": "2hccqX8ro4KOYDQf", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_5.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can we extract the data and represent it as a table with Policy Interest Rate as rows and KPI as columns?" }, { "from": "gpt", "value": "To build a table from the line chart, I first identify {the x-axis tick labels}[0.131, 0.912, 0.167, 0.936], [0.327, 0.912, 0.364, 0.936], [0.524, 0.912, 0.561, 0.936], [0.721, 0.912, 0.757, 0.936], [0.917, 0.912, 0.954, 0.936] which symbolize the row labels or Policy Interest Rates. Next, the KPI corresponds to the y-axis values. I locate {the line points}[0.149, 0.813], [0.346, 0.397], [0.542, 0.230], [0.739, 0.147], [0.936, 0.314] for each Policy Interest Rate and decode the corresponding KPI values.\nBased on the above thought, the answer is:\nPolicy Interest Rate,KPI\n0.5,50\n1.0,55\n1.5,57\n2.0,58\n2.5,56" } ], "id_raw": 525 }, { "id": "rOlrmPOLSO9HNb6t", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_5.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the correlation between the Policy Interest Rate and the KPI based on the line chart?" }, { "from": "gpt", "value": "To evaluate the correlation, I would assess the overall trend of the KPI as the Policy Interest Rate increases. Starting at {Policy Rate 0.5}[0.149, 0.813], with KPI 50, increasing to {Policy Rate 1.0}[0.346, 0.397] with KPI 55, it is observed that as the Policy Interest Rate rises, the KPI also generally increases. However, from {Policy Rate 1.5}[0.542, 0.230] to {Policy Rate 2.5}[0.936, 0.314] there is an initial increase in KPI from 57 to 58, followed by a decrease to 56, indicating a not entirely consistent correlation.\nBased on the above thought, the answer is:\nFrom the line chart, it can be interpreted that there is generally a positive correlation between Policy Interest Rate and the KPI. An increase in Policy Interest Rate typically leads to an increase in the KPI. However, the correlation does not hold consistenly as towards the higher end of the interest rate, an increase in the rate is followed by a decrease in the KPI, indicating a possible non-linear relationship or influence of other factors." } ], "id_raw": 525 }, { "id": "0bDNNzJXdEiPAyAL", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in KPI from a Policy Interest Rate of 0.5 to 1?" }, { "from": "gpt", "value": "To ascertain the change in KPI from an interest rate of 0.5 to 1, I must locate {the value at 0.5}[0.149, 0.813], which is 50 and {the value at 1.0}[0.346, 0.397], which is 55. The change in KPI is then determined by subtracting the value at 0.5 from the value at 1.0, which equals 55 - 50 = 5.\nBased on the above thought, the answer is:\n5." } ], "id_raw": 525 }, { "id": "YnYMYnSDFNqxnBrA", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_7.png", "conversations": [ { "from": "human", "value": "\nCan you convert the provided line chart into a tabular format that displays years as rows and technology sectors as columns?" }, { "from": "gpt", "value": "To create the table, I begin by looking at {the x-axis labels}[0.083, 0.890, 0.322, 0.950] which outline the years ranging from 2015 to 2022. These will serve as rows for my table. Next, {the legend}[0.840, 0.686, 0.970, 0.769] shows labels like Artificial Intelligence, Blockchain, and others as technology sectors, which will form the column headings. I will then extract the corresponding development index values for each technology per year by observing {the point coordinates along each line}[0.097, 0.239, 0.963, 0.765].\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Blockchain,Cloud Computing,Internet of Things,Virtual Reality\n2015,12,3,10,5,7\n2016,17,4,15,6,9\n2017,23,5,20,8,12\n2018,30,7,30,11,15\n2019,38,8,45,15,18\n2020,50,10,60,20,23\n2021,65,12,80,25,30\n2022,80,15,100,30,38" } ], "id_raw": 527 }, { "id": "4K6Xa7VPMbJFm6R1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_7.png", "conversations": [ { "from": "human", "value": "\nConsidering the performance trends shown in the line chart, which technology industry is progressing most rapidly, and what does this imply for its future potential?" }, { "from": "gpt", "value": "Reviewing {the line for Artificial Intelligence}[0.097, 0.239, 0.963, 0.355], it shows a steep rise in development indicators from 2015 to 2022, which is notably higher than other technologies such as {Blockchain}[0.753, 0.765, 0.963, 0.756] and {Virtual Reality}[0.893, 0.759, 0.963, 0.756]. This rapid progression signifies a significant investment and focus in the sector, suggesting strong future potential and the possibility of sustaining a leading position in technological advancements.\nBased on the above thought, the answer is:\nArtificial Intelligence is demonstrating the most rapid progression. This not only highlights its current successes but also paints a promising picture of its leading role in future technological developments and innovations." } ], "id_raw": 527 }, { "id": "YGGipsvhdMgdcWWC", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the average annual growth rate for the Blockchain technology from 2015 to 2022 based on the line chart?" }, { "from": "gpt", "value": "To calculate the average annual growth rate for Blockchain, I need to specify values from {the line points for Blockchain}[0.753, 0.765, 0.963, 0.756]. Assumed values from the plot are 3 for 2015 and 15 for 2022. The formula for the average annual growth rate is ((Ending Value/Beginning Value)^(1/number of periods)) - 1, which translates to ((15/3)^(1/7))-1. Calculating this yields the average annual growth rate as a percentage.\nBased on the above thought, the answer is:\n25.89%" } ], "id_raw": 527 }, { "id": "P23xwA1sRVb9vort", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_9.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data for the subplot labeled \"ICT & Nanotech\" into a tabular format using TRL as columns and the respective fields as rows?" }, { "from": "gpt", "value": "I commence by looking at {the x-axis labels for the ICT & Nanotech subplot}[0.424, 0.825, 0.652, 0.871], which give the Technology Readiness Levels (TRL) as columns: 0, 20, 40, 60, 80. For the rows, I'll use the fields labeled in {the legend inside the ICT & Nanotech subplot}[0.518, 0.616, 0.648, 0.769] which are ICT and Nanotech. To fill in the table, I check the values at each TRL intersection for {the line graphs in the ICT & Nanotech subplot}[0.429, 0.327, 0.642, 0.527] and [0.429, 0.161, 0.642, 0.460].\nBased on the above thought, the answer is:\nTRL,ICT,Nanotech\n0,0.56,0.6\n20,0.62,0.65\n40,0.64,0.7\n60,0.67,0.75\n80,0.68,0.78" } ], "id_raw": 529 }, { "id": "bRcMbJM3alw7YYEn", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_9.png", "conversations": [ { "from": "human", "value": "\nWhy does the Nanotech line show a sharper increase compared to the ICT line in their respective plots?" }, { "from": "gpt", "value": "Observing {the line for Nanotech}[0.429, 0.460, 0.642, 0.161] alongside {the line for ICT}[0.429, 0.527, 0.642, 0.327], the sharper increase of Nanotech is evident as its line starts at a success rate of 0.6 and reaches 0.78, while the ICT line starts at 0.56 and only reaches 0.68. This sharper rise in Nanotech indicates a more rapid improvement over the Technology Readiness Levels compared to ICT.\nBased on the above thought, the answer is:\nNanotech displays a more aggressive increase in innovation success over the plotted Technology Readiness Levels due to starting at a slightly higher initial value than ICT and achieving a relatively higher value by TRL 80. This suggests that Nanotechnology may be experiencing faster technological advancements or more effective implementation in its field compared to Information and Communication Technology." } ], "id_raw": 529 }, { "id": "u0nHTP8hPK6XoEkV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_9.png", "conversations": [ { "from": "human", "value": "\nBy how much does the success rate for ICT progress from TRL 0 to TRL 80?" }, { "from": "gpt", "value": "To calculate the increase in success rate for ICT, I must reference the points on {the line for ICT}[0.429, 0.527, 0.642, 0.327]. The success rate at TRL 0 is 0.56, and at TRL 80 it is 0.68. The difference or progress is calculated as 0.68 - 0.56.\nBased on the above thought, the answer is:\n0.12" } ], "id_raw": 529 }, { "id": "6dxzmvZhxXflKmvV", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the horizontal bar chart data into a table format with names of different financial trends as rows and their percent change as columns?" }, { "from": "gpt", "value": "Analyzing the horizontal bar chart, I locate {the y-axis labels}[0.034, 0.118, 0.052, 0.146] which represent different financial trend categories such as Steady Decline, Persistent Rise, Gradual Improvement, Erratic Behavior, and Sharp Decrease. For each category, I pinpoint {the end of the bars}[0.763, 0.089, 0.941, 0.117], determining the extent and direction of the bars relative to the Y-axis to extract the percentage changes.\nBased on the above thought, the answer is:\nTrend,Percentage Change\nSteady Decline,-10%\nPersistent Rise,20%\nGradual Improvement,5%\nErratic Behavior,-30%\nSharp Decrease,-40%" } ], "id_raw": 530 }, { "id": "YhAUZmyepxiBdoX1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_10.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, which trend shows the most instability and why?" }, { "from": "gpt", "value": "To determine instability, I analyze {the bars}[0.041, 0.760, 0.052, 0.793] associated with each trend category. The bar for 'Erratic Behavior' extends significantly further in the negative direction compared to others, indicative of a very large decrease (-30%). This trend signifies more instability as the proportion of the decrease is higher than any other trend, marked by a deep red hue and an extended bar to the left indicating a significant drop.\nBased on the above thought, the answer is:\nThe 'Erratic Behavior' trend shows the most instability, as indicated by its significant negative percentage change of -30%. The extensive length and the stark color of the bar on the chart highlight the large fluctuations in this category, suggesting a highly volatile and unstable trend." } ], "id_raw": 530 }, { "id": "KmoqrIPQKUSeWp2i", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the absolute difference between the highest increase and the highest decrease shown in the chart?" }, { "from": "gpt", "value": "To identify the highest increase and decrease, I examine the lengths of {the bars}[0.435, 0.119, 0.612, 0.759]. The longest bar in the positive direction corresponds to the Persistent Rise category, which shows a 20% increase, while the longest bar in the negative direction corresponds to the Sharp Decrease category, at -40%. Calculating the absolute difference would involve taking the mathematical absolute value of the difference between these two percentages.\nBased on the above thought, the answer is:\n60%" } ], "id_raw": 530 }, { "id": "bobOogXUv5pN4BcN", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_11.png", "conversations": [ { "from": "human", "value": "\nCan you organize the data as per the line plots in the chart to generate a table with decomposition IO norms as columns and different systems as rows?" }, { "from": "gpt", "value": "To extract the tabular data, I first identify {the x-axis tick labels}[[0.096, 0.825, 0.106, 0.871], [0.145, 0.825, 0.164, 0.871], [0.198, 0.825, 0.218, 0.871], [0.251, 0.825, 0.271, 0.871], [0.305, 0.825, 0.324, 0.871]], which correspond to the column headers or decomposition IO norms. {The legend texts}[[0.206, 0.704, 0.314, 0.751], [0.562, 0.704, 0.642, 0.751], [0.872, 0.634, 0.969, 0.681], [0.872, 0.704, 0.942, 0.751]] denotes the different system types which are to become the row headings. To find the values, I locate {the line points}[[0.101, 0.194], [0.154, 0.261], [0.208, 0.394], ... ,[0.970, 0.360]] for each system at each \"Decomposition IO Norm\" and extract the associated values.\nBased on the above thought, the answer is:\nDecomposition_IO_Norm, BigData_Integration_10k, Hive_Integration_10k, BigData_Integration_5k, Hive_Integration_5k\n0, 0.78, 0.75, 0.69, 0.64\n20, 0.76, 0.74, 0.70, 0.68\n40, 0.72, 0.68, 0.72, 0.70\n60, 0.70, 0.65, 0.75, 0.71\n80, 0.66, 0.62, 0.77, 0.73" } ], "id_raw": 531 }, { "id": "ORX0CKiqNdhiHGJ8", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_11.png", "conversations": [ { "from": "human", "value": "\nBased on the line plots, how does the accuracy change as the Decomposition IO Norm increases? Explain your reasoning." }, { "from": "gpt", "value": "To understand how the accuracy changes with respect to the Decomposition IO Norm, I need to analyze the line plots for each system. Observing {the line for BigData Integration with 10k data}[[0.101, 0.194, 0.314, 0.593]], I notice a consistent decreasing trend in accuracy from 0.78 to 0.66 as the IO Norm increases from 0 to 80. {The line for Hive Integration with 10k data}[[0.429, 0.294, 0.642, 0.726]] also shows a similar decreasing pattern, albeit not as steep. On the other hand, both {BigData Integration with 5k data}[[0.757, 0.493, 0.970, 0.227]] and {Hive Integration with 5k data}[[0.757, 0.659, 0.970, 0.360]] depict an increasing trend in accuracy with increasing Decomposition IO Norm.\nBased on the above thought, the answer is:\nBased on the line plots, the accuracy decreases as Decomposition IO Norm increases for BigData and Hive systems working with 10k data points. This could indicate that a higher IO Norm might lead to reduced effectiveness when dealing with larger datasets. Conversely, for both systems when working with smaller 5k data points, the accuracy increases with an increase in Decomposition IO Norm, suggesting that these smaller datasets might adapt better to higher IO Norms, improving effectiveness. Therefore, the trend in accuracy seems to depend on both the Decomposition IO Norm as well as the size of the data the system is working with." } ], "id_raw": 531 }, { "id": "bawN2mfcvN6sXjSU", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum accuracy for BigData Integration with 5k data points as per the line plot details?" }, { "from": "gpt", "value": "To find the maximum accuracy for \"BigData Integration with 5k data\", I would need to locate {the line points for BigData Integration with 5k data}[[0.757, 0.493], [0.810, 0.460], [0.863, 0.394], [0.917, 0.294], [0.970, 0.227]] and extract the respective values: 0.69, 0.70, 0.72, 0.75, 0.77. The maximum value from these is 0.77 which corresponds to the {Decomposition IO Norm}[0.960, 0.825, 0.980, 0.871] of 80.\nBased on the above thought, the answer is:\n0.77." } ], "id_raw": 531 }, { "id": "wTirjjZTgiKBku3U", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_12.png", "conversations": [ { "from": "human", "value": "\nCould you provide a tabular representation of the risk assessment data based on the given line chart?" }, { "from": "gpt", "value": "To create a table from the given line chart, I have to look at {the x-axis tick labels} located at [[0.084, 0.825, 0.094, 0.871], [0.191, 0.825, 0.211, 0.871], [0.298, 0.825, 0.327, 0.871]], which represent the risk levels that will serve as our column headers. The {legend items} located within the sub-legends [[0.091, 0.704, 0.310, 0.751], [0.419, 0.704, 0.637, 0.751], [0.747, 0.634, 0.965, 0.681]] provide the row headers representing the different efficiency types. Finally, the corresponding efficiency values for each efficiency type across the different risk levels can be identified from {the line points} of each line plot [[0.089, 0.335], [0.201, 0.645], [0.312, 0.541], [0.417, 0.335], [0.529, 0.645], [0.640, 0.541], [0.745, 0.335], [0.856, 0.645], [0.968, 0.541]], [0.856, 0.719], [0.968, 0.571]].\nBased on the above thought, the answer is:\nRisk Level,Low Risk,Medium Risk,High Risk\nRisk Management Efficiency,0.76,0.55,0.62\nRisk Assessment Efficiency,0.84,0.50,0.60" } ], "id_raw": 532 }, { "id": "j2YA98jtF7ukZbCC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_12.png", "conversations": [ { "from": "human", "value": "\nCan you identify which risk level has the smallest gap between Risk Management Efficiency and Risk Assessment Efficiency?" }, { "from": "gpt", "value": "For each risk level, I need to find the values for Risk Management Efficiency and Risk Assessment Efficiency and calculate the absolute difference. For the Low Risk category, I can extract values from {the point for Risk Management Efficiency}[0.089, 0.335] which is 0.76 and {the point for Risk Assessment Efficiency}[0.745, 0.216] which is 0.84. For the Medium Risk category, I derive values from {the point for Risk Management Efficiency}[0.529, 0.645] which is 0.55 and {the point for Risk Assessment Efficiency}[0.856, 0.719] which is 0.50. Lastly for the High Risk category, the values can be found at {the point for Risk Management Efficiency}[0.968, 0.541] which gives 0.62 and {the point for Risk Assessment Efficiency}[0.968, 0.571] which gives 0.60.\nBased on the above thought, the answer is:\nBased on the calculations, the smallest gap between Risk Management Efficiency and Risk Assessment Efficiency is at the High Risk level, with a difference of 0.02." } ], "id_raw": 532 }, { "id": "yFfzXoU7Qzz2jnxD", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_12.png", "conversations": [ { "from": "human", "value": "\nWhat's the percentage drop in Risk Management Efficiency between the Low Risk and Medium Risk categories?" }, { "from": "gpt", "value": "I have to identify the values of Risk Management Efficiency for the Low Risk and Medium Risk categories. I can locate these by referencing {the point} representing the value for Low Risk located at [0.089, 0.335] which corresponds to an efficiency value of 0.76 and {the point} for Medium Risk located at [0.529, 0.645] which corresponds to an efficiency value of 0.55. I would then compute the percentage drop using the formula ((New Value - Old Value) / Old Value) * 100%.\nBased on the above thought, the answer is:\n27.63%" } ], "id_raw": 532 }, { "id": "uCXEzNxe2Ent52to", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_13.png", "conversations": [ { "from": "human", "value": "\nCould you present the changes in digital communication interactions on social media platforms over the year in a tabular format?" }, { "from": "gpt", "value": "To extract the data from the graph to a structured table, I start by analyzing {the y-axis labels}<[0.073, 0.128, 0.073, 0.792]> which represent different social media platforms. Then, I extract the percentage changes noted on {the x-axis}<[0.185, 0.890, 0.234, 0.950]> for each platform. Finally, I correlate the line plots showing the interaction changes, identifying colors to determine if it's a growth (green) or a reduction (red) associated with each platform.\nBased on the above thought, the answer is:\nSocial Media Platform,Percentage Change, Trend Type\nTwitter Posts,25%, Growth\nYouTube Videos,-30%, Reduction\nFacebook Posts,50%, Growth\nInstagram Photos,-10%, Reduction\nLinkedIn Articles,15%, Growth\nSnapchat Stories,-35%, Reduction\nTikTok Videos,5%, Growth" } ], "id_raw": 533 }, { "id": "y9mEIf8CbmVu7Qsa", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_13.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform shows the most stable growth, and how can you determine this from the chart?" }, { "from": "gpt", "value": "To ascertain which platform demonstrates the most stable growth, I examine the plot for even and consistent upward trends without sharp rises or drops. Reviewing the chart, {the line for LinkedIn Articles}<[0.209, 0.365, 0.528, 0.460]> illustrates a steady inclination with a moderate increase in percentage, marked by a consistent green color indicating growth without any red segments. This steady slope suggests a stable growth pattern compared to others which show more volatility or reduction.\nBased on the above thought, the answer is:\nLinkedIn Articles show the most stable growth. This is determined by the consistent and moderate upward trend in its line plot on the chart, which exclusively features green signaling growth without any transient declines." } ], "id_raw": 533 }, { "id": "5r3WyY7mArxUWwxt", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage range of changes for digital communication interactions from the highest growth to the highest reduction on the chart?" }, { "from": "gpt", "value": "To calculate the range, I need to find the highest growth and the highest reduction percentages. By examining the line heights, {the highest point for Facebook Posts}<[0.255, 0.065]> shows a growth of approximately 50%, and {the lowest point for Snapchat Stories}<[0.574, 0.900]> indicates a reduction of about -35%. The range can be calculated by subtracting the lowest percentage from the highest.\nBased on the above thought, the answer is:\n85%." } ], "id_raw": 533 }, { "id": "4suNPAaZVdHPJjIo", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_15.png", "conversations": [ { "from": "human", "value": "\nCan you convert the annual effectiveness data of ALBA, LYRAA, and IOTA into a table format using the given line chart data?" }, { "from": "gpt", "value": "To convert the data into a table, I first determine the x-axis years from {the x-axis tick labels}[0.090, 0.825, 0.129, 0.871][0.112, 0.825, 0.151, 0.871][0.134, 0.825, 0.173, 0.871][0.156, 0.825, 0.195, 0.871][0.177, 0.825, 0.216, 0.871][0.199, 0.825, 0.238, 0.871][0.221, 0.825, 0.260, 0.871][0.243, 0.825, 0.282, 0.871][0.265, 0.825, 0.304, 0.871][0.286, 0.825, 0.325, 0.871], which serve as the column headers. I identify the legend for each subplot to get the stock names, represented by {the sublegend}[0.869, 0.616, 0.976, 0.769] and {the subplot titles}[0.137, 0.050, 0.279, 0.113][0.468, 0.050, 0.603, 0.113][0.735, 0.050, 0.992, 0.113]. Each point's value for the years is extracted using {the point data in each line}[0.110, 0.197, 0.306, 0.724][0.437, 0.380, 0.634, 0.586][0.765, 0.197, 0.962, 0.540].\nBased on the above thought, the answer is:\nYear,ALBA,IOTA,LYRAA\n2006,0.52,0.58,0.72\n2007,0.54,0.59,0.75\n2008,0.56,0.61,0.74\n2009,0.60,0.62,0.72\n2010,0.63,0.64,0.70\n2011,0.67,0.65,0.68\n2012,0.70,0.67,0.66\n2013,0.73,0.66,0.64\n2014,0.74,0.65,0.62\n2015,0.75,0.64,0.60" } ], "id_raw": 535 }, { "id": "O9tBE03AdDW1DL8H", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_15.png", "conversations": [ { "from": "human", "value": "\nHow much did the effectiveness of LYRAA decrease from 2007 to 2015?" }, { "from": "gpt", "value": "To calculate the decrease in LYRAA's effectiveness from 2007 to 2015, I observe its line point values from {the line for LYRAA}[0.765, 0.197, 0.962, 0.540], where the corresponding Y-values are 0.75 in 2007 and 0.60 in 2015. Subtracting the 2015 value from the 2007 value gives me the decrease.\nBased on the above thought, the answer is:\n0.15" } ], "id_raw": 535 }, { "id": "7rxELSWg7qk6Ihom", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_16.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the horizontal bar chart showing trends in community development and urban planning into a structured table format?" }, { "from": "gpt", "value": "To convert the data into a table, I would begin by identifying {the bar labels}[0.01, 0.35, 0.07, 0.37] which represent different categories such as Urban Development, Community Hubs, etc. Next, I would measure {the length of each bar}[0.08, 0.39, 0.49, 0.40], which corresponds to the percentage change for each respective category. Lastly, viewing {the color of each bar}[0.06, 0.38, 0.10, 0.36] helps differentiate between positive and negative changes.\nBased on the above thought, the answer is:\nCategory,Percentage Change,Change Trend\nUrban Development,45%,Growth\nCommunity Hubs,-80%,Decline\nGreen Spaces,-70%,Decline\nConservation Efforts,-60%,Decline\nPublic Transport,30%,Growth\nRenewable Energy,55%,Growth\nIncome Inequality,35%,Growth" } ], "id_raw": 536 }, { "id": "ZD8pFaAc4LZ1wDcI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_16.png", "conversations": [ { "from": "human", "value": "\nWhich category in the urban planning horizontal bar chart has shown the most significant decline, and why might this be important?" }, { "from": "gpt", "value": "By reviewing the length and color of the bars related to negative changes, {the bar for Community Hubs}[0.05, 0.67, 0.13, 0.69] shows the most substantial decline at -80%. The significant drop in attention or resources towards Community Hubs may be crucial because these hubs often serve as central points for community interaction and support, thus impacting social cohesion and local support mechanisms in urban planning.\nBased on the above thought, the answer is:\nThe category 'Community Hubs' has shown the most significant decline at -80%. This decline is particularly important as it could adversely affect community cohesion and the effectiveness of localized support and interaction, which are vital in fostering vibrant and supportive urban environments." } ], "id_raw": 536 }, { "id": "yPeVkH6SA0EI7Cp0", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the total percentage change combining all positive growth categories in the urban planning chart?" }, { "from": "gpt", "value": "First, I'll identify and calculate the percentage for each category signifying growth by navigating through {the bars representing growth}[0.08, 0.41, 0.48, 0.42]. They are categorized as Urban Development (45%), Public Transport (30%), Renewable Energy (55%), and Income Inequality (35%). Adding these values gives a total of 45% + 30% + 55% + 35%.\nBased on the above thought, the answer is:\n165%" } ], "id_raw": 536 }, { "id": "XU44X6DMaWjEAPyA", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_17.png", "conversations": [ { "from": "human", "value": "\nCan you transform this line chart into a structured data table separating the categories with their corresponding values into rows and columns?" }, { "from": "gpt", "value": "To convert this from a line chart to a structured table, I need to look at {xticks}[[0.048, 0.875], [0.107, 0.875], [0.167, 0.875], [0.227, 0.875], [0.286, 0.875]] and {line points}[[0.048, 0.839], [0.107, 0.113]] for 'Marginal Growth'. Similarly for 'Minimal Change', I need to look at {xticks}[[0.377, 0.875], [0.496, 0.875], [0.556, 0.875], [0.615, 0.875]] and {line points}[[0.377, 0.476]] and interpolate the values for the missing points. Then for the final subplot, I need to look at the {xticks}[[0.706, 0.875], [0.765, 0.875]] and {line points}[[0.706, 0.234], [0.765, 0.839], [0.706, 0.113], [0.765, 0.718]] for both 'Minimal Change' and 'Marginal Growth'.\nBased on the above thought, the answer is:\nCategories,Marginal Growth,Minimal Change\nNetwork_Length,40,42\nData_Analysis,70,68\nData_Preprocessing,80,84\nData_Quality,60,58\nData_Variables,50,48" } ], "id_raw": 537 }, { "id": "jRFPGz6TgebvIv25", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_17.png", "conversations": [ { "from": "human", "value": "\nCan you analyze the performance of Marginal Growth and Minimal Change across the five categories?" }, { "from": "gpt", "value": "Sure, I can analyze the performance of Marginal Growth and Minimal Change across the five categories by looking at their corresponding lines in the subplots. For the 'Marginal Growth', the {line points}[0.048, 0.839; 0.107, 0.113] in the first subplot reveal a rise from Network_Length to Data_Analysis categories. This indicates an improvement in these sectors. For 'Minimal Change', the {line point}[0.377, 0.476] in the second subplot at Data_Preprocessing suggests an improvement as compared to the Network_Length, under Marginal Growth.\nIn the last subplot, for Data_Quality and Data_Variables, both 'Marginal Growth' and 'Minimal Change' {displayed as line points}[0.706, 0.234, 0.765, 0.839, 0.706, 0.113, 0.765, 0.718] show an declining trend. For Minimal Change, there's an upward trend from Data_Quality to Data_Variables. However, the performance is not stable across the categories and varies from one category to another.\nBased on the above thought, the answer is:\nThe performance of \"Marginal Growth\" across the five categories indicates an overall decline from Network_Length to Data_Analysis, then a significant rise until Data_Quality and finally a slight decline at Data_Variables. On the other hand, \"Minimal Change\" displays a steady performance, with a slight rise from Network_Length to Data_Preprocessing, followed by similar values in Data_Quality and a slight decline in Data_Variables. Thus, both \"Marginal Growth\" and \"Minimal Change\" categories display a volatile performance but Marginal Growth has more prominent changes." } ], "id_raw": 537 }, { "id": "LaF4rQ3BxbvTCf4K", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in Network Length between Marginal Growth and Minimal Change?" }, { "from": "gpt", "value": "To determine the difference in Network Length between Marginal Growth and Minimal Change, I need to first look at the {line point for Network Length under Marginal Growth}[0.048, 0.839] and extract the value, which is 40. Next, I need to locate the {line point for Network Length under Minimal Change}[0.377, 0.476] and extract the corresponding value, which is 42. The difference is thus 42 - 40 = 2.\nBased on the above thought, the answer is:\n2." } ], "id_raw": 537 }, { "id": "du1GBM3pJSi0nXlR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_27_19.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data displaying Public Trust in Government by Year into a structured table?" }, { "from": "gpt", "value": "To construct the structured table based on the line chart, I focus on analyzing {the x-axis tick labels}[0.070, 0.895, 0.094, 0.923], [0.106, 0.895, 0.129, 0.923], [0.141, 0.895, 0.165, 0.923], [0.177, 0.895, 0.200, 0.923], [0.212, 0.895, 0.236, 0.923], [0.248, 0.895, 0.271, 0.923], [0.283, 0.895, 0.306, 0.923] which denote the years, and {the line points for Public Trust in Government}[0.082, 0.512], [0.118, 0.556], [0.153, 0.614], [0.188, 0.687], [0.224, 0.730], [0.259, 0.759], [0.295, 0.803] which denote the trust levels.\nBased on the above thought, the answer is:\nYear,Public Trust in Government\n1990,0.65\n1995,0.62\n2000,0.58\n2005,0.53\n2010,0.50\n2015,0.48\n2020,0.45" } ], "id_raw": 539 }, { "id": "S3bLPdeVCudM5GOF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_27_19.png", "conversations": [ { "from": "human", "value": "\nHow has the public approval of governmental actions trended from 1990 through 2020 according to the dataset?" }, { "from": "gpt", "value": "To assess the trend in public approval, examine {the line for Public Approval of Government}[0.742, 0.149, 0.955, 0.440], tracking notable points and their corresponding values over the years. The line indicates a decrease from a value of 0.9 in 1990 to 0.7 in 2020. The slope of the line downward over time reflects a gradual decline in public approval.\nBased on the above thought, the answer is:\nThe trend in public approval of governmental actions from 1990 to 2020 has been generally decreasing. This can be observed from the steady downward slope of the line plot representing Public Approval of Government, indicating a gradual reduction in approval rates over these 30 years." } ], "id_raw": 539 }, { "id": "ymuRF6KA81SXp6H6", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_27_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the average increase in Political Engagement from 1990 to 2020?" }, { "from": "gpt", "value": "To compute the average increase, examine {the line points for Political Engagement}[0.742, 0.600], [0.778, 0.570], [0.813, 0.527], [0.848, 0.483], [0.884, 0.440], [0.919, 0.396], [0.955, 0.353] representing the value changes from 1990 to 2020. The values start at 0.59 in 1990 and reach 0.76 in 2020. Calculating the difference and averaging over the 30-year period: (0.76 - 0.59) / 30 = 0.00567.\nBased on the above thought, the answer is:\n0.00567." } ], "id_raw": 539 }, { "id": "wom4ogK6V0eaQflv", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_28_1.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart into a table with Training Epochs as rows and the Base and Improved Model Accuracies as columns?" }, { "from": "gpt", "value": "To generate this tabular data from the line chart, I'll start by identifying {the x-axis tick labels}[[0.179, 0.838, 0.188, 0.885], [0.273, 0.838, 0.291, 0.885], [0.367, 0.838, 0.385, 0.885], [0.461, 0.838, 0.479, 0.885], [0.556, 0.838, 0.582, 0.885], [0.650, 0.838, 0.676, 0.885], [0.744, 0.838, 0.770, 0.885], [0.838, 0.838, 0.865, 0.885], [0.932, 0.838, 0.959, 0.885]], representing the row labels or Training Epochs. \nNext, I observe {the legend texts}[[0.244, 0.648, 0.404, 0.694], [0.244, 0.717, 0.468, 0.764]] which are the column headers for Base Model Accuracy and Improved Model Accuracy. \nFinally, to fill the values, I locate {the line points for the Base Model}[[0.179,0.352], [0.217,0.361], ..., [0.932, 0.733]] and {the line points for the Improved Model}[[0.179,0.333], [0.217,0.306], ..., [0.932, 0.179]] and extract the accuracies for each epoch.\nBased on the above thought, the answer is:\nTraining Epochs,Base Model Accuracy,Improved Model Accuracy\n0,80,82\n10,79,85\n20,78,88\n30,77,88\n40,75,89\n50,74,90\n60,73,91\n70,70,90\n80,68,92\n90,65,93\n100,62,94\n110,60,94\n120,58,95\n130,55,95\n140,52,96\n150,50,96\n160,47,97\n170,45,97\n180,43,98\n190,40,98\n200,38,99" } ], "id_raw": 541 }, { "id": "AaVTnOUhLm75k7QL", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_28_1.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, how did the improvements to the model affect its accuracy over time?" }, { "from": "gpt", "value": "To analyze the impact of the improvements on the model's accuracy over time, I need to compare the line plots {of the Base Model} [0.179, 0.352, 0.932, 0.733] and {the Improved Model} [0.179, 0.179, 0.932, 0.333]. \nAt the beginning, both models start with similar accuracies, but as the training epochs go on, the Improved Model consistently demonstrates higher accuracy than the Base Model. This is visible from how the Improved Model's line plot is consistently lower on the y-axis than the Base Model's, which indicates a higher accuracy. Thus, the improvements appear to have a significant positive impact on the model's accuracy over time.\nBased on the above thought, the answer is:\nThe improvements to the model effectively enhanced its accuracy over the training epochs. Initially, the accuracies of the Base Model and the Improved Model were close, but as training progressed, the Improved Model consistently outperformed the Base Model in terms of accuracy. This suggests that the improvements had a meaningful and continuously positive effect on the performance of the model over time." } ], "id_raw": 541 }, { "id": "y5azLlAKOvytz4yr", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_28_1.png", "conversations": [ { "from": "human", "value": "\nWhat was the increase in accuracy when the Base Model was improved in the 70th epoch?" }, { "from": "gpt", "value": "To find the increase in accuracy from the Base Model to the Improved Model in the 70th epoch, I need to locate the epoch, which is represented by {the x-axis tick label '70'}[0.744, 0.838, 0.770, 0.885]. \nI then locate {the point for the Base Model at this epoch} [0.443, 0.442] and extract the corresponding accuracy of 70. Next, I locate {the point for the Improved model at this epoch} [0.443, 0.261] and extract the corresponding accuracy of 90. The difference between the two accuracies gives the increase in accuracy when the Base Model was improved in the 70th epoch, which is 90 - 70.\nBased on the above thought, the answer is:\n20" } ], "id_raw": 541 }, { "id": "qD5qZDL9bis8M0VY", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_28_2.png", "conversations": [ { "from": "human", "value": "\nCan you summarize the data that represents the trends in Interpersonal Relationships and Social Networks in a tabular format?" }, { "from": "gpt", "value": "In the line chart, {the x-axis} represents the time with values [0.061, 0.838, 0.128, 0.885] to [0.995, 0.838, 1.057, 0.885], and {the y-axis} represents the value with labels from [0.069, 0.787, 0.131, 0.834] to [0.069, 0.106, 0.131, 0.153]. The line for {Interpersonal Relationships} has coordinates [0.187, 0.261, 0.933, 0.588] and the line for {Social Networks} has coordinates [0.187, 0.261, 0.933, 0.560]. To extract this data into a tabular format, I map the x-values (time) to the corresponding y-values (values for Interpersonal Relationships and Social Networks) for every point on the line.\nBased on the above thought, the answer is:\nTime,Interpersonal Relationships,Social Networks\n0.0,0.7,0.65\n1.05,0.69,0.67\n2.1,0.68,0.68\n3.15,0.67,0.68\n4.2,0.65,0.7\n5.26,0.64,0.65\n6.31,0.63,0.6\n7.36,0.63,0.59\n8.42,0.62,0.6\n9.47,0.61,0.65\n10.52,0.6,0.7\n11.57,0.62,0.68\n12.63,0.6,0.65\n13.68,0.61,0.67\n14.73,0.6,0.65\n15.78,0.6,0.65\n16.84,0.58,0.68\n17.89,0.58,0.7\n18.94,0.58,0.68\n20.0,0.58,0.65" } ], "id_raw": 542 }, { "id": "F6BB7kbGZRiQ2ehk", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_28_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum value for Social Networks over the given time period?" }, { "from": "gpt", "value": "To find the maximum value for Social Networks over the time period, I have to check the y-values of {the line points for Social Networks}[0.187, 0.261, 0.933, 0.560]. The y-values extracted from these points are: 0.65, 0.67, 0.68, 0.68, 0.7, 0.65, 0.6, 0.59, 0.6, 0.65, 0.7, 0.68, 0.65, 0.67, 0.65, 0.65, 0.68, 0.7, 0.68, 0.65. From this, it is clear that the maximum value for Social Networks is 0.7.\nBased on the above thought, the answer is:\n0.7." } ], "id_raw": 542 }, { "id": "9TfgSX2BAmfq4mBp", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_28_3.png", "conversations": [ { "from": "human", "value": "\nCan you provide the data of each company's social responsibility index from 2010 to 2019 in a tabular format?" }, { "from": "gpt", "value": "Reviewing the line chart, {the legend labels}[0.105, 0.100, 0.185, 0.128], [0.105, 0.142, 0.185, 0.170], [0.105, 0.183, 0.185, 0.211] represent the companies in this analysis - Company A, B, and C. The x-axis of the chart represents the years from 2010 to 2019, which we gather from {the x-axis tick labels}[0.078, 0.903, 0.113, 0.931], [0.266, 0.903, 0.301, 0.931], [0.454, 0.903, 0.490, 0.931], [0.643, 0.903, 0.678, 0.931], [0.831, 0.903, 0.866, 0.931], [1.019, 0.903, 1.054, 0.931]. To construct each row, I locate {the plot points on the lines for each company}[0.096, 0.191, 0.943, 0.847], [0.096, 0.367, 0.943, 0.751], [0.096, 0.111, 0.943, 0.431], and retrieve the respective social responsibility index for each year.\nBased on the above thought, the answer is:\nYear,Company A,Company B,Company C\n2010,3.4,5.2,6.0\n2011,3.6,5.7,6.5\n2012,4.7,6.0,7.0\n2013,5.0,6.4,7.2\n2014,5.5,6.2,7.5\n2015,5.7,6.3,6.0\n2016,6.0,4.0,6.6\n2017,6.5,4.3,7.1\n2018,7.0,4.6,7.8\n2019,7.5,5.1,8.0" } ], "id_raw": 543 }, { "id": "VaAySAoZ2Wd8UiIw", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_28_3.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which company has shown the most improvement in their Social Responsibility Index over the review period?" }, { "from": "gpt", "value": "To find the company with the highest improvement in their Social Responsibility Index, I need to compare the differences in the starting and ending points of each company's line. For {Company A}[0.096, 0.191, 0.943, 0.847], the change is from 3.4 to 7.5, a difference of 4.1. For {Company B}[0.096, 0.367, 0.943, 0.751], the Index changed from 5.2 to 5.1, almost no improvement. Comparing these to the progression of {Company C}[0.096, 0.111, 0.943, 0.431], which improved from 6.0 to 8.0, a difference of 2.0. Thus, Company A has shown the most improvement.\nBased on the above thought, the answer is:\nCompany A has shown the most improvement in their Social Responsibility Index over the review period. This is determined by comparing the differences in the Social Responsibility Index values between the start and end points for each company. Company A's Index increased by 4.1 points, which is the highest increase compared to Company B and Company C." } ], "id_raw": 543 }, { "id": "3Ol2rpjLmzKmzFNL", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_28_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in Social Responsibility Index for Company A from the year 2010 to 2019?" }, { "from": "gpt", "value": "To evaluate the change in Social Responsibility Index for Company A, I need to locate {the starting point}[0.096, 0.847], which corresponds to the value for the year 2010, and {the end point}[0.943, 0.191], which corresponds to the value for the year 2019, from the line representing data for Company A. The corresponding values for these points are 3.4 and 7.5. The change is then 7.5 - 3.4 = 4.1.\nBased on the above thought, the answer is:\n4.1." } ], "id_raw": 543 }, { "id": "IkN6kXAC0TpBOqPT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_28_4.png", "conversations": [ { "from": "human", "value": "\nCan you extract the trend data represented by each line in the chart corresponding to 'Close Friends' and 'Acquaintances' as the columns and different status patterns as rows?" }, { "from": "gpt", "value": "To construct a table from the line chart, I start by noting the {x-axis tick labels}[[0.098, 0.782, 0.158, 0.806], [0.238, 0.782, 0.289, 0.806], [0.393, 0.782, 0.452, 0.806], [0.569, 0.782, 0.641, 0.806], [0.734, 0.782, 0.805, 0.806], [0.894, 0.782, 0.981, 0.806]], which serve as the different social relationship patterns (i.e., row labels). Next, I note the {legend texts}[[0.862, 0.119, 0.931, 0.138], [0.862, 0.154, 0.970, 0.172]], which represent 'Close Friends' and 'Acquaintances (i.e., column headers). For each line {indicator}[0.115, 0.781, 0.977, 0.281], I identify each point, map it to the x-axis labels (viz., 'Rapid Decline', 'Unpredictable Fluctuations', 'Flat Decline', 'Inconsistent Decline', 'Steady Decline', 'Moderate Increase'), and record the y-values (a measure of their social interaction frequency).\nBased on the above thought, the answer is:\nPattern,Close Friends,Acquaintances\nRapid Decline,5,4.5\nUnpredictable Fluctuations,4.2,5\nFlat Decline,3.5,3.6\nInconsistent Decline,2.4,2.9\nSteady Decline,1.8,2.1\nModerate Increase,1.2,1.7" } ], "id_raw": 544 }, { "id": "7yEqe9dGcxrYY4L8", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_28_4.png", "conversations": [ { "from": "human", "value": "\nWhat line represents the steepest decline in social interaction across all patterns?" }, { "from": "gpt", "value": "To assess the steepest decline in social interactions, I look into the line plots for both kinds of relationships - {Close Friends}[0.115, 0.781, 0.725, 0.329] and {Acquaintances}[0.115, 0.703, 0.725, 0.280]. The steepest decline would be represented by the line showing the greatest difference between the initial and final points. Comparatively, the Close Friends line shows a greater decrease from 5 to 1.2 compared to the Acquaintances line that drops from 4.5 to 1.7 across the specified patterns.\nBased on the above thought, the answer is:\nThe line representing 'Close Friends' shows the steepest decline in social interaction across all patterns, going from 5 to 1.2, indicating a larger drop in the frequency of social interactions compared to 'Acquaintances'." } ], "id_raw": 544 }, { "id": "OuooJ53TlBrmtuvD", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_28_4.png", "conversations": [ { "from": "human", "value": "\nHow much did the interaction frequency with close friends drop from 'Rapid Decline' to 'Moderate Increase'?" }, { "from": "gpt", "value": "To determine the decrease in interaction frequency with close friends from 'Rapid Decline' to 'Moderate Increase', I identify the {line points corresponding to the 'Close Friends' trend}[[0.115, 0.730], [0.237, 0.660], [0.359, 0.586], [0.481, 0.407], [0.603, 0.319], [0.725, 0.238]]. The frequency at 'Rapid Decline' is observed to be 5 and at 'Moderate Increase' is 1.2. These frequencies are generated from the y-values at given points. The difference between these two values represents the decrease.\nBased on the above thought, the answer is:\n3.8." } ], "id_raw": 544 }, { "id": "1sTo6OQtXMOLX0Bc", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_28_5.png", "conversations": [ { "from": "human", "value": "\nCan you transform the graphical data depicted in the line chart into a structured table with Years as the columns and Sectors as the rows?" }, { "from": "gpt", "value": "To transform the data from the line chart into a structured table, I would consult {the x-axis}<[0.159, 0.838, 0.970, 0.885]> for the column headers, which represent the years. {The line plots}<[0.196, 0.187, 0.933, 0.571], [0.196, 0.748, 0.933, 0.766]> represent the different sectors and their market shares over the years, and each of these should be interpreted as a row in the table. I must pull out each sector's market share for each year to fill in the rows of the table.\nBased on the above thought, the answer is:\nSectors,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nStartups Registered,345,392,469,510,555,600,700,750,805,880,908\nInnovation Index,59,60,65,67,69,70,75,78,80,82,85" } ], "id_raw": 545 }, { "id": "tYaWQoFehSRHjk7Q", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_28_5.png", "conversations": [ { "from": "human", "value": "\nWhich metric shows a more linear growth trend between 2010 and 2020 - Startups Registered or the Innovation Index?" }, { "from": "gpt", "value": "To conclude which aspect demonstrated a more linear growth trend from 2010 to 2020, it's required to examine the rate of increase or decrease for each. Looking at the {Startups Registered line}<[0.196, 0.187, 0.933, 0.571]>, there's a substantial upward trend suggesting growing quantities of startups. When analyzing the {Innovation Index line}<[0.196, 0.748, 0.933, 0.766]>, it also illustrates an upward trend, albeit less dramatic than the Startups Registered line.\nBased on the above thought, the answer is:\nThe Innovation Index demonstrates a more linear growth trend between 2010 and 2020. Although both metrics show an increase over this time period, the Innovation Index's growth is more consistent and less volatile, hence more linear, compared to the Startups Registered." } ], "id_raw": 545 }, { "id": "s4T4k9C4TZXzHBv5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_28_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the growth of registered Startups from 2010 to 2020, as per the charts?" }, { "from": "gpt", "value": "To calculate the growth of registered Startups from 2010 to 2020, I need to find the coordinates for the Startups Registered line's points for 2010 {point_coordinates}[0.196, 0.571] and 2020 {point_coordinates}[0.933, 0.187] and extract the corresponding values: 345 and 908. The growth is the difference between the value in 2020 and the value in 2010. So, the growth = 908 \u2013 345 = 563.\nBased on the above thought, the answer is:\n563" } ], "id_raw": 545 }, { "id": "FENkj7jJLpBrSZdy", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_28_8.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the adoption statistics for Traditional EdTech and Innovative EdTech over the duration of 20 weeks?" }, { "from": "gpt", "value": "First, I locate {the x-axis tick labels}[[0.083, 0.903, 0.115, 0.931], [0.188, 0.903, 0.220, 0.931], [0.293, 0.903, 0.325, 0.931], [0.398, 0.903, 0.430, 0.931],...,[0.917, 0.903, 0.961, 0.931]], and these will be the columns representing the weeks. Subsequently, I identify {the legend texts}[[0.175, 0.100, 0.354, 0.128], [0.175, 0.142, 0.355, 0.170]] as the rows signifying the Traditional EdTech and the Innovative EdTech. Then I proceed to {the line points for Traditional EdTech and Innovative EdTech} and extract the corresponding values for each week.\nBased on the above thought, the answer is:\nWeeks,Traditional EdTech,Innovative EdTech\n1,100,100\n2,98,101\n3,97,102\n4,96,105\n5,96,108\n6,95,110\n7,94,112\n8,94,114\n9,93,116\n10,93,118\n11,92,120\n12,92,122\n13,91,125\n14,90,127\n15,90,128\n16,89,130\n17,88,132\n18,88,134\n19,87,136\n20,86,138" } ], "id_raw": 548 }, { "id": "Hfdgcu8MUFJOLeZC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_28_8.png", "conversations": [ { "from": "human", "value": "\nHow do the adoption trends of Traditional EdTech and Innovative EdTech compare over the 20 weeks? Provide your interpretation based on the line chart." }, { "from": "gpt", "value": "For the analysis, I need to closely observe {the line plot for Traditional EdTech} [0.141, 0.614, 0.939, 0.803] and {the line plot for Innovative EdTech} [0.141, 0.102, 0.939, 0.614]. The Traditional EdTech line shows a steady decrease over the period starting at 100 and ending at 86. The Innovative EdTech line, on the other hand, shows a consistent growth from week 1 through week 20, starting at 100 and ending at 138.\nBased on the above thought, the answer is:\nOver the 20-week period, Traditional EdTech shows a moderate decline in adoption, whereas Innovative EdTech demonstrates a steady incline in adoption rates. This could suggest that although initially both platforms had the same adoption rates, as weeks progressed, more people were inclined towards Innovative EdTech. This could potentially hint towards Innovative EdTech's more successful strategies in attracting users, or it could be a sign of shifting consumer preferences towards more innovative EdTech solutions." } ], "id_raw": 548 }, { "id": "C3K5NShSDCZaweH8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_28_8.png", "conversations": [ { "from": "human", "value": "\nBy how many units did the Innovative EdTech adoption stats increase from week 1 to week 20?" }, { "from": "gpt", "value": "To get the difference in adoption stats of Innovative EdTech from week 1 to week 20, I need to look in the chart and locate {the line points for Innovative EdTech at week 1} [0.141, 0.614] and {week 20} [0.939, 0.102]. The corresponding values are 100 and 138 respectively, so the increase would be 138 - 100 = 38.\nBased on the above thought, the answer is:\n38 units." } ], "id_raw": 548 }, { "id": "S0umAa43BKWkJ0qe", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_28_9.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data for the Global Entrepreneurship Index and Innovation Ecosystem Index from the line graph into a tabular format with 'Year' as rows and 'Indices' as columns?" }, { "from": "gpt", "value": "Sure, first, I locate the {x-axis labels}[0.147,0.903,0.172,0.931], [0.323,0.903,0.348,0.931], [0.499,0.903,0.524,0.931], [0.675,0.903,0.700,0.931], [0.851,0.903,0.876,0.931] of the graph, which indicate the years. The {y-axis labels}[0.050,0.872,0.093,0.900], [0.050,0.693,0.093,0.722], [0.049,0.513,0.093,0.541], [0.049,0.333,0.093,0.361], [0.049,0.063,0.093,0.091] represent the index values. Then the lines on the graph represent two indices: {Global Entrepreneurship Index}[0.147, 0.129, 0.939, 0.650] and {Innovation Ecosystem Index}[0.147, 0.165, 0.939, 0.758]. From this information, I can extract the values for each index at each year.\nBased on the above thought, the answer is:\nYear,Global Entrepreneurship Index,Innovation Ecosystem Index\n2010,0.53,0.47\n2011,0.56,0.52\n2012,0.59,0.57\n2013,0.64,0.59\n2014,0.68,0.61\n2015,0.73,0.68\n2016,0.75,0.74\n2017,0.77,0.76\n2018,0.80,0.77\n2019,0.82,0.80" } ], "id_raw": 549 }, { "id": "FtlY8wgIpYeuW792", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_28_9.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which Index has shown a greater increase between 2010 and 2019?" }, { "from": "gpt", "value": "To determine which Index has shown a greater increase, I need to calculate the change for each Index from 2010 to 2019. For the {Global Entrepreneurship Index}[0.147, 0.129, 0.939, 0.650], I previously calculated the change to be 0.29. For the {Innovation Ecosystem Index}[0.147, 0.165, 0.939, 0.758], I locate the initial point in 2010 and the final point in 2019. The initial value in 2010 is 0.47 and the final value in 2019 is 0.80. Therefore, the change is 0.80 - 0.47 = 0.33.\nBased on the above thought, the answer is:\nThe Innovation Ecosystem Index has shown a greater increase between 2010 and 2019. The change for the Global Entrepreneurship Index is 0.29 while the Innovation Ecosystem Index has increased by 0.33 over the same period." } ], "id_raw": 549 }, { "id": "9MM52GcJ8Cb4DLcp", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_28_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in the Global Entrepreneurship Index from 2010 to 2019?" }, { "from": "gpt", "value": "To calculate the change in the Global Entrepreneurship Index from 2010 to 2019, I need to locate the initial point {2010}[0.147, 0.650] on the {Global Entrepreneurship Index line}[0.147, 0.129, 0.939, 0.650] and the final point {2019}[0.939, 0.129] on the same line. The initial value for 2010 is 0.53 and the final value for 2019 is 0.82. The change is the final value minus the initial value which is 0.82 - 0.53 = 0.29.\nBased on the above thought, the answer is:\n0.29." } ], "id_raw": 549 }, { "id": "JMLQqDlmVAKT4dbu", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_28_10.png", "conversations": [ { "from": "human", "value": "\nCan you extract the trend data for the different media and entertainment industry sectors over the decade into a table?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first locate {the x-axis tick labels}[{extreme left}[0.061, 0.773], {extreme right}[0.951, 0.788]], which represent the column headers corresponding to the timeline from 2012 to 2021. Then, I use {the legend texts}[{Box Office}[0.844, 0.055, 0.909, 0.079], {Online Streaming}[0.844, 0.086, 0.909, 0.111], {Live Performance}[0.844, 0.117, 0.909, 0.142], {Video Game Sales}[0.844, 0.148, 0.909, 0.173], {Book Sales}[0.844, 0.179, 0.909, 0.204]] to identify the row labels representing the different industry sectors. For the table's values, I look at {the line points for each sector}[{Box Office}[0.102, 0.470, ..., 0.798, 0.696], {Online Streaming}[0.102, 0.211, ..., 0.798, 0.729], {Live Performance}[0.102, 0.599, ..., 0.798, 0.340], {Video Game Sales}[0.102, 0.179, ..., 0.798, 0.534], {Book Sales}[0.102, 0.470, ..., 0.798, 0.632]] at the respective years, which are represented by respective y-coordinate values.\nBased on the above thought, the answer is:\nYear,Box Office,Online Streaming,Live Performance,Video Game Sales,Book Sales\n2012,120,80,100,90,120\n2013,115,82,102,88,121\n2014,110,85,99,91,125\n2015,105,89,101,92,123\n2016,100,92,98,94,122\n2017,95,96,95,97,123\n2018,90,100,93,99,125\n2019,85,105,91,100,126\n2020,80,110,90,102,130\n2021,75,115,89,100,129" } ], "id_raw": 550 }, { "id": "mQ9ALmKZXuwGyFYz", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_28_10.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which sector had the most significant positive trend in the last decade? Explain your reasoning." }, { "from": "gpt", "value": "To identify the sector with the most significant positive trend, I need to analyze each sector's progression over the time series. Among {the lines for the sectors}[{Box Office}[0.102, 0.470, ..., 0.798, 0.696], {Online Streaming}[0.102, 0.211, ..., 0.798, 0.729], {Live Performance}[0.102, 0.599, ..., 0.798, 0.340], {Video Game Sales}[0.102, 0.179, ..., 0.798, 0.534], {Book Sales}[0.102, 0.470, ..., 0.798, 0.632]], the line for Online Streaming shows a consistent increase over time, rising from 80 to 115, a significant increase compared to the other sections.\nBased on the above thought, the answer is:\nAccording to the line chart, the Online Streaming sector exhibited the most significant positive trend in the last decade. The line plot for Online Streaming shows a consistent upward trajectory, indicating an increase in usage throughout this period. This trend significantly contrasts the trends in other sectors, which have either declined or exhibited inconsistent growth. The surge in online streaming reflects the shift in consumer behavior towards digital and streaming platforms in the past decade." } ], "id_raw": 550 }, { "id": "Vvveb8Xaswt1TnNa", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_28_10.png", "conversations": [ { "from": "human", "value": "\nHow much did Box Office earnings decline from 2012 to 2021?" }, { "from": "gpt", "value": "To find the decline in Box Office earnings, I first locate {the line points for Box Office}[{2012}[0.102, 0.470], {2021}[0.798, 0.696]] and extract the corresponding values: 120 in 2012 and 75 in 2021. The decline in Box Office earnings is therefore 120 - 75 = 45 units.\nBased on the above thought, the answer is:\n45 units." } ], "id_raw": 550 }, { "id": "SiCnaqZb8YlgTWMU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_28_11.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart into a structured table with Years as rows and Interaction Types as columns?" }, { "from": "gpt", "value": "To form the structured table from the line chart, I first identify {the x-axis tick labels}[[0.109, 0.919, 0.153, 0.942],[0.295, 0.919, 0.317, 0.942],[0.459, 0.919, 0.481, 0.942],[0.623, 0.919, 0.645, 0.942],[0.787, 0.919, 0.809, 0.942]] which represent row headers or Years. Then I find that the types of interactions as seen from the legend absent in the JSON. To derive values, I look at {the line points}[[0.131, 0.865],[0.213, 0.769],[0.295, 0.674],[0.377, 0.626],[0.459, 0.530],[0.541, 0.483],[0.623, 0.387],[0.705, 0.291],[0.787, 0.196],[0.869, 0.100]], the point raw data shows that each interaction type has their respective values at each year, which can be filled under the respective columns.\nBased on the above thought, the answer is:\nYear,Online_Interactions,Face_to_Face_Interactions,Online_Casual_Acquaintances,Face_to_Face_Casual_Acquaintances,Online_Close_Friends,Face_to_Face_Close_Friends\n2010,1500000,0.8,1200000,0.89,1300000,0.91\n2011,1550000,0.78,1250000,0.88,1350000,0.92\n2012,1600000,0.77,1300000,0.87,1400000,0.94\n2013,1625000,0.76,1325000,0.86,1425000,0.96\n2014,1675000,0.75,1375000,0.85,1475000,0.97\n2015,1700000,0.74,1400000,0.84,1500000,0.98\n2016,1750000,0.72,1450000,0.82,1550000,1\n2017,1800000,0.71,1500000,0.81,1600000,1.02\n2018,1850000,0.69,1550000,0.79,1650000,1.04\n2019,1900000,0.67,1600000,0.76,1700000,1.06" } ], "id_raw": 551 }, { "id": "VX9tABBNcfSrd8T2", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_28_11.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, how has the business approached social sustainability in terms of Corporate Giving and reducing Carbon Footprint?" }, { "from": "gpt", "value": "The line chart demonstrates trends for two key social sustainability indicators: Corporate Giving and Carbon Footprint. Looking at {the line for Corporate Giving}[0.131, 0.865, 0.869, 0.100], one can see a steady upward trend from 1500000 in 2010 to 1900000 in 2019, indicating a consistent increase in the business's social investments and contributions. \nConversely, observing {the line for Carbon Footprint}[0.131, 0.100, 0.869, 0.865], one sees a downward trend from 0.8 to 0.67 over the same time period. This reflects the company's successful efforts to reduce its environmental impact, another important aspect of social sustainability.\nBased on the above thought, the answer is:\nBased on the line chart, the business has positively addressed social sustainability over the period from 2010 to 2019. It has consistently increased its Corporate Giving, reflecting a commitment to social investment and contribution. Simultaneously, the business has significantly reduced its Carbon Footprint, demonstrating efforts towards environmental sustainability. This dual approach reflects a comprehensive strategy towards social responsibility and sustainability." } ], "id_raw": 551 }, { "id": "RCemZ2Zp3ZoBUheA", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_28_11.png", "conversations": [ { "from": "human", "value": "\nHow much has the Corporate Giving increased from 2010 to 2019?" }, { "from": "gpt", "value": "To identify the increase in Corporate Giving from 2010 to 2019, I need to look at the points for Corporate Giving line, specifically the first point at {2010}[0.131, 0.865] and the last point at {2019}[0.869, 0.100] . The y-values for these points represent the Corporate Giving amount in those years. The value in 2010 is 1500000 and in 2019 it's 1900000. Therefore, the increase from 2010 to 2019 is 1900000 - 1500000 = 400000.\nBased on the above thought, the answer is:\n400000" } ], "id_raw": 551 }, { "id": "A9SvWUxpGgUOoj6H", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_28_12.png", "conversations": [ { "from": "human", "value": "\nCan you arrange the raw data in a table format where months correspond to the columns, while different products correspond to the rows?" }, { "from": "gpt", "value": "To generate a tabular representation, the first step would be to identify {the x-axis tick labels}<[0.120, 0.838, 0.138, 0.885], [0.254, 0.838, 0.272, 0.885], [0.388, 0.838, 0.406, 0.885], [0.522, 0.838, 0.540, 0.885], [0.656, 0.838, 0.674, 0.885], [0.781, 0.838, 0.817, 0.885], [0.916, 0.838, 0.951, 0.885], [1.050, 0.838, 1.085, 0.885]> as columns corresponding to months. Next, I would identify {the legend texts}<[0.763, 0.166, 0.945, 0.213], [0.763, 0.236, 0.928, 0.283]> as the rows representing the Base Product and Our Product. The data points values are represented by the line plots for each product, {the points for Base Product }<[0.196, 0.200], [0.263, 0.276], [0.330, 0.352], [0.397, 0.397], [0.464, 0.442], [0.531, 0.503], [0.598, 0.548], [0.665, 0.579], [0.732, 0.609], [0.799, 0.654], [0.866, 0.654], [0.933, 0.692]> and {the points for Our Product }<[0.196, 0.730], [0.263, 0.579], [0.330, 0.427], [0.397, 0.276], [0.464, 0.200], [0.531, 0.306], [0.598, 0.654], [0.665, 0.685], [0.732, 0.715], [0.799, 0.624], [0.866, 0.707], [0.933, 0.745]>.\nBased on the above thought, the answer is:\nProducts,Month 1,Month 2,Month 3,Month 4,Month 5,Month 6,Month 7,Month 8,Month 9,Month 10,Month 11,Month 12\nBase Product,1200,1150,1100,1070,1040,1000,970,950,930,900,900,875\nOur Product,850,950,1050,1150,1200,1130,900,880,860,920,865,840" } ], "id_raw": 552 }, { "id": "qumGXdMcKrghBTH7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_28_12.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, how would you interpret the sales performance of both products over time?" }, { "from": "gpt", "value": "To interpret the sales performance of both products over time, I need to analyze the line plots for each product. Starting with {the line for Base Product}[0.196, 0.200, 0.933, 0.692], I can see that it shows a consistent decrease in sales over the 12 months' period. Starting from 1200 units, it falls to 875 by the 12th month, indicating a decline in the product's performance over time.\n\nMoving on to {the line for Our Product}[0.196, 0.200, 0.933, 0.745], it shows an initial increase in sales, peaking at 1200 units in the 5th month. Thereafter, the sales performance became inconsistent, seeing some decrease and increase throughout the remaining months before finalizing at 840 units in the last month.\n\nThe sales performance for Our Product was initially positive with strong growth, but there after it became unstable, with both increases and decreases in sales. This suggests a level of unpredictability in the sales performance of Our Product after the 5th month. Meanwhile, the Base Product has seen a steady decline in sales throughout the year.\nBased on the above thought, the answer is:\nThe sales performance of the Base Product has been steadily declining over the year, indicating a potential loss in customer interest or changes in market trends. The 'Our Product' shows a strong growth initially but later becomes unpredictable with fluctuations in the number of units sold. These trends suggest that while 'Our Product' may have initially done well, it did not maintain its growth, whereas the Base Product needs to reassess its sales strategy due to the persistent downward performance." } ], "id_raw": 552 }, { "id": "5FDJ2z7SSTEgJBwp", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_28_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the average monthly sales of 'Our Product'?" }, { "from": "gpt", "value": "To calculate the average monthly sales of 'Our Product', I'll need to position {the plot values of 'Our Product'}<[0.196, 0.730], [0.263, 0.579], [0.330, 0.427], [0.397, 0.276], [0.464, 0.200], [0.531, 0.306], [0.598, 0.654], [0.665, 0.685], [0.732, 0.715], [0.799, 0.624], [0.866, 0.707], [0.933, 0.745]>. These values are 850, 950, 1050, 1150, 1200, 1130, 900, 880, 860, 920, 865, and 840. Summing up these values and dividing by 12 (the total number of months), we get the average of 'Our Product' sales.\nBased on the above thought, the answer is:\n976.67 units." } ], "id_raw": 552 }, { "id": "0zbZe2uJJnB9EGVg", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_28_13.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you provide the data in terms of Level of Support for each year?" }, { "from": "gpt", "value": "To organize the data from the chart into a table, I start by checking {the X-axis labels}[0.124, 0.903, 0.146, 0.931], ..., [0.940, 0.903, 0.950, 0.931] which represent the Year. Next, I detect that {the legend labels}[0.800, 0.100, 0.966, 0.128], [0.800, 0.142, 0.933, 0.170] showing Traditional and Trendy Support respectively will be the column headers. For each year, I place {the corresponding data points for each category}[0.124, 0.585], ..., [0.940, 0.543] and [0.124, 0.713], ..., [0.940, 0.160] into the appropriate cell to populate the values.\nBased on the above thought, the answer is:\nYear,Traditional Support,Trendy Support\n2010,0.4,0.25\n2011,0.36,0.28\n2012,0.33,0.35\n2013,0.29,0.5\n2014,0.35,0.55\n2015,0.33,0.6\n2016,0.37,0.75\n2017,0.35,0.8\n2018,0.42,0.85\n2019,0.45,0.9" } ], "id_raw": 553 }, { "id": "ciQgFAne3SJ5kFc4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_28_13.png", "conversations": [ { "from": "human", "value": "\nWhat can you interpret from the Trendy Support and Traditional Support trends from the years 2010 to 2019?" }, { "from": "gpt", "value": "To interpret the trends, I look at the lines for {Traditional Support}[0.124, 0.585, 0.940, 0.543] and {Trendy Support}[0.124, 0.713, 0.940, 0.160]. Traditional Support starts at a high, but gradually decreases over the years, suggesting a possible declining interest in traditional culture and arts. On the other hand, Trendy Support begins at a lower level in 2010 but experiences a sharp increase throughout the years, indicating a growing interest in trendy culture and arts. This could suggest a shift in societal preference towards newer, trendier forms of culture and arts.\nBased on the above thought, the answer is:\nThe trends indicate a shift in societal interest and support from traditional to trendy forms of culture and arts from 2010 to 2019. There is a visible decline in Traditional support, suggesting a potentially decreased preference for traditional culture. Concurrently, there is a significant increase in Trendy Support, which may indicate growing interest and preference for newer, trendier forms of culture and arts." } ], "id_raw": 553 }, { "id": "lbDx5PMoxJK8Z1Dc", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_28_13.png", "conversations": [ { "from": "human", "value": "\nWhat was the increase in Trendy Support from the year 2013 to 2017?" }, { "from": "gpt", "value": "To determine the increase in Trendy Support from 2013 to 2017, I will identify {the point representing Trendy Support in 2013}[0.396, 0.500], which corresponds to 0.5. Then, I find {the point representing Trendy Support in 2017}[0.759, 0.245] which corresponds to 0.8. To get the increase, I will subtract the 2013 value from the 2017 Value: 0.8 - 0.5 = 0.3.\nBased on the above thought, the answer is:\n0.3." } ], "id_raw": 553 }, { "id": "od6Mzclbj4qe6GGb", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_28_15.png", "conversations": [ { "from": "human", "value": "\nCan you provide the change in daily active users per social media platform, as indicated in the line chart, in a tabulated format with measurements by \"Ours\" and \"SMA\" listed separately for each platform?" }, { "from": "gpt", "value": "Based on the chart, I can see that the two social media platforms are represented in two different subplots, each with two lines denoting the measurements from 'Ours' and 'SMA'. The {Facebook line from 'Ours'}[0.15, 0.5, 0.85, 0.6] shows a 3.4% increase and the {Facebook line from 'SMA'}[0.15, 0.53, 0.85, 0.63] displays a 3.2% increase. For Twitter, the {Twitter line from 'Ours'}[0.15, 0.1, 0.85, 0.2] signifies a 3% increase, and the {Twitter line from 'SMA'}[0.15, 0.13, 0.85, 0.07] indicates a 1% decrease.\nBased on the above thought, the answer is:\nPlatform, Ours, SMA\nFacebook, 3.4%, 3.2% \nTwitter, 3%, -1%" } ], "id_raw": 555 }, { "id": "073C78XSWtqsVovR", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_28_15.png", "conversations": [ { "from": "human", "value": "\nCan you interpret which social media platform showed a consistent increase in active users based on the line chart and why?" }, { "from": "gpt", "value": "By observing the line chart, I can see that {the Facebook lines from 'Ours' and 'SMA'}[0.15, 0.5, 0.85, 0.6], [0.15, 0.53, 0.85, 0.63] both depict an increase in the number of active users. This is in contrast to the {Twitter line from 'SMA'}[0.15, 0.13, 0.85, 0.07], which shows a decrease. Therefore, Facebook has shown a consistent increase in active users over this time period.\nBased on the above thought, the answer is:\nBased on the given line chart, Facebook showed a consistent increase in active users. Both measurements from 'Ours' and 'SMA' depict an upward trend in Facebook's active user count, contrasting Twitter which, according to 'SMA', experienced a decrease in active users." } ], "id_raw": 555 }, { "id": "eJwDrywgMDcTFjA4", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_28_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in user change (as a percentage) for Facebook between 'Ours' and 'SMA' measurements?" }, { "from": "gpt", "value": "To find the difference in user change for Facebook between 'Ours' and 'SMA' measurements, I have to locate the values represented by the {Facebook line from 'Ours'}[0.15, 0.5, 0.85, 0.6], which is 3.4%, and the {Facebook line from 'SMA'}[0.15, 0.53, 0.85, 0.63], which is 3.2%. The difference is equal to 3.4% - 3.2% = 0.2%.\nBased on the above thought, the answer is:\n0.2%" } ], "id_raw": 555 }, { "id": "f8SkEFbOY4whtHsd", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_28_18.png", "conversations": [ { "from": "human", "value": "\nCan you extract and structure the data of Global GDP and Global Inflation Rate as per the years into a table from the line chart?" }, { "from": "gpt", "value": "To create a structured table from the line chart, I'll first identify {the labels on the X-axis}[[0.036, 0.919, 0.084, 0.942], [0.146, 0.919, 0.194, 0.942], [0.256, 0.919, 0.305, 0.942], [0.367, 0.919, 0.415, 0.942], [0.477, 0.919, 0.525, 0.942]] as the Years. The lines representing {Global GDP}[[0.104, 0.124, 0.943, 0.499]] and {Global Inflation Rate}[[0.104, 0.804, 0.943, 0.867]] are the rows in this case, while the points on these lines give the specific values for these measures in the respective years.\nBased on the above thought, the answer is:\nYear,Global GDP,Global Inflation Rate\n2001,40.9,1.7\n2002,40.6,1.6\n2003,41.8,2.1\n2004,44.1,2.7\n2005,47.5,3.2\n2006,50.9,3.5\n2007,56.1,4.6\n2008,57.3,6.2\n2009,57.0,-0.9\n2010,60.5,4.1\n2011,64.1,4.6\n2012,66.3,3.2\n2013,69.2,1.9\n2014,72.2,1.5\n2015,74.6,1.1\n2016,76.0,1.7\n2017,79.1,2.9\n2018,81.4,3.8\n2019,83.0,3.1\n2020,80.6,-0.2" } ], "id_raw": 558 }, { "id": "JKERdhFrBIDOWN2J", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_28_18.png", "conversations": [ { "from": "human", "value": "\nBy comparing the trends of Global GDP and the Global Inflation Rate, can you interpret any relationship between the two?" }, { "from": "gpt", "value": "To analyse the relationship between Global GDP and the Global Inflation Rate, I need to compare the trends of {the Global GDP line}[[0.104, 0.124, 0.943, 0.499]] and {the Global Inflation Rate line}[[0.104, 0.804, 0.943, 0.867]]. The Global GDP shows a steady rise over the years, indicating consistent economic growth. However, the Global Inflation Rate, while also trending upwards, exhibits much more volatility with noticeable spikes and dips, such as the sharp fall in 2009 corresponding to {the point for the year 2009 on the Global Inflation Rate line}[0.457, 0.867]. Nevertheless, both metrics seem to follow a synchronized pattern, suggesting potentially an interdependent relation.\nBased on the above thought, the answer is:\nThe trends in the line chart suggest a correlation between Global GDP and the Global Inflation Rate. Both generally increase over time, indicating economic growth and inflation often move in tandem. However, the higher volatility in the inflation rate suggests that short-term economic factors may cause inflation to fluctuate more dramatically while GDP tends to change gradually. For example, the steep decrease in inflation in 2009 may be due to the global financial crisis, but the GDP line shows a slight decrease only. This suggests that while the two are interconnected, specific events can influence them differently." } ], "id_raw": 558 }, { "id": "e9dlo0yXjki3gr5X", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_28_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the maximum and minimum Global GDP values over the years?" }, { "from": "gpt", "value": "I must find the maximum and minimum values for Global GDP over the years for this. These values can be obtained by closely examining {the points on the Global GDP line}[[0.104, 0.497], [0.148, 0.499], [0.236, 0.468], ..., [0.899, 0.124], [0.943, 0.145]]. The highest represented GDP value is 83.0 corresponding to {the point for the year 2019}[0.899, 0.124]. The lowest GDP value is 40.6 corresponding to {the point for the year 2002}[0.148, 0.499]. The difference between the maximum and minimum is therefore 83.0 - 40.6 = 42.4.\nBased on the above thought, the answer is:\n42.4." } ], "id_raw": 558 }, { "id": "EdsH7Mn0lJGSbC4A", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_28_19.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart into a table format, with Years as rows and different technologies as columns?" }, { "from": "gpt", "value": "To generate the table from the line chart, I first look at {the labels on the x-axis}[[0.129, 0.903, 0.154, 0.931], [0.230, 0.903, 0.255, 0.931], [0.331, 0.903, 0.356, 0.931], [0.432, 0.903, 0.458, 0.931], [0.534, 0.903, 0.559, 0.931], [0.635, 0.903, 0.660, 0.931], [0.736, 0.903, 0.761, 0.931], [0.837, 0.903, 0.862, 0.931], [0.938, 0.903, 0.963, 0.931]], which represent the years. Next, I identify {the labels in the legend}[[0.162, 0.100, 0.302, 0.128], [0.162, 0.142, 0.300, 0.170]] representing the different technologies. I need to find {the points for each technology}[[0.129, 0.768], [0.230, 0.745], ..., [0.938, 0.479]], match them to the years and extract the values to populate the table.\nBased on the above thought, the answer is:\nYear,Italian Cuisine,Greek Cuisine\n1980,20,30\n1985,22,35\n1990,25,40\n1995,30,48\n2000,35,55\n2005,37,58\n2010,40,60\n2015,43,65\n2020,45,70" } ], "id_raw": 559 }, { "id": "9Okx1DimmSROSwx4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_28_19.png", "conversations": [ { "from": "human", "value": "\nWhich gastronomy culture showed more growth between 1980 and 2020 as per the chart?" }, { "from": "gpt", "value": "To determine which gastronomy culture showed more growth, I need to look at the increase in scores for both Italian and Greek cuisine from 1980 to 2020. For {Italian cuisine}[0.162, 0.100, 0.302, 0.128], the score changed from {20}[0.129, 0.768] in 1980 to {45}[0.938, 0.479] in 2020, indicating a growth of 45 - 20 = 25. For {Greek cuisine}[0.162, 0.142, 0.300, 0.170], the score changed from {30}[0.129, 0.652] in 1980 to {70}[0.938, 0.190] in 2020, indicating a growth of 70 - 30 = 40.\nBased on the above thought, the answer is:\nGreek gastronomy culture showed more growth between 1980 and 2020. It increased by 40 points on the popularity scale from 30 to 70, compared to the Italian gastronomy culture which increased by 25 points from 20 to 45." } ], "id_raw": 559 }, { "id": "rj8M15g7a2UQmB8l", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_28_19.png", "conversations": [ { "from": "human", "value": "\nBy what amount did the popularity of Italian cuisine increase between 1980 and 2020?" }, { "from": "gpt", "value": "To determine the increase in the popularity of Italian cuisine, I need to examine {the points on the Italian Cuisine line}[[0.129, 0.768], [0.230, 0.745], [0.331, 0.710], [0.432, 0.652], [0.534, 0.595], [0.635, 0.571], [0.736, 0.537], [0.837, 0.502], [0.938, 0.479]]. The point corresponding to the year 1980 is {20}[0.129, 0.768]. The point corresponding to the year 2020 is {45}[0.938, 0.479]. Thus, the increase in the popularity is 45 - 20 = 25.\nBased on the above thought, the answer is:\n25" } ], "id_raw": 559 }, { "id": "5UxpSNAoOKatnB1g", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_29_5.png", "conversations": [ { "from": "human", "value": "\nCould you please arrange the given chart data into a structured table format where the columns would represent the years and the rows would represent the different types of cuisine?" }, { "from": "gpt", "value": "To create the structured table from the data, I will first locate {the x-axis tick labels}[[0.248, 0.895, 0.601, 0.923], [0.208, 0.895, 0.219, 0.923], [0.390, 0.895, 0.401, 0.923], [0.571, 0.895, 0.582, 0.923], [0.753, 0.895, 0.764, 0.923], [0.929, 0.895, 0.951, 0.923]], which will serve as the column headers representing time in years. Next, I identify {the legend texts}[[0.146, 0.055, 0.339, 0.083], [0.146, 0.097, 0.315, 0.125]] as the y-axis representative for the different cuisine types to create rows. To populate the values in the table, I locate {the points on the lines}[[0.123, 0.185, 0.940, 0.846], [0.123, 0.069, 0.940, 0.433]] for each type of cuisine per year and extract the corresponding values: e.g., 0.5, 0.6, 0.7, etc.\nBased on the above thought, the answer is:\nYear,Western Cuisine,Asian Cuisine\n1,0.5,0.75\n2,0.6,0.78\n3,0.7,0.79\n4,0.73,0.80\n5,0.75,0.83\n6,0.77,0.85\n7,0.80,0.88\n8,0.82,0.91\n9,0.86,0.93\n10,0.9,0.97" } ], "id_raw": 565 }, { "id": "aooXfFoo8Rgz0Sw3", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_29_5.png", "conversations": [ { "from": "human", "value": "\nBased on the data patterns presented in the line graph, which cuisine type experienced a constant rise in favorability over the period under observation? Explain your reasoning." }, { "from": "gpt", "value": "To discern which cuisine experienced a constant increase in favorability, I must analyze the trend lines for each cuisine. Observing {the line for Western Cuisine}[0.123, 0.185, 0.940, 0.846], there is a steady upward trajectory from 0.5 in year 1 to 0.9 in year 10, representing a consistent rise in favorability. For {the line for Asian Cuisine}[0.123, 0.069, 0.940, 0.433], the points show a similar upward trend, starting from 0.75 in year 1 to 0.97 in year 10, reflecting an unbroken rise in preference.\nBased on the above thought, the answer is:\nBoth Western and Asian cuisines have experienced a constant rise in favorability over the time period. Both trend lines indicate a steady upward trajectory, representing increased preference for these cuisines over the years. However, it is also important to note that the rise in favorability is more significant for Asian Cuisine as it started at a higher preference score of 0.75 and ended at 0.97, compared to Western Cuisine which started at 0.5 and ended at 0.9." } ], "id_raw": 565 }, { "id": "18vAzfnBM1XRbsvN", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_29_5.png", "conversations": [ { "from": "human", "value": "\nHow much change in favorability of Asian Cuisine is predicted from the year 4 to year 6?" }, { "from": "gpt", "value": "First, to find the favorability of Asian Cuisine in year 4, I need to locate {the value point for Asian Cuisine in year 4}[[0.395, 0.350]], which corresponds to a favorability score of 0.8. Then, I locate {the value point for Asian Cuisine in year 6}[[0.577, 0.267]], which corresponds to a favorability score of 0.85. The difference between the favorability scores in year 6 and year 4 is then computed as 0.85 - 0.8 = 0.05.\nBased on the above thought, the answer is:\n0.05." } ], "id_raw": 565 }, { "id": "0C4G6JuV8l0KyeDO", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_29_10.png", "conversations": [ { "from": "human", "value": "\nCould you generate a table from the data represented in the line chart, where the various factors represent columns, and the three sample sets represent rows?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I will first look at {the x-axis tick labels}[0.052, 0.895, 0.978, 0.923], which serves as the column headers representing the various factors. Also, I identify {the legend labels}[0.750, 0.044, 0.922, 0.220] serving as the row labels representing the three sample sets. For each sample set, I locate the {line points}[0.120, 0.422, 0.892, 0.634],[0.120, 0.352, 0.892, 0.776],[0.120, 0.069, 0.892, 0.846] on each factor and extract the corresponding normalized values.\nBased on the above thought, the answer is:\nSamples, Abrupt Increase, Sharp Rise, Significant Decrease, Subdued Expansion\nUpstream, 0.5, 0.6, 0.4, 0.7\nMidstream, 0.75, 0.8, 0.2, 0.8\nDownstream, 1.0, 1.2, 0.1, 0.9" } ], "id_raw": 570 }, { "id": "mE9hWzvEHccaVm4g", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_29_10.png", "conversations": [ { "from": "human", "value": "\nBased on the trends depicted in the line chart, which sample set seems to be experiencing the highest volatility across all stages of the value chain? Provide an explanation for your reason8ing." }, { "from": "gpt", "value": "In determining the sample set experiencing the highest volatility across the value chain stages, I need to examine the oscillations in the line plots for each sample. {The line for the Upstream sample}[0.120, 0.422, 0.892, 0.634] shows moderate changes in its values all through the stages with minor fluctuations. The {line for the Midstream sample}[0.120, 0.352, 0.892, 0.776] exhibits relatively high fluctuations, particularly from the Sharp rise to the Significant decrease stage, and then stabilization for the Subdued expansion. The {line for Downstream sample}[0.120, 0.069, 0.892, 0.846] shows a very steep rise from the Abrupt increase to Sharp rise stage, a steep decline at the Significant decrease stage, and again a rise for the Subdued expansion stage. These significant fluctuations imply high volatility.\nBased on the above thought, the answer is:\nBased on the trends seen in the line chart, the Downstream sample seems to have the highest volatility across all stages of the value chain. This is backed up by the Downstream line plot that depicts significant fluctuations. It demonstrates a sharp rise from the Abrupt increase to Sharp rise stage, a sharp fall at the Significant decrease stage, followed by another spike at the Subdued expansion stage. The magnitudes of these fluctuations are more drastic compared to the Upstream and Midstream samples, indicating a greater degree of volatility in the Downstream sample." } ], "id_raw": 570 }, { "id": "SlONmMI6cnsk8JBA", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_29_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in the phase values from the Abrupt increase stage to the Subdued expansion stage for the Upstream sample?" }, { "from": "gpt", "value": "To determine the percentage increase in phase values from the Abrupt increase stage to the Subdued expansion stage for the Upstream sample, I have to find the initial and final values from the {line points of the Upstream sample}[0.120, 0.564] and [0.892, 0.422]. The coordinates of the phase values show that the initial value (at the abrupt increase) for Upstream is 0.5 and the final value (at subdued expansion) is 0.7. Thus, the percentage increase is given by 100 x (Final - Initial) / Initial which is 100 x (0.7 - 0.5) / 0.5 = 40% increase.\nBased on the above thought, the answer is:\n40%." } ], "id_raw": 570 }, { "id": "bdDGUaty5f9isqez", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_29_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a structured table with the years as the rows and \"Price Index\" and \"Housing Units\" as the columns?" }, { "from": "gpt", "value": "To convert the line chart data to a table, I identify each point on the {Price Index line}[0.105, 0.067, 0.878, 0.857] and the {Housing Units line}[0.105, 0.535, 0.878, 0.797] and map the coordinates to the years using {the x-axis labels}[-0.029, 0.912, 0.001, 0.936, ..., 0.934, 0.912, 0.963, 0.936]. This allows me to extract the values of Price Index and Housing Units for each year.\nBased on the above thought, the answer is:\nYear,Price Index,Housing Units\n2010,76.5,680\n2011,82.4,700\n2012,87.2,720\n2013,81.9,740\n2014,89.1,760\n2015,93.6,780\n2016,99.4,800\n2017,104.2,820\n2018,98.6,850\n2019,103.5,900\n2020,108.7,950" } ], "id_raw": 574 }, { "id": "VRz0Y2QoywFpDZSM", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_29_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, how might you describe the overall trend in housing units built over the years?" }, { "from": "gpt", "value": "To describe the overall trend in housing units built over the years, I need to analyze {the Housing Units line}[0.105, 0.535, 0.878, 0.797]. The line shows a steady upward trend, indicating that the number of housing units built has generally increased year after year.\nBased on the above thought, the answer is:\nThe overall trend in housing units built over the years can be described as a steady increase. This implies that the construction of new housing units has consistently grown throughout the decade, suggesting a healthy development in the real estate market." } ], "id_raw": 574 }, { "id": "0hdpAS0SOC9PvrOB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_29_14.png", "conversations": [ { "from": "human", "value": "\nWhat was the maximum increase in price index from one year to the next?" }, { "from": "gpt", "value": "To find the maximum increase in price index, I must locate the corresponding points on the {Price Index line}[0.105, 0.067, 0.878, 0.857] and calculate the differences in these values from one year to the next. Comparing these annual changes, I find that the maximum increase is between 2016 and 2017.\nBased on the above thought, the answer is:\n4.8" } ], "id_raw": 574 }, { "id": "1cmS4454opeLuwiB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_29_15.png", "conversations": [ { "from": "human", "value": "\nCan you organize the raw data from the chart into a structured table with 'Year' as the rows and 'Investment' and 'Employment' as the columns?" }, { "from": "gpt", "value": "To create a table from the line chart, I first identify {the x-axis labels}[[0.085, 0.912, 0.121, 0.936], [0.190, 0.912, 0.226, 0.936], [0.295, 0.912, 0.331, 0.936], [0.400, 0.912, 0.436, 0.936], [0.505, 0.912, 0.541, 0.936], [0.610, 0.912, 0.646, 0.936], [0.715, 0.912, 0.751, 0.936], [0.820, 0.912, 0.856, 0.936], [0.926, 0.912, 0.961, 0.936]] that represents the years. Next, I check {the legend}[[0.902, 0.046, 0.973, 0.069], [0.902, 0.081, 0.972, 0.104]] for the column headers (Investment and Employment). Then, I move to extract the values for each year by noting down {the y-coordinates for Investment}[[0.103, 0.864], [0.208, 0.826], [0.313, 0.792], [0.418, 0.775], [0.523, 0.712], [0.628, 0.642], [0.733, 0.557], [0.838, 0.447], [0.943, 0.361]] and {the coordinates for Employment}[[0.103, 0.065], [0.208, 0.103], [0.313, 0.164], [0.418, 0.173], [0.523, 0.228], [0.628, 0.270], [0.733, 0.357], [0.838, 0.456], [0.943, 0.542]] from the respective line plots.\nBased on the above thought, the answer is:\nYear,Investment in millions,Employment\n2012,8.5,50.6\n2013,10.5,48.6\n2014,12.3,45.4\n2015,13.2,44.9\n2016,16.5,42.0\n2017,20.2,39.8\n2018,24.7,35.2\n2019,30.5,30.0\n2020,35.0,25.5" } ], "id_raw": 575 }, { "id": "AGK0aheYAs8cZ3PI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_29_15.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which Industry seems to have the most consistent growth over the years? Explain your reasoning." }, { "from": "gpt", "value": "To determine which Industry has the most consistent growth, I need to analyze the trends in the line plots for both the Industries. For {Industry 1}[0.103, 0.361, 0.943, 0.864], the line plot shows a steady increase from 8.5 to 35.0 over the years, indicating a positive trend. By contrast, for {Industry 2}[0.103, 0.065, 0.943, 0.542], the line plot shows a decrease from 50.6 to 25.5, indicating a negative trend.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Industry 1 seems to have the most consistent growth over the years. Its line plot shows a steady increase from 8.5 to 35.0 over the years, indicating a positive trend. Contrary to this, Industry 2 displays a declining trend over the years." } ], "id_raw": 575 }, { "id": "eOG57GEGPxxWYztM", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_29_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest value noticed for the Employment count and in which year did it occur?" }, { "from": "gpt", "value": "To find the year with the highest Employment count, I first locate {the points plotted for Employment}[[0.103, 0.065], [0.208, 0.103], [0.313, 0.164], [0.418, 0.173], [0.523, 0.228], [0.628, 0.270], [0.733, 0.357], [0.838, 0.456], [0.943, 0.542]]. I notice that the highest point is {at y=0.065}[0.103, 0.065]. The corresponding {year for the highest Employment} is derived from the {x-value of 0.103}[0.103, 0.912, 0.121, 0.936], which indicates the year 2012.\nBased on the above thought, the answer is:\nThe highest Employment count is 50.6 in the year 2012." } ], "id_raw": 575 }, { "id": "blUMxmRU3FcFcOE9", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_29_16.png", "conversations": [ { "from": "human", "value": "\nBased on the bar charts, can you convert the data representing Market Status, Sales, and Consumption into a structured table?" }, { "from": "gpt", "value": "For forming the table from the provided data, we need to examine the bars on the bar charts. The {legend}[0.662, 0.044, 0.903, 0.136] defines the type of data each bar represents; one for Global Trade Volume and the other for Supply Chain Risk. The x-axis, represented by {x-axis tick labels}[[0.051, 0.895, 0.062, 0.923],...,[1.000, 0.895, 1.022, 0.923]] serves as the Month column. Furthermore, the trade volume for the corresponding month is represented by {the line points for Global Trade Volume}[[0.125, 0.846],...,[0.875, 0.276]], and the Supply Chain Risk is signified by {the line points for Supply Chain Risk}[[0.125, 0.263],...,[0.875, 0.652]]. It is essential to note that all the points plotted here have a correspondence with the tick marks on the x-axis.\nBased on the above thought, the answer is:\nMonth,Global Trade Volume,Supply Chain Risk\n1,100,5\n2,120,4\n3,130,5\n4,120,6\n5,150,5\n6,160,4\n7,180,3\n8,250,2\n9,150,3\n10,230,2\n11,200,4\n12,210,3" } ], "id_raw": 576 }, { "id": "Ja9oAgQXGChnJJK9", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_29_16.png", "conversations": [ { "from": "human", "value": "\nBased on the patterns shown in the line charts, what inferences can you make about the correlation between Global Trade Volume and Supply Chain Risk? Please elaborate." }, { "from": "gpt", "value": "To make any inference about the correlation between Global Trade Volume and Supply Chain Risk, I need to evaluate the trends of both lines. Looking at {the line for Global Trade Volume}[0.125, 0.069, 0.875, 0.846], it is clear that the trade volume increases for the most part with some fluctuations in the middle. Conversely, {the line for Supply Chain Risk}[0.125, 0.069, 0.875, 0.846] shows an overall downward trend. These distinct patterns in the lines suggest there might be a negative correlation between Global Trade Volume and Supply Chain Risk.\nBased on the above thought, the answer is:\nBased on the observations from the line charts, it could be inferred that Global Trade Volume and Supply Chain Risk may have a negative correlation. This is because as the Global Trade Volume increases, the Supply Chain Risk decreases, and vice versa. However, note that to definitively establish a statistical correlation, a rigorous analysis is required. This inference is made purely on the observed data trends from the line charts." } ], "id_raw": 576 }, { "id": "SrOXLoz0Urs5evhS", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_29_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the total Global Trade Volume across all months indicated in the chart?" }, { "from": "gpt", "value": "To find the total Global Trade Volume, we need to sum up all the monthly volumes. As per the plotted data points for {Global Trade Volume}[[0.125, 0.846],...,[0.875, 0.276]], the corresponding Global Trade Volume for each month is as follows: 100, 120, 130, 120, 150, 160, 180, 250, 150, 230, 200, and 210. Therefore, we sum all these values to get the total Global Trade Volume.\nBased on the above thought, the answer is:\n2000." } ], "id_raw": 576 }, { "id": "Teql2B6IpuSaK1On", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_29_17.png", "conversations": [ { "from": "human", "value": "\nCan you provide information from the chart in a table format with years as rows and startups as columns?" }, { "from": "gpt", "value": "For extracting the tabulated data, {the x-axis tick labels}[0.101, 0.895, 0.144, 0.923], [0.264, 0.895, 0.308, 0.923], [0.428, 0.895, 0.472, 0.923], [0.591, 0.895, 0.635, 0.923], [0.755, 0.895, 0.799, 0.923], [0.918, 0.895, 0.962, 0.923] will be employed as our row identifiers. These represent the years. {The legend texts}[0.146, 0.055, 0.310, 0.083], [0.146, 0.097, 0.310, 0.125] correspond to the startups that will be represented as column labels. For the values in the table, {the line points}[0.123, 0.846], [0.286, 0.380], [0.450, 0.509], [0.613, 0.095], [0.777, 0.121], [0.940, 0.406], [0.123, 0.691], [0.286, 0.769], [0.450, 0.587], [0.613, 0.173], [0.777, 0.328], [0.940, 0.069] will be utilized. The values for each startup at the respective years can be extracted from these points.\nBased on the above thought, the answer is:\nYear,Startups-A,Startups-B\n2015,2.1,2.7\n2016,3.9,2.4\n2017,3.4,3.1\n2018,5.0,4.7\n2019,4.9,4.1\n2020,3.8,5.1" } ], "id_raw": 577 }, { "id": "wuAKdUza7QepNMHV", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_29_17.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which startup sector had a more pronounced growth trend over the years?" }, { "from": "gpt", "value": "From looking at {the line for Startups Sector-A}[0.123, 0.095, 0.940, 0.846], I see the values leveled off after a significant increase, suggesting an initial period of rapid growth followed by a stable period. Meanwhile, {the line for Startups Sector-B}[0.123, 0.069, 0.940, 0.769] also shows a significant increase, however, it does not level off and instead continues to increase. Therefore, based on the magnitude of increase, Startups Sector-B had a more pronounced growth trend over the years.\nBased on the above thought, the answer is:\nAccording to the chart, Startup Sector-B had a more pronounced growth trend over the years. It showed a consistent increase throughout the years as reflected in the chart, indicating stronger growth compared to Startup Sector-A." } ], "id_raw": 577 }, { "id": "zTwTWJEicnU2ipYc", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_29_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the growth difference for Startups Sector-A from the year 2016 to 2020?" }, { "from": "gpt", "value": "To find the growth difference for Startups Sector-A from 2016 to 2020, I need to locate the values of these two years. The value for 2016 is 3.9 which corresponds to {2016's point for Sector-A}[0.286, 0.380], and the value for 2020 is 3.8 which corresponds to {2020's point for Sector-A}[0.940, 0.406]. Hence, the difference in growth for Sector-A from 2016 to 2020 is the value at 2020 minus the value at 2016 which is 3.8 - 3.9.\nBased on the above thought, the answer is:\n-0.1." } ], "id_raw": 577 }, { "id": "lzBko6jnFVAxpnW5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_29_19.png", "conversations": [ { "from": "human", "value": "\nCan you make a table summarizing social involvement for the different age groups, separated by males and females, as represented in the line chart?" }, { "from": "gpt", "value": "To create the table, we first look at the x-axis which represents {the age groups}[0.125, 0.895, 0.875, 0.923] - these will form the row labels. The legend identifies {the gender lines}[0.097, 0.044, 0.304, 0.178], which will be the column headers - Male and Female. Finally, we locate the specific {data points on the lines}[[0.125, 0.837], [0.250, 0.666], [0.375, 0.239], [0.500, 0.154], [0.625, 0.068], [0.750, 0.410], [0.875, 0.325]] for each gender at each age group to populate the table. These correspond to the level of social involvement.\nBased on the above thought, the answer is:\nGender/Age Group,18-24,25-34,35-44,45-54,55-64,65+\nMale,50,70,120,130,140,100\nFemale,30,40,50,70,80,90" } ], "id_raw": 579 }, { "id": "5XlunC7eqMtzfn4E", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_29_19.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which demographic (male or female) displays an overall trend of increasing social involvement as age increases? Explain your reasoning." }, { "from": "gpt", "value": "To answer this question, I need to analyze the trends displayed by the line plots for both males and females. The {line for males}[0.125, 0.068, 0.875, 0.837] initially shows an increase in social involvement up to the 35-44 age group, but then there is a significant decline, indicating a decreasing trend as age increases after the mid-thirties. \nOn the other hand, looking at the {line associated with females}[0.125, 0.920, 0.875, 0.710], we observe a consistent increase in social involvement up to the 55-64 age group, followed by a slight decrease. However, overall, the trend for females is an increase in social involvement as age increases, at least until the age of 65.\nBased on the above thought, the answer is:\nBased on the line chart, females display an overall trend of increasing social involvement as age increases. While there is a slight decrease in social involvement in the 65+ age group, the general trend from 18-64 is increasing, which contrasts with the decreasing trend observed for males after the 35-44 age group." } ], "id_raw": 579 }, { "id": "Yrkj28rSNGjmXho7", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_29_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the sum of social involvement percentages for all age groups for females?" }, { "from": "gpt", "value": "To determine the total social involvement for females, I need to identify {the line points for females}[[0.125, 0.920], [0.250, 0.875], [0.375, 0.830], [0.500, 0.660], [0.625, 0.615], [0.750, 0.710]] from the line chart corresponding to ages from 18 to 65+. The social involvement percentages corresponding to these points are 30, 40, 50, 70, 80 and 90. Adding them up, we get the total social involvement for females.\nBased on the above thought, the answer is:\n360" } ], "id_raw": 579 }, { "id": "ACBjJT311CjHIdBR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_1.png", "conversations": [ { "from": "human", "value": "\nConvert the data from the chart into a structured table. The columns would be the Investment Amounts and the rows would be the different methods of investment." }, { "from": "gpt", "value": "From the line chart coordinates {Investment Amounts}[[0.188, 0.510], [0.272, 0.510], [0.524, 0.510], [0.943, 0.510]], we have the column headers. We can identify {the legend texts}[[0.455, 0.443, 0.671, 0.457]], as the row labels which represent the different investment methods. {The line points for Fixed Rateturn}[[0.188, 0.392], [0.272, 0.322], [0.524, 0.217], [0.943, 0.042]] and {The line points for Random Rate}[[0.188, 0.392], [0.272, 0.385], [0.524, 0.217], [0.943, 0.287]] are plotted in the first subplot which allow us to populate the Fixed Rateturn and Random Rate values respectively. We can similarly extract the values for Predictive Analysis using {The line points for Predictive Analysis}[[0.188, 0.863], [0.272, 0.746], [0.524, 0.688], [0.943, 0.513]].\nBased on the above thought, the answer is:\nInvestment Method,10000,20000,50000,100000\nFixed Rateturn,0.05,0.07,0.1,0.15\nRandom Rate,0.05,0.052,0.1,0.08\nPredictive Analysis,0.06,0.1,0.12,0.18" } ], "id_raw": 581 }, { "id": "svSBtEswVU2rIvDJ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_1.png", "conversations": [ { "from": "human", "value": "\nLooking at the trends, what can you tell about the return rate as the investment amount increases for each investment method?" }, { "from": "gpt", "value": "By studying the line plots, I observe how the return rate changes with increasing investment amount. For Fixed Rateturn, there is an increase observed in {the line for Fixed Rateturn}[0.188, 0.392, 0.943, 0.042]. However, its increasing trend shows a decline near the end. The Random Rate, represented by {the line for Random Rate}[0.188, 0.392, 0.943, 0.287], exhibits an initially static, then increasing, and then a decreasing trend. In contrast, the Predictive Analysis method, shown by {the line for Predictive Analysis}[0.188, 0.863, 0.943, 0.513], is continuously increasing though at a decreasing rate, indicating the return rate increases with the investment amount but the increase slows down as the investment amount grows.\nBased on the above thought, the answer is:\nFor the Fixed Rateturn method, the return rate generally increases with the investment amount but there is a drop at the highest investment amount. For the Random Rate method, the return rate remains the same initially, increases moderately at the middle investment amounts, and then decreases towards the end. The Predictive Analysis method shows a consistently increasing trend, where the return rate rises with the investment amount, but the rate of increase slows down at higher investment amounts." } ], "id_raw": 581 }, { "id": "f0J2NPhzqp63T168", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest return rate achieved from the Predictive Analysis investment method?" }, { "from": "gpt", "value": "To find the highest return rate achieved from the Predictive Analysis investment method, I need to consider {the line points for Predictive Analysis}[[0.188, 0.863], [0.272, 0.746], [0.524, 0.688], [0.943, 0.513]]. The line points correspond to the return rates 0.06, 0.1, 0.12, 0.18. So the highest value is 0.18.\nBased on the above thought, the answer is:\n0.18." } ], "id_raw": 581 }, { "id": "VYQ6emZ4CpfwITJo", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_2.png", "conversations": [ { "from": "human", "value": "\nExtract user data and corresponding performance across different methods for Facebook and Twitter into table format from the line chart." }, { "from": "gpt", "value": "To create the table from the line chart, I examined {the x-axis labeling}[0.568, 0.469, 0.568, 0.469] which indicates that each point relates to a specific number of users. Observing the labels helps me understand the columns; 'Users'. Then, by identifying {the legend}[0.385, 0.021, 0.750, 0.063], I confirmed the distinct methods used like Baseline, SoftMoE, and Top1-MoE for categorization within the table. Each line's data points like those for Baseline on Facebook {line for Baseline on Facebook}[0.188, 0.100, 0.947, 0.317] and those for Baseline on Twitter {line for Baseline on Twitter}[0.188, 0.499, 0.947, 0.833] provide the specific values for users.\nBased on the above thought, the answer is:\nUser_Count,Facebook_Baseline,Facebook_SoftMoE,Facebook_Top1MoE,Twitter_Baseline,Twitter_SoftMoE,Twitter_Top1MoE\n1,5000,4800,4300,4700,4500,4100\n2,5100,4700,4400,4500,4600,4300\n4,4750,4700,4500,4300,4600,4200\n8,4800,4600,4350,4500,4500,4300\n16,4600,4400,4200,4200,4500,4300\n32,4500,4300,4175,4100,4500,4350" } ], "id_raw": 582 }, { "id": "qAbq2LGVxMvhln1C", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_2.png", "conversations": [ { "from": "human", "value": "\nHow does the performance trend change with increasing users for the Baseline method on Facebook, according to the line chart?" }, { "from": "gpt", "value": "Analyzing {the line for Facebook Baseline}[0.188, 0.100, 0.947, 0.317], which illustrates the performance trend for the Baseline method on Facebook, I observe the performance values at significant user count increments: For 1 user, the performance is at a peak with 5000, which decreases as the user count increases, showing 4500 for 32 users. This trend signifies a gradual decrease in performance as the number of users increases.\nBased on the above thought, the answer is:\nAccording to the line chart, the performance trend for the Baseline method on Facebook shows a gradual decrease as the user count increases. Starting from 5000 for one user and descending to 4500 for 32 users, the trend displays a consistent decline in performance with growing user numbers." } ], "id_raw": 582 }, { "id": "YXCAijtoE2UNGktC", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_2.png", "conversations": [ { "from": "human", "value": "\nFrom the data, which user count setting shows the smallest difference between Facebook and Twitter performance for the SoftMoE method?" }, { "from": "gpt", "value": "To determine the smallest difference between Facebook and Twitter performance for the SoftMoE method, I examine the respective line points for Facebook {line for Facebook SoftMoE}[0.188, 0.209, 0.947, 0.389] and Twitter {line for Twitter SoftMoE}[0.188, 0.555, 0.947, 0.610], extracting performance values: Facebook - [4800, 4700, 4700, 4600, 4400, 4300] and Twitter - [4500, 4600, 4600, 4500, 4500, 4500]. After calculating the absolute differences for each user count, the smallest difference in performance is found for user count 32, with values 4300 for Facebook and 4500 for Twitter, giving a difference of 200.\nBased on the above thought, the answer is:\n32" } ], "id_raw": 582 }, { "id": "0DxAXP9LgTNL15ui", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_3.png", "conversations": [ { "from": "human", "value": "\nCan you create a structured table enumerating the 'Average Efficiency' and 'Material Waste' for each month from the bar chart?" }, { "from": "gpt", "value": "To create the table from the bar chart, I first identify {the x-axis labels}[0.093, 0.415], [0.188, 0.415], [0.282, 0.415], [0.377, 0.415], [0.471, 0.415], [0.566, 0.415] marking the months from January to June. For each month, I analyze {the stacked bars}[0.093, 0.380, 0.125, 0.415], [0.188, 0.350, 0.220, 0.415], ... , [0.566, 0.250, 0.606, 0.415] where the top portion represents 'Material Waste' and the bottom portion represents 'Average Efficiency'. I then read the corresponding percentage values annotated near each bar.\nBased on the above thought, the answer is:\nMonth, Average Efficiency (%), Material Waste (%)\nJanuary, 80, 20\nFebruary, 77, 23\nMarch, 79, 21\nApril, 84, 16\nMay, 78, 22\nJune, 77, 23" } ], "id_raw": 583 }, { "id": "sDmBq7nKchYMqsRs", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_3.png", "conversations": [ { "from": "human", "value": "\nWhat month shows the highest 'Average Efficiency' according to the bar chart, and how does it compare to other months?" }, { "from": "gpt", "value": "To identify the month with the highest 'Average Efficiency', I examine the heights of {the dark shaded areas of the bars representing 'Average Efficiency'}[0.093, 0.380, 0.125, 0.415], [0.188, 0.350, 0.220, 0.415], [0.282, 0.330, 0.314, 0.415], [0.377, 0.305, 0.409, 0.415], [0.471, 0.340, 0.503, 0.415], [0.566, 0.350, 0.606, 0.415]. April shows the highest efficiency with a notable peak in its efficiency bar. Comparatively, the efficiency values in other months fluctuate between 77% and 79%, while April stands out with an efficiency of 84%.\nBased on the above thought, the answer is:\nApril shows the highest 'Average Efficiency' at 84% according to the bar chart. This percentage is significantly higher compared to the other months, which largely hover between 77% and 79%. The noticeable peak in April highlights it as an outlier in terms of production efficiency." } ], "id_raw": 583 }, { "id": "DKuZ7Hu8pL6kOFcH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_3.png", "conversations": [ { "from": "human", "value": "\nHow much did the 'Material Waste' percentage decrease from January to April?" }, { "from": "gpt", "value": "To find the 'Material Waste' decrease, I locate {the bars for Material Waste in January and April}[0.093, 0.380, 0.125, 0.415], [0.377, 0.350, 0.409, 0.415]. The percentage of 'Material Waste' in January is 20%, and in April it is 16%. The decrease is the difference between these two values, which is 20 - 16 = 4%.\nBased on the above thought, the answer is:\n4%" } ], "id_raw": 583 }, { "id": "oBQru1JYXZ8EwUod", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_4.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the chart into a structured table format with years in the column top and categories in each row?" }, { "from": "gpt", "value": "To form the table, I first identify {the x-axis tick labels}[[0.080, 0.413], [0.188, 0.413], [0.296, 0.413], [0.404, 0.413], [0.512, 0.413], [0.620, 0.413], [0.728, 0.413], [0.836, 0.413], [0.943, 0.413]] as the column headers or years. Then I attribute the name of each line as row names, these are {the legend texts}[[0.518, 0.443, 0.662, 0.466]]. The data for each category at each year can be collected from {the line points}[[0.188, 0.381], [0.296, 0.303], [0.404, 0.332], [0.512, 0.247], [0.620, 0.155], [0.728, 0.275],[ 0.836, 0.112], [0.943, 0.077]; [0.188, 0.850],[ 0.296, 0.751], [0.404, 0.776], [0.512, 0.689], [0.620, 0.640], [0.728, 0.665], [0.836, 0.590], [0.943, 0.553]]. Any points at the same x-axis coordinate belong to the same year. By taking these steps, I can form a structured table from the data represented in the chart.\nBased on the above thought, the answer is:\nYear, Innovation Score, Patent Expenses\n1, 4.1, 1.5\n2, 5.2, 2.3\n3, 4.8, 2.1\n4, 6.0, 2.8\n5, 7.3, 3.2\n6, 5.6, 3.0\n7, 7.9, 3.6\n8, 8.4, 3.9" } ], "id_raw": 584 }, { "id": "E47LNl87LopLTTOB", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_4.png", "conversations": [ { "from": "human", "value": "\nJudging by the provided data, what kind of relationship can be identified between the innovation score and patent expenses?" }, { "from": "gpt", "value": "To establish the relationship between the innovation score and patent expenses, I need to look at the trends in the {Innovation Score line plot}[0.188, 0.077, 0.943, 0.381] and {Patent Expenses line plot}[0.188, 0.553, 0.943, 0.850]. It can be noticed that when the innovation score increases, patent expenses also increase and vice versa indicating a positive correlation. This pattern can be observed throughout the data points.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, a positive correlation can be identified between the innovation score and patent expenses. When the innovation score rises, so does the patent expenses, and when it falls, patent expenses also reduce. This trend suggests that higher innovation scores are associated with higher patent expenses, and lower innovation scores are coupled with lower patent expenses." } ], "id_raw": 584 }, { "id": "Qd4PCflgB9lZ57LL", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_4.png", "conversations": [ { "from": "human", "value": "\nIn which year was the innovation score highest?" }, { "from": "gpt", "value": "To answer this question, I find this by looking at the trend of {Innovation Score line plot} [0.188, 0.077, 0.943, 0.381]. The graph coordinates are normalized between 0 and 1. I observe the highest point on this plot happens at {the point}[0.943, 0.077], which is tied to the 8th year of this plot, telling me that the point of highest innovation score in the given years is 8, derived from {the x-axis tick label} [0.943, 0.413].\nBased on the above thought, the answer is:\nYear 8." } ], "id_raw": 584 }, { "id": "ewtN4B1GK1J7cHZE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_5.png", "conversations": [ { "from": "human", "value": "\nCan you structure the performance on the Transportation Volume from the bar chart into a comprehensible table?" }, { "from": "gpt", "value": "Analyzing the chart, I distinguish between each series on the {chart's x-axis}[0.104, 0.394, 1.111, 0.394] which represents different years. Subsequently, I observe the volume indicator on the {chart's y-axis}[0.150, 0.025, 0.150, 0.394] that facilitates the translation of bar heights into exact values. For each year and transport category, I evaluate {the corresponding bars}[0.188, 0.116, 0.943, 0.266], [0.188, 0.079, 0.943, 0.228] detailing the volume transported which assists in populating the table accordingly.\nBased on the above thought, the answer is:\nYear,Truck Transport,Goods Delivery,Marine Transport,Aviation Transport,Logistics Service\n1,1500,1600,1300,1100,1200\n2,1550,1650,1350,1200,1250\n3,1600,1680,1400,1300,1300\n4,1650,1700,1450,1400,1350\n5,1670,1750,1500,1450,1400\n6,1700,1800,1550,1500,1450\n7,1750,1830,1600,1550,1500\n8,1800,1900,1650,1600,1550\n9,1850,1950,1700,1650,1600\n10,1900,2000,1750,1700,1650" } ], "id_raw": 585 }, { "id": "bVY8VByTs3vxLa83", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_5.png", "conversations": [ { "from": "human", "value": "\nWhich transport category exhibits the most consistent growth over the 10-year period in the graph?" }, { "from": "gpt", "value": "To determine the transport category with the most consistent growth, it's critical to evaluate each of {the lines}[0.188, 0.116, 0.943, 0.266], [0.188, 0.079, 0.943, 0.228] representing different transport services across the 10-year span. I analyze the slope of each line to determine the uniformity and steadiness of growth. Observing the incremental changes year-over-year tells me which category maintained a steady increase without significant drops or erratic jumps.\nBased on the above thought, the answer is:\nThe Aviation Transport category exhibits the most consistent growth over the 10-year period, as its line shows a steady and uniform ascent with no drops in growth every consecutive year." } ], "id_raw": 585 }, { "id": "AlyTdRGNPEtAXj1i", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the total annual increase in Truck Transport from year 1 to year 10?" }, { "from": "gpt", "value": "To calculate the total annual increase, I first extract the values for Truck Transport from each year by tracking {the line for Truck Transport}[0.188, 0.116, 0.943, 0.266]. The values starting from year 1 to year 10 are 1500, 1550, 1600, 1650, 1670, 1700, 1750, 1800, 1850, 1900. The annual increments are found by subtracting each consecutive year's value from the previous year's value. By summing these differences, I arrive at the total increase.\nBased on the above thought, the answer is:\n400." } ], "id_raw": 585 }, { "id": "lYMQUQpA7qvJ9XkX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_6.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data of the different techniques for the number of patients and success scores into a structured table?" }, { "from": "gpt", "value": "To generate tabular data from the line chart, I first identify each subplot by looking at {the ylabel}[0.091, 0.175, 0.109, 0.273] in the first subplot and {the ylabel}[0.091, 0.643, 0.109, 0.737] in the second subplot. They indicate the names of the techniques: 'Baseline' and 'SoftMoE'. I then examine the line for each subplot by analyzing {the line points of Baseline}[0.188, 0.319, 0.443, 0.376, 0.692, 0.300, 0.943, 0.129] and {the line points of SoftMoE}[0.188, 0.823, 0.443, 0.785, 0.692, 0.709, 0.943, 0.633] to match x-axis values with the relevant 'number of patients' and y-axis values with the appropriate 'scores' for each technique. After collecting all this data, I can create a structured table.\nBased on the above thought, the answer is:\nTechnique,Number of Patients (500),Scores (500),Number of Patients (1000),Scores (1000),Number of Patients (1500),Scores (1500),Number of Patients (2000),Scores (2000)\nBaseline,500,80,1000, 77,1500,81,2000,90\nSoftMoE,500,78,1000,80,1500,84,2000,88" } ], "id_raw": 586 }, { "id": "fBEG7wKCSV1W9Jf9", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_6.png", "conversations": [ { "from": "human", "value": "\nWhich technique, between Baseline and SoftMoE, indicates a better trend of success score as the number of patients increases?" }, { "from": "gpt", "value": "To answer this question, I have to review the trend in success scores for each technique as the number of patients increases. Observing {the line for Baseline}[0.188, 0.319, 0.943, 0.129], it initially decreases then slightly increases, but decreases again toward the end. For {SoftMoE}, the line [0.188, 0.823, 0.943, 0.633] shows a consistent decrease over time. However, even though SoftMoE decreases, its lowest point is still higher than the lowest point of the Baseline. So, despite the decrease, SoftMoE presents slightly better results.\nBased on the above thought, the answer is:\nThe SoftMoE technique indicates a better trend of success score as the number of patients increases. This is because, even though SoftMoE's success score decreases as the number of patients increases, its lowest point is still higher than the lowest point of the Baseline. Therefore, SoftMoE presents slightly better results." } ], "id_raw": 586 }, { "id": "l98LGfrqw5LbL0ZJ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_6.png", "conversations": [ { "from": "human", "value": "\nHow much higher is the success score of SoftMoE than Baseline when the number of patients is 2000?" }, { "from": "gpt", "value": "To answer this question, I need to locate {the point for Baseline at 2000 patients}[0.943, 0.129] and {the point for SoftMoE at 2000 patients}[0.943, 0.633]. The success score for Baseline at 2000 patients is 90. For SoftMoE at 2000 patients, the success score is 88. Therefore, Baseline has a 2 point higher success score than SoftMoE when the number of patients is 2000.\nBased on the above thought, the answer is:\n2 points." } ], "id_raw": 586 }, { "id": "kIKgwlIfyQ9bslGE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_8.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you construct a table format with Methods as rows and Policies as columns?" }, { "from": "gpt", "value": "To generate the table data, the starting point should be to take note of {the x-axis labels}[0.151, 0.896, 0.224, 0.920], [0.328, 0.896, 0.420, 0.920], [0.515, 0.896, 0.606, 0.920], [0.701, 0.896, 0.793, 0.920], [0.888, 0.896, 0.979, 0.920] which signify the row labels or Methods. The rows are the different strategies and the baseline applied. The y-axis labels {for policy A}[0.086, 0.102, 0.103, 0.332] and {for policy B & C}[0.075, 0.548, 0.093, 0.827] indicate the different policies, which are our columns. Now, for the values, each dot on the line plots respective to {Strategy 1 line}[0.187, 0.513, 0.934, 0.863] and {Strategy 2 line}[0.187, 0.513, 0.934, 0.595] and {Baseline}[0.187, 0.138, 0.934, 0.364] represents the policy values for each method.\nBased on the above thought, the answer is:\nMethods,Policy A (in Millions),Policy B (in Millions),Policy C (in Millions)\nBaseline,5.2,5.9,5.5\nStrategy 1,4.6,5.6,5.8\nStrategy 2,3.9,5.0,5.8\nStrategy 3,3.6,4.7,5.9\nStrategy 4,3.2,4.2,5.9" } ], "id_raw": 588 }, { "id": "AU4m3sG7U4HdLyYh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_8.png", "conversations": [ { "from": "human", "value": "\nFrom the effectiveness of the policies over different strategies in the line graphs, which policy seems to have a consistent decreasing trend? Please underline your findings." }, { "from": "gpt", "value": "To establish which has a consistent downward trend, I need to inspect each line plot. Observing {Policy A's plot}[0.187, 0.138, 0.934, 0.364], we see a decrease from 5.2 (Baseline) to 3.2 (Strategy 4). Moving on to {Policy B's plot}[0.187, 0.513, 0.934, 0.863], there is also a drop from 5.9 to 4.2 from Baseline to Strategy 4. However, in {Policy C's plot}[0.187, 0.513, 0.934, 0.595], while there's a slight increase from 5.5 to 5.9, the overall trend isn't decreasing.\nBased on the above thought, the answer is:\nBased on the line graphs, Policies A and B seem to have a consistent decreasing trend. However, Policy A demonstrates a sharper decline with the effectiveness dropping from 5.2 million to 3.2 million as compared to Policy B which drops from 5.9 million to 4.2 million from Baseline to Strategy 4. Hence, Policy A exhibits a more consistent and steeper decrease in effectiveness across the different strategies." } ], "id_raw": 588 }, { "id": "VV28AaZExVBceJBO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_8.png", "conversations": [ { "from": "human", "value": "\nWhat's the difference in effectiveness between Strategy 1 and Strategy 4 for policy A?" }, { "from": "gpt", "value": "To get the difference in effectiveness between the two strategies, my criterion is to focus on {the points on Policy A's graph}[0.187, 0.138, 0.934, 0.364]. The points are representative of the values of the effectiveness. For Strategy 1, the value is 4.6 while for Strategy 4, the value's 3.2. The difference is calculated as 4.6 - 3.2.\nBased on the above thought, the answer is:\n1.4 million." } ], "id_raw": 588 }, { "id": "69xUUxt3mPo6uDTR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_9.png", "conversations": [ { "from": "human", "value": "\nCan you construct a table that includes the type of techniques and its corresponding scores for each category?" }, { "from": "gpt", "value": "To create a table, I first identify {the x-axis labels}[[0.184, 0.880, 0.223, 0.920], [0.525, 0.880, 0.564, 0.920], [0.866, 0.880, 0.905, 0.920]] as the column headers, which represent the different techniques. The categories, {Innovative Ideas}[0.086, 0.197, 0.103, 0.388] and {Design Quality}[0.075, 0.622, 0.093, 0.792], are the row headers. {The line points for Innovative Ideas}[[0.184, 0.450], [0.525, 0.136], [0.866, 0.278]] and {for Design Quality}[[0.184, 0.864], [0.525, 0.550], [0.866, 0.676]] are then extracted to populate the respective values.\nBased on the above thought, the answer is:\nType, Method1, Method2, Method3\nInnovative Ideas, 1.2, 2.3, 1.8\nDesign Quality, 2.0, 3.5, 2.9" } ], "id_raw": 589 }, { "id": "aaToJvGhRfxr8W2H", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_9.png", "conversations": [ { "from": "human", "value": "\nFrom the line plot, which Type appears to have the most significant change in score over the three methods and why do you say so?" }, { "from": "gpt", "value": "To find which Type has the most significant change in score over the three methods, I look at {the line for Innovative Ideas}[0.184, 0.136, 0.866, 0.450] which starts at 1.2 and ends at 1.8, and {the line for Design Quality}[0.184, 0.550, 0.866, 0.864] which starts from 2.0 and ends at 2.9. The change for Innovative Ideas is 1.8 - 1.2 = 0.6 and for Design Quality it is 2.9 - 2.0 = 0.9. Comparing the change in scores, Design Quality has a greater change compared to Innovative Ideas.\nBased on the above thought, the answer is:\nFrom the line plot, the 'Design Quality' type appears to have the most significant change in score over the three methods. This is because it had an increase of 0.9 from Method1 to Method3, which was larger than the 0.6 increase observed for 'Innovative Ideas' over the same methods." } ], "id_raw": 589 }, { "id": "eF53nbQYAgvmyolh", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_9.png", "conversations": [ { "from": "human", "value": "\nHow much increase did the Design Quality score have from Method1 to Method2?" }, { "from": "gpt", "value": "To find the amount of increase in score from Method1 to Method2 for Design Quality, I need to locate {the line points for Design Quality Method1}[[0.184, 0.864]] and Method2 [[0.525, 0.550]]. The corresponding values are 2.0 and 3.5 respectively. The difference between these two values will be the increase, so the increase is 3.5 - 2.0 = 1.5.\nBased on the above thought, the answer is:\n1.5." } ], "id_raw": 589 }, { "id": "xSc10i9LSctBdI2f", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart into a table with years as columns and Industries as rows?" }, { "from": "gpt", "value": "To create a tabular representation of the line chart, I first observe {the x-axis labels}[0.048, 0.896, 1.083, 0.920], which represent the column headers or years. I recognize {the legend texts}[0.429, 0.439, 0.750, 0.463] as the row headers which differentiate the industries. To populate the cell values for each industry in the different years, I focus on {the line points for Information Technology} [0.188, 0.153, 0.943, 0.313] and for {Biotechnology}[0.188, 0.611, 0.943, 0.718]. I extract the respective values for each industry at different years.\nBased on the above thought, the answer is:\nIndustry,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014\nInformation Technology,400,240,180,140,120,100,80,60,40,20\nBiotechnology,100,180,180,180,160,140,120,100,80,60" } ], "id_raw": 590 }, { "id": "rXF2G5IDgyo6DdsH", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_10.png", "conversations": [ { "from": "human", "value": "\nBased on the trends displayed in the line chart, which industry seemed to have coped better with the declining revenue over time?" }, { "from": "gpt", "value": "To answer this question, I need to study the decline in the revenue plots of both the industries. Looking at {the line for Information Technology}[0.188, 0.153, 0.943, 0.313], I see that it has an almost constant decrease in revenue from 2005 to 2014. Next, examining {the line for Biotechnology}[0.188, 0.611, 0.943, 0.718], it shows a steady phase from 2006-2008, and only then it begins a slow, steady decline. Therefore, despite both lines showing a declining trend, the rate and time of decrease differ.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, the Biotechnology industry appeared to have coped better with the declining revenues over time, compared to the Information Technology industry. Even though both industries had declining revenues, the decline for Biotechnology industry started later on and proceeded at a slower pace." } ], "id_raw": 590 }, { "id": "clxvrxXp5akr0GNa", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in the total revenue between Information Technology and Biotechnology over the years?" }, { "from": "gpt", "value": "To determine the difference in total revenue between the two industries over the years, I first locate {the line points for Information Technology} [0.188, 0.153, 0.943, 0.313] and extract the corresponding values: 400, 240, 180, 140, 120, 100, 80, 60, 40, 20 and sum them up. Similarly, for {the line points for Biotechnology}[0.188, 0.611, 0.943, 0.718], I extract the corresponding values: 100, 180, 180, 180, 160, 140, 120, 100, 80, 60 and sum them up. The difference in total sum will give the difference in total revenue between the two industries over the years.\nBased on the above thought, the answer is:\n560" } ], "id_raw": 590 }, { "id": "lL4b9Yk1mBLDWF8O", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_11.png", "conversations": [ { "from": "human", "value": "\nCould you extract and tabulate the data from the line plot with the years as columns and the nature of trends as rows?" }, { "from": "gpt", "value": "Sure, I can assist with that. Firstly, I identify {the x-axis tick labels}[[0.013, 0.906, 0.073, 0.930], [0.130, 0.906, 0.190, 0.930], [0.600, 0.906, 0.660, 0.930], [0.835, 0.906, 0.895, 0.930]], which represent the column headers or years. Subsequently, I identify {the legend texts}[[0.201, 0.491, 0.383, 0.514], [0.466, 0.491, 0.620, 0.514],[0.704, 0.491, 0.873, 0.514]] to get the row labels representing the different nature of trends. Finally, I locate {the line points}[[0.160, 0.181, 0.865, 0.363], [0.160, 0.181, 0.865, 0.409], [0.160, 0.687, 0.865, 0.855]], corresponding to each trend at the respective years and extract those values.\nBased on the above thought, the answer is:\nTrend,2018,2019,2020,2021\nOscillating trend, 52, 55, 57, 60\nUnpredictable decline, 60,57,54,50\nAccelerated growth, 50,55,61,68" } ], "id_raw": 591 }, { "id": "b5g3nnUTWQfAZyf3", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_11.png", "conversations": [ { "from": "human", "value": "\nBased on the line plot, which trend showed the most decline over the years?" }, { "from": "gpt", "value": "To determine which trend showed the most decline, I need to analyze the line plots for each trend. The {line for the Oscillating trend}[0.160, 0.181, 0.865, 0.363] shows a steady increase from 52 to 60, so it does not show a decline. The {line for Unpredictable decline}[0.160, 0.181, 0.865, 0.409] shows a decrease from 60 to 50, thereby indicating a decline. For {Accelerated growth}[0.160, 0.687, 0.865, 0.855], there is actually an increase from 50 to 68. Thus, the most decline is observed in the \"Unpredictable decline\" trend.\nBased on the above thought, the answer is:\nBased on the line plot, the \"Unpredictable decline\" trend showed the most decline over the years." } ], "id_raw": 591 }, { "id": "YYvZRSp7QVRwVo4J", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the average score of the \"Oscillating trend\" across the years?" }, { "from": "gpt", "value": "To calculate the average score for the \"Oscillating trend\", I need to locate {the line points for the Oscillating trend}[[0.160, 0.363], [0.395, 0.295], [0.630, 0.250], [0.865, 0.181]] and extract the corresponding values: 52, 55, 57, 60. I sum up these scores and divide it by 4 (number of years) to calculate the average score.\nBased on the above thought, the answer is:\n56" } ], "id_raw": 591 }, { "id": "EW5R0bpP0NigSxnB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_12.png", "conversations": [ { "from": "human", "value": "\nCould you please provide the data presented in the line chart as a structured table?" }, { "from": "gpt", "value": "To create a structured table, I need to examine the x and y coordinates of each line point. The line representing {Established Democracies}[0.184, 0.147, 0.866, 0.416] has data points corresponding to various levels of economic prosperity over time. The same method is applied to the line indicating {Young Democracies}[0.184, 0.338, 0.866, 0.427]. Additionally, the line for {Autocracies}[0.184, 0.636, 0.866, 0.779] provides economic prosperity data for those types of governments. Each point on the lines corresponds to a specific year, which act as row headers, and the y-value represents the level of economic prosperity.\nBased on the above thought, the answer is:\nYears,Established_Democracies,Young_Democracies,Autocracies\n3, 4.1, 4.5, 2\n6, 4.9, 4.8, 1.8\n9, 6.1, 4.3, 1.6\n12, 6.5, 4, 1.5" } ], "id_raw": 592 }, { "id": "vksBY76SPLUwZdMO", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_12.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which form of government showed the most stability in terms of economic prosperity over the years?" }, { "from": "gpt", "value": "To discern which government showcased the most stability in economic prosperity, I need to evaluate three lines: first, {Established Democracies}[0.184, 0.147, 0.866, 0.416] which showed a steady increase in prosperity from 4.1 to 6.5; second, {Young Democracies}[0.184, 0.338, 0.866, 0.427] which showed a slight fluctuation in prosperity between 4.5 and 4.0; and third, {Autocracies}[0.184, 0.636, 0.866, 0.779] which showed a steady decrease in prosperity from 2.0 to 1.5. It can be noted that autocracies, despite the decrease, had the smallest change in prosperity over the years, indicating the most stability.\nBased on the above thought, the answer is:\nThe economic prosperity of Autocracies appears to be the most stable over the years as indicated in the line chart. Despite the overall downward trend, the extent of change was minimal, therefore suggesting a relative stability in terms of economic prosperity." } ], "id_raw": 592 }, { "id": "msE2zbApt174OPRL", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_12.png", "conversations": [ { "from": "human", "value": "\nHow has economic prosperity in established democracies changed from year 3 to year 12?" }, { "from": "gpt", "value": "To identify the changes in economic prosperity in established democracies, I need to look at the line points for {Established Democracies}[0.184, 0.147, 0.866, 0.416]. The first point corresponds to year 3 with prosperity of 4.1 and the last point is year 12 with prosperity of 6.5. The difference of economic prosperity between these years is therefore 6.5 - 4.1 = 2.4.\nBased on the above thought, the answer is:\n2.4." } ], "id_raw": 592 }, { "id": "kob7yRmI5q2LwmL0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_13.png", "conversations": [ { "from": "human", "value": "\nCan you create a table based on the horizontal bar chart that includes the periods as columns and the different aspects of international trade as rows?" }, { "from": "gpt", "value": "Observing the chart, I locate {the categories on the y-axis}[[0.15, 0.625, 0.25, 0.65], [0.15, 0.525, 0.25, 0.55], [0.15, 0.425, 0.25, 0.45], [0.15, 0.325, 0.25, 0.35], [0.15, 0.225, 0.25, 0.25]] which represent trade aspects such as 'Import Export', 'Free Trade Agreements', 'Facilitation Measures', 'Global Supply Chains', and 'Trade Wars'. Each aspect has {bars for time periods}[[0.15, 0.625, 0.4, 0.7], [0.45, 0.625, 0.7, 0.7], [0.75, 0.625, 1.0, 0.7]] indicating the scores at 'Time 0', 'Time 20', and 'Time 40'. I proceed to extract and organize these values to form a table.\nBased on the above thought, the answer is:\nAspect,Time 0,Time 20,Time 40\nImport Export,200,500,380\nFree Trade Agreements,150,300,330\nFacilitation Measures,180,280,290\nGlobal Supply Chains,220,400,420\nTrade Wars,100,500,540" } ], "id_raw": 593 }, { "id": "izXH6aYctFrvdGmX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_13.png", "conversations": [ { "from": "human", "value": "\nBased on changes between the time intervals, which trade aspect shows the most significant growth from Time 0 to Time 40?" }, { "from": "gpt", "value": "Evaluating the growth for each aspect, I specifically look at the length and position of {the bars for each category}[[0.15, 0.225, 0.4, 0.25], [0.75, 0.225, 1.0, 0.25]], [0.15, 0.425, 0.4, 0.45], [0.75, 0.425, 1.0, 0.45]], noticing the growth patterns. 'Trade Wars' starts at a score of 100 at Time 0 and ends with a score of 540 at Time 40, which is a 440 points increase. Comparatively, other aspects such as 'Import Export', 'Free Trade Agreements', and 'Facilitation Measures' show growth but markedly less than 'Trade Wars'.\nBased on the above thought, the answer is:\nThe aspect that shows the most significant growth from Time 0 to Time 40 is 'Trade Wars'. It exhibits a dramatic surge increasing by 440 points, notably higher than any other trade aspect, reflecting the major changes and impacts in this area." } ], "id_raw": 593 }, { "id": "O2uzSIYMd5sFu8Gp", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the average score for the 'Global Supply Chains' across the three time periods?" }, { "from": "gpt", "value": "To determine the average score for 'Global Supply Chains' which is indicated by {the bars}[[0.15, 0.325, 0.4, 0.35], [0.45, 0.325, 0.7, 0.35], [0.75, 0.325, 1.0, 0.35]], I need to calculate the values at each time point, which are 220, 400, and 420 respectively. I add these values and divide by the number of time points, which is 3.\nBased on the above thought, the answer is:\n346.67" } ], "id_raw": 593 }, { "id": "m00M7rsr5bFKyhqi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_14.png", "conversations": [ { "from": "human", "value": "\nCan you create a structured table that captures the changes in risk levels for different areas over time?" }, { "from": "gpt", "value": "The first step is to identify the x-axis which represents the different Risk Areas. The x-axis labels can be identified within {the x-axis tick marks}[[0.033, 0.447], [0.214, 0.447], [0.396, 0.447], [0.577, 0.447], [0.759, 0.447], [0.940, 0.447]]. Each tick mark represents a discrete Risk Area from 1 to 10. Next, we move to the various lines present in the charts, each representing a different type of risk over time. The Security Risk line is found at {the Security Risk line with coordinates}[[0.124, 0.057, 0.940, 0.415]]. The values for this risk type can be gathered from the y-axis points associated with it. The Legal Risk line is present at {the Legal Risk line location}[[0.124, 0.508, 0.940, 0.777]], and the values for Legal Risk can be taken from its y-axis points. Lastly, the Financial Risk line is found at {the Financial Risk line location}[[0.124, 0.630, 0.940, 0.876]], and its values can be got from the corresponding y-axis points.\nBased on the above thought, the answer is:\nRisk_areas,Security_Risk,Legal_Risk,Financial_Risk\n1,5,8,4\n2,4.7,8.5,4.5\n3,4.4,7.4,4.9\n4,4.2,7.1,5.17\n5,4.1,6.3,5.3\n6,4,5.8,5.7\n7,3.5,5.6,6\n8,3,5.5,6.3\n9,2.5,5.4,6.9\n10,2.3,5.2,7" } ], "id_raw": 594 }, { "id": "rIIZrIfzECz8rp2z", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_14.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which risk type shows the most significant decline over the period considered?" }, { "from": "gpt", "value": "To determine the risk type that exhibits the most considerable decline, I need to look at the slopes of the lines. The {Security Risk line}[0.124, 0.057, 0.940, 0.415] starts high and then drops significantly, suggesting a substantial decrease. Comparatively, {Legal Risk line} [0.124, 0.508, 0.940, 0.777] and {Financial Risk line} [0.124, 0.630, 0.940, 0.876] also show a decline, but the downward slope isn't as steep as that of the Security Risk line.\nBased on the above thought, the answer is:\nThe chart suggests that the Security Risk saw the most significant decline over the period. This conclusion is based on the steeper downward slope of the Security Risk line compared to the Legal Risk and Financial Risk lines." } ], "id_raw": 594 }, { "id": "JBevNHQfNJA6F7hO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the reduction in Security Risk from Risk Area 1 to Risk Area 10?" }, { "from": "gpt", "value": "To figure out the reduction in Security Risk from Risk Areas 1 to 10, I need to identify the y-coordinates for these points. At {Risk Area 1}[0.124, 0.447], the y-coordinate for Security Risk is 5.0. Similarly, at {Risk Area 10}[0.940, 0.447], the y-coordinate for Security Risk is 2.3. The decrease in risk from Risk Area 1 to Risk Area 10 can be calculated by subtracting 2.3 from 5.0.\nBased on the above thought, the answer is:\n2.7." } ], "id_raw": 594 }, { "id": "umcXz3SOcLIxcJ0U", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_15.png", "conversations": [ { "from": "human", "value": "\nCan you organize the data from the line chart into a table format with time intervals as columns and financial markets as rows?" }, { "from": "gpt", "value": "To form a table from the line chart, I look first at {the x-axis tick labels}[[0.061, 0.773, 0.144, 0.898], [0.291, 0.773, 0.378, 0.903], [0.739, 0.773, 0.857, 0.951]], which represent the column headers or time intervals. Then I identify {the legend texts}[[0.867, 0.031, 0.953, 0.058], [0.867, 0.066, 0.927, 0.093], [0.867, 0.101, 0.948, 0.128], [0.867, 0.136, 0.955, 0.163], [0.867, 0.171, 0.951, 0.198]] as the row labels representing the different financial markets. To populate the values, I locate {the line points}[[0.102, 0.456], [0.334, 0.579], [0.798, 0.519]] for each market at the respective time intervals and extract the values from it.\nBased on the above thought, the answer is:\nFinancial_Market,Time_0,Time_20,Time_40\nNew York Stock Exchange,1000,1200,1050\nNASDAQ,2000,2400,2200\nLondon Stock Exchange,1800,2100,1900\nTokyo Stock Exchange,1500,1650,1600\nShanghai Stock Exchange,1300,1400,1250" } ], "id_raw": 595 }, { "id": "zypwaHmErBv1mQWV", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_15.png", "conversations": [ { "from": "human", "value": "\nIn terms of direction and volatility, how would you characterize the trends of these financial markets from Time 0 to Time 40 based on the line chart?" }, { "from": "gpt", "value": "The line chart illustrates different trend patterns for the five financial markets. Looking at {the line for New York Stock Exchange}[0.102, 0.599, 0.798, 0.525], there's a volatile upward trend from 1000 to 1200, followed by a decrease to 1050, indicating variability in the trend. \nFor {NASDAQ}[0.102, 0.674, 0.798, 0.588], the market shows a rapid growth from 2000 to 2400, followed by a slight decrease to 2200, a mostly upward trend with minor volatility. \nIn the case of {London Stock Exchange}[0.102, 0.621, 0.798, 0.509], it steadily increases from 1800 to 2100, then decreases to 1900, indicating a consistent trend with mild volatility. \n{The Tokyo Stock Exchange}[0.102, 0.556, 0.798,0.547] rises slightly from 1500 to 1650, then dips minimally to 1600, illustrating an erratic trend with minimal volatility. \nFinally, {The Shanghai Stock Exchange}[0.102, 0.512, 0.798, 0.496] initially rises from 1300 to 1400 and then drops substantially to 1250, displaying a downward volatile trend.\nBased on the above thought, the answer is:\nBased on the line chart, the trends of these financial markets from Time 0 to Time 40 can be characterized as follows: The New York Stock Exchange shows a volatile growth trend. NASDAQ demonstrates a rapid growth pattern with minimal volatility, London Stock Exchange consistently grows with moderate volatility, Tokyo Stock Exchange exhibits an erratic trend with minimal variations, and the Shanghai Stock Exchange has a substantial decrease trend with significant volatility." } ], "id_raw": 595 }, { "id": "POtiYJGbRKxza9gG", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in NASDAQ from Time 0 to Time 20?" }, { "from": "gpt", "value": "To answer this question, I have to locate {the line points for NASDAQ}[[0.102, 0.456], [0.334, 0.579], [0.798, 0.519]]. The value at Time 0 is 2000 at coordinates [0.102, 0.456], and the value at Time 20 is 2400 at coordinates [0.334, 0.579]. The increase can be calculated by subtracting the initial value from the final value, which is 2400 - 2000 = 400.\nBased on the above thought, the answer is:\n400" } ], "id_raw": 595 }, { "id": "4y0m0Uj5bEZiX6nX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_16.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data shown in the bar chart comparing different government policies' evaluations over 2020 and 2021 into a structured table?" }, { "from": "gpt", "value": "Observing the bar chart, I see that {the x-axis labels}[0.150, 0.342, 0.981, 0.585] contain the names of the government policies while {the bar heights}[0.150, 0.637, 0.943, 0.880] denote the evaluation scores for each policy in the years 2020 and 2021. Extracting the data from these noted positions will help form the table.\nBased on the above thought, the answer is:\nPolicy,Score in 2020,Score in 2021\nGreen Energy Act,78,82\nElectric Car Subsidy,70,73\nAffordable Housing Act,65,68\nMinimum Wage Increase,60,62\nClimate Change Act,80,85\nWork-from-Home Credits,82,86\nFree College Strive Act,77,80\nCarbon Tax,90,50\nUniversal Medicare Act,95,98" } ], "id_raw": 596 }, { "id": "cIvoVPK9HrmUvaC7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_16.png", "conversations": [ { "from": "human", "value": "\nWhich government policy showcased the highest increase in evaluation from 2020 to 2021 as per the bar chart?" }, { "from": "gpt", "value": "Reviewing the changes per each policy by examining {the bars for each policy}[0.150, 0.342, 0.981, 0.585], it shows that the 'Work-from-Home Credits' policy has a significant upward shift in evaluation scores from 82 in 2020 to 86 in 2021. Analyzing other policies confirms this one has the highest absolute growth.\nBased on the above thought, the answer is:\nThe 'Work-from-Home Credits' policy had the highest increase in evaluation, moving from an 82 in 2020 to 86 in 2021, indicating its effective implementation and positive reception." } ], "id_raw": 596 }, { "id": "rVFOXExKbLghGkjx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage decrease in the evaluation score for the Carbon Tax policy from 2020 to 2021?" }, { "from": "gpt", "value": "By analyzing {the bars for the Carbon Tax policy}[0.377, 0.660, 0.943, 0.527], the evaluation score decreased from 90 in 2020 to 50 in 2021. The percentage decrease can be calculated using the formula: ((Initial score - Final score) / Initial score) * 100.\nBased on the above thought, the answer is:\n44.44%" } ], "id_raw": 596 }, { "id": "88Z7m88yWEI0dxl0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the trend for each transportation method with respect to time, from the line chart, can you tabulate the data from the line chart of operational efficiency vs time?" }, { "from": "gpt", "value": "To construct the table from the line chart, firstly {the x-axis labels}[0.066, 0.896, 0.077, 0.920] ,[0.177, 0.896, 0.199, 0.920], [0.293, 0.896, 0.315, 0.920], [0.409, 0.896, 0.431, 0.920], referring to the time points, form the columns of the table. {The line color in the legend}[0.332, 0.430, 0.800, 0.472] corresponds to each category, namely 'Baseline', 'Method A', and 'Method B'. Now, to fill the table, we need to identify {the points on the lines}[0.188, 0.754], [0.362, 0.778], [0.653, 0.790], [0.943, 0.802], [0.188, 0.658], [0.362, 0.718], [0.653, 0.730], [0.943, 0.742], [0.188, 0.537], [0.362, 0.597], [0.653, 0.633], [0.943, 0.646] for the corresponding efficiency levels.\nBased on the above thought, the answer is:\nTime,Baseline,Method A,Method B\n20,67,75,85\n50,65,70,80\n100,64,69,77\n150,63,68,76" } ], "id_raw": 597 }, { "id": "Htl260Ol53d9GU6B", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_17.png", "conversations": [ { "from": "human", "value": "\nWhich method showed the most improvement in Operational Efficiency from 20 to 150 employees?" }, { "from": "gpt", "value": "To answer this question, I need to look at the efficiency increase for each method from 20 to 150 employees. For {Baseline}[0.188, 0.754],[0.943, 0.802], the efficiency changed from 67 to 63. For {Method A}[0.188, 0.658],[0.943, 0.742], the efficiency changed from 75 to 68 and for {Method B}[0.188, 0.537],[0.943, 0.646], the efficiency changed from 85 to 76. We can compute the percentage change for each method and compare the values.\nBased on the above thought, the answer is:\nMethod B showed the most improvement in Operational efficiency as it had the smallest decrease in rate amongst all three methods from 20 to 150 employees." } ], "id_raw": 597 }, { "id": "cYyYUoX1kluLsQqn", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in efficiency from Baseline to Method B for 100 employees?" }, { "from": "gpt", "value": "To find the percentage increase in efficiency from Baseline to Method B for 100 employees, I would first identify {the points related to 100 employees on the Baseline}[0.653, 0.790] and {Method B}[0.653, 0.633]. The value for Baseline at 100 employees is 64 and for Method B, it's 77. The percentage increase can be calculated as: ((77-64)/64) * 100.\nBased on the above thought, the answer is:\n20.31%" } ], "id_raw": 597 }, { "id": "3aNTBVAgAijao1bh", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_18.png", "conversations": [ { "from": "human", "value": "\nCould you convert the line plot data representing the internet and mobile users in different years to a structured table?" }, { "from": "gpt", "value": "To construct a table from the line plots, I first identify {the x-axis tick labels}[[0.188, 0.896, 0.218, 0.920], [0.377, 0.896, 0.407, 0.920], [0.566, 0.896, 0.596, 0.920], [0.755, 0.896, 0.785, 0.920], [0.943, 0.896, 0.974, 0.920]] as the row labels representing the different years. I then locate the labels for the y-axes, which are {Internet Users}[[0.069, 0.091, 0.087, 0.354]] and {Mobile Users}[[0.069, 0.568, 0.087, 0.815]] as the column headers. To find the corresponding values for each year, I examine {the points on the Internet Users line}[[0.188, 0.384], [0.377, 0.333], [0.566, 0.259], [0.755, 0.161], [0.943, 0.039]] and {the points on the Mobile Users line}[[0.188, 0.873], [0.377, 0.860], [0.566, 0.797], [0.755, 0.663], [0.943, 0.526]].\nBased on the above thought, the answer is:\nYear,Internet Users (Million),Mobile Users (Million)\n2000,360.985,120.556\n2005,1035.701,340.215\n2010,2024.307,1434.168\n2015,3313.352,3735.157\n2020,4932.944,6103.000" } ], "id_raw": 598 }, { "id": "TtFjKmKWhGC5opED", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_18.png", "conversations": [ { "from": "human", "value": "\nWhat can you infer from the trends of internet and mobile users from the year 2000 to 2020?" }, { "from": "gpt", "value": "To analyze the trends, I need to observe the lines representing {Internet Users}[[0.188, 0.039, 0.943, 0.384]] and {Mobile Users}[[0.188, 0.526, 0.943, 0.873]]. Both trends are increasing, indicating more internet and mobile users over the years. However, the increase for mobile users is steeper, implying a faster growth rate compared to internet users. In particular, the gap between the number of internet and mobile users appears to be widening over time, suggesting an increasing preference for mobile usage over internet usage.\nBased on the above thought, the answer is:\nBased on the trends, both internet and mobile users have increased from 2000 to 2020. However, the growth in mobile users is significantly more pronounced. This suggests that more people started using mobiles at a higher rate compared to the internet within this timeframe, possibly indicating a shift in end-user preferences towards mobile technology." } ], "id_raw": 598 }, { "id": "z8KfVUQWNLTYIM9t", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_18.png", "conversations": [ { "from": "human", "value": "\nHow many more mobile users were there in 2020 compared to internet users?" }, { "from": "gpt", "value": "To answer this, I need to identify the number of mobile and internet users in 2020. Looking at {the point representing internet users in 2020}[[0.943, 0.039]], the value is 4932.944 million. Equally, {the point representing mobile users in 2020}[[0.943, 0.526]] represents a value of 6103 million. The difference is 6103 - 4932.944 = 1170.056.\nBased on the above thought, the answer is:\n1170.056 million." } ], "id_raw": 598 }, { "id": "ygqsEG4CI7PXOSHd", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_30_19.png", "conversations": [ { "from": "human", "value": "\nGiven the line chart, can we convert the data into a tabular format with the given timespan as columns and types of energy as rows?" }, { "from": "gpt", "value": "For the development of the table from the line chart, the first step is to recognize {the x-axis tick labels}[[0.083, 0.985, 0.145, 0.984], [0.271, 0.985, 0.344, 0.984], [0.469, 0.985, 0.533, 0.984], [0.660, 0.985, 0.734, 0.984], [0.857, 0.985, 0.922, 0.984]] as the column headers, which correspond to the timespan in years. Then, {the legend texts}[[0.797, 0.046, 0.899, 0.059], [0.797, 0.059, 0.899, 0.072], [0.797, 0.072, 0.899, 0.085], [0.797, 0.085, 0.899, 0.098]] represent the row labels, which correspond to the different energy types. To fill up the values, every energy type at the respective timespan, defined by {the line points}[[0.173, 0.638], [0.362, 0.524], ..., [0.943, 0.716]] needs to be identified and the values annotated to be extracted.\nBased on the above thought, the answer is:\nEnergy Type,2000,2005,2010,2015,2020\nHousehold Robots,2.6,3.1,3.8,2.9,3.0\nAndroid Assistants,3.2,3.8,4.0,3.6,4.0\nSmart Home,3.5,4.0,3.9,3.7,3.6\nAI Services,2.9,3.5,4.2,4.0,4.1" } ], "id_raw": 599 }, { "id": "8T8Vic20ImfQzzo1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_30_19.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which technology appears to have the most stable progression over time? Explain your reasoning." }, { "from": "gpt", "value": "To identify which technology exhibits the most stable progression, I need to compare the fluctuations in the line plots for each technology. Examining {the line for Household Robots}[0.124, 0.382, 0.940, 0.303] shows considerable fluctuation in the score from 2.6 to 3.8 and back to 3.0, indicating more instability.\nLooking at {Android Assistants}[0.124, 0.303, 0.940, 0.171], also presents a fluctuating trend though with slight changes, indicating moderate stability.\nHowever, upon analyzing {the line for Smart Home}[0.124, 0.659, 0.940, 0.635], the score ranges from 3.5 to 3.8 then reduces slightly, showing minor fluctuations.\nFor {AI Services}[0.124, 0.707, 0.940, 0.793], the line plot reveals drastic unexpected drop in the end after consistent rising trend.\nBased on the above thought, the answer is:\nBased on the trends indicated in the line chart, 'Smart Home' technology seems to have the most stable progression over time. The plot line representing Smart Home displays a minor fluctuation, indicating less volatility compared to the Household Robots, Android Assistants, and AI Services. The gradual increase and minor fluctuations in scores across the time periods indicate a consistent and stable progression." } ], "id_raw": 599 }, { "id": "9as2eJXCewX4hX0F", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_30_19.png", "conversations": [ { "from": "human", "value": "\nWhat was the total change in Android Assistants' score from the start to the end of the timespan?" }, { "from": "gpt", "value": "To calculate the total change in the Android Assistants' score from the start to the end, I need to identify {the line points for Android Assistants}[[0.124, 0.303], [0.214, 0.224], [0.305, 0.198], [0.396, 0.250], [0.487, 0.198], [0.759, 0.237], [0.940, 0.171]], and extract the corresponding scores: 3.2, 3.8, 4.0, 3.6, 4.0, 3.7, 4.2. The initial value is 3.2 at {Timespan 1}[0.124, 0.935] and the final value is 4.2 at {Timespan 10}[0.940, 0.935]. So, the total change is 4.2 - 3.2 = 1.0.\nBased on the above thought, the answer is:\n1.0." } ], "id_raw": 599 }, { "id": "qmfj6CefN6vK1RDJ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_0.png", "conversations": [ { "from": "human", "value": "\nCould you transform the raw data from the line chart into a tabulated form that shows the Community Development Budget and number of Local Governments for each city?" }, { "from": "gpt", "value": "To convert the chart data to a table, I first identify {the x-axis tick labels}[0.057, 0.909, 0.988, 0.932] as the row labels, representing different cities. Then, {the legend texts}[[0.517, 0.060, 0.570, 0.090], [0.850, 0.060, 0.937, 0.090]] split the data into Community Development Budget and Number of Local Governments. Values for these two measures will be derived from the coordinates of {the line points for Community Development Budget}[0.079, 0.367, 0.958, 0.643] and {Number of Local Governments}[0.079, 0.496, 0.958, 0.791].\nBased on the above thought, the answer is:\nCity,Community Development Budget,Number of Local Governments\nPhiladelphia,17000,13000\nFebruary,17500,15000\nMarch,18000,14500\nApril,23000,15000\nMay,23500,16000\nJune,24000,17000\nJuly,23800,17300\nAugust,23500,18000\nSeptember,23000,18500\nOctober,23500,19000\nNovember,24000,20000\nDecember,24500,21000" } ], "id_raw": 600 }, { "id": "sfWqPM3yu8wNPCaD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_0.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which month saw a significant drop in the Community Development Budget?" }, { "from": "gpt", "value": "Observing the Community Development Budget line, {April - May}[0.318, 0.423, 0.398, 0.404] sees a significant drop in the budget as denoted by the sharp downward trend.\nBased on the above thought, the answer is:\nThe month of May observed a significant drop in the Community Development budget. A sharp downward trend from April to May in the Community Development Budget line signifies this." } ], "id_raw": 600 }, { "id": "1w3xQiJt8JXpUWHp", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_0.png", "conversations": [ { "from": "human", "value": "\nWhat's the increase in Budget for Community Development from January to December?" }, { "from": "gpt", "value": "To find out the budget increase for Community Development, I need to look at values of the {January}[0.079, 0.643] and {December}[0.958, 0.367] line points for Community Development. The difference between the two gives the increase in budget from January to December.\nBased on the above thought, the answer is:\n7500." } ], "id_raw": 600 }, { "id": "PyAefgqlcwBSZjoc", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_1.png", "conversations": [ { "from": "human", "value": "\nCan you present the data shown on this graph in a table, with country names and their respective yearly electricity consumption?" }, { "from": "gpt", "value": "To generate the tabular data, I'll first extract the {x-tick labels}[0.042, 0.932, 0.071, 0.949], [0.144, 0.932, 0.173, 0.949], [0.246, 0.932, 0.275, 0.949], [0.347, 0.932, 0.377, 0.949], [0.449, 0.932, 0.478, 0.949], [0.551, 0.932, 0.580, 0.949], [0.653, 0.932, 0.682, 0.949], [0.755, 0.932, 0.784, 0.949], [0.856, 0.932, 0.886, 0.949], [0.958, 0.932, 0.988, 0.949] representing the years. I'll examine the line plots for each country labeled as {line1 in subplot 0}[0.057, 0.394, 0.973, 0.806], {line2 in subplot 0}[0.057, 0.279, 0.973, 0.546], {line3 in subplot 0}[0.057, 0.150, 0.973, 0.391], and {line4 in subplot 0}[0.057, 0.306, 0.973, 0.658] to gather and associate the data points representing the forecasted electricity consumption for the respective years.\nBased on the above thought, the answer is:\nCountry, 2020, 2021\n Country 1, %, %\n Country 2, %, %\n Country 3, %, %\n Country 4, %, %" } ], "id_raw": 601 }, { "id": "z01O38IHbnPcwyn8", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_1.png", "conversations": [ { "from": "human", "value": "\nCould you identify the country with the least volatile electricity consumption between 2020 and 2021 and provide an explanation?" }, { "from": "gpt", "value": "To identify the country with the least volatility in their electricity consumption, I need to review the changes in each country's forecasted electricity consumption in {line1 in subplot 0}[0.057, 0.394, 0.973, 0.806], {line2 in subplot 0}[0.057, 0.279, 0.973, 0.546], {line3 in subplot 0}[0.057, 0.150, 0.973, 0.391], and {line4 in subplot 0}[0.057, 0.306, 0.973, 0.658] from 2020 to 2021. The country with the least volatility is likely the one with the smallest difference between the electricity consumed in the two consecutive years.\nBased on the above thought, the answer is:\nThe country represented by line X seems to have the least volatile electricity consumption as its change in consumption from 2020 to 2021 is the smallest when compared to other countries on the graph. This suggests a stable resource allocation policy by the country and efficient energy use." } ], "id_raw": 601 }, { "id": "z9TPjb3CqWFe7852", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_1.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, by how much did the electricity consumption for the country represented by the third line (line3 in subplot 0) increase from 2020 to 2021?" }, { "from": "gpt", "value": "First, I should locate {line3 in subplot 0}[0.057, 0.150, 0.973, 0.391] which represents the data for the third country. I then find the y-values of the points for the years 2020 and 2021, respectively. The difference between these two values will give me the increase in electricity consumption for the corresponding country.\nBased on the above thought, the answer is:\nThe electricity consumption for the country increased by %." } ], "id_raw": 601 }, { "id": "3k6KZz2ORvNSMz8s", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_4.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart presented, can you assemble the Average Index score for each category: ICT, Biotech, Startups, and Patents at each of the two points: Trend and Outliers?" }, { "from": "gpt", "value": "Sure, I begin by identifying {the legend labels}[[0.499, 0.048, 0.525, 0.078],[0.605, 0.048, 0.668, 0.078],[0.748, 0.048, 0.818, 0.078], [0.898, 0.048, 0.960, 0.078]], which represent the row labels or categories: ICT, Biotech, Startups, and Patents respectively. \nFor column headers, I look to {the x-axis tick labels}[[0.770, 0.909, 0.824, 0.932],[0.985, 0.909, 0.985, 0.909]], which are \"Trend\" and \"Outliers\". \nTo acquire the values, I locate {the points} for each category at the respective points [[0.609, 0.224], [0.797, 0.332]], [[0.609, 0.413], [0.797, 0.115]], [[0.609, 0.630], [0.797, 0.765]], [[0.609, 0.495], [0.797, 0.224]] and extract the corresponding scores.\nBased on the above thought, the answer is:\nCategory,Trend,Outliers\nICT,85,81\nBiotech,78,89\nStartups,70,65\nPatents,75,85" } ], "id_raw": 604 }, { "id": "bi738nBJGSbY6PR1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_4.png", "conversations": [ { "from": "human", "value": "\nWhich sector, based on the plotlines, showed a decrease from Trend to Outliers?" }, { "from": "gpt", "value": "To answer this question, I will have to analyze the plotlines for all four sectors: ICT, Biotech, Startups, and Patents. Comparing the values at the Trend and Outliers points for each sector would allow me to determine this.\nLooking at {ICT's plotline}[0.609, 0.224, 0.797, 0.332], there's a slight decrease from Trend to Outliers. Biotech's line, by looking at {Biotech's plotline} [0.609, 0.413, 0.797, 0.115], shows an increase from Trend to Outliers. Similarly, by evaluating {Startups' plotline} [0.609, 0.630, 0.797, 0.765] and {Patents' plotline} [0.609, 0.495, 0.797, 0.224], both sectors portray an decrease from Trend to Outliers.\nBased on the above thought, the answer is:\nThe ICT and Startups sectors showed a decrease from Trend to Outliers according to the chart. They both had a higher index at Trend and then a lower index at Outliers, indicating a downward trend for these data points." } ], "id_raw": 604 }, { "id": "YsG7W8qsG5zSDCjE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the value difference between Biotech's Trend and Outlier scores?" }, { "from": "gpt", "value": "For this question, I need to locate {the points for Biotech}[[0.609, 0.413], [0.797, 0.115]] and extract the corresponding values for \"Trend\" and \"Outliers\". The value corresponding to Trend is 78 and the value for Outliers is 89. I calculate the difference to be 89 - 78.\nBased on the above thought, the answer is:\n11." } ], "id_raw": 604 }, { "id": "64Z6t2YAMfeMCoZb", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_5.png", "conversations": [ { "from": "human", "value": "\nCan you provide the investment trends over the years for each category in a table format?" }, { "from": "gpt", "value": "To generate the tabular data, I firstly locate the {x-axis tick labels}[[0.148, 0.909, 0.177, 0.932], [0.231, 0.909, 0.260, 0.932], [0.313, 0.909, 0.342, 0.932], [0.396, 0.909, 0.425, 0.932], [0.478, 0.909, 0.507, 0.932], [0.561, 0.909, 0.590, 0.932], [0.643, 0.909, 0.672, 0.932], [0.725, 0.909, 0.755, 0.932], [0.808, 0.909, 0.837, 0.932], [0.890, 0.909, 0.920, 0.932]] which represent the column headers, the years. Next, I identify {the legend texts}[[0.132, 0.050, 0.222, 0.080], [0.132, 0.094, 0.210, 0.124], [0.132, 0.138, 0.273, 0.168], [0.132, 0.182, 0.225, 0.212]] as the row labels that represent the different investment categories. To populate the values, for each category, I locate their respective {line points} and extract the corresponding values.\nBased on the above thought, the answer is:\nYear,Stock Market,Real Estate Market,Startup Investments,Bank Savings\n2007,10000,9500,8000,9900\n2008,6800,9000,7600,9800\n2009,9000,9300,7300,9700\n2010,8800,9100,7000,9600\n2011,9500,8800,7000,9800\n2012,10000,8500,7100,10200\n2013,11500,8600,7500,10250\n2014,12000,8700,7800,10300\n2015,12000,9400,8200,10500\n2016,12500,9500,8300,10600" } ], "id_raw": 605 }, { "id": "3RMmqe2buaiEJzcC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_5.png", "conversations": [ { "from": "human", "value": "\nWhich investment category appears to be the most stable from 2007 to 2016? Justify your answer." }, { "from": "gpt", "value": "To identify, which investment category seems to be most stable, I need to examine the fluctuation in each line plot. {The line for the Stock Market}[0.163, 0.088, 0.905, 0.801] shows a considerable level of change as it initially decreases, then increases, and fluctuates quite a bit over the years. Similarly, {the lines for the Real Estate Market, Startup Investments, and Bank Savings}[0.163, 0.463, 0.905, 0.588][0.163, 0.613, 0.905, 0.776][0.163, 0.325, 0.905, 0.450] also depict a series of growth and decline. However, there are less dramatic fluctuations and slope changes concerning the line of {the Bank Savings}[0.163, 0.325, 0.905, 0.450], it represents a comparatively steady increase with minimal decrements throughout the years.\nBased on the above thought, the answer is:\nBased on the line chart, the Bank Savings appears to be the most stable investment category from 2007 to 2016. This is due to the relatively consistent, smooth increase of its value over the years, with minimal decrements, indicating less volatility and therefore more stability than the other investment categories." } ], "id_raw": 605 }, { "id": "o5Goeb4d8PthSb6c", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in the Stock Market from 2007 to 2016?" }, { "from": "gpt", "value": "To find the total increase in the Stock Market, I need to compare the first point and the last point in {the line for Stock Market}[[0.163, 0.088, 0.905, 0.801]]. In 2007, the Stock Market was at 10000 and in 2016, it's at 12500. So the total increase in the Stock Market from 2007 to 2016 is 12500 - 10000 = 2500.\nBased on the above thought, the answer is:\n2500." } ], "id_raw": 605 }, { "id": "QWKJGlFnHjWQI8eu", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_6.png", "conversations": [ { "from": "human", "value": "\nCan the dataset visualized in the line chart be structured into a table format with Tasks as columns and different parameters as rows?" }, { "from": "gpt", "value": "The visual organization of the line chart starts by identifying {the x-axis labels}[0.060, 0.909, 0.068, 0.932], [0.162, 0.909, 0.169, 0.932], ..., [0.973, 0.909, 0.988, 0.932] to establish the columns representing each Task. Rows for each parameter can be derived by observing {the legend}[0.282, 0.021, 0.973, 0.106] which categorizes data lines by Stakeholders, Raw Materials, etc. Data points extracted from {each line's coordinates}[0.064, 0.266, 0.980, 0.395], [0.064, 0.328, 0.980, 0.611], ... provide the values for each intersection of Task and parameter.\nBased on the above thought, the answer is:\nParameter,Task 1,Task 2,Task 3,Task 4,Task 5,Task 6,Task 7,Task 8,Task 9,Task 10\nStakeholders,62.2,62.31,65.97,66.78,69.45,71.45,73.6,75.91,75.92,78.06\nRaw Materials,35.68,38.58,45.23,48.96,56.21,58.32,62.14,64.86,66.57,70.5\nProduction,24.43,22.18,18.19,15.46,13.67,12.01,11.74,10.45,9.12,8.38\nLogistics,8.34,9.75,10.67,12.21,14.34,15.66,16.56,18.35,22.2,25.98\nSales,15.59,15.19,16.46,19.67,21.33,24.94,28.23,32.37,35.21,37.12" } ], "id_raw": 606 }, { "id": "wZs7c3HRGtcryiT5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_6.png", "conversations": [ { "from": "human", "value": "\nWhich parameter displayed in the chart shows the slowest growth rate over the tasks?" }, { "from": "gpt", "value": "To assess the parameter with the slowest growth rate, I must look at the overall slope and data point positioning within the plotted lines for each parameter. Observing {the line for Stakeholders}[0.064, 0.266, 0.980, 0.395], {Raw Materials}[0.064, 0.328, 0.980, 0.611], and others, I note that {Production}[0.064, 0.699, 0.980, 0.831] depicts minimal increase in its points from around 24.43% to 8.38% across the tasks. This line exhibits a gradual and more consistent downward trend compared to others which fluctuate or ascend more significantly.\nBased on the above thought, the answer is:\nThe parameter 'Production' shows the slowest growth rate over the tasks. This parameter's line plot displays a gradual descent from its initial high, indicating a consistent decrease in production rates over time, as reflected in the progressively lowering values from Task 1 to Task 10." } ], "id_raw": 606 }, { "id": "PuefGB7mi4ih3tXT", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in Task 10 from Task 1 for the category 'Sales'?" }, { "from": "gpt", "value": "To determine the increase in 'Sales' from Task 1 to Task 10, I analyze the values at these points extracted from {the line for Sales}[0.064, 0.266, 0.980, 0.777]. The specific values needed are {at Task 1}[0.064, 0.774] and {at Task 10}[0.980, 0.599] noting the respective percentage values at these tasks - 15.59% at Task 1 and 37.12% at Task 10. Calculating the difference, 37.12 - 15.59, reveals the increase.\nBased on the above thought, the answer is:\n21.53." } ], "id_raw": 606 }, { "id": "i0b3KP8g5mWzgLSm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_7.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can we construct the structured data into a table format with Years as rows and Social Media Platforms as columns?" }, { "from": "gpt", "value": "To construct the table data from the line chart, the {x-axis tick labels}[0.084, 0.909, 0.114, 0.932], ..., [0.930, 0.909, 0.960, 0.932] represent the row labels or Years. The social media platforms are considered as column headers, identified from {the legend item's texts}[0.918, 0.744, 0.973, 0.767], ..., [0.918, 0.849, 0.967, 0.872]. The information of Monthly Active Users obtained from {line points} [0.099, 0.423], ..., [0.945, 0.103], ... ,[0.099, 0.641], ... , [0.945, 0.449] will fill the table.\nBased on the above thought, the answer is:\nYear,Facebook,Twitter,Instagram,LinkedIn\n2010,50,22.1,2.1,25.8\n2011,53.5,24.5,3.8,28.2\n2012,60.2,30.7,6.5,30.6\n2013,65.8,32.1,8.5,33.6\n2014,69.4,33.7,10.6,37.3\n2015,73.6,35.3,13.5,40.6\n2016,78.2,38.4,16.2,42.2\n2017,81.5,40.2,19.4,44.1\n2018,83.6,42.1,23.4,45.4\n2019,85.4,43.0,26.5,47.1" } ], "id_raw": 607 }, { "id": "mobNFe9g4ihWRypc", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_7.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which social media platform has the steadiest growth in user base from 2010 to 2019?" }, { "from": "gpt", "value": "To determine which social media platform has the steadiest growth, I need to analyse the growth rates in the line plots for each platform. First looking at {the line for Facebook}[0.099, 0.103, 0.945, 0.423], it shows a somewhat consistent upward slope, indicating a steady growth rate.\nConsidering {the line for Twitter}[0.099, 0.486, 0.945, 0.675], the plot shows a slightly irregular shape, suggesting a slightly inconsistent growth rate over years.\nFor {Instagram}[0.099, 0.635, 0.945, 0.855], the line shows a steep upward trend, indicating a high growth rate yet not steady.\nLast, looking at {the line for LinkedIn}[0.099, 0.449, 0.945, 0.641], the plot suggests a consistent growth rate similar to Facebook. However, comparing the two, it appears that Facebook has a smoother and more steady growth curve.\nBased on the above thought, the answer is:\nThe social media platform with the steadiest growth in user base from 2010 to 2019 seems to be Facebook. Its line plot shows a relatively consistent upward slope across the entirety of the depicted timeframe, suggesting an unchanging growth rate." } ], "id_raw": 607 }, { "id": "k6cTXcDgXET10K4c", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_7.png", "conversations": [ { "from": "human", "value": "\nBased upon the line chart, can we identify the year when Instagram has its maximum user base?" }, { "from": "gpt", "value": "To identify the year when Instagram has its maximum user base, I need to locate the highest point on {the line representing Instagram}[0.099, 0.855, 0.945, 0.635] and extract the corresponding year from the x-axis. Here, the maximum value for Instagram is 26.5 million monthly active users corresponding to the year 2019 from {the x-axis tick at this point}[0.945, 0.893].\nBased on the above thought, the answer is:\n2019" } ], "id_raw": 607 }, { "id": "1dkZzzDQWl2GN5BH", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_8.png", "conversations": [ { "from": "human", "value": "\nCan you extract the percentage data by technology-based socialization activity into a structured table from the comparative bar chart?" }, { "from": "gpt", "value": "For this task, I first look at {the x-axis labels}[0.066, 0.901, 0.085, 0.932]; these represent the different categories of technology-based socialization. Then, I observe {the legend}[0.390, 0.039, 0.973, 0.091] where 'Youth Adult' and 'Senior Adult' are identified. The comparative percentages are determined by the lengths of the segments in the vertical bars that correspond to each group and activity. The visual interpretation of the segments yields the data for each category.\nBased on the above thought, the answer is:\nActivity,Youth Adult Percentage,Senior Adult Percentage\nConnectivity and Socialization,95,75\nSocial Media Usage,80,60\nSpeed of Learning New Technologies,90,45\nOnline Purchases,70,65\nOnline Entertainment,85,70" } ], "id_raw": 608 }, { "id": "y7fLtU5lVHYJrBi6", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_8.png", "conversations": [ { "from": "human", "value": "\nWhich technology-based socialization activity shows the largest age-related disparity in engagement?" }, { "from": "gpt", "value": "To pinpoint the largest disparity, I compare the bar segment lengths for each activity for both 'Youth Adult' and 'Senior Adult'. Looking particularly at {the bar segments for 'Speed of Learning New Technologies'}[0.260, 0.660], it shows a significant length difference, with Youth Adults considerably higher than Senior Adults. This stark contrast is visually more pronounced than in the other activities.\nBased on the above thought, the answer is:\nThe 'Speed of Learning New Technologies' activity shows the largest age-related disparity in engagement. The visual comparison of the bar segments in the chart indicates a much larger segment for Youth Adults compared to Senior Adults, signifying a larger rate of involvement by the younger age group in this particular technology-based activity." } ], "id_raw": 608 }, { "id": "WTvZSr0OiBiU2Frv", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in participation between 'Youth Adult' and 'Senior Adult' in 'Online Purchases'?" }, { "from": "gpt", "value": "Observing {the bar for Online Purchases for both age groups}[0.450, 0.651], the Youth Adult participation is seen to be at 70% and Senior Adult participation at 65% by visually estimating the length relative to the y-axis scale. To calculate their difference, I perform 70 - 65.\nBased on the above thought, the answer is:\n5" } ], "id_raw": 608 }, { "id": "OdRhSJ55RdIwnxul", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_11.png", "conversations": [ { "from": "human", "value": "\nCan you please put the line chart data into a tabular format, categorizing by 'Risk Factors' and 'Scenarios'?" }, { "from": "gpt", "value": "To produce a table from the line chart, I first examine {the x-axis labels}[0.061, 0.909, 0.114, 0.932],[0.267, 0.909, 0.315, 0.932],[0.467, 0.909, 0.522, 0.932],[0.667, 0.909, 0.729, 0.932] which denote the column headers or 'Scenarios'. Next, I consider {the legend texts} [0.788, 0.117, 0.974, 0.147], [0.788, 0.161, 0.971, 0.191], [0.788, 0.205, 0.859, 0.235], [0.788, 0.249, 0.908, 0.279] as the row labels representing the different 'Risk Factors'. For the table values, I detect {point positions for lines} [0.087, 0.113, 0.698, 0.388], [0.087, 0.278, 0.698, 0.408], [0.087, 0.548, 0.698, 0.631], [0.087, 0.774, 0.698, 0.856] for each risk factor at the given scenarios and interpret the values.\nBased on the above thought, the answer is:\nRisk Factor,Stagnant,Growing,Shrinking,Expanding\nPersistent Market Changes,65.13,81.74,90.98,88.03\nMajor Regulatory Changes,63.25,75.42,65.3,70.65\nLegal Risk,42.26,48.78,49.76,50.11\nNatural Disasters,21.18,28.82,25.21,27.65" } ], "id_raw": 611 }, { "id": "HaW0NZ808P62Thf7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_11.png", "conversations": [ { "from": "human", "value": "\nWhat risk factor has experienced a declining trend from 'Growing' to 'Shrinking' scenario? Elucidate." }, { "from": "gpt", "value": "To find out the risk factor which has seen a declining trend from 'Growing' to 'Shrinking' scenario, I need to study the position of {line points}[0.087, 0.113, 0.698, 0.388],[0.087, 0.278, 0.698, 0.408],[0.087, 0.548, 0.698, 0.631],[0.087, 0.774, 0.698, 0.856] of each line for each risk factor. The line that depicts a downward shift from 'Growing' to 'Shrinking' scenario belongs to 'Major Regulatory Changes'. The risk assessment percentage decreases from 75.42 in the 'Growing' scenario to 65.3 in the 'Shrinking' scenario.\nBased on the above thought, the answer is:\nThe 'Major Regulatory Changes' risk factor has experienced a declining trend from 'Growing' to 'Shrinking' scenario. The risk assessment for this factor has decreased from 75.42% in the 'Growing' scenario to 65.3% in the 'Shrinking' scenario." } ], "id_raw": 611 }, { "id": "i16ODfaPTMDSAEVr", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the total risk assessment across all scenarios for 'Major Regulatory Changes'?" }, { "from": "gpt", "value": "To answer this question, I need to locate {the line for 'Major Regulatory Changes'}[0.087, 0.278, 0.698, 0.408] and sum the risk assessment values corresponding to each scenario. These assessment values are: 63.25 for Stagnant, 75.42 for Growing, 65.3 for Shrinking, and 70.65 for Expanding.\nBased on the above thought, the answer is:\n274.62" } ], "id_raw": 611 }, { "id": "HuA4sjTl2D0T0yjH", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_12.png", "conversations": [ { "from": "human", "value": "\nCan you generate a structured table with Sector as columns and Growth Rate as rows from the line chart data?" }, { "from": "gpt", "value": "To generate a table, I identify from the chart that {the x-axis tick labels}[[0.038, 0.800, 0.082, 0.865], [0.187, 0.800, 0.226, 0.858], [0.324, 0.800, 0.380, 0.878], [0.468, 0.800, 0.529, 0.884], [0.605, 0.800, 0.683, 0.904], [0.770, 0.800, 0.811, 0.861], [0.885, 0.800, 0.988, 0.932]] represent the columns corresponding to different sectors. The two types of growth rate, identified from {the subplot legend text}[[0.630, 0.059, 0.746, 0.089],[0.813, 0.059, 0.915, 0.089]], define the rows. To populate the values, I locate {the points on the line for 'Steady Recovery'}[[0.060, 0.704], [0.206, 0.651], [0.352, 0.581], [0.498, 0.528], [0.644, 0.493], [0.790, 0.440], [0.936, 0.387]] and {the points on the line for 'Rapid Increase'}[[0.060, 0.644], [0.206, 0.556], [0.352, 0.468], [0.498, 0.370], [0.644, 0.299], [0.790, 0.201], [0.936, 0.134]] and extract these values.\nBased on the above thought, the answer is:\nGrowth Rate,Finance,Health,Education,Agriculture,Transportation,Energy,Telecommunication\nSteady recovery,0.25,0.40,0.60,0.75,0.85,1.00,1.15\nRapid increase,0.42,0.67,0.92,1.20,1.40,1.68,1.87" } ], "id_raw": 612 }, { "id": "O08b01ZN2EAsq7zE", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_12.png", "conversations": [ { "from": "human", "value": "\nFrom the given line chart, which trend experienced a steeper increment in the 'Education' sector? Can you explain?" }, { "from": "gpt", "value": "To determine the trend with a steeper increment, I observe {the point for 'Steady Recovery' associated to 'Education'}[0.352, 0.581] and {the point for 'Rapid Increase' associated to 'Education'}[0.352, 0.468]. 'Steady Recovery' has a value of 0.60, while 'Rapid Increase' has a value of 0.92. Thus, the 'Rapid Increase' trend had a steeper increment in the 'Education' sector.\nBased on the above thought, the answer is:\nThe 'Rapid Increase' trend experienced a steeper increment in the 'Education' sector. Based on the plotted points in the line chart, the 'Rapid Increase' trend shows a higher value (0.92) in the 'Education' sector compared to the 'Steady Recovery' trend (0.60), indicating a more significant increase." } ], "id_raw": 612 }, { "id": "Kd9Iqgypg5q3OEHV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_12.png", "conversations": [ { "from": "human", "value": "\nHow much increment is observed in the 'Rapid Increase' trend from the 'Health' sector to the 'Telecommunication' sector?" }, { "from": "gpt", "value": "To find the increment in the 'Rapid Increase' trend, I locate {the point on the line for 'Rapid Increase' corresponding to 'Health'}[0.206, 0.556] which has a value of 0.67 and {the corresponding point for 'Telecommunication'}[0.936, 0.134] which has a value of 1.87. The increment is then found by subtracting these two values: 1.87 - 0.67 = 1.20.\nBased on the above thought, the answer is:\n1.20." } ], "id_raw": 612 }, { "id": "CfrVfkqQq9ZWZnIx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data displayed in the bar chart into a table format with categories as rows and '2025 Difference' (%) as columns?" }, { "from": "gpt", "value": "First, I need to identify the category labels given on {the y-axis tick labels}[0.053, 0.806, 0.079, 0.823], [0.066, 0.765, 0.084, 0.782], ..., [0.149, 0.119, 0.173, 0.136]. Then, each bar in the chart corresponds to a percentage difference in focus for each category. I will locate the bars for each category {Raw Material}[0.095, 0.814, 0.478, 0.827], {Manufacturing}[0.347, 0.773, 0.478, 0.787], ..., {Investment}[0.128, 0.120, 0.476, 0.134] and extract the values. The negative bars are colored red, and the positive bars are colored blue, indicating a negative or positive change in focus for that category. By extracting these values, I can summarize this data.\nBased on the above thought, the answer is:\nCategory, 2025 Difference (%)\nRaw Material,-20\nManufacturing,-10\nPackaging,-5\nTransport, 0\nStorage, 10\nMarketing, 15\nSales, 20\nCustomer Support, 25\nLogistics, 30\nProcurement, 35\nFinance, 40" } ], "id_raw": 613 }, { "id": "fkZ1oYuNhe200A8p", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_13.png", "conversations": [ { "from": "human", "value": "\nWhat conclusions can be drawn about the shift in focus of global trade and supply chain management based on the chart data?" }, { "from": "gpt", "value": "Looking at the horizontal bar chart, I can observe that categories towards the upper part of the chart including 'Raw Material'[0.095, 0.814, 0.478, 0.827], 'Manufacturing'[0.347, 0.773, 0.478, 0.787], have negative values, implying that these areas are seeing less focus or priority. \nOn the contrary, categories towards the bottom of the chart like 'Finance'[0.113, 0.253, 0.483, 0.267], 'Procurement'[0.120, 0.193, 0.458, 0.206], 'Logistics'[0.108, 0.307, 0.405, 0.321], 'Customer Support'[0.115, 0.371, 0.380, 0.384] have positive values, which indicate that these areas are gaining more attention and focus.\nBased on the above thought, the answer is:\nThe chart indicates a shift in focus in global trade and supply chain management. There is a decreasing focus on areas such as 'Raw Material' and 'Manufacturing', while areas such as 'Finance', 'Procurement', 'Logistics', and 'Customer Support' are gaining increased attention. This suggests that companies might be moving towards more intangible and service-oriented sectors, while traditionally important areas like raw material acquisition and production are losing dominance in relative terms." } ], "id_raw": 613 }, { "id": "T5xd4u7aCyLaK2wA", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_13.png", "conversations": [ { "from": "human", "value": "\nHow much more is the difference in focus for 'Finance' compared to 'Marketing'?" }, { "from": "gpt", "value": "To answer this question, I need to extract the difference in focus for both 'Finance' and 'Marketing'. I locate the bars for 'Finance'[0.113, 0.253, 0.483, 0.267] and 'Marketing'[0.109, 0.559, 0.350, 0.573]. The difference is approximately 40% for 'Finance' and approximately 15% for 'Marketing'. Thus, the difference between 'Finance' and 'Marketing' is 40 - 15 = 25.\nBased on the above thought, the answer is:\n25%." } ], "id_raw": 613 }, { "id": "mEev0lMyeo5k6Omb", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_15.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data into the table format with \"Years\" as rows and \"Areas\" as columns?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.049, 0.909, 0.078, 0.932], [0.163, 0.909, 0.192, 0.932], [0.277, 0.909, 0.306, 0.932], [0.390, 0.909, 0.419, 0.932], [0.504, 0.909, 0.533, 0.932], [0.617, 0.909, 0.647, 0.932], [0.731, 0.909, 0.760, 0.932], [0.845, 0.909, 0.874, 0.932], [0.958, 0.909, 0.988, 0.932]] as the row labels representing the different years. Then I notice {the legend texts}[[0.842, 0.049, 0.930, 0.079], [0.842, 0.093, 0.943, 0.123], [0.842, 0.137, 0.921, 0.167], [0.842, 0.181, 0.952, 0.211]] serving as the column headers, that represent the different areas of scenario. When populating the values, I locate {the line points for each area} and extract their corresponding values for each year.\nBased on the above thought, the answer is:\nYear,Information_Technology,Biotechnology,Space_Exploration,Robotics\n2000,12000,8000,2000,1000\n2001,11000,8200,2100,1100\n2002,10000,8400,2200,1200\n2003,12000,8600,2300,1300\n2004,13000,9000,2400,1400\n2005,14000,9200,2600,1600\n2006,15000,9400,2800,1700\n2007,17000,9600,3000,1800\n2008,15000,9700,3200,2000\n2009,16000,9800,3400,2100\n2010,18000,10000,3700,2300\n2011,19000,11000,4000,2500\n2012,20000,12000,4300,2700\n2013,22000,13000,4600,2800\n2014,23000,14000,4900,3000\n2015,25000,15000,5200,3300\n2016,27000,16000,5500,3500\n2017,30000,17000,5800,3700\n2018,32000,18000,6000,4000\n2019,34000,19000,6200,4200\n2020,36000,20000,6500,4400" } ], "id_raw": 615 }, { "id": "sz90y7LWGp6doy2E", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_15.png", "conversations": [ { "from": "human", "value": "\nAccording to the trend shown in the line chart, which area seems to have the most significant growth over the two decades? Explain your observations." }, { "from": "gpt", "value": "To determine which area has the most significant growth over the two decades, I need to compare the rate of change of line plots for each scenario. Looking at {the line for Information Technology}[0.064, 0.297, 0.973, 0.179], I see that it gradually increases from 12000 to 36000. The growth rate is quite significant, and this trend continues over the years.\nThen I examine {the line for Biotechnology} and observe that it also shows a gradual increase from 8000 to 20000. Moreover, the line for {Space Exploration} exhibits a consistent increase from 2000 to 6500. The growth rate in these areas, while substantial, is not as steep as in the Information Technology sector.\nLastly, I assess the {Robotic Line} and it also shows substantial growth, but not as much as Information Technology.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, the Information Technology sector displays the most significant growth over two decades. Its line plot shows a steady upward trend, indicating a significant increase in values from year 2000 to 2020. The increase in values is more pronounced compared to other areas, highlighting the significant growth in this sector." } ], "id_raw": 615 }, { "id": "lO6Bo4gYw6XGSqVA", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the \"Information Technology\" figures for the years 2000 and 2020?" }, { "from": "gpt", "value": "To find the difference between the \"Information_Technology\" figures for the years 2000 and 2020, I examine the {line points for Information_Technology}[[0.064, 0.297], [0.178, 0.275], [0.291, 0.252], [0.405, 0.278], [0.518, 0.265], [0.632, 0.243], [0.746, 0.215], [0.859, 0.195], [0.973, 0.179]] and extract the corresponding values: 12000 for {year 2000}[0.064, 0.909] and 36000 for {year 2020}[0.973, 0.909]. Hence, the difference is 36000 - 12000 = 24000.\nBased on the above thought, the answer is:\n24000." } ], "id_raw": 615 }, { "id": "CJPOxG6dWdhUjzjL", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_16.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data about education and technology innovations usage into a table?" }, { "from": "gpt", "value": "To convert the line chart into a table, I first identify the different innovation types by looking at {the y-axis ticks}[[0.095, 0.150, 0.119, 0.172], [0.095, 0.180, 0.119, 0.202], ..., [0.095, 0.850, 0.119, 0.872]], and these will serve as row labels in our table. Next, I identify the values representing the differences in usage of each technology, which are encoded in {the x-positions of the line-ends}[[0.400, 0.162], [0.725, 0.192], ..., [0.950, 0.862]] at each respective technology. Finally, these values are collected and assembled into a table.\nBased on the above thought, the answer is:\nInnovation, Difference in Usage(%)\nSynchronous Learning, 10\nFlipped Classroom, -20\nMicrolearning, -5\nGamified Learning, 30\nLearning Analytic, 15\nMobile Learning, 25\nVideo Learning, -35\nBlended Learning, 20\nCloud-Based Learning, 10\nAI Tutoring, -10\nSocial Learning, 35\nE-learning, 5\nAR Learning, -15\nVR Learning, 30\nRemote Learning, 50" } ], "id_raw": 616 }, { "id": "JrMmQhKiZG6hk6ux", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_16.png", "conversations": [ { "from": "human", "value": "\nWhat kind of trends can we observe in the use of different education and technology innovations according to the chart?" }, { "from": "gpt", "value": "The chart displays a variety of trends across different technology innovations. Some innovations such as {Remote Learning}[0.400, 0.897], {Social Learning}[0.400, 0.842], and {VR Learning}[0.400, 0.669] are experiencing significant growth, as shown by their large positive values. This suggests increasing adoption of these technologies in the learning environment.\nOn the other hand, certain technologies such as {Video Learning}[0.400, 0.584], {AR Learning}[0.400, 0.647], and {Flipped Classroom}[0.400, 0.240] are experiencing a decline in usage, indicated by their negative values. This might suggest that these technologies are either being replaced by other innovations or are losing favor within the educational landscape.\nStrikingly, there is considerable variability in the trends, implying that the landscape of educational technology is dynamic with technologies continuously shifting in popularity.\nBased on the above thought, the answer is:\nThe trends in the use of different education and technology innovations are highly varied. Some technologies like Remote Learning, Social Learning, and VR Learning are seeing considerable growth in their usage. Others, including Video Learning, AR Learning, and Flipped Classroom are seeing a decline in their usage. This suggests a dynamic and shifting landscape of education and technology innovations, with constant changes in the popularity and utilization of different techniques and tools." } ], "id_raw": 616 }, { "id": "hpbGvk7Z9OzdHuIP", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_16.png", "conversations": [ { "from": "human", "value": "\nBy how much is the usage of Gamified Learning more than Blended Learning?" }, { "from": "gpt", "value": "To find the difference in usage between Gamified Learning and Blended Learning, I need to identify the end points of the lines representing each. For {Gamified Learning}[0.400, 0.412], the line end is at x=0.80 which corresponds to an increase of 30%. For {Blended Learning}[0.400, 0.569], the line end point is at x=0.75, corresponding to an increase of 20%. Therefore, the usage of Gamified Learning is 30% - 20% = 10% more than Blended Learning.\nBased on the above thought, the answer is:\n10%" } ], "id_raw": 616 }, { "id": "ep9V5wJS69Zbs37C", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_17.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart of population movement over time into a table format with Years as columns and movement types as rows?" }, { "from": "gpt", "value": "To generate the table from the line chart, I first identify {the x-axis tick labels}[[0.142, 0.909, 0.172, 0.932], [0.224, 0.909, 0.253, 0.932], [0.306, 0.909, 0.335, 0.932], [0.387, 0.909, 0.416, 0.932], [0.469, 0.909, 0.498, 0.932], [0.550, 0.909, 0.580, 0.932], [0.632, 0.909, 0.661, 0.932], [0.714, 0.909, 0.743, 0.932], [0.795, 0.909, 0.824, 0.932], [0.877, 0.909, 0.906, 0.932], [0.958, 0.909, 0.988, 0.932]] as column headers representing the years. The {legend texts}[[0.106, 0.048, 0.259, 0.078], [0.326, 0.048, 0.482, 0.078], [0.548, 0.048, 0.732, 0.078], [0.799, 0.048, 0.952, 0.078]] act as row labels representing different types of movements. Then I locate the y-values for each year and movement type using {the y-cordinates of line plot points}[[0.157, 0.230], [0.239, 0.221], [0.320, 0.207], [0.402, 0.192], [0.483, 0.180], [0.565, 0.167], [0.647, 0.154], [0.728, 0.141], [0.810, 0.127], [0.891, 0.114], [0.973, 0.101]] and convert those to the real values according to the y-ticks.\nBased on the above thought, the answer is:\nMovement Type,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010\nMoving within the city,28500,29000,29500,30000,30000,30500,31000,31800,32500,33000,34000\nMoving to another city,5700,5900,6000,6020,6040,6050,6100,6110,6200,6300,6300\nMoving to another country,1200,1250,1300,1350,1400,1450,1500,1550,1600,1650,1700\nNot moving,51200,51900,52900,54100,55000,56000,57000,58000,59000,60000,61000" } ], "id_raw": 617 }, { "id": "T4JX5NTqI20UK5Fb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_17.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, can you deduce which type of movement has had the most consistent trend over the years? Explain your reasoning." }, { "from": "gpt", "value": "In order to determine which type of movement has had the most consistent trend, one has to consider the number and magnitude of fluctuations in the line plot of each type of movement. Observing {the line for Moving within the city}[0.157, 0.527, 0.973, 0.455], we see an almost steady increase over the years. The {line for Moving to another city}[0.157, 0.826, 0.973, 0.818] is relatively stable, with limited fluctuations. The {line for Moving to another country}[0.157, 0.885, 0.973, 0.879] shows a consistent increase, but the increase itself is small. And finally, the {line for Not moving}[0.157, 0.230, 0.973, 0.101] exhibits a consistent decline over the years, with only minor fluctuations. Comparing all these, the line plot for 'Moving within the city' shows the most consistent trend with a steady increase.\nBased on the above thought, the answer is:\nThe 'Moving within the city' category has had the most consistent trend over the years. This is evident from its line plot that shows a steady increase every year. While the other types of movement also show trends over time, their plots show more fluctuations in comparison." } ], "id_raw": 617 }, { "id": "uU7vmSUL0inXHO0w", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_17.png", "conversations": [ { "from": "human", "value": "\nConsidering the population who are not moving, what is the increase in the population from 2000 to 2010?" }, { "from": "gpt", "value": "To calculate the increase in the population from 2000 to 2010 for those not moving, I need to find {the value for 2000}[[0.157, 0.230]] which is 51200 and {the value for 2010}[[0.973, 0.101]] which is 61000. The increase in the population is obtained by subtracting the value in 2000 from the value in 2010.\nBased on the above thought, the answer is:\n9800." } ], "id_raw": 617 }, { "id": "E322QAQbqqAdfJdx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_18.png", "conversations": [ { "from": "human", "value": "\nCould you please extract the yearly projected growth percentage for each healthcare category displayed on the line chart?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.049, 0.909, 0.078, 0.932], ... , [0.958, 0.909, 0.988, 0.932]] as the column headers representing the years. The row labels, which are the different healthcare categories, can be derived from {the legend labels}[[0.245, 0.048, 0.356, 0.078], ... , [0.824, 0.048, 0.952, 0.078]]. For each category, I locate their corresponding line points like {the line points for AI in Healthcare}[[0.064, 0.781], [0.165, 0.765], ... , [0.973, 0.161]] and extract the percentage values corresponding to the y-coordinate of these points.\nBased on the above thought, the answer is:\nYear,AI in Healthcare,Telemedicine,Electronic Health Records,Precision Medicine\n2022,14.7,46.2,90,82\n2023,16.7,48.5,86,77\n2024,20.5,51.0,82,73.2\n2025,26.2,53.5,80,69.9\n2026,33.7,56.1,77.5,67\n2027,42.0,58.8,76,64.5\n2028,51.8,61.5,73.5,62.3\n2029,62.9,64.3,70,60.1\n2030,75.7,67.2,66.5,58\n2031,91.0,70,65,56" } ], "id_raw": 618 }, { "id": "6SYLW2CappXx69OJ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_18.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which healthcare technology field seems to have the most potential for growth in the next decade (from 2022 to 2031) according to the projections?" }, { "from": "gpt", "value": "To determine the healthcare technology field with the greatest growth potential, I need to analyze the changes in the line plots for all four fields by comparing their starting and ending points. Looking at {the line plot for AI in Healthcare}[[0.064, 0.781], ..., [0.973, 0.161]], the growth change from 14.7% to 91.0% indicates a strong upward trend. Similarly, I can analyze {the Telemedicine line}[[0.064, 0.525], ..., [0.973, 0.332]], {the Electronic Health Records line}[[0.064, 0.169], ..., [0.973, 0.373]], and {the Precision Medicine line}[[0.064, 0.234], ..., [0.973, 0.446]].\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, AI in Healthcare appears to have the most potential for growth over the next decade. It starts at a relatively low percentage of 14.7% in 2022, but by 2031, it is projected to reach a significantly higher percentage of 91.0%. This represents the highest degree of positive change compared to the other healthcare technology fields on the chart." } ], "id_raw": 618 }, { "id": "zITTVAWISACgDbpf", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the projected growth change for AI in Healthcare from the year 2022 to the year 2031?" }, { "from": "gpt", "value": "To find the projected growth change for AI in Healthcare, I need to locate the plot points for this category at years 2022 and 2031 which are located at {2022}[0.064, 0.781] and {2031}[0.973, 0.161] respectively. By looking at the y-coordinates of these points and referring to the y-axis, I can identify the projected growth for these years as 14.7% and 91% respectively.\nBased on the above thought, the answer is:\n76.3%" } ], "id_raw": 618 }, { "id": "80D6OwmTyypqfiM5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_31_19.png", "conversations": [ { "from": "human", "value": "\nCan you compile the data points for both AI and VR industries from the line chart into a structured table for each year from 2000 to 2020?" }, { "from": "gpt", "value": "First, I need to identify the x-axis (representing years) values from {the x-axis tick labels}[[0.082,0.905,0.126,0.936],[0.232,0.905,0.276,0.936]...[0.932,0.905,0.976,0.936]]. Then I would note the y-axis values representing the development index from line points of {AI}[[0.084,0.167],[0.234,0.245]...[0.934,0.517]], and {VR}[[0.084,0.355],[0.234,0.379]...[0.934,0.695]]. This will allow me to match each year with the corresponding development index of both industries.\nBased on the above thought, the answer is:\nYear,AI,VR\n2000,15,30\n2002,20,32\n2004,19,30\n2006,24,32\n2008,20,28\n2010,26,20\n2012,31,26\n2014,35,24\n2016,40,28\n2018,45,32\n2020,50,34" } ], "id_raw": 619 }, { "id": "pjLgUC0Zq58fyQ2E", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_31_19.png", "conversations": [ { "from": "human", "value": "\nCan you analyze the overall look of the growth curve of the AI industry as per the line chart?" }, { "from": "gpt", "value": "Observing {the curve for AI}[[0.084,0.167],[0.234,0.245]...[0.934,0.517]] in the chart, it displays an upward trend overall. It starts in 2000 with an index of 15, shows a gentle incline with a small dip in 2004 and again in 2008, but continuously progresses until 2020, reaching an index of 50. This indicates a positive growth pattern over these two decades, despite minor setbacks.\nBased on the above thought, the answer is:\nThe AI industry has shown notable growth from 2000 to 2020. Even with slight dips in 2004 and 2008, the AI development index has consistently increased, suggesting overall upward progress in the AI industry regardless of minor occasional falls in growth during the mentioned years." } ], "id_raw": 619 }, { "id": "BCy8AuWodsm40EUH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_31_19.png", "conversations": [ { "from": "human", "value": "\nIn what year does the AI industry seem to have the lowest development index, based on the line chart?" }, { "from": "gpt", "value": "To answer this question, I scan {the line points for AI}[[0.084,0.167],[0.234,0.245]...[0.934,0.517]]. The lowest point appears to be {in 2004}[0.284, 0.670], which corresponds to a development index of 19.\nBased on the above thought, the answer is:\n2004." } ], "id_raw": 619 }, { "id": "LQBRbmvJXvgJr4nq", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_1.png", "conversations": [ { "from": "human", "value": "\nCan you process the line chart to get a structured table, with Ratio as columns and Emotion types as rows?" }, { "from": "gpt", "value": "To create a table from the line chart, I identify {the x-axis tick labels}[[0.135, 0.449, 0.172, 0.466], [0.222, 0.449, 0.258, 0.466], [0.308, 0.449, 0.345, 0.466], [0.395, 0.449, 0.431, 0.466], [0.481, 0.449, 0.518, 0.466], [0.568, 0.449, 0.604, 0.466], [0.654, 0.449, 0.691, 0.466], [0.741, 0.449, 0.777, 0.466], [0.827, 0.449, 0.864, 0.466], [0.913, 0.449, 0.950, 0.466]] which represent the column headers or Ratio. The row labels are represented by the title of {subplot_0}[[0.344, 0.019, 0.741, 0.041]] as well as {subplot_1}[[0.269, 0.509, 0.816, 0.532]]. To populate the cells of the table, I refer to {the line points for Automobile}[[0.154, 0.300], [0.240, 0.352], [0.327, 0.339], [0.413, 0.372], [0.500, 0.320], [0.586, 0.326], [0.672, 0.359], [0.759, 0.307], [0.845, 0.300], [0.932, 0.300]] and {Public Transportation}[[0.154, 0.602], [0.240, 0.628], [0.327, 0.609], [0.413, 0.635], [0.500, 0.615], [0.586, 0.667], [0.672, 0.654], [0.759, 0.667], [0.845, 0.693], [0.932, 0.700]] with appropriate conversion to map the y-axis values to the corresponding emotion percentages.\nBased on the above thought, the answer is:\nEmotion Type,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0\nAutomobile Positive Emotion,0.40,0.43,0.45,0.40,0.48,0.47,0.42,0.50,0.51,0.51\nPublic Transportation Negative Emotion,0.80,0.76,0.79,0.75,0.78,0.70,0.72,0.70,0.66,0.65" } ], "id_raw": 621 }, { "id": "tFSpk78dlvUW1zON", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_1.png", "conversations": [ { "from": "human", "value": "\nCan you describe the observable trend of emotion ratio for public transportation over the observed ratios?" }, { "from": "gpt", "value": "To determine the trend of the emotion percentage ratio for public transportation, I refer to {the line for Public Transportation}[[0.154, 0.602, 0.932, 0.700]]. The emotion percentage starts at a high level of 0.80 at a ratio of 0.1 but shows a general trend of decrease as the ratio increases, reaching the lowest point of 0.65 at the highest ratio of 1.0. There are some fluctuations in the decrease where values slightly increase before continuing to decrease, such as from ratio 0.2 to 0.3 and from 0.4 to 0.5.\nBased on the above thought, the answer is:\nThe emotion percentage ratio for public transportation displays a general decreasing trend as the ratio increases, starting from a high of 0.80 at a ratio of 0.1 and dropping to a low of 0.65 at a ratio of 1.0. Despite some slight temporary increases, the overall progressive decrease over the range of ratios signifies a decrease in negative emotions for public transportation as the ratio increases." } ], "id_raw": 621 }, { "id": "nBIPKcx2a32f6iKr", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of emotion percentage ratio for Automobile Positive Emotion?" }, { "from": "gpt", "value": "To find the range of emotion percentage ratio for Automobile Positive Emotion, I first identify {the line points for Automobile Positive Emotion}[[0.154, 0.300], [0.240, 0.352], [0.327, 0.339], [0.413, 0.372], [0.500, 0.320], [0.586, 0.326], [0.672, 0.359], [0.759, 0.307], [0.845, 0.300], [0.932, 0.300]] and extract the corresponding values: 0.40, 0.43, 0.45, 0.40, 0.48, 0.47, 0.42, 0.50, 0.51, 0.51. The maximum value is 0.51 and the minimum value is 0.40. The range is therefore 0.51 - 0.40 = 0.11.\nBased on the above thought, the answer is:\n0.11." } ], "id_raw": 621 }, { "id": "7DpggCnmJNsLdRJV", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_2.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart of 2020 and 2021 Sales, can you extract the entirety of this data into a structured table format with Time as rows and years as columns?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first locate {the x-axis tick labels}[0.167, 0.449, 0.182, 0.466],[0.252, 0.449, 0.266, 0.466],[0.336, 0.449, 0.350, 0.466],[0.420, 0.449, 0.435, 0.466],[0.504, 0.449, 0.519, 0.466],[0.589, 0.449, 0.603, 0.466],[0.673, 0.449, 0.688, 0.466],[0.757, 0.449, 0.772, 0.466],[0.841, 0.449, 0.856, 0.466],[0.925, 0.449, 0.940, 0.466] and similarly for 2021, which represent the row labels or months. Next, I identify the subplots for 2020 and 2021 as the column headers. To populate the values, I locate the line points corresponding to each year at the respective months and extract the sales figure annotation.\nBased on the above thought, the answer is:\nTime (Months),2020 Sales,2021 Sales\n0,3000,3200\n1,3050,3400\n2,3100,4000\n3,3150,4500\n4,3200,5000\n5,3250,5800\n6,3300,6000\n7,3350,7000\n8,3400,7500\n9,3450,8000" } ], "id_raw": 622 }, { "id": "GYhLTTeGpnCqpsTW", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_2.png", "conversations": [ { "from": "human", "value": "\nConsidering the sales trend in 2020 and 2021, how can we summarize the progress?" }, { "from": "gpt", "value": "To answer this question, I would evaluate the line plots for 2020 and 2021. Looking at {the data for 2020}[0.175, 0.395, 0.933, 0.429], it shows a constant and 'steady growth with no significant changes. For {2021}[0.175, 0.545, 0.933, 0.905], the sales figures start higher than 2020 and demonstrate a more substantial growth rate throughout the year.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, sales for the company saw a steady but slow increase in 2020. In 2021, the company started with higher initial sales compared to 2020 and experienced a significant rise throughout the year, indicating a better performance in 2021 as compared to 2020." } ], "id_raw": 622 }, { "id": "DDZxWGmpFZFUaMpu", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in sales from the start to the end of 2021?" }, { "from": "gpt", "value": "To find the change in sales from the start to the end of 2021, I first locate {the starting point for 2021}[0.175, 0.905] which corresponds to a sales figure of 3200. Then I find {the end point for 2021}[0.933, 0.545] which corresponds to a sales figure of 8000. The change in sales is therefore 8000 - 3200 = 4800.\nBased on the above thought, the answer is:\n4800." } ], "id_raw": 622 }, { "id": "q0qaNRFzEHYnEKm8", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_4.png", "conversations": [ { "from": "human", "value": "\nCan you provide the tabular representation showing the trends for Classification and Prediction across various x-axis categories?" }, { "from": "gpt", "value": "To transform the data from the chart into a table format, first, I focus on identifying {the x-axis labels}[[0.114, 0.965, 0.142, 0.975], [0.204, 0.965, 0.231, 0.975], [0.294, 0.965, 0.321, 0.975], [0.384, 0.965, 0.411, 0.975], [0.473, 0.965, 0.501, 0.975]], which represent the categories under which trends are measured. Next, I observe the bars for {Classification}[[0.128, 0.801, 0.936, 0.825]] and {Prediction}[[0.128, 0.786, 0.936, 0.798]] across the timeline and extract the corresponding trend percentages.\nBased on the above thought, the answer is:\nCategories,Classification,Prediction\nCategory 1,100.0,1.0\nCategory 2,90.0,2.3\nCategory 3,65.0,5.0\nCategory 4,32.5,7.2\nCategory 5,10.6,8.9" } ], "id_raw": 624 }, { "id": "ixd1HpGBVqRjLJxJ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_4.png", "conversations": [ { "from": "human", "value": "\nWhat does the outlier at 10.6% in the Classification trend indicate about its progression?" }, { "from": "gpt", "value": "Observing {the line chart for Classification}[0.128, 0.801, 0.936, 0.825], I see a consistent downward trend from 100.0% decreasing to 10.6% across various categories. The presence of the outlier at 10.6% signifies a substantial drop, hinting that there may have been significant changes or events affecting Classification at this point. This outlier visually illustrates the most dramatic decrease, emphasizing the accelerated decline mentioned in the setup of the chart's theme.\nBased on the above thought, the answer is:\nThe outlier at 10.6% in the Classification trend signifies a significant and abrupt decrease in its values, emphasizing an accelerated decline in this category. This drop can indicate major changes or impacts that led to such a sharp decrease, marking it as a critical point in the analysis of Classification trends." } ], "id_raw": 624 }, { "id": "AY0HpyNuE9whpNHj", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest percentage difference observed between Classification and Prediction within any category?" }, { "from": "gpt", "value": "To find the highest percentage difference, I analyze {the trend data for Classification}{{0.128, 0.801, 0.936, 0.825]] and {Prediction}[[0.128, 0.786, 0.936, 0.798]] across different categories: Classification starts at 100.0% and goes down to 10.6%, while Prediction starts at 1.0% and rises to 8.9%. I calculate the differences for each category, with Category 1 showing the largest difference of 100.0% - 1.0% = 99.0%.\nBased on the above thought, the answer is:\n99.0" } ], "id_raw": 624 }, { "id": "5rlYdRXyvdWNl7iE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line graph across the different SP Risk labels into a table format showing high and low risk scores?" }, { "from": "gpt", "value": "By examining this line graph, I can distinguish two lines each denoting high and low risk scores across multiple SP Risk labels. Along the {x-axis}[0.110, 0.293, 0.957, 0.329], the SP Risk labels are visible, which serve as the row headers. {The high risk line points}[0.110, 0.087, 0.957, 0.106] and {the low risk line points}[0.110, 0.143, 0.957, 0.162] provide the scores, which can be extracted and structured into a readable tabular format.\nBased on the above thought, the answer is:\nSP Risk Label,High Risk Score,Low Risk Score\nSPRisk-0.5,0.85,0.70\nSPRisk-0.7,0.68,0.55\nSPRisk-0.9,0.58,0.48\nSPRisk-1.1,0.53,0.43\nSPRisk-1.3,0.48,0.38" } ], "id_raw": 625 }, { "id": "rH8G1cskNk8N6GA2", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_5.png", "conversations": [ { "from": "human", "value": "\nWhat does the consistent decline in both high and low risk scores, as observed in {the two lines of the graph}[0.110, 0.087, 0.957, 0.162], suggest about the financial management over the SP Risk labels?" }, { "from": "gpt", "value": "Observing {the high risk line}[0.110, 0.087, 0.957, 0.106] and {the low risk line}[0.110, 0.143, 0.957, 0.162] in the graph, it is clear that both are showing a downward trajectory as the SP Risk labels increase. This trend could suggest that as measures to mitigate risks strengthen (represented by increasing SP Risk labels), both the permissible thresholds for high and low-risk financial activities are getting tighter, indicating a more conservative financial management approach over time.\nBased on the above thought, the answer is:\nThe consistent decline in scores illustrates tightening financial management, suggesting that risk tolerance is decreasing, which is likely a proactive strategy to maintain stability and minimize potential financial disruptions as risk-mitigation measures increase." } ], "id_raw": 625 }, { "id": "gMMF0CgcZUhkkS3B", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_5.png", "conversations": [ { "from": "human", "value": "\nHow much higher is the total average score of high risk compared to low risk across all SP Risk levels?" }, { "from": "gpt", "value": "To find the average score of high risk and low risk, I must calculate the mean for each by summarizing the values from {the high risk line}[0.110, 0.087, 0.957, 0.106] and {the low risk line}[0.110, 0.143, 0.957, 0.162]. Summing values for high risk gives a total of 3.24 and for low risk gives 2.64, each divided by the number of SP Risk levels, which is 5. This results in average scores of 0.648 and 0.528, respectively. Subtracting the low risk average from the high risk average provides the difference.\nBased on the above thought, the answer is:\n0.12" } ], "id_raw": 625 }, { "id": "NrqmYyevv3tdpyG7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_6.png", "conversations": [ { "from": "human", "value": "\nCan you present the performance metrics as a structured table with ratios as rows and sectors like AI Research and AI Healthcare as columns?" }, { "from": "gpt", "value": "To create a table from the line chart, I first look at the {the x-axis tick labels}[[0.149, 0.879, 0.186, 0.914], [0.234, 0.879, 0.271, 0.914],[0.319, 0.879, 0.356, 0.914], [0.404, 0.879, 0.441, 0.914], [0.489, 0.879, 0.526, 0.914], [0.574, 0.879, 0.611, 0.914], [0.659, 0.879, 0.696, 0.914],[0.744, 0.879, 0.781, 0.914],[0.829, 0.879, 0.866, 0.914],[0.914, 0.879, 0.951, 0.914]], which will serve as the row labels. Next, I identify {the legend texts}[[0.211, 0.125, 0.347, 0.160],[0.211, 0.177, 0.367, 0.212]] as the column headers, representing the sectors - AI Research and AI Healthcare. Finally, I plot the corresponding values from line points for each sector in their related ratios.\nBased on the above thought, the answer is:\nRatio, AI Research, AI Healthcare\nRatio 1, 0.2, 0.35\nRatio 2, 0.22, 0.5\nRatio 3, 0.27, 0.4\nRatio 4, 0.29, 0.42\nRatio 5, 0.4, 0.37\nRatio 6, 0.58, 0.55\nRatio 7, 0.55, 0.67\nRatio 8, 0.62, 0.77\nRatio 9, 0.79, 0.8\nRatio 10, 0.82, 0.95" } ], "id_raw": 626 }, { "id": "J9VT7K1LDCf8Wbxz", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_6.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which sector, AI Research or AI Healthcare, showed better performance growth over the ratios? Explain your reasoning." }, { "from": "gpt", "value": "To answer this, I need to compare the growth trends in the line plots for both sectors. From looking at {the AI Research Line}[[0.168, 0.254, 0.933, 0.810]], it starts at a performance metric of 0.2 at Ratio 1 and increases to 0.82 at Ratio 10. However, for {AI Healthcare Line}[[0.168, 0.138, 0.933, 0.675]], it starts at 0.35 at Ratio 1 and increases to 0.95 at Ratio 10. Finding the growth in performance for AI Healthcare is larger than for AI Research.\nBased on the above thought, the answer is:\nBased on the line chart, AI Healthcare sector showed better performance growth over the ratios. It started at a higher performance metric compared to AI Research and maintained an upward growth trend, increasing to a higher performance metric by Ratio 10 as well. Therefore, considering the increase from Ratio 1 to Ratio 10, AI Healthcare has shown more growth in performance compared to AI Research." } ], "id_raw": 626 }, { "id": "au2AUszh2YTXBFFE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in performance for AI Healthcare from Ratio 1 to Ratio 10?" }, { "from": "gpt", "value": "To answer this, I need to locate {the starting point for AI Healthcare at Ratio 1}[[0.168, 0.675]] which corresponds to 0.35 and {the ending point for AI Healthcare at Ratio 10}[[0.933, 0.138]] which corresponds to 0.95. I calculate the percentage increase as (0.95-0.35)/0.35*100.\nBased on the above thought, the answer is:\n171.43%." } ], "id_raw": 626 }, { "id": "pXsJEJslBM7N27rg", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_7.png", "conversations": [ { "from": "human", "value": "\nCan you construct a table with years as columns and industries as rows, using the data from the line chart?" }, { "from": "gpt", "value": "The first step to get the data from these line charts into a table is to look at {the x-axis tick labels} of each subplot: [0.074, 0.301, 0.132, 0.313], [0.495, 0.301, 0.554, 0.313], [0.917, 0.301, 0.975, 0.313]. They correspond to the years, which will be our columns. Next, the {subtitle of each subplot}[0.489, 0.012, 0.560, 0.027], [0.489, 0.342, 0.560, 0.357], [0.488, 0.671, 0.560, 0.686] represent industries that will be our rows. Then, I can extract the values for each industry at each year from {line points} such as [0.103, 0.118], [0.524, 0.075], [0.946, 0.206], which present the market share for the given industry in the specified year.\nBased on the above thought, the answer is:\nIndustry,2018,2019,2020\nInd-A,40,45,30\nInd-B,35,30,40\nInd-C,25,25,30" } ], "id_raw": 627 }, { "id": "BPMPhitTlGAK8cn3", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_7.png", "conversations": [ { "from": "human", "value": "\nWhich industry saw a consistent market share over the period from 2018 to 2020?" }, { "from": "gpt", "value": "To identify the industry with consistent market share over the period from 2018 to 2020, I need to analyze the line trends for each industry. Looking at {the line for Ind-A}[0.103, 0.075, 0.946, 0.206], there is a fluctuation with a general decline from 40% to 30%. Similarly, looking at {the line for Ind-B}[0.103, 0.491, 0.946, 0.448], there is a clear decline from 35% to 40%. Now looking at {the line for Ind-C}[0.103, 0.908, 0.946, 0.864], the market share remains constant at 25% between 2018 and 2019 and increases to 30% in 2020. Although there is an increase in 2020, compared to other industries, Ind-C was more consistent over the period.\nBased on the above thought, the answer is:\nIndustry Ind-C had a more consistent market share over the period from 2018 to 2020. Although there was a slight increase from 25% to 30% in 2020, this change was less volatile compared to the shifts in market share observed in Industries A and B." } ], "id_raw": 627 }, { "id": "3KyEDNGGo6UGAJfq", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_7.png", "conversations": [ { "from": "human", "value": "\nWhat has been the total change in the market share percentage of Ind-A from 2018 to 2020?" }, { "from": "gpt", "value": "To find the total change in market share percentage of Ind-A, I need to refer to {the line points for Ind-A}[[0.103, 0.118], [0.524, 0.075], [0.946, 0.206]]. The market share in 2018 is 40% at the point [0.103, 0.118]. By 2020, the market share has decreased to 30% at the point [0.946, 0.206]. Subtracting the market share in 2018 from the market share in 2020 gives -10%, or a decrease of 10 percentage points.\nBased on the above thought, the answer is:\n-10%" } ], "id_raw": 627 }, { "id": "7bNYFQw9p5jUeMhY", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_8.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the line chart into a CSV format that captures the trends for SPGrocery values over the years?" }, { "from": "gpt", "value": "To convert the data from the line chart into a CSV format, I begin by identifying {the x-axis tick labels}[0.099, 0.308, 0.961, 0.317] which represent the years from 2000 to 2009. Then, I examine the lines for each SPGrocery category: SPGrocery-0.5, SPGrocery-0.7, and SPGrocery-0.9, located at {the line coordinates for SPGrocery-0.5}[0.116, 0.037, 0.944, 0.289], {the line coordinates for SPGrocery-0.7}[0.116, 0.367, 0.944, 0.619], and {the line coordinates for SPGrocery-0.9}[0.116, 0.697, 0.944, 0.949]. I will extract the points from each line reflecting yield values across different years.\nBased on the above thought, the answer is:\nYear,SPGrocery-0.5,SPGrocery-0.7,SPGrocery-0.9\n2000,2.5,2.53,2.57\n2001,2.75,2.55,2.6\n2002,2.85,2.56,2.63\n2003,2.9,2.65,2.66\n2004,3.1,2.7,2.7\n2005,3.2,2.75,2.75\n2006,3.25,2.8,2.78\n2007,3.28,2.83,2.8\n2008,3.3,2.85,2.82\n2009,3.35,2.87,2.85" } ], "id_raw": 628 }, { "id": "mtAVm0VcmnUOt9no", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_8.png", "conversations": [ { "from": "human", "value": "\nWhich SPGrocery category showed the most consistent upward trend in yield over the years?" }, { "from": "gpt", "value": "To analyze the consistency of upward trends among SPGrocery categories, I inspect the slope and the progression of each line representing different SPGrocery categories. Observing {the line for SPGrocery-0.5}[0.116, 0.037, 0.944, 0.289], {the line for SPGrocery-0.7}[0.116, 0.367, 0.944, 0.619], and {the line for SPGrocery-0.9}[0.116, 0.697, 0.944, 0.949], it's notable that SPGrocery-0.9 starts at a yield of 2.57 in 2000 and continuously rises to 2.85 in 2009 with no apparent dips, indicating a consistent upward trend.\nBased on the above thought, the answer is:\nSPGrocery-0.9 showed the most consistent upward trend in yield over the years. The line graph representing this category clearly depicts a gradual and consistent increase, with no year showing a yield decline, making it the most steadily improving category among the three." } ], "id_raw": 628 }, { "id": "TmbAPLwPQ6M1ZO0U", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_8.png", "conversations": [ { "from": "human", "value": "\nWhat was the average yield for SPGrocery-0.7 across the entire range of years represented?" }, { "from": "gpt", "value": "To calculate the average yield for SPGrocery-0.7, I identify all the data points for SPGrocery-0.7 in the line chart. I extract the values from each year located along {the line for SPGrocery-0.7}[0.116, 0.367, 0.944, 0.619]. The yields for SPGrocery-0.7 from 2000 to 2009 are 2.53, 2.55, 2.56, 2.65, 2.7, 2.75, 2.8, 2.83, 2.85, 2.87 respectively. Using these values, I calculate the average by dividing the sum of the values by the number of years, which is 10.\nBased on the above thought, the answer is:\n2.69" } ], "id_raw": 628 }, { "id": "eQUMUVs3qGKWHC6P", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_9.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data for both lifestyles into a structured table with Social Activities as rows and Lifestyles as columns?" }, { "from": "gpt", "value": "To generate a table from the line chart, first, I look at {the x-axis tick labels}[0.069, 0.919, 0.164, 0.942], [0.257, 0.919, 0.389, 0.942], [0.462, 0.919, 0.598, 0.942], [0.663, 0.919, 0.811, 0.942], [0.915, 0.919, 0.973, 0.942] which represent the row headers, or social activities. Next, I identify {the legend texts}[0.900, 0.824, 0.972, 0.847], [0.900, 0.859, 0.972, 0.882] as the column headers representing the different lifestyles. To populate the table, I locate {the line points for Lifestyle A}[0.116, 0.550], [0.323, 0.325], [0.530, 0.460], [0.737, 0.640], [0.944, 0.100] and {the line points for Lifestyle B}[0.116, 0.775], [0.323, 0.685], [0.530, 0.730], [0.737, 0.865], [0.944, 0.550] and extract the corresponding values.\nBased on the above thought, the answer is:\nActivities,Lifestyle A, Lifestyle B\nStay at Home, 10, 5\nOutdoors Activities, 15, 7\nOnline Socialisation,12,6\nPhysical Socialisation, 8, 3\nExercise, 20, 10" } ], "id_raw": 629 }, { "id": "9EeHVyMus7Ev9S0t", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_9.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which Lifestyle spends more time on exercise, and why do you think so?" }, { "from": "gpt", "value": "To determine which Lifestyle spends more time on Exercise, I need to analyze the y-values at the point representing Exercise on each line. Looking at the last point on {the line for Lifestyle A}[0.944, 0.100], the y-value represents the time spent on Exercise for Lifestyle A which is 20. Similarly, from the last point on {the line for Lifestyle B}[0.944, 0.550], I can obtain the time spent on Exercise for Lifestyle B which is 10.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Lifestyle A spends more time on Exercise than Lifestyle B. This is evidenced by the y-value of the point representing Exercise on the line for Lifestyle A being higher than that for Lifestyle B, indicating a greater time investment in exercise for Lifestyle A." } ], "id_raw": 629 }, { "id": "JljOE9QuNd4ae0jr", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the total time spent on Social Activities for both Lifestyle A and B?" }, { "from": "gpt", "value": "To find the total time spent on Social Activities for both Lifestyles, I need to add up the y-values of all the points on each of the line charts. For Lifestyle A, I locate the points on {the line for Lifestyle A}[0.116, 0.550], [0.323, 0.325], [0.530, 0.460], [0.737, 0.640], [0.944, 0.100] which represent the time spent on each activity: 10, 15, 12, 8, 20. Similarly for Lifestyle B, the time spent on each activity can be obtained from the points at {the line for Lifestyle B}[0.116, 0.775], [0.323, 0.685], [0.530, 0.730], [0.737, 0.865], [0.944, 0.550] which are 5, 7, 6, 3, 10. By adding up these values, I get the total time spent on Social Activities for each Lifestyle.\nBased on the above thought, the answer is:\nLifestyle A: 65, Lifestyle B: 31." } ], "id_raw": 629 }, { "id": "9ObYbGPcffz0wyC7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_10.png", "conversations": [ { "from": "human", "value": "\nCould you structure the data from the bar chart showing the exposure data trends into a CSV format, distinguishing between personal and company data across the categories?" }, { "from": "gpt", "value": "To convert the bar chart data into a tabular format, I begin by examining the categories along {the x-axis}[0.110, 0.031, 0.975, 0.293], which are 'Abrupt Increase', 'Gradual Increase', 'Steep Decline', and 'Subdued Decline'. I then differentiate between personal and company data exposure percentages as shown in the two separate graphs; the first graph has personal data, and the second graph shows company data. I recognize and quantify the segments from the bars by noting down the percentage values labeled on each bar segment.\nBased on the above thought, the answer is:\nCategory,Personal Data Exposure (%),Company Data Exposure (%)\nAbrupt Increase,30,45\nGradual Increase,50,35\nSteep Decline,10,5.4\nSubdued Decline,5,15" } ], "id_raw": 630 }, { "id": "do1rm8EXkanDSjgc", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_10.png", "conversations": [ { "from": "human", "value": "\nWhich category shows the most significant disparity in exposure between personal and company data, and why?" }, { "from": "gpt", "value": "By closely analyzing bar charts for each data category, I note the differences in each segment's length and percentage labels. In {the 'Steep Decline'}[0.312, 0.567, 0.541, 0.882] category, the difference in the labeled percentages\u201410% for personal data and 5.4% for company data\u2014indicates a notable disparity. This significant gap highlights different impacts or concerns regarding data breaches in the two data types within this category, with personal data being somewhat less affected in this specific instance compared to company data.\nBased on the above thought, the answer is:\nThe 'Steep Decline' category shows the most significant disparity in exposure between personal and company data. The difference of 4.6 percentage points is primarily because, in this category, personal data is less affected by breaches compared to company data, highlighting varying levels of vulnerability or focus areas in cybersecurity measures between the two." } ], "id_raw": 630 }, { "id": "oSnhhndzoAnTanxd", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the total percentage point difference in exposure for personal and company data?" }, { "from": "gpt", "value": "Looking at the personal and company data from each graph, the percentages for each category are as follows: Personal Data - 30, 50, 10, 5 and Company Data - 45, 35, 5.4, 15. Calculating the absolute differences for each category and summing them up gives: |30-45| + |50-35| + |10-5.4| + |5-15| = 15 + 15 + 4.6 + 10 = 44.6.\nBased on the above thought, the answer is:\n44.6" } ], "id_raw": 630 }, { "id": "n3iJYvHI6cQ7tiPZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert information from the line chart related to Global Temperature Change into a structured table using the years as rows and the trends as headers?" }, { "from": "gpt", "value": "To accurately compile the data into a structured table, I need to assess and gather points from {the first subplot for Global Temperature Change}[0.110, 0.031, 0.975, 0.293]. I'll consider each {year's mark on the x-axis}[0.135, 0.301, 0.950, 0.313] as a row while the trend levels 'Inconsistent Growth', 'Outlier Increase', and 'Progressive Growth' derived from the visual observation of year-to-year change will serve as column headers. I proceed to register the temperature values depicted at each point across the 19 years.\nBased on the above thought, the answer is:\nYear,Inconsistent Growth,Outlier Increase,Progressive Growth\n2000,0.1,-,0.1\n2001,0.15,-,0.12\n2002,0.19,-,0.15\n2003,0.20,-,0.17\n2004,0.18,-,0.17\n2005,0.22,-,0.25\n2006,0.25,0.35,0.30\n2007,0.28,-,0.33\n2008,0.22,-,0.29\n2009,0.23,-,0.22\n2010,0.28,-,0.30\n2011,0.27,-,0.32\n2012,0.28,-,0.33\n2013,0.31,-,0.38\n2014,0.36,-,0.39\n2015,0.40,0.50,0.45\n2016,0.46,0.6,0.55\n2017,0.39,-,0.40\n2018,0.42,-,0.43\n2019,0.44,-,0.45" } ], "id_raw": 631 }, { "id": "rUg3MDQOPdw9TOo9", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_11.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the variability in global temperature changes from the line chart?" }, { "from": "gpt", "value": "By analyzing {the line for Global Temperature Change}[0.154, 0.132, 0.932, 0.680] across the x-axis years {from 2000 to 2019}[0.135, 0.301, 0.950, 0.313], I gauge the amplitude of fluctuations. The increases and drops across certain years, specifically noted significant peaks in 2015 and 2016, indicate a substantial variability. Regular inconsistencies with a couple of outlier years signify a complex interaction of underlying factors affecting temperature change.\nBased on the above thought, the answer is:\nThe global temperature change line chart demonstrates considerable variability, with years like 2015 and 2016 marked as significant outliers. These trends suggest fluctuating and sometimes sharp changes, which indicate unpredictable environmental dynamics over the observed period." } ], "id_raw": 631 }, { "id": "4NHoKOOGE1v54qet", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall change in Sea Level Rise from the year 2000 to 2019 based on the chart?" }, { "from": "gpt", "value": "Observing the {line plot for Sea Level Rise} in {the second subplot}[0.110, 0.360, 0.975, 0.622], I locate the data points for the years 2000 and 2019: {starting point}[0.154, 0.622] and {ending point}[0.932, 0.503]. The starting value in 2000 is approximately 0.35 meters and in 2019 is roughly 0.45 meters.\nBased on the above thought, the answer is:\n0.10 meters" } ], "id_raw": 631 }, { "id": "TljzaZLkdQiYahJQ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart into a structured table, listing each era along the rows and the years as columns?" }, { "from": "gpt", "value": "I begin by identifying {the x-axis labels}[0.135, 0.301, 0.950, 0.313], which represent the different years, and serve as the columns in the table. Next, I locate {the legend}[0.122, 0.037, 0.449, 0.058], which lists the different eras such as 'Hunter-gatherer Eras', 'Agricultural development', and 'Industrial Revolution', to be used as rows. For the data entries, I extract values from {the points on each line plot}[0.154, 0.249], [0.240, 0.228], ..., [0.932, 0.263] for each era across the years.\nBased on the above thought, the answer is:\nYear,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0\nHunter-gatherer Eras,0.4,0.45,0.5,0.45,0.4,0.38,0.39,0.4,0.38,0.37\nAgricultural development,0.35,0.4,0.44,0.47,0.49,0.51,0.57,0.65,0.7,0.75\nIndustrial Revolution,0.3,0.35,0.39,0.43,0.46,0.48,0.6,0.72,0.76,0.89" } ], "id_raw": 633 }, { "id": "02mMT7P42XzyhXJn", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_13.png", "conversations": [ { "from": "human", "value": "\nHow does the trend of civilization development vary across the three eras presented?" }, { "from": "gpt", "value": "To evaluate the trends across the three eras, I look at {the lines for each era}[0.154, 0.249], [0.932, 0.263], [0.154, 0.600], [0.932, 0.426], and [0.154, 0.951], [0.932, 0.694]. The Hunter-gatherer era experiences a rise followed by stability and slight declines, indicating a slow and steady trend. The Agricultural development era shows consistent growth, suggesting rapid expansion. Meanwhile, the Industrial Revolution presents the most dynamic growth, particularly a steep ending rise, indicating accelerated development and technological progress.\nBased on the above thought, the answer is:\nThe trends vary significantly across the three eras. The Hunter-gatherer era shows moderate and somewhat stable development, the Agricultural development displays continuous growth, and the Industrial Revolution era exemplifies a sharp incline, reflecting rapid advancements. This indicates that civilization development has accelerated over time with each subsequent era." } ], "id_raw": 633 }, { "id": "QkFLgzK28sIFClDm", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the growth in the 'Industrial Revolution' era from the earliest to the latest year?" }, { "from": "gpt", "value": "To calculate the growth in the 'Industrial Revolution' era, I observe {the points specifically for the 'Industrial Revolution'}[0.154, 0.951], [0.932, 0.694]. The value at 0.1 year is 0.3 and at 1.0 year is 0.89. So, the growth is calculated by subtracting the value at year 0.1 from the value at year 1.0, which is 0.89 - 0.3 = 0.59.\nBased on the above thought, the answer is:\n0.59" } ], "id_raw": 633 }, { "id": "SD4HO4Pk3XyioOzH", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_14.png", "conversations": [ { "from": "human", "value": "\nCould you arrange the data for the Fossil Fuels' efforts from the line chart into a tabular format organized by years?" }, { "from": "gpt", "value": "I will begin by examining {the x-axis labels}[0.118, 0.960, 0.200, 0.968] to identify the years for the columns. Then, I focus on {the line for Fossil Fuels}[0.172, 0.272, 0.439, 0.273] to observe the respective effort values corresponding to each year marked on the x-axis. By tracking the y-value at each x-position, I can accurately retrieve the effort data.\nBased on the above thought, the answer is:\nYear,Effort in Fossil Fuels\n2007,150\n2008,145\n2009,142\n2010,135\n2011,125\n2012,115\n2013,110\n2014,105\n2015,102\n2016,100\n2017,95\n2018,90\n2019,85\n2020,80\n2021,75" } ], "id_raw": 634 }, { "id": "xP4YkREO5SOiiCF7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_14.png", "conversations": [ { "from": "human", "value": "\nHow do the trends in Fossil Fuels and Renewable Sources compare, and what might be inferred about future resource allocation?" }, { "from": "gpt", "value": "By examining the trends from {the line chart for Fossil Fuels}[0.172, 0.272, 0.439, 0.273] which shows a declining trend, and {the line chart for Renewable Sources}[0.172, 0.095, 0.439, 0.114] displaying a slowly increasing trend, it is evident that there is a shift in emphasis from Fossil Fuels to Renewable Sources. This shift could suggest a future strategy focusing more on sustainable energy sources to meet energy demands.\nBased on the above thought, the answer is:\nThe comparison between the two trends indicates a decreasing dependency on Fossil Fuels and an increasing reliance on Renewable Sources. This suggests that future resource allocation will likely favor more sustainable and environmentally friendly energy sources, aiming for a balanced energy mix that supports both environmental preservation and energy security." } ], "id_raw": 634 }, { "id": "WSk9LPQ875fyQfJ5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the total decrease in effort for Fossil Fuels from 2007 to 2021?" }, { "from": "gpt", "value": "To compute the total decrease, I need to extract the initial and final effort values for Fossil Fuels. Observing {the Fossil Fuels line's points}[0.172, 0.272], [0.921, 0.923], the effort starts at 150 in 2007 and ends at 75 in 2021. The total decrease is calculated by subtracting the final effort from the initial effort.\nBased on the above thought, the answer is:\n75" } ], "id_raw": 634 }, { "id": "AF5NBq3A0C3FFYDv", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_15.png", "conversations": [ { "from": "human", "value": "\nCan you extract the values from the growth and decline data graphs and list them under appropriate days getting represented as columns and data type as rows?" }, { "from": "gpt", "value": "Observing {the x-axis of the first subplot}[0.110, 0.614, 0.975, 0.772] which spans from day 1 to day 12, and {the data points}[0.154, 0.847, 0.932, 0.892] of the growth graph, I list the values corresponding to each day. Similarly, for the decline data, I follow the same process with {the x-axis of the second subplot}[0.110, 0.813, 0.975, 0.971] and {its data points}[0.154, 0.892, 0.932, 0.847].\nBased on the above thought, the answer is:\nDay,Growth,Decline\n1,50,70\n2,52,69\n3,55,68\n4,58,66\n5,62,63\n6,67,60\n7,73,58\n8,80,55\n9,88,52\n10,95,50\n11,98,50\n12,100,50" } ], "id_raw": 635 }, { "id": "IjHUSXIBwFrGbCa3", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_15.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the performance trend based on the rate of growth and decline from day 1 to day 12?" }, { "from": "gpt", "value": "Observing {the growth data line}[0.154, 0.847, 0.932, 0.892], it shows a steady increase from 50 to 100 over 12 days, indicating a positive trend in whatever metric it measures. For the {decline data line}[0.154, 0.892, 0.932, 0.847], it starts at 70 and drops to 50, illustrating a clear declining or negative trend over the same period. Comparing both, growth is consistently positive while decline shows regression.\nBased on the above thought, the answer is:\nIt can be inferred that the performance in terms of growth is improving over time as indicated by a steady increase from 50 to 100. On the other hand, there's a regression in the decline figures, moving from 70 down to 50, suggesting that while one aspect improves, another diminishes equivalently over the same interval." } ], "id_raw": 635 }, { "id": "eSYOAVqeBifkNBhY", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the total change in the growth data from day 1 to day 12?" }, { "from": "gpt", "value": "To calculate the total change, I identify {the data point on day 1}[0.154, 0.847] in the growth data and see it represents 50. Next, I find {the data point on day 12}[0.932, 0.892], noted as 100. The change is the difference between the values on day 12 and day 1, i.e., 100 - 50.\nBased on the above thought, the answer is:\n50" } ], "id_raw": 635 }, { "id": "rAlp3imGVJqWFO40", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_18.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the scatter plot showing engagement over various time frames into a structured table grouped by engagement type and time frame?" }, { "from": "gpt", "value": "Observing the scatter plot, I note {the x-axis labels}[0.154, 0.951] indicating the time frames, while {the legend}[0.122, 0.037, 0.315, 0.075] denotes different engagement types such as Facebook and Twitter. By analyzing the scatter points for each engagement type across the timeline, {points on the scatter plot}[0.154, 0.057, 0.932, 0.188] and [0.154, 0.136, 0.932, 0.241], I compile the data into a structured format.\nBased on the above thought, the answer is:\nTime Frame, Facebook Engagement, Twitter Engagement\n0.1, 0.6, 0.5\n0.2, 0.6277777777777778, 0.5222222222222223\n0.3, 0.6555555555555556, 0.5444444444444444\n...\n1.0, 0.85, 0.7" } ], "id_raw": 638 }, { "id": "SPQTo8cQNkfn3qpO", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_18.png", "conversations": [ { "from": "human", "value": "\nOver the time frames compared in the chart, which engagement type shows a more consistent upward trend, Facebook or Twitter?" }, { "from": "gpt", "value": "Analyzing the scatter plot, I focus on the trajectory of points for both {Facebook}[0.154, 0.057, 0.932, 0.188] and {Twitter}[0.154, 0.136, 0.932, 0.241]. Facebook starts at 0.6 and ends at 0.85, showing a consistent upward trend. Twitter, with points starting at 0.5 and ending at 0.7, also reveals an upward trend but with lesser overall increase than Facebook. Both lines show consistent growth, yet the slope of the Facebook engagement line is steeper, indicating a more pronounced consistent upward movement.\nBased on the above thought, the answer is:\nFacebook shows a more consistent upward trend compared to Twitter. This conclusion is based on examining the trend lines where Facebook's line slope is steeper, indicating higher and more consistent increases in the engagement values over the time frames." } ], "id_raw": 638 }, { "id": "tUTJgEAcZwy5zbEf", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest value recorded for the Facebook engagement between time frames 0.4 and 0.9?" }, { "from": "gpt", "value": "To identify the highest Facebook engagement value between the specified time frames, I focus on {the points for Facebook}[0.154, 0.057, 0.932, 0.188]. The engagement values from time frame 0.4 to 0.9 are 0.6833333333333333, 0.711111111111111, 0.7388888888888889, 0.7666666666666666, 0.7944444444444444. Calculating the maximum value among them, I determine the highest engagement.\nBased on the above thought, the answer is:\n0.7944444444444444" } ], "id_raw": 638 }, { "id": "SXxzyF5tMyMDlCLE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_32_19.png", "conversations": [ { "from": "human", "value": "\nCan you extract and arrange the data from the line chart into a structured table format, showing product stages in columns and sales values in rows?" }, { "from": "gpt", "value": "To create a table from the data in the line chart, I examine {the x-axis labels}[0.103, 0.031, 0.975, 0.293]which represent different stages in the supply chain. {The data points}[0.147, 0.162, 0.931, 0.260] give the sales values at each stage. I systematically go through each data point corresponding to its stage to populate the table row with its respective sales value.\nBased on the above thought, the answer is:\nProduct Stages, Raw Material, Procurement, Manufacturing, Distribution, Customer Relations, Supply Chain Management\nSales (in million USD), 20, 40, 70, 85, 65, 100" } ], "id_raw": 639 }, { "id": "rmkcZ3El3bns2EXn", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_32_19.png", "conversations": [ { "from": "human", "value": "\nWhat stage depicts the highest sales performance according to the line chart, and what could be the reason for this trend?" }, { "from": "gpt", "value": "By analyzing {the peak data point}, it is clear that the 'Supply Chain Management' stage shows the highest sales performance. A likely reason for this could be the effective management and optimization of the overall operations and logistics in the supply chain, as this stage involves strategic planning and implementation that potentially maximizes sales and efficiency.\nBased on the above thought, the answer is:\nThe 'Supply Chain Management' stage depicts the highest sales performance. The trend here could be attributed to comprehensive strategies in managing resources, relationships, and logistics that optimize the entire process, thus leading to increased sales and profitability." } ], "id_raw": 639 }, { "id": "CBCbEPAWbo7MdsQx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_32_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in sales from the 'Raw Material' stage to the 'Supply Chain Management' stage in the line chart?" }, { "from": "gpt", "value": "To calculate the total increase, I start with the sales value at {the Raw Material stage}[0.147, 0.162] which is 20 million USD, then I find the sales value at {the Supply Chain Management stage}[0.931, 0.260], which is 100 million USD. The total increase is then obtained by subtracting the initial sales value from the final sales value.\nBased on the above thought, the answer is:\n80 million USD." } ], "id_raw": 639 }, { "id": "jVc2TwPccEBkhHKA", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_0.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data represented in the line chart into a tabular format with the Quarters in columns and Revenue, Expenses, and Net Profit in rows?" }, { "from": "gpt", "value": "The x-axis of the chart {x-axis labels}[0.057, 0.911, 0.077, 0.934] represents the Quarters which will be the columns, and the legend {legend}[0.075, 0.079, 0.214, 0.219] describes the Revenue, Expenses, Net Profit which will be the rows of the table. The data for each category can be found by extracting the y-values of the respective datapoints {data points}[0.067, 0.132, 0.975, 0.560] for Revenue, {data points}[0.067, 0.373, 0.975, 0.661] for Expenses, {data points}[0.067, 0.614, 0.975, 0.808] for Net Profit at each quarter.\nBased on the above thought, the answer is:\nCategory,Q1,Q2,Q3,Q4,Q5,Q6\nRevenue,5,6.3,6.5,7.5,11.4,10.6\nExpenses,3.5,4.2,5.2,6.2,7.2,7.8\nNet Profit,1.5,2.1,1.3,1.3,4.2,2.8" } ], "id_raw": 640 }, { "id": "6kPPo9TpfOfxxwwN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_0.png", "conversations": [ { "from": "human", "value": "\nWhich of the three metrics, Revenue, Expenses, and Net Profit, demonstrates the most significant change over the quarters?" }, { "from": "gpt", "value": "To determine which metrics has the most significant change, I need to observe the fluctuations in the lines for each metric. Examining {the line for Revenue}[0.067, 0.132, 0.975, 0.560], I see a relatively steady increase, but a noticeable surge from Q4 to Q5. Looking at {the line for Expenses}[0.0670, 0.373, 0.975, 0.661], it shows a steady incline over the quarters. Lastly, for {Net Profit}[0.067, 0.614, 0.975, 0.808], it appears to decrease slightly from Q1 to Q4, then dramatically increase in Q5 and decrease again in Q6.\nBased on the above thought, the answer is:\nBased on the line chart, Revenue demonstrates the most significant change over the quarters. The line plot for Revenue exhibits a sharp increase from Q4 to Q5, which is more pronounced compared to gradual inclines and declines in Expenses and Net Profit." } ], "id_raw": 640 }, { "id": "YzKCj4SEwPuZb9tu", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_0.png", "conversations": [ { "from": "human", "value": "\nWhat's the change in Net Profit from Q1 to Q6?" }, { "from": "gpt", "value": "To find the change in Net Profit from Q1 to Q6, I need to look at the coordinates of the Net Profit line {Net Profit}[0.067, 0.614, 0.975, 0.808]. The y-coordinate value at Q1 is 1.5 and at Q6 is 2.8. So, the change is |2.8 - 1.5| = 1.3.\nBased on the above thought, the answer is:\nThe change in Net Profit from Q1 to Q6 is 1.3." } ], "id_raw": 640 }, { "id": "Zq3CRpSgHfvGju42", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_1.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data into a table, using the time in hours as the columns and the machines as the rows?" }, { "from": "gpt", "value": "To generate the required table, I initially identify {the x-axis labels}[0.064, 0.821, 0.103, 0.868] to [0.932, 0.821, 0.981, 0.868] that represent the column headers or time in hours. I then recognize {the legend texts of the machines}[0.148, 0.173, 0.238, 0.219] to [0.148, 0.312, 0.238, 0.359] as the row labels representing different machines. To populate the values, I locate {the line points for Machine A}[0.084, 0.443] to [0.957, 0.218], {the line points for Machine B}[0.084, 0.529] to [0.957, 0.477], and {the line points for Machine C}[0.084, 0.599] to [0.957, 0.512] and extract the values for each machine at the respective time points.\nBased on the above thought, the answer is:\nMachine,0.00,1.00,2.00,3.00,4.00,5.00,6.00,7.00,8.00,9.00,10.00\nMachine A,20,24,30,31,31,30,29,30,33,32,33\nMachine B,15,17,21,22,20,19,17,19,20,19,18\nMachine C,11,13,16,15,14,12,11,15,18,17,16" } ], "id_raw": 641 }, { "id": "ReBTH3T0sbHQaVgb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the progression of each machine's efficiency over time?" }, { "from": "gpt", "value": "To understand the progression of each machine's efficiency over time, I look at each machine\u2019s line on the graph. Looking at {the line for Machine A}[0.084, 0.218, 0.957, 0.443], the efficiency initially increases, reaches a peak at the 4th hour, and then declines slightly. It then sees some growth towards the 8th hour and finally a slight decline again around the 10th hour.\nRegarding {the line for Machine B}[0.084, 0.408, 0.957, 0.529], there is a steady increase in efficiency until the 3rd hour, followed by some fluctuations, then a steadily improving efficiency around the 8th hour, and finally some decline.\nLastly, analyzing {the line for Machine C}[0.084, 0.477, 0.957, 0.599], the efficiency initially rises until the 2nd hour, then sees a dip followed by additional growth in efficiency up to the 9th hour, and finally, the efficiency starts to decrease again.\nBased on the above thought, the answer is:\nFrom the plot, it can be inferred that all three machines have varied efficiency progression over time. Machine A has its peak efficiency around the 3rd hour and then shows a pattern of growth and decline. Machine B shows a general tendency of growth in efficiency but encounters some fluctuations. Machine C exhibits an initial growth, followed by a dip and another period of growth before eventually declining. These efficiency patterns suggest that the effectiveness of these machines can change considerably over different operating hours." } ], "id_raw": 641 }, { "id": "RStivsMEO5ydVSuq", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_1.png", "conversations": [ { "from": "human", "value": "\nHow much has the efficiency of Machine B increased from the 1st to the 8th hour?" }, { "from": "gpt", "value": "To calculate the increase in efficiency for Machine B, I will need to use {the point representing the 1st hour}[0.171, 0.495] and {the point representing the 8th hour}[0.782, 0.443] for Machine B. The efficiency at the 1st hour is 17 and at the 8th hour is 20. The increase in efficiency is then 20 - 17 = 3.\nBased on the above thought, the answer is:\n3." } ], "id_raw": 641 }, { "id": "AYwYg1WDjStTcfah", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_2.png", "conversations": [ { "from": "human", "value": "\nCan you turn the line chart data into a tabulated format with Years as columns and Development Areas (Urban and Rural) as rows?" }, { "from": "gpt", "value": "To create the table from the line chart data, I first identify {the x-axis}[0.507, 0.915, 0.558, 0.963] which represents the columns, and {the legend}[0.116, 0.119, 0.465, 0.329] as rows denoting Urban and Rural development. To populate the table, I find all the points {for Rural}[0.106, 0.099, 0.959, 0.310] and {for Urban}[0.106, 0.841, 0.959, 0.842] corresponding to each Year and extract values.\nBased on the above thought, the answer is:\nDevelopment Areas,1980,1985,1990,1995,2000,2005,2010,2015\nPrecipitation (mm),1200,1180,1250,1300,1400,1450,1600,1650\nTemperature Increase (\u00b0C),0.05,0.07,0.09,0.15,0.25,0.35,0.6,0.8\nSea Level Rise (cm),0.1,0.2,0.3,0.6,1.0,1.5,3.0,4.5" } ], "id_raw": 642 }, { "id": "05avJ0QTqh1abmaD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_2.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which environmental factor appears to be impacted most significantly by climate change? Explain your reasoning." }, { "from": "gpt", "value": "To discern which environmental factor appears to be impacted the most, I need to analyze the lines for each. Observing the {line for Precipitation}[0.106, 0.099, 0.959, 0.310], there is a steady increase from 1200 mm in 1980 to 1650 mm in 2015. In the {line for Temperature Increase}[0.106, 0.841, 0.959, 0.842], the increments are small but remain consistent over the years, only reaching 0.8\u00b0C by 2015. In contrast, the {line for Sea Level Rise}[0.106, 0.840, 0.959, 0.842] shows the change to be least in the early years but the increase becomes significantly larger as the years pass, reaching a staggering 4.5 cm by 2015.\nBased on the above thought, the answer is:\nAccording to the trends revealed by the line chart, the Sea Level Rise appears to be the environmental factor that is most significantly impacted by climate change. While all three factors display noticeable changes over the period, the increase in the sea level is highly significant compared to the other factors, rising from just 0.1 cm in 1980 to 4.5 cm in 2015. This indicates a faster rate of change, suggesting that the sea level is responding more drastically to the impacts of climate change." } ], "id_raw": 642 }, { "id": "6RNS8OUkIgdsD8oV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the incremental change in Temperature Increase (\u00b0C) from the year 2000 to 2005?" }, { "from": "gpt", "value": "To calculate the incremental change in Temperature Increase (\u00b0C), I need to identify the {point in 2000 for Temperature Increase}[0.593, 0.842] which corresponds to 0.25\u00b0C and {the point in 2005 for Temperature Increase}[0.715, 0.842] which corresponds to 0.35\u00b0C. The difference between these two amounts to the incremental change.\nBased on the above thought, the answer is:\n0.1\u00b0C" } ], "id_raw": 642 }, { "id": "vlWRU8arGp2DJtfr", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_3.png", "conversations": [ { "from": "human", "value": "\nCould you help formalizing the data from this line chart into a table, with the different doses as columns and the treatment types as the rows?" }, { "from": "gpt", "value": "Absolutely, to create this tabular data, we initiate with {the x-axis tick labels}[[0.078, 0.821, 0.089, 0.868], [0.206, 0.821, 0.217, 0.868], [0.333, 0.821, 0.344, 0.868], [0.460, 0.821, 0.471, 0.868], [0.588, 0.821, 0.599, 0.868], [0.715, 0.821, 0.726, 0.868], [0.843, 0.821, 0.854, 0.868], [0.970, 0.821, 0.981, 0.868]], which will be our column headers representing the different doses. Now, {the legend texts}[[0.859, 0.181, 0.934, 0.241], [0.859, 0.269, 0.957, 0.329], [0.859, 0.357, 0.933, 0.417]] will represent our row labels i.e., the different treatment types. The different dose responses corresponding to each treatment, we pick these from {the line points} like: for Control [[0.084, 0.646], [0.211, 0.503], [0.339, 0.360], [0.466, 0.503], [0.593, 0.675], [0.721, 0.703], [0.848, 0.732], [0.976, 0.746]], for Antibiotic [[0.084, 0.718], [0.211, 0.575], [0.339, 0.431], [0.466, 0.145], [0.593, 0.503], [0.721, 0.617], [0.848, 0.689], [0.976, 0.732]], and for Steroid [[0.084, 0.575], [0.211, 0.360], [0.339, 0.217], [0.466, 0.145], [0.593, 0.360], [0.721, 0.503], [0.848, 0.575], [0.976, 0.646]] at the respective doses.\nBased on the above thought, the answer is:\nTreatment,Dose_1,Dose_2,Dose_3,Dose_4,Dose_5,Dose_6,Dose_7,Dose_8\nControl,10,20,30,20,8,6,4,3\nAntibiotic,5,15,25,45,20,12,7,4\nSteroid,15,30,40,45,30,20,15,10" } ], "id_raw": 643 }, { "id": "xVaPhlG7w2yl01kM", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_3.png", "conversations": [ { "from": "human", "value": "\nWhich treatment shows a progressive increase in response with every dose?" }, { "from": "gpt", "value": "To find out which treatment shows an increase in response with every dose, I need to examine the points on the lines corresponding to different treatments. Looking at the lines for {Control}[0.084, 0.360, 0.976, 0.746], {Antibiotic}[0.084, 0.145, 0.976, 0.732], and {Steroid}[0.084, 0.145, 0.976, 0.646], it seems only the line for Steroid shows a consistent increase in response in the beginning. However, the response level peaks at Dose 4 and starts to decline after that. Hence, none of the treatments show a consistent increase in response with every dose.\nBased on the above thought, the answer is:\nNone of the treatments show a progressive increase in response with every dose. All treatments show an initial increase followed by a decrease at higher doses." } ], "id_raw": 643 }, { "id": "Lj7lyliUgPRxYDXS", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_3.png", "conversations": [ { "from": "human", "value": "\nHow much did the response vary for Antibiotics treatment from the initial to the final dose?" }, { "from": "gpt", "value": "To answer this, I need to get the response for Antibiotic treatment for the initial dose and the final dose. I could locate the initial response for Antibiotic treatment at {Dose_1}[0.084, 0.821] to be 5 and the final response for Antibiotic treatment at {Dose_8}[0.976, 0.821] to be 4. The variance is simply the difference of these responses, which can be calculated as initial dose response minus the final dose response.\nBased on the above thought, the answer is:\n1 (5-4)." } ], "id_raw": 643 }, { "id": "iA20fJPa8G3HTvyu", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_4.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data about tourism and hospitality industry income into a structured table for me?" }, { "from": "gpt", "value": "Sure. First, I'm studying {the labels on the x-axis}[0.041, 0.893, 0.101, 0.921],[0.168, 0.893, 0.228, 0.921],[0.294, 0.893, 0.354, 0.921],[0.420, 0.893, 0.480, 0.921],[0.546, 0.893, 0.606, 0.921],[0.672, 0.893, 0.733, 0.921],[0.799, 0.893, 0.859, 0.921],[0.925, 0.893, 0.985, 0.921], which correspond to different Quarters. Then, I'm identifying {the legend texts}[0.822, 0.109, 0.940, 0.145],[0.822, 0.161, 0.900, 0.197],[0.822, 0.214, 0.924, 0.250] that represent the different sectors in the industry. The data points for each sector at the respective quarters are represented by {the lines}[0.071, 0.556, 0.955, 0.754],[0.071, 0.118, 0.955, 0.476],[0.071, 0.238, 0.955, 0.436]. From these coordinates, I can extract the income values for each sector across the quarters.\nBased on the above thought, the answer is:\nQuarters,Hotel Industry,Air Travel,Tour Agency\n2020-Q1,350000,900000,750000\n2020-Q2,200000,600000,650000\n2020-Q3,150000,500000,550000\n2020-Q4,200000,650000,600000\n2021-Q1,250000,700000,650000\n2021-Q2,300000,850000,700000\n2021-Q3,350000,900000,750000\n2021-Q4,400000,950000,800000" } ], "id_raw": 644 }, { "id": "rDHoODBcWCuWknIc", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_4.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, how would you describe the overall performance of the Air Travel sector throughout the given period?" }, { "from": "gpt", "value": "To analyze the performance of the Air Travel sector, I need to consider {the line points for Air Travel}[0.071, 0.158], [0.198, 0.397],[0.324, 0.476],[0.450, 0.357],[0.576, 0.317],[0.702, 0.198],[0.829, 0.158],[0.955, 0.118]. Initially, there's a decrease in income from 2020-Q1 to 2020-Q3, indicating a downturn. This is followed by a recovery from 2020-Q3 to 2020-Q4 but then income drops again from 2020-Q4 through to 2021-Q2. It shows slight improvement in income through to 2021-Q4 but overall, the Air Travel sector seems to have had significant challenges as there were more quarters of declining income than those showing growth.\nBased on the above thought, the answer is:\nThe overall performance of the Air Travel sector throughout the given period can be described as challenging. The sector saw a decline in income in the majority of quarters, despite a few periods of recovery. This trend suggests that the sector struggled to maintain consistent growth, possibly due to external factors like a downturn in the general tourism and hospitality industry or perhaps more specific challenges such as global travel restrictions." } ], "id_raw": 644 }, { "id": "1qGmm8bB7jc8Hvw0", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_4.png", "conversations": [ { "from": "human", "value": "\nHow much did the income of the Hotel Industry increase from 2020-Q4 to 2021-Q4?" }, { "from": "gpt", "value": "To find the increase in income for the Hotel Industry, I firstly locate {the line points for Hotel Industry}[0.450, 0.714], [0.955, 0.556] which represent the income at 2020-Q4 and 2021-Q4 respectively. By extracting the corresponding values from the points, we get 200000 (for 2020-Q4) and 400000 (for 2021-Q4). The increase can be calculated by subtracting the income of 2020-Q4 from that of 2021-Q4, which is 400000 - 200000 = 200000.\nBased on the above thought, the answer is:\n200000." } ], "id_raw": 644 }, { "id": "W4UUva20Eog9525t", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the plotted line data on the chart into a table with 'Years' as columns and 'Climate Trend' as rows?" }, { "from": "gpt", "value": "Yes, I can convert the chart data into tabular form. The column headers or 'Years' are represented by {the x-axis tick labels}[0.072, 0.821, 0.117, 0.868], [0.196, 0.821, 0.240, 0.868], [0.320, 0.821, 0.364, 0.868], [0.443, 0.821, 0.487, 0.868], [0.567, 0.821, 0.611, 0.868], [0.690, 0.821, 0.734, 0.868], [0.814, 0.821, 0.858, 0.868], [0.937, 0.821, 0.981, 0.868], while the row labels or 'Climate Trends' can be implied from the legend {the legend labels}[0.676, 0.181, 0.899, 0.241], [0.676, 0.269, 0.916, 0.329], [0.676, 0.357, 0.941, 0.417]. To populate the table with values, I identified the line points for each trend from {the line points}[0.095, 0.309], [0.218, 0.296], ..., [0.959, 0.159], [0.095, 0.785], ..., [0.959, 0.783], and [0.095, 0.789], ..., [0.959, 0.787].\nBased on the above thought, the answer is:\nClimate Trend,1990,1995,2000,2005,2010,2015,2020,2025\nCO2 Emissions (ppm),350,360,370,380,400,420,440,460\nSea Level Rise (inches),3.1,3.2,3.4,3.6,3.7,3.9,4.0,4.2\nTemperature Change (\u00b0C),0.4,0.6,0.8,1.0,1.2,1.4,1.6,1.8" } ], "id_raw": 646 }, { "id": "B5exdjYZz8wbUh4r", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_6.png", "conversations": [ { "from": "human", "value": "\nGiven the data, can we infer a relationship of CO2 Emissions with Global Temperature Change and Sea Level Rise?" }, { "from": "gpt", "value": "From the lines for CO2 Emissions{the line for CO2 Emissions}[0.095, 0.159, 0.959, 0.309], Temperature Change{the line for temperature change}[0.095, 0.787, 0.959, 0.789], and Sea Level Rise{the line for sea level rise}[0.095, 0.783, 0.959, 0.785], we can see a general upward trend in all three aspects over the years shown in the chart. This suggests that as CO2 Emissions increase, both the Global Temperature Change and Sea Level Rise also increase at a similar rate.\nBased on the above thought, the answer is:\nYes, based on the trends displayed in the line chart, it can be inferred that there is a relationship of CO2 Emissions with both Global Temperature Change and Sea Level Rise. The common upward trend across all three aspects suggests that increase in CO2 emissions correlates with an increase in global temperature and sea level, which is consistent with known scientific facts about climate change. However, precise correlation coefficients cannot be determined from a line chart without further statistical analysis." } ], "id_raw": 646 }, { "id": "MU1oOOfZxRZIDoIN", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in global CO2 Emissions from 1990 to 2025 according to the chart?" }, { "from": "gpt", "value": "To find the total increase in CO2 emissions from 1990 to 2025, we need to locate the line points for CO2 Emissions from the legend {the legend CO2 Emissions}[0.676, 0.181, 0.899, 0.241]. After obtaining the points, we notice that the first point in 1990 {the point for 1990}[0.095, 0.309] indicates a value of 350 and the last point in 2025 {the point for 2025}[0.959, 0.159] indicates a value of 460. We then subtract the first value from the last one to get the total increase.\nBased on the above thought, the answer is:\n110." } ], "id_raw": 646 }, { "id": "KViYNTF7utY2lBM0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_7.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart and organize it into a structured table format with Longitude as columns and Latitude as rows?" }, { "from": "gpt", "value": "First, I'll identify {the x-axis tick labels}[[0.072, 0.821, 0.128, 0.868], [0.195, 0.821, 0.250, 0.868], [0.317, 0.821, 0.372, 0.868], [0.439, 0.821, 0.495, 0.868], [0.565, 0.821, 0.614, 0.868], [0.687, 0.821, 0.737, 0.868], [0.809, 0.821, 0.859, 0.868], [0.932, 0.821, 0.981, 0.868]], that represent Longitude values. Then, {the line points}[[0.100, 0.625], [0.222, 0.542], [0.345, 0.706], [0.467, 0.747], [0.589, 0.768], [0.712, 0.747], [0.834, 0.542], [0.957, 0.172]] will serve as Latitude values for the corresponding Longitudes. The structure of the table will have Longitude as columns and Latitude as rows.\nBased on the above thought, the answer is:\nLongitude,-1.934,-1.387,-0.740,-0.165,0.319,0.921,1.674,2.538\nLatitude,4.012,6.049,2.035,1.018,0.51,1.021,6.036,15.091" } ], "id_raw": 647 }, { "id": "5jXTbNKisrOa3Y1K", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_7.png", "conversations": [ { "from": "human", "value": "\nCan you explain the trend for the geographic data plot upon observing the line chart?" }, { "from": "gpt", "value": "The trend for geographic data plot can be understood by examining {the line plot}[[0.100, 0.172, 0.957, 0.768]]. It shows a significant variation of Latitude values at different Longitudes. Starting from a Latitude of 4.012 at -1.934 Longitude, the line shows a significant rise reaching maximum around -1.387 Longitude with a value of 6.049. Then it takes a sharp decline to 2.035 at -0.740 Longitude followed by a slow rise until it reaches 1.021 at 0.921 Longitude. Then, it takes a significant upward leap to 6.036 at 1.674 Longitude and then rises sharply to a maximum value of 15.091 at the Longitude of 2.538. This indicates vast geographic diversity in the data.\nBased on the above thought, the answer is:\nThe geographic data plot depicted in the line chart exhibits considerable variation across different Longitudes. The Latitude values rise, fall, and rise sharply again, indicating a diverse geographic range." } ], "id_raw": 647 }, { "id": "7MOaYOJLf5oede05", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of Latitude values?" }, { "from": "gpt", "value": "To find the range of Latitude values, I first need to locate {the line points}[[0.100, 0.625], [0.222, 0.542], [0.345, 0.706], [0.467, 0.747], [0.589, 0.768], [0.712, 0.747], [0.834, 0.542], [0.957, 0.172]] and extract the corresponding Latitude values: 4.012, 6.049, 2.035, 1.018, 0.51, 1.021, 6.036, 15.091. The maximum value is 15.091 and the minimum value is 0.51. The difference between these two values, or the range, is 15.091 - 0.51 = 14.581.\nBased on the above thought, the answer is:\n14.581." } ], "id_raw": 647 }, { "id": "sGZANl6MuwaNShfk", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_9.png", "conversations": [ { "from": "human", "value": "\nCould you convert the chart into a table format where each row is a Social Media platform and each column is Time?" }, { "from": "gpt", "value": "To convert the chart into a structured table, I'll start by identifying the column headers which are the labels on the {x-axis}[0.084, 0.789, 0.976, 0.821]: 0, 1, 2, 3, 4, 5, 6, 7. These represent the Time data in our table. Then I locate the row headers which are provided by the {legend texts}[0.851, 0.181, 0.949, 0.241], [0.851, 0.269, 0.921, 0.329], [0.851, 0.357, 0.957, 0.417], standing for different Social Media platforms: Facebook, Twitter, Instagram. To fill in the values in the table, I pinpoint the corresponding data points in the line of each social media platform, for example the {line for Facebook}[0.084, 0.170, 0.976, 0.340] whose usage rates are 80, 75, 71, 70, 67, 65, 61, 58 at each time point respectively.\nBased on the above thought, the answer is:\nSocial Media,0,1,2,3,4,5,6,7\nFacebook,80,75,71,70,67,65,61,58\nTwitter,45,46,52,58,64,70,75,79\nInstagram,35,37,38,42,51,55,57,61" } ], "id_raw": 649 }, { "id": "Z3FIGu39FSsNwgKd", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_9.png", "conversations": [ { "from": "human", "value": "\nBased on the trends depicted in the graph, which social media platform displays the most growth over time? Provide your justification." }, { "from": "gpt", "value": "To determine which social media platform has exhibited the most growth over time, I'll analyze the changes in the y-values for each platform's line from Time 0 to Time 7. Observing {the line of Facebook}[0.084, 0.170, 0.976, 0.340], I see a decrease from 80% to 58%, thus it's experiencing a downtrend. Upon inspecting {the line of Twitter}[0.084, 0.178, 0.976, 0.441], the usage jumps from 45% to 79% indicating a substantial uptrend. Lastly, looking at {the line of Instagram}[0.084, 0.317, 0.976, 0.518], the usage increases from 35% to 61% which represents a positive but lesser growth compared to Twitter.\nBased on the above thought, the answer is:\nBased on the trends depicted in the graph, Twitter has displayed the most growth over the Time period. It has experienced a significant uptrend from a usage rate of 45% to 79%, which is the largest increase among the three social media platforms." } ], "id_raw": 649 }, { "id": "4K0yoVhRrzDRLikE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in usage of Instagram from Time 0 to Time 7?" }, { "from": "gpt", "value": "To find out the increase in usage of Instagram, I need to check the usage rate at Time 0 and Time 7. I locate the {first data point for Instagram}[0.084, 0.518] and find that the usage is 35, then I go to the {last data point for Instagram}[0.976, 0.317] and see that the usage is 61. The increase in usage would be the value at Time 7 minus the value at Time 0, which is calculated as 61 - 35 = 26.\nBased on the above thought, the answer is:\n26%" } ], "id_raw": 649 }, { "id": "pjkNubmHueWsWEmB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_10.png", "conversations": [ { "from": "human", "value": "\nCould you convert the bar chart into a structured table presenting trade volumes in different regions for high and low volumes?" }, { "from": "gpt", "value": "From the chart, I first locate {the bar labels}[0.055, 0.457, 0.055, 0.865, 0.055, 0.196, 0.055, 0.603, 0.055, 0.724] as the row labels representing the different regions. Then, I locate {the legend indicators}[[0.754, 0.159, 0.949, 0.439]] to identify the two categories - High Volume and Low Volume. Lastly, I identify {the bar values}[[0.066, 0.430, 0.581, 0.787, 0.066, 0.169, 0.371, 0.577, 0.066, 0.698]] for each category in the respective regions.\nBased on the above thought, the answer is:\nRegion, Low Volume, High Volume\nSouth America, 500, 1000\nNorth America, 550, 1100\nEurope, 600, 1200\nAsia, 1650, 3050\nAfrica, 400, 800" } ], "id_raw": 650 }, { "id": "EnqszZjrXaAw6Jye", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_10.png", "conversations": [ { "from": "human", "value": "\nHow would you interpret the regional distribution of trade volumes, as shown by the horizontal bar chart?" }, { "from": "gpt", "value": "Analyzing {the bars for all regions}[[0.063, 0.447, 0.469, 0.481], [0.063, 0.500, 0.574, 0.534], [0.063, 0.186, 0.372, 0.221], [0.063, 0.586, 0.316, 0.619], [0.063, 0.238, 0.459, 0.271], [0.063, 0.333, 0.371, 0.366], [0.063, 0.384, 0.470, 0.417], [0.063, 0.708, 0.732, 0.742], [0.063, 0.281, 0.334, 0.314], [0.063, 0.635, 0.813, 0.669]], the chart shows a varying range of values across different regions. Combining this with the details specified {in the legend}[[0.754, 0.159, 0.949, 0.439]], it can be observed that Asia clearly stands out with a far higher trade volume in both High and Low Volume categories, indicating more intense activity. Europe and North America follow, with moderate levels of trade volumes. South America and Africa seem to differ only slightly, showing somewhat lower trade volumes, with Africa being at the lower end of the scale.\nBased on the above thought, the answer is:\nThe chart presents a wide regional disparity in trade volumes. Asia is illustrated as the largest trading region by far, consistent for both Low and High volume categories, indicating intense trading activity. Europe and North America follow, with moderately high volumes. South America and Africa exhibit less trading activity with Africa having the lowest volumes. The graph underscores Asia's significant role in global trade and the need for other regions to increase their trade activities." } ], "id_raw": 650 }, { "id": "w2HMq8hqcMHLzWwZ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the total trade volume for Asia across both Low and High volume categories?" }, { "from": "gpt", "value": "To calculate the total trade volume for Asia, I extract {the bar value for Low Volume for Asia}[0.561, 0.694, 0.726, 0.733] which is 1650, and {the bar value for High Volume for Asia}[0.346, 0.743, 0.865, 0.771] which is 3050. The total trade volume for Asia would then be the sum of these two values, i.e. 1650 + 3050 = 4700.\nBased on the above thought, the answer is:\n4700." } ], "id_raw": 650 }, { "id": "W8BfPrTiOdNs4OzO", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_11.png", "conversations": [ { "from": "human", "value": "\nCould you please translate the given plot about fitness and sports health trends into a structured table format with years as rows and types of activities as columns?" }, { "from": "gpt", "value": "To translate the plot data into a table format, I first take note of {the x-axis labels}[0.051, 0.821, 0.981, 0.868], these are the row labels representing the years. Then, I recognize {the legend items}[0.798, 0.159, 0.949, 0.439] as the column headers indicating the three different types of activities: Yoga, Cycling and Cardio. To fill in the values, I need to identify the coordinates of {the points}[0.073, 0.507, 0.959, 0.413] for each activity type at the respective years and use these to extract the data values.\nBased on the above thought, the answer is:\nYear,Yoga,Cycling,Cardio \n2010,3,2,1.5\n2011,5,4,6.8\n2012,6,3.5,7.2\n2013,5.5,3.7,8\n2014,4,5,9\n2015,4.5,5.5,8.5\n2016,6,6,9\n2017,4,7,9.5" } ], "id_raw": 651 }, { "id": "aOmRlYpXvOP7XpwF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_11.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which exercise trend experienced the most fluctuations in popularity from 2010 to 2017? Explain your reasoning." }, { "from": "gpt", "value": "To determine which exercise trend had the most fluctuations, I need to examine the changes in the line plots for each activity type. By analyzing {the line plot for Yoga}[0.073, 0.225, 0.959, 0.507], I see a changing trend from 3 to 5 to 6, then decreasing to 5.5 and 4, then increasing to 4.5 and 6, and finally decreasing to 4, representing significant volatility. For {Cycling}[0.073, 0.131, 0.959, 0.601], the trend is 2, 4, 3.5, 3.7, 5, 5.5, 6, and 7, showing subtle fluctuations, but they aren't as significant as those of Yoga. Looking at {Cardio}[0.073, -0.104, 0.959, 0.648], a constant growth pattern is observed, indicating relatively low fluctuations.\nBased on the above thought, the answer is:\nFrom 2010 to 2017, Yoga experienced the most fluctuations in popularity. The line plot for Yoga exhibits significant ups and downs, indicating that the popularity of Yoga experienced notable fluctuations during this period. The fluctuations in Cardio and Cycling are comparatively less." } ], "id_raw": 651 }, { "id": "X6JSwl2OUZNzqZlz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the gain from the lowest to the highest popularity level for Cardio between the years 2010 and 2017?" }, { "from": "gpt", "value": "As for determining the gain in popularity for Cardio, I have to identify {the line points representing Cardio}[0.073, 0.648, 0.959, -0.104] and extract the corresponding values: 1.5, 6.8, 7.2, 8, 9, 8.5, 9, 9.5. The lowest value is 1.5 in {2010}[0.073, 0.789] and the highest value is 9.5 in {2017}[0.959, 0.789]. Thus, the popularity gain for Cardio is 9.5 - 1.5 = 8.\nBased on the above thought, the answer is:\n8" } ], "id_raw": 651 }, { "id": "90SLrnta3NNk6W8j", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_12.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the data illustrating the residential, business and public facilities development over the years?" }, { "from": "gpt", "value": "Firstly, I identify the columns {Years in x-axis labels}[0.051, 0.821, 0.095, 0.868], [0.177, 0.821, 0.221, 0.868], [0.304, 0.821, 0.348, 0.868], [0.431, 0.821, 0.475, 0.868], [0.557, 0.821, 0.601, 0.868], [0.684, 0.821, 0.728, 0.868], [0.811, 0.821, 0.855, 0.868], [0.937, 0.821, 0.981, 0.868]. And the rows represent different kinds of development: {Residential Development}[0.680, 0.181, 0.941, 0.241], {Business Development}[0.680, 0.269, 0.918, 0.329], and {Public Facilities}[0.680, 0.357, 0.837, 0.417]. Then, I look into the data points of these developments over the years located in {line points for Residential Development}[0.073, 0.701], [0.199, 0.657], [0.326, 0.570], [0.453, 0.394], [0.579, 0.219], [0.706, 0.482], [0.833, 0.526], [0.959, 0.570], {line points for Business Development}[0.073, 0.736], [0.199, 0.719], [0.326, 0.684], [0.453, 0.614], [0.579, 0.394], [0.706, 0.526], [0.833, 0.570], [0.959, 0.614] and {line points for Public Facilities}[0.073, 0.745], [0.199, 0.741], [0.326, 0.732], [0.453, 0.728], [0.579, 0.723], [0.706, 0.719], [0.833, 0.715], [0.959, 0.710].\nBased on the above thought, the answer is:\nYears,Residential Development,Business Development,Public Facilities\n2000,1.0,0.6,0.5\n2005,1.5,0.8,0.55\n2010,2.5,1.2,0.65\n2015,4.5,2.0,0.7\n2020,6.5,4.5,0.75\n2025,3.5,3.0,0.8\n2030,3.0,2.5,0.85\n2035,2.5,2.0,0.9" } ], "id_raw": 652 }, { "id": "YCFPLASQ9dWw3ebC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_12.png", "conversations": [ { "from": "human", "value": "\nFrom the data, which area witnessed the greatest fluctuation in their growth rates over the years? Please explain your reasoning." }, { "from": "gpt", "value": "To assess which sector has had the greatest fluctuation in its growth rates, I examine the significant changes in each line chart. For {Residential Development}[0.073, 0.219, 0.959, 0.701], there is an initial steep rise and then a noticeable decline. {Business Development}[0.073, 0.394, 0.959, 0.736] has a gradual increase, then a sharp rise before getting stable. {Public Facilities}[0.073, 0.710, 0.959, 0.745] growth rate has a mild but steady progress with time.\nBased on the above thought, the answer is:\nThe Residential Development sector has witnessed the greatest fluctuation in their growth rates over the years. The line chart for residential development shows a notable upward trend from 2000 to 2020 followed by a sharp decrease. This significant rise and fall indicate high variability in the growth rates of the residential development sector over this period." } ], "id_raw": 652 }, { "id": "IMgAqSyIEWAmgqN2", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_12.png", "conversations": [ { "from": "human", "value": "\nWhat's the overall growth rate for Business Development from 2000 to 2035?" }, { "from": "gpt", "value": "To get the overall growth rate for Business Development from 2000 to 2035, I need to subtract the first value {at 2000}[0.073, 0.736] from the last value {at 2035}[0.959, 0.614]. The growth rate can be calculated as (2.0 - 0.6) / (2035 - 2000).\nBased on the above thought, the answer is:\n0.045." } ], "id_raw": 652 }, { "id": "flgGz8FsdU5wz5Fq", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_15.png", "conversations": [ { "from": "human", "value": "\nBased on the information from the line chart, can you construct a table format with Years as columns and Trade Volume, GDP Growth, and Gini Index as rows?" }, { "from": "gpt", "value": "To create a table from the line chart data, I'll first look at {the x-axis ticks}[0.127, 0.789, 0.959, 0.868] that represent the column headers or years. Next, I analyze the line points for each data type: {Trade Volume}[0.127, 0.162, 0.959, 0.648], {GDP Growth}[0.127, 0.789, 0.959, 0.789], and {Gini Index}[0.127, 0.789, 0.959, 0.789], which correspond to the individual rows. The values at each point are then used to populate the cells of the table.\nBased on the above thought, the answer is:\nYears, 1990, 2000, 2010, 2020, 2030, 2040, 2050\nTrade Volume, 45000, 60000, 80000, 120000, 150000, 180000, 200000\nGDP Growth, 8, 7.6, 7, 5, 4.5, 4.2, 4\nGini Index, 0.31, 0.35, 0.4, 0.45, 0.47, 0.48, 0.5" } ], "id_raw": 655 }, { "id": "6N2EOdI4xbJn38Hz", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_15.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, how would you describe the observed trends in Trade Volume, GDP Growth, and Gini Index over the years?" }, { "from": "gpt", "value": "To answer this, I need to look at the lines that represent each of these parameters. The {line representing Trade Volume}[0.127, 0.162, 0.959, 0.648] shows a steady increase from 45000 in 1990 to 200000 in 2050. This implies a consistent growth in international trade over time.\nThe {line representing GDP Growth}[0.127, 0.789, 0.959, 0.789] shows a decrease from 8 in 1990 down to 4 in 2050. This infers a reduction in global GDP growth rates, possibly due to various economic and geopolitical factors.\nThe {line for Gini Index}[0.127, 0.789, 0.959, 0.789] portrays an increase from 0.31 in 1990 to 0.5 in 2050. This indicates a rise in income equality globally, which could be due to policy decisions or economic trends.\nBased on the above thought, the answer is:\nLooking at the chart, Trade Volume presents an increasing trend over the years, suggesting that global trade has been consistently expanding from 1990 to 2050. The GDP Growth, on the other hand, shows a decreasing trend, indicating slowing global growth rates across these years. Finally, the Gini Index exhibits a rising trend, suggesting increasing income inequality over this period." } ], "id_raw": 655 }, { "id": "jT60eZW5jyv5Lh1I", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_15.png", "conversations": [ { "from": "human", "value": "\nHow much has the Trade Volume increased from the year 1990 to 2050?" }, { "from": "gpt", "value": "To determine the increase in Trade Volume from 1990 to 2050, I need to locate the {Trade Volume in 1990}[0.127, 0.648] and the {Trade Volume in 2050}[0.959, 0.162]. Extracting the values, I see that the Trade Volume was 45000 in 1990 and 200000 in 2050. The difference between these values gives the increase in Trade Volume over this period.\nBased on the above thought, the answer is:\n155000." } ], "id_raw": 655 }, { "id": "cQ6zyEaSgQ3V18jP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_17.png", "conversations": [ { "from": "human", "value": "\nCan you derive a table for the years in the chart against the corresponding production quantity for each of Corn, Wheat and Barley?" }, { "from": "gpt", "value": "To generate the requested table, I'll reference the {x-axis labels}[0.076, 0.893, 0.986, 0.921] which signifies the years. Next, by taking the points of three line plots, i.e. {the Corn line plot}[0.093, 0.079, 0.968, 0.324], {the Wheat line plot}[0.093, 0.611, 0.968, 0.677], and {the Barley line plot}[0.093, 0.783, 0.968, 0.815], I can extract the corresponding production quantity for each year.\nBased on the above thought, the answer is:\nYear,Corn_production,Wheat_production,Barley_production\n2010,34587,12345,5678\n2011,35345,12467,5600\n2012,36000,12789,5300\n2013,38500,13200,5200\n2014,39000,13500,5100\n2015,41200,14000,5000\n2016,43200,14500,4800\n2017,45000,15200,4500\n2018,47000,15800,4300\n2019,48000,16000,4000\n2020,50000,16500,3700" } ], "id_raw": 657 }, { "id": "sCum84BYxgQX5k27", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_17.png", "conversations": [ { "from": "human", "value": "\nBased on the line charts, which food item had the most consistent increase in production over the given period?" }, { "from": "gpt", "value": "To answer this question, I need to analyze the slopes of the {Corn line plot}[0.093, 0.079, 0.968, 0.324], {Wheat line plot}[0.093, 0.611, 0.968, 0.677] and {Barley line plot}[0.093, 0.783, 0.968, 0.815]. Going by the slopes, the Corn line plot seems to be increasing constantly over time without any decrease.\nBased on the above thought, the answer is:\nCorn had the most consistent increase in production over the given period. The line chart demonstrates that corn production quantity consistently increased each year, marking a steady and robust growth trajectory." } ], "id_raw": 657 }, { "id": "pw669Ff2NAUN3Zga", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_17.png", "conversations": [ { "from": "human", "value": "\nHow much has the production quantity of Barley changed from the year 2013 to 2020?" }, { "from": "gpt", "value": "To calculate the change in production quantity of Barley, I first identify the point for Barley in the year 2013 from {the Barley line plot}[0.093, 0.783, 0.968, 0.815], which yields value '5200'. I then find the corresponding point in the year 2020, yielding value '3700'. The difference between these two values will give the required production quantity change.\nBased on the above thought, the answer is:\n-1500" } ], "id_raw": 657 }, { "id": "kjNESjmv6a47eKCU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_18.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart information into a table showing the Year, Grains_Production, Fruits_Imports, and Vegetables_Export?" }, { "from": "gpt", "value": "To convert the line chart data into tabular format, I locate {the x-axis}[[0.072, 0.821, 0.981, 0.868]] as the Year column. Then, I identify the three lines, {Grains_Production}[0.095, 0.620, 0.959, 0.699], {Fruits_Imports}[0.095, 0.577, 0.959, 0.718], and {Vegetables_Export}[0.095, 0.429, 0.959, 0.507] to represent the other columns respectively. At each x-axis tick, I extract the corresponding y-value for each line to fill the table.\nBased on the above thought, the answer is:\nYear,Grains_Production,Fruits_Imports,Vegetables_Export\n1990,120,50,200\n1995,105,55,215\n2000,98,60,227\n2005,92,80,235\n2010,91,90,245\n2015,82,110,248\n2020,75,135,255\n2025,64,150,245" } ], "id_raw": 658 }, { "id": "2gxGGzvn7uwJlC8k", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_18.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which of the three categories showed the most growth from 1990 to 2025 and please provide your analysis." }, { "from": "gpt", "value": "Assessing the chart, there are lines for {Grains_Production}[0.095, 0.620, 0.959, 0.699], {Fruits_Imports}[0.095, 0.577, 0.959, 0.718], and {Vegetables_Export}[0.095, 0.429, 0.959, 0.507]. Grains_Production decreased from 120 in 1990 to 64 in 2025. Fruits_Imports shows an increase from 50 in 1990 to 150 in 2025. Similarly, Vegetables_Export also shows an increase, but from 200 in 1990 to 245 in 2025. Hence, it appears that the Fruits_Imports has the most growth.\nBased on the above thought, the answer is:\nThe Fruits Imports category has shown the most growth from 1990 to 2025. This is because it starts at a value of 50 in 1990 and rises to 150 in 2025, a difference of 100 units. While Vegetables Export also increased during this time frame, it did so by just 45 units. Meanwhile, Grains Production decreased over this window of time, reducing by 56 units. So, between these three categories, Fruits Imports showed the most growth." } ], "id_raw": 658 }, { "id": "FyQcVgvAz0tao2Fi", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_18.png", "conversations": [ { "from": "human", "value": "\nFrom 1990 to 2025, what is the total decrease in Grains Production?" }, { "from": "gpt", "value": "To find the total decrease, I'll evaluate the points on the {Grains_Production}[[0.095, 0.620, 0.959, 0.699]] line. The starting point in 1990 is {120}[0.095, 0.620] and the end point in 2025 is {64}[0.959, 0.699]. Therefore, the total reduction equals 120 - 64.\nBased on the above thought, the answer is:\n56." } ], "id_raw": 658 }, { "id": "f6zrCdcEUxl2Irb3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_33_19.png", "conversations": [ { "from": "human", "value": "\nCould you please extract and structure the data presented in the line chart into a table format with Years as columns and Urban/Rural Development as rows?" }, { "from": "gpt", "value": "Sure, to get the raw data from the line chart into a structured table, I first identify {the x-axis tick labels}[[0.051, 0.911, 0.095, 0.934], [0.272, 0.911, 0.316, 0.934], [0.494, 0.911, 0.538, 0.934], [0.716, 0.911, 0.760, 0.934], [0.938, 0.911, 0.981, 0.934]], which represent the column headers or years. Next, I locate {the legend texts}[[0.878, 0.091, 0.932, 0.121], [0.878, 0.134, 0.941, 0.164]] which represents the rows of the table corresponding to Rural and Urban development. For each type of development, I identify {the line points for Rural}[[0.073, 0.685], [0.294, 0.406], [0.516, 0.205], [0.738, 0.188], [0.959, 0.170]] and {the line points for Urban}[[0.073, 0.563], [0.294, 0.345], [0.516, 0.223], [0.738, 0.179], [0.959, 0.153]] and extract the corresponding data values for each year.\nBased on the above thought, the answer is:\nDevelopment,2000,2005,2010,2015,2020\nRural,2.4,5.6,7.9,8.1,8.3\nUrban,3.8,6.3,7.7,8.2,8.5" } ], "id_raw": 659 }, { "id": "mgLrZIvsotXtbuk8", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_33_19.png", "conversations": [ { "from": "human", "value": "\nJudging by the depicted line chart, which area, Rural or Urban, has seen a more steady growth in development from 2000 to 2020?" }, { "from": "gpt", "value": "To answer this question, I need to analyze the line plots for Rural and Urban areas. For instance, looking at {the line plot for Rural development}[0.073, 0.170, 0.959, 0.685], it shows a considerable growth from 2.4 in 2000 to 8.3 in 2020, with a continuous increase over the years. {The Urban development line plot}[0.073, 0.153, 0.959, 0.563], on the other hand, displays a similar pattern, but with slight fluctuations between 2010 and 2020. Both trends show continuous growth, but Rural development appears slightly smoother over the 20 years period.\nBased on the above thought, the answer is:\nThe line chart suggests that the Rural area has seen a more steady development growth from 2000 through 2020, with a continuous linear increase, while the Urban area, despite a similar growth pattern, shows slight fluctuations between 2010 and 2020, indicating less steadiness." } ], "id_raw": 659 }, { "id": "6hQxZY4zM0wcW8HV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_33_19.png", "conversations": [ { "from": "human", "value": "\nBy how much does the Urban development percentage exceed Rural development percentage in 2020?" }, { "from": "gpt", "value": "To answer the question, I need to compare the Urban and Rural values for 2020. I locate {the Urban value for 2020}[0.959, 0.153] and determine it as 8.5. I find {the Rural value for 2020}[0.959, 0.170] and determine it as 8.3. Therefore, to conclude the difference I need to compute the Urban value minus the Rural value: 8.5 - 8.3.\nBased on the above thought, the answer is:\n0.2." } ], "id_raw": 659 }, { "id": "dw2ukKesOOmwmPa8", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_0.png", "conversations": [ { "from": "human", "value": "\nCan you please extract the data values from the line chart and structure them into a table with metric categories as the column headers and community types as the rows?" }, { "from": "gpt", "value": "To create a table from the line chart, I identify {the labels on the y-axis}[[0.040, 0.753, 0.054, 0.788], [0.025, 0.669, 0.054, 0.704], [0.025, 0.585, 0.054, 0.620], [0.025, 0.501, 0.054, 0.536], [0.025, 0.416, 0.054, 0.451], [0.025, 0.332, 0.054, 0.367], [0.025, 0.248, 0.054, 0.283], [0.025, 0.164, 0.054, 0.199]] as the different metrics which will be the column headers. Subsequently, I take note of {the legends}[[0.518, 0.899, 0.715, 0.934], [0.826, 0.899, 0.984, 0.934]] as the row headers indicating the community types. To populate the table cells, I locate {the line point values}[[0.062, 0.178], [0.126, 0.099], [0.185, 0.051], [0.237, 0.025], [0.332, 0.011], [0.413, 0.011], [0.511, 0.011], [0.607, 0.011], [0.722, 0.011], [0.810, 0.011]] for each community type under the corresponding metrics.\nBased on the above thought, the answer is:\nMetrics,Traditional Communities,Modern Communities\nPopulation Density,3200,800\nUrbanization Rate,60,95\nLocal Businesses Establishment,150,220" } ], "id_raw": 660 }, { "id": "TJHMZy9AmErnLub6", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_0.png", "conversations": [ { "from": "human", "value": "\nWhich category shows the largest difference between the Traditional and Modern Communities? Provide a brief explanation of your process." }, { "from": "gpt", "value": "To answer this question, I need to compare the difference between Traditional and Modern Communities for each category. Looking at {the line point for Population Density for Traditional Communities}[0.062, 0.178] and for Modern Communities[0.062, 0.430], the difference is 3200 - 800 = 2400 persons per square mile. For {the line point for Urbanization Rate for Traditional Communities}[0.126, 0.211] and for Modern Communities[0.126, 0.099], the difference is 95% - 60% = 35%. For {the line point for Local Businesses Establishment for Traditional Communities}[0.185, 0.051] and for Modern Communities[0.185, 0.025], the difference is 220 - 150 = 70 businesses per year. Hence, the largest difference between Traditional and Modern communities occurs in the category of Population Density.\nBased on the above thought, the answer is:\nThe largest difference between Traditional and Modern Communities is observed in the category of Population Density, with a difference of 2400 persons per square mile, which is significantly higher compared to the differences in the rates of Urbanization and Local Businesses Establishment." } ], "id_raw": 660 }, { "id": "BBDEdWXdfp7IIv7v", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_0.png", "conversations": [ { "from": "human", "value": "\nBy what factor is the Urbanization Rate greater in Modern Communities compared to Traditional Communities?" }, { "from": "gpt", "value": "To answer this question, I locate {the line point for the Urbanization Rate for Modern Communities}[0.126, 0.099] and extract its corresponding value of 95, and {the line point for the Urbanization Rate for Traditional Communities}[0.126, 0.211] and extract its corresponding value of 60. The factor by which the Urbanization Rate is greater in Modern Communities compared to Traditional Communities is therefore 95 / 60 = 1.58 (rounded to two decimal places).\nBased on the above thought, the answer is:\n1.58." } ], "id_raw": 660 }, { "id": "I8pd0Hs0SDBbBuC0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_1.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the given line chart into a structured table representing the trends and projected growth for AI and VR technologies?" }, { "from": "gpt", "value": "To create a table, I will start by identifying {the x-axis tick labels}[[0.031, 0.879, 0.074, 0.903], [0.212, 0.879, 0.256, 0.903], [0.393, 0.879, 0.437, 0.903], [0.575, 0.879, 0.619, 0.903], [0.756, 0.879, 0.800, 0.903], [0.938, 0.879, 0.981, 0.903]] as the years from 2020 to 2030. Then, I will locate {the legend texts}[[0.415, 0.948, 0.485, 0.972], [0.568, 0.948, 0.645, 0.972]] for identifying the AI and VR trends. The line points for {AI Trend}[[0.052, 0.135, 0.959, 0.709]] and {VR Trend}[[0.052, 0.645, 0.959, 0.871]] will be used to obtain the respective values for each year.\nBased on the above thought, the answer is:\nYear,AI_trend,VR_trend\n2020,0.70,0.78\n2021,0.76,0.77\n2022,0.82,0.75\n2023,0.90,0.71\n2024,0.98,0.67\n2025,1.08,0.65\n2026,1.20,0.63\n2027,1.25,0.61\n2028,1.29,0.58\n2029,1.35,0.54\n2030,1.41,0.50" } ], "id_raw": 661 }, { "id": "Q0SFVfzpnlvYRsb1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_1.png", "conversations": [ { "from": "human", "value": "\nWhich trend, AI or VR, is projected to grow more significantly from 2020 to 2030 based on the line chart?" }, { "from": "gpt", "value": "To determine which trend is projected to grow more significantly, I need to look at {the line for AI trend}[0.052, 0.135, 0.959, 0.709], starting at 0.70 in 2020 and reaching 1.41 in 2030, and compare it to {the line for VR trend}[0.052, 0.645, 0.959, 0.871], which starts at 0.78 in 2020 and declines to 0.50 in 2030. The line of AI trend shows a clear significant growth compared to the declining line of VR trend between 2020 and 2030.\nBased on the above thought, the answer is:\nBased on the line chart, the AI trend is projected to grow more significantly from 2020 to 2030. It begins with less than the VR trend but surpasses it over time and sees a substantial upward trend ending at a noticeably higher rate in 2030, whereas the VR trend declines over the same period." } ], "id_raw": 661 }, { "id": "WDr5b7RQub5FYNSi", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_1.png", "conversations": [ { "from": "human", "value": "\nAs the chart reflects, how much greater is the AI industry trend in 2030 compared to VR?" }, { "from": "gpt", "value": "To find the difference between AI and VR trends in 2030, I need to locate {the point for AI in 2030}[0.959, 0.135] which corresponds to a value of 1.41 and {the point for VR in 2030}[0.959, 0.871] which corresponds to a value of 0.50. The difference would then be 1.41 - 0.50 = 0.91.\nBased on the above thought, the answer is:\n0.91." } ], "id_raw": 661 }, { "id": "SqLjpmvgGwXQlcWW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_2.png", "conversations": [ { "from": "human", "value": "\nCould you extract the patent filing data represented in the line chart for the period from 2015 to 2019 for the five major technologies into a table?" }, { "from": "gpt", "value": "To generate a structured table from the line chart, I first need to observe {the x-axis tick labels}[[0.061, 0.879, 0.083, 0.914], [0.174, 0.879, 0.195, 0.914], [0.286, 0.879, 0.308, 0.914], [0.398, 0.879, 0.420, 0.914], [0.510, 0.879, 0.532, 0.914]]. They represent the years and will form the columns of the table. Next, to establish the rows of the table, I refer to {the legend texts}[[0.125, 0.125, 0.248, 0.160], [0.125, 0.177, 0.249, 0.212]], indicating the major technology trends. For the data population, I locate {the line points}[[0.061, 0.823], [0.174, 0.523], ..., [0.959, 0.147]] for each technology trend for the respective years and extract their values.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,3D Printing,Virtual Reality,Blockchain,Autonomous Vehicles\n2015,23,12,17,1,7\n2016,36,16,20,3,11\n2017,48,22,26,9,15\n2018,59,27,29,18,18\n2019,73,30,33,24,21" } ], "id_raw": 662 }, { "id": "POfmcVZRWzvlUZV7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_2.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you identify the technology trend that has seen the most consistent growth in patent filings from 2015 to 2019?" }, { "from": "gpt", "value": "To identify the technology trend with the most consistent growth, I need to study {the lines in the chart}[0.061, 0.147, 0.959, 0.858]. All the lines represent the trends of different technologies over time. Artificial Intelligence has a line that displays a steady and consistent upward trajectory. Looking at {the line for Artificial Intelligence}[[0.061,147], [0.174,0.328], [0.286,0.113], [0.398,0.324], [0.510,0.240], [0.623,0.402], [0.735,0.172], [0.847,0.223], [0.959,0.267]], the values are consistently increasing every year from 23 in 2015 to 73 in 2019.\nBased on the above thought, the answer is:\nThe technology trend that has seen the most consistent growth in patent filings from 2015 to 2019 according to the line chart is Artificial Intelligence. Its line shows a consistent increase in patent filings each year over the observed period." } ], "id_raw": 662 }, { "id": "EpxLMB10npdozHVh", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_2.png", "conversations": [ { "from": "human", "value": "\nFrom 2015 to 2019, by how much has the number of patent filings for Blockchain increased?" }, { "from": "gpt", "value": "To calculate the increase in the number of Blockchain patent filings, I need to examine the starting and ending points of {the line for Blockchain}[0.061, 0.854, 0.959, 0.854]. The starting point represents the number of filings in 2015, which is 1, and the ending point indicates the number of filings in 2019, which is 24. So the increase over this period is 24 - 1 = 23.\nBased on the above thought, the answer is:\n23" } ], "id_raw": 662 }, { "id": "XHwRu3GPmp0XIWzd", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_3.png", "conversations": [ { "from": "human", "value": "\nCan you construct a table comparing the startup count and innovation index per year based on the chart data?" }, { "from": "gpt", "value": "The first step in constructing this table is to identify the x-axis and y-axis labels. The {x-axis labels}[0.033, 0.879, 0.092, 0.914], [0.210, 0.879, 0.268, 0.914], [0.387, 0.879, 0.445, 0.914], [0.563, 0.879, 0.622, 0.914], [0.740, 0.879, 0.799, 0.914], [0.917, 0.879, 0.975, 0.914] represent the years - these will be the rows of the table. \nThe {labels of the legend}[0.728, 0.747, 0.888, 0.782], [0.728, 0.800, 0.925, 0.835], \"startup count\" and \"innovation index\", are the columns of the table. I can get the corresponding data by parsing the points on the {line representing startup count}[0.062, 0.721, 0.946, 0.866] and the {line representing innovation index}[0.062, 0.142, 0.946, 0.383].\nBased on the above thought, the answer is:\nYear,Startup Count,Innovation Index\n2010,10,60\n2011,15,65\n2012,13,62\n2013,16,63\n2014,20,70\n2015,18,72\n2016,21,75\n2017,19,73\n2018,22,80\n2019,20,79\n2020,25,85" } ], "id_raw": 663 }, { "id": "rSWnSggF4NcSiwtv", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in the innovation index from 2010 to 2020?" }, { "from": "gpt", "value": "Looking at the {innovation index line}[0.062, 0.142, 0.946, 0.383], I can see the value of the innovation index for each year. In 2010, the innovation index was 60 at {point}[0.062, 0.383]. In 2020, it increased to 85 at {point}[0.946, 0.142]. The total increase is therefore 85 - 60 = 25.\nBased on the above thought, the answer is:\n25" } ], "id_raw": 663 }, { "id": "rVbpRBfxenQsZJpD", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_4.png", "conversations": [ { "from": "human", "value": "\nBased on the data in the line chart, can you provide a structured table that correlates career stages with the corresponding workplace development trends?" }, { "from": "gpt", "value": "To formulate the data into a tabular format, I have to look at {the x-axis tick labels}[[0.101, 0.919, 0.148, 0.942], [0.161, 0.919, 0.250, 0.942], [0.244, 0.919, 0.330, 0.942], [0.346, 0.919, 0.390, 0.942], [0.403, 0.919, 0.495, 0.942], [0.496, 0.919, 0.564, 0.942], [0.586, 0.919, 0.636, 0.942], [0.670, 0.919, 0.715, 0.942], [0.752, 0.919, 0.795, 0.942], [0.814, 0.919, 0.895, 0.942], [0.892, 0.919, 0.980, 0.942]] for career stages. This will make up the first column in the table. Next, I find {the data points}[[0.124, 0.833], [0.206, 0.756], [0.287, 0.693], [0.368, 0.679], [0.449, 0.728], [0.530, 0.721], [0.611, 0.679], [0.692, 0.118], [0.774, 0.833], [0.855, 0.679], [0.936, 0.819]] associated with each career stage, translating the visual information into numerical values based on the position of the points in relation to {the y-axis tick labels}[[0.035, 0.894, 0.074, 0.917], [0.035, 0.754, 0.074, 0.777], [0.035, 0.613, 0.074, 0.637], [0.035, 0.473, 0.074, 0.497], [0.035, 0.333, 0.074, 0.357], [0.035, 0.193, 0.074, 0.216], [0.035, 0.053, 0.074, 0.076]]. These y-values will make up the second column in the table, referred to as 'Workplace Development Trend'.\nBased on the above thought, the answer is:\nCareer Stage,Workplace Development Trend\nNovice,0.0050\nIntermediate,0.0061\nExperienced,0.0070\nSenior,0.0072\nManagement,0.0065\nExecutive,0.0066\nRetired,0.0072\nHealth,0.0152\nLayoff,0.0050\nResignation,0.0072\nUnemployed,0.0052" } ], "id_raw": 664 }, { "id": "1xZZ5BnVxvpfDHS0", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_4.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, how does the workplace development trend evolve throughout different career stages?" }, { "from": "gpt", "value": "To answer this, I would follow {the line plot for Workplace Development Trend}[0.124, 0.118, 0.936, 0.833] and analyze the movement. Starting from {Novice}[0.101, 0.919, 0.148, 0.942], the trend value is low. It then appears to slowly rise through {Intermediate}[0.161, 0.919, 0.250, 0.942], {Experienced}[0.244, 0.919, 0.330, 0.942], and reach a minor peak at {Senior}[0.346, 0.919, 0.390, 0.942]. Afterwards, there is a slight dip at {Management}[0.403, 0.919, 0.495, 0.942], and a minor rise again towards {Executive}[0.496, 0.919, 0.564, 0.942] and {Retired}[0.586, 0.919, 0.636, 0.942]. Significantly, there is a drastic rise when employees reach {Health}[0.670, 0.919, 0.715, 0.942] stage, followed by a sharp fall back to low levels at {Layoff}[0.752, 0.919, 0.795, 0.942], slightly rising again towards {Resignation}[0.814, 0.919, 0.895, 0.942], and remaining steady through {Unemployed}[0.892, 0.919, 0.980, 0.942].\nBased on the above thought, the answer is:\nThe Workplace Development Trend, overall, shows a steady growth from the Novice to Retired stages, suggesting an increase in productivity, innovation, and other positive workplace factors up until retirement. An abnormal surge can be observed at the Health stage, pointing out a spike in certain contributing elements to workplace development. However, following this stage, there is a drastic decline in the trend, returning to the levels close to the beginning of a career." } ], "id_raw": 664 }, { "id": "M9wck00dJ6Uz9pmy", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_4.png", "conversations": [ { "from": "human", "value": "\nAt what career stage does the Workplace Development Trend observe the highest value?" }, { "from": "gpt", "value": "To find out the stage at which the Workplace Development Trend is the highest, I analyze {the data line}[[0.124, 0.118, 0.936, 0.833]] and see the point with the lowest y-coordinate represents the highest value of the trend. From the graph, I can see that this point corresponds to {the label 'Health'}[0.670, 0.919, 0.715, 0.942] with the y-coordinate being significantly lower at the rest of the data points.\nBased on the above thought, the answer is:\nHealth." } ], "id_raw": 664 }, { "id": "Ad4AyDI5CZRxySI9", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_5.png", "conversations": [ { "from": "human", "value": "\nFrom the data presented in the bar chart, can you construct a table presenting Sleep Quality, Mental Focus, and Physical Activity percentages for both the Stable Daily Routine and the Erratic Daily Routine?" }, { "from": "gpt", "value": "To gather the information necessary for the creation of the table, I first refer to {the y-axis labels}[[0.032, 0.191, 0.056, 0.222], [0.032, 0.294, 0.064, 0.325]], which dictate the row labels. For each row, I then examine the corresponding {bar segments for Stable Daily Routine}[[0.062, 0.189, 0.360, 0.223], [0.360, 0.189, 0.650, 0.223], [0.650, 0.189, 0.902, 0.223]], and {Erratic Daily Routine}[[0.062, 0.292, 0.300, 0.326], [0.300, 0.292, 0.540, 0.326], [0.540, 0.292, 0.746, 0.326]] to extract the values of Sleep Quality, Mental Focus, and Physical Activity as represented by their respective lengths.\nBased on the above thought, the answer is:\nRoutine, Sleep Quality (%), Mental Focus (%), Physical Activity (%)\nStable Daily Routine, 50, 48, 62\nErratic Daily Routine, 40, 40, 34" } ], "id_raw": 665 }, { "id": "iyTQja5L4ZAmXAbp", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_5.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what can you infer about an individual's behavioral psychology and mental health in the context of daily routines?" }, { "from": "gpt", "value": "To understand the influence of daily routines on an individual's behavioral psychology and mental health, I study the {bars}[[0.062, 0.189, 0.902, 0.223], [0.062, 0.292, 0.746, 0.326]]. Clearly, the sections representing Sleep Quality, Mental Focus, and Physical Activity have longer lengths in the Stable Daily Routine compared to the Erratic Daily Routine, indicating better sleep quality, mental focus, and physical activity levels respectively when the individual follows a stable routine.\nBased on the above thought, the answer is:\nBased on the chart, it can be inferred that the stability of an individual\u2019s daily routine has a significant impact on their behavioral psychology and mental health. When an individual sticks to a stable daily routine, they demonstrate higher percentages of sleep quality, mental focus, and physical activity. On the other hand, an erratic or unregulated routine is associated with lower values for these aspects. Therefore, a well-organized routine promotes better mental health and behavior." } ], "id_raw": 665 }, { "id": "2VtU61GMcZCwz2JE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in the Physical Activity percentage between the Stable Daily Routine and the Erratic Daily Routine according to the chart?" }, { "from": "gpt", "value": "To find the differences in the Physical Activity percentage between the two routines, I examine {the third segment of the bar pertaining to Stable Daily Routine}[[0.650, 0.189, 0.902, 0.223]] and determine its value to be 62%. Similarly, I locate {the third segment of the bar pertaining to Erratic Daily Routine}[[0.540, 0.292, 0.746, 0.326]], determined to be 34%. The difference is therefore 62% - 34% = 28%.\nBased on the above thought, the answer is:\n28%." } ], "id_raw": 665 }, { "id": "ZzfPx0OkLkJYTEaq", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into a table format with Categories as rows and Concepts as columns?" }, { "from": "gpt", "value": "To extract the data from the bar chart into a table format, I first identify {the y-axis tick labels}[[0.200, 0.504, 0.220, 0.552], [0.725, 0.360, 0.750, 0.420]] as the column headers - these are the concepts of 'Business Management' and 'Organizational Leadership'. Next, I recognize the colors of the segments within the bars, denoted by {color code}[[0.35,0.5,0.36,0.51],[0.82,0.37,0.83,0.38]] as markers for identifying different categories - 'Profit Margin' and 'Investment Rate'. To find the values, I observe the lengths of various segments in the full bar for each concept. For 'Business Management', the 'Profit Margin' segment takes up 45% of the bar and 'Investment Rate' takes up the remaining 55%. For 'Organizational Leadership', 'Profit Margin' takes 65% of the bar and 'Investment Rate' takes the remaining 35%. Hence, these percentages are the respective values for each category in the concepts.\nBased on the above thought, the answer is:\nCategories,Business Management,Organizational Leadership\nProfit Margin,45%,65%\nInvestment Rate,55%,35%" } ], "id_raw": 666 }, { "id": "Pkd363ocDfQvFJzg", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_6.png", "conversations": [ { "from": "human", "value": "\nWhich area seems to be a more balanced investment and why?" }, { "from": "gpt", "value": "To determine which area seems to be a more balanced investment, I need to evaluate the distribution of 'Profit Margin' and 'Investment Rate' for 'Business Management' and 'Organizational Leadership'. With regard to 'Business Management', {the bar for Business Management}[[0.14, 0.515, 0.70, 0.595]] shows a close balance between 'Profit Margin' (45%) and 'Investment Rate' (55%). On the other hand, for 'Organizational Leadership', {the bar for Organizational Leadership}[[0.14, 0.295, 0.81, 0.347]] demonstrates a more skewed distribution, with 'Profit Margin' at 65% and 'Investment Rate' at a lower 35%. Therefore, 'Business Management' appears to have a more balanced investment between 'Profit Margin' and 'Investment Rate'.\nBased on the above thought, the answer is:\n'Business Management' seems to be a more balanced investment. This is because the distribution between 'Profit Margin' and 'Investment Rate' for 'Business Management' is closer to 50-50, suggesting a more balanced investment in both profit-making and further investments. On the other hand, 'Organizational Leadership' has a higher 'Profit Margin' but lower 'Investment Rate', indicating a more profit-focused approach at the expense of further investments." } ], "id_raw": 666 }, { "id": "6NOIWioaZXMO0E68", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in Profit Margin between 'Business Management' and 'Organizational Leadership'?" }, { "from": "gpt", "value": "To find the difference in Profit Margin between 'Business Management' and 'Organizational Leadership', I need to locate the 'Profit Margin' segments for both. For 'Business Management', {the 'Profit Margin' segment}[[0.14, 0.515, 0.55, 0.595]] denotes a profit margin of 45%. For 'Organizational Leadership', {the 'Profit Margin' segment}[[0.14, 0.295, 0.75, 0.347]] signifies a profit margin of 65%. Therefore, the difference in profit margin is 65% - 45% = 20%.\nBased on the above thought, the answer is:\n20%." } ], "id_raw": 666 }, { "id": "HxscTRrKwkYjGPwn", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_7.png", "conversations": [ { "from": "human", "value": "\nCould you provide a table format to present the trend data for each type of investment, over time, from the given line chart?" }, { "from": "gpt", "value": "To construct a table from this chart, I observe that there are line plots for each type of investment. Specifically, I refer to the {x-axis tick labels}[0.106, 0.761, 0.121, 0.796], [0.276, 0.761, 0.290, 0.796], [0.445, 0.761, 0.460, 0.796], [0.614, 0.761, 0.629, 0.796], [0.784, 0.761, 0.798, 0.796], [0.946, 0.761, 0.975, 0.796] to denote the \"Layer\" timescale representing the row headers. Collectively, {the sublegend items}[0.269, 0.879, 0.869, 0.914] represent the different investments as column headers. To find the values for each investment type at each layer timepoint, I locate the line points for each investment, e.g., {Social Responsibility}[0.114, 0.140, 0.960, 0.468], and {Sustainable Business}[0.114, 0.618, 0.960, 0.683] and extract corresponding values for each point.\nBased on the above thought, the answer is:\nLayer, Social_Responsibility, Sustainable_Business\n0, 0.0060, 0.0040\n1, 0.0065, 0.0042\n2, 0.0080, 0.0042\n3, 0.0090, 0.0040\n4, 0.0095, 0.0037\n5, 0.0075, 0.0038\n6, 0.0072, 0.0042\n7, 0.0068, 0.0044\n8, 0.0080, 0.0040\n9, 0.0074, 0.0041\n10, 0.0065, 0.0043" } ], "id_raw": 667 }, { "id": "0vdCATanfzb6Traz", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_7.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which area, Social Responsibility or Sustainable Business, seems to be more volatile over time? Explain your reasoning." }, { "from": "gpt", "value": "To determine which area has the most volatile progression, I need to analyze the fluctuations in the line plots for each area. Observing {the line for Social Responsibility}[0.114, 0.140, 0.960, 0.468], there are small changes between adjacent layers, indicating a mild fluctuation over time. For {Sustainable Business}[0.114, 0.618, 0.960, 0.683], the line shows little change over time, which manifests the stable nature of this investment.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Social Responsibility seems to be more volatile over time. Its line plot reveals mild but consistent fluctuation between different stages as compared to Sustainable Business, which shows little change over time." } ], "id_raw": 667 }, { "id": "VUCIDvDvRfXuzHJy", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_7.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, what is the total increase of Social Responsibility from layer 0 to layer 10?" }, { "from": "gpt", "value": "To compute the total increase, I need to locate {the line points for Social Responsibility}[0.114, 0.140, 0.960, 0.468] and extract the corresponding values at layer 0 and layer 10, which has values of 0.0060 and 0.0065, respectively. The total increase is the value at layer 10 minus the value at layer 0, which is 0.0065 - 0.0060.\nBased on the above thought, the answer is:\n0.0005." } ], "id_raw": 667 }, { "id": "0bUb1uWJNAgIese7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_8.png", "conversations": [ { "from": "human", "value": "\nCan you gather the data from this line chart into a tabular structure where the rows represent different years and the columns represent 'Creativity-Level' and 'Design-Innovation-Level'?" }, { "from": "gpt", "value": "To generate the table from the line chart, I first inspect {the x-axis tick labels}[[0.050, 0.763, 0.062, 0.791], [0.163, 0.763, 0.176, 0.791], ... [0.953, 0.763, 0.979, 0.791]], which represent the row headers or years. Then, I identify the labels from {the legend texts}[[0.322, 0.903, 0.477, 0.931], [0.573, 0.903, 0.812, 0.931]], representing 'Creativity-Level' and 'Design-Innovation-Level' columns. To populate the values, I need to locate and interpret {the line points for Creativity-Level}[[0.113, 0.527], [0.170, 0.542], ... [0.966, 0.497]] and {the line points for Design-Innovation-Level}[[0.113, 0.738], [0.170, 0.708], ... [0.966, 0.662]], obtaining the corresponding values for each year.\nBased on the above thought, the answer is:\nLayers,Creativity_Level,Design_Innovation_Level\n3,0.050,0.036\n4,0.049,0.038\n5,0.072,0.040\n6,0.071,0.039\n7,0.052,0.035\n8,0.057,0.045\n9,0.048,0.043\n10,0.055,0.041\n11,0.062,0.048\n12,0.060,0.044\n13,0.065,0.046\n14,0.062,0.049\n15,0.058,0.042\n16,0.055,0.045\n17,0.056,0.039\n18,0.052,0.041" } ], "id_raw": 668 }, { "id": "CwhgbR7g7jwz73aj", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_8.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which level sees more consistency over the years and why?" }, { "from": "gpt", "value": "To comprehend the consistency in the two trends, I need to assess the oscillations in the line graphs for 'Creativity-Level' and 'Design-Innovation-Level'. {The line for Creativity-Level}[0.113, 0.195, 0.966, 0.557] indicates somewhat little fluctuations, with few spikes and troughs, especially between the years associated with coordinates [0.340, 0.966]. \nHowever, when I analyze {the line for Design-Innovation-Level}[0.113, 0.542, 0.966, 0.753], I can see more consistent and smaller fluctuations over the years. This consistency suggests that 'Design-Innovation-Level' remains relatively stable in comparison to 'Creativity-Level'.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the 'Design-Innovation-Level' appears to be more consistent across the years. The fluctuation in the 'Design-Innovation-Level' line plot is less pronounced compared to the 'Creativity-Level', suggesting a higher degree of stability in the 'Design-Innovation-Level' over time." } ], "id_raw": 668 }, { "id": "VzR4NbrF5aBPquYY", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum Creativity Level recorded?" }, { "from": "gpt", "value": "To find the maximum Creativity Level recorded, I need to locate and analyze {the line points for Creativity-Level}[[0.113, 0.527], [0.170, 0.542], ... [0.966, 0.497]]. Here, I note the y-values corresponding to each point, which represent the Creativity Level for each year. The maximum y-value corresponds to the point {at the 5th Layer}[0.226, 0.195], which is 0.072.\nBased on the above thought, the answer is:\n0.072." } ], "id_raw": 668 }, { "id": "eUjlqdN7MhC9rwrj", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_9.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table from the line chart, using Layers as columns and the two series (Social Inequality and Rights Protection) as rows?" }, { "from": "gpt", "value": "To convert the data in the line chart into a table, I will use {the x-axis tick labels}[[0.145, 0.879, 0.160, 0.914], [0.327, 0.879, 0.341, 0.914], [0.508, 0.879, 0.523, 0.914], [0.689, 0.879, 0.704, 0.914], [0.863, 0.879, 0.892, 0.914], [1.044, 0.879, 1.073, 0.914]] to represent the column headers or Layers. Then I'll recognize {the legend texts}[[0.298, 0.878, 0.489, 0.913], [0.600, 0.878, 0.798, 0.913]] as the row labels for Social Inequality and Rights Protection. Subsequently, I'll map {the line points for Social Inequality}[[0.062, 0.577], [0.153, 0.596], [0.244, 0.625], [0.334, 0.673], [0.425, 0.664], [0.515, 0.721], [0.606, 0.760], [0.696, 0.789], [0.787, 0.808], [0.877, 0.818], [0.968, 0.837]] and {the line points for Rights Protection}[[0.062, 0.461], [0.153, 0.441], [0.244, 0.412], [0.334, 0.383], [0.425, 0.316], [0.515, 0.287], [0.606, 0.238], [0.696, 0.190], [0.787, 0.229], [0.877, 0.209], [0.968, 0.229]] to the respective layers and extract their values.\nBased on the above thought, the answer is:\nLayer, Social Inequality, Rights Protection\n1, 5.0, 6.2\n2, 4.8, 6.4\n3, 4.5, 6.7\n4, 4.0, 7.0\n5, 4.1, 7.7\n6, 3.5, 8.0\n7, 3.1, 8.5\n8, 2.8, 9.0\n9, 2.6, 8.6\n10, 2.5, 8.8\n11, 2.3, 8.6" } ], "id_raw": 669 }, { "id": "IoqzrdbAJRSsENn1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_9.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which topic shows a steady decrease over time, and why would you reach that conclusion?" }, { "from": "gpt", "value": "To determine which topic shows a steady decrease over time, I need to examine the behaviors of both {the line for Social Inequality}[0.062, 0.577, 0.968, 0.837] and {the line for Rights Protection}[0.062, 0.461, 0.968, 0.229]. Close observation reveals that the line for Social Inequality was steadily descending from a value of 5.0 to 2.3 across the layers. Even though it has a slight increase in Layer 5, the overall trend is decreasing. Conversely, with the exception of the dip in Layer 9, the line for Rights Protection is generally increasing.\nBased on the above thought, the answer is:\nThe topic of Social Inequality shows a steady decrease over time. Though there are slight reprieves like at Layer 5 where it slightly increases, the overall trend for Social Inequality is a decrease from 5.0 at Layer 1 to 2.3 at Layer 11. This is in contrast to the Rights Protection topic, which demonstrates more of an increasing trend." } ], "id_raw": 669 }, { "id": "PK4e5LoMRhVHoAlx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest value for Rights Protection across all layers?" }, { "from": "gpt", "value": "To find the highest value for Rights Protection, I need to identify {the line points for Rights Protection}[[0.062, 0.461], [0.153, 0.441], [0.244, 0.412], [0.334, 0.383], [0.425, 0.316], [0.515, 0.287], [0.606, 0.238], [0.696, 0.190], [0.787, 0.229], [0.877, 0.209], [0.968, 0.229]]. After reviewing these data points, I observe that the highest value is 9.0 at {Layer 8}[0.696, 0.190].\nBased on the above thought, the answer is:\n9.0" } ], "id_raw": 669 }, { "id": "Ry5z0l4cLJ4QV9oI", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_10.png", "conversations": [ { "from": "human", "value": "\nCan you depict the data for 'Social Inequality & Rights Protection over Years' in a tabular format, where the years are in columns?" }, { "from": "gpt", "value": "To create a table, I first recognize {the years from x-axis}[[0.073, 0.879, 0.132, 0.914], [0.242, 0.879, 0.301, 0.914], [0.411, 0.879, 0.469, 0.914], [0.579, 0.879, 0.638, 0.914], [0.748, 0.879, 0.806, 0.914], [0.917, 0.879, 0.975, 0.914]] as column headers. Then, {the legend text}[[0.669, 0.748, 0.854, 0.783], [0.669, 0.800, 0.925, 0.835]] denotes the two indices representing the row labels. To fetch the values, I follow {the lines for Inequality Index}[[0.102, 0.535, 0.946, 0.811]] and {the lines for Right Protection Index}[0.102, 0.149, 0.946, 0.425] for each year and extract the corresponding values.\nBased on the above thought, the answer is:\nIndices\\Years,1980,1985,1990,1995,2000,2005,2010,2015,2020,2025,2030\nInequality Index,0.15,0.18,0.20,0.22,0.24,0.27,0.30,0.33,0.35,0.37,0.40\nRight Protection Index,0.50,0.52,0.54,0.56,0.58,0.60,0.62,0.64,0.66,0.70,0.75" } ], "id_raw": 670 }, { "id": "KGMlbMaioi8gM7mB", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_10.png", "conversations": [ { "from": "human", "value": "\nBased on the graph, which among the Inequality Index and the Right Protection Index shows a faster rate of growth during the time period observed?" }, { "from": "gpt", "value": "To answer this question, I need to find the change in value from 1980 to 2030 for each index. To do so, I locate {the point for Inequality Index in 1980}[0.102, 0.811] and {the point for Inequality Index in 2030}[0.946, 0.535] and find the difference between the values. Similarly, I locate {the point for Right Protection Index in 1980}[0.102, 0.425] and {the point for Right Protection Index in 2030}[0.946, 0.149] and calculate the difference. The index with the higher difference in value will indicate the faster rate of growth.\nBased on the above thought, the answer is:\nBased on the graph, it can be observed that the Inequality Index shows a faster rate of growth between 1980 to 2030 relative to the Right Protection Index. The Inequality Index grows from 0.15 to 0.40, a jump of 0.25. On the other hand, the Right Protection Index increases from 0.50 to 0.75, a growth of 0.25. However, considering that the Inequality Index started from a lower point, the rate of its growth appears faster. Thus, the Inequality Index experienced a quicker surge in growth during the observed time period." } ], "id_raw": 670 }, { "id": "95ibFStpzlxPOJPy", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in the Inequality Index from 1980 to 2030?" }, { "from": "gpt", "value": "To identify the change, I first locate {the point for Inequality Index in 1980}[0.102, 0.811], which corresponds to a value of 0.15. Then, I locate {the point for Inequality Index in 2030}[0.946, 0.535], which corresponds to a value of 0.40. Finally, the difference between 1980 and 2030 is calculated as 0.40 - 0.15 = 0.25.\nBased on the above thought, the answer is:\n0.25." } ], "id_raw": 670 }, { "id": "JpdHZtvOOCIQfWMB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert the freight volume data from the line chart into a structured table categorized by year and volume type?" }, { "from": "gpt", "value": "To construct a table from the line chart, I observe {the x-axis ticks}[[0.082, 0.911], [0.258, 0.911], [0.433, 0.911], [0.609, 0.911], [0.784, 0.911], [0.959, 0.911]] representing years. The legend entries, {E-commerce Freight Volume}[0.093, 0.835, 0.138, 0.849] and {Heavy Industrial Transport Volume}[0.093, 0.870, 0.138, 0.884], are to be the row labels. Using {the line points for E-commerce Freight Volume}[[0.082, 0.861], [0.170, 0.811], ..., [0.959, 0.112]] and {the line points for Heavy Industrial Transport Volume}[[0.082, 0.512], [0.170, 0.542], ..., [0.959, 0.711]], I extract the corresponding values for each year and volume type.\nBased on the above thought, the answer is:\nYear,E-commerce Freight Volume,Heavy Industrial Transport Volume\n2010,50,85\n2011,55,82\n2012,60,78\n2013,68,79\n2014,75,77\n2015,84,75\n2016,94,73\n2017,105,71\n2018,111,70\n2019,120,68\n2020,125,65" } ], "id_raw": 671 }, { "id": "0SIQiKa7LC58qLqE", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_11.png", "conversations": [ { "from": "human", "value": "\nCan you describe the overall trends shown in the line chart for E-commerce Freight Volume and Heavy Industrial Transport Volume?" }, { "from": "gpt", "value": "By analyzing both {the line for E-commerce Freight Volume}[0.082, 0.112, 0.959, 0.861] and {the line for Heavy Industrial Transport Volume}[0.082, 0.512, 0.959, 0.711], I can derive the overall trends. \nFor E-commerce Freight Volume, there's a clear upward trend, indicating a consistent increase in e-commerce freight volume over the years.\nIn contrast, the line for Heavy Industrial Transport Volume shows a decreasing trend over the years, indicating a consistent decline in heavy industrial transport volume.\nBased on the above thought, the answer is:\nOverall, the chart indicates that the E-commerce Freight Volume has been increasing year-on-year, whereas the Heavy Industrial Transport Volume has been decreasing over the same period. This might suggest a shift in the transportation and logistics industry towards more E-commerce related transportation." } ], "id_raw": 671 }, { "id": "NNsjk4qWnC1diX6c", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_11.png", "conversations": [ { "from": "human", "value": "\nIn which year did Heavy Industrial Transport Volume reach its lowest point?" }, { "from": "gpt", "value": "To identify when the Heavy Industrial Transport Volume was at its minimum, I need to analyze {the line points for Heavy Industrial Transport Volume}[[0.082, 0.512], [0.170, 0.542], ..., [0.959, 0.711]]. The y-values represent the volume, and the corresponding x-values represent the years. The smallest y-value corresponds to the lowest volume. Comparing the y-values, the smallest is at point {65}[0.959, 0.711].\nBased on the above thought, the answer is:\n2020" } ], "id_raw": 671 }, { "id": "sCph3tDPXyV2acRL", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_12.png", "conversations": [ { "from": "human", "value": "\nCan you extract the maximum efficiency data for each manufacturing strategy (Automated and Hand Assembly) and organize it in a tabular format?" }, { "from": "gpt", "value": "Analyzing the line chart, I can see that {the x-axis labels}[[0.142, 0.758, 0.165, 0.772], ..., [0.902, 0.758, 0.925, 0.772]] representing the months of the year. Next, the {legend texts}[[0.952, 0.245, 0.972, 0.275], [0.952, 0.295, 0.972, 0.325]] provide the two different types of manufacturing strategies. I spot the peak values on both lines by locating the highest points on {the automated lines}[[0.175, 0.264, ..., 0.927, 0.215]] and {the hand assembly lines}[[0.175, 0.397, ..., 0.927, 0.415]]. Using these, I can generate a table with the maximum efficiency achieved each month, for each manufacturing strategy.\nBased on the above thought, the answer is:\nMonth,Automated Assembly,Hand Assembly\nJanuary,820,845\nFebruary,840,860\nMarch,872,870\nApril,900,877\nMay,915,882\nJune,925,890\nJuly,940,888\nAugust,960,884\nSeptember,1000,878\nOctober,1035,875\nNovember,1080,868\nDecember,1120,860" } ], "id_raw": 672 }, { "id": "zPON3yK0hT07xtH9", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_12.png", "conversations": [ { "from": "human", "value": "\nBetween automated and hand assembly, which manufacturing strategy seems more reliable based on the chart?" }, { "from": "gpt", "value": "Assessing the reliability of manufacturing strategies necessitates studying the consistency of the trend throughout the period. Examining the chart, {the line for Automated Assembly}[0.175, 0.264, ..., 0.927, 0.215] shows a steady upward trend with few fluctuations. On the other hand, {the line for Hand Assembly}[0.175, 0.397, ..., 0.927, 0.415] has periodic fluctuations without a prominent trend. Fluctuations often indicate uncertainties and inconsistencies.\nBased on the above thought, the answer is:\nBased on the chart, Automated Assembly appears to be a more reliable manufacturing strategy. It demonstrates a steady upward trend throughout the year, indicating consistent performance and predictable outcomes. In contrast, Hand Assembly shows significant fluctuations, thus introducing elements of unpredictability and inconsistency." } ], "id_raw": 672 }, { "id": "HC8og6sPcklVER6c", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_12.png", "conversations": [ { "from": "human", "value": "\nIn which month was the gap between Automated and Hand Assembly efficiencies the greatest?" }, { "from": "gpt", "value": "To find the month where the difference in efficiencies between automated and hand assembly was the greatest, I need to locate the points for each type on the chart and subtract the coordinates. Looking at {the points for automated}[[0.175, 0.264], ..., [0.927, 0.215]] and {the points for hand assembly}[[0.175, 0.397], ..., [0.927, 0.415]], I calculate the differences between the pairs of values and find the maximum difference and the corresponding month.\nBased on the above thought, the answer is:\nNovember." } ], "id_raw": 672 }, { "id": "WKnYsJCPKNsgpHl0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from this Line chart into a tabulated format with 'Years' as the rows and 'Charity Components' as the columns?" }, { "from": "gpt", "value": "To extract table data, let's first recognize that {the x-axis ticks}<[0.071, 0.764, 0.115, 0.787], [0.167, 0.764, 0.211, 0.787], [0.264, 0.764, 0.308, 0.787], [0.360, 0.764, 0.404, 0.787], [0.456, 0.764, 0.500, 0.787], [0.552, 0.764, 0.596, 0.787], [0.649, 0.764, 0.693, 0.787], [0.745, 0.764, 0.789, 0.787], [0.841, 0.764, 0.885, 0.787], [0.937, 0.764, 0.981, 0.787]> are the row labels indicating Years. Next, let me identify {the legend texts}<[0.411, 0.919, 0.521, 0.943], [0.605, 0.919, 0.690, 0.943]> as column headers representing different Charity Components. For table values, I need to locate {the line points for Charity Fund}<[0.093, 0.447], [0.189, 0.324], [0.286, 0.293], [0.382, 0.216], [0.478, 0.185], [0.574, 0.370], [0.671, 0.139], [0.767, 0.232], [0.863, 0.108], [0.959, 0.108]> and {the line points for Direct Aid}<[0.093, 0.709], [0.189, 0.632], [0.286, 0.571], [0.382, 0.555], [0.478, 0.509], [0.574, 0.386], [0.671, 0.309], [0.767, 0.278], [0.863, 0.247], [0.959, 0.170]> and extract these as the corresponding values.\nBased on the above thought, the answer is:\nYear,Charity_Fund,Direct_Aid\n2011,6000,4300\n2012,6800,4800\n2013,7000,5200\n2014,7500,5300\n2015,7700,5600\n2016,6500,6400\n2017,8000,6900\n2018,7400,7100\n2019,8200,7300\n2020,8200,7800" } ], "id_raw": 673 }, { "id": "Xsl4YMd4fn6SZThh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_13.png", "conversations": [ { "from": "human", "value": "\nCan you comment on the trend of the charity fund versus direct aid based on the line chart?" }, { "from": "gpt", "value": "Upon examining {the line for Charity Fund}[0.093, 0.108, 0.959, 0.447], it presents a general upward trend from 6000 in 2011 to 7700 in 2015, experiences a dip in 2016 to 6500, and then rises again to reach 8200 by 2019. While it seems to witness fluctuations, the overall trend is an increase in the Charity Fund allocation over the years.\nIn contrast, {the line for Direct Aid}[0.093, 0.170, 0.959, 0.709] reveals a consistently rising trend from 4300 in 2011 to 7800 in 2020 with minor fluctuations. Meaning, the Direct Aid allocation has seen a quite close to linear growth over the years.\nBased on the above thought, the answer is:\nBased on the line chart, the charity fund has seen an overall increase despite several fluctuations over the years. In contrast, Direct Aid has presented a more consistent upward trend. This suggests that while both areas have seen progress, the allocation for Direct Aid seems more stable and consistent than the Charity Fund." } ], "id_raw": 673 }, { "id": "ErDSfAqvqxrkD3l9", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest amount collected in charity fund throughout these years, and in which year did this happen?" }, { "from": "gpt", "value": "To find this, I should identify the highest value point in {the line plot for Charity Fund}[0.093, 0.108, 0.959, 0.447]. Upon examining the line plot, it becomes clear that the highest points occur in the years 2019 and 2020 with the coordinates {the points for 2019 and 2020}[0.863, 0.108], [0.959, 0.108]. Both these points correspond to the value of 8200, which is the maximum amount collected in the Charity Fund over these years.\nBased on the above thought, the answer is:\nThe highest amount collected in the Charity Fund was $8200 thousand, obtained in the years 2019 and 2020." } ], "id_raw": 673 }, { "id": "jKi5J6TPOZMuKfGx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_14.png", "conversations": [ { "from": "human", "value": "\nCould you create a table summarizing the information presented in the horizontal bar chart?" }, { "from": "gpt", "value": "To generate a table from the figure, I extract {the x-axis tick labels}[0.224, 0.763, 0.263, 0.791], [0.296, 0.763, 0.335, 0.791], [0.368, 0.763, 0.407, 0.791], [0.440, 0.763, 0.479, 0.791], [0.512, 0.763, 0.551, 0.791], [0.584, 0.763, 0.623, 0.791], [0.656, 0.763, 0.695, 0.791], [0.728, 0.763, 0.767, 0.791], [0.800, 0.763, 0.839, 0.791], [0.872, 0.763, 0.911, 0.791], [0.944, 0.763, 0.983, 0.791] which represent the years from 2010 to 2020. Next, the {legend entries}[0.478, 0.903, 0.578, 0.931],[0.652, 0.903, 0.772, 0.931] provide the labels for \"Twitter Users\" and \"Facebook Users\". The length of {the bars for Twitter Users and Facebook Users}[0.243, 0.210, 0.964, 0.482],[0.243, 0.256, 0.964, 0.617] correspond to the values for each year, which I extract and convert to numerical values for tabulation.\nBased on the above thought, the answer is:\nYear,Twitter Users,Facebook Users\n2010,0.0042,0.0037\n2011,0.0048,0.0043\n2012,0.0053,0.0046\n2013,0.0046,0.0049\n2014,0.0049,0.0052\n2015,0.0050,0.0048\n2016,0.0051,0.0050\n2017,0.0054,0.0047\n2018,0.0052,0.0039\n2019,0.0052,0.0036\n2020,0.0054,0.0038" } ], "id_raw": 674 }, { "id": "62WFDVaUi0PoYalO", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_14.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, how did the user growth of Facebook and Twitter change over the years?" }, { "from": "gpt", "value": "To answer this, I need to study the pattern of {the line plots for Twitter Users and Facebook Users}[0.243, 0.210, 0.964, 0.482],[0.243, 0.256, 0.964, 0.617]. I can observe that both lines display an initial increase till around 2012-13. Post this, there is a noticeable shift in trend pattern for both the lines. The Facebook Users line shows a fairly consistent decrease in user growth from 2013 to 2020. On the contrary, the Twitter Users line has more fluctuations but overall it's also on a decline with a more steady slope across years.\nBased on the above thought, the answer is:\nBased on the chart, user growth for both Facebook and Twitter initially increased till 2012-13. After this period, user growth for both platforms showed a declining trend. However, the decline was more consistent for Facebook, with the user growth rate decreasing every year from 2013 to 2020. For Twitter, although the overall trend was a decline, there were fluctuations during the period, indicating less consistency compared to Facebook. The Twitter line plot showed a slight increase between 2013 and 2014, and again between 2018 and 2019, which were not present in the Facebook line plot. Therefore, it can be inferred that while both platforms faced a reduction in user growth over the years, the decrease was more gradual and consistent for Facebook than for Twitter." } ], "id_raw": 674 }, { "id": "jNEIrPTQCJoZSeuc", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_14.png", "conversations": [ { "from": "human", "value": "\nIn what year did Twitter's user growth exceed that of Facebook's for the first time?" }, { "from": "gpt", "value": "To answer this question, I need to compare {the points for the Twitter Users line}[0.243, 0.482], [0.315, 0.346], [0.387, 0.233], [0.459, 0.391], [0.531, 0.323], [0.603, 0.301], [0.676, 0.278], [0.748, 0.210], [0.820, 0.256], [0.892, 0.256], [0.964, 0.210] and {the points for the Facebook Users line}[0.243, 0.595], [0.315, 0.459], [0.387, 0.391], [0.459, 0.323], [0.531, 0.256], [0.603, 0.346], [0.676, 0.301], [0.748, 0.369], [0.820, 0.549], [0.892, 0.617], [0.964, 0.572] year by year, and find the first year when the growth rate of Twitter Users was higher.\nBased on the above thought, the answer is:\n2017." } ], "id_raw": 674 }, { "id": "qyhv5wl7wSiDKSJN", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_15.png", "conversations": [ { "from": "human", "value": "\nCould you extract the monthly performance values for the two financial market algorithms 'Recursion Slice' and 'Chained List' and represent those values in a structured table with 'Months' as rows and 'Algorithms' as columns?" }, { "from": "gpt", "value": "First, I have to identify the x-axis labels which represent the months of the year. I located the x-axis labels by looking for the {x-tick labels}[0.017, 0.761, 0.032, 0.796], [0.173, 0.761, 0.188, 0.796], ..., [0.946, 0.761, 0.975, 0.796]. Then I have to find the line points which correspond to the performance of each algorithm for each month. The line for {Recursion Slice}[0.103, 0.146, 0.960, 0.723] and {Chained List}[0.103, 0.251, 0.960, 0.631] both have data points representing the performance for each month. I will have to extract the corresponding values for each point on both lines to generate the complete data for the table.\nBased on the above thought, the answer is:\nMonth,RecursionSlice,ChainedList\n1,0.44,0.58\n2,0.56,0.62\n3,0.65,0.67\n4,0.76,0.70\n5,0.82,0.76\n6,0.92,0.78\n7,0.97,0.83\n8,1.02,0.86\n9,1.12,0.84\n10,1.18,0.89\n11,1.27,1.08\n12,1.32,1.16" } ], "id_raw": 675 }, { "id": "jvIXYutZUcRH4Ap2", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_15.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which algorithm seems to show consistent improvement over time? Explain your reasoning." }, { "from": "gpt", "value": "To determine which algorithm shows consistent improvement, I need to analyze the trend in the line plots for each algorithm. Looking at {the line for Recursion Slice}[0.103, 0.146, 0.960, 0.723], I see its performance is generally increasing, showing a steady upward trend over time. On the other hand, analyzing {the line for Chained List}[0.103, 0.251, 0.960, 0.631], there seems to be slight fluctuations, but overall, it also shows steady improvement. Among the two, Recursion Slice shows a more consistent rise and less fluctuation compared to Chained List.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Recursion Slice algorithm seems to show more consistent improvement over time. It presents a steady and almost linear upward trend indicating consistent growth. While the Chained List algorithm also shows improvement, it is accompanied by slight fluctuations. Therefore, Recursion Slice maintains a more consistent performance improvement over the depicted time frame." } ], "id_raw": 675 }, { "id": "L8eXAT4UbzAhybQ8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the average monthly performance improvement for Chained List algorithm over the 12 month period?" }, { "from": "gpt", "value": "To calculate the average monthly performance improvement, I need to subtract the Performance value of the first month from that of the last month and divide by the total number of months. Looking at {the line points for Chained List}[0.103, 0.251, 0.960, 0.631], the Performance value in the first month is 0.58 while in the last month it is 1.16, so the average monthly performance improvement is (1.16 - 0.58) / 12 = 0.0483.\nBased on the above thought, the answer is:\n0.0483." } ], "id_raw": 675 }, { "id": "le81RtsHaJrZ5Tmy", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_17.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart, which portrays synthetic and human-authored progress in various scientific research areas, into a table format?" }, { "from": "gpt", "value": "To generate the table, I first identify the {x-axis tick labels}[0.077, 0.227, 0.135, 0.262], [0.245, 0.227, 0.303, 0.262], [0.413, 0.227, 0.471, 0.262], [0.581, 0.227, 0.639, 0.262], [0.749, 0.227, 0.807, 0.262], [0.917, 0.227, 0.975, 0.262] as the column headers i.e., the years. Secondly, I perceive {the legend texts} [0.324, 0.185, 0.480, 0.220], [0.591, 0.185, 0.793, 0.220] that represent the row labels the 'Inflation Rate' and 'GDP Growth Rate'. Finally, to populate the data, I fetch the line points, {line_0}[0.106, 0.100, 0.946, 0.180], {line_1}[0.106, 0.116, 0.946, 0.332] for Inflation Rate and GDP Growth Rate respectively at different years and extract the corresponding values.\nBased on the above thought, the answer is:\nYear,Inflation Rate,GDP Growth Rate\n2010,0.165,0.275\n2011,0.33,0.145\n2012,0.21,0.235\n2013,0.165,0.285\n2014,0.25,0.225\n2015,0.11,0.265\n2016,0.20,0.185\n2017,0.175,0.25\n2018,0.20,0.155\n2019,0.115,0.195\n2020,0.1,-0.335" } ], "id_raw": 677 }, { "id": "tOI1uO9UkdeDxkdF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_17.png", "conversations": [ { "from": "human", "value": "\nIf someone were interested in investing and cared about both inflation and GDP growth, which year or years would you suggest based on the trend of the two lines?" }, { "from": "gpt", "value": "For optimal investment, one may look for a year with low inflation and high GDP growth. I see that {the line for GDP Growth Rate}[0.106, 0.116, 0.946, 0.332] reaches its peak during {2013}[0.358, 0.276, 0.407, 0.311], indicating the highest GDP growth rate. On the other hand, in 2013, {the Inflation Rate line}[0.106, 0.100, 0.946, 0.180] is at a relatively low point, indicating lower inflation. Both these factors make 2013 an appealing year for investment when considering both GDP Growth Rate and Inflation Rate.\nBased on the above thought, the answer is:\nBased on both the inflation rate and GDP growth rate trends, 2013 may be an optimal year for investment as it had high GDP growth and relatively low inflation." } ], "id_raw": 677 }, { "id": "Or2nIfVsapu7JoUx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_17.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the highest GDP growth rate during the decade and which year it happened?" }, { "from": "gpt", "value": "To find out the highest GDP growth rate and the corresponding year, I look at the points of the {GDP Growth Rate line} [0.106, 0.116, 0.946, 0.332] and identify the point with the highest Y-coordinate which indicates the highest growth rate. The year for the highest point can be found by locating its corresponding x-coordinate on the x-axis (Year). The highest point on the GDP growth rate line corresponds to the coordinate [0.358, 0.116], and the corresponding year is {2013}[0.358, 0.276, 0.407, 0.311].\nBased on the above thought, the answer is:\nThe highest GDP growth rate was 0.285 and it occurred in 2013." } ], "id_raw": 677 }, { "id": "UACtP9hLVB8jLMV7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_18.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart, which shows the trend of government support and private investment across grades, into a structured format?" }, { "from": "gpt", "value": "To extract the data into a structured format, first I'll consider {the x-axis tick labels}[[0.014, 0.879, 0.029, 0.914], [0.119, 0.879, 0.148, 0.914], [0.230, 0.879, 0.260, 0.914], [0.342, 0.879, 0.372, 0.914], [0.454, 0.879, 0.484, 0.914], [0.566, 0.879, 0.596, 0.914], [0.678, 0.879, 0.708, 0.914]] representing the grades. Next, I take {the legend texts}[[0.723, 0.125, 0.966, 0.160], [0.723, 0.177, 0.942, 0.212]] representing Government Support and Private Investment respectively. Magic matching of these entities with {the line points of Government Support}[[0.077, 0.544], [0.133, 0.564], [0.189, 0.589], [0.245, 0.609],[0.301, 0.628],[0.357, 0.654],[0.413, 0.673],[0.469, 0.699],[0.525, 0.738],[0.581, 0.770],[0.637, 0.834]] and {the line points of Private Investment}[[0.077, 0.577], [0.133, 0.557], [0.189, 0.525], [0.245, 0.493],[0.301, 0.461],[0.357, 0.422],[0.413, 0.383],[0.469, 0.338],[0.525, 0.287],[0.581, 0.222],[0.637, 0.126]] will fetch the data for each grade.\nBased on the above thought, the answer is:\nGrade,Government Support,Private Investment\n9,80,75\n10,77,78\n11,73,83\n12,70,88\n13,67,93\n14,63,99\n15,60,105\n16,56,112\n17,50,120\n18,45,130\n19,35,145" } ], "id_raw": 678 }, { "id": "AS0E75CqlZEQdXs7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_18.png", "conversations": [ { "from": "human", "value": "\nWhich channel of education investment, government or private, exhibits a more significant rising trend?" }, { "from": "gpt", "value": "To understand which channel shows a stronger rising trend, I need to analyze {the government support line}[0.077, 0.544, 0.637, 0.834] and {the private investment line}[0.077, 0.126, 0.637, 0.577]. For government support, it starts at 80 at grade 9 and reduces to 35 at grade 19, indicating a declining trend. On the other hand, private investment begins at 75 at grade 9 and increases steadily to reach 145 at grade 19, a clear rising trend.\nBased on the above thought, the answer is:\nComparing the trends indicated in the chart, the Private Investment line exhibits a more significant rising trend than Government Support. This is evidenced by the steady rise in private investment values across the grades, as opposed to the declining trend of government support." } ], "id_raw": 678 }, { "id": "IiGNfuRQuBp4UKPi", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of values covered by the private investment according to the chart?" }, { "from": "gpt", "value": "To find the range of values covered by the private investment, I first need to find the maximum and minimum values. Based on the {line plot for private investment}[0.077, 0.126, 0.637, 0.577] and its corresponding points, the maximum value in the chart is 145 at grade 19, and the minimum value in the chart is 75 at grade 9. The difference between the maximum and minimum (range) is 145 - 75 = 70.\nBased on the above thought, the answer is:\n70." } ], "id_raw": 678 }, { "id": "rz4k2uoLCCVO4mPM", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_34_19.png", "conversations": [ { "from": "human", "value": "\nCould you please transform the information from the line graph into a structured table, with the years as columns and the two societal values as rows?" }, { "from": "gpt", "value": "To create a table from the line chart, I first take note of the {x-axis tick labels}[[0.133, 0.764, 0.176, 0.787], [0.315, 0.764, 0.359, 0.787], [0.498, 0.764, 0.542, 0.787], [0.680, 0.764, 0.724, 0.787], [0.863, 0.764, 0.907, 0.787]], representing the column headers or years. Next, I identify {the legend texts}[[0.352, 0.919, 0.477, 0.943], [0.561, 0.919, 0.736, 0.943]] as the row labels, symbolizing the societal values. Lastly, to fill the values in the table, I locate {the line points for 'Moral Conscience'}[[0.063, 0.478], [0.155, 0.461], ..., [0.976, 0.582]] and {the line points for 'Cultural Exchange'}[[0.063, 0.669], [0.155, 0.652], ..., [0.976, 0.149]] at the respective years and map them to the corresponding values.\nBased on the above thought, the answer is:\nValue, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015\nPublic Support, 0.56, 0.57, 0.59, 0.61, 0.55, 0.54, 0.57, 0.61, 0.54, 0.52, 0.50\nMilitary Expenditure, 0.45, 0.46, 0.47, 0.49, 0.51, 0.60, 0.63, 0.67, 0.70, 0.72, 0.75" } ], "id_raw": 679 }, { "id": "qngZJV1A3mWkw4Qo", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_34_19.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which societal value showed a greater trend of change over the 10 years period and why?" }, { "from": "gpt", "value": "To identify the societal value that showed a greater trend of change, I have to consider the magnitudes of changes in both \"Public Support\" and \"Military Expenditure\". Starting with {Public Support}[0.063, 0.392, 0.976, 0.582], there was a decrease from 0.61 to 0.5, which is a change of 0.11. On the other hand, looking at {Military Expenditure}[0.063, 0.149, 0.976, 0.669], the change was from 0.45 to 0.75, which is a difference of 0.3. Therefore, \"Military Expenditure\" showed a greater magnitude of change compared to \"Public Support\".\nBased on the above thought, the answer is:\nFrom 2005 to 2015, \"Military Expenditure\" showed a greater trend of change than \"Public Support\" based on the line chart. This is evident as the difference in \"Military Expenditure\" over the ten-year span was larger compared to the difference in \"Public Support\". Specifically, \"Military Expenditure\" rose by 0.3, whereas \"Public Support\" decreased by 0.11. This suggests that the focus on military spending increased significantly more than the decrease in public support over this time period." } ], "id_raw": 679 }, { "id": "UnFQRpa2k9EuDEo8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_34_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in Public Support in 2005 and 2015 based on the chart data?" }, { "from": "gpt", "value": "To find the difference, I have to look at the values for Public Support in 2005 and 2015. The data point for 2005 can be found at {Public Support in 2005}[0.063, 0.478] which corresponds to 0.56. Similarly, the data point for 2015 is at {Public Support in 2015}[0.976, 0.582] showing a value of 0.50. So, the difference between these two years is 0.56 - 0.50 = 0.06.\nBased on the above thought, the answer is:\n0.06" } ], "id_raw": 679 }, { "id": "zjlCmetaQ6ws3yMC", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_0.png", "conversations": [ { "from": "human", "value": "\nAccording to the visual data plotted in the chart, please extract structured tables with years as rows and the different aspects as columns." }, { "from": "gpt", "value": "To generate structured tabular data from the chart, I first observe {the x-axis tick labels}[0.082, 0.901, 0.094, 0.932], [0.079, 0.901, 0.104, 0.932], [0.082, 0.901, 0.107, 0.932], [0.085, 0.901, 0.110, 0.932], [0.088, 0.901, 0.113, 0.932] for years. Next, I find {the legend texts}[0.149, 0.830, 0.310, 0.860], [0.440, 0.830, 0.592, 0.860], [0.722, 0.830, 0.948, 0.860], [1.078, 0.830, 1.296, 0.860], [1.426, 0.830, 1.739, 0.860] which represents different aspects like 'CO2 in atmosphere', 'Deforestation rate', 'Polar Ice Caps Melting Rate', 'Human Population Growth', 'Invention of Sustainable Technologies'.Then, I note down {the points on the lines}[0.087, 0.156], [0.101, 0.156], [0.088, 0.750], [0.091, 0.759], [0.094, 0.768], [0.098, 0.777], [0.101, 0.786], [0.088, 0.868], [0.091, 0.872], [0.094, 0.871], [0.098, 0.870], [0.101, 0.868], [0.088, 0.863], [0.091, 0.870], [0.094, 0.868], [0.098, 0.867], [0.101, 0.863], [0.088, 0.864], [0.091, 0.876], [0.094, 0.873], [0.098, 0.869], [0.101, 0.864] to extract the values of all these aspects for each year.\nBased on the above thought, the answer is:\nYears,CO2 in atmosphere,Deforestation rate,Polar Ice Caps Melting Rate,Human Population Growth,Invention of Sustainable Technologies\n3,405,75,6.5,7.7,2.1\n10,410,70,7.0,8.2,4.6\n15,413,65,7.5,9.0,6.5\n30,420,60,8.0,10.0,8.8\n40,425,55,9.0,12.0,11.7" } ], "id_raw": 680 }, { "id": "oogUokvthFvDIrVf", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the environmental aspect that shows the highest rate of improvement between the 3rd year and the 40th year, according to the chart?" }, { "from": "gpt", "value": "To determine the environmental aspect with the highest rate of improvement, I need to compare the differences between the values of all aspects from the 3rd year to the 40th year. I can find the points for each aspect for both years: {CO2 in atmosphere}[0.087, 0.156], [0.101, 0.864], {Deforestation rate}[0.088, 0.750], [0.101, 0.786], {Polar Ice Caps Melting Rate}[0.088, 0.868], [0.101, 0.881], {Human Population Growth}[0.088, 0.863], [0.101, 0.871], {Invention of Sustainable Technologies}[0.088, 0.864], [0.101, 0.881]. Based on the figures, the 'Invention of Sustainable Technologies' increases from 2.1 to 11.7, which is higher compared to others.\nBased on the above thought, the answer is:\nThe environmental aspect that shows the highest rate of improvement between the 3rd year and the 40th year according to the chart is the 'Invention of Sustainable Technologies'." } ], "id_raw": 680 }, { "id": "MpAGhu2PfhL1R6Jc", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in atmospheric CO2 from the 3rd year to the 40th year?" }, { "from": "gpt", "value": "To find out the increase in atmospheric CO2 from the 3rd year to the 40th year, I need to find out the values of CO2 in atmosphere for these years. I locate {the points of CO2 in atmosphere for the 3rd year and 40th year}[0.087, 0.156], [0.101, 0.864], then I can see the corresponding values are 405 for the 3rd year and 425 for the 40th year. So the increase in atmospheric CO2 is 425 - 405 = 20.\nBased on the above thought, the answer is:\n20." } ], "id_raw": 680 }, { "id": "l1tVE963rakooMfk", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_1.png", "conversations": [ { "from": "human", "value": "\nCan you provide the data depicted in the chart in a table format with \"Year\" as rows and the respective \"Fields\" as columns?" }, { "from": "gpt", "value": "To create the table, I begin by identifying the x-axis labels found at {the x-axis tick labels}[0.123, 0.801, 0.205, 0.865], [0.692, 0.801, 0.774, 0.865] as the row headers '1990' and '2010'. Then, I identify the object labels in {the legend}[0.238, 0.571, 0.356, 0.631], [0.238, 0.659, 0.345, 0.719], [0.572, 0.571, 0.651, 0.631], [0.572, 0.659, 0.719, 0.719], [0.936, 0.571, 1.112, 0.631], [0.936, 0.659, 1.024, 0.719] as the column headers 'GPT-Neo', 'Theatre', 'Music', 'Visual Arts', 'Photography', 'Dance'. The data values can be derived based on {the line points}[0.164, 0.336], [0.733, 0.336], [0.164, 0.249], [0.733, 0.379], [0.164, 0.336], [0.733, 0.249], [0.164, 0.163], [0.733, 0.206], [0.164, 0.379], [0.733, 0.466], [0.164, 0.509], [0.733, 0.423] corresponding to each cell of the table.\nBased on the above thought, the answer is:\nYear,GPT-Neo,Theatre,Music,Visual Arts,Photography,Dance\n1990,0.5,0.6,0.5,0.7,0.45,0.3\n2010,0.5,0.45,0.6,0.65,0.55,0.35" } ], "id_raw": 681 }, { "id": "huk7sgGSGX188Iv1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_1.png", "conversations": [ { "from": "human", "value": "\nBased on the chart data, which field has seen negative growth from 1990 to 2010? Justify your answer." }, { "from": "gpt", "value": "To answer this question, I need to analyze the line plots for each field. I see that {the line for Theatre}[0.164, 0.249], [0.733, 0.379] starts from 0.6 in 1990 and ends at 0.45 in 2010 indicating negative growth. Similarly, {the line for Visual Arts}[0.164, 0.163], [0.733, 0.206] starts from 0.7 in 1990 and ends at 0.65 in 2010 which also indicates a negative growth.\nBased on the above thought, the answer is:\nThe fields which have seen negative growth from 1990 to 2010 are Theatre and Visual Arts. In both cases, the growth rate in 1990 is higher than the growth rate in 2010, indicating a decrease or negative growth over these years." } ], "id_raw": 681 }, { "id": "yYmKaI2pOcfYIGij", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_1.png", "conversations": [ { "from": "human", "value": "\nWhat was the growth change in Visual Arts from 1990 to 2010?" }, { "from": "gpt", "value": "I can find the growth values for Visual Arts in 1990 and 2010 by looking at the points on {the line for Visual Arts}[0.164, 0.163], [0.733, 0.206] which are represented as 0.7 and 0.65 respectively. So, the growth change from 1990 to 2010 would be 0.65 - 0.7 = -0.05.\nBased on the above thought, the answer is:\n-0.05" } ], "id_raw": 681 }, { "id": "7XZVMLIo3Z8TKhJi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_2.png", "conversations": [ { "from": "human", "value": "\nCould you please turn the line chart into a table that includes Quarters in columns and Industries as rows?" }, { "from": "gpt", "value": "To generate the table from the line chart, the first step is to recognize {the x-axis tick labels}[[0.118, 0.902, 0.152, 0.934], [0.386, 0.902, 0.421, 0.934], [0.655, 0.902, 0.690, 0.934], [0.924, 0.902, 0.958, 0.934]] as the column headers or Quarters. Next, I identify {the legend texts}[[0.815, 0.656, 0.962, 0.686], [0.815, 0.700, 0.899, 0.730], [0.815, 0.743, 0.854, 0.773], [0.815, 0.787, 0.906, 0.817], [0.815, 0.831, 0.867, 0.861]] as the row labels representing the different industries. To populate the values, I locate the points for each industry {Aviation}[[0.135, 0.589], [0.404, 0.461], etc.], {Rail}[[0.135, 0.716], [0.404, 0.674], etc.], {Shipping}[[0.135, 0.249], [0.404, 0.334], etc.], {Road}[[0.135, 0.844],[0.404, 0.759], etc.] at the respective quarters and convert the corresponding y-coordinate to the growth rate.\nBased on the above thought, the answer is:\nIndustries,Q1,Q2,Q3,Q4\nAviation,1.5,1.8,2.1,1.7\nRail,1.2,1.3,1.7,1.6\nShipping,2.3,2.1,2.4,2.6\nRoad,0.9,1.1,1.3,1.2" } ], "id_raw": 682 }, { "id": "BuFXFdT8qFP2QZw1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_2.png", "conversations": [ { "from": "human", "value": "\nAs per the trends depicted in the line chart, which industry demonstrated the most stable growth over the year?" }, { "from": "gpt", "value": "To identify which industry demonstrated the most stable growth, I need to examine the changes in the line plots for each industry. Analyzing {the line for Aviation}[[0.135, 0.589], [0.404, 0.461], [0.672, 0.334], [0.941, 0.504]], I see some fluctuations across quarters. The similar scenario can be observed for the {Road}[[0.135, 0.844],[0.404, 0.759], etc.], {Shipping}[[0.135, 0.249], [0.404, 0.334], etc.], and {Rail}[[0.135, 0.716], [0.404, 0.674], etc.] industries too. However, the fluctuations in {the line for Rail} appear less severe and more stable in comparison to the other industries.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Rail industry seems to have demonstrated the most stable growth over the year. The line plot for Rail shows relatively less fluctuations across quarters, indicating a more stable growth rate compared to other industries, such as Aviation, Shipping, and Road." } ], "id_raw": 682 }, { "id": "hWA8LVhxCQB1qvhO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_2.png", "conversations": [ { "from": "human", "value": "\nWhat\u2019s the difference between the highest and lowest growth rate for the Aviation industry?" }, { "from": "gpt", "value": "To identify the highest and lowest values, I need to locate {the points for the Aviation line}[[0.135, 0.589], [0.404, 0.461], [0.672, 0.334], [0.941, 0.504]]. The highest value is at the point corresponding to {Q3}[0.672, 0.334] with a growth rate of 2.1, while the lowest value is at the point corresponding to {Q1}[0.135, 0.589] with a growth rate of 1.5. Therefore, the difference between the highest and lowest growth rate for Aviation is 2.1-1.5=0.6.\nBased on the above thought, the answer is:\n0.6." } ], "id_raw": 682 }, { "id": "ZfwSnSApqRyqqYI3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_3.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the line chart to a structured table with the 'Parameters' acting as the column headers and each social media platform as the rows?" }, { "from": "gpt", "value": "To create a table from the line chart, I note {the axis labels}[0.450, 0.930, 0.577, 0.970], which represent the column headers or 'Parameters'. Now, looking at {the legend items}, I find labels for Facebook[0.119, 0.055, 0.185, 0.083], Twitter[0.119, 0.097, 0.166, 0.125], Instagram[0.119, 0.139, 0.190, 0.167] and LinkedIn[0.119, 0.181, 0.177, 0.209], which will form the row headings. To generate the values, I observe {the line points for Facebook}[0.108, 0.776, 0.919, 0.551], {Twitter}[0.108, 0.824, 0.919, 0.334], {Instagram}[0.108, 0.809, 0.919, 0.068] and {LinkedIn}[0.108, 0.833, 0.919, 0.406], respectively and extract these corresponding data values.\nBased on the above thought, the answer is:\nPlatform,Active Users in billions,Growth Rate in %\nFacebook,2.7,12\nTwitter,0.69,21\nInstagram,1.32,32\nLinkedIn,0.31,18" } ], "id_raw": 683 }, { "id": "1893b0uQUjQosCPm", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_3.png", "conversations": [ { "from": "human", "value": "\nConsidering the active user count and growth rate, which platform shows the most balanced growth and why?" }, { "from": "gpt", "value": "To evaluate balanced growth, I need to consider both active user count and growth rate. {Facebook}[0.108, 0.776, 0.919, 0.551] has a substantial active user count but a relatively lower growth rate. {Instagram}[0.108, 0.809, 0.919, 0.068] possesses the highest growth rate, but its active user count is less than half of Facebook's. {Twitter}[0.108, 0.824, 0.919, 0.334] has a decent growth rate but far fewer active users than Facebook or Instagram. {LinkedIn}[0.108, 0.833, 0.919, 0.406] has the least number of active users but maintains a growth rate that's competitive with other platforms except Instagram. Therefore, attributing balanced growth depends on whether equal weight is given to active users and growth rate.\nBased on the above thought, the answer is:\nIf both active user count and growth rate are given equal importance, then Facebook arguably demonstrates the most balanced growth. Despite its relatively lower growth rate, it compensates with its massive active user base. Otherwise, LinkedIn seems to be growing steadily with a reasonable growth rate, despite starting with a lower user base, showing potential for balanced growth in the future." } ], "id_raw": 683 }, { "id": "8W8nuQy9jMhOWxYB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the growth rate of the social media platform with the highest growth rate and the one with the lowest growth rate?" }, { "from": "gpt", "value": "To find the growth rates, I need to refer to the line points corresponding to 'growth rate in %'. Here, {Instagram}[0.919, 0.068] has the highest growth rate of 32% while {Facebook}[0.919, 0.551] has the lowest growth rate of 12%. Hence, the difference is 32% - 12% = 20%.\nBased on the above thought, the answer is:\n20%." } ], "id_raw": 683 }, { "id": "z6GBU7TDltQoKroi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_4.png", "conversations": [ { "from": "human", "value": "\nCan you please create a table showing how sales changed from 6AM to 6PM for each product sold?" }, { "from": "gpt", "value": "I'm looking at {the x-axis labels}[0.173, 0.801, 0.194, 0.865], [0.917, 0.801, 0.958, 0.865] that state the two timestamps \"6\" and \"18\". With those as my column labels, I'm noticing {the legend items}[0.860, 0.308, 0.950, 0.368], [0.860, 0.396, 0.916, 0.456], [0.860, 0.484, 0.906, 0.544], [0.860, 0.571, 0.926, 0.631], [0.860, 0.659, 0.942, 0.719] are \"Coffee\", \"Milk\", \"Tea\", \"Juice\", and \"Water\". Now, I'll extract the quantities sold for each, represented by the lines' positions {for Coffee}[0.183, 0.334, 0.937, 0.435], {for Milk}[0.183, 0.267, 0.937, 0.334], {for Tea}[0.183, 0.201, 0.937, 0.468], {for Juice}[0.183, 0.100, 0.937, 0.602] and {for Water}[0.183, 0.167, 0.937, 0.612].\nBased on the above thought, the answer is:\nProduct,6AM,6PM\nCoffee,100,130\nMilk,130,150\nTea,90,170\nJuice,50,200\nWater,47,180" } ], "id_raw": 684 }, { "id": "hVnAwvikhERaH66l", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_4.png", "conversations": [ { "from": "human", "value": "\nWhich product shows the greatest increase in sales from 6AM to 6PM?" }, { "from": "gpt", "value": "To determine which product had the greatest increase in sales, I need to compare the changes in sales from 6AM to 6PM. Looking at {the line for Coffee}[0.183, 0.334, 0.937, 0.435], I see the sales increased from 100 to 130. For {Milk}[0.183, 0.267, 0.937, 0.334], the sales increased from 130 to 150. {The line for Tea}[0.183, 0.201, 0.937, 0.468] shows an increase from 90 to 170. {The line for Juice}[0.183, 0.100, 0.937, 0.602] shows the largest increase in sales from 50 to 200. Finally, {Water}[0.183, 0.167, 0.937, 0.612] sales increased from 47 to 180.\nBased on the above thought, the answer is:\nThe product that shows the greatest increase in sales from 6AM to 6PM is Juice, with sales increasing by 150 units which is the largest increase among all the products." } ], "id_raw": 684 }, { "id": "WGWeamXFk1NIpavF", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_4.png", "conversations": [ { "from": "human", "value": "\nAt 6PM, how much more is sold in terms of Tea than Water units?" }, { "from": "gpt", "value": "I would have to locate and compare the values for Tea and Water at the 6PM time. Among the plot lines, I find {the point for Tea at 6PM}[0.937, 0.201], which corresponds to 170 units sold and {the point for Water at 6PM}[0.937, 0.167], corresponding to 180 units sold. By subtracting the sales of Water from Tea, I can find the difference.\nBased on the above thought, the answer is:\n-10" } ], "id_raw": 684 }, { "id": "5ujYnge9ZzoJy7aD", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_5.png", "conversations": [ { "from": "human", "value": "\nPlease convert the line chart that shows the population trend over years in different Areas into a table with Years as rows and Area names as columns." }, { "from": "gpt", "value": "To generate the table, I first locate {the x-axis tick labels}[[0.119, 0.881, 0.168, 0.919], [0.920, 0.881, 0.970, 0.919]] which represent the row headers or Years. Next, I identify {the legend texts}[[0.154, 0.599, 0.267, 0.627], [0.154, 0.641, 0.200, 0.669], [0.154, 0.683, 0.201, 0.711], [0.154, 0.725, 0.201, 0.753], [0.154, 0.767, 0.202, 0.795], [0.154, 0.808, 0.200, 0.836]] as the column headers representing the different Areas. For the values, I would identify each {line points}[[0.143, 0.666], [0.945, 0.813], [0.143, 0.372], [0.945, 0.177], [0.143, 0.226], [0.945, 0.079], [0.143, 0.421], [0.945, 0.275], [0.143, 0.519], [0.945, 0.617]] for each Area for the specific Year and extract these population values.\nBased on the above thought, the answer is:\nYears,AreaA,AreaB,AreaC,AreaD,AreaE\n2000,12000,18000,21000,17000,15000\n2010,9000,22000,24000,20000,13000" } ], "id_raw": 685 }, { "id": "j0LEoKc2saD5m3lp", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_5.png", "conversations": [ { "from": "human", "value": "\nConsidering the data, which area has had the largest growth over this period, and why?" }, { "from": "gpt", "value": "To answer this question, I will compare the increase or decrease in values for each area. From the {line points for Area A}[[0.143, 0.666], [0.945, 0.813]], I am able to see that the population decreased from 12000 to 9000. By analyzing {the line points for Area B}[[0.143, 0.372], [0.945, 0.177]], I can determine that it increased from 18000 to 22000. For {Area C's line points}[[0.143, 0.226], [0.945, 0.079]], the population sees a growth from 21000 to 24000. Similarly, by examining {the line points for Area D}[[0.143, 0.421], [0.945, 0.275]], there is an increase from 17000 to 20000. Lastly, looking at {the line points for Area E}[[0.143, 0.519], [0.945, 0.617]], the values decrease from 15000 to 13000.\nBased on the above thought, the answer is:\nFrom the information, it is evident that Area C has had the largest growth over the last decade, with an increase of 3000 from 21000 to 24000, compared to the growth of other areas like Area B (4000 from 18000 to 22000) and Area D (3000 from 17000 to 20000). The reasons could be various ranging from better facilities, economic development or migration trends, without additional context we can't pinpoint a specific reason why." } ], "id_raw": 685 }, { "id": "2w1qmPQ1lV5avWWx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_5.png", "conversations": [ { "from": "human", "value": "\nOver the decade, what is the absolute change in the population for Area A?" }, { "from": "gpt", "value": "To calculate the absolute change in the population for Area A, I need to find {the line points representing Area A}[[0.143, 0.666], [0.945, 0.813]]. The population in 2000 is 12000 at point [0.143, 0.666] and in 2010, it's 9000 at point [0.945, 0.813]. The absolute difference is 12000 - 9000 = 3000.\nBased on the above thought, the answer is:\n3000." } ], "id_raw": 685 }, { "id": "puaZV7yABQNgIZnn", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_6.png", "conversations": [ { "from": "human", "value": "\nCould you please extract the data from the line chart into a tabular format with 'Year' as the columns and 'Categories' as the rows?" }, { "from": "gpt", "value": "To transform the line chart data into a tabular format, I start by recognizing the labels on {the x-axis}['xticks'][0.103, 0.901, 0.165, 0.932], [0.910, 0.901, 0.972, 0.932] as the column headers representing the years. Next, I identify {the legend texts}[[0.171, 0.062, 0.256, 0.093], [0.171, 0.106, 0.262, 0.136], [0.171, 0.150, 0.271, 0.180], [0.371, 0.062, 0.525, 0.093], [0.371, 0.106, 0.548, 0.136]] as the row labels representing the different Technology Services. The values are extracted from the corresponding {line points} for each service at the respective years.\nBased on the above thought, the answer is:\nYear,Telecom,Software,Hardware,Cloud Services,Internet Services\n2009,20,30,35,15,10\n2019,30,45,40,60,75" } ], "id_raw": 686 }, { "id": "TwrMRJQPE4gl4N8z", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_6.png", "conversations": [ { "from": "human", "value": "\nBased solely on the line chart, which technology service category experienced the most significant growth from 2009 - 2019?" }, { "from": "gpt", "value": "To find the service category with the most substantial growth, I need to inspect the difference in start and end points for each line. The lines representing the various categories are {Telecom}['line_0'][0.134, 0.673, 0.941, 0.567], {Software}['line_1'][0.134, 0.567, 0.941, 0.408], {Hardware}['line_2'][0.134, 0.514, 0.941, 0.461], {Cloud Services}['line_3'][0.134, 0.726, 0.941, 0.249], and {Internet Services}['line_4'][0.134, 0.779, 0.941, 0.090]. The y-values for these lines correspond to the growth levels for the respective years 2009 and 2019. As per my evaluation, Internet Services have demonstrated the highest growth.\nBased on the above thought, the answer is:\nOf all the technology service categories, Internet Services has experienced the most significant growth from 2009 to 2019. This is evident from the large vertical distance covered by the corresponding line from its point in 2009 to its point in 2019." } ], "id_raw": 686 }, { "id": "E176LvouoykmekBz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the absolute growth from 2009 to 2019 in the Hardware category?" }, { "from": "gpt", "value": "To determine the absolute growth in the Hardware category, I need to identify the start and end point of {the line representing Hardware}{'line_2'}[0.134, 0.514, 0.941, 0.461]. The starting point corresponds to the year 2009 and its value is 35. However, the concluding point represents the year 2019 and its value is 40. Hence, the absolute growth is calculated as 40 - 35 = 5.\nBased on the above thought, the answer is:\n5" } ], "id_raw": 686 }, { "id": "RrqVbYeOF9mYvDsS", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_7.png", "conversations": [ { "from": "human", "value": "\nCould you state the earnings per share for all the quarters in a structured table for each company?" }, { "from": "gpt", "value": "To construct the structured table, I first recognize {the x-axis ticks}[0.153, 0.851, 0.192, 0.899], [0.408, 0.851, 0.448, 0.899], [0.664, 0.851, 0.704, 0.899], [0.920, 0.851, 0.960, 0.899] which represent the quarters. Next, I identify the company names from {the legend}[0.676, 0.670, 0.727, 0.702], [0.676, 0.718, 0.709, 0.751], [0.676, 0.766, 0.739, 0.799], [0.878, 0.670, 0.963, 0.702], [0.878, 0.718, 0.950, 0.751]. For the earnings values, I locate the points on {the lines for each company}[0.172, 0.185, 0.940, 0.284], [0.172, 0.120, 0.940, 0.366], [0.172, 0.498, 0.940, 0.613], [0.172, 0.416, 0.940, 0.531], [0.172, 0.629, 0.940, 0.745] at each quarter and extract the corresponding values.\nBased on the above thought, the answer is:\nCompany,Q1,Q2,Q3,Q4\nApple,0.68,0.72,0.74,0.70\nIBM,0.75,0.78,0.65,0.63\nGoogle,0.5,0.48,0.52,0.55\nFacebook,0.53,0.57,0.6,0.58\nAmazon,0.47,0.4,0.44,0.45" } ], "id_raw": 687 }, { "id": "ygSvkCCynVFBmJbB", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_7.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which company seems to have the most stable earnings per share across the four quarters?" }, { "from": "gpt", "value": "To determine which company has the most stable earnings across all quarters, I need to analyze the fluctuations in the line plots for each company. Looking at the {line for Apple}[0.172, 0.185, 0.940, 0.284], I see a minor increase from 0.68 to 0.72, followed by another minor increase and a decrease which indicates variability. The {line for IBM}[0.172, 0.120, 0.940, 0.366] starts at 0.75 and ends at 0.63, exhibiting a decrease over time with a small upward movement in Q2. {Google}[0.172, 0.498, 0.940, 0.613] and {Facebook}[0.172, 0.416, 0.940, 0.531] both show a generally rising trend with minor fluctuations. For {Amazon}[0.172, 0.629, 0.940, 0.745], the line shows a consistently increasing trend from 0.47 to 0.45 with almost no fluctuation.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Amazon seems to have the most stable earnings per share across the four quarters. Its plot line exhibits the smallest fluctuation among all companies, indicating the highest level of stability in their earnings." } ], "id_raw": 687 }, { "id": "vL5c1l37AvI2fF05", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in Earnings per Share from Q1 to Q4 for Google?" }, { "from": "gpt", "value": "To find the change in earnings for Google from Q1 to Q4, I need to locate {the line points for Google}[0.172, 0.580], [0.428, 0.613], [0.684, 0.547], [0.940, 0.498] and extract the corresponding values: 0.5, 0.48, 0.52, 0.55. The value for Q1 is 0.5 corresponding to {Q1}[0.153, 0.851, 0.192, 0.899], and the value for Q4 is 0.55 corresponding to {Q4}[0.920, 0.851, 0.960, 0.899]. The difference between Q1 and Q4 is therefore 0.55 - 0.5 = 0.05.\nBased on the above thought, the answer is:\n0.05." } ], "id_raw": 687 }, { "id": "OOat2zMjcG00lUpB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_8.png", "conversations": [ { "from": "human", "value": "\nCan the data from the line chart showing the progression stages of both \"Knowledge Economy\" and \"Innovation Industries\" be arrayed into a table format?" }, { "from": "gpt", "value": "To convert the line chart data into a tabular format, I begin by identifying {the x-axis tick labels}[[0.128, 0.856, 0.142, 0.901], [0.390, 0.856, 0.417, 0.901], [0.659, 0.856, 0.685, 0.901], [0.921, 0.856, 0.961, 0.901]] as column headers which represent the progression stages. Then I look at {the legend}[[0.519, 0.766, 0.700, 0.801], [0.783, 0.766, 0.966, 0.801]] to figure out the row labels, which here represent the entities \"Knowledge Economy\" and \"Innovation Industries\". Using {the line points for Knowledge Economy}[[0.135, 0.210], [0.404, 0.172], [0.672, 0.366], [0.941, 0.599]] and {the line points for Innovation Industries}[[0.135, 0.443], [0.404, 0.443], [0.672, 0.288], [0.941, 0.172]] I fetch the values for each stage of progression for both entities.\nBased on the above thought, the answer is:\nProgression Stages|3|10|30|100\nKnowledge Economy|.8|.85|.6|.3\nInnovation Industries|.5|.5|.7|.85" } ], "id_raw": 688 }, { "id": "Ik4p6WoT61GeUVSW", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, has there been a steady increase or decrease in the metrics for both \"Knowledge Economy\" and \"Innovation Industries\" with increasing progression stages?" }, { "from": "gpt", "value": "Determining if there has been a steady increase or decrease in the metrics requires an analysis of the lines and their slopes for both the entities. {The line for Knowledge Economy}[0.135, 0.210, 0.941, 0.599] first shows an upward trend until {Progression Stage 10}[0.404, 0.856], afterwards which a downward trend is evident, showing an overall decrease in metrics. Similarly, {the line for Innovation Industries}[0.135, 0.443, 0.941, 0.172] initially reflects a steady metric until {Progression Stage 10}[0.404, 0.856], followed by an increase until {Progression Stage 30}[0.659, 0.856] and another decrease until {Progression Stage 100}[0.921, 0.856], showing overall fluctuation in metrics.\nBased on the above thought, the answer is:\nThe metrics for \"Knowledge Economy\" show an overall decrease with increasing progression stages, indicating an initial growth followed by a decline. On the other hand, the metrics for \"Innovation Industries\" stage an overall fluctuation with an initial stagnancy followed by an increase and subsequent decrease." } ], "id_raw": 688 }, { "id": "NpFK5Ad9ReOx2Ebo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the maximum and minimum metrics of \"Knowledge Economy\"?" }, { "from": "gpt", "value": "To attain the difference, I first have to get the minimum and maximum metrics for \"Knowledge Economy\". Close examination of {the line points for Knowledge Economy}[[0.135, 0.210], [0.404, 0.172], [0.672, 0.366], [0.941, 0.599]] manifests the maximum metric as 0.85 corresponding to {Progression Stage 10}[0.404, 0.856] and the minimum metric as 0.3 corresponding to {Progression Stage 100}[0.921, 0.856]. The difference thus is 0.85 - 0.3 = 0.55.\nBased on the above thought, the answer is:\n0.55." } ], "id_raw": 688 }, { "id": "yczBhXeaV1fMiDoo", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_9.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line graph data into structured tables based on efficiency levels and types of meaning?" }, { "from": "gpt", "value": "To recreate the tables from the line graph, firstly, I am focusing on the {x-axis tick labels}[[0.147, 0.801, 0.239, 0.865], [0.339, 0.801, 0.414, 0.865], [0.517, 0.801, 0.603, 0.865], [0.685, 0.801, 0.802, 0.865], [0.881, 0.801, 0.974, 0.865]], which indicate the efficiency levels. Then, I use {the legend texts}[[0.259, 0.571, 0.473, 0.631], [0.259, 0.659, 0.498, 0.719]] that include \"Literal Meaning\" and \"Learned Meaning\" that represent different types of meaning. For the corresponding values, I consider {the line points} for both \"Literal Meaning\"[[0.193, 0.457], [0.376, 0.577], [0.560, 0.649], [0.743, 0.649], [0.927, 0.721]] and \"Learned Meaning\" [[0.193, 0.170], [0.376, 0.290], [0.560, 0.410], [0.743, 0.529], [0.927, 0.601]] to populate the values.\nBased on the above thought, the answer is:\nEfficiency Level,Literal Meaning,Learned Meaning\nAlpha,0.93,1.05\nBeta,0.88,1.0\nDelta,0.85,0.95\nEpsilon,0.85,0.9\nTheta,0.82,0.87" } ], "id_raw": 689 }, { "id": "XWAtc17pUOEVsavw", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_9.png", "conversations": [ { "from": "human", "value": "\nBased on the line graph, how does the efficiency evolve from Alpha to Theta for each type of meaning?" }, { "from": "gpt", "value": "Observing the lines on the graph for each type, I can analyze the trends in efficiency from Alpha to Theta for both types of meaning. For {Literal Meaning}[0.193, 0.457, 0.927, 0.721], the value starts at 0.93 and ends at 0.82, showing a decrease in efficiency. In contrast, the line for {Learned Meaning}[0.193, 0.170, 0.927, 0.601] points to an overall decrease as well, starting from 1.05 at Alpha, and reducing to 0.87 at Theta.\nBased on the above thought, the answer is:\nThe efficiency for both types of meaning decreases from Alpha to Theta, as per the trends shown on the line graph. \"Literal Meaning\" starts at a high efficiency of 0.93 at Alpha and decreases to 0.82 at Theta, while \"Learned Meaning\" also shows a reduction from 1.05 at Alpha to 0.87 at Theta." } ], "id_raw": 689 }, { "id": "LCv2yf1H64OJnfTn", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in efficiency from Alpha to Theta for Literal Meaning?" }, { "from": "gpt", "value": "To determine the increase in efficiency from Alpha to Theta in Literal Meaning, I need to identify {the line points for Literal Meaning starting at Alpha and ending at Theta}[[0.193, 0.457], [0.927, 0.721]] and extract the corresponding values: Alpha = 0.93 and Theta = 0.82. The increase is therefore 0.82 - 0.93 = -0.11.\nBased on the above thought, the answer is:\n-0.11." } ], "id_raw": 689 }, { "id": "ImtWLqTMzY2tLMqN", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_10.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table from the line chart to show the values of Patent Filed, Patent Granted, Patent Expired, Innovation Index, and Patent Transferred for each year?" }, { "from": "gpt", "value": "To construct the table, I first identify {the x-axis tick labels}[[0.115, 0.881, 0.164, 0.919], [0.316, 0.881, 0.366, 0.919], [0.518, 0.881, 0.567, 0.919], [0.719, 0.881, 0.768, 0.919], [0.920, 0.881, 0.969, 0.919]] which represent the year or the row labels of our table. Then, I locate the {legend texts}[[0.151, 0.070, 0.232, 0.098], [0.151, 0.112, 0.257, 0.140], [0.151, 0.154, 0.253, 0.182], [0.151, 0.196, 0.267, 0.224], [0.151, 0.238, 0.279, 0.266]] for the columns corresponding to Patent Filed, Patent Granted, Patent Expired, Innovation Index, and Patent Transferred. The values for each category can be obtained from the coordinates of the {line points}[[0.140, 0.487], [0.341, 0.372], ..., [0.945, 0.113]]. Corresponding to these various years are extracted.\nBased on the above thought, the answer is:\nYear,Patent Filed,Patent Granted,Patent Expired,Innovation Index,Patent Transferred\n2000,550,320,50,2.5,30\n2005,720,400,100,3,50\n2010,890,450,150,3.5,100\n2015,1000,500,200,4.2,150\n2020,1100,600,250,5,200" } ], "id_raw": 690 }, { "id": "qEoocNv8DujP6jEG", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_10.png", "conversations": [ { "from": "human", "value": "\nBased on the trends seen in the line chart, which category had the most significant growth over the years and why do you believe so?" }, { "from": "gpt", "value": "To find the category with the most significant growth, I need to analyze the lines for different categories. The {line for Patent Filed}[0.140, 0.113, 0.945, 0.487] starts at 550 in 2000 and ends at 1100 in 2020, showing an increase of 550. The {line for Patent Granted}[0.140, 0.453, 0.945, 0.644] indicates an increase of 280 over the years. {Patent Expired}[0.140, 0.691, 0.945, 0.827] increased by 200, {Innovation Index}[0.140, 0.858, 0.945, 0.860] only increased by 2.5, and {Patent Transferred}[0.140, 0.725, 0.945, 0.841] significantly increased by 170.\nBased on the above thought, the answer is:\nThe category that has shown the most significant growth over the years, as depicted in the line chart, is the Patent Filed category. This is evident as the Patent Filed line plot shows an increase from 550 in the year 2000 to 1100 in the year 2020, which is a 550 increases, this is the largest increase compared to the other categories within the same period." } ], "id_raw": 690 }, { "id": "Cb5BGyRDUuSdBntk", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_10.png", "conversations": [ { "from": "human", "value": "\nWhat was the overall net increase in Patent Transferred from the year 2000 to 2020?" }, { "from": "gpt", "value": "To find the net increase in Patent Transferred, I have to find the corresponding values of Patent Transferred for the years 2000 and 2020. The 2000 value is at {the point for 2000}[[0.140, 0.841]], which corresponds to 30. The 2020 value is at {the point for 2020}[[0.945, 0.725]], which is 200. The net increase is therefore 200 - 30 = 170.\nBased on the above thought, the answer is:\n170." } ], "id_raw": 690 }, { "id": "akfAJeqU3IdZZqFE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the chart into a table that reflects the year and corresponding values for Cultural Heritage and Cultural Integration?" }, { "from": "gpt", "value": "To extract the information from the chart, I need to observe {the line for Cultural Heritage}[0.114, 0.857, 0.946, 0.853] and {the line for Cultural Integration}[0.114, 0.127, 0.946, 0.029], along with {the x-axis labels}[0.094, 0.901, 0.966, 0.932] representing the years. For each year, the values associated with Cultural Heritage and Cultural Integration need to be accurately collected from where the points intersect with each corresponding line, ensuring precise data capture.\nBased on the above thought, the answer is:\nYear,Cultural Heritage (%),Cultural Integration (%)\n2000,13.6,370\n2001,13.75,372\n2002,13.85,375\n2003,13.77,379\n2004,13.7,381\n2005,14.0,382\n2006,13.93,385\n2007,13.85,387\n2008,13.7,390\n2009,14.2,393\n2010,14.4,396\n2011,14.42,400\n2012,14.6,402\n2013,14.65,404\n2014,14.75,407\n2015,15.01,409\n2016,15.15,410\n2017,15.2,413\n2018,15.3,415\n2019,15.4,418" } ], "id_raw": 691 }, { "id": "qxkfhBLiN3CiJlAX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_11.png", "conversations": [ { "from": "human", "value": "\nHow does the line plot for Cultural Integration indicate the effects of multicultural policies?" }, { "from": "gpt", "value": "The analysis of {the line for Cultural Integration}[0.114, 0.127, 0.946, 0.029] reveals subtle fluctuations across the years. It portrays a marginal decline with one significant drop, which can help interpret the effects of multicultural policies and their adaptation in society over time. Given that the line mostly exhibits a declining trend rather than sharp falls or rises, it suggests a gradual adaptation and normalization of multicultural principles, with the noted outlier possibly indicating a temporary cultural disturbance or an issue which momentarily impacted cultural integration.\nBased on the above thought, the answer is:\nThe line plot demonstrates a generally stable yet slightly declining trend in Cultural Integration, signifying that while there are ongoing efforts for cultural adaptation through multicultural policies, there are challenges and disturbances which might temporarily affect the integration process." } ], "id_raw": 691 }, { "id": "h24ASjbppKymCgiB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_11.png", "conversations": [ { "from": "human", "value": "\nWhat was the average percentage decline for Cultural Heritage over the observed years?" }, { "from": "gpt", "value": "To calculate the average decline, I will review the data points along {the line for Cultural Heritage}[0.114, 0.857, 0.946, 0.853]. From the start year 2000 with a value of 13.6 to the end year 2019 with a value of 15.4, calculations involve determining the decline each year and then finding the average. The total decline is computed by subtracting the final value from the initial value, divided by the number of years.\nBased on the above thought, the answer is:\n0.09%" } ], "id_raw": 691 }, { "id": "pMEi4H7lLsDNfbd4", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_13.png", "conversations": [ { "from": "human", "value": "\nCan you structure the provided raw data into a table showcasing the trend of each sector or category over the years from 2009 to 2019?" }, { "from": "gpt", "value": "To structure the data into a tabular form, I will extract each sector's data across the years by analyzing the line plot for each sector. First, I will locate {the x-axis tick labels}[0.109, 0.881, 0.972, 0.919] which represent the years 2009 to 2019. For each sector such as {the line for Building Materials}[0.149, 0.453, 0.942, 0.233], I will extract the corresponding y-values at every x-point. Repeating this process for each sector, I will compile a comprehensive table showcasing the trend over the years.\nBased on the above thought, the answer is:\nYear,Building_Materials,Real_Estate_Agencies,Residential_Buildings,Commercial_Buildings,Public_Sector_Buildings,Infrastructure_Projects\n2009,50,60,55,42,58,65\n2010,52,62,57,45,60,67\n2011,58,65,60,50,63,70\n2012,60,70,63,52,65,75\n2013,62,70,67,55,67,77\n2014,67,72,70,57,68,78\n2015,68,75,75,60,70,80\n2016,70,78,78,65,72,85\n2017,72,79,80,67,75,86\n2018,75,80,83,70,77,86\n2019,77,85,85,73,80,90" } ], "id_raw": 693 }, { "id": "derD4hAB7Thq6i8q", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_13.png", "conversations": [ { "from": "human", "value": "\nWhich sector shows a consistently upward trend throughout the period from 2009 to 2019?" }, { "from": "gpt", "value": "To identify which sector shows a consistently upward trend, it is necessary to analyze the slope of the lines for each sector throughout the period. Observing the line plots, I notice that {the line for Infrastructure Projects}[0.149, 0.208, 0.942, 0.167] starts at a lower position and consistently rises to a higher position by the end of 2019. This indicates that Infrastructure Projects have a consistent upward trend.\nBased on the above thought, the answer is:\nThe Infrastructure Projects sector shows a consistently upward trend throughout the period from 2009 to 2019. This is evident from its line plot starting at a lower position and gradually ascending to a higher position by the end of the period." } ], "id_raw": 693 }, { "id": "bhYWVi08ANfgWgOh", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in value for Commercial Buildings from 2009 to 2019 according to the chart?" }, { "from": "gpt", "value": "To calculate the increase, I need to identify the points for Commercial Buildings in the years 2009 and 2019. Locating {the point for Commercial Buildings in 2009}{[0.149, 0.519]}, the value is 42, and for {2019}{[0.942, 0.265]}, it is 73. The increase is calculated by subtracting the initial value from the final value (73 - 42).\nBased on the above thought, the answer is:\n31." } ], "id_raw": 693 }, { "id": "oRm64ZxjzszauqWy", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_14.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured data table reflecting the user numbers over years for each platform as shown in the line chart?" }, { "from": "gpt", "value": "To create the structured data table from the line chart, I first locate {the axis labels for years}[0.098, 0.895, 0.961, 0.923] that will represent the rows in our table. Next, I identify {the legend entries}[0.087, 0.102, 0.181, 0.213] that denote each platform to establish the columns for our table. Each column corresponds to Google, Youtube, Twitter, Facebook, and Netflix respectively. Then, I assess the {data points}[0.116, 0.275, 0.944, 0.875] for each platform over the years, calculating the corresponding user numbers from their positions relative to the Y-axis.\nBased on the above thought, the answer is:\nYear,Google,Youtube,Twitter,Facebook,Netflix\n2006,65,30,0,25,0\n2007,75,40,5,30,0\n2008,75,60,10,50,10\n2009,80,80,15,55,20\n2010,85,110,20,60,40\n2011,90,140,30,70,60\n2012,100,170,45,80,80\n2013,115,200,60,90,110\n2014,130,230,100,110,150\n2015,145,270,150,130,200\n2016,160,310,200,160,260\n2017,180,345,225,190,320\n2018,200,380,260,220,375\n2019,220,400,300,250,420\n2020,240,425,340,290,500\n2021,260,450,380,330,570" } ], "id_raw": 694 }, { "id": "lwPMXIhual1qz5fq", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_14.png", "conversations": [ { "from": "human", "value": "\nHow did the user growth trend for Netflix compare to Google\u2019s from 2006 to 2021 based on the chart?" }, { "from": "gpt", "value": "Upon analyzing their growth trends, I observe {the line for Netflix}[0.116, 0.017, 0.944, 0.117] starting at 0 million users in 2006 and escalating to 570 million in 2021, showing sharp upward trend particularly after 2010. In contrast, {Google\u2019s line}[0.116, 0.529, 0.944, 0.370] starts at 65 million users in 2006 and gradually increases to 260 million by 2021. It's evident that both have sustained growth; however, Netflix\u2019s growth is more exponential in the later years compared to Google's more linear increase.\nBased on the above thought, the answer is:\nNetflix exhibited a more drastic increase in user numbers, especially from 2010 onwards, compared to Google, which had steady, linear growth. Netflix's graph reveals sharp ascensions and higher growth rates, denoting more volatile but higher gains, as opposed to Google's consistent upward trajectory." } ], "id_raw": 694 }, { "id": "19xI2ii5Hgo5Z7RE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_14.png", "conversations": [ { "from": "human", "value": "\nWhat was the percent increase in Youtube users from 2006 to 2021 as shown on the chart?" }, { "from": "gpt", "value": "To compute the percent increase for Youtube users from 2006 to 2021, I examine the y-values for Youtube at the years {2006 and 2021}[0.116, 0.015, 0.944, 0.117]. In 2006, Youtube had 30 million users, and by 2021, the users increased to 450 million. The percent increase is calculated as ((450 - 30) / 30) * 100.\nBased on the above thought, the answer is:\n1400%" } ], "id_raw": 694 }, { "id": "I9UGBhRLcxoMm1JT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_15.png", "conversations": [ { "from": "human", "value": "\nCould you extract a CSV data table from the chart with a structure based on trends over the years as columns, and the sector type, i.e. 'Knowledge Economy' and 'Innovation Industries' as rows?" }, { "from": "gpt", "value": "To create the table data, I will first identify {the x-axis tick labels}[0.117, 0.912, 0.174, 0.943], [0.277, 0.912, 0.334, 0.943], [0.437, 0.912, 0.494, 0.943], [0.597, 0.912, 0.654, 0.943], [0.757, 0.912, 0.814, 0.943], [0.917, 0.912, 0.974, 0.943] as the column headers representing different trends. Following this, I can see {the subplot text labels}[0.048, 0.465, 0.169, 0.489], [0.048, 0.040, 0.199, 0.064] indicating 'Knowledge Economy' and 'Innovation Industries'. Then, I will locate positions of {line points for 'Knowledge Economy'}[0.117, 0.797], [0.277, 0.668], [0.437, 0.697], [0.597, 0.476], [0.757, 0.627], [0.917, 0.527], and {line points for 'Innovation Industries'}[0.117, 0.535], [0.277, 0.708], [0.437, 0.726], [0.597, 0.710], [0.757, 0.551], [0.917, 0.605] to extract the respective values for each trend and each sector to populate the rows of the table.\nBased on the above thought, the answer is:\nTrend,Knowledge_Economy,Innovation_Industries\nAccelerating Decline,79.7,53.5\nMarginal Growth,66.8,70.8\nSluggish Growth,69.7,72.6\nUneven Development,47.6,71.0\nSubstantial Decrease,62.7,55.1\nDownward Trajectory,52.7,60.5" } ], "id_raw": 695 }, { "id": "vJ2G4ShjOQ6fsCgx", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_15.png", "conversations": [ { "from": "human", "value": "\nCould you explain which industry between 'Knowledge Economy' and 'Innovation Industries' displayed more consistent performance under different trends?" }, { "from": "gpt", "value": "To determine the consistency in performance, we should look at the fluctuation or the volatility of the lines for each sector. The {line for 'Knowledge Economy'}[0.117, 0.797], [0.277, 0.668], [0.437, 0.697], [0.597, 0.476], [0.757, 0.627], [0.917, 0.527] varies widely throughout different trends, showing highs near 79.7% and lows approximately at 47.6%. On the other hand, the {line for 'Innovation Industries'}[0.117, 0.535], [0.277, 0.708], [0.437, 0.726], [0.597, 0.710], [0.757, 0.551], [0.917, 0.605] fluctuates less, with highest at roughly 72.6% and lowest around 53.5%.\nBased on the above thought, the answer is:\nThe 'Innovation Industries' sector displayed more consistent performance under different trends compared to 'Knowledge Economy'. Though it saw both highs and lows, the margin between those extremes was less prominent for 'Innovation Industries', indicating less volatility and thus, more consistency." } ], "id_raw": 695 }, { "id": "amisJ0EsoThFtku8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_15.png", "conversations": [ { "from": "human", "value": "\nCalculate the range of performance values in the 'Knowledge Economy' sector." }, { "from": "gpt", "value": "For calculating the range of performance values in the 'Knowledge Economy' sector, I should spot the maximum and minimum values. Observing {the line for 'Knowledge Economy'}[0.117, 0.797], [0.277, 0.668], [0.437, 0.697], [0.597, 0.476], [0.757, 0.627], [0.917, 0.527], the maximum value is near about 79.7% and the minimum value is approximately 47.6%. The range can be calculated as maximum - minimum, i.e., 79.7 - 47.6 = 32.1.\nBased on the above thought, the answer is:\n32.1." } ], "id_raw": 695 }, { "id": "XLKHmOKGuxSCtl3I", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_17.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data showing GDP % in R&D for different countries into a structured table with years as rows and countries as columns?" }, { "from": "gpt", "value": "Observing the line chart, I identify distinct data points for each country over the years. By examining {the x-axis labels}[0.084, 0.801, 0.166, 0.865] for years and {the line points for each country}[0.971, 0.165, 0.975, 0.290] for the USA, [0.971, 0.630, 0.975, 0.672] for China, and so on, I then align these with their respective years and percentage values. The ticks on the x-axis help to block exact years, and the y-axis provides the percentage values for each point on the lines.\nBased on the above thought, the answer is:\nYear,USA,China,India,Germany,Brazil\n2000,8.5,1.4,2.7,5.6,4.3\n2001,8.7,1.8,2.9,5.4,4.0\n2002,8.1,1.6,3.2,5.2,3.5\n2003,7.6,1.4,2.8,5.8,3.7\n2004,7.8,1.7,3.1,5.9,4.2\n2005,7.5,1.6,2.9,6.0,4.0\n2006,7.6,1.9,3.2,5.7,3.8\n2007,7.3,2.0,3.0,5.8,4.3\n2008,7.1,1.8,2.7,5.5,3.9\n2009,7.0,1.6,2.5,5.4,3.6\n2010,6.9,1.8,2.6,5.6,4.0" } ], "id_raw": 697 }, { "id": "tubXGkYsinUj4oMf", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_17.png", "conversations": [ { "from": "human", "value": "\nOver the span of the decade, which country shows a consistent increase in % GDP towards R&D according to the chart?" }, { "from": "gpt", "value": "By scrutinizing the lines for each country in {the plotted data}[0.971, 0.165, 0.975, 0.290], I observe the general trends. The USA and Brazil exhibit some fluctuations, but Germany's line [0.971, 0.353, 0.975, 0.408] particularly demonstrates a constant upward trajectory, starting from 5.6% in 2000 and peaking at 6.0% in 2005 before stabilizing around 5.7% to 5.8% towards the end of the decade.\nBased on the above thought, the answer is:\nGermany exhibits a consistent increase in % GDP towards R&D over the ten-year span, marked by a trend that predominantly climbs with minor fluctuations." } ], "id_raw": 697 }, { "id": "4ivAd3hL2WwC2Lp3", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in India's % GDP on R&D from 2000 to 2010 according to the chart data?" }, { "from": "gpt", "value": "To determine the increase over the decade, I track {India's line points}[0.971, 0.547, 0.975, 0.596] specifically from the points in 2000 and 2010. Extracting the starting and ending values which are 2.7 in 2000 and 2.6 in 2010, I calculate the total increase which involves subtracting the initial value from the final value.\nBased on the above thought, the answer is:\n-0.1." } ], "id_raw": 697 }, { "id": "KUfl2fnEATSrqsf8", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_18.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into a table with Investment strategies as rows and Market Trends as columns?" }, { "from": "gpt", "value": "To do this, I need to first identify {the x-axis labels}[0.116, 0.881, 0.230, 0.919], [0.871, 0.881, 0.982, 0.919], which represent the rows of the table i.e., the investment strategies. Then, I recognize {the legend}[0.083, 0.677, 0.502, 0.845] symbols which represent the column headers or market trends. I then extract the height of various {bars}[0.116, 0.861, 0.166, 0.698], [0.871, 0.861, 0.922, 0.251] in relation to the y-axis to note down the performance metrics of the respective investment strategies under various market trends.\nBased on the above thought, the answer is:\nInvestment Strategy,Sluggish Growth,Fluctuating Decline,Sluggish Progress,Upward Trajectory,Dramatic Decrease\nHigh Risk,4.8,3.0,0.2,1.6,0.5\nLow Risk,2.0,1.6,0.5,1.2,1.0" } ], "id_raw": 698 }, { "id": "ViLe0rH7giD80Z4S", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_18.png", "conversations": [ { "from": "human", "value": "\nBy how much does the performance of Low Risk Investment exceed that of High Risk Investment during the period of Upward Trajectory?" }, { "from": "gpt", "value": "This requires locating the bars representing Upward Trajectory for both Investment strategies. Here are {the bar for High Risk in Upward Trajectory}[0.427, 0.861, 0.478, 0.251] with a value of 1.6 and {the bar for Low Risk in Upward Trajectory}[0.830, 0.861, 0.881, 0.567] with a value of 1.2. The difference is then 1.2 - 1.6 = -0.4.\nBased on the above thought, the answer is:\n-0.4" } ], "id_raw": 698 }, { "id": "8TjluMECqszeX7ZE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_35_19.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, could you turn the data into a table format, with Months as columns and Companies as rows?" }, { "from": "gpt", "value": "To convert the chart into a table, I'll first examine {the x-axis tick labels}[[0.102, 0.885, 0.114, 0.921], [0.195, 0.885, 0.207, 0.921], [0.288, 0.885, 0.299, 0.921], [0.380, 0.885, 0.392, 0.921], [0.473, 0.885, 0.484, 0.921], [0.565, 0.885, 0.577, 0.921], [0.658, 0.885, 0.670, 0.921], [0.751, 0.885, 0.762, 0.921], [0.843, 0.885, 0.855, 0.921], [0.930, 0.885, 0.953, 0.921]], which represent the months of the year (from March to December 2020). {The legend texts}[[0.135, 0.694, 0.335, 0.730], [0.135, 0.747, 0.313, 0.783], [0.135, 0.799, 0.247, 0.835], [0.461, 0.694, 0.628, 0.730], [0.461, 0.747, 0.663, 0.783]] serve as the row labels. These are the names of the different companies (TechCo, RetailCo, HealthCo, FoodCo, GreenEnerCo). To populate the numerical values, it's necessary to locate {the line points for each company} like {[0.108, 0.352], [0.201, 0.332], ..., [0.942, 0.095]} for Average Health Score as an example and assign the appropriate normalized stock prices.\nBased on the above thought, the answer is:\nCompanies,March,April,May,June,July,August,September,October,November,December\nTechCo,100,95,90,95,90,85,90,95,90,85\nRetailCo,100,95,90,95,90,85,90,95,90,85\nHealthCo,100,105,110,115,120,125,130,135,140,145\nFoodCo,100,105,110,115,120,125,130,135,140,145\nGreenEnerCo,100,100,100,100,100,100,100,100,100,100" } ], "id_raw": 699 }, { "id": "0AEFWxbBxm8NEB0R", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_35_19.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, which company shows the most growth over the year 2020 and why?" }, { "from": "gpt", "value": "To identify which company shows the most growth, I need to evaluate the trends for each company. Looking at the line for {HealthCo}[0.108, 0.249, 0.942, 0.095], there is a steady increase in the stock price from a base value of 100 in March to 145 in December. Similarly, for {FoodCo}[0.108, 0.557, 0.942, 0.95], the stock price also increases from 100 in March to 145 in December. Comparatively, the lines for {TechCo}[Line coordinates] and {RetailCo}[Line coordinates] show a decrease in stock price over the same period, and {GreenEnerCo}[Line coordinates] fluctuates around the base value of 100. Therefore, HealthCo and FoodCo show the most growth.\nBased on the above thought, the answer is:\nThe companies HealthCo and FoodCo show the most growth over the year 2020. Both lines display a steady and consistent increase in stock price from March to December, indicating significant growth. In contrast, both TechCo and RetailCo showed a steady decrease, and GreenEnerCo's stock price remained steady with minor fluctuations. Hence, HealthCo and FoodCo had the highest growth trend among the companies represented in the chart." } ], "id_raw": 699 }, { "id": "JjiiMwIiSHUTuW1F", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_35_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in stock price for the FoodCo from March to December?" }, { "from": "gpt", "value": "To find the increase, I need to locate the {starting line point for FoodCo in March} [0.108, 0.557] which corresponds to the score 100 and {the end line point in December} [0.942, 0.095] which corresponds to the score 145. Shown directly on the chart from the coordinates and legend, I can see that the stock price in March is 100 and in December is 145, so the increase is 145 - 100 = 45.\nBased on the above thought, the answer is:\n45." } ], "id_raw": 699 }, { "id": "lp4KrPpfpVdguL48", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_0.png", "conversations": [ { "from": "human", "value": "\nCould you provide the efficiency data from the chart in a structured table format with Teams as columns and Product Types as rows?" }, { "from": "gpt", "value": "To convert the data from the chart into a structured table, I first analyze the x-axis, where {the teams' labels}[0.1, 0.8, 0.2, 0.9] are provided as columns. Then, I identify {the legend}[0.88, 0.1, 0.98, 0.18] which details the row headers as Product Types: New Product, Old Product, and Outsourced Product. I examine {the points on the lines}[0.1, 0.3, 0.9, 0.5] for each team and product combination to gather the efficiency values.\nBased on the above thought, the answer is:\nProduct Type,Innovation,Finance,Human Resources\nNew Product,75%,80%,70%\nOld Product,65%,70%,65%\nOutsourced Product,70%,75%,60%" } ], "id_raw": 700 }, { "id": "9phl2W63Nfx6bIzc", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_0.png", "conversations": [ { "from": "human", "value": "\nWhich team displays the most consistent performance across different product types according to the chart?" }, { "from": "gpt", "value": "To evaluate consistency, I analyze the variances in {line heights for each team}[0.1, 0.2, 0.9, 0.7] across different product types. The Finance team line exhibits minor variations between 70% and 80%, while the Innovation and Human Resources teams show greater fluctuations. Observing the more stable positioning of {Finance team's line}[0.2, 0.3, 0.8, 0.5] across the products concludes its performance consistency.\nBased on the above thought, the answer is:\nThe Finance team displays the most consistent performance across different product types as indicated by the minor variations in their efficiencies from the chart." } ], "id_raw": 700 }, { "id": "GKHv1tqtLm6jhq6B", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in efficiency between the highest and lowest performing team for the New Product?" }, { "from": "gpt", "value": "To determine the performance difference, I review {the line points for New Product}[0.1, 0.3, 0.2, 0.5] for each team and extract their efficiency percentages. The highest efficiency is 80% for the Finance team at {coordinates}[0.2, 0.5], and the lowest is 70% for the Human Resources team at {coordinates}[0.3, 0.8]. The difference is calculated by subtracting the lowest value from the highest.\nBased on the above thought, the answer is:\n10%" } ], "id_raw": 700 }, { "id": "JBEZ2E6bkODKp1e0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_1.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart showing the annual trends into a structured table format?" }, { "from": "gpt", "value": "To create the table, I need to gather information from {the x-axis labeled 'Years'}[0.063, 0.047, 0.494, 0.437] representing each year. The values for each category, 'Box Office Revenue', 'Ad Spending', and 'Digital Media Consumption', must be extracted from {the three line graphs}[0.082, 0.064, 0.474, 0.419] respectively, at each year's position along the x-axis. I must carefully delineate the corresponding values for each category taking note of their color codes and marker styles.\nBased on the above thought, the answer is:\nYear,Box Office Revenue (in millions),Ad Spending (in millions),Digital Media Consumption (hours/person/day)\n2019,150,20,1.1\n2020,155,22,1.3\n2021,160,23,1.5\n2022,170,25,1.8\n2023,180,27,2.0\n2024,200,28,2.3\n2025,195,30,2.5\n2026,250,33,2.8\n2027,240,35,2.6\n2028,245,37,2.9\n2029,255,38,3.1\n2030,300,40,3.5" } ], "id_raw": 701 }, { "id": "G4t0SatWIiIXUtQx", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_1.png", "conversations": [ { "from": "human", "value": "\nHow did the fluctuating 'Digital Media Consumption' trend in the chart correlate with 'Ad Spending' over the years?" }, { "from": "gpt", "value": "Analyzing the trends from the line chart, I observe the line graphs for {Digital Media Consumption}[0.082, 0.064, 0.474, 0.419] and {Ad Spending}[0.082, 0.064, 0.474, 0.419]. Both show a general increase as time progresses. Specifically, notable increases in media consumption in 2026 and 2030 are matched with spikes in ad spending the same years. This suggests that increased advertising possibly encouraged higher digital media consumption or vice versa; the exact causality isn't clear and could be influenced by external factors or synchronized market strategies.\nBased on the above thought, the answer is:\nThe trends suggest a correlation where spikes in 'Ad Spending' coincide with increases in 'Digital Media Consumption,' particularly notable in the years 2026 and 2030. This implies that higher ad spending is possibly related to driving up media consumption, though the linkage might also involve other external market factors or strategic choices by media firms." } ], "id_raw": 701 }, { "id": "0Kyr6RcHPMeaPlIk", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in 'Box Office Revenue' from the year 2025 to 2026?" }, { "from": "gpt", "value": "To calculate the percentage increase, I first locate the respective values for 'Box Office Revenue' in 2025 and 2026 using {the line graph for Box Office Revenue}[0.082, 0.064, 0.474, 0.419]. In 2025, the revenue was $195 million and in 2026, it increased sharply to $250 million. Using the formula (New Value - Old Value) / Old Value * 100, I calculate the increase to be (250 - 195) / 195 * 100.\nBased on the above thought, the answer is:\n28.21%" } ], "id_raw": 701 }, { "id": "jel19qm1edBjM77g", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_4.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data into a CSV table format with social media platforms as columns and recovery types as rows?" }, { "from": "gpt", "value": "First, I observe {the x-axis labels}[0.055, 0.449, 0.095, 0.466], [0.193, 0.449, 0.222, 0.466], [0.318, 0.449, 0.363, 0.466], [0.455, 0.449, 0.491, 0.466] representing the social media platforms (Facebook, Twitter, Instagram, LinkedIn). I then identify {the legend}[0.372, 0.055, 0.489, 0.113] that lists the recovery types (Stable Engagement, Volatile Feedbacks). By examining the plotted data points for each combination of social media platform and recovery type, I extract the numerical user counts.\nBased on the above thought, the answer is:\nRecovery Type, Facebook, Twitter, Instagram, LinkedIn\nStable Engagement, 5234, 4523, 2765, 3893\nVolatile Feedbacks, 9437, 8234, 3457, 1293" } ], "id_raw": 704 }, { "id": "Yb3zSVqqh1ARzFis", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_4.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform shows the most significant difference in user engagement between the two recovery types?" }, { "from": "gpt", "value": "Analyzing the difference in user counts across recovery types, I observe {the points for Facebook}[0.075, 0.262], [0.075, 0.101], {Twitter}[0.208, 0.290], [0.208, 0.147], {Instagram}[0.341, 0.357], [0.341, 0.331], and {LinkedIn}[0.473, 0.314], [0.473, 0.414]. The differences in engagement are calculated for each platform, revealing the most significant difference.\nBased on the above thought, the answer is:\nBased on the observed data, Facebook shows the most significant difference in user engagement between the recovery types, with a robust initial engagement in Volatile Feedbacks compared to Stable Engagement." } ], "id_raw": 704 }, { "id": "LzU31Fbb8RY0sXX2", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_4.png", "conversations": [ { "from": "human", "value": "\nWhat's the total number of users engaging on Instagram considering both recovery types?" }, { "from": "gpt", "value": "I need to sum the individual user counts at the points marked for Instagram across both recovery types. Observing {the points for Instagram under Stable Engagement}[0.341, 0.357] and {Volatile Feedbacks}[0.341, 0.331], the counts are 2765 and 3457 respectively. Adding these gives the total engagement for Instagram.\nBased on the above thought, the answer is:\n6222" } ], "id_raw": 704 }, { "id": "QdKtDzHHBbEDC7E3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_6.png", "conversations": [ { "from": "human", "value": "\nCan you arrange the data presented in the chart as a structured table with Metrics as columns and Procedures as rows?" }, { "from": "gpt", "value": "To format the line chart data into a table, I first consider {the x-axis tick labels}[[0.015, 0.939, 0.029, 0.957], [0.106, 0.939, 0.119, 0.957], [0.196, 0.939, 0.210, 0.957], [0.287, 0.939, 0.301, 0.957], [0.377, 0.939, 0.391, 0.957], [0.468, 0.939, 0.482, 0.957], [0.559, 0.939, 0.572, 0.957]], which represent the column headers or Metrics. Proceeding, I identify the subchart titles {Manufacturing Procedure A}[0.201, 0.019, 0.342, 0.041] and {Manufacturing Procedure B}[0.696, 0.019, 0.837, 0.041] as row labels representing the different Procedures. To populate the values, I locate the line points [[0.067, 0.514], [0.149, 0.333], [0.230, 0.308], [0.312, 0.127], [0.393, 0.850], [0.475, 0.644]] and [[0.563, 0.514], [0.644, 0.325], [0.726, 0.430], [0.807, 0.116], [0.889, 0.850], [0.970, 0.577]] for Procedure A and Procedure B at the respective stages and extract the associated values.\nBased on the above thought, the answer is:\nProcedure,0.0,0.2,0.4,0.6,0.8,1.0\nProcedure A,0.75,0.82,0.83,0.9,0.62,0.7\nProcedure B,0.76,0.85,0.8,0.95,0.6,0.73" } ], "id_raw": 706 }, { "id": "gYhNpZ2qGYEDgC3s", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_6.png", "conversations": [ { "from": "human", "value": "\nJudging from the plottings, which manufacturing procedure expresses a more roller-coaster-like performance across different ratio categories? Explain your selection." }, { "from": "gpt", "value": "To decide which manufacturing procedure denotes more ups and downs, it's necessary to scrutinize the variation in the line plots for both procedures. Glancing at {the line for Manufacturing Procedure A}[0.067, 0.127, 0.475, 0.850], we see a steady climb from 0.75 to 0.9, with a sudden drop to 0.62 and a minor rise to 0.7. This indicates moderate fluctuations in the trend.\nObserving {the line for Manufacturing Procedure B}[0.563, 0.116, 0.970, 0.850], there is an increase from 0.76 to 0.95, followed by a drastic plunge to 0.6 and a slight climb to 0.73. The ups and downs are more acute compared to Manufacturing Procedure A, suggesting more rollicking performance.\nBased on the above thought, the answer is:\nBased on the visual assessment of the chart, Manufacturing Procedure B seems to display a more roller-coaster-like performance across different ratio categories. The line plotting for Procedure B presents more severe fluctuations, exhibiting a high leap followed by a deep fall and then another ascension. This upheaval in the trend line exhibits a more turbulent progression compared to Manufacturing Procedure A, which suggests more potential inconsistencies in Procedure B's performance." } ], "id_raw": 706 }, { "id": "TzA07nNOWS09yGdr", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_6.png", "conversations": [ { "from": "human", "value": "\nBy how much does the performance of Manufacturing Procedure B exceed that of Manufacturing Procedure A at the 0.6 Ratio r?" }, { "from": "gpt", "value": "To find the difference in performances of Manufacturing Procedure A and Manufacturing Procedure B at 0.6 Ratio r, I should identify the corresponding line points for both procedures at this ratio. The points are Procedure A {point}[0.312, 0.127] and Procedure B {point}[0.807, 0.116]. Using the y-values, the performance for Procedure A is 0.9, and for Procedure B it's 0.95. The difference in performance is therefore 0.95 - 0.9 = 0.05.\nBased on the above thought, the answer is:\n0.05." } ], "id_raw": 706 }, { "id": "xwFm5V7bnckr7hRd", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_8.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information about trends and their corresponding values for Video Content into a structured table format?" }, { "from": "gpt", "value": "Observing the chart, I locate {the x-axis labels}[[0.070, 0.449, 0.478, 0.466]] which show the trend stages. Additionally, I focus on the line associated with {Video Content}[0.072, 0.242, 0.475, 0.419] and see the specific values at points {1 and 2}[0.072, 0.419], [0.475, 0.242]. I will use these to construct the table.\nBased on the above thought, the answer is:\nTrend Stage,Value\n1,0.2\n2,0.3" } ], "id_raw": 708 }, { "id": "hov3TXymu13BPPmE", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_8.png", "conversations": [ { "from": "human", "value": "\nWhich line shows a more consistent trend in the first subplot, and how can you tell?" }, { "from": "gpt", "value": "To determine consistency, I analyze the lines in the first subplot. The line for {Video Content}[0.072, 0.242, 0.475, 0.419] varies from a value of 0.2 to 0.3, which shows a consistent increase. Conversely, the line for {Interactive Media}[0.072, 0.064, 0.475, 0.242] starts at 0.3 and increases to 0.4, showing a smaller range of variation and thus, more consistency compared to Video Content.\nBased on the above thought, the answer is:\nThe line for Interactive Media shows a more consistent trend in the first subplot as it has a smaller range in variation, moving from 0.3 to 0.4, which is less varied than the movement from 0.2 to 0.3 in Video Content." } ], "id_raw": 708 }, { "id": "3t1uxI93OLFHoZEb", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of values for the 'Interactive Media' trend between the first and second stage?" }, { "from": "gpt", "value": "To calculate the range, I need to find the maximum and minimum values for 'Interactive Media' at stages 1 and 2. I locate the values at {stage 1}[0.072, 0.242] and {stage 2}[0.475, 0.064]. The values are 0.3 and 0.4 respectively. The range is the difference between the maximum and minimum values, which is 0.4 - 0.3.\nBased on the above thought, the answer is:\n0.1" } ], "id_raw": 708 }, { "id": "ch70opUrywoRkGfB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_9.png", "conversations": [ { "from": "human", "value": "\nCan you extract the values into a structured table listing company names and their production efficiency changes from start to end?" }, { "from": "gpt", "value": "Observing the chart, I analyze each company's line from {the starting point}[[0.054, 0.916], [0.054, 0.829], [0.054, 0.642]] to {the ending point}[[0.473, 0.556], [0.972, 0.829], [0.972, 0.642]] and record the efficiency values corresponding to each company across these points.\nBased on the above thought, the answer is:\nCompany,Start Efficiency,End Efficiency\nTesla,1.0,5.9\nAmazon,3.0,3.2\nLG,2.0,3.2\nPanasonic,5.0,3.3\nMicrosoft,5.0,3.0\nOracle,4.5,3.0\nNokia,2.5,3.1\nToshiba,4.8,3.0\nLamborghini,3.2,3.2\nFerrari,4.7,3.0\nApple,3.0,3.1\nNetflix,2.0,3.3\nSalesforce,3.9,3.0\nCisco,3.0,3.2\nSony,3.0,3.0\nVolkswagen,4.1,3.0\nAudi,4.0,3.0\nGeneral Electric,3.0,3.0" } ], "id_raw": 709 }, { "id": "UDZg8uiyEnac3RjZ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_9.png", "conversations": [ { "from": "human", "value": "\nWhich company shows a drastic increase in production efficiency?" }, { "from": "gpt", "value": "I analyze all the lines, particularly identifying {Tesla's line}[[0.054, 0.916], [0.473, 0.556]] as it starts at the lowest point with a value of 1.0 and ends at 5.9. This signifies a substantial increase compared to other companies whose lines display more modest or negative changes.\nBased on the above thought, the answer is:\nTesla shows a drastic increase in production efficiency. Its line starts at the bottom and rises to the highest efficiency level by the end of the observed period, indicating significant improvement in production efficiency." } ], "id_raw": 709 }, { "id": "A2j5WWSCwofbEkmP", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of efficiency changes for Tesla based on the chart data?" }, { "from": "gpt", "value": "To determine Tesla's range of efficiency changes, I need to locate {Tesla's line start}[[0.054, 0.916]] and {Tesla's line end}[[0.473, 0.556]], and extract the corresponding values of 1.0 at the start and 5.9 at the end. Thus, the range is calculated as 5.9 - 1.0.\nBased on the above thought, the answer is:\n4.9" } ], "id_raw": 709 }, { "id": "dpxViVxAPcOouI64", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data displayed in the line chart into a structured table showcasing the country names as rows and trade types (Imports and Exports) as columns?" }, { "from": "gpt", "value": "To create the table, I first identify {the legend}[0.381, 0.055, 0.491, 0.113] to understand the categories, which are Imports and Exports. Then, I analyze each line in the chart representing different countries, using the labels from the x-axis and translate the data into columns of Imports and Exports alongside the y-axis for trade values. {the lines of Italy and the Netherlands}[0.067, 0.081, 0.475, 0.405], [0.542, 0.537, 0.991, 0.927] exhibit distinctly erratic behavior confirming their import and export values.\nBased on the above thought, the answer is:\nCountry,Imports,Exports\nGermany,134000,148000\nSouth Korea,89000,93000\nFrance,97000,101200\nItaly,76000,118000\nNetherlands,56000,112000\nBrazil,48000,41000\nIndia,17000,15000" } ], "id_raw": 710 }, { "id": "iZyd62R5XF4DVkMK", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_10.png", "conversations": [ { "from": "human", "value": "\nWhich country shows the most stable trend in both imports and exports, and how can this be observed in the chart?" }, { "from": "gpt", "value": "To determine the most stable trend, I assess the variability and consistency in the lines representing each country's trade. Observing {the lines for Germany and South Korea}[0.067, 0.108, 0.970, 0.378], [0.542, 0.537, 0.991, 0.927], both have smoother trajectories with minor fluctuations, indicative of stable trends. Germany\u2019s lines for both imports and exports rise gently, while South Korea\u2019s lines are parallel with a small, consistent gap between them, highlighting stability in trade values over time.\nBased on the above thought, the answer is:\nGermany shows the most stable trend in both imports and exports. The line charts for Germany present gentle, consistent inclines without sharp peaks or troughs, illustrating stable growth in international trade figures." } ], "id_raw": 710 }, { "id": "74aFyM1FSHeBBNHi", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in exports for Germany when comparing the earliest to the latest data point?" }, { "from": "gpt", "value": "To find the increase, I examine {the line points for Germany's exports}[0.067, 0.108, 0.970, 0.378]. The earliest data point is at 120000 and the latest is at 148000. Calculating the increase: 148000-120000 yields the required difference.\nBased on the above thought, the answer is:\n28000." } ], "id_raw": 710 }, { "id": "FSfJ0ucB9esqAFMW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_11.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the bar chart that measures the effectiveness of government policies into a CSV format table?" }, { "from": "gpt", "value": "To begin, I organize the information by identifying each bar which represents a different government policy, locating {the bars for each policy}[0.1, 0.2, 0.9, 0.8]. Then, I look at {the y-axis}[0.05, 0.1, 0.95, 0.2] to understand the range of effectiveness values. Finally, I correlate these with {the policy labels}[0.1, 0.9, 0.2, 0.2] to compile the data into a structured table.\nBased on the above thought, the answer is:\nPolicy,Effectiveness\nImmigration,-5.5\nHealthcare,1.5\nEnvironmental,0.5\nTaxes,-4.5\nEducation,3.0\nForeign Affairs,2.5\nInfrastructure,3.5\nDefence,-0.5" } ], "id_raw": 711 }, { "id": "NFvlxKTclDW7yc1G", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_11.png", "conversations": [ { "from": "human", "value": "\nWhich government policy, based on the chart, shows the most significant shift in its past effectiveness?" }, { "from": "gpt", "value": "Identifying the most changed policy requires examining the length and direction of each bar in the bar plot. Observing the {bars}[0.1, 0.2, 0.9, 0.8], I ascertain that the Immigration policy bar, which extends the furthest below the axis while others remain between -5.0 and 3.5, showing immense negative shift.\nBased on the above thought, the answer is:\nThe Immigration policy shows the most significant shift with the greatest decrease in effectiveness, which is notably more prominent than the rest of the policies, evident from the substantial negative length of its bar on the chart." } ], "id_raw": 711 }, { "id": "nFwx68BIYUCY5ft4", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the average effectiveness of all the studied government policies?" }, { "from": "gpt", "value": "Analyzing the effectiveness of all government policies involves locating each bar on the chart representing a specific policy. I start from {bars for policies}[0.1, 0.2, 0.9, 0.8], extract the values from {the y-axis}[0.05, 0.1, 0.95, 0.2], and calculate the average. The values are -5.5, 1.5, 0.5, -4.5, 3.0, 2.5, 3.5, and -0.5.\nBased on the above thought, the answer is:\n0.125" } ], "id_raw": 711 }, { "id": "XxBubNFXRAPqD8rC", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_12.png", "conversations": [ { "from": "human", "value": "\nCan you create a table representing the values of residential density and economic activity across the various cities according to the given line chart?" }, { "from": "gpt", "value": "Analyzing the line chart, I begin by noting {x-axis labels}[0.243, 0.964, 0.303, 0.981], which contain the names of the cities. The line charts representing residential density and economic activity can be traced through their associated colors and data points. Each point on the lines, marked by specific coordinates, shows the values for the respective city.\nBased on the above thought, the answer is:\nCity,Residential Density,Economic Activity\nNew York,90,75\nLondon,85,70\nMumbai,95,55\nTokyo,80,60\nMoscow,75,65\nSydney,70,68\nCairo,100,50\nJohannesburg,78,58" } ], "id_raw": 712 }, { "id": "G8WjxGIl7bePLeGv", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_12.png", "conversations": [ { "from": "human", "value": "\nWhich city exhibits a significant difference in residential density compared to its economic activity, and why does this stand out?" }, { "from": "gpt", "value": "Reviewing the line graph, I closely flag the data points and line difference between residential density and economic activity. Focusing on {Cairo}[0.342, 0.605], I observe a contrast where the residential density peaks higher (100) compared to economic activity (50). This significant gap is notable because while most cities have closer values between residential density and economic activity, Cairo's is distinctly polarized.\nBased on the above thought, the answer is:\nCairo exhibits a significant difference where the residential density (100) is notably higher than its economic activity (50). This stands out as it suggests a unique situation of high population density paired with lower economic vitality, unlike the more balanced figures seen in other cities." } ], "id_raw": 712 }, { "id": "y4HetzMslCZGDaQT", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the average residential density value across all cities shown in the line graph?" }, { "from": "gpt", "value": "To calculate the average, I refer to the data points representing residential density for each city displayed on the line graph. Extracting values from {New York}[0.067, 0.181], {London}[0.113, 0.134], {Mumbai}[0.159, 0.181], {Tokyo}[0.205, 0.322], {Moscow}[0.250, 0.369], {Sydney}[0.296, 0.510], {Cairo}[0.342, 0.605], {Johannesburg}[0.387, 0.699], we get the values 90, 85, 95, 80, 75, 70, 100, 78 respectively. Adding these and dividing by the number of cities (8) gives the average residential density.\nBased on the above thought, the answer is:\n84.375" } ], "id_raw": 712 }, { "id": "SGK370heN9hkzotX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the trends presented in the line chart into a table format listing the technologies and their associated trend values at each ratio milestone?" }, { "from": "gpt", "value": "To extract the data points for each technology from the line chart, I locate the positions of {the x-tick labels}[[0.015, 0.449, 0.029, 0.466], [0.106, 0.449, 0.120, 0.466], [0.197, 0.449, 0.211, 0.466], [0.288, 0.449, 0.302, 0.466], [0.380, 0.449, 0.393, 0.466], [0.471, 0.449, 0.484, 0.466], [0.562, 0.449, 0.576, 0.466]] to match with the corresponding values of trend indexes from {the lines}[[0.067, 0.064, 0.478, 0.400], [0.067, 0.232, 0.478, 0.381], [0.067, 0.307, 0.478, 0.381], [0.067, 0.251, 0.478, 0.419]].\nBased on the above thought, the answer is:\nTechnology,0.0,0.2,0.4,0.6,0.8,1.0\nErratic Growth,2.3,3.0,2.9,2.7,3.5,2.8\nInconsistent Decline,3.3,3.1,2.8,3.0,2.5,1.4\nSubdued Decline,2.8,2.9,2.6,2.8,3.0,1.1\nMarginal Growth,2.5,2.7,2.9,3.1,3.4,1.8" } ], "id_raw": 713 }, { "id": "iK56PiOBlokrcwiy", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_13.png", "conversations": [ { "from": "human", "value": "\nObserving the trends in the line chart, which technology sector seems to show the most consistent incline in adoption trend values?" }, { "from": "gpt", "value": "To determine which technology sector shows the most consistent incline, I analyze the direction and smoothness of {the line trends}[[0.067, 0.064, 0.478, 0.400], [0.067, 0.232, 0.478, 0.381], [0.067, 0.307, 0.478, 0.381], [0.067, 0.251, 0.478, 0.419]]. Each line\u2019s end point relative to its starting point helps evaluate the incline. Noticing that the Marginal Growth has both a high starting and ending point and consistent upward tendency, this sector seems to have the most consistent incline.\nBased on the above thought, the answer is:\nThe Marginal Growth technology sector shows the most consistent incline in its adoption trend values, as evidenced by the continuous increase in trend index from beginning to end without any dips." } ], "id_raw": 713 }, { "id": "zQXPW2NwugTg7Fpg", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum adoption trend index observed for 'Erratic Growth' across all the provided ratios?" }, { "from": "gpt", "value": "I observe the trend index values for 'Erratic Growth' by examining {the line for Erratic Growth}[[0.067, 0.064, 0.478, 0.400]]. The points' values are found to be 2.3, 3.0, 2.9, 2.7, and 3.5 for ratios 0.1 through 1.0 respectively. The maximum value in this sequence is 3.5.\nBased on the above thought, the answer is:\n3.5" } ], "id_raw": 713 }, { "id": "RIbvTosJcZmIVnqs", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_14.png", "conversations": [ { "from": "human", "value": "\nHow can you convert the data from the line graph showing trends in the Media and Entertainment Industry segments into a structured table format?" }, { "from": "gpt", "value": "Observing the x-axis, I see different labels representing the Media and Entertainment segments such as Music, Film, Gaming, etc., which will form the columns of the table. The y-axis shows trends, which help in understanding the exact values for each segment. By analyzing {the lines in the chart}[0.065, 0.060, 0.317, 0.425] for each segment, I can determine the type of trend (e.g., Dramatic decrease, Slow increase) and convert these observations into rows in a table.\nBased on the above thought, the answer is:\nIndustry,Trend\nMusic,Dramatic Decrease\nFilm,Marginal Decline\nGaming,Slow Increase\nDigital Media,Slow Increase\nLive Events,Dramatic Decrease\nPublishing,Slow Increase\nTV & Radio,Marginal Decline\nToys & Games,Slow Increase\nFashion & Beauty,Dramatic Decrease\nPerforming Arts,Marginal Decline" } ], "id_raw": 714 }, { "id": "Ks1gaVvAEEryN2UN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_14.png", "conversations": [ { "from": "human", "value": "\nWhich industry sector is showing the most positive growth trend in the Media and Entertainment Industry according to the chart?" }, { "from": "gpt", "value": "To determine the sector with the most positive growth trend, I will look at the color and the y-coordinate of each segment line. {The line representing Gaming}[0.191, 0.242] shows a slow increase as indicated by {the green color}[0.191, 0.242] on the line symbolizing a positive trend. Compared to other segments that predominantly show declines, this marks it as the most positively growing sector.\nBased on the above thought, the answer is:\nAccording to the chart, the Gaming industry is showing the most positive growth trend in the Media and Entertainment Industry. This is evident from its position on the y-axis and its representation through a green line, which indicates a slow increase." } ], "id_raw": 714 }, { "id": "V2vRRlyIpCgincfK", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in trend value between the Gaming and Film industries according to the graph?" }, { "from": "gpt", "value": "By analyzing {the point coordinates for Gaming}[0.191, 0.242] and {the point coordinates for Film}[0.128, 0.370], I notice the trend values are 113 for Gaming and 109 for Film. The difference can be computed by subtracting the trend value of Film from Gaming which is 113 - 109.\nBased on the above thought, the answer is:\n4." } ], "id_raw": 714 }, { "id": "SWSwKTqYJFPcGBuP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_15.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data presented in the line chart showing the trend of government policy effectiveness into a CSV format table?" }, { "from": "gpt", "value": "To achieve this conversion, I need to reference {the x-axis labels}[0.055, 0.023, 0.495, 0.468] which indicate policy names, and {the y-axis labels}[0.009, 0.232, 0.018, 0.260] which highlight the effectiveness scores. Each data point on {the policy effectiveness line}[0.075, 0.044, 0.475, 0.448] provides the effectiveness scores for each respective policy over the study period. With each policy named on the x-axis and corresponding effectiveness scores marked on the line graph, I can compile them into a structured CSV table.\nBased on the above thought, the answer is:\nPolicy,Effectiveness\nEconomic Recovery Package,88.5\nPatient Protection and Affordable Care Act,57.0\nSmall Business Jobs Act,44.0\nAmerican Jobs Act,6.5" } ], "id_raw": 715 }, { "id": "igH60pYT8MsVF2Bo", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_15.png", "conversations": [ { "from": "human", "value": "\nWhich government policy stands out as an outlier in terms of effectiveness according to the line chart, and why?" }, { "from": "gpt", "value": "Observing the effectiveness line chart, I locate {the outliers}[0.148, 0.179] specifically {the point for the Patient Protection and Affordable Care Act}[0.148, 0.179] which is significantly lower in effectiveness (57.0) despite being implemented around the same time as other more effective policies like the Economic Recovery Package (88.5). This deviation from the steep decline trend depicted on the line observation marks it as a clear outlier in terms of its effectiveness.\nBased on the above thought, the answer is:\nThe Patient Protection and Affordable Care Act stands out as an outlier in terms of effectiveness. Its effectiveness is significantly lower compared to its contemporaneous policies despite being part of a similar implementation period, defining it uniquely amid the general steep decline observed in the policy effectiveness trend." } ], "id_raw": 715 }, { "id": "DomEF53Q8KrwJTXN", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the total change in effectiveness from the beginning to the end of the policies based on the line chart?" }, { "from": "gpt", "value": "To determine the total change in effectiveness, I first reference {the starting point on the line}[0.075, 0.044] which provides the effectiveness score at the start, 88.5 for the Economic Recovery Package, and {the ending point on the line}[0.475, 0.448] which indicates the effectiveness of the last policy, the American Jobs Act, which is 6.5. The change is calculated by subtracting the ending value from the starting value, 88.5 - 6.5.\nBased on the above thought, the answer is:\n82.0" } ], "id_raw": 715 }, { "id": "KBX4FR2uNkpk8CRn", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_16.png", "conversations": [ { "from": "human", "value": "\nCould you provide a table format of the data from the line chart showing the GDP Growth Rate and Unemployment Rate for the US between 2003 and 2020?" }, { "from": "gpt", "value": "To transform the chart data into a table format, I first identify the two key pieces of information represented by distinct lines on the chart {the GDP Growth Rate line}[0.060, 0.300, 0.950, 0.700] and {the Unemployment Rate line}[0.060, 0.300, 0.950, 0.700]. I then correlate each data point on these lines to its corresponding year along {the x-axis}[0.05, 0.945, 0.100, 0.940] and read off the values as plotted for each year from 2003 to 2020.\nBased on the above thought, the answer is:\nYear,GDP Growth Rate (%),Unemployment Rate (%)\n2003,2.80,6.00\n2004,3.80,5.50\n2005,3.50,5.10\n2006,2.90,4.60\n2007,1.90,5.00\n2008,-0.10,7.30\n2009,-2.50,9.90\n2010,2.60,9.60\n2011,1.60,8.90\n2012,2.20,7.90\n2013,1.80,6.70\n2014,2.50,6.20\n2015,3.10,5.30\n2016,1.70,4.90\n2017,2.30,4.40\n2018,3.00,3.90\n2019,2.20,3.50\n2020,-3.50,8.10" } ], "id_raw": 716 }, { "id": "LbfwGii3G8b4YAuj", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_16.png", "conversations": [ { "from": "human", "value": "\nWhat trends in GDP Growth Rate are evident from the economic events of 2008-2009 financial recession and the COVID-19 pandemic in 2020?" }, { "from": "gpt", "value": "The line chart shows {the GDP Growth Rate line}[0.060, 0.300, 0.950, 0.700] capturing key trends. Prior to the 2008-2009 financial recession, the GDP Growth Rate was increasing, reaching a peak around 2006 at {2006 point}[0.473, 0.588]. During the 2008-2009 recession, there is a noticeable sharp decline, reaching a low in 2009 at {2009 point}[0.565, 0.801]. Following this period, the GDP Growth Rate gradually recovered, demonstrating consistent growth until 2019. However, in 2020, the chart indicates another sharp decline at {2020 point}[0.950, 0.870], reflecting the economic impact of the COVID-19 pandemic.\nBased on the above thought, the answer is:\nThe trends indicate that the GDP Growth Rate was increasing steadily until the 2008-2009 recession when a significant dip occurred, followed by a recovery phase that lasted until another drop in 2020 due to the COVID-19 pandemic, highlighting the sensitivity of economic growth to major global incidents." } ], "id_raw": 716 }, { "id": "W1lu3WMbVD2XCSO6", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_16.png", "conversations": [ { "from": "human", "value": "\nWhat was the difference in Unemployment Rate between the years 2020 and 2009?" }, { "from": "gpt", "value": "To determine the difference in Unemployment Rate between 2020 and 2009, I inspect the values marked on {the Unemployment Rate line}[0.060, 0.300, 0.950, 0.700]. In 2009, the Unemployment Rate was 9.90% at {2009 point}[0.565, 0.861] and in 2020, it was 8.10% at {2020 point}[0.950, 0.650]. Calculating the difference, it is 9.90% - 8.10% = 1.80%.\nBased on the above thought, the answer is:\n1.80%" } ], "id_raw": 716 }, { "id": "i5X6IZm3Vtz1EuRm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_17.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line graph in the form of a table listing Scenarios as columns and Media Outlets as rows?" }, { "from": "gpt", "value": "To transform the line graph data into a table, {the x-axis tick labels}[[0.059, 0.449, 0.130, 0.466], [0.199, 0.449, 0.271, 0.466], [0.344, 0.449, 0.408, 0.466], [0.470, 0.449, 0.564, 0.466], [0.619, 0.449, 0.696, 0.466], [0.764, 0.449, 0.833, 0.466], [0.895, 0.449, 0.984, 0.466]] are considered representing the different Scenarios, which should be table columns. Then, I identify {the legend texts}[[0.955, 0.062, 0.974, 0.080], [0.955, 0.553, 0.974, 0.570]] as the Media Outlets which are to be the rows of the table. For values, I'll associate {the line points}[[0.095, 0.215], [0.235, 0.098], ..., [0.940, 0.391], [0.095, 0.882], [0.235, 0.568], ..., [0.940, 0.898]] to the respective media outlets at the scenario stages using the y-axis labels for measures.\nBased on the above thought, the answer is:\nMedia Outlet,Marginal decline,Rapid expansion,Steady decline,Pronounced decrease,Sluggish progress,Steady increase,Accelerating decline\nBBC,0.82,0.88,0.8,0.76,0.79,0.81,0.73\nCNN,0.39,0.79,0.51,0.5,0.49,0.55,0.37" } ], "id_raw": 717 }, { "id": "hYw3EwPM2k6n8kwC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_17.png", "conversations": [ { "from": "human", "value": "\nBased on the line graph trends, which media outlet seems to exhibit more fluctuating influence across the scenarios?" }, { "from": "gpt", "value": "To determine which media outlet has more fluctuating influence, I'll analyze the line plots for each outlet. Looking at {the line for BBC}[[0.095, 0.098, 0.940, 0.391]], it shows changes from 0.82 to 0.88, then to 0.8, 0.76, 0.79, 0.81, and finally 0.73, implying a degree of oscillation.\nExamining {CNN}[[0.095, 0.568, 0.940, 0.898]], the values deviate from 0.39 to 0.79, then to 0.51, 0.5, 0.49, 0.55, and end with 0.37. Despite some fluctuations, the visual pattern suggests a general downward trend.\nBased on the above thought, the answer is:\nBased on the line graph trends, CNN appears to have a more fluctuating influence across the scenarios. It starts with a significant increase from 'Marginal decline' to 'Rapid expansion', followed by continuous changes until 'Accelerating decline'. Despite the overall descending inclination, the journey from the highest point at 'Rapid expansion' to the lowest at 'Accelerating decline' still shows noteworthy fluctuations." } ], "id_raw": 717 }, { "id": "eVIYkhjFma24jn7F", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the disparity between BBC's influence measure at the scenarios of Steady decline and Steady increase?" }, { "from": "gpt", "value": "First, I need to locate {the line points for BBC at 'Steady decline' and 'Steady increase'}[[0.376, 0.254], [0.799, 0.235]] and extract the corresponding values, which are 0.8 and 0.81, respectively. The difference between 'Steady increase' and 'Steady decline' can be calculated as 0.81 - 0.8 = 0.01.\nBased on the above thought, the answer is:\n0.01." } ], "id_raw": 717 }, { "id": "8XdOAGFT8u5QFkIK", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_36_18.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, can the data points related to social networks and interpersonal relationships indices be structured into a two-column CSV format?" }, { "from": "gpt", "value": "To structure the data points into a two-column CSV format, I first identify {the x-axis labels}[[0.1, 0.9, 0.12, 0.94]] which denote 'Social Networks' and 'Interpersonal Relationships' as the respective column headers. I then examine {the line points with their respective y-values}[[0.1, 0.25], [0.9, 0.75], [0.6, 0.65]] and extract the values from these coordinates corresponding to each category.\nBased on the above thought, the answer is:\n\"Social Networks\",\"Interpersonal Relationships\"\n0.25,0.75" } ], "id_raw": 718 }, { "id": "8sVDDKS6iGgTr2TN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_36_18.png", "conversations": [ { "from": "human", "value": "\nHow does the trend in connectivity change from social networks to interpersonal relationships based on the chart?" }, { "from": "gpt", "value": "Examining the chart, I observe {the line trajectory starting from social networks to interpersonal relationships}[[0.1, 0.25], [0.9, 0.75]]. The trend begins at a lower connectivity index for social networks and ascends to a higher index for interpersonal relationships. This upward movement indicates a positive change in connectivity, reflecting an overall improvement as we move from social networks to closer, more personal interactions.\nBased on the above thought, the answer is:\nThe trend in connectivity notably improves from social networks to interpersonal relationships. Beginning at a lower index, the data clearly shows an ascending trend towards a higher connectivity index, illustrating a progressive improvement as interpersonal interactions become more personal and engaged." } ], "id_raw": 718 }, { "id": "OGV05Ci5JmRs7qvJ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_36_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the connectivity index increase from social networks to interpersonal relationships based on the chart?" }, { "from": "gpt", "value": "To calculate the connectivity index increase, I look at {the y-values for social networks}[[0.1, 0.25]] and {the y-values for interpersonal relationships}[[0.9, 0.75]]. The difference is calculated by subtracting the social networks index from the interpersonal relationships index: 0.75 - 0.25.\nBased on the above thought, the answer is:\n0.50" } ], "id_raw": 718 }, { "id": "1ZaDHWZMcsDwbsn4", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_37_0.png", "conversations": [ { "from": "human", "value": "\nCan you structure the viewership data for SVoD and Linear TV across three years into a CSV format?" }, { "from": "gpt", "value": "First, I'll take notice of {the x-axis labels which represent the years}[0.086, 0.903, 0.112, 0.931], [0.168, 0.903, 0.194, 0.931], [0.250, 0.903, 0.276, 0.931] indicating 2018, 2019, and 2020 respectively. Then, I identify {the legend}[0.087, 0.059, 0.257, 0.151] to differentiate data for 'Subscription Video on Demand' and 'Linear TV'. Extracting viewership values from {line plot points for SVoD and Linear TV}[0.099, 0.556], [0.181, 0.456], ..., [0.427, 0.157], will populate their respective year columns.\nBased on the above thought, the answer is:\nYear,SVoD,Linear TV\n2018,50 million,100 million\n2019,60 million,95 million\n2020,80 million,90 million" } ], "id_raw": 720 }, { "id": "B324T5kFURzCLf27", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_37_0.png", "conversations": [ { "from": "human", "value": "\nWhich service demonstrated a stronger growth trend from 2018 to 2020 according to the line chart?" }, { "from": "gpt", "value": "To determine which service demonstrated a stronger growth trend, I observe {the line points for SVoD}[0.099, 0.556], [0.140, 0.481], [0.263, 0.394] and see an upward trajectory indicating growth. Meanwhile, {the line points for Linear TV}[0.099, 0.494], [0.140, 0.481], [0.263, 0.606] show a downward trajectory, indicating a decline. The visual comparison clearly shows that SVoD not only grew but also increased at a more rapid pace towards 2020.\nBased on the above thought, the answer is:\nSubscription Video on Demand (SVoD) demonstrated a stronger growth trend from 2018 to 2020. The upward trajectory of its line plot clearly outpaced the consistent decline seen in Linear TV's viewership." } ], "id_raw": 720 }, { "id": "vdjIetIVpkMJo97E", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_37_0.png", "conversations": [ { "from": "human", "value": "\nWhat was the average annual decrease in viewership for Linear TV from 2018 to 2020?" }, { "from": "gpt", "value": "Observing the decreasing trend from the {Linear TV line points}[0.099, 0.494], [0.140, 0.481], [0.263, 0.606] which represent the viewership values from 2018 to 2020, I calculate the decrease each year: from 100 million in 2018 to 95 million in 2019 (5 million decrease), and from 95 million in 2019 to 90 million in 2020 (5 million decrease). The average annual decrease is the sum of these decreases divided by the number of years, which is (5+5)/3.\nBased on the above thought, the answer is:\n3.33 million viewers" } ], "id_raw": 720 }, { "id": "bPbKaCQKctymvIs6", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_37_1.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table of the loan amounts disbursed for Public and Government Schools over the years?" }, { "from": "gpt", "value": "To create the table from the chart, I observe {the x-axis tick labels}[[0.103, 0.903, 0.138, 0.931], [0.143, 0.903, 0.179, 0.931], [0.184, 0.903, 0.219, 0.931], [0.225, 0.903, 0.260, 0.931], [0.265, 0.903, 0.300, 0.931], [0.306, 0.903, 0.341, 0.931], [0.346, 0.903, 0.382, 0.931], [0.387, 0.903, 0.422, 0.931], [0.428, 0.903, 0.463, 0.931], [0.468, 0.903, 0.503, 0.931]] which indicate the years. I use the y-coordinates to estimate the loan amounts visually from {the line points}[[0.120, 0.797], [0.161, 0.818], [0.202, 0.563], [0.242, 0.481], [0.283, 0.329], [0.323, 0.426], [0.364, 0.274], [0.405, 0.164], [0.445, 0.143], [0.486, 0.068]] for Public School and similar coordinates for Government School. These loan amounts are color-coded in the chart, represented by orange and blue lines.\nBased on the above thought, the answer is:\nYear,Public School Loan Amount,Government School Loan Amount\n2009,30000,31500\n2010,28500,32750\n2011,47000,35875\n2012,53000,35500\n2013,64000,38750\n2014,57000,42350\n2015,68000,47520\n2016,76000,47010\n2017,77500,53000\n2018,83000,59500" } ], "id_raw": 721 }, { "id": "3j0JwCaIUMa563zt", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_37_1.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the trend of loan amounts for Public and Government Schools from 2009 to 2018?" }, { "from": "gpt", "value": "Looking at {the line for Public School Loan Amount}[0.120, 0.797, 0.486, 0.068] and {the line for Government School Loan Amount}[0.120, 0.777, 0.486, 0.391], there is an overall increasing trend in loan amounts for both Public and Government Schools over the span from 2009 to 2018. The curvature of the lines shows a steady rise, with some fluctuations\u2014Public School loans tend to be consistently higher, with a more pronounced rise. In contrast, the Government School's line depicts a steadier increase, demonstrating progressive policies over the years.\nBased on the above thought, the answer is:\nFrom the analysis of the chart, it is evident that there is an increasing trend in the loan amounts disbursed to both Public and Government Schools from 2009 to 2018. The consistent rise can be interpreted as a substantial public and governmental commitment to supporting education through increasing financial aid and resources." } ], "id_raw": 721 }, { "id": "0C9e0Ly3TzmwKbVu", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_37_1.png", "conversations": [ { "from": "human", "value": "\nHow many more loan amounts were given to Government Schools compared to Public Schools in 2017?" }, { "from": "gpt", "value": "To calculate the difference for 2017 between the loan amounts for Government Schools and Public Schools, I firstly locate the amounts from {the line points for 2017}[[0.445, 0.143], [0.445, 0.481]] for Public School and Government School respectively. The corresponding values are 77500 for Public School loan and 53000 for Government School loan. The difference is then calculated as 53000 - 77500.\nBased on the above thought, the answer is:\n-24500" } ], "id_raw": 721 }, { "id": "1PaDfVNuCsgxQKPK", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_37_4.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data of the various sectors over each quarter into a structured table?" }, { "from": "gpt", "value": "To generate the table data from the line chart, I first identify the tick labels on {the x-axis}[[0.117, 0.903, 0.126, 0.931], [0.323, 0.903, 0.331, 0.931], [0.528, 0.903, 0.537, 0.931], [0.734, 0.903, 0.743, 0.931], [0.940, 0.903, 0.948, 0.931]], which will form the column headers representing the quarters, mentioned as \"1\", \"2\", \"3\", \"4\", \"5\" respectively. Next, I locate the legends {Supply Chain, Procurement, Manufacturing, Delivery, After-sale Service}[0.491, 0.635, 0.613, 0.831] which will serve as the row headers representing the different sectors. Finally, I extract the values which form the cells in the table corresponding to each sector and quarter from {the line points}[[0.121, 0.353, 0.944, 0.640], [0.121, 0.138, 0.944, 0.425], [0.121, 0.066, 0.944, 0.425], [0.121, 0.497, 0.944, 0.784], [0.121, 0.209, 0.944, 0.568]].\nBased on the above thought, the answer is:\nQuarter,Supply Chain,Procurement, Manufacturing, Delivery, After-sale Service\nQuarter 1,0.8,1.1,1.5,1.0,0.9\nQuarter 2,1.1,1.2,1.6,0.7,1.0\nQuarter 3,1.2,1.3,1.3,0.9,1.1\nQuarter 4,1.0,1.4,1.2,0.6,1.3\nQuarter 5,0.9,1.5,1.1,0.7,1.4" } ], "id_raw": 724 }, { "id": "jkFFKzLpH5sjAdXh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_37_4.png", "conversations": [ { "from": "human", "value": "\nObserving the trend in the line chart, which sector appears to have the most consistently increasing efficiency across the quarters?" }, { "from": "gpt", "value": "To determine the sector with the most consistently increasing efficiency, I need to see the trend of values for each of the line plots in the sectors. By observing {the line for Supply Chain}[[0.121, 0.640], [0.327, 0.425], [0.533, 0.353], [0.738, 0.497], [0.944, 0.568]], I see a slight increase and decrease, indicating some variation. The line plot for {Procurement}[[0.121, 0.425], [0.327, 0.353], [0.533, 0.281], [0.738, 0.209], [0.944, 0.138]] shows a consistent upward trend. For the other sectors, such as {Manufacturing}[[0.121, 0.138], [0.327, 0.066], [0.533, 0.281], [0.738, 0.353], [0.944, 0.425]], {Delivery}[[0.121, 0.497], [0.327, 0.712], [0.533, 0.568], [0.738, 0.784], [0.944, 0.712]], and {After-sale Service}[[0.121, 0.568], [0.327, 0.497], [0.533, 0.425], [0.738, 0.281], [0.944, 0.209]], all show variations in efficiency across the quarters.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Procurement sector appears to have the most consistently increasing efficiency across the quarters. The line plot for procurement shows a steady upward trend with each passing quarter, indicating an increase in efficiency over time. Unlike the other sectors, which show more unpredictable variations in efficiency, the Procurement sector demonstrates a consistent increase from quarter 1 to 5." } ], "id_raw": 724 }, { "id": "TMuq3c8gnjH1KDIh", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_37_4.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, what is the difference in efficiency between the Procurement sector in Quarter 1 and Quarter 5?" }, { "from": "gpt", "value": "To find the difference in efficiency, I need to locate {the line points for Procurement}[[0.121, 0.425], [0.327, 0.353], [0.533, 0.281], [0.738, 0.209], [0.944, 0.138]]. The efficiency in Quarter 1 is located at {the point}[0.121, 0.425], which corresponds to a value of 1.1, and in Quarter 5 is located at {the point}[0.944, 0.138], corresponding to an efficiency value of 1.5. The difference between the efficiency of Procurement in Quarter 5 and Quarter 1 is therefore 1.5 - 1.1 = 0.4.\nBased on the above thought, the answer is:\n0.4" } ], "id_raw": 724 }, { "id": "7t1e2YQYhdqSZEhA", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_37_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the historical course registration data from the line chart into a structured table format with Years as columns and Courses as rows?" }, { "from": "gpt", "value": "Observing the chart, I first notice {the x-axis labels}[0.103, 0.903, 0.112, 0.931] representing the years from '2015' to '2020'. These labels serve as column headers. Then, I identify {the legend}[0.096, 0.044, 0.191, 0.136] which details the three courses: Web Design, AI, and Business Management as row headers. I locate {the data points for each course}[0.108, 0.068, 0.480, 0.443], [0.593, 0.068, 0.966, 0.362] distributed over the lines representing each course for the respective years and extract these registrations values.\nBased on the above thought, the answer is:\nYear, Web Design, AI, Business Management\n2015, 1000, 2500, 5000\n2016, 1500, 3500, 4700\n2017, 1800, 4500, 4500\n2018, 2000, 6000, 4300\n2019, 2200, 8000, 4100\n2020, 2300, 9500, 4000" } ], "id_raw": 726 }, { "id": "TYJObwnByR2JGQ1x", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_37_6.png", "conversations": [ { "from": "human", "value": "\nWhich course demonstrated the highest growth rate in registrations over the observed years according to the chart?" }, { "from": "gpt", "value": "To evaluate the highest growth rate, I analyze {the slopes of the lines representing the courses}[0.108, 0.068, 0.480, 0.443], [0.593, 0.068, 0.966, 0.362]. The AI course line, which initially starts at 2500 registrations in 2015 at [0.593, 0.215] and spikes to 9500 by 2020 at [0.966, 0.156], depicts a steep gradient relative to the other courses. This suggests a much faster growth compared to the moderate increase in Web Design and the decrease in Business Management courses.\nBased on the above thought, the answer is:\nThe AI course demonstrated the highest growth rate in registrations over the observed years, as its line on the chart shows the steepest ascending slope, indicating a vigorous increase." } ], "id_raw": 726 }, { "id": "Dw07FbJ8PjtEXBCQ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_37_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in registrations for the AI course from 2015 to 2020?" }, { "from": "gpt", "value": "To calculate the total increase, I need to observe {the data points for the AI course}[0.593, 0.068, 0.966, 0.362]. The number of registrations depicted in 2015 is 2500, located at [0.593, 0.215], while those in 2020 are 9500, located at [0.966, 0.156]. The increase is simply calculated as 9500 (2020 registrations) minus 2500 (2015 registrations).\nBased on the above thought, the answer is:\n7000." } ], "id_raw": 726 }, { "id": "zPizkbG2yt2BQe0A", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_37_8.png", "conversations": [ { "from": "human", "value": "\nCan you extract the values of each social network's user counts into a table format for each year?" }, { "from": "gpt", "value": "To create the table, I first notice {the x-axis tick labels}[[0.094, 0.903, 0.129, 0.931], [0.184, 0.903, 0.219, 0.931], [0.275, 0.903, 0.310, 0.931], [0.365, 0.903, 0.400, 0.931], [0.455, 0.903, 0.490, 0.931]], which represent the years. {The subplot legends}[[0.100, 0.044, 0.216, 0.136], [0.593, 0.044, 0.714, 0.136]] correspond to the social networks. Utilizing this, I can form the column headers as the social networks and the rows for each year. The values are then extracted from each {line points}[[0.111, 0.419], [0.202, 0.367], [0.292, 0.262], [0.383, 0.163], [0.473, 0.065], [0.111, 0.766], [0.202, 0.762], [0.292, 0.761], [0.383, 0.760], [0.473, 0.762], [0.604, 0.457], [0.694, 0.337], [0.785, 0.257], [0.875, 0.058], [0.965, 0.058], [0.604, 0.616], [0.694, 0.537], [0.785, 0.457], [0.875, 0.377], [0.965, 0.317]] of respective social networks for each year.\nBased on the above thought, the answer is:\nYear,Facebook Users (Millions),Twitter Users (Millions),WhatsApp Users (Millions),Instagram Users (Millions)\n2016,1543,317,1000,600\n2017,1729,330,1300,800\n2018,2098,335,1500,1000\n2019,2449,340,2000,1200\n2020,2796,330,2000,1350" } ], "id_raw": 728 }, { "id": "dViqhjLf3fWEaDT6", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_37_8.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which social media platform has had the most consistent growth? Explain your reasoning." }, { "from": "gpt", "value": "To determine which social media platform has had the most consistent growth, I need to examine the line plots for each platform. Observing {the line for Facebook}[0.111, 0.065, 0.473, 0.419], it shows an upward trend in user counts from 1543 million to 2796 million. Meanwhile, {the line for Twitter}[0.111, 0.760, 0.473, 0.766] exhibits a very slight uptrend followed by leveling off. Looking at {the line for WhatsApp}[0.604, 0.058, 0.965, 0.457], it shows a consistent increase from 1000 million to 2000 million users but then horizontally straight from 2019 to 2020. Finally, {the line for Instagram}[0.604, 0.317, 0.965, 0.616] shows an upward trend but at a slower pace than Facebook.\nBased on the above thought, the answer is:\nBased on the trends, Facebook has had the most consistent growth. Its user count consistently increased over the years, from 1543 million in 2016 to 2796 million in 2020. The growth trajectory of Facebook is also steeper than other social media platforms in the line chart, signifying a more rapid and consistent growth rate." } ], "id_raw": 728 }, { "id": "CSPxFMCUDzA477SY", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_37_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase of Instagram users from 2016 to 2020?" }, { "from": "gpt", "value": "To find the total increase of Instagram users from 2016 to 2020, I look at {the line points for Instagram}[[0.604, 0.616], [0.694, 0.537], [0.785, 0.457], [0.875, 0.377], [0.965, 0.317]]. The user count in 2016 is 600 million at {point}[0.604, 0.616] and in 2020 is 1350 million at {point}[0.965, 0.317]. The total increase is therefore 1350 - 600 = 750 million.\nBased on the above thought, the answer is:\n750 million." } ], "id_raw": 728 }, { "id": "icM9TAUYbPGu0V24", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_37_11.png", "conversations": [ { "from": "human", "value": "\nBased on the line graph, could you extract the data into a table with categories (teenagers and adults) as columns and behaviors (impulsive and procrastination) as rows?" }, { "from": "gpt", "value": "To get the table from the line graph, I first recognize {the x-axis tick labels}[[0.033, 0.903, 0.118, 0.931], [0.076, 0.903, 0.159, 0.931], [0.112, 0.903, 0.208, 0.931], [0.173, 0.903, 0.232, 0.931], [0.228, 0.903, 0.262, 0.931], [0.263, 0.903, 0.311, 0.931], [0.312, 0.903, 0.347, 0.931], [0.344, 0.903, 0.399, 0.931], [0.393, 0.903, 0.435, 0.931], [0.422, 0.903, 0.491, 0.931]] as the column headers: 'Teenagers' and 'Adults'. Next, I see {the legend texts}[[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156], [0.884, 0.167, 0.946, 0.189]] which represent the rows: 'Impulsive Behavior' and 'Procrastination'. Each of these datasets is represented by {the line points}[[0.102, 0.470], [0.218, 0.082], [0.334, 0.179], [0.450, 0.534], [0.566, 0.632], [0.682, 0.437], [0.798, 0.243]] in the line plot, from which I can extract the corresponding values to fill in our table.\nBased on the above thought, the answer is:\nCategory, Teenagers, Adults\nImpulsive Behavior, 80, 60\nProcrastination, 75, 50" } ], "id_raw": 731 }, { "id": "Hf60Uv65koVZ8EWq", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_37_11.png", "conversations": [ { "from": "human", "value": "\nExamining the line chart, which group, teenagers or adults, tends to exhibit more impulsive behavior and procrastination, and why?" }, { "from": "gpt", "value": "To answer this question, I need to analyze the line graph. Looking at the line plot for {Teenagers}[0.075, 0.081, 0.456, 0.750], the behavior rates for impulsive behavior and procrastination are higher relative to the {Adults}[0.118, 0.328, 0.160, 0.433] line plot, whose behavior rates for impulsive behavior and procrastination are lower.\nBased on the above thought, the answer is:\nAccording to the line chart, Teenagers tend to exhibit more impulsive behavior and procrastination. This is because both the rates of impulsive behavior and procrastination are noticeably higher for Teenagers as compared to Adults, as visualized by the respective line plots on the graph." } ], "id_raw": 731 }, { "id": "P3mwtPvjEKafDMr3", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_37_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the average rate of impulsive behavior for teenagers and adults as indicated in the line graph?" }, { "from": "gpt", "value": "To calculate the average rate of impulsive behavior for teenagers and adults, I need to find {the line points for impulsive behavior for teenagers}[[0.075, 0.504], [0.118, 0.328]], which represent the rates: 80 and 60 respectively. The average is therefore (80+60)/2 = 70.\nBased on the above thought, the answer is:\n70." } ], "id_raw": 731 }, { "id": "bU10cuDQbvufV7yT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_37_12.png", "conversations": [ { "from": "human", "value": "\nCan we transform the year-wise data of 'Population Density' and 'Renewable Energy Production' from the line chart to table form?" }, { "from": "gpt", "value": "I can convert the visual data from the chart to tabular form by locating {the x-axis labels}_{the x-axis tick labels}[[0.098, 0.903, 0.133, 0.931], [0.170, 0.903, 0.205, 0.931], [0.241, 0.903, 0.276, 0.931], [0.313, 0.903, 0.348, 0.931], [0.384, 0.903, 0.419, 0.931], [0.456, 0.903, 0.491, 0.931]] as the 'Year', {line plot for 'Population Density'}{line_0}[0.116, 0.793, 0.473, 0.818] and {line plot for 'Renewable Energy Production'}{line_1}[0.116, 0.068, 0.473, 0.294] as the rows for 'Population Density' and 'Renewable Energy Production' respectively.\nBased on the above thought, the answer is:\nYear,Population Density,Renewable Energy Production\n2020,52.8,500\n2021,37.8,520\n2022,40.8,560\n2023,50.9,615\n2024,59.2,550\n2025,60.3,700" } ], "id_raw": 732 }, { "id": "eua4v1wg2jzmgmqB", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_37_12.png", "conversations": [ { "from": "human", "value": "\nBased on the comparative trends in the line chart, can it be inferred that a higher population density corresponds to higher renewable energy production?" }, { "from": "gpt", "value": "The trends of both parameters should be considered here. By observing {Population Density line plot}[0.116, 0.793, 0.473, 0.818], initially, I see that population density decreases from 52.8 in 2020 to 37.8 in 2021, slightly increases to 40.8 in 2022 and then shows an increasing trend till 2025. On the other hand, looking at the {Renewable Energy Production line plot}[0.116, 0.068, 0.473, 0.294], the renewable energy production consistently shows an increasing trend from 500 in 2020 to 700 in 2025. So, while the population density shows fluctuations, the renewable energy production shows a consistently increasing trend. Therefore, we cannot conclusively infer that a higher population density corresponds to higher renewable energy production based on these trends.\nBased on the above thought, the answer is:\nNo, we cannot conclusively infer that a higher population density corresponds to higher renewable energy production based on the trends depicted in the line chart." } ], "id_raw": 732 }, { "id": "bbnETSl7hF9mXcaP", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_37_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the population densities of the year 2020 and 2022 according to the line chart?" }, { "from": "gpt", "value": "The population densities for the years 2020 and 2022 can be obtained from the {Points on Population Density line plot}[[0.116, 0.801], [0.259, 0.815]], which represent 52.8 for 2020 and 40.8 for 2022. The difference can be calculated as 52.8 - 40.8 = 12.\nBased on the above thought, the answer is:\n12" } ], "id_raw": 732 }, { "id": "TgKGm8fp79AZymn9", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_37_13.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data of the graph into a table format, with Time as rows and Measures as columns?" }, { "from": "gpt", "value": "To begin converting the graph to a table format, firstly, I identify {the x-axis tick labels}[0.094, 0.903, 0.103, 0.931], [0.150, 0.903, 0.168, 0.931], [0.206, 0.903, 0.232, 0.931], [0.267, 0.903, 0.293, 0.931], [0.327, 0.903, 0.353, 0.931], [0.388, 0.903, 0.414, 0.931], [0.448, 0.903, 0.474, 0.931]\nwhich serve as row labels for time in months. \nThen, I look at {the legend texts}[0.132, 0.055, 0.246, 0.083], [0.132, 0.097, 0.250, 0.125] as column headers signifying the Measures. For data extraction I locate {the line points for Social Inequality}[0.098, 0.065, 0.461, 0.737] and {the line points for Rights Protection}[0.098, 0.421, 0.461, 0.757] at each respective time and extract corresponding values.\nBased on the above thought, the answer is:\nTime (months),Social Inequality,Rights Protection\n0,1.4,1.0\n50,2.0,0.9\n100,1.6,1.1\n150,0.9,0.9\n200,0.3,0.8\n250,0.9,0.25\n300,1.0,0.3" } ], "id_raw": 733 }, { "id": "Wad8zTGyF0kt8gIP", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_37_13.png", "conversations": [ { "from": "human", "value": "\nJudging by the graphs, which measure remains more stable over time - Social Inequality or Rights Protection? Provide your reasoning." }, { "from": "gpt", "value": "To determine the stability of the measures, I need to examine the variability in the data points for each one. Observing {the line for Social Inequality}[0.098, 0.065, 0.461, 0.737], it displays high variability, with a rise from 1.4 to 2.0, a drop to 0.3, and an increase again to 1.0. In contrast, the {line for Rights Protection}[0.098, 0.421, 0.461, 0.757] also varies but not as significantly, with values ranging between 1.1 and 0.3.\nBased on the above thought, the answer is:\nThe Rights Protection measure remains more stable over time. Although both measures showcase fluctuations, the Rights Protection line exhibits smaller changes when compared to the Social Inequality line, indicating lesser fluctuation and therefore, greater stability." } ], "id_raw": 733 }, { "id": "QQXjIJwwr407le2Y", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_37_13.png", "conversations": [ { "from": "human", "value": "\nAfter how many months does the Intensity of Social Inequality first drop below 1?" }, { "from": "gpt", "value": "I need to find out when the Intensity of Social Inequality first falls below 1. To do this, I look at {the data points for Social Inequality}[0.098, 0.065, 0.461, 0.737], the corresponding Intensity values are 1.4, 2.0, 1.6, 0.9, 0.3, 0.9, 1.0. The first time value where the Intensity drops below 1 corresponds to {150}[0.267, 0.903, 0.293, 0.931].\nBased on the above thought, the answer is:\n150 months." } ], "id_raw": 733 }, { "id": "2DNCur74bAZMAXW8", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_37_14.png", "conversations": [ { "from": "human", "value": "\nPlease convert the plotted data into a structured table form with time intervals as the rows and 'On Fire' values and 'Ice Cool' values as the columns." }, { "from": "gpt", "value": "To create the tabular data, I'll start by identifying {the x-axis tick labels}[[0.090, 0.903, 0.099, 0.931], [0.184, 0.903, 0.193, 0.931], [0.278, 0.903, 0.286, 0.931], [0.371, 0.903, 0.380, 0.931], [0.465, 0.903, 0.474, 0.931]] representing the rows which are time intervals. The column headers are represented by {the legend labels}[[0.127, 0.074, 0.176, 0.102], [0.747, 0.074, 0.845, 0.102]]. The 'On Fire' values can be retrieved from {the line points for 'On Fire'}[[0.095, 0.261], [0.188, 0.595], [0.282, 0.717], [0.376, 0.081], [0.469, 0.220]]. Also, 'Ice Cool' values from {the line points for 'Ice Cool'}[[0.591, 0.383], [0.685, 0.660], [0.779, 0.689], [0.873, 0.372], [0.966, 0.303]].\nBased on the above thought, the answer is:\nTime Intervals, On Fire Value, Ice Cool Value\n1, 73, 82\n2, 32, 34\n3, 17, 29\n4, 95, 84\n5, 78, 96" } ], "id_raw": 734 }, { "id": "8ydUGZA2gCFLlvRO", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_37_14.png", "conversations": [ { "from": "human", "value": "\nBased on the charts, is there a direct correlation between the 'On Fire' and 'Ice Cool' trends across the time intervals?" }, { "from": "gpt", "value": "To analyze the correlation between 'On Fire' and 'Ice Cool' trends, I need to examine {the plot of 'On Fire'}[0.095, 0.081, 0.469, 0.717] and {the plot of 'Ice Cool'}[0.591, 0.293, 0.966, 0.699]. The 'On Fire' plot starts at a high point, decreases significantly, increases again, and then has a gradual decline. On the other hand, the 'Ice Cool' plot shows both decrease and increase but as a general trend, it decreases. While both plots exhibit declines and rises, their rates and timings don't directly correspond.\nBased on the above thought, the answer is:\nWhile both the 'On Fire' and 'Ice Cool' trends have their peaks and troughs across the time intervals, they seem to not mirror each other perfectly, indicating that there is not a strong direct correlation between the two variables. They both exhibit volatile behaviors in different ways which does not necessarily establish a direct correlation." } ], "id_raw": 734 }, { "id": "dcaAJ0B8DeyaO15G", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_37_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, what is the average value of the 'On Fire' variable across all time intervals?" }, { "from": "gpt", "value": "To find the average of the 'On Fire' values, I'll have to find {the points for 'On Fire'}[[0.095, 0.261], [0.188, 0.595], [0.282, 0.717], [0.376, 0.081], [0.469, 0.220]], where the corresponding values are 73, 32, 17, 95, and 78. Adding these and dividing by the number of time intervals (5), will yield the average.\nBased on the above thought, the answer is:\n59" } ], "id_raw": 734 }, { "id": "uDs5nqqgG78DChdI", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_37_17.png", "conversations": [ { "from": "human", "value": "\nCan you produce a structured table from the bar graph displaying growth percentages for both Western and Eastern civilizations during the Industrial Age and Digital Age?" }, { "from": "gpt", "value": "Analyzing the two bar graphs, I first recognize the labeling of the sections. Starting with the digital representation of the growth percentages for both civilizations, I extract values based on the lengths of {the bars for Western civilization}[0.450, 0.200, 0.750, 0.300] and {the bars for Eastern civilization}[0.450, 0.350, 0.680, 0.450] during {the Industrial Age}[0.100, 0.150, 0.300, 0.500] and {the Digital Age}[0.550, 0.150, 0.750, 0.500]. These visuals and coordinates are interpreted to represent the respective growth percentages.\nBased on the above thought, the answer is:\nPeriod,Civilization,Growth Percentage\nIndustrial Age,Western,70.4\nIndustrial Age,Eastern,63.6\nDigital Age,Western,89.2\nDigital Age,Eastern,80.1" } ], "id_raw": 737 }, { "id": "fQlY5JSDGTpOh4h0", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_37_17.png", "conversations": [ { "from": "human", "value": "\nWhat trend can be observed in the growth of civilizations from the Industrial Age to the Digital Age based on the bar graph?" }, { "from": "gpt", "value": "By examining the progression of the bars, specifically {the bar for the Western civilization}[0.450, 0.150, 0.750, 0.250] showing growth from the Industrial Age to the Digital Age, there's a visible increase in bar length, indicating a higher growth percentage. Similarly, for {the Eastern civilization}[0.450, 0.350, 0.680, 0.450], there's also an increase, but slightly less pronounced compared to the Western civilization. This suggests that while both civilizations show growth in moving from the Industrial Age to the Digital Age, Western civilization exhibits a relatively faster growth.\nBased on the above thought, the answer is:\nThe trend observed is a clear increase in growth percentages from the Industrial Age to the Digital Age for both civilizations. Western civilization shows a more rapid increase compared to Eastern civilization, indicating a faster rate of development in recent periods." } ], "id_raw": 737 }, { "id": "fc83G5ivU3ZOTIxT", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_37_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage point difference in growth between the Digital Age and the Industrial Age for the Western civilization?" }, { "from": "gpt", "value": "To calculate the percentage point difference for the Western civilization, I refer to the values indicated by {the bar for Western civilization in the Industrial Age}[0.450, 0.200, 0.750, 0.300] and {the bar for Western civilization in the Digital Age}[0.450, 0.150, 0.750, 0.250]. The value for the Industrial Age is 70.4 and for the Digital Age is 89.2. The difference is computed as 89.2 - 70.4.\nBased on the above thought, the answer is:\n18.8" } ], "id_raw": 737 }, { "id": "mvvj52wM1bYfh7KU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_37_18.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data from the bar chart in a table format with columns for policy and effectiveness evaluation?" }, { "from": "gpt", "value": "To generate the tabular data from the bar chart, I must first look at {the x-axis tick labels}[0.090, 0.903, 0.099, 0.931], [0.132, 0.903, 0.141, 0.931], [0.175, 0.903, 0.183, 0.931], [0.217, 0.903, 0.225, 0.931], [0.259, 0.903, 0.268, 0.931], [0.301, 0.903, 0.310, 0.931], [0.343, 0.903, 0.352, 0.931], [0.385, 0.903, 0.394, 0.931], [0.427, 0.903, 0.436, 0.931], [0.469, 0.903, 0.478, 0.931], which represent the effectiveness evaluation scores. Next, I identify {the bar values}[0.095, 0.093, 0.474, 0.522], [0.587, 0.066, 0.966, 0.556] as the policy effectiveness evaluations. These values will be in the second column. The first column will be the policy names, which I recognize as the {y-axis tick labels}[0.036, 0.845, 0.052, 0.873], [0.035, 0.771, 0.052, 0.799], [0.036, 0.697, 0.052, 0.725], [0.036, 0.622, 0.052, 0.650], [0.036, 0.548, 0.052, 0.576], [0.036, 0.474, 0.052, 0.502], [0.035, 0.400, 0.052, 0.428], [0.036, 0.326, 0.052, 0.354], [0.035, 0.252, 0.052, 0.280], [0.035, 0.178, 0.052, 0.206], [0.035, 0.104, 0.052, 0.132], [0.035, 0.030, 0.052, 0.058].\nBased on the above thought, the answer is:\nPolicy, Effectiveness Evaluation\nCorporate Tax Cuts, 10\nClimate Change Policies, -6\nHealth Care Reform, 5\nEducation Funding, 12\nGun Control Policies, -8" } ], "id_raw": 738 }, { "id": "aE6Bi22SH2MCya5Y", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_37_18.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which policy received the most positive public opinion and which received the least positive response?" }, { "from": "gpt", "value": "To determine which policy has the most positive public opinion, we need to look for the bar with the greatest positive value. This corresponds to the wheel labeled {Education Funding}[0.263, 0.352, 0.474, 0.522]. It extends to the right, implying a positive rating, and its length shows its rating of 12, the highest among all policies. On the other hand, the policy that received the least positive response corresponds to the bar extending towards the left, which signifies a negative rating. This bar is assigned to {Gun Control Policies}[0.966, 0.066, 0.587, 0.556], indicating it has the lowest rating of -8 amongst all policies.\nBased on the above thought, the answer is:\nRegarding policies represented through public opinion in the bar chart, the most positive response was given to Education Funding, and the least positive response was to Gun Control Policies. The Education Funding policy received a rating of 12, which was the highest amongst all, while Gun Control Policies received a significantly less favorable rating of -8." } ], "id_raw": 738 }, { "id": "AL5rwlwDgilw8puo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_37_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest policy effectiveness evaluations?" }, { "from": "gpt", "value": "To find the difference between the highest and lowest policy effectiveness evaluations, I need to search for the longest positive- and negative-value bars, which correspond {Education Funding}[0.263, 0.352, 0.474, 0.522] and {Gun Control Policies}[0.966, 0.066, 0.587, 0.556], respectively. Extracting the corresponding values, these are 12 and -8. Therefore, the difference between the maximum and minimum is |12 - (-8)| = 20.\nBased on the above thought, the answer is:\n20" } ], "id_raw": 738 }, { "id": "sPzDRtFzXhbTgSWf", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_37_19.png", "conversations": [ { "from": "human", "value": "\nCan you create a structured table from the grouped bar chart showing environmental issues and their severity over four time periods?" }, { "from": "gpt", "value": "To convert the grouped bar chart data into a tabular format, I analyze the magnitude of each bar within its bounding coordinates, reflecting the severity of the respective environmental issue. First, I inspect {the bars for 'Ice Melt'}[0.150, 0.200, 0.400, 0.300], {the bars for 'Land Temperature Rise'}[0.150, 0.350, 0.500, 0.450], {the bars for 'Oceanic Temperature Rise'}[0.150, 0.500, 0.550, 0.600], {the bars for 'Air Quality Index'}[0.150, 0.650, 0.320, 0.750], and {the bars for 'Greenhouse Gas Emission'}[0.150, 0.800, 0.700, 0.900]. I arrange these values according to their y-coordinates which align with different environmental issues and x-coordinates indicating the severity across different time periods.\nBased on the above thought, the answer is:\nEnvironmental Issue,Period 1,Period 2,Period 3,Period 4\nIce Melt,10,15,20,25\nLand Temperature Rise,20,25,30,35\nOceanic Temperature Rise,25,30,35,40\nAir Quality Index,15,20,18,22\nGreenhouse Gas Emission,30,35,40,45" } ], "id_raw": 739 }, { "id": "zTpJB4kQWkA4ufEF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_37_19.png", "conversations": [ { "from": "human", "value": "\nWhich environmental issue demonstrated the most significant growth in severity over the given time periods according to the bar chart?" }, { "from": "gpt", "value": "To find which issue showed the most significant growth, I compare the length and increase of the bar groups for each environmental issue. Scanning through the bar groups, I notice {the group for 'Greenhouse Gas Emission'}[0.150, 0.800, 0.700, 0.900] shows a steep rise from the start to the end coordinate compared to others such as {the group for 'Ice Melt'}[0.150, 0.200, 0.400, 0.300] and {the group for 'Air Quality Index'}[0.150, 0.650, 0.320, 0.750]. The increased width distinctly across four periods indicates a significant growth in severity compared to others.\nBased on the above thought, the answer is:\n'Greenhouse Gas Emission' demonstrated the most significant growth in severity over the given time periods. This was observed by the substantial increase in the width of its bars from Period 1 to Period 4, visually representing a greater change in severity relative to other environmental issues." } ], "id_raw": 739 }, { "id": "aAMzGyoNS76SDM4u", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_37_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in severity for 'Greenhouse Gas Emission' from Period 1 to Period 4?" }, { "from": "gpt", "value": "To solve this, I locate the severity values for 'Greenhouse Gas Emission' across the four periods by referring to {the bars representing 'Greenhouse Gas Emission'}[0.150, 0.800, 0.700, 0.900]. These values are 30, 35, 40, 45. The increase from Period 1 (30) to Period 4 (45) is calculated as 45 - 30.\nBased on the above thought, the answer is:\n15" } ], "id_raw": 739 }, { "id": "fduMTv612FcUYNZ6", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_1.png", "conversations": [ { "from": "human", "value": "\nCan you present the data shown on the line chart in a structured table format with 'Years' as columns and 'Expenditures' as rows?" }, { "from": "gpt", "value": "I will translate the data displayed in the line chart into a structured table. First, {the x-axis tick labels}[-0.133, 0.894, -0.085, 0.931], [1.329, 0.894, 1.376, 0.931] are observed as column headers which correspond to the years. Next, I identify {the legend texts}[0.168, 0.055, 0.219, 0.083], [0.168, 0.097, 0.218, 0.125] as the row labels, indicating the types of expenditures. To populate the values, I find {the line points}[0.135, 0.066], [0.281, 0.605], [0.427, 0.515], [0.598, 0.695], [0.768, 0.784], [0.938, 0.066] for each expenditure type corresponding to the respective years.\nBased on the above thought, the answer is:\nType,2010,2020\nHealthcare,15000,25000\nEducation,20000,22000" } ], "id_raw": 741 }, { "id": "3HK2oRfnPYs3VgBS", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_38_1.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which area has seen more focus in terms of expenditure over the decade? Provide your analysis." }, { "from": "gpt", "value": "To analyze the focus of expenditure, I look at {the lines for Healthcare and Education}[0.135, 0.066, 0.938, 0.066], [0.281, 0.605, 0.427, 0.515]. It's observed that the expenditure on Healthcare saw a more significant rise, increasing from $15,000 in 2010 to $25,000 in 2020. The spending on Education, in contrast, shows only a minor increase from $20,000 to $22,000 during the same period.\nBased on the above thought, the answer is:\nThe Healthcare sector saw a more significant increase in expenditure over the decade, implying a greater focus on Healthcare compared to Education. The line chart shows an upward trajectory in Healthcare spending, which contrasts with the sluggish progress of Education spending. Thus, the chart suggests that more focus has been placed on Healthcare in terms of expenditure over the decade." } ], "id_raw": 741 }, { "id": "3ougVTQ4NwenmFGJ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_1.png", "conversations": [ { "from": "human", "value": "\nHow much did the expenditure on Healthcare increase from 2010 to 2020?" }, { "from": "gpt", "value": "To determine the increase in Healthcare expenditure, I need to locate {the line points for Healthcare}[0.135, 0.066], [0.938, 0.066] and extract the related values for the years 2010 and 2020. The value for 2010 is $15,000 while the value for 2020 is $25,000. The difference between these two values represents the increase in Healthcare expenditure over the ten-year period.\nBased on the above thought, the answer is:\n10000." } ], "id_raw": 741 }, { "id": "vgAAK45dEJHd33PT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_2.png", "conversations": [ { "from": "human", "value": "\nCan you please prepare a table that outlines the cost per serving for each cuisine type as indicated in the bar chart?" }, { "from": "gpt", "value": "Sure, I will first look for the x-axis labels which represent the cuisine types. I spot these at {the x-axis tick labels}[[0.114, 0.888, 0.209, 0.928], [0.242, 0.888, 0.303, 0.928], [0.401, 0.888, 0.446, 0.928], [0.581, 0.888, 0.636, 0.928], [0.718, 0.888, 0.805, 0.928], [0.871, 0.888, 0.931, 0.928]]. The y-value at the top of each bar plot represents the cost per serving for these cuisine types, which I find at {the bar top points}[[0.160, 0.188], [0.284, 0.604], [0.450, 0.604], [0.613, 0.500], [0.756, 0.542], [0.902, 0.292]]. I now have all the details to construct the table.\nBased on the above thought, the answer is:\nCuisine Types,Cost per Serving\nGerman,100\nItalian,200\nAmerican,200\nFrench,175\nJapanese,190\nMexican,130" } ], "id_raw": 742 }, { "id": "cJo1AmA0VRTmCGxU", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_38_2.png", "conversations": [ { "from": "human", "value": "\nComment on the cost per serving trends based on the bar chart. What information can you infer about the cost of cuisines?" }, { "from": "gpt", "value": "To evaluate the trends, I need to look at the order and heights of the bars in the chart. Starting from the left, {German cuisine}[0.160, 0.188] is the least expensive. Next, there is a significant rise as we move to {Italian}[0.284, 0.604] and {American Cuisines}[0.450, 0.604] which are both priced higher. The cost remains relatively high for {French}[0.613, 0.500] and {Japanese cuisines}[0.756, 0.542], with Japanese cuisine being the most expensive. The cost drops again for {Mexican cuisine}[0.902, 0.292].\nBased on the above thought, the answer is:\nThe cost of cuisines varies significantly. German cuisine is the cheapest, while Japanese cuisine is the most expensive. Italian and American cuisines are similarly priced and are moderately costly. The price for French cuisine also falls in this range, although it is a bit cheaper. Finally, Mexican cuisine is relatively affordable, although not as cheap as German cuisine. The significant disparities in the cost per serving of these cuisines infer the different ingredients, preparation techniques, and perceived value associated with each cuisine." } ], "id_raw": 742 }, { "id": "GI0HzriZtEAsUXVR", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the cost difference between the most expensive and least expensive cuisine types as per the bar chart?" }, { "from": "gpt", "value": "To aid my calculation, I need to identify the highest and lowest points on the bar plots. These are {Japanese cuisine} [0.756, 0.542] with a height that corresponds to $190, making it the most expensive, and {German cuisine} at [0.160, 0.188] with a height that corresponds to $100, making it the least expensive. The difference in cost is therefore 190 - 100 = $90 dollars.\nBased on the above thought, the answer is:\n90" } ], "id_raw": 742 }, { "id": "2pe2FsOyPRrxHOqY", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_3.png", "conversations": [ { "from": "human", "value": "\nCan you extract the online and offline shopping habits data into a table, with Years as columns and Shopping Modes as rows from the trend shown in the line chart?" }, { "from": "gpt", "value": "To generate the tabular data, I'll look at {the x-axis tick labels}[[-0.392, 0.852, -0.337, 0.899], [-0.131, 0.852, -0.075, 0.899], [0.131, 0.852, 0.187, 0.899], [0.393, 0.852, 0.449, 0.899], [0.655, 0.852, 0.711, 0.899], [0.917, 0.852, 0.973, 0.899], [1.179, 0.852, 1.235, 0.899]] to identify the years. Then, the {legend labels}[[0.232, 0.078, 0.636, 0.123], [0.232, 0.146, 0.682, 0.191]] represent the shopping modes. The data points to populate the table are given by {line points for online shopping}[[0.421, 0.828], [0.604, 0.828]] and {line points for offline shopping}[[0.421, 0.037], [0.604, 0.037]] for each year.\nBased on the above thought, the answer is:\nShopping Mode,2010,2020\nOnline,1000,2000\nOffline,2000,1500" } ], "id_raw": 743 }, { "id": "iEQ1UUarTtrxQTPc", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_38_3.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, has the preference for online shopping increased or decreased from 2010 to 2020?" }, { "from": "gpt", "value": "To determine the trend in preference for online shopping, let's take {the line for online shopping}[[0.421, 0.828], [0.604, 0.828]]. It's clearly visible that the online shopping line shows a rise from 1000 units in 2010 to 2000 units in 2020. Hence, the preference for online shopping has increased.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the preference for online shopping has increased from 2010 to 2020." } ], "id_raw": 743 }, { "id": "ZM2NivV0ml2FmYIQ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_3.png", "conversations": [ { "from": "human", "value": "\nBy how much percent did the online shopping increment from 2010 to 2020?" }, { "from": "gpt", "value": "To find out the percent increment in online shopping, I need to take {the line points for online shopping}[[0.421, 0.828], [0.604, 0.828]] which correspond to the values 1000 in 2010 and 2000 in 2020. The percentage increase is calculated by ((2000 - 1000) / 1000)*100 = (1000/1000)*100 = 100%.\nBased on the above thought, the answer is:\n100%." } ], "id_raw": 743 }, { "id": "NpznWfiRxZB2KF5b", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data showing the annual costs for Public Service and Basic Welfare into a structured table?" }, { "from": "gpt", "value": "To generate the table, I'm examining the data. The x-axis tick labels can be seen at {[0.095, 0.911, 0.130, 0.934], [0.261, 0.911, 0.296, 0.934], [0.428, 0.911, 0.463, 0.934], [0.594, 0.911, 0.629, 0.934], [0.760, 0.911, 0.795, 0.934], [0.926, 0.911, 0.961, 0.934]}, which define the 'Year'. Then, looking at the line points for each 'Public Service Cost' and 'Basic Welfare Cost' from {[0.113, 0.100, 0.943, 0.449]} and {[0.113, 0.449, 0.943, 0.857]}, I can derive the annual costs for each year.\nBased on the above thought, the answer is:\nYear,Public_Service_Cost,Basic_Welfare_Cost\n2000,2.5,1.1\n2001,2.6,1.2\n2002,2.7,1.3\n2003,2.8,1.4\n2004,2.9,1.5\n2005,3.0,1.6\n2006,3.1,1.7\n2007,3.2,1.8\n2008,3.3,2.0\n2009,3.5,2.2\n2010,3.7,2.5" } ], "id_raw": 744 }, { "id": "Kbon5ppMl0e06rcv", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_38_4.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which expenditure type shows the most steady growth rate between 2000 to 2010 and why?" }, { "from": "gpt", "value": "To answer this, I need to observe the volatility in the growth trends. Observing the lines {[0.113, 0.100, 0.943, 0.449]} for 'Public Service Cost' and {[0.113, 0.449, 0.943, 0.857]} for 'Basic Welfare Cost', the line for 'Public Service Cost' shows a steady uphill movement, with the growth rate incrementally increasing each year, and no noticeable fluctuations. In contrast, the line for 'Basic Welfare Cost' shows steeper gradients, indicating more significant changes in the costs year by year.\nBased on the above thought, the answer is:\nThe 'Public Service Cost' appears to have the most steady growth rate between 2000 to 2010. This conclusion is based on the smooth, gradually increasing pattern observed in the corresponding line plot, which suggests a consistent and steady increase in cost over the years, with no drastic fluctuations or large jumps. In comparison, the 'Basic Welfare Cost' line plot shows more steep gradients, indicating a more volatile growth rate." } ], "id_raw": 744 }, { "id": "3psQPZFkK1xlnE01", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_4.png", "conversations": [ { "from": "human", "value": "\nWhat was the rate of increase in Basic Welfare Costs from 2008 to 2010?" }, { "from": "gpt", "value": "To find the increase in Basic Welfare Costs between these two years, I need to locate {the line points for Basic Welfare Cost}[[0.777, 0.595], [0.860, 0.537], [0.943, 0.449]] and extract the corresponding values: 2.0 from {2008}[0.777, 0.911], 2.2 from {2009}[0.860, 0.911], and 2.5 from {2010}[0.943, 0.911]. The increase from 2008 to 2010 is therefore 2.5 - 2.0 = 0.5 billion.\nBased on the above thought, the answer is:\n0.5 billion." } ], "id_raw": 744 }, { "id": "Zxl7es5echg7mjzF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_6.png", "conversations": [ { "from": "human", "value": "\nCould you provide the usage ratio for each learning platform before and during the pandemic in a table format?" }, { "from": "gpt", "value": "Firstly, I identify that {the x-axis labels}[0.088, 0.912, 0.131, 0.936], [0.172, 0.912, 0.310, 0.936], [0.337, 0.912, 0.407, 0.936], [0.434, 0.912, 0.573, 0.936], [0.571, 0.912, 0.698, 0.936], [0.681, 0.912, 0.851, 0.936], [0.814, 0.912, 0.981, 0.936] correspond to different learning modules, which can act as row labels. Then, looking at {the legend}[0.079, 0.079, 0.280, 0.176], I distinguish \"Before Pandemic\" and \"During Pandemic\" as column headers. I then examine the line data of these two states and find out the corresponding usage ratio for each learning module.\nBased on the above thought, the answer is:\nLearning Module,Usage Ratio Before,Usage Ratio During\nMOOC,0.6,1.75\nSimulation Platform,1.5,2.1\ne-Reading,1.6,2.9\nAI-assisted Platform,0.5,1.6\nGamification Tools,1.3,2.15\nOnline Assessment Tools,1.75,2.9\nLearning Analytics Tools,0.9,1.75" } ], "id_raw": 746 }, { "id": "Ho3lAtSyvQDudSDR", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_38_6.png", "conversations": [ { "from": "human", "value": "\nWhich learning module experienced the most significant increase in usage ratio during the pandemic compared to before the pandemic? Explain." }, { "from": "gpt", "value": "In order to find out the learning module with the most significant increase in usage ratio during the pandemic, I need to compare the change in usage ratios across all learning modules. Starting with the point for MOOC before the pandemic {at this location}[0.110, 0.827] and during the pandemic {at this location}[0.110, 0.465], I calculate an increase of 1.75 - 0.6 = 1.15. I repeat the same calculation for all other learning modules using their respective coordinates. However, taking a closer look at the e-Reading platform, with its previous value {here}[0.372, 0.512] and the current value {here}[0.372, 0.103], I compute an increase of 2.9 - 1.6 = 1.3. This is the most significant increase among all the learning modules.\nBased on the above thought, the answer is:\nThe e-Reading platform experienced the most significant increase in usage ratio during the pandemic compared to before the pandemic at 1.3. This indicates that many learners have shifted towards electronic reading resources during the pandemic." } ], "id_raw": 746 }, { "id": "DuGIp0KTl56RrvAE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_6.png", "conversations": [ { "from": "human", "value": "\nHow much has the usage ratio of e-Reading increased during the pandemic compared to before the pandemic?" }, { "from": "gpt", "value": "To find the increase in usage ratio for e-Reading platform during the pandemic, I first identify the point coordinates associated with the e-Reading platform for both states. For the state before the pandemic, {the point}[0.372, 0.512] corresponds to a usage ratio of 1.6. For the state during the pandemic, {the point}[0.372, 0.103] corresponds to a usage ratio of 2.9. Therefore, the increase in usage ratio during the pandemic is 2.9 - 1.6 = 1.3.\nBased on the above thought, the answer is:\n1.3" } ], "id_raw": 746 }, { "id": "hPWqv4DogtPlxpm4", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_7.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data from the line chart into a table with Return Ratio as the columns and Risk Factor as rows?" }, { "from": "gpt", "value": "To generate a table from this graphical data, first I recognize that {the x-axis} values< r>[0.432, 0.917, 0.647, 0.963] represent the columns which are Return Ratios. Meanwhile, the y-axis values< r>[0.020, 0.291, 0.058, 0.588] correspond to the rows for Risk Factors. The points on the line plot for the Risk Return Trade-off< r>[0.429, 0.721, 0.935, 0.710] yield values corresponding to each Return Ratio and Risk Factor pair.\nBased on the above thought, the answer is:\nReturn Ratio,Risk Factor\n0.1,1\n0.4,1.05\n0.25,1.15\n0.15,1.20\n0.8,2\n0.03,6.6" } ], "id_raw": 747 }, { "id": "gOzzxpLSg2j6pE78", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_38_7.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the investment strategy based on the risk-return trade-off shown in the chart?" }, { "from": "gpt", "value": "The risk-return trade-off depicted by the plotted line< r>[0.429, 0.721, 0.935, 0.710] shows that as the return ratio increases, the risk factor tends to increase as well. Specifically, we have a very high-risk factor at a low return point< r>[0.138, 0.096 ], a moderate risk factor at moderate return ratio< r>[0.653, 0.700] and< r>[0.767, 0.711], and an elevated risk at high return ratio< r>[0.935, 0.606]. The investments A, B, and C< r>[0.653, 0.051, 0.767, 0.051, 0.935, 0.051] represent constant-risk options with their risks roughly equivalent respectively to the risks at low, moderate, and high returns in the trade-off line.\nBased on the above thought, the answer is:\nThe chart shows a typical risk-return trade-off where higher potential returns are associated with greater risk. Investment A has a low return but also a low risk, making it a conservative investment. Investment B, with a moderate return ratio, represents a balanced investment. Investment C, with a high return ratio, represents a high-risk, high-reward strategy. These observations underscore the importance of diversification when building an investment portfolio, balancing lower-risk and higher-risk investments. This distribution of investments allows one to achieve a desirable risk-return trade-off." } ], "id_raw": 747 }, { "id": "EdjswtgnBCwg7NRI", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the Risk Factor when the Return Ratio is 0.8?" }, { "from": "gpt", "value": "To determine the Risk Factor at a Return Ratio of 0.8, I need to locate the point on the {Risk Return Trade-off line}< r>[0.429, 0.721, 0.935, 0.710] corresponding to 0.8 Return Ratio. This point< r>[0.935, 0.606] indicates the Risk Factor at this Return Ratio.\nBased on the above thought, the answer is:\n2.0" } ], "id_raw": 747 }, { "id": "z7z2v6kGRHaCblvB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_8.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line graph displaying market shares of the products across three quarters into a structured table please?" }, { "from": "gpt", "value": "To export the data from the line graph, I firstly identify the labels corresponding to the quarters on {the x-axis}[[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903]], which will be used as the column headers. Next, by identifying the legend texts of {product_A}[[0.884, 0.101, 0.982, 0.123]], {product_B}[[0.884, 0.134, 0.933, 0.156]], {product_C}[[0.884, 0.167, 0.946, 0.189]], {product_D}[[0.884, 0.200, 0.983, 0.220]], and {product_E}[[0.884, 0.233, 0.982, 0.255]], I understand that they will represent the different rows. Then, by locating points on the line for each product like {product_A's points}[[0.102, 0.470], [0.218, 0.082], [0.333, 0.179]] for the respective quarters and extracting the annotated values, I can populate the table.\nBased on the above thought, the answer is:\nProduct,Q1,Q2,Q3\nProduct_A,24,20,18\nProduct_B,18,22,26\nProduct_C,22,15,19\nProduct_D,20,25,19\nProduct_E,16,15,14" } ], "id_raw": 748 }, { "id": "x2mmtu6zVEReXzHO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in market share percentage for Product_B from Q1 to Q3?" }, { "from": "gpt", "value": "To discover the variation in market share percentage for Product_B between Q1 and Q3, I have to find {the point of Product_B for Q1}[[0.102,0.211]] and {the point of Product_B for Q3}[[0.333,0.470]]. The corresponding values extracted from these points are 18 and 26 respectively. Subtraction of the Q1 value from the Q3 value gives the difference which is 26 - 18 = 8.\nBased on the above thought, the answer is:\n8%" } ], "id_raw": 748 }, { "id": "xt3TpY9Jf0mrHGw0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_9.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you arrange the progression of the two trends 'Gradual increase' and 'Rapid growth' per year in a tabular format?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.153, 0.852, 0.174, 0.899], ...[0.936, 0.852, 0.936, 0.899]], which represent the column headers or years from 2000 to 2015. The row labels representing the trend names 'Gradual increase' and 'Rapid growth' are defined by {the legend texts}[[0.068, 0.239, 0.097, 0.274], [0.068, 0.167, 0.097, 0.202]]. To populate the values, I locate {the line points of 'Gradual increase'}[[0.153, 0.109], [0.173, 0.181], ...[0.936, 0.500]] and {the line points of 'Rapid growth'}[[0.153, 0.109], [0.173, 0.262],...[0.936, 0.835]] for each year and extract the values annotated.\nBased on the above thought, the answer is:\nYear,Gradual increase,Rapid growth\n2000,100,100\n2001,102,200\n2002,104,400\n2003,106,800\n2004,108,1600\n2005,110,3200\n2006,112,6400\n2007,114,12800\n2008,116,25600\n2009,118,51200\n2010,120,102400\n2011,122,51200\n2012,124,25600\n2013,126,12800\n2014,128,6400\n2015,130,3200" } ], "id_raw": 749 }, { "id": "L3jeK2A3f5cGJOE6", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_38_9.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which trend shows a more drastic change during the observed period from 2000 to 2015?" }, { "from": "gpt", "value": "To answer this question, I need to look at the progression of both trends during the period from 2000 to 2015. I note that while {the line for 'Gradual increase'}[0.153, 0.109, 0.936, 0.500] shows a steady and slow increase in value throughout the period, {the line for 'Rapid growth'}[0.153, 0.109, 0.936, 0.835] initially shows a steep increase reaching a peak in 2010 before dropping back down. This drop in the 'Rapid growth' trend indicates a more drastic change compared to the 'Gradual increase' trend.\nBased on the above thought, the answer is:\nThe 'Rapid growth' trend shows a more drastic change during the observed period from 2000 to 2015. This is evident in the steep increase in the initial years until 2010, after which there's a drastic drop. This up and down progression indicates a much more drastic change in comparison to the steady and slow growth of the 'Gradual Increase' trend." } ], "id_raw": 749 }, { "id": "qjy8S8xZxF3z5yYG", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in value of 'Gradual increase' from the year 2000 to 2015?" }, { "from": "gpt", "value": "To calculate the percentage increase in value of 'Gradual increase' from the year 2000 to 2015, I locate {the line point for 'Gradual increase' in 2000}[0.153, 0.109], which gives me the value of 100, and {the line point for 'Gradual increase' in 2015}[0.936, 0.500], which corresponds to a value of 130. The percentage increase is therefore calculated as ((130 - 100) / 100) * 100%.\nBased on the above thought, the answer is:\n30%" } ], "id_raw": 749 }, { "id": "wSfF9kuYkwm2ivoF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_11.png", "conversations": [ { "from": "human", "value": "\nCould you please extract educational spending in different types of institutions into tabular format?" }, { "from": "gpt", "value": "Sure, to extract the data, I analyze {the x-axis labels}[0.061, 0.934, 0.167, 0.952], [0.226, 0.934, 0.325, 0.952], [0.408, 0.934, 0.467, 0.952], [0.537, 0.934, 0.660, 0.952], [0.704, 0.934, 0.817, 0.952], [0.864, 0.934, 0.980, 0.952] which gives the types of schools. Then, I locate {the line points for spending}[0.114, 0.882],[0.276, 0.500],[0.437, 0.529],[0.599, 0.249],[0.760, 0.365],[0.922, 0.086] and extract the corresponding figures.\nBased on the above thought, the answer is:\nInstitutions,Spending\nPrivate Schools,1000000\nPublic Schools,19845627\nColleges,15789463\nGraduate Schools,140781300\nTraining Centers,56921336\nOnline platforms,500000000" } ], "id_raw": 751 }, { "id": "SHO66lFeI589xwqa", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_38_11.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, how would you describe the spending trend on educational institutions?" }, { "from": "gpt", "value": "To describe the spending trend, I'm analyzing the {points on the line chart}[0.114, 0.882], [0.276, 0.500], [0.437, 0.529], [0.599, 0.249], [0.760, 0.365], [0.922, 0.086]. From this, I infer that the spending has overall increased from Private Schools to Online platforms, though there were fluctuations.\nBased on the above thought, the answer is:\nThe spending on educational institutions has seen an overall increase from Private Schools to Online platforms, however there have been substantial fluctuations in between. While the expenditure on Public Schools was significantly higher than Private Schools, there was a drop for Colleges and another noticeable increase for Graduate Schools. Spending decreased again for Training Centers but saw the most substantial rise for Online platforms." } ], "id_raw": 751 }, { "id": "TlffQSH2fS0ecSNm", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest spending on educational institutions?" }, { "from": "gpt", "value": "To find the difference, I need to locate {the maximum and minimum points on the line chart}[0.922, 0.086] and [0.114, 0.882], which correspond to the highest and lowest spendings respectively. The difference is 500000000 - 1000000 = 499000000.\nBased on the above thought, the answer is:\n499000000" } ], "id_raw": 751 }, { "id": "8wgZzOBb6inhBBBe", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_12.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data from the line chart into a table format with 'Time' as the rows and 'Types of Interaction' as the columns for each age group?" }, { "from": "gpt", "value": "To summarize the chart in a table, I first identify the {x-axis}[0.491, 0.912, 0.582, 0.963] as the 'Time' parameter. I then locate the {legend texts}[0.210, 0.078, 0.537, 0.123], [0.210, 0.146, 0.507, 0.191], [0.210, 0.213, 0.694, 0.258], [0.210, 0.281, 0.664, 0.326] to set the 'Type of Interaction' for each age group. Finally, I analyze {the y-values of the data points}[0.509, 0.750, 0.509, 0.839], [0.787, 0.750, 0.787, 0.839], [0.666, 0.750, 0.666, 0.839], [0.926, 0.750, 0.926, 0.839] for each corresponding 'Time' to create row entries for each type of interaction in both age groups.\nBased on the above thought, the answer is:\nTime,Education Technology,Training Technology,Average (Education Technology),Average (Training Technology)\n3.3,3.5,null,null\n3.9,3.7,null,null\n5.1,4.4,null,null\n6.5,5.2,null,null\n5.3,6.8,6.15,null\n7.6,8.9,null,7.55" } ], "id_raw": 752 }, { "id": "rZyN25aRmQEVrrym", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_38_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which technology, Education Technology or Training Technology, indicates a more positive trend in increasing its Innovation Index over time?" }, { "from": "gpt", "value": "To answer this question, I need to understand the overall trends of the line points for Education Technology and Training Technology. Looking at the {line for Education Technology}[0.138, 0.046, 0.935, 0.527], it indicates an overall upward trend from 3.3 to 7.6. Similarly, the trend in the {line for Training Technology} [0.210, 0.146, 0.507, 0.191] also shows an upward trend but starting at 3.5 and ending at 8.9, with a steeper rise compared to that for Education Technology.\nBased on the above thought, the answer is:\nBased on the chart, Training Technology indicates a more positive trend in increasing its Innovation Index over time. Despite both technologies showing an upward trend, Training Technology starts at a slightly higher index and ends at a significantly higher index. Moreover, the slope of the line indicates a steeper rise, thus demonstrating a more dramatic increase in its Innovation Index over time as compared to Education Technology." } ], "id_raw": 752 }, { "id": "71RQ7372fLoeXlON", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest Innovation Index observed for Education Technology over the time period?" }, { "from": "gpt", "value": "To find the highest Innovation Index for Education Technology, I need to observe the {Education Technology line points}[0.509, 0.750, 0.509, 0.839], [0.787, 0.750, 0.787, 0.839], [0.666, 0.750, 0.666, 0.839] and extract the corresponding values: 3.3, 3.9, 5.1, 6.5, 5.3, 7.6, from which the highest value is 7.6 at time 7.6.\nBased on the above thought, the answer is:\n7.6." } ], "id_raw": 752 }, { "id": "G7PJcfOo1bumOveW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_13.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data depicted in the line chart into a table displaying Time Periods as columns and Supply Chain Factors as rows?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first note {the x-axis tick labels}[[0.110, 0.909, 0.121, 0.932], [0.288, 0.909, 0.309, 0.932], [0.471, 0.909, 0.493, 0.932], [0.654, 0.909, 0.676, 0.932], [0.837, 0.909, 0.859, 0.932], [1.015, 0.909, 1.048, 0.932]], representing the Time Periods. Then, {the legend texts}[[0.085, 0.049, 0.510, 0.238]] point out the Supply Chain Factors. Finally, by locating {the line points for Risk Moderation}[[0.115, 0.280, 0.161, 0.427]] and {the line points for Supply Chain Integration}[[0.115, 0.034, 0.161, 0.169]], I can extract their respective Performance Indices over the given Time Periods.\nBased on the above thought, the answer is:\nSupply Chain Factor,-20,0,20,40,60,80\nRisk Moderation,5,4.5,4.7,4.2,3.9,3.8\nSupply Chain Integration,7,6.9,6.5,6.2,6,5.9" } ], "id_raw": 753 }, { "id": "0iGzLtQh84vSXrl3", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_38_13.png", "conversations": [ { "from": "human", "value": "\nBased on the depicted trends in the line chart, which factor shows a continuous decreasing trend in Performance Index over time and why?" }, { "from": "gpt", "value": "In order to discern which factor shows a continuous decreasing trend, I need to inspect the line plots for each factor. Upon evaluating {the line plot for Risk Moderation}[[0.115, 0.280, 0.161, 0.427]], it appears to not be decreasing continuously, especially with the dip and rise around the 20 mark. On the other hand, looking at {the line plot for Supply Chain Integration}[[0.115, 0.034, 0.161, 0.169]], I observe a sustained reduction in performance index from 7 at '-20'to 5.9 at '80'.\nBased on the above thought, the answer is:\nThe factor Supply Chain Integration shows a continuous decreasing trend in Performance Index over time. This is observed from the continuous decrease in its values from 7 to 5.9 as the time period progresses from -20 to 80." } ], "id_raw": 753 }, { "id": "W78Fj3hSjc9LjXEP", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum value of Performance Index reached by Supply Chain Integration over the Time Periods?" }, { "from": "gpt", "value": "To find the maximum value of Performance Index for Supply Chain Integration, I examine {the line points for Supply Chain Integration}[[0.115, 0.034, 0.161, 0.169]], which correspond to the values 7, 6.9, 6.5, 6.2, 6, and 5.9. As seen, the highest value is 7 at the Time Period '-20'.\nBased on the above thought, the answer is:\n7." } ], "id_raw": 753 }, { "id": "JrZoOPbpaFeiQjhX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_15.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line dataset into a structured table with years as the columns and the different metrics (like CSR Score, Environment, etc.) as rows?" }, { "from": "gpt", "value": "To create a structured table from the line plot, I first identify {the x-axis labels}[0.035, 0.869, 0.069, 0.904], [0.132, 0.869, 0.146, 0.904], [0.219, 0.869, 0.234, 0.904], [0.306, 0.869, 0.321, 0.904], [0.394, 0.869, 0.408, 0.904], [0.481, 0.869, 0.495, 0.904], [0.568, 0.869, 0.583, 0.904], [0.655, 0.869, 0.670, 0.904] as the columns i.e. years. I then identify {the legend}[0.633, 0.069, 0.960, 0.341] as the row labels representing different metrics. Based on the plot, I can then map the corresponding {line points} [0.139, 0.483], [0.226, 0.531], [0.314, 0.501], [0.401, 0.522], [0.488, 0.513], [0.575, 0.557], [0.139, 0.682], [0.226, 0.726], [0.314, 0.700], [0.401, 0.721], [0.488, 0.712], [0.575, 0.739], [0.139, 0.610], [0.226, 0.548], [0.314, 0.575], [0.401, 0.566], [0.488, 0.545], [0.575, 0.527], [0.139, 0.573], [0.226, 0.633], [0.314, 0.527], [0.401, 0.598], [0.488, 0.562], [0.575, 0.668], [0.139, 0.393], [0.226, 0.196], [0.314, 0.351], [0.401, 0.263], [0.488, 0.337], [0.575, 0.148] values as the data under respective years for each metric.\nBased on the above thought, the answer is:\nMetrics,-1,0,1,2,3,4,5\nCSR Score,2.05,1.78,1.95,1.83,1.88,1.63,0.63\nEnvironment,0.92,0.67,0.82,0.70,0.75,0.60,0.60\nEconomic,1.33,1.68,1.53,1.583,1.70,1.80,1.80\nEmployee Wellness,1.54,1.20,1.80,1.40,1.60,1.00,1.00\nSustainable Business,2.56,3.68,2.80,3.30,2.88,3.95,3.95" } ], "id_raw": 755 }, { "id": "WApGzlFLpOUYmDrc", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_38_15.png", "conversations": [ { "from": "human", "value": "\nBased on the trend shown in the line chart, which factor seems to be improving the most over the years?" }, { "from": "gpt", "value": "To determine the factor that improved the most over the years, I observe the slopes of the line plots for each factor. The slope of {Employee Wellness}[0.139, 0.573, 0.575, 0.668] line plotted seems the steepest as it increases from 1.54 to 1.0 over this period.\nBased on the above thought, the answer is:\nBased on the trend shown in the line chart, the factor, Employee Wellness seems to be improving the most over the years. Its line plot shows the steepest upward slope indicating a continuous improvement over the period." } ], "id_raw": 755 }, { "id": "Voha3nfzLVHeKQzl", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the CSR Score of -1 year and 1 year?" }, { "from": "gpt", "value": "To find the difference between the CSR Score of -1 year and 1 year, I first locate {the point for -1 year}[0.139, 0.483] with a value of 2.05 and {the point for 1 year}[0.314, 0.501] with a value of 1.95. The difference is then 2.05 - 1.95 = 0.1.\nBased on the above thought, the answer is:\n0.1" } ], "id_raw": 755 }, { "id": "VxUD7dyc5L74svYb", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_16.png", "conversations": [ { "from": "human", "value": "\nCan you structure the line plot data into a table format with Investment categories as rows and Rapid Recovery as columns?" }, { "from": "gpt", "value": "Sure, I need to first identify {the legend texts}[0.210, 0.092, 0.536, 0.137], [0.210, 0.159, 0.491, 0.204], [0.210, 0.227, 0.376, 0.272], [0.210, 0.295, 0.461, 0.340], [0.210, 0.362, 0.500, 0.407], [0.210, 0.430, 0.554, 0.475] as the row labels representing the different Investment categories. To populate the columns, I locate the x coordinates of {the line points for each category}[0.841, 0.634], [0.654, 0.634], [0.701, 0.634], [0.279, 0.634], [0.138, 0.634], [0.935, 0.634] on the x-axis which represent the investment for respective categories. Next, I look at the corresponding y coordinates which represent the Rapid recovery rates for each category.\nBased on the above thought, the answer is:\nInvestment,Rapid_Recovery\nTraditional_Innovation,1.4\nHigh-Tech_Startups,1.1\nAI_Startups,1.0\nBioTech_Startups,2.6\nCleanTech_Startups,3.4\nQuantumTech_Startups,2.0" } ], "id_raw": 756 }, { "id": "wYv9qMSFgFzM0lva", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_38_16.png", "conversations": [ { "from": "human", "value": "\nCan you infer the category that the investors are likely to invest in more for a higher rapid recovery rate?" }, { "from": "gpt", "value": "To infer the category that the investors are likely to invest in more for a higher rapid recovery rate, I need to locate the line point with the highest y coordinate, which indicates the highest rapid recovery rate. Looking at the y coordinates of {the line points for each category}[0.841, 0.634], [0.654, 0.634], [0.701, 0.634], [0.279, 0.634], [0.138, 0.634], [0.935, 0.634], the point with the highest y-coordinate is at 3.4 corresponding to {CleanTech Startups}[0.138, 0.634]. This suggests that CleanTech Startups is the category that investors are likely to invest in more for a higher rapid recovery rate.\nBased on the above thought, the answer is:\nFrom the chart figure, it is likely that investors may invest more in the \"CleanTech Startups\" category as it is associated with the highest rapid recovery rate." } ], "id_raw": 756 }, { "id": "bWZLjOgjM8AjPQa8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_16.png", "conversations": [ { "from": "human", "value": "\nHow much more is the Rapid Recovery rate for CleanTech Startups as compared to AI Startups?" }, { "from": "gpt", "value": "To determine this, I need to compare the y coordinates of the line points for {CleanTech Startups}[0.138, 0.634] and {AI Startups}[0.701, 0.634]. The Rapid Recovery rate for CleanTech Startups is 3.4 while that for AI Startups is 1.0. By subtracting the Rapid Recovery rate for AI Startups from that for CleanTech Startups (3.4 - 1.0), I can find out how much more the former is.\nBased on the above thought, the answer is:\n2.4" } ], "id_raw": 756 }, { "id": "pif8CeooNsiK3iVA", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_18.png", "conversations": [ { "from": "human", "value": "\nCan you please construct a table that summaries the error ratio values for the models." }, { "from": "gpt", "value": "To create the table, I will first note down {the y-ticks}[[0.142, 0.828], [0.142, 0.684], [0.142, 0.540], [0.142, 0.397], [0.142, 0.253],[0.142, 0.109]]. These represent the model's complexity with values 0, 200, 400, 600, 800, and 1000, respectively. Then, I locate {the line points}[[0.180, 0.821], [0.445, 0.792], [0.559, 0.756], [0.625, 0.720], [0.823, 0.469], [0.937, 0.109]] corresponding to each model from the line, which represents the values of Error Ratio: 0.00001, 0.00005, 0.0001, 0.00015, 0.0005, 0.001, respectively. Combining these together, we can construct a table.\nBased on the above thought, the answer is:\nModel Complexity,Error Ratio\n10,0.00001\n50,0.00005\n100,0.0001\n150,0.00015\n500,0.0005\n1000,0.001" } ], "id_raw": 758 }, { "id": "OQtqolQFOlOeRJOi", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_38_18.png", "conversations": [ { "from": "human", "value": "\nWhich model shows a significant increase in error ratio as its complexity increases?" }, { "from": "gpt", "value": "To determine which model has a significant increase in error ratio, I am reviewing the plot and specifically {the line graph for Error Ratio}[0.180, 0.109, 0.937, 0.821]. The trend shows that the error ratio increases significantly from 0.00001 to 0.0005 as we move from M-LVQ-4x10 to M-LVQ-4x500 model. The M-LVQ-4x500 model shows a steep increase in error ratio indicating a significant increase.\nBased on the above thought, the answer is:\nThe M-LVQ-4x500 model shows a significant increase in error ratio as its complexity increases. The trend depicts that as the complexity of this model increases, the error ratio also rises significantly, which might indicate overfitting or a lack of generalization capability for complex scenarios in this model." } ], "id_raw": 758 }, { "id": "jGLq2TRzRxXsPjUd", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in Error Ratio between M-LVQ-4x10 and M-LVQ-4x500 models?" }, { "from": "gpt", "value": "To determine the difference in Error Ratio, I need to look at {the line points for M-LVQ-4x10 and M-LVQ-4x500 models}[[0.180, 0.821], [0.823, 0.469]]. The corresponding values of Error Ratio for M-LVQ-4x10 and M-LVQ-4x500 models are 0.00001 and 0.0005 respectively. The difference will therefore be calculated as 0.0005 - 0.00001 = 0.00049.\nBased on the above thought, the answer is:\n0.00049" } ], "id_raw": 758 }, { "id": "mKGdEH8X3032l1Uj", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_38_19.png", "conversations": [ { "from": "human", "value": "\nAccording to the grouped bar chart, can you extract the data into a table format with Methods as columns and Trends as rows?" }, { "from": "gpt", "value": "To generate the table, I first need to identify {the x-axis ticks}[[0.124, 0.864, 0.161, 0.899], [0.276, 0.864, 0.312, 0.899], [0.427, 0.864, 0.463, 0.899], [0.578, 0.864, 0.615, 0.899], [0.730, 0.864, 0.766, 0.899], [0.881, 0.864, 0.918, 0.899] as the row labels or trends. Next, I identify {the legend}[0.157, 0.074, 0.566, 0.560] as the column headers representing the different methods. To populate the table, I locate the points for each trend at the respective methods and extract the values annotated at {the bar tops}.\nBased on the above thought, the answer is:\nTrends,Volatile Growth,Pronounced Increase,Incremental Growth,Dramatic Decrease,Outlier \nMachine Learning,6.2,7.1,6.8,6.1,6.3\nRobotic Process Automation,6.3,7.3,6.9,6.0,7.1" } ], "id_raw": 759 }, { "id": "3bfKt5wvLdFvb5AU", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_38_19.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which trend showed the least difference between the maximum and minimum score? Provide your reasoning." }, { "from": "gpt", "value": "To determine which trend showed the least difference between the maximum and minimum scores, I need to compare the range of scores for each trend. Looking at {the bars for Machine Learning}[[0.294, 0.839, 0.294, 0.839], [0.521, 0.839, 0.521, 0.839], [0.672, 0.839, 0.672, 0.839], [0.824, 0.839, 0.824, 0.839]], the maximum score is 7.1 and the minimum score is 6.1, making a difference of 1.0. For {Robotic Process Automation}[0.369, 0.839, 0.369, 0.839], the maximum score is 7.3 and the minimum score is 6.0, with a difference of 1.3. Hence, Machine Learning has the least difference between the maximum and minimum score.\nBased on the above thought, the answer is:\nAccording to the bar chart, the trend for Machine Learning showed the least difference between the maximum and minimum score. This is identified by comparing the spread of scores under each method for both trends, where Machine Learning exhibits a smaller range of 1.0 compared to the 1.3 for Robotic Process Automation." } ], "id_raw": 759 }, { "id": "z4ffdEmfhYjv2QJw", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_38_19.png", "conversations": [ { "from": "human", "value": "\nHow much higher was the score for Robotic Process Automation than Machine learning in the Pronounced Increase method?" }, { "from": "gpt", "value": "To find the difference in scores between Robotic Process Automation and Machine Learning for the Pronounced Increase method, I need to locate {the bar for Machine Learning}[0.294, 0.839, 0.294, 0.839] in Pronounced Increase and extract the corresponding value: 7.1. Similarly, I locate {the bar for Robotic Process Automation}[0.369, 0.839, 0.369, 0.839] and extract the corresponding value: 7.3. The difference between the two scores is therefore 7.3 - 7.1 = 0.2.\nBased on the above thought, the answer is:\n0.2." } ], "id_raw": 759 }, { "id": "t6V0sfbcHv0uRY1n", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_0.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data from the bar chart into a structured table where the rows represent the career path development methods and the columns represent the gradual recovery and dramatic surge?" }, { "from": "gpt", "value": "The first step in creating a table from the bar chart data is to identify the column heads, corresponding to {the x-axis labels}[0.129, 0.919, 0.151, 0.942],[0.329, 0.919, 0.455, 0.942] that represent Gradual Recovery and Dramatic Surge. Next, we identify the row labels that indicate different career development methods, shown in {the legend}[0.338, 0.090, 0.536, 0.113] and [0.338, 0.125, 0.524, 0.148]. The data within the table is extracted from the corresponding {bar heights}[0.140, 0.268, 0.895, 0.794],[0.140, 0.180, 0.895, 0.736] in each category.\nBased on the above thought, the answer is:\nCareer Path Development,Gradual Recovery,Dramatic Surge\nCareer Coaching,7.1,9.5\nLeadership Training,6.8,8.8\nEmployee Onboarding,6.0,8.0\nPerformance Management,7.0,7.8\nCareer Development,6.5,8.5" } ], "id_raw": 760 }, { "id": "TT4REcLCwYEdIsZg", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_0.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which career development method seems to have the highest overall effectiveness across both trends? Justify your reasoning." }, { "from": "gpt", "value": "To identify the career development method with the highest effectiveness, I compare the heights of the bars across both trends. Notably, {Career Coaching in Gradual Recovery}[0.170, 0.343, 0.236, 0.676] has a score of 7.1, and {Career Coaching in Dramatic Surge}[0.380, 0.343, 0.446, 0.815] has the highest score of 9.5. By considering the summed scores across both trends, Career Coaching presents the highest cumulative score thus suggesting it as the most effective method for career development according to the chart.\nBased on the above thought, the answer is:\nBased on the bar chart, Career Coaching appears to have the highest overall effectiveness across both Gradual Recovery and Dramatic Surge trends. This is because it boasts the highest cumulative score across these trends, outperforming other career development methods in the Dramatic Surge scenario and being competitive in the Gradual Recovery scenario." } ], "id_raw": 760 }, { "id": "BvBUKxUladk96mQf", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the score of Leadership Training in Gradual Recovery and Dramatic Surge scenarios?" }, { "from": "gpt", "value": "To find the difference, I need to identify {the bar of Leadership Training in the Gradual Recovery}[0.220, 0.343, 0.286, 0.602] which corresponds to a score of 6.8 and {the bar of Leadership Training in the Dramatic Surge}[0.430, 0.343, 0.496, 0.746] which corresponds to a score of 8.8. The difference between these scores is 8.8 - 6.8 = 2.0\nBased on the above thought, the answer is:\n2.0" } ], "id_raw": 760 }, { "id": "QMSfw2ujRmagdhii", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_1.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the grouped bar chart into a structured table with 'Entities' as columns and 'Method' as rows?" }, { "from": "gpt", "value": "Starting with the x-axis tick values, which are {the entities}[0.117, 0.919, 0.192, 0.942], [0.852, 0.919, 0.972, 0.942]. I assess these as the column headers 'Pear_Tree' and 'Log_Saw'. I then identify {the legend texts}[0.771, 0.090, 0.851, 0.113], [0.771, 0.125, 0.929, 0.148] for the row headers representing the different methods. Lastly, from the bars of the chart, I locate {the bar heights}[0.155, 0.253, 0.912, 0.779], [0.155, 0.166, 0.912, 0.867] corresponding to each score for the respective method and entity. I use these values to fill in the table.\nBased on the above thought, the answer is:\nMethod,Pear_Tree,Log_Saw\nClasses,2.0,2.2\nServices,1.8,1.9\nOrganizations,1.5,1.4\nIndividuals,1.2,1.1\nFundraisers,1.0,0.8" } ], "id_raw": 761 }, { "id": "0anLA07nE7qtwNSC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_1.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which entity exhibits a larger range of scores across different methods?" }, { "from": "gpt", "value": "To answer this question, I need to examine the range of scores for both entities by subtracting the lowest score from the highest score for each. Looking at {the bar heights for Pear_Tree}[0.155, 0.253], [0.306, 0.341], [0.458, 0.473], [0.609, 0.604], [0.761, 0.692] and {the bar heights for Log_Saw}[0.155, 0.166], [0.306, 0.297], [0.458, 0.516], [0.609, 0.648], [0.761, 0.779], I see the scores range from 1.0 to 2.0 for Pear_Tree and from 0.6 to 2.2 for Log_Saw. Therefore, Log_Saw has a larger range of scores (1.6) compared to Pear_Tree (1.0).\nBased on the above thought, the answer is:\nBased on the bar chart, 'Log_Saw' exhibits a larger range of scores across different methods than 'Pear_Tree'. This is determined by comparing the difference between the highest and lowest scores for each entity." } ], "id_raw": 761 }, { "id": "UnquGJaZPX9LYztK", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the gap between the highest and lowest scores for the entity 'Pear_Tree'?" }, { "from": "gpt", "value": "To address this, I examine {the bar heights for Pear_Tree}[0.155, 0.253], [0.306, 0.341], [0.458, 0.473], [0.609, 0.604], [0.761, 0.692] which gives us the values: 2.0, 1.8, 1.5, 1.2, 1.0. The highest value is 2.0 under 'Classes' and the lowest is 1.0 under 'Fundraisers'. Therefore, the difference between the highest and lowest scores for 'Pear_Tree' is 2.0 - 1.0 = 1.\nBased on the above thought, the answer is:\n1." } ], "id_raw": 761 }, { "id": "L2qluQ0g7AW8aYMx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_2.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the successful integration percentages of Traditional and Contemporary Techniques in both Cultural Events and Museums?" }, { "from": "gpt", "value": "Firstly, I observe that {the x-axis labels}[0.057, 0.939, 0.146, 0.957], [0.217, 0.939, 0.316, 0.957]represent the categories Traditional and Contemporary techniques, respectively. Then, looking at the y-axis, I can understand that it represents the score in terms of percentages. I can see {the line points} for Integration at Cultural Events[0.102, 0.897], [0.267, 0.824] and for Integration in Museums[0.432, 0.389], [0.597, 0.461] which correspond to the relevant scores, respectively. Using these points I can form the table.\nBased on the above thought, the answer is:\nTechniques,Integration at Cultural Events,Integration in Museums\nTraditional,89.8,92.1\nContemporary,95.8,87.3" } ], "id_raw": 762 }, { "id": "W8PjUq0dJV84H5RP", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_2.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which technique has shown a better performance in integration at cultural events and museums, and why?" }, { "from": "gpt", "value": "To comprehend which technique performs better, I need to contrast their successful integration percentages in both the scenarios. Referring to {the line points for Cultural Events}[0.102, 0.897], [0.267, 0.824] and for Museums[0.432, 0.389], [0.597, 0.461] I observe that for Cultural events the Contemporary technique has a higher value of 95.8 % compared to the Traditional technique which is 89.8 %. However, in the context of Museums, the Traditional technique shows a higher performance of 92.1 % whereas Contemporary technique shows 87.3 %. It appears that the individual performance of the techniques is highly context dependent.\nBased on the above thought, the answer is:\nNeither the Traditional nor the Contemporary technique has shown universally improved performance in both scenarios. In the context of Cultural Events, Contemporary technique performs better, whereas the Traditional technique shows a higher degree of successful integration in Museums. The performance of the techniques seems to be dependent on the specific context they are deployed in, underscoring the importance of context-specific strategies." } ], "id_raw": 762 }, { "id": "edOWwOV3rfuMTFIK", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the Traditional and Contemporary techniques' successful integration percentage for Cultural events?" }, { "from": "gpt", "value": "I refer to {the line points}[0.102, 0.897], [0.267, 0.824] which represent the successful integration percentages corresponding to the Traditional and Contemporary techniques for Cultural events. The values for these techniques are 89.8 and 95.8 respectively. Hence, the difference would be 95.8 - 89.8 = 6.\nBased on the above thought, the answer is:\n6." } ], "id_raw": 762 }, { "id": "yqOqO9ZhoJDfXQLV", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_3.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table containing the scores of career development measures for each trend?" }, { "from": "gpt", "value": "To transform the bar chart data into a table, the primary step is to understand the X-axis and Y-axis. Noting {the x-axis categories}[[0.155, 0.801, 0.273, 0.833], [0.441, 0.801, 0.574, 0.838]], I observe that the categories are the two different trends - \"Gradual Recovery\" and \"Dramatic Surge\", which should be two columns of my table data. Moving to the Y-axis, I see it represents the scores of each career path under the respective trends. Hence, these become the rows of our table. To populate the values, I locate {the bars}[[0.163, 0.338, 0.229, 0.710], [0.241, 0.338, 0.318, 0.595], [0.318, 0.338, 0.394, 0.700], [0.394, 0.338, 0.470, 0.705], [0.470, 0.338, 0.547, 0.600], [0.450, 0.338, 0.516, 0.845], [0.528, 0.338, 0.604, 0.785], [0.604, 0.338, 0.681, 0.800], [0.681, 0.338, 0.757, 0.788], [0.757, 0.338, 0.833, 0.790]] and extract the values based on their positions with the Y-axis values.\nBased on the above thought, the answer is:\nCareer Development Measures,Gradual Recovery,Dramatic Surge\nCareer Coaching,7.1,9.5\nLeadership Training,6.8,8.3\nEmployee Onboarding,6.0,7.8\nPerformance Management,7.0,8.0\nCareer Development,6.2,7.5" } ], "id_raw": 763 }, { "id": "jDq4S45KuFopETQ7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_3.png", "conversations": [ { "from": "human", "value": "\nWhich career development measure has shown the most improvement when shifting from a trend of Gradual Recovery to Dramatic Surge?" }, { "from": "gpt", "value": "To identify the career development measure with the greatest improvement when shifting from a Gradual Recovery to a Dramatic Surge, I would compare the difference between scores for all the career pathways from Gradual Recovery to Dramatic Surge. Looking at {the bars in the left group}[[0.163, 0.338, 0.229, 0.710], [0.241, 0.338, 0.318, 0.595], [0.318, 0.338, 0.394, 0.700], [0.394, 0.338, 0.470, 0.705], [0.470, 0.338, 0.547, 0.600]] and {the corresponding bars in the right group}[[0.450, 0.338, 0.516, 0.845], [0.528, 0.338, 0.604, 0.785], [0.604, 0.338, 0.681, 0.800], [0.681, 0.338, 0.757, 0.788], [0.757, 0.338, 0.833, 0.790]], I find that Career Coaching has the highest shift from 7.1 to 9.5, a change of 2.4 units.\nBased on the above thought, the answer is:\nThe career development measure of Career Coaching has shown the biggest improvement when the trend shifts from Gradual Recovery to Dramatic Surge. The score for this category rose from 7.1 to 9.5, indicating a 2.4 points improvement, which is the highest compared to other career pathways." } ], "id_raw": 763 }, { "id": "XkCtOlXYQQUykCQN", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in scores between trends for Performance Management?" }, { "from": "gpt", "value": "To determine the difference in scores between trends for Performance Management, I should locate the bars specifically for this category. The bar for Gradual Recovery is found at {the second to last position in the left group}[[0.394, 0.338, 0.470, 0.705]], corresponding to a score of 7.0. For Dramatic Surge, its position is found at {the second to last in the right group}[[0.681, 0.338, 0.757, 0.788]], corresponding to a score of 8.0. The difference between the values under two trends could then be found by subtracting 7.0 from 8.0.\nBased on the above thought, the answer is:\n1.0" } ], "id_raw": 763 }, { "id": "8zL6BzIWNqShsqe4", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_4.png", "conversations": [ { "from": "human", "value": "\nCan you arrange the chart data in a tabulated format where the performance scores for each Artificial Intelligence Application based on different software are categorized in rows and columns respectively?" }, { "from": "gpt", "value": "To present the data in a tabular format, I first identify {the x-axis labels}[0.054, 0.919, 0.069, 0.942], [0.229, 0.919, 0.244, 0.942], [0.404, 0.919, 0.419, 0.942], [0.579, 0.919, 0.593, 0.942], [0.753, 0.919, 0.768, 0.942], [0.921, 0.919, 0.950, 0.942], [1.096, 0.919, 1.125, 0.942] as column headers, representing the Years of Development. Next, I view the {sublegend}[0.404, 0.090, 0.473, 0.113], [0.404, 0.125, 0.477, 0.148] to identify the row headers, representing the Sales and Profits. To situate the data, each {line point}[0.149, 0.701], [0.236, 0.613], [0.324, 0.490], [0.411, 0.420], [0.499, 0.402], [0.586, 0.357], [0.673, 0.350], [0.761, 0.280], [0.848, 0.210], [0.936, 0.174] for every application represents the score in the corresponding year.\nBased on the above thought, the answer is:\nYear, Sales, Profits\n1,60,30\n2,85,55\n3,120,75\n4,140,90\n5,145,100\n6,158,110\n7,160,111\n8,180,120\n9,200,150\n10,210,170" } ], "id_raw": 764 }, { "id": "JXZudienchH7keuC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_4.png", "conversations": [ { "from": "human", "value": "\nInterpreting from the chart figure, could you provide an overview of the performance of the Entertainment & Gaming Industry based on the given chart data?" }, { "from": "gpt", "value": "To understand the performance of the Entertainment & Gaming Industry, I analyze the trend of {the Sales line}[0.149, 0.174, 0.936, 0.701] and {the Profits line}[0.149, 0.315, 0.936, 0.806]. It is observed that both lines show a general upward trend, indicating that Sales and Profits increased over the Years of Development.\nBased on the above thought, the answer is:\nThe Entertainment & Gaming industry has demonstrated a growth pattern over the 10-year development span. Both revenues, specifically Sales and Profits showed a generally increasing trend. While profits started lower than sales at year 1, the growth rate was more pronounced, eventually surpassing sales around the mid-point of the time span. This indicates that the industry experienced improvements in cost-efficiency or economies of scale over time." } ], "id_raw": 764 }, { "id": "iePb7jRUXr7UhuAs", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the average increase in Profit over the 10 years period of development?" }, { "from": "gpt", "value": "To calculate the average increase in profits over the 10 years of development, I first locate the {line points for Profits}[0.149, 0.806], [0.236, 0.718], [0.324, 0.648], [0.411, 0.595], [0.499, 0.560], [0.586, 0.525], [0.673, 0.522], [0.761, 0.490], [0.848, 0.420], [0.936, 0.315] and extract the corresponding values: 30, 55, 75, 90, 100, 110, 111, 120, 150, and 170. The increase from one year to another can be calculated as the difference between the current year's profit and the previous year's profit. The average increase can then be calculated as the total increase divided by the number of years, which is 10.\nBased on the above thought, the answer is:\n14." } ], "id_raw": 764 }, { "id": "8qeBFYFGQrPzJECC", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_5.png", "conversations": [ { "from": "human", "value": "\nCan you create a table showing the development scores for rural and urban areas from 2001 to 2010 according to the line chart data?" }, { "from": "gpt", "value": "To construct the table, I retrieve the data from the line chart by identifying {the x-axis tick labels}[0.117, 0.898, 0.225, 0.938] corresponding to the years from 2001 to 2010. I also examine {the points on the line for Rural Development}[0.156, 0.122], [0.511, 0.297], [0.581, 0.188] and {the points on the line for Urban Development}[0.156, 0.188], [0.511, 0.319], [0.581, 0.210]. I correlate these points with their respective values on the y-axis marked from 30 to 60 to extract numerical values for each year.\nBased on the above thought, the answer is:\nYear,Rural Development,Urban Development\n2001,32,34\n2002,36,37\n2003,40,39\n2004,42,42\n2005,45,46\n2006,47,48\n2007,55,49\n2008,50,50\n2009,48,52\n2010,45,55" } ], "id_raw": 765 }, { "id": "L634GHa1qymW9GtL", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_5.png", "conversations": [ { "from": "human", "value": "\nHow does the trend in Urban Development compare to the trend in Rural Development from 2001 to 2010 based on the line chart?" }, { "from": "gpt", "value": "Observing the plotted lines for Urban and Rural Development, I analyze {the line for Urban Development}[0.156, 0.188, 0.936, 0.692] which shows a gradual increase, and {the line for Rural Development}[0.156, 0.122, 0.936, 0.613] which indicates a fluctuating progression with a sharp increase around 2007. This visual comparison highlights the distinct differences in growth patterns and development scores between the two areas.\nBased on the above thought, the answer is:\nThe trend in Urban Development shows a steady and gradual growth over the years, while the trend in Rural Development is more erratic with significant fluctuations, especially notable with an abrupt peak in 2007. Overall, Urban Development appears to be more stable and consistent compared to the more volatile pattern of Rural Development." } ], "id_raw": 765 }, { "id": "0FgaDJ0dCaTPndgI", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_5.png", "conversations": [ { "from": "human", "value": "\nWhat was the highest peak value for Rural Development according to the chart data, and in which year did it occur?" }, { "from": "gpt", "value": "To determine the highest peak value for Rural Development, I observe and evaluate {the line for Rural Development}[0.156, 0.122, 0.936, 0.613] and pinpoint the highest point on the line located at {peak point at 2007}[0.581, 0.188]. Referring to the y-axis tick labels mapped from 30 to 60, I find that this peak corresponds to a development score.\nBased on the above thought, the answer is:\n55 in 2007." } ], "id_raw": 765 }, { "id": "fpPuL0e9GWUKv6pU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_6.png", "conversations": [ { "from": "human", "value": "\nCould you please extract the plotting points from this line chart into a structured table?" }, { "from": "gpt", "value": "The first step to extracting data involves looking at the {x-axis' ticks}[[0.057, 0.919, 0.225, 0.942], [0.849, 0.919, 0.972, 0.942]] which represent the two periods in question: \"Strong Growth\" and \"No Growth\". The two segmented lines: {Sporty}[[0.141, 0.143, 0.910, 0.616]], and {Luxury}[[0.141, 0.379, 0.910, 0.793]] are the rows of the table. These lines' points correspond to the Y-value or Market Share for each segment at the specific periods.\nBased on the above thought, the answer is:\nMarket Segment,Sporty,Luxury\nStrong Growth,1.3,0.9\nNo Growth,0.5,0.2" } ], "id_raw": 766 }, { "id": "aKLPBRaSOdZbA35p", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_6.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which market segment observed a decline in their market share from the \"Strong Growth\" period to the \"No Growth\" period?" }, { "from": "gpt", "value": "To figure out which market segment experienced a decline, I have to follow the direction of the line segments {for Sporty}[[0.141, 0.143, 0.910, 0.616]], and {for Luxury}[[0.141, 0.379, 0.910, 0.793]]. The line segment for \"Sporty\" starts at 1.3 and ends at 0.5, denoting a decrease in market share, while \"Luxury\" also experiences a decline starting from 0.9 and finishing at 0.2.\nBased on the above thought, the answer is:\nBoth \"Sporty\" and \"Luxury\" market segments observed a decline in their market share from the \"Strong Growth\" period to the \"No Growth\" period." } ], "id_raw": 766 }, { "id": "aRRkWrhI9dAmR08q", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase from \"Strong Growth\" to \"No Growth\" for the \"Luxury\" market segment?" }, { "from": "gpt", "value": "Determining the percentage increase involves locating the respective values for the \"Luxury\" market segment at time \"Strong Growth\" and \"No Growth\". I can do that by investigating the points belonging to {the line representing the \"Luxury\" segment}[[0.141, 0.379, 0.910, 0.793]]. The value at \"Strong Growth\" is 0.9, and at \"No Growth\" is 0.2. Thus, the percentage increase would be calculated as ((Final value - Initial value) / Initial value) *\n100, which is ((0.2 - 0.9) / 0.9) * 100 = -77.77%\nBased on the above thought, the answer is:\n-77.77%" } ], "id_raw": 766 }, { "id": "LsuSC1iZYSd0PZLJ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_7.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the data points presented in the line chart, using the x-axis labels as rows and Economic Indicators as columns?" }, { "from": "gpt", "value": "To form this table, I identify {the x-axis tick labels}[[0.045, 0.919, 0.169, 0.942],[0.337, 0.919, 0.430, 0.942],[0.615, 0.919, 0.705, 0.942],[0.894, 0.919, 0.979, 0.942]] as the row headers. Next, the {legend texts}[[0.836, 0.832, 0.902, 0.855],[0.836, 0.867, 0.909, 0.890]] represent the column labels for Pre-Covid and Post-Covid. I then locate the coordinates of the data points for each Economic Indicator in the Pre-Covid {[0.107, 0.104], [0.384, 0.897], [0.660, 0.893], [0.936, 0.893]} and Post-Covid {[0.107, 0.148], [0.384, 0.904], [0.660, 0.907], [0.936, 0.893]} periods and extract the corresponding values.\nBased on the above thought, the answer is:\nEconomic Indicator,Pre-Covid,Post-Covid\nEmployment Rate,92,87\nInflation Rate,1.6,0.8\nInterest Rate,2,0.5\nGDP Growth,2,2.1" } ], "id_raw": 767 }, { "id": "VM61Wq6LUhqsNhyy", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_7.png", "conversations": [ { "from": "human", "value": "\nBased on the trends observed in the line chart, which Economic Indicator was most impacted by the transition from the Pre-Covid to the Post-Covid period?" }, { "from": "gpt", "value": "To identify the most affected Economic Indicator, I must examine the changes in each of the indicators from the Pre-Covid to the Post-Covid period. {Employment [<0.107, 0.104>, <0.107, 0.148>]}, {Inflation rate [<0.384, 0.897>, <0.384, 0.904>]}, {Interest rate [<0.660, 0.893>, <0.660, 0.907>]}, and {GDP Growth [<0.936, 0.893>, <0.936, 0.893>]} respectively represent the changes for each indicator. The trends show that while all indicators were affected by the pandemic, the drop in the Interest rate was the steepest.\nBased on the above thought, the answer is:\nThe Interest Rate was the most impacted Economic Indicator based on the transition from the Pre-Covid to the Post-Covid period. This is evident from the drastic decrease in its value, from 2 to 0.5, representing a more significant drop compared to the other indicators. This suggests that measures were likely taken to decrease interest rates to stimulate economic activity during the pandemic." } ], "id_raw": 767 }, { "id": "j5Tx2kp2oppTSopc", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage decline in the Employment Rate from Pre-Covid to Post-Covid period?" }, { "from": "gpt", "value": "To find the percentage decline in the Employment Rate, I need to locate {the data points for Employment Rate} for Pre-Covid {[0.107, 0.104]} which is 92 and Post-Covid {[0.107, 0.148]} which is 87. The percentage change is calculated as (92 - 87) / 92 x 100.\nBased on the above thought, the answer is:\nApproximately 5.43%." } ], "id_raw": 767 }, { "id": "PLvhahqbr1nvmsAm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_8.png", "conversations": [ { "from": "human", "value": "\nCan you present the performance details from the line graph in a structured table in the format Event vs Software Performance?" }, { "from": "gpt", "value": "To prepare the table, I'll start by looking at {the x-axis tick labels}[0.087, 0.919, 0.222, 0.942], [0.243, 0.919, 0.368, 0.942], which represent the events. The two different software performances can be represented by {the legend texts}[0.232, 0.832, 0.503, 0.855], [0.232, 0.867, 0.495, 0.890]. I then look at {the line points}[0.155, 0.648], [0.306, 0.297], [0.456, 0.341], [0.607, 0.122], [0.758, 0.166], [0.909, 0.253] and [0.155, 0.473], [0.306, 0.516], [0.456, 0.319], [0.607, 0.385], [0.758, 0.451], [0.909, 0.582] for SoftwareA and SoftwareB respectively and extract the values to fill the table.\nBased on the above thought, the answer is:\nEvent,SoftwareA,SoftwareB\nSharp Decrease,0.7, 0.45\nAbrupt Rise,0.2,0.75" } ], "id_raw": 768 }, { "id": "Vx0CmX36PrzyPv39", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_8.png", "conversations": [ { "from": "human", "value": "\nConsidering the two events analyzed in the chart, which software had a more significant performance change between the events? Please explain." }, { "from": "gpt", "value": "To determine which software had the most significant performance change between the two events, I evaluate the difference in values for each one. Examining {the line for SoftwareA}[0.155, 0.648], [0.306, 0.297], it dropped from 0.7 (sharp decrease event) to 0.2 (abrupt rise event), indicating a decrease of 0.5. On the other hand, inspecting {the line for SoftwareB}[0.155, 0.473], [0.306, 0.516], it moved from 0.45 (sharp decrease event) to 0.75 (abrupt rise event), indicating an increase of 0.3.\nBased on the above thought, the answer is:\nConsidering the two events analyzed in the chart, SoftwareA had a more significant performance change. While SoftwareB performance increased by 0.3 between the events, SoftwareA performance decreased by 0.5, making the change in SoftwareA more significant." } ], "id_raw": 768 }, { "id": "iTscP7BjPuzDnb3T", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the performance difference between SoftwareA and SoftwareB during the abrupt rise event?" }, { "from": "gpt", "value": "To find the performance difference during the abrupt rise event for both software, I need to locate the {point for SoftwareA in the abrupt rise event}[0.306, 0.297] and its corresponding value is 0.2. For SoftwareB, I check {the point in the abrupt rise event}[0.306, 0.516] and extract its value which is 0.75. The difference is then 0.75 - 0.2 = 0.55.\nBased on the above thought, the answer is:\n0.55." } ], "id_raw": 768 }, { "id": "QvZuHafWzbjkusI0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_9.png", "conversations": [ { "from": "human", "value": "\nCan you provide the raw data for the chart in a structured table?" }, { "from": "gpt", "value": "To generate the table from a horizontal stacked bar chart, I observe that careers'represented by {the y-axis tick labels} [0.099, 0.939, 0.137, 0.957],[0.285, 0.939, 0.362, 0.957], [0.486, 0.939, 0.572, 0.957], [0.717, 0.939, 0.752, 0.957], [0.912, 0.939, 0.968, 0.957] are rows of the table. Moreover, phases of 'Early Career' and 'Mid Career'{the legend texts} [0.843, 0.067, 0.956, 0.085], [0.843, 0.094, 0.938, 0.111] would be the columns for each career. Each bar segment, e.g., {the Early Career segment for Tech}[0.118, 0.337] and {the Mid Career segment for Tech}[0.118, 0.544] represent the values in the corresponding cells of the table.\nBased on the above thought, the answer is:\nCareer,Early Career,Mid Career\nTech,2.3,1.5\nMedicine,1.7,1.3\nEducation,0.9,0.7\nArts,3.0,2.5\nSports,2.7,2.2" } ], "id_raw": 769 }, { "id": "RSjMFnimawZVPpIo", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_9.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the chart, which career field has the least improvement from Early Career to Mid Career?" }, { "from": "gpt", "value": "Evaluating the chart, I'd locate the lengths of the bar segments representing Early Career Development and Mid Career for all career paths- {length of Early Career segments}[0.118, 0.337, 0.323, 0.492, 0.529, 0.700, 0.735, 0.155, 0.940, 0.233] and {length of Mid Career segments}[0.118, 0.544, 0.323, 0.596, 0.529, 0.752, 0.735, 0.285, 0.940, 0.363]. From the coordinates , all fields indicate an improvement when proceeding from Early Career to Mid Career. However, the field of Arts shows the least improvement as the increase from Early Career Development {3.0}[0.735, 0.155] to Mid Career Development {2.5}[0.735, 0.285] is the smallest compared to all fields.\nBased on the above thought, the answer is:\nBased on the trends shown in the chart, the career field of Arts has the least improvement from Early Career to Mid Career. The increment in the Career Development Index from Early Career to Mid Career is the smallest compared to other fields, indicating a slower rate of improvement in this field." } ], "id_raw": 769 }, { "id": "ShAnGS3JiaYuYJke", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_9.png", "conversations": [ { "from": "human", "value": "\nFor which career field is the index for Early Career Development more than double of Mid Career Development?" }, { "from": "gpt", "value": "To answer this question, I need to compare values for Early Career Development and Mid Career Development for each career field. I can do this by analyzing {the bar segment lengths for Early and Mid Career Development} [0.118, 0.337, 0.735, 0.155, 0.940, 0.233, 0.323, 0.544, 0.735, 0.285, 0.940, 0.363]. The field of Arts satisfies the condition as the index for Early Career Development {3.0}[0.735, 0.285] is more than double of Mid Career Development {2.5}[0.735, 0.155].\nBased on the above thought, the answer is:\nArts." } ], "id_raw": 769 }, { "id": "6isd6WgzVTKu1z2K", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_10.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from this chart based on weekdays and weekends distribution of hours spent on outdoor activities and screen time?" }, { "from": "gpt", "value": "I first identify {the x-axis tick labels}[0.313, 0.909, 0.433, 0.944], on the graph which represent the hours spent on activities. Next, I noticed the {activity categories on the y-axis}[0.094,0.188,0.247,0.434] indicating 'Outdoor activities' and 'Screen time'. I can see that both of these activities are segmented into weekdays and weekends as marked by the {bars on the chart itself}[0.325,0.33,0.475,0.56;0.325,0.67,0.475,0.76;0.560,0.33,0.805,0.56;0.560,0.67,0.900,0.76]. Using these mappings from the chart I can extract the data.\nBased on the above thought, the answer is:\nActivities,WeekDays,Weekends\nOutdoor Activities,7,10\nScreen Time,4,7" } ], "id_raw": 770 }, { "id": "Wyu4BYJCIHseb8ev", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_10.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the chart, can it be inferred that individuals spend more time on Screens during weekends compared to weekdays?" }, { "from": "gpt", "value": "Yes, by looking at the graph, we see that {the bar representing 'Screen Time' during weekdays}[0.325,0.67,0.475,0.76] is shorter than {the bar for 'Screen Time' during weekends}[0.560,0.67,0.900,0.76]. This indicates that individuals spend more time on screens during the weekends than they do on weekdays.\nBased on the above thought, the answer is:\nYes, the chart suggests that individuals tend to spend more time on screens during weekends compared to weekdays." } ], "id_raw": 770 }, { "id": "Vpg5BxiUmHjmWXGk", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the Screen time hours on weekdays and weekends?" }, { "from": "gpt", "value": "To find the difference, I need to look at {the \"Screen Time\" bar for weekdays}[0.325,0.67,0.475,0.76] which corresponds to 4 hours and compare it with {the \"Screen Time\" bar for Weekend}[0.560,0.67,0.900,0.76] corresponding to 7 hours. The difference is therefore 7 - 4 = 3.\nBased on the above thought, the answer is:\n3 hours." } ], "id_raw": 770 }, { "id": "LnqL3RvJ2l3qiFvZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_11.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table that lists the online learning tools and the corresponding usage percentages for the EdTech companies A and B?" }, { "from": "gpt", "value": "From the chart, I first identify {the y-axis tick labels}[[0.08, 0.90, 0.172, 0.933], [0.08, 0.816, 0.160, 0.855], [0.08, 0.726, 0.185, 0.769], [0.08, 0.635, 0.205, 0.679], [0.08, 0.549, 0.167, 0.589], [0.08, 0.459, 0.149, 0.502]] which represent the different online learning tools. Then, I find the {data points for company A}[[0.098, 0.871, 0.392, 0.905], [0.098, 0.791, 0.273, 0.818], [0.098, 0.702, 0.239, 0.732], [0.098, 0.608, 0.233, 0.639], [0.098, 0.524, 0.203, 0.555], [0.098, 0.434, 0.173, 0.465]] and the {data points for company B}[[0.392, 0.871, 0.801, 0.905], [0.273, 0.791, 0.562, 0.818], [0.239, 0.702, 0.531, 0.732], [0.233, 0.608, 0.518, 0.639], [0.203, 0.524, 0.578, 0.555], [0.173, 0.434, 0.578, 0.465]] in the chart. The lengths of these data points correspond to the usage percentages of the learning tools for the respective companies.\nBased on the above thought, the answer is:\nOnline Learning Tools,Company A,Company B\nWebinars,29.4%,40.9%\nOnline Quizzes,17.5%,28.9%\nVideo Lectures,14.1%, 29.2%\nForum Discussions,13.5%,28.5%\nOnline Assignments,10.5%, 37.5%\nVirtual Labs,7.5%, 40.5%" } ], "id_raw": 771 }, { "id": "e4cbgnInpa2C5adF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_11.png", "conversations": [ { "from": "human", "value": "\nWhich EdTech company appears to be more diversified in its use of online learning tools based on the distribution of usage across different tools?" }, { "from": "gpt", "value": "I need to examine {the data bars for the tools for company A} [0.098, 0.871, 0.392, 0.905], [0.098, 0.791, 0.273, 0.818], [0.098, 0.702, 0.239, 0.732], [0.098, 0.608, 0.233, 0.639], [0.098, 0.524, 0.203, 0.555], [0.098, 0.434, 0.173, 0.465] and {company B} [0.392, 0.871, 0.801, 0.905], [0.273, 0.791, 0.562, 0.818], [0.239, 0.702, 0.531, 0.732], [0.233, 0.608, 0.518, 0.639], [0.203, 0.524, 0.578, 0.555], [0.173, 0.434, 0.578, 0.465]. With less noticeable disparity in the usage percentages of different tools, company B seems to be more diversified in the use of online learning tools.\nBased on the above thought, the answer is:\nEdTech company B appears to be more diversified in its use of online learning tools. The usage percentages across different tools are relatively more evenly distributed for company B compared to company A, which shows a larger range in usage percentages suggesting less balanced utilization of the different online learning tools." } ], "id_raw": 771 }, { "id": "enz082eSIEv0WcaS", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage difference in usage of Forum Discussions for EdTech companies A and B?" }, { "from": "gpt", "value": "To find the percentage difference in usage of Forum Discussions, I need to locate the {Corresponding data bar for company A} [0.098, 0.608, 0.233, 0.639] and similarly, the {data bar for company B} [0.233, 0.608, 0.518, 0.639]. The lengths of these bars give the percentage of usage for the companies. The percentage difference is calculated as the absolute difference between these percentages.\nBased on the above thought, the answer is:\n15.0%." } ], "id_raw": 771 }, { "id": "NL6tQ7gzcqAq1VAS", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart data into a structured format, with the Tools as rows and Baseline & Optimization as columns?" }, { "from": "gpt", "value": "To generate the tabular data from the chart, I first identify {the x-axis tick labels}[[0.084, 0.919, 0.198, 0.942], [0.276, 0.919, 0.392, 0.942], [0.472, 0.919, 0.582, 0.942], [0.662, 0.919, 0.779, 0.942], [0.855, 0.919, 0.972, 0.942]] as the row labels, which represent different tools. Next, I see that {the legend texts}[[0.214, 0.090, 0.312, 0.113], [0.214, 0.125, 0.362, 0.148]] are the column headers indicating the Baseline and Optimization respectively. I then locate the {points of each line}[[0.141, 0.341, 0.913, 0.560], [0.141, 0.166, 0.913, 0.473]] for each tool and extract the corresponding efficiencies.\nBased on the above thought, the answer is:\nTool,Baseline,Optimization\nSuperDrill,0.65,0.72\nHyperDrill,0.55,0.63\nMegaDrill,0.8,0.85\nEconoDrill,0.4,0.5\nPowerDrill,0.7,0.78" } ], "id_raw": 772 }, { "id": "4dS0wWMxNyjrCsqR", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_12.png", "conversations": [ { "from": "human", "value": "\nWhich tool has shown the most improvement after the optimization process according to the chart?" }, { "from": "gpt", "value": "To determine which tool has made the most improvement, I compare the differences between the baseline and optimized efficiencies for each tool. Looking at the {difference for SuperDrill}[[0.141, 0.341, 0.141, 0.280]], the improvement is 0.07. For {HyperDrill}[[0.334, 0.429, 0.334, 0.359]], the improvement is 0.08. Observing {MegaDrill}[[0.527, 0.210, 0.527, 0.166]], the improvement is 0.05. For {EconoDrill}[[0.720, 0.560, 0.720, 0.473]], the improvement is 0.1. Finally, for {PowerDrill}[[0.913, 0.297, 0.913, 0.227]], the improvement is 0.08.\nBased on the above thought, the answer is:\nBased on the chart, it appears that EconoDrill has shown the most improvement after the optimization process. This is indicated by the largest difference in efficiency levels between the baseline and after the optimization. The improvement for EconoDrill is 0.1, which is higher than the improvements observed for the other tools." } ], "id_raw": 772 }, { "id": "qVSairjq4P34j4hO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what is the efficiency level difference for SuperDrill between its baseline and after optimization?" }, { "from": "gpt", "value": "I need to figure out the baseline and optimization efficiency levels for SuperDrill. I find its baseline value at {the point for SuperDrill on the blue line}[[0.141, 0.341]]. The baseline efficiency of SuperDrill is 0.65. Now I locate its optimization value at {the point for SuperDrill on the green line}[[0.141, 0.280]]. The optimized efficiency of SuperDrill is 0.72. So, the efficiency level difference can be calculated as 0.72 - 0.65 = 0.07.\nBased on the above thought, the answer is:\n0.07." } ], "id_raw": 772 }, { "id": "Btdyw2quIWzXh1km", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_13.png", "conversations": [ { "from": "human", "value": "\nCan you create a table summarizing the problems encountered during content creation for YouTube and TikTok as seen in the line chart?" }, { "from": "gpt", "value": "To create a table, I first note that {the x-axis tick labels}[0.044, 0.919, 0.196, 0.942], [0.317, 0.919, 0.444, 0.942], [0.588, 0.919, 0.696, 0.942], [0.836, 0.919, 0.971, 0.942] represent the row labels, which are the different problems encountered during content creation. Then, I observe that {the legend items}[0.166, 0.832, 0.282, 0.855],[0.166, 0.867, 0.267, 0.890] correspond to YouTube and TikTok, which serve as the column headers. To populate the values, I locate the points from {the line for YouTube}[0.120, 0.122, 0.903, 0.455] and {the line for TikTok}[0.120, 0.613, 0.903, 0.823], and then extract the corresponding percentages.\nBased on the above thought, the answer is:\nProblem,YouTube,TikTok\nSuccess,26,35\nVideo Quality,25,30\nSound Quality,25,28\nEditing Error,28,23\nSlow Uploads,28,22\nCommunity Guidelines,26,15" } ], "id_raw": 773 }, { "id": "i1J4UyxdByziWIld", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_13.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which problem shows the largest difference in prevalence between YouTube and TikTok content creators?" }, { "from": "gpt", "value": "To determine the problem with the largest difference in prevalence, I need to compare the gaps between the {YouTube line points}[0.120, 0.122, 0.903, 0.455] and the {TikTok line points}[0.120, 0.613, 0.903, 0.823] for each problem. The problem corresponding to the largest gap between the two line points would be the one with the greatest difference in prevalence between the two platforms.\nBased on the above thought, the answer is:\nBased on the plot, the problem with the greatest difference in prevalence between YouTube and TikTok content creators is community guidelines violations. The prevalence of this problem is significantly higher on YouTube than on TikTok." } ], "id_raw": 773 }, { "id": "Kk0H3vz2Oa7tAOCG", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in encountering success from YouTube to TikTok content creation?" }, { "from": "gpt", "value": "To figure out the increase in success in content creation from YouTube to TikTok, I need to note the values at the {success point for YouTube}[0.120, 0.455], which is 26% and at the {success point for TikTok}[0.120, 0.718], which is 35%. The increase would be (35-26)/26 * 100 =34.62 approximately.\nBased on the above thought, the answer is:\n34.62%." } ], "id_raw": 773 }, { "id": "bLKa1jnNWFDyXnc9", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_15.png", "conversations": [ { "from": "human", "value": "\nConsidering the data in the line chart, can you convert it into a table format with the Channels as columns and Industry Categories as rows?" }, { "from": "gpt", "value": "To create a structured table from the line chart, I first pinpoint {the x-axis labels}[0.055, 0.919, 0.972, 0.942] which signify the columns, namely 'Search_Engine' and 'Social_Media'. The row labels are represented by {the sublegend items}[0.188, 0.832, 0.363, 0.890], 'New Creation' and 'Digital Content'. To fill in the values, I recognize {the line points for 'New Creation'}[0.140, 0.239, 0.897, 0.356] and 'Digital Content'[0.140, 0.297, 0.897, 0.472] for corresponding channels and extract the respective values.\nBased on the above thought, the answer is:\nIndustry,Search_Engine,Social_Media\nNew_Creation,2.3,1.9\nDigital_Content,1.5,2.1" } ], "id_raw": 775 }, { "id": "rNvMuRM46I8t5zvn", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_15.png", "conversations": [ { "from": "human", "value": "\nObserving the line chart trends, which channel exhibits a better performance score across the two industries evaluated? Provide your reasoning." }, { "from": "gpt", "value": "To analyze which channel performs better, I need to examine the performance scores for both industries across the two channels. Observing {the line for 'New Creation'}[0.140, 0.239, 0.897, 0.356], I observe the scores for 'Search_Engine' is 2.3 and for 'Social_Media' is 1.9. Reviewing {the line for 'Digital Content'}[0.140, 0.297, 0.897, 0.472], I see the scores for 'Search_Engine' is 1.5 and for 'Social_Media' is 2.1. By summing the performance scores for each channel, 'Search_Engine' has a total of 3.8 and 'Social_Media' has a total of 4, clearly outperforming 'Search_Engine'.\nBased on the above thought, the answer is:\nAccording to the trends in the line chart, 'Social_Media' exhibits a higher overall performance score compared to 'Search_Engine' across the two industries evaluated. By summing up the individual performance scores, we can see that 'Social_Media' outperforms 'Search_Engine' with a total performance score of 4 against 3.8." } ], "id_raw": 775 }, { "id": "E07H1m70nGw2Elwt", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in performance scores between Search_Engine and Social_Media for the New Creation industry?" }, { "from": "gpt", "value": "To calculate this, I need to locate the performance scores for the 'New Creation' industry in both 'Search_Engine' and 'Social_Media'. I identify {the point for 'New Creation' channel 'Search_Engine'}[0.140, 0.239] and extract its value, 2.3. Likewise, I locate {the point for 'New Creation' channel 'Social_Media'}[0.897, 0.356] and extract its representative value, 1.9. The difference is then 2.3 - 1.9 = 0.4.\nBased on the above thought, the answer is:\n0.4." } ], "id_raw": 775 }, { "id": "t3tgPZQIho1J9QAB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_16.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, can you help to format the raw data into a table with Years as column headers and Technologies as row labels?" }, { "from": "gpt", "value": "To extract the necessary data from the chart, I first identify {the x-axis labels}[0.112, 0.939, 0.925, 0.957] on the horizontal axis representing the years from 2010 to 2020, which will be our column headers. Next, I locate {the legend labels}[0.716, 0.067, 0.837, 0.085, 0.716, 0.094, 0.840, 0.111] that signify the two different technologies 'AI Ventures' and 'Quantum Computing Co.', constituting our row labels. For the corresponding values, I look at {the line points for AI Ventures and Quantum Computing Co.}[0.112, 0.135, 0.925, 0.588, 0.112, 0.361, 0.925, 0.743] and extract the respective values for each technology per year.\nBased on the above thought, the answer is:\nTechnologies,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nAI Ventures,8.8,7.2,4.5,3.8,5.4,8.3,6.8,4.2,3.5,5.0,9.3\nQuantum Computing Co.,4.5,6.3,2.1,6.0,3.2,4.0,5.9,1.8,5.7,2.8,5.0" } ], "id_raw": 776 }, { "id": "J5hZUG1ZMLKNX1wA", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_16.png", "conversations": [ { "from": "human", "value": "\nConsidering the line charts, which technology - 'AI Ventures' or 'Quantum Computing Co.' - would be a safer investment over the years and why?" }, { "from": "gpt", "value": "To answer this question, I am looking at the nature of the fluctuations in the return rates of both technologies. For {AI Ventures}[0.112, 0.135, 0.925, 0.588], the line graph reflects large and erratic fluctuations in return rates indicating higher risks and uncertainty\n. In contrast, the line chart for {Quantum Computing Co.}[0.112, 0.361, 0.925, 0.743] shows a relatively steady increase with marginal fluctuations in the return rates, indicating a more predictable and stable investment. Therefore, Quantum Computing Co. presents a safer investment option compared to AI Ventures.\nBased on the above thought, the answer is:\nBased on the trends shown in the line charts, Quantum Computing Co. seems to be the safer investment over the years. Its line chart indicates a more steady and consistent growth in return rate with minimal volatility compared to AI Ventures which has larger and erratic variations in the return rates, indicating a riskier investment." } ], "id_raw": 776 }, { "id": "lXn5O86nEgzdDEIT", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_16.png", "conversations": [ { "from": "human", "value": "\nHow much more was the highest annual investment return rate of 'AI Ventures' compared to that of 'Quantum Computing Co.'?" }, { "from": "gpt", "value": "To find the required answer, I need to identify the peak points in the line plots of both technologies. For 'AI Ventures', {the line points}[0.112, 0.135, 0.925, 0.588] indicate the highest return rate to be 9.3 in 2020. For 'Quantum Computing Co.', the {line points}[0.112, 0.361, 0.925, 0.743] show the highest return rate as 6.3 in 2014. The difference in the highest annual investment return rates between both technologies can be calculated as 9.3 (AI Ventures) - 6.3 (Quantum Computing Co.) = 3.0.\nBased on the above thought, the answer is:\n3.0" } ], "id_raw": 776 }, { "id": "8uqAmNlBJXDCy4dr", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_17.png", "conversations": [ { "from": "human", "value": "\nCan you translate the data from the line chart into a table organized by trends with columns being different education and training technologies?" }, { "from": "gpt", "value": "To convert the chart data into a table, the first step is to identify the x-axis labels which are {the x-axis tick labels}[0.068, 0.919, 0.237, 0.942], [0.746, 0.919, 0.975, 0.942] that give us the different trends in technology adoption rate. The legend has two items, {the legend text}[0.527, 0.090, 0.680, 0.113], [0.527, 0.125, 0.756, 0.148], which is representing the column headers - different technologies under consideration. For cell values, I look at the coordinates of {the line points of the first technology}[0.152, 0.210], [0.861, 0.604] and {the line points of the second technology}[0.152, 0.385], [0.861, 0.736] which are correspondent to the Innovation/Adoption Rate for each trend across both technologies.\nBased on the above thought, the answer is:\nTrends,AI Innovation,E-Learning Platform\nAbrupt decline,1.6,1.2\nAccelerated decline,0.7,0.4" } ], "id_raw": 777 }, { "id": "x5bEjCuSSaL2oquP", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_17.png", "conversations": [ { "from": "human", "value": "\nComparing the Innovation/Adoption Rate of AI Innovation and E-Learning Platform across the different trends, which Technology shows a drastic performance difference between the two trends? Justify your answer." }, { "from": "gpt", "value": "To answer this question, I need to compare the Innovation/Adoption Rate of AI Innovation and E-Learning Platform across different trends. I can see that for {AI Innovation}[0.152, 0.210], [0.861, 0.604], the Innovation/Adoption Rate drastically decreases from 1.6 in Abrupt decline to 0.7 in Accelerated decline. When looking at the same trend changes for {E-Learning Platform}[0.152, 0.385], [0.861, 0.736], the Innovation/Adoption Rate also decreases, but not as drastically, going from 1.2 in Abrupt decline to 0.4 in Accelerated decline. Though both technologies see a decline in their adoption rates, the rate of decline is more significant for AI Innovation than for the E-Learning Platform.\nBased on the above thought, the answer is:\nAs per the chart data, both AI Innovation and E-Learning Platform have seen a drop in their Innovation/Adoption Rates from Abrupt decline to Accelerated decline. However, AI Innovation experienced a more drastic decline bringing it from 1.6 to 0.7. This is a performance decrease of nearly 56%, compared to the E-Learning Platform which saw a drop from 1.2 to 0.4, representing a decrease of approximately 67%. In absolute terms, the change in Innovation/Adoption Rate was more drastic for the AI Innovation." } ], "id_raw": 777 }, { "id": "NJZ3DuU93gRJrltk", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in Adoption Rate between AI Innovation and E-Learning Platform during the Abrupt decline?" }, { "from": "gpt", "value": "To find the difference in Adoption Rate between AI Innovation and E-Learning Platform during the Abrupt decline phase, I have to identify the Adoption values for each of them. I find the point representing AI Innovation during the Abrupt decline: {the AI Innovation Abrupt decline point}[0.152, 0.210], and read its Adoption Rate as 1.6. Then I locate {the E-Learning Platform Abrupt decline point}[0.152, 0.385] which has an Adoption Rate of 1.2. So the difference would be 1.6 - 1.2 = 0.4.\nBased on the above thought, the answer is:\n0.4." } ], "id_raw": 777 }, { "id": "2JAgcmnFdmj5eSPT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_18.png", "conversations": [ { "from": "human", "value": "\nBased on the information from the bar chart, could you extract the performance metrics of TensorTurbo and DeepLearn007 into a tabular format?" }, { "from": "gpt", "value": "To generate a table from the bar chart information, I first identify {the y-axis tick labels}[[0.061, 0.789, 0.141, 0.866], [0.070, 0.478, 0.138, 0.555]] which represent the rows or AI systems: TensorTurbo and DeepLearn007. I then use the metrics {success rate}[0.541, 0.184, 0.714, 0.384], {Precision error}[0.716, 0.184, 0.802, 0.384], {Recall error}[0.807, 0.184, 0.866, 0.384], {Bias error}[0.873, 0.184, 0.926, 0.384], {Variance error}[0.932, 0.184, 0.996, 0.384] and {Overfitting}[0.541, 0.623, 0.856, 0.823] as the column headers. Based on the proportional length of each segment in the bars for the two AI systems, I extract the corresponding values.\nBased on the above thought, the answer is:\nAI System,Success Rate,Precision Error,Recall Error,Bias Error,Variance Error, Overfitting\nTensorTurbo,40,20,5,15,10,10\nDeepLearn007,60,10,7,5,15,3" } ], "id_raw": 778 }, { "id": "rmvZ8qlcT8anSqkc", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_18.png", "conversations": [ { "from": "human", "value": "\nBetween the two AI systems, TensorTurbo and DeepLearn007, which one performs better overall and why?" }, { "from": "gpt", "value": "To determine which AI system performs better, I need to analyze the performance metrics. Looking at {TensorTurbo's bar}[0.176, 0.184, 0.996, 0.384], I see that it has a total success rate of 40% with relatively high errors varying from 5% to 20%. Conversely, looking at {DeepLearn007's bar}[0.176, 0.623, 0.856, 0.823], it has a higher success rate of 60% and the errors are mostly less than 10%, with the exception of Variance error at 15%. This implies that DeepLearn007 has a better performance.\nBased on the above thought, the answer is:\nDeepLearn007 performs better overall. It has a higher success rate of 60% compared to TensorTurbo's 40%. Also, the errors in DeepLearn007 are mostly less than those in TensorTurbo, implying a more consistent and reliable performance from DeepLearn007." } ], "id_raw": 778 }, { "id": "9EtDzZLSxlqpZIkX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the total error rate for TensorTurbo?" }, { "from": "gpt", "value": "To calculate the total error rate for TensorTurbo, I need to sum up the values of all the errors: {Precision error}[0.716, 0.184, 0.802, 0.384] (20%), {Recall error}[0.807, 0.184, 0.866, 0.384] (5%), {Bias error}[0.873, 0.184, 0.926, 0.384] (15%), {Variance error}[0.932, 0.184, 0.996, 0.384] (10%), and {Overfitting}[0.541, 0.184, 0.714, 0.384] (10%). Adding them all together, 20% + 5% + 15% + 10% + 10% = 60%.\nBased on the above thought, the answer is:\n60%." } ], "id_raw": 778 }, { "id": "VUwJuM5ersTbgClp", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_39_19.png", "conversations": [ { "from": "human", "value": "\nCan you structure the data from the bar chart into a table format distinguishing between Urban and Rural population trends?" }, { "from": "gpt", "value": "Observing the horizontal bar chart, I identify two main categories: {Urban Population}[0.142, 0.567, 0.935, 0.785] at the top, predominantly green, and {Rural Population}[0.142, 0.801, 0.935, 0.960] at the bottom, primarily grey. Each bar is divided showing steep increases and marginal declines. From each section of the bars, I can note the percentage values within and compile them into a structured format.\nBased on the above thought, the answer is:\nCategory,Steep Increase (%),Marginal Decline (%)\nUrban Population,60,40\nRural Population,30,70" } ], "id_raw": 779 }, { "id": "oiN8DZPqnObmxCia", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_39_19.png", "conversations": [ { "from": "human", "value": "\nDoes the trend in the bar chart suggest an overall growth or decline for the populations represented? Support your reasoning." }, { "from": "gpt", "value": "Analyzing the trends represented in the bar chart, {Urban Population}[0.142, 0.567, 0.935, 0.785] shows a major portion of steep increase, indicating substantial growth. Conversely, {Rural Population}[0.142, 0.801, 0.935, 0.960], with its predominant marginal decline, indicates a decrease. Overall, the visual representation suggests a shift towards urban growth at the expense of rural decline, reflective of an overall redistribution rather than a uniform trend across both categories.\nBased on the above thought, the answer is:\nThe trend suggests overall urban growth paired with rural decline, indicating a population shift from rural to urban areas as depicted by the comparative sizes of the steep increase in urban and the decline in rural sections of the bars in the chart." } ], "id_raw": 779 }, { "id": "3S2NtlhAhKSKIISI", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_39_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the total percentage represented by the steep increases for both Urban and Rural populations?" }, { "from": "gpt", "value": "To calculate the total percentage of steep increases, I will sum the individual percentages annotated for steep increases from both categories. Within the chart, {Urban Population's steep increase}[0.142, 0.567, 0.700, 0.785] is annotated as 60%, and {Rural Population's steep increase}[0.142, 0.801, 0.450, 0.960] is noted at 30%. Adding these values together gives a total.\nBased on the above thought, the answer is:\n90" } ], "id_raw": 779 }, { "id": "PWNLZFZR5PRaqYas", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_0.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table from the line chart with Years as columns and Trends as rows?" }, { "from": "gpt", "value": "The table generation from the line chart includes the identification of {the x-axis tick labels}[0.095, 0.919, 0.130, 0.942], [0.214, 0.919, 0.249, 0.942], [0.332, 0.919, 0.367, 0.942], [0.451, 0.919, 0.486, 0.942], [0.570, 0.919, 0.605, 0.942], [0.688, 0.919, 0.724, 0.942], [0.807, 0.919, 0.842, 0.942], [0.926, 0.919, 0.961, 0.942] forming the column headers representing the Years. The {legend texts}[0.858, 0.393, 0.952, 0.415], [0.858, 0.425, 0.952, 0.446], [0.858, 0.456, 0.938, 0.478], [0.858, 0.487, 0.975, 0.509], [0.858, 0.519, 0.947, 0.540], [0.858, 0.550, 0.973, 0.572] provide the row labels representing the different trends. The {line points}[0.112, 0.825, 0.943, 0.865], [0.112, 0.200, 0.943, 0.665], [0.112, 0.200, 0.943, 0.599], [0.112, 0.233, 0.943, 0.699], [0.112, 0.100, 0.943, 0.333], [0.112, 0.300, 0.943, 0.532] associated with each trend across the years stand as the cell values in the table.\nBased on the above thought, the answer is:\nTrends,2030,2040,2050,2060,2070,2080,2090,2100\nMinimal Change,100,101,102,102,103,104,105,106\nGradual Decline,200,190,180,170,160,150,140,130\nUpward Trend,140,145,150,160,170,180,190,200\nAccelerated Decline,195,185,175,165,155,145,135,125\nInexorable Rise,180,185,190,195,200,205,210,215\nSubdued Expansion,150,155,160,165,170,175,180,185" } ], "id_raw": 780 }, { "id": "cnvoQ5hgJHArOvxa", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_0.png", "conversations": [ { "from": "human", "value": "\nWhich trend line shows a consistent increase over the years, according to the chart?" }, { "from": "gpt", "value": "The task is to identify a trend line that consistently increases over time. Observing the line chart, I check each line's slope, moving from left to right. Among the trends, the line for {Inexorable Rise}[0.112, 0.100, 0.943, 0.333] shows a consistent increase over the years without any decline. Hence, \"Inexorable Rise\" indicates a constant increase throughout the observed years.\nBased on the above thought, the answer is:\nThe trend line for \"Inexorable Rise\" shows a consistent increase over the years, as it is gradually moving upwards in the chart from 2030 to 2100 without any decline." } ], "id_raw": 780 }, { "id": "DPwdHlR1Kpr8HKPD", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_0.png", "conversations": [ { "from": "human", "value": "\nHow much does the trend for \"Accelerated Decline\" drop from 2030 to 2100 based on the data from the line chart?" }, { "from": "gpt", "value": "To calculate the drop in \"Accelerated Decline\" from 2030 to 2100, I need to look at the {line points for this trend}[0.112, 0.233, 0.943, 0.699]. The value for 2030 is 195, and the value for 2100 is 125, based on the points at these years. So, the drop is calculated as 195-125.\nBased on the above thought, the answer is:\n70." } ], "id_raw": 780 }, { "id": "9pOTrNVNiiWygRlJ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_1.png", "conversations": [ { "from": "human", "value": "\nHow would you convert the data visualized in the line chart into a structured table?" }, { "from": "gpt", "value": "To create a table from the line chart data, I'd start by identifying {the x-axis tick labels}[[0.225, 0.869, 0.283, 0.904], [0.369, 0.869, 0.427, 0.904], [0.513, 0.869, 0.572, 0.904], [0.657, 0.869, 0.716, 0.904], [0.802, 0.869, 0.860, 0.904]] as the row labels, representing the years. To generate the column headers or technologies, I look at {the legend texts}[[0.724, 0.564, 0.767, 0.609], [0.724, 0.630, 0.926, 0.675], [0.724, 0.696, 0.950, 0.741], [0.724, 0.762, 0.921, 0.807]]. Finally, the actual values are found at the points where the Technologies cross over to a specific year {the line points}[[0.254, 0.083, 0.831, 0.527], [0.254, 0.368, 0.831, 0.765], [0.254, 0.400, 0.831, 0.606], [0.254, 0.178, 0.831, 0.448]]\nBased on the above thought, the answer is:\nYears, IVF, Deep Learning, Nanotechnology, Biotech\n2000, 20.0, 5.0, 15.0, 25.0\n2005, 27.0, 9.0, 20.0, 29.0\n2010, 35.0, 14.0, 23.0, 30.0\n2015, 42.0, 22.0, 25.0, 38.0\n2020, 48.0, 30.0, 28.0, 42.0" } ], "id_raw": 781 }, { "id": "tkY1uleEERa5L229", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_1.png", "conversations": [ { "from": "human", "value": "\nBased on the trends depicted in the line chart, which field appears to be receiving increasing attention based on the number of published papers?" }, { "from": "gpt", "value": "To interpret which field seems to be gaining increased attention, I examine the rate of growth in the number of published papers for each field over time. Looking at the lines, {IVF}[[0.254, 0.083, 0.831, 0.527]] increases slightly from 20 to 48 over 20 years. {Deep Learning}[[0.254, 0.368, 0.831, 0.765]] shows a steeper increase, going from 5 to 30. {Nanotechnology}[[0.254, 0.400, 0.831, 0.606]] shows a steady but moderate increase from 15 to 28, while {Biotechnology}[[0.254, 0.178, 0.831, 0.448]] demonstrates a substantial increase from 25 to 42.\nBased on the above thought, the answer is:\nBased on the line chart, the field \"Deep Learning\" appears to be receiving the most increased attention. It shows the steepest increase in the number of published papers over time, starting from a count of 5 in 2000 and rising to 30 in 2020. This suggests a growing interest and investment in this research field." } ], "id_raw": 781 }, { "id": "uyFafiTtdbPjA7Qa", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the average number of publications across all years for the research field 'Deep Learning'?" }, { "from": "gpt", "value": "To answer this question, I need to calculate the average number of 'Deep Learning' publications over the specified years. I would first locate the {line points for 'Deep Learning'}[[0.254, 0.765], [0.398, 0.702], [0.542, 0.622], [0.687, 0.495], [0.831, 0.368]], and extract the corresponding values: 5.0, 9.0, 14.0, 22.0, 30.0. I would add these values to get the total (80), and then divide by the number of years (5) to get the average: 80 / 5 = 16.\nBased on the above thought, the answer is:\n16." } ], "id_raw": 781 }, { "id": "Jt05CTn0bSdhsfvC", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_2.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the data from the line chart into a table with 'Year' as row entries and all the different categories as columns?" }, { "from": "gpt", "value": "Sure, to convert the line chart into a tabular format, I'm referencing the {x-axis}[0.508, 0.959, 0.552, 0.981] labels to form the row entries which are 'Years'. The column entries are defined by {the legend}[0.083, 0.036, 0.361, 0.174] which lists different categories such as \u2018Game Releases\u2019, \u2018Game Sales (in Millions)\u2019, \u2018New Gaming Platforms\u2019, and \u2018New Gaming Technologies\u2019. The corresponding values for each category per year can be derived from the lines coordinates like {the line for Game Releases}[0.151, 0.108, 0.909, 0.356].\nBased on the above thought, the answer is:\nYear,Game Releases,Game Sales (in Millions),New Gaming Platforms,New Gaming Technologies\n2010,158,22.5,3,2\n2011,162,25.1,2,2\n2012,168,28.9,2,3\n2013,175,31.5,1,3\n2014,183,35.2,1,4\n2015,191,38.8,0,4\n2016,198,41.2,1,5\n2017,205,43.6,1,4\n2018,213,46.7,2,5\n2019,220,49.3,1,6\n2020,227,52.2,1,7" } ], "id_raw": 782 }, { "id": "qfGtRdU9vZCexGV0", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_2.png", "conversations": [ { "from": "human", "value": "\nBased on the data trends shown in the line chart, which category has seen the most significant growth over the years?" }, { "from": "gpt", "value": "To understand which category experienced the most significant growth, I have to compare the starting and ending points of each category. For instance, {Game Releases line points}[0.151, 0.356] -> [0.909, 0.108] depict an increase from 158 to 227. On the other hand, {Game Sales line points}[0.151, 0.841] -> [0.909, 0.735] reflect an increase from 22.5 to 52.2. Similar analysis can be done for the other lines.\nBased on the above thought, the answer is:\nBased on the given trends, 'Game Sales (in Millions)' has experienced the most significant growth over the years. This category started at 22.5 in 2010 and ended at 52.2 in 2020 which is a substantial increase compared to other categories." } ], "id_raw": 782 }, { "id": "Rng6Rd1vrlDJnEJH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in the number of Game Releases from 2010 to 2020?" }, { "from": "gpt", "value": "To find the total increase in the number of game releases, I need to compare {the point for 2010 in Game Releases}[0.151, 0.356] which corresponds to 158 and {the point for 2020 in Game Releases}[0.909, 0.108] which corresponds to 227. The total increase is then 227 - 158.\nBased on the above thought, the answer is:\n69" } ], "id_raw": 782 }, { "id": "FmkUnr3hI3JG2sSl", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_4.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, are you capable of structuring the provided data into a tabulated form that identifies each Economic Sector as the row headers and their respective annual revenue as the columns?" }, { "from": "gpt", "value": "To transpose this visual data into a table, it is crucial to first, determine the column headers which have been represented as {x-axis tick labels}[[0.111, 0.912, 0.146, 0.936], [0.201, 0.912, 0.236, 0.936], ..., [0.922, 0.912, 0.958, 0.936]]. Moving on, I identify the {legend texts}[[0.145, 0.089, 0.318, 0.119], [0.145, 0.133, 0.320, 0.163], ..., [0.145, 0.221, 0.293, 0.251]] as the row headers that symbolize different Economic Sectors. Lastly, to populate the values, I look for the {line points}[[0.129, 0.493, 0.940, 0.829], [0.129, 0.118, 0.940, 0.614], ..., [0.129, 0.091, 0.940, 0.859]], recorded as the annual revenue for each Economic Sector and extract the respective numbers.\nBased on the above thought, the answer is:\nYear,Knowledge Economy,Innovation Industries,Smart Manufacturing,Internet Economy\n2000,675,825,310,370\n2002,542,876,390,520\n2004,425,925,450,625\n2006,522,954,550,800\n2008,600,1062,650,950\n2010,660,1150,745,1143\n2012,784,1290,840,1300\n2014,900,1450,910,1500\n2016,965,1600,1050,1650\n2018,1050,1750,1200,1800" } ], "id_raw": 784 }, { "id": "qH8uMQpr695zjz2l", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_4.png", "conversations": [ { "from": "human", "value": "\nGiven the trends depicted in the line chart, which Economic Sector seems to have experienced the most substantial growth over the years 2000 - 2018? Please elaborate on your thought process." }, { "from": "gpt", "value": "To determine the Economic Sector that demonstrates the most considerable growth, it is essential to scrutinize the slope of each line on the plot. Observing the line for the {Knowledge Economy}[[0.129, 0.493, 0.940, 0.829]], it portrays an initial decline followed by a steady rise. For {Innovation Industries}[[0.129, 0.118, 0.940, 0.614]], the line shows a consistent uptrend with few fluctuations. {Smart Manufacturing}[[0.129, 0.413, 0.940, 0.891]] also displays a consistent increase but at a slower pace. Lastly, looking at {Internet Economy}[[0.129, 0.091, 0.940, 0.859]], the line plot indicates a steady spike in revenue growth over the years, with a steeper slope compared to the other sectors.\nBased on the above thought, the answer is:\nExamining the trends highlighted in the line chart, the Internet Economy seems to have experienced the most substantial growth over the years 2000 - 2018. The slope of its line plot is steeper in comparison to the plots of the other Economic Sectors, indicating a faster growth rate. This analysis is indicative of hefty revenue generation by the Internet Economy sector over the years." } ], "id_raw": 784 }, { "id": "Xx963UkVJDWT67YK", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_4.png", "conversations": [ { "from": "human", "value": "\nCan you estimate the total revenue accumulated by the Smart Manufacturing sector from 2000 to 2018 based on the line plot?" }, { "from": "gpt", "value": "To determine the total revenue for the Smart Manufacturing sector over these years, it's important to first, locate all its corresponding {data points}[[0.129, 0.891], [0.219, 0.848], ..., [0.940, 0.413]]. Next, I extract the assigned values from these points, which are broken down as follows: 310, 390, 450, 550, 650, 745, 840, 910, 1050, 1200. Adding these up will derive the total revenue for Smart Manufacturing from 2000 to 2018.\nBased on the above thought, the answer is:\n6695 billion dollars." } ], "id_raw": 784 }, { "id": "s6AlsNjldGz9Go5e", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_6.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the bar chart into a table format with processes and their respective success rates?" }, { "from": "gpt", "value": "To generate the tabular data from the bar chart, I first look at {the y-axis tick labels}[[0.685, 0.058, 0.780, 0.075], [0.569, 0.058, 0.632, 0.075], [0.414, 0.058, 0.487, 0.075], [0.298, 0.058, 0.386, 0.075], [0.168, 0.058, 0.258, 0.075]], which represent the row headers or different processes. Next, I locate and measure {the length of the bars}[[0.092, 0.772, 0.972, 0.808], [0.092, 0.652, 0.942, 0.688], [0.092, 0.532, 0.922, 0.568], [0.092, 0.412, 0.892, 0.448], [0.092, 0.292, 0.882, 0.328]] which corresponds to the success rate for each process.\nBased on the above thought, the answer is:\nProcess,Success Rate\nFinancial auditing,0.98\nSupply chain management,0.95\nCustomer relationship management,0.92\nRisk management,0.90\nProduct development,0.88" } ], "id_raw": 786 }, { "id": "pGPAtMoPEkfktSA0", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_6.png", "conversations": [ { "from": "human", "value": "\nIn the organization's internal processes represented in the chart, which process should the management potentially look into improving upon, based on its success rate?" }, { "from": "gpt", "value": "To identify the process that management might want to improve upon, I would look for the process with the shortest bar, representing the lowest success rate. Locating {the bar}[0.092, 0.292, 0.882, 0.328] I identify Product development as the process with the lowest success rate.\nBased on the above thought, the answer is:\nBased on the success rates indicated, the management should potentially look into improving upon the 'Product development' process, as it has the lowest success rate among the represented internal processes." } ], "id_raw": 786 }, { "id": "xEDo8e6ZOkchyOpU", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in success rate between the highest and the lowest performing process?" }, { "from": "gpt", "value": "To find the difference between the success rates of the highest and lowest performing process, I need to locate {the bar for Financial auditing}[[0.092, 0.772, 0.972, 0.808]] and extract the corresponding value which is 0.98. Similarly, I locate {the bar for Product development}[[0.092, 0.292, 0.882, 0.328]] and see that the success rate is 0.88. The difference between the highest and lowest success rate would be 0.98 - 0.88.\nBased on the above thought, the answer is:\n0.1" } ], "id_raw": 786 }, { "id": "jEt5seduxM3uSWJZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_7.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data represented in the line chart to a table, with Years as the rows and the types being Income, Expenses, Risk_Controller, and Outliers as the columns?" }, { "from": "gpt", "value": "Sure. To create the table, I'll first identify {the x-axis}[0.123, 0.869, 0.975, 0.904] as representing the Years, which will serve as rows in the table. For the column headers, I'll use the labels in {the legend}[0.257, 0.564, 0.476, 0.823]. Next, I will collect the corresponding values for each of these categories by observing the coordinates of line points for {Income}[0.154, 0.165, 0.946, 0.278], {Expenses}[0.154, 0.334, 0.946, 0.391], {Risk_Controller}[0.154, 0.844, 0.946, 0.844], and {Outliers}[0.154, 0.844, 0.946, 0.844] over the Years.\nBased on the above thought, the answer is:\nYears,Income,Expenses,Risk_Controller,Outliers\n2015,50000,40000,0.39,0.60\n2016,52000,40500,0.42,0.62\n2017,53000,41000,0.43,0.63\n2018,54000,42000,0.45,0.65\n2019,56000,43000,0.47,0.67\n2020,60000,45000,0.50,0.70" } ], "id_raw": 787 }, { "id": "FTIK4DTCFmPepqqt", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_7.png", "conversations": [ { "from": "human", "value": "\nConsidering the plotted lines in the chart, which area showed the most significant change over the years?" }, { "from": "gpt", "value": "To determine the area of most change, we need to analyze the slopes of the different lines. Observing the trend-lines for {Income}[0.154, 0.165, 0.946, 0.278], {Expenses}[0.154, 0.334, 0.946, 0.391], {Risk_Controller}[0.154, 0.844, 0.946, 0.844] and {Outliers}[0.154, 0.844, 0.946, 0.844], we see that Income has a moderate upward slope, Expenses also show an upward trend but less steep compared to Income. In contrast, Risk_Controller and Outliers show horizontal lines suggesting there's no change in their values across the years.\nBased on the above thought, the answer is:\nTaking into account the trends represented by the lines, Income has shown the most significant change over the years. This is based on the steepness of its upward slope, indicating a constant increase in income from 2015 to 2020." } ], "id_raw": 787 }, { "id": "6lapIBejzJSMZdXg", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_7.png", "conversations": [ { "from": "human", "value": "\nWhat's the income growth from 2015 to 2020?" }, { "from": "gpt", "value": "The income values for the year 2015 and 2020 are plotted as the starting and ending points of {the line representing Income}[0.154, 0.165, 0.946, 0.278]. The coordinates correspond to income values of 50000 and 60000 respectively. Subtracting the value in 2015 from that of 2020 yields the increment in income over these years.\nBased on the above thought, the answer is:\n10000" } ], "id_raw": 787 }, { "id": "ALSJFmts8Pleq6n0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_8.png", "conversations": [ { "from": "human", "value": "\nCould you convert the chart data into a table format? The concept names should be considered as row labels and the corresponding scores as values in the table." }, { "from": "gpt", "value": "To create a structured table from the bar chart, I can use the labels on the y-axis as the row labels and the width of the bars for each concept as the corresponding values. For instance, I identify {Organizational Restructure}[0.089, 0.737, 0.225, 0.765] on the y-axis and cross-reference that with its bar ending at approximately {0.68}[0.678, 0.714] on the x-axis. I continue this process for each bar in the chart.\nBased on the above thought, the answer is:\nConcept,Score\nOrganizational Restructure,0.68\nEmployee Engagement,0.52\nManagement Styles,0.62\nDecision Making,0.48\nPerformance Evaluation,0.75\nConflict Resolution,0.65" } ], "id_raw": 788 }, { "id": "m9nquZEPBplwe4e9", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_8.png", "conversations": [ { "from": "human", "value": "\nWhat can we discern about the effectiveness of these management and leadership concepts based on their scores featured in the chart?" }, { "from": "gpt", "value": "To understand the effectiveness of these concepts, I analyze the corresponding score bars. Noticing {Performance Evaluation}[0.675, 0.239, 0.788, 0.260] has the longest bar indicating the highest score. In contrast, {Decision Making}[0.41, 0.423, 0.527, 0.444] has the shortest bar, signifying the lowest score. Overall, the bars' lengths demonstrate how well each concept is performing in the business context, with longer bars indicating greater effectiveness.\nBased on the above thought, the answer is:\nBased on the scores in the chart, it appears that Performance Evaluation is the most effective concept in this business context, as it has the highest score. On the other hand, Decision Making seems to be the least effective, given that it has the lowest score. The rest of the concepts are moderately effective with relatively similar scores, indicating that there is an overall high level of effectiveness across most of the management and leadership concepts." } ], "id_raw": 788 }, { "id": "CQFnjsPyyYEn6oKB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_8.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, what is the difference between the highest and lowest score carried by the concepts?" }, { "from": "gpt", "value": "For me to get the difference between the highest and lowest score, I first locate the concepts with the longest and shortest bars, in this case {Performance Evaluation}[0.762, 0.737] with the highest score, and {Decision Making}[0.478, 0.332] with the lowest score. The difference between the highest and lowest score is 0.75 - 0.48 = 0.27.\nBased on the above thought, the answer is:\n0.27" } ], "id_raw": 788 }, { "id": "tyYvACVJYa5UKoRX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_9.png", "conversations": [ { "from": "human", "value": "\nCan you derive the data in a tabular format from the line chart, by taking the years along the columns and the research areas as rows?" }, { "from": "gpt", "value": "Yes, to create a table from the line chart, I begin by identifying {the x-axis labels}[0.135, 0.912, 0.179, 0.936, 0.285, 0.912, 0.329, 0.936, 0.435, 0.912, 0.479, 0.936, 0.585, 0.912, 0.629, 0.936, 0.734, 0.912, 0.779, 0.936, 0.884, 0.912, 0.928, 0.936], which indicate the column headers as years. Following this, I recognize {the legend texts}[0.159, 0.059, 0.325, 0.089, 0.159, 0.103, 0.418, 0.133, 0.159, 0.147, 0.308, 0.177, 0.159, 0.191, 0.368, 0.221] to be the row labels symbolizing the different research areas. To fill in the values, I locate the points for each research area at the corresponding years and extract the annotated values.\nBased on the above thought, the answer is:\nResearch Area,2015,2016,2017,2018,2019,2020\nProtein Analysis,50,48,46,45,42,40\nCell Membrane Research,45,44,46,48,50,51\nDNA Structure,40,42,44,46,48,50\nAnaerobic Digestion,35,35,40,44,46,48" } ], "id_raw": 789 }, { "id": "orgjC1csR0YK41gj", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_9.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which area of research seems to have garnered the most attention over the past years? Provide your reasoning." }, { "from": "gpt", "value": "To decide which research area has gained the most attention, it is necessary to examine the trends in the line plots for each research area. Looking at the line for {Protein Analysis}[0.157, 0.207, 0.906, 0.551], the attention has decreased significantly from 50% to 40%. Meanwhile, the line for {Cell Membrane Research}[0.157, 0.172, 0.906, 0.413] has seen an increase in focus from 45% to 51%. The line for {DNA Structure}[0.157, 0.207, 0.906, 0.551] shows a slow yet steady increase from 40% to 50%. Finally, {Anaerobic Digestion}[0.157, 0.276, 0.906, 0.724] seems to have experienced steady growth in focus until it levels off after 2017.\nBased on the above thought, the answer is:\nFrom scrutinizing the trends in the line chart, it seems that the research area 'Cell Membrane Research' has garnered the most attention over the years. Despite starting with smaller focus percentages in 2015, 'Cell Membrane Research' shows a steady increase till 2020 when it reaches the highest attention percentage among all research areas." } ], "id_raw": 789 }, { "id": "5yQ4QAH5A5JWRSSC", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the total change in the degree of focus for Protein Analysis from 2015 to 2020?" }, { "from": "gpt", "value": "To ascertain the total change for 'Protein Analysis' from 2015 to 2020, I need to locate the points for {Protein Analysis}[0.157, 0.207, 0.307, 0.276, 0.457, 0.345, 0.607, 0.379, 0.756, 0.482, 0.906, 0.551] and extract the corresponding values: 50, 48, 46, 45, 42, 40. The initial value in 2015 is 50 and the final value in 2020 is 40. Hence, the total change is 50 - 40 = 10.\nBased on the above thought, the answer is:\n10." } ], "id_raw": 789 }, { "id": "3neJ5XUzS2NHXmdU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_10.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data from the line chart and organize it into a table with Global Trade Flow units as columns and the corresponding trade performance index as rows?" }, { "from": "gpt", "value": "To compile this data into a table, I first consider {the x-axis tick labels}[[0.159, 0.869, 0.189, 0.904], [0.244, 0.869, 0.273, 0.904], [0.328, 0.869, 0.357, 0.904], [0.412, 0.869, 0.441, 0.904], [0.497, 0.869, 0.526, 0.904], [0.581, 0.869, 0.610, 0.904], [0.665, 0.869, 0.694, 0.904], [0.749, 0.869, 0.779, 0.904], [0.834, 0.869, 0.863, 0.904], [0.911, 0.869, 0.955, 0.904]], which give me the column headers depicting Global Trade Flow in Units. Next, I identify the y-axis tick labels for Trade Performance Index values. To populate the table data, I look at {the line points}[[0.933, 0.712], [0.849, 0.316], [0.764, 0.227], [0.680, 0.161], [0.596, 0.117], [0.511, 0.382], [0.427, 0.448], [0.343, 0.580], [0.258, 0.602], [0.174, 0.536]] for each Global Trade Flow value and extract the corresponding Trade Performance Index values.\nBased on the above thought, the answer is:\nGlobal Trade Flow (Units),Trade Performance Index\n100,0.50\n90,0.68\n80,0.72\n70,0.75\n60,0.77\n50,0.65\n40,0.62\n30,0.56\n20,0.55\n10,0.58" } ], "id_raw": 790 }, { "id": "tL3oO2GNPR35ZlMP", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_10.png", "conversations": [ { "from": "human", "value": "\nIs there a general trend between Global Trade Flow and Trade Performance Index, based on the information given in the line chart?" }, { "from": "gpt", "value": "To understand the general trend between Global Trade Flow and the Trade Performance Index, I need to analyze {the plot of the Global Trade Flow line}[0.174, 0.117, 0.933, 0.712]. There is a noticeable decline in the Trade Performance Index as the Global Trade Flow increases from 10 units to 60 units, followed by a slight rebound and a further decline. This indicates a general negative correlation between Global Trade Flow and Trade Performance Index, which means when the Global Trade Flow increases, Trade Performance Index tends to decrease, and vice versa.\nBased on the above thought, the answer is:\nYes, a general trend can be observed in the line chart. It reveals a negative correlation between Global Trade Flow and Trade Performance Index. In this context, it implies that as the Global Trade Flow increases, the Trade Performance Index tends to decrease. However, it should be noted that this is a general trend, and there could be exceptions or slight rebounds, as seen from 50 to 60 units of Global Trade Flow." } ], "id_raw": 790 }, { "id": "CO7cjEjbBqjLPkRK", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest Trade Performance Index in the line chart?" }, { "from": "gpt", "value": "To determine this, I need to identify {the largest point in the Y-axis}[0.933, 0.117] and {the smallest point in the Y-axis}[0.174, 0.712] of the line chart. The largest value is 0.77 and it corresponds to the Global Trade Flow of 60 Units. The smallest value is 0.50 that corresponds to Global Trade Flow of 100 units. Therefore, the difference between the highest and the lowest Trade Performance Index value is 0.77-0.50=0.27 units.\nBased on the above thought, the answer is:\n0.27 units." } ], "id_raw": 790 }, { "id": "u2seFRQ1VXq96icd", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_11.png", "conversations": [ { "from": "human", "value": "\nWhat are the values for each category over the years 1900, 1950, 2000, and 2050?" }, { "from": "gpt", "value": "To extract the data for each category, I'll need to locate and organize the various points on the line graphs. To begin, I look at the points along the {line for Human Population}[0.313, -3245074.638, 0.939, -550426.882]. From here, I will extract values for the years 1900, 1950, 2000, and 2050. I repeat this process for the {line for Accumulated Knowledge}[0.313, 0.096, 0.939, 0.863], {line for Warfare Intensity}[0.313, 0.866, 0.939, 0.885], and {line for Social Equality}[0.313, 0.866, 0.939, 0.884].\nBased on the above thought, the answer is:\nYear, Human Population, Accumulated Knowledge, Warfare Intensity, Social Equality\n1900, 1650000000, 100, 60, 40\n1950, 2525149000, 200, 90, 36\n2000, 6115974486, 800, 75, 60\n2050, 9727660644, 2400, 33, 90" } ], "id_raw": 791 }, { "id": "CsDdvj2q9hMRYVVy", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_11.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, what can we say about the correlation between Accumulated Knowledge and Social Equality over time?" }, { "from": "gpt", "value": "Based on {the line for Accumulated Knowledge}[0.313, 0.096, 0.939, 0.863] and {the line for Social Equality}[0.313, 0.866, 0.939, 0.884], both of them show upward trends. Accumulated Knowledge increased from 100 in 1900 to 2400 in 2050, and Social Equality increased from 40 in 1900 to 90 in 2050. This positive correlation suggests that as Accumulated Knowledge increased, Social Equality also tended to increase over the years.\nBased on the above thought, the answer is:\nThe chart indicates a positive correlation between Accumulated Knowledge and Social Equality. As the values of Accumulated Knowledge increased over time, the values of Social Equality showed a corresponding increase. This may suggest that as society accumulated more knowledge, there might be strides towards social equality." } ], "id_raw": 791 }, { "id": "VmJTaJajKpgvtxdB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_11.png", "conversations": [ { "from": "human", "value": "\nHow much did Human Population increase from 1900 to 2050?" }, { "from": "gpt", "value": "To calculate the increase in Human Population from 1900 to 2050, I would need to find the values for those years. The value for Human Population in 1900 can be obtained from the first data point in {the line for Human Population}[0.313, -3245074.638, 0.939, -550426.882], which is 1650000000. The value for 2050 can be obtained from the last data point on the same line, which is 9727660644. The increase would then be the value for 2050, 9727660644, subtracted by the value for 1900, 1650000000, and that comes to 8077660644.\nBased on the above thought, the answer is:\n8077660644" } ], "id_raw": 791 }, { "id": "tdxFTSdEH7GC0pSu", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data showing the number of reforms or projects from 2015 to 2019 in different departments into tabular format?" }, { "from": "gpt", "value": "To generate the tabular data, I'll first locate {the x-axis tick labels}[[0.081, 0.869, 0.139, 0.904],[0.289, 0.869, 0.348, 0.904],[0.498, 0.869, 0.557, 0.904],[0.707, 0.869, 0.766, 0.904],[0.916, 0.869, 0.975, 0.904]], which represent the column headers or years. I then identify {the legend texts}[[0.693, 0.327, 0.843, 0.372],[0.693, 0.392, 0.921, 0.437],[0.693, 0.458, 0.913, 0.503],[0.693, 0.524, 0.897, 0.569]] as the row labels representing different departments. Mapping these correctly will get the required data for each department for every year.\nBased on the above thought, the answer is:\nDepartment,2015,2016,2017,2018,2019\nHealthcare,10,15,18,14,7\nPublic Education,12,13,10,6,7\nPublic Transport,8,10,7,6,4\nSocial Security,15,14,12,13,14" } ], "id_raw": 793 }, { "id": "kXbO9wK0oYio1oFq", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_13.png", "conversations": [ { "from": "human", "value": "\nWhich sector showed a sharp decline in the number of reforms or projects during the analyzed period? Provide your reasoning." }, { "from": "gpt", "value": "A sharp decline would be indicated by a downward trend in the data points. Looking at {the line for Healthcare}[0.110, 0.448, 0.946, 0.567], there is a decline from 18 in 2017 to 7 in 2019. In comparison, {Public Education}[0.110, 0.368, 0.946, 0.567] and {Public Transport}[0.110, 0.527, 0.946, 0.686] also show a decline, but not as sharply. {The Social Security line}[0.110, 0.250, 0.946, 0.368] shows a meandering progression, not a sharp decline.\nBased on the above thought, the answer is:\nThe Healthcare sector showed a sharp decline in the number of reforms or projects. This is evident from the downward trend seen in the Healthcare line plot, with an abrupt drop from 18 projects in 2017 to 7 by 2019. This reduction is steeper when compared to the less dramatic declines observed for the Public Education and Public Transport lines." } ], "id_raw": 793 }, { "id": "AbfpjAF15HDMg7Hj", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the total number of projects or reforms in the Social Security department over the years?" }, { "from": "gpt", "value": "The total number of projects or reforms in the Social Security department can be calculated by adding the y-values for all the years. To find the y-values, I refer to {the line points for Social Security}[[0.110, 0.250, 0.319, 0.289, 0.528, 0.368, 0.737, 0.329, 0.946, 0.289]], yielding the numbers 15, 14, 12, 13, 14.\nBased on the above thought, the answer is:\n68" } ], "id_raw": 793 }, { "id": "KrkuIibSu5Gt3VNg", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_14.png", "conversations": [ { "from": "human", "value": "\nCould you structure the given dataset into a readable table format with Years as columns, and Start-Up Spirit, Innovation, Disruption, and Large Corporations as rows and their corresponding scores?" }, { "from": "gpt", "value": "By interpreting the line chart, {the x-axis tick labels}\n[[0.102, 0.869, 0.161, 0.904], [0.306, 0.869, 0.364, 0.904], [0.509, 0.869, 0.568, 0.904], [0.713, 0.869, 0.772, 0.904], [0.917, 0.869, 0.975, 0.904]] represent the years, which will be the columns headers. I identify the {legend texts} \n[[0.656, 0.564, 0.853, 0.609], [0.656, 0.630, 0.803, 0.675], [0.656, 0.696, 0.800, 0.741], [0.656, 0.762, 0.921, 0.807]] as the rows labels representing Start-Up Spirit, Innovation, Disruption, and Large Corporations respectively. I'll extract the corresponding values by locating the {line points} for each respective cell from the line plots: [0.4, 0.45, 0.5, 0.52, 0.55], [0.38, 0.4, 0.42, 0.45, 0.48], [0.24, 0.3, 0.35, 0.4, 0.42], and [0.58, 0.55, 0.52, 0.48, 0.42].\nBased on the above thought, the answer is:\nEvaluation Type,2000,2005,2010,2015,2020\nStart-Up Spirit,0.4,0.45,0.5,0.52,0.55\nInnovation,0.38,0.4,0.42,0.45,0.48\nDisruption,0.24,0.3,0.35,0.4,0.42\nLarge Corporations,0.58,0.55,0.52,0.48,0.42" } ], "id_raw": 794 }, { "id": "h29TT3EoOl3zI69t", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_14.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which evaluation type seems to have consistent improvement over the years?" }, { "from": "gpt", "value": "In order to determine which evaluation type shows consistent improvement over the years, I'll have to analyze the trend of the line plots:\n{Start-Up Spirit}[0.132, 0.138, 0.946, 0.441] indicated a continuous upward trend from 0.4 to 0.55. \n{Innovation}[0.132, 0.280, 0.946, 0.481] has a steady growth from 0.38 to 0.48.\n{Disruption}[0.132, 0.401, 0.946, 0.764] displayed a gradual improvement from 0.24 to 0.42.\n{Large Corporations}[0.132, 0.078, 0.946, 0.401] showed a noticeable decline from 0.58 to 0.42.\nBased on the above thought, the answer is:\nFrom the analysis of the trends, it seems that the evaluation type 'Start-Up Spirit' shows the most consistent improvement over the years, with a steady increase in the score from 0.4 in 2000 to 0.55 in 2020." } ], "id_raw": 794 }, { "id": "FVsZqdy3MjPEY6Cj", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_14.png", "conversations": [ { "from": "human", "value": "\nWhat was the increase in Disruption score from 2000 to 2020?" }, { "from": "gpt", "value": "To find the increase in the Disruption score from year 2000 to year 2020, l can see the data point in year 2000 at the coordinates {Disruption in 2000}[[0.132, 0.764]] representing a score of 0.24 and in 2020 at coordinates {Disruption in 2020}[[0.946, 0.401]] representing the score of 0.42. Thus, the increase would be 0.42 - 0.24 = 0.18.\nBased on the above thought, the answer is:\n0.18." } ], "id_raw": 794 }, { "id": "S0QuKHBNnlXzOlzD", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_15.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table that outlines the annual counts of Patent Applications, Patent Grants, Trademark Filings, and Trademark Registrations from 2000 to 2020 according to the line chart?" }, { "from": "gpt", "value": "To construct the table, I will first identify {the x-axis labels}[[0.223, 0.912, 0.258, 0.936], [0.372, 0.912, 0.407, 0.936], [0.521, 0.912, 0.556, 0.936], [0.670, 0.912, 0.705, 0.936], [0.819, 0.912, 0.854, 0.936]] which indicate the years. Then, I will recognize {the legend}[[0.099, 0.075, 0.319, 0.222]] for patent categories that are the rows. Using the data points from {the lines}[{line:[0.241, 0.116, 0.836, 0.636]},{line:[0.241, 0.427, 0.836, 0.792]},{line:[0.241, 0.220, 0.836, 0.740]},{line:[0.241, 0.428, 0.836, 0.844]}], I can deduce the corresponding values which represent the count of each category of patented items for each year.\nBased on the above thought, the answer is:\nYears,Patent Applications,Patent Grants,Trademark Filings,Trademark Registrations\n2000,5000,3500,4000,3000\n2005,6000,4000,5000,4000\n2010,7000,5500,6000,5000\n2015,9000,6500,7000,6000\n2020,10002,7008,9000,7002" } ], "id_raw": 795 }, { "id": "zf9diPlizWMTpYZb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_15.png", "conversations": [ { "from": "human", "value": "\nBased on the line charts, which Intellectual Property category has seen the most growth in counts from 2000 to 2020?" }, { "from": "gpt", "value": "I'll assess the growth for each category by examining the difference between their 2000 and 2020 counts. Starting off with {Patent Applications}[line:[0.241, 0.636, 0.836, 0.116]], the difference is 5002. Checking {Patent Grants}[line:[0.241, 0.792, 0.836, 0.427]], the growth is 3508. For {Trademark Filings}[line:[0.241, 0.740, 0.836, 0.220]], the increase is 5000. Lastly, {Trademark Registrations}[line:[0.241, 0.844, 0.836, 0.428]] reveals a growth by 4002.\nBased on the above thought, the answer is:\nThe Intellectual Property category that has seen the most growth in counts from 2000 to 2020 is Trademark Filings. It showed an increase of 5000 counts over this time period, which is the highest growth among all the categories." } ], "id_raw": 795 }, { "id": "c25alqJuONSHxetg", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_15.png", "conversations": [ { "from": "human", "value": "\nWhat's the difference in the count of Patent Applications from 2000 to 2020?" }, { "from": "gpt", "value": "To find the difference, I need to identify the points for Patent Applications from {the line points}[{x:2000,y:5000, point_coordinates:'[0.241, 0.636]'},\n{x:2020,y:10002, point_coordinates:'[0.836, 0.116]'}]. The count of Patent Applications in 2000 is 5000, and in 2020, it is 10002. Thus the difference is 10002 - 5000.\nBased on the above thought, the answer is:\n5002" } ], "id_raw": 795 }, { "id": "RvqHZbXJhn8ddHjJ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_16.png", "conversations": [ { "from": "human", "value": "\nCan you parse the line subplots into a structured table that shows age against weight and height for each environment?" }, { "from": "gpt", "value": "To generate this table, I first identify {the x-axis tick labels}[[0.116, 0.912, 0.125, 0.936], [0.281, 0.912, 0.289, 0.936], [0.445, 0.912, 0.454, 0.936], [0.610, 0.912, 0.619, 0.936], [0.775, 0.912, 0.783, 0.936], [0.939, 0.912, 0.948, 0.936]] as representing age for both environments. Then I establish {the legend texts}[[0.489, 0.747, 0.614, 0.771], [0.489, 0.782, 0.593, 0.806], [0.489, 0.817, 0.588, 0.840], [0.489, 0.852, 0.577, 0.875]] as representations of weight and height in both environment types. Finally, I extract the vertical value of {the line points}[[0.120, 0.245, 0.944, 0.408], [0.120, 0.550, 0.944, 0.794], [0.120, 0.062, 0.944, 0.224], [0.120, 0.347, 0.944, 0.530]] for each age and environment to conclude the corresponding measurements.\nBased on the above thought, the answer is:\nAge, Urban Weight, Urban Height, Rural Weight, Rural Height\n1, 0.75, 0.85, 0.7, 0.65\n2, 0.78, 0.83, 0.77, 0.64\n3, 0.74, 0.88, 0.76, 0.67\n4, 0.76, 0.89, 0.73, 0.65\n5, 0.79, 0.91, 0.72, 0.60\n6, 0.82, 0.90, 0.68, 0.55" } ], "id_raw": 796 }, { "id": "jPXK8yQLdlYXg0ug", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_16.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which metric seems to have the most stable performance over the quarters? Explain your reasoning." }, { "from": "gpt", "value": "To determine which metric has the most stable performance, I will have to look at the fluctuations in the line plots for each metric. Looking at {the line for Process Efficiency}[0.120, 0.245, 0.944, 0.408], it shows a relatively consistent increase from 0.75 to 0.82, indicating stability.\nFor {Process Timing}[0.120, 0.550, 0.944, 0.794], there is an overall decrease from 0.65 to 0.55, with some minor increases in the 3rd and 4th quarters. This indicates some volatility.\nFor {Approval Rate}[0.120, 0.062, 0.944, 0.224], there is a significant increase from 0.85 to 0.90, which while it does show improvement, also indicates a degree of fluctuation.\nFinally, for {Process Cost}[0.120, 0.347, 0.944, 0.530], it fluctuates between 0.7 and 0.68, over the quarters, again indicating some level of instability.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Process Efficiency seems to have the most stable performance over the quarters. While all other metrics show varying degrees of fluctuation across the quarters, Process Efficiency maintains a relatively consistent increase, suggesting it experiences the least volatility among the included metrics." } ], "id_raw": 796 }, { "id": "hNtVi9XWMpgoRE0w", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in the weight from 1st to the 6th quarter for the urban environment?" }, { "from": "gpt", "value": "To determine the increase in the weight from 1st to the 6th quarter for the urban environment, I will have to locate the {line points for Process Efficiency}[0.120, 0.245, 0.944, 0.408]. The weight in the 1st quarter is 0.75 and in the 6th quarter is 0.82. So the increase in weight = 0.82 - 0.75.\nBased on the above thought, the answer is:\n0.07" } ], "id_raw": 796 }, { "id": "3d3w6DSLdasa6ROi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_17.png", "conversations": [ { "from": "human", "value": "\nCan you generate a structured table from this line chart with Companies as columns and Quarters as rows?" }, { "from": "gpt", "value": "I first identify {the x-axis tick labels}[0.215, 0.911, 0.224, 0.934],[0.368, 0.911, 0.377, 0.934],[0.522, 0.911, 0.530, 0.934] as the row labels indicating the Quarters. Next, I find {the legend texts}[0.128, 0.050, 0.244, 0.080],[0.128, 0.094, 0.253, 0.124],[0.128, 0.138, 0.275, 0.168],[0.128, 0.182, 0.290, 0.212] as the column headers representing the Companies. To extract the values, I locate {the line point for Control group}[[0.220, 0.842],[0.373, 0.789],[0.526, 0.736]], {the line points for Therapy Group}[[0.220, 0.736],[0.373, 0.684],[0.526, 0.552]], {the line points for Medication Group}[[0.220, 0.789],[0.373, 0.710],[0.526, 0.631]] and {the line points for Combination Group}[[0.220, 0.578],[0.373, 0.526],[0.526, 0.394]] and their corresponding values for each quarter.\nBased on the above thought, the answer is:\nQuarter,Control Group,Therapy Group,Medication Group,Combination Group\nDay 1,20,24,22,30\nDay 2,22,26,25,32\nDay 3,24,31,28,37" } ], "id_raw": 797 }, { "id": "gmK51r2tzuh22e3R", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_17.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which group has shown the most consistent improvement over the three days? Please explain your reasoning." }, { "from": "gpt", "value": "To determine which group has the most consistent improvement, I need to analyze the trend in the line plots for each group. Looking at {the line for Control Group}[0.220, 0.842, 0.526, 0.736], it shows a increasing trend from 20 to 24 through out the three days. For {Therapy Group}[0.220, 0.736, 0.526, 0.552], there is an increase from 24 to 31, indicating consistent improvement. Then looking at the {Medication Group}[0.220, 0.789, 0.526, 0.631], the line indicates an increasing trend from 22 to 28. Finally, the line for {Combination Group}[0.220, 0.578, 0.526, 0.394] shows a strong increase from 30 to 37, indicating the most consistent improvement.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Combination Group has shown the most consistent improvement over the three days. The line plot for this group shows a strong increasing trend across the three days, indicating continued improvement over the period." } ], "id_raw": 797 }, { "id": "BzpXspvmFjxj2sTR", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the average observed value for Day 2 for all groups?" }, { "from": "gpt", "value": "To find the average observed value for Day 2, I need to locate {the line points for Day 2}[[0.373, 0.789], [0.373, 0.684], [0.373,0.710], [0.373, 0.526]] and extract the corresponding values: 22, 26, 25, 32. The average value for Day 2 is therefore (22 + 26 + 25 + 32)/4 = 26.25.\nBased on the above thought, the answer is:\n26.25." } ], "id_raw": 797 }, { "id": "lfF4PrKqdFu5mNOJ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_18.png", "conversations": [ { "from": "human", "value": "\nCould you tabulate the sales values for each product over 12 months using the data given in the line chart?" }, { "from": "gpt", "value": "To compile sales values for each product from the line chart over 12 month period, I start with locating {the X-axis labels}[0.132, 0.869, 0.975, 0.904] for representing the time sequence as rows. {The line graphs}[0.139, 0.580, 0.960, 0.820],[0.139, 0.146, 0.960, 0.435],[0.139, 0.531, 0.960, 0.652],[0.139, 0.219, 0.960, 0.531] represent 'Product_A', 'Product_B', 'Product_C' and 'Product_D' respectively. The individual points on these lines [0.139, 0.724], [0.214, 0.734]... correspond to respective sales values for each product in each month.\nBased on the above thought, the answer is:\nMonth,Product_A,Product_B,Product_C,Product_D\n1,500,1200,800,1500\n2,480,1300,720,1550\n3,460,1400,750,1450\n4,440,1320,690,1420\n5,420,1310,650,1400\n6,400,1200,700,1300\n7,300,1100,680,1000\n8,350,1150,670,900\n9,500,1120,900,1100\n10,650,1400,850,1300\n11,750,1600,800,1500\n12,800,1700,820,1550" } ], "id_raw": 798 }, { "id": "qzZcOrpF63K5Inkb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_18.png", "conversations": [ { "from": "human", "value": "\nWhich product showed the most consistent sales performance over the year, and which product showed the most fluctuation?" }, { "from": "gpt", "value": "I need to study the trends and patterns of {the line graphs representing each product}[0.139, 0.580, 0.960, 0.820],[0.139, 0.146, 0.960, 0.435],[0.139, 0.531, 0.960, 0.652],[0.139, 0.219, 0.960, 0.531]. {The line graph for Product_A}[0.139, 0.580, 0.960, 0.820] exhibits a steady decline from the start to the 7th month and increases quite gradually afterwards. Similarly, {the line graph for Product_B}[0.139, 0.146, 0.960, 0.435] depicts a steady rise until the 5th month, and a more volatile pattern henceforth. {The Line graph for Product_C}[0.139, 0.531, 0.960, 0.652] shows a mixed trend with multiple ups and downs. Lastly, {The line graph for Product_D}[0.139, 0.219, 0.960, 0.531] follows a similar pattern but with less volatility.\nBased on the above thought, the answer is:\nBased on the line chart, Product_A displayed the most consistent sales performance over the year, with a clear trend and less fluctuation. On the other hand, Product_B exhibited the most fluctuation, with a steady rise initially and several ups and downs afterwards." } ], "id_raw": 798 }, { "id": "ZUhekYsjcx8VWNqY", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_18.png", "conversations": [ { "from": "human", "value": "\nHow much did the sales of Product_C drop from Month 1 to Month 7?" }, { "from": "gpt", "value": "To find out the drop in sales of Product_C from Month 1 to Month 7, I need to look at {the specific points for Product_C on the line graph}[0.139, 0.724, 0.587, 0.820]. The sales value at Month 1 is 800, and the sales value at Month 7 is 680. The decrease in sales would therefore be 800 - 680 = 120.\nBased on the above thought, the answer is:\n120" } ], "id_raw": 798 }, { "id": "KI3hOrCtL6z75stU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_40_19.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a structured table with Years as rows and the labels 'CBD Construction', 'Suburbs Construction', 'CBD Planning Contacts', and 'Suburbs Planning Contacts' as columns?" }, { "from": "gpt", "value": "To extract and structure the data from the line chart, I would first locate {the x-axis tick labels}[[0.140, 0.895, 0.184, 0.923], [0.300, 0.895, 0.343, 0.923], [0.459, 0.895, 0.503, 0.923], [0.618, 0.895, 0.662, 0.923], [0.778, 0.895, 0.822, 0.923], [0.937, 0.895, 0.981, 0.923]], which represent the row headers or years. Then, {the legend texts}[[0.661, 0.071, 0.845, 0.107], [0.661, 0.124, 0.885, 0.160], [0.661, 0.176, 0.900, 0.212], [0.661, 0.229, 0.941, 0.265]] would provide the column labels representing the different categories for each year. Then, I would access each line's points for instances of {CBD Construction}[[0.162, 0.637], [0.242, 0.661], ..., [0.880, 0.852]], {Suburbs Construction}[[0.162, 0.279], [0.242, 0.303], ..., [0.880, 0.685]], {CBD Planning Contacts}[[0.162, 0.303], [0.242, 0.494], ..., [0.880, 0.518]], and {Suburbs Planning Contacts}[[0.162, 0.089], [0.242, 0.136], ..., [0.880, 0.303]] to obtain the values for each category at the respective years.\nBased on the above thought, the answer is:\nYears, CBD_Construction, Suburbs_Construction, CBD_Planning_Contacts, Suburbs_Planning_Contacts\n2000, 10, 25, 15, 30\n2001, 9, 24, 16, 31\n2002, 8, 22, 18, 32\n2003, 7, 20, 20, 33\n2004, 5, 18, 21, 32\n2005, 4, 16, 21, 31\n2006, 3, 14, 22, 29\n2007, 3, 12, 23, 28\n2008, 2, 10, 24, 26\n2009, 1, 8, 24, 24" } ], "id_raw": 799 }, { "id": "TZTqxpCtXQVhdkkT", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_40_19.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which sector had a steadier trend, CBD or Suburbs?" }, { "from": "gpt", "value": "To ascertain which sector had a steadier trend, I need to look at the variance in the values over the years. I see that {the line for CBD Construction}[[0.162, 0.637, 0.880, 0.852]], while it starts at a higher value in 2000, it ends at the lowest point in 2009. There are also sharp declines over the years. On the contrary, {the line for Suburbs Construction}[[0.162, 0.279, 0.880, 0.685]], even though it starts lower in 2000, it maintains a steadier, declining trend over the years. Furthermore, {the line for CBD Planning Contacts}[[0.162, 0.303, 0.880, 0.518]] shows an overall increase, however, there is a sharp increase between 2003 and 2004. Whereas, {the line for Suburbs Planning Contacts}[[0.162, 0.089, 0.880, 0.303]] maintains a steadier, declining trend over the years.\nBased on the above thought, the answer is:\nBased on the chart, the Suburbs sector had a steadier trend overall, with a consistent decrease over the years in Construction and a more gradual, albeit unsteady rise in Planning Contacts. On the other hand, the CBD sector showed much more fluctuation in both Construction and Planning Contacts. Therefore, looking at the overall trends, the Suburbs sector seems to be steadier." } ], "id_raw": 799 }, { "id": "7s1ibuMxFFcQr65i", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_40_19.png", "conversations": [ { "from": "human", "value": "\nHow much has the number of CBD Construction projects decreased from 2000 to 2009?" }, { "from": "gpt", "value": "To answer this question, I need to find the initial point of {the line for CBD Construction}[[0.162, 0.637]] and the final point of the same line[[0.880, 0.852]]. These points represent the values of CBD Construction projects in 2000 and 2009 respectively. Subtracting the final value from the initial value will give us the answer.\nBased on the above thought, the answer is:\n9." } ], "id_raw": 799 }, { "id": "CZvfx6N5V6ObatLZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_0.png", "conversations": [ { "from": "human", "value": "\nCan you present the data from the line chart in a structured table form, where the row categories are Temperate_Climate and Tropical_Climate and the column categories are based on the Latitude?" }, { "from": "gpt", "value": "To extract data to a table from the line chart, I'll start by identifying {the x-axis tick labels}[[0.112, 0.911, 0.148, 0.934], [0.157, 0.911, 0.193, 0.934], [0.202, 0.911, 0.238, 0.934], [0.247, 0.911, 0.283, 0.934], [0.292, 0.911, 0.328, 0.934], [0.337, 0.911, 0.373, 0.934], [0.382, 0.911, 0.418, 0.934], [0.427, 0.911, 0.463, 0.934], [0.472, 0.911, 0.508, 0.934], [0.530, 0.911, 0.541, 0.934], [0.569, 0.911, 0.591, 0.934], [0.614, 0.911, 0.636, 0.934], [0.659, 0.911, 0.681, 0.934], [0.705, 0.911, 0.726, 0.934], [0.750, 0.911, 0.771, 0.934], [0.795, 0.911, 0.817, 0.934], [0.840, 0.911, 0.862, 0.934], [0.885, 0.911, 0.907, 0.934], [0.930, 0.911, 0.952, 0.934]], which will be the column headers or Latitude. I then choose {the legend texts}[[0.166, 0.091, 0.367, 0.121], [0.166, 0.135, 0.336, 0.165]] to be the row categories or Temperate_Climate and Tropical_Climate. To get the values, I look at {the points for Temperate_Climate and Tropical_Climate}[[0.130, 0.397], [0.175, 0.729], ..., [0.941, 0.770], [0.130, 0.231], [0.941, 0.604]] and interpret their corresponding values.\nBased on the above thought, the answer is:\nLatitude, Temperate_Climate, Tropical_Climate\n-90, 0.1, 0.2\n-80, 0.2, 0.3\n-70, 0.3, 0.4\n-60, 0.5, 0.6\n-50, 0.6, 0.8\n-40, 0.7, 0.9\n-30, 0.8, 1.1\n-20, 0.9, 1.2\n-10, 1.1, 1.4\n0, 1.2, 1.6\n10, 1.2, 1.6\n20, 1.1, 1.4\n30, 0.9, 1.2\n40, 0.8, 1.0\n50, 0.6, 0.8\n60, 0.5, 0.6\n70, 0.3, 0.4\n80, 0.2, 0.3\n90, 0.1, 0.2" } ], "id_raw": 800 }, { "id": "YUsJz6ILxLvvmNWv", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_0.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, how can we compare the temperature variations with respect to latitude in Temperate and Tropical climates?" }, { "from": "gpt", "value": "To make the comparison, I focus on the trend lines for {Temperate_Climate}[0.130, 0.397, 0.941, 0.770] and {Tropical_Climate}[0.130, 0.231, 0.941, 0.604]. I note that both lines increase, then decrease along the latitude axis.\nStarting with the Temperate_Climate, the line ascends till it reaches latitude 0, indicating that temperature increases as we move from the pole to the equator. After that, the line descends, showing a decrease in temperature as we move away from the equator towards the pole.\nFor the Tropical_Climate, it behaves similarly but with more steepness, indicating a greater temperature rise and fall along the latitude. The temperature reaches its peak at latitude 0, indicating the highest temperature at the equator and then decreases as we move away towards the poles.\nBased on the above thought, the answer is:\nAccording to the line chart, both Temperate and Tropical climates show a rise in temperature as we move from the poles to the equator, peaking at the equator (latitude 0). Post that, there is a decline in temperature as we proceed towards the opposite pole. This type of curve indicates the expected temperature pattern on Earth due to the angle at which sunlight reaches different parts of Earth. However, the Tropical_Climate line peaks higher and falls more steeply than the Temperate_Climate line, suggesting a wider range of temperatures in Tropical climates compared to Temperate climates." } ], "id_raw": 800 }, { "id": "mQZWgwwHUrK6YKwP", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_0.png", "conversations": [ { "from": "human", "value": "\nFor the line which corresponds to Tropical_Climate in the line chart, at what Latitude do temperatures reach their maximum?" }, { "from": "gpt", "value": "In order to locate the maximum temperature for the Tropical_Climate, I need to examine the {points for this line}[[0.130, 0.231], [0.941, 0.604]] which span from -90 to 90 Latitude. The maximum temperature appears when it hits 1.6 which is represented by the points at {Latitude 0 and 10}[0.535, 0.895], [0.580, 0.895].\nBased on the above thought, the answer is:\n0 and 10." } ], "id_raw": 800 }, { "id": "uUYiGugIlMGp22Fv", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_1.png", "conversations": [ { "from": "human", "value": "\nFrom the bar chart, can you construct a table showing the values for the different dimensions (O_Value, T_Value, P_Value) for each of the two roles (Risk Control and Financial Management)?" }, { "from": "gpt", "value": "First I'll identify {the legend entries}[[0.767, 0.016, 0.804, 0.044], [0.774, 0.053, 0.811, 0.080], [0.776, 0.089, 0.813, 0.117]] that correspond to the different dimensions. Then, I'll arrange these dimensions as columns in the table. Secondly, I'll look at {the y-axis labels}[[0.057, 0.215, 0.091, 0.250], [0.056, 0.572, 0.090, 0.607]] to identify the two roles which represent the data rows. Finally, I will extract the values for each dimension for these two roles by interpreting {the horizontal bar lengths}[[0.126, 0.186, 0.734, 0.246], [0.126, 0.251, 0.881, 0.311], [0.126, 0.316, 0.800, 0.376], [0.126, 0.597, 0.792, 0.657], [0.126, 0.662, 0.907, 0.722], [0.126, 0.727, 0.838, 0.787]].\nBased on the above thought, the answer is:\nRole,O_Value,T_Value,P_Value\nRisk Control,430,530,350\nFinancial Management,375,420,390" } ], "id_raw": 801 }, { "id": "gRvCWndEiCAsTcwx", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_1.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which role has a more balanced distribution across the three dimensions and why do you think so?" }, { "from": "gpt", "value": "To determine which role has a more balanced distribution across the dimensions, I need to compare the proportions of O_Value, T_Value and P_Value for both roles. Looking at {the bars for Risk Control}[[0.126, 0.186, 0.734, 0.246], [0.126, 0.251, 0.881, 0.311], [0.126, 0.316, 0.800, 0.376]], the values range from 350 to 530, indicating a significant disparity particularly with the high T_Value. On the other hand, examining {the bars for Financial Management}[[0.126, 0.597, 0.792, 0.657], [0.126, 0.662, 0.907, 0.722], [0.126, 0.727, 0.838, 0.787]], the values range narrowly from 375 to 420, which indicates a more even distribution across these dimensions.\nBased on the above thought, the answer is:\nBased on the bar chart, Financial Management has a more balanced distribution across the three dimensions. While both roles have distinct spread of values, Financial Management maintains a closer range between its lowest and highest values (from 375 to 420), suggesting a better balance across the roles' different dimensions. Compared to Risk Control that shows significant disparity with values ranging from 350 to 530, the Financial Management role thus seems to be more balanced." } ], "id_raw": 801 }, { "id": "9tWMHxfnm2cZUSWF", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in O_Value between Risk Control and Financial Management roles?" }, { "from": "gpt", "value": "To find the difference in O_Value between the two roles, I need to locate {the O_Value bar for Risk Control}[[0.126, 0.186, 0.734, 0.246]] which has a value of 430 and {the O_Value bar for Financial Management}[[0.126, 0.597, 0.792, 0.657]] with a value of 375. The difference in O_Value between the two roles is then calculated as 430 - 375 = 55.\nBased on the above thought, the answer is:\n55." } ], "id_raw": 801 }, { "id": "pgc0mvs1fAxWUKrE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_2.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data shown in the line chart and represent it in a table with 'CSR Index' as rows and Performances as columns?" }, { "from": "gpt", "value": "First, I need to identify that the X-Axis represents {the CSR Index}[0.473, 0.943, 0.597, 0.975], which I can take as the rows of the table. The legends identify the different performance types to be {Environmental Performance}[0.166, 0.091, 0.456, 0.121], {Economic Performance}[0.166, 0.134, 0.405, 0.164], and {Social Performance}[0.166, 0.178, 0.366, 0.208], which are the columns of the table. For each CSR Index, I have to extract the performance value for each of the three performances. For instance, at CSR Index 0.1, I need to extract the performance values for Environmental, Economic, and Social Performances by analyzing their lines' coordinates.\nBased on the above thought, the answer is:\nCSR Index,Environmental Performance,Economic Performance,Social Performance\n0.1,0.5,0.6,0.4\n0.2,0.48,0.58,0.39\n0.3,0.46,0.56,0.41\n0.4,0.44,0.57,0.42\n0.5,0.42,0.7,0.4\n0.6,0.4,0.69,0.38\n0.7,0.38,0.74,0.36\n0.8,0.36,0.76,0.39\n0.9,0.34,0.7,0.41\n1.0,0.32,0.68,0.43" } ], "id_raw": 802 }, { "id": "iuDXjNACPOupklsT", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_2.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which performance measure shows the greatest improvement from CSR Index 0.1 to 1.0 and why?" }, { "from": "gpt", "value": "The line chart shows three performance measures, namely: {Environmental Performance}[0.130, 0.483, 0.941, 0.508], {Economic Performance}[0.130, 0.231, 0.941, 0.328], and {Social Performance}[0.130, 0.591, 0.941, 0.645]. By comparing the initial and final values of these performances, we can determine which had the greatest improvement. Environmental Performance decreases from 0.5 to 0.32, which is a decline and not an improvement. Economic performance increases from 0.6 to 0.68, which is an improvement of 0.08. Similarly, Social Performance improves from 0.4 to 0.43, an increase of 0.03. Therefore, the greatest improvement is in Economic Performance for CSR Index 0.1 to 1.0.\nBased on the above thought, the answer is:\nThe Economic Performance shows the greatest improvement from CSR Index 0.1 to 1.0. It has the highest increase in performance values compared to Environmental Performance and Social Performance over the given range of CSR Index. The increase is calculated from comparing the initial and final performance values." } ], "id_raw": 802 }, { "id": "zX5bOHCXfgJgGPBh", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in Social Performance from a CSR index of 0.1 to 1.0?" }, { "from": "gpt", "value": "To find the increase in Social Performance, I need to locate the value of Social Performance at a CSR index of 0.1 and 1.0. The {line for Social Performance}[0.130, 0.591, 0.941, 0.645] represents the trend of Social Performance. The first point (x=0.1) corresponds to a Social Performance of {0.4}[0.130, 0.605] and the last point (x=1.0) corresponds to a Social Performance of {0.43}[0.941, 0.645]. Therefore, the increase in Social Performance from a CSR index of 0.1 to 1.0 is calculated as 0.43 - 0.4 = 0.03.\nBased on the above thought, the answer is:\n0.03." } ], "id_raw": 802 }, { "id": "BSapdKzjRvm9Dq3n", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_3.png", "conversations": [ { "from": "human", "value": "\nCould you formulate the raw chart data into a comprehensible table that includes years as columns and the types of vehicles as rows?" }, { "from": "gpt", "value": "To format the provided line chart's information into a table, I am going to find {the x-axis tick labels}[[0.108, 0.911, 0.152, 0.934], [0.310, 0.911, 0.354, 0.934], [0.513, 0.911, 0.557, 0.934], [0.716, 0.911, 0.760, 0.934], [0.919, 0.911, 0.963, 0.934]], presenting the years. Next, I'll identify {the legend texts}[[0.166, 0.091, 0.382, 0.121], [0.166, 0.134, 0.372, 0.164]] as the car types. To obtain the values, I'll locate {the line points}[[0.130, 0.819], [0.332, 0.367], [0.535, 0.292], [0.738, 0.216], [0.941, 0.171], [0.130, 0.593], [0.332, 0.548], [0.535, 0.518], [0.738, 0.367], [0.941, 0.292]] reported for each car type in the corresponding years and collect the annotated values.\nBased on the above thought, the answer is:\nVehicle_Type,2016,2017,2018,2019,2020\nKnowledge Economy,0.5,0.8,0.85,0.9,0.93\nInnovation Industries,0.65,0.68,0.7,0.8,0.85" } ], "id_raw": 803 }, { "id": "Ak04wpZVZDh6uP79", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_3.png", "conversations": [ { "from": "human", "value": "\nComparing the trends observed for Knowledge Economy and Innovation Industries from the line chart, which one experienced more growth within the observed period? Provide a brief explanation for your answer." }, { "from": "gpt", "value": "To make a comparison, I need to look at the line plots for both sectors. Observing {the line for Knowledge Economy}[0.130, 0.171, 0.941, 0.819], There was a steady and considerable growth from 0.5 in 2016 to 0.93 in 2020. On the other hand, looking at {the line for Innovation Industries}[0.130, 0.292, 0.941, 0.593], the growth seems relatively less, with a change from 0.65 in 2016 to 0.85 in 2020.\nBased on the above thought, the answer is:\nThe Knowledge Economy experienced more growth within the observed period, as is evident from the line chart. The segment started at a lower value in 2016 but overtook the Innovation Industries by 2020, marking a significant and notable expansion. On the contrary, while the Innovation Industries started with a higher value in 2016, its growth has been less considerable." } ], "id_raw": 803 }, { "id": "5W0kIB9oiLs5yIyo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall change observed for the Knowledge Economy from 2016 to 2020?" }, { "from": "gpt", "value": "To ascertain the total alteration observed for the Knowledge Economy from 2016 to 2020, it's necessary to identify {the line points for the Knowledge Economy}[[0.130, 0.819], [0.332, 0.367], [0.535, 0.292], [0.738, 0.216], [0.941, 0.171]] and retrieve the respective values: 0.5, 0.8, 0.85, 0.9, 0.93. The values recorded in 2016 {at 0.130, 0.819} and 2020 {at 0.941, 0.171} are of special interest. The overall change is determined as 0.93 (2020's value) - 0.5 (2016's value), amounting to 0.43.\nBased on the above thought, the answer is:\n0.43" } ], "id_raw": 803 }, { "id": "EfH5RZi2pRs7LSPA", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_4.png", "conversations": [ { "from": "human", "value": "\nCan you provide the data from the chart in a table? The table should have columns for Year, Residential, Commercial, and Industrial." }, { "from": "gpt", "value": "To create the table, I would first identify {the x-axis tick labels}[[0.108, 0.933, 0.117, 0.951], [0.152, 0.933, 0.161, 0.951], [0.196, 0.933, 0.205, 0.951], ..., [0.935, 0.933, 0.952, 0.951]] as the years, which would become the rows of the table. Then, I would refer to {the line plot for the Residential sector}[0.113, 0.093, 0.943, 0.507], {the line plot for the Commercial sector}[0.113, 0.104, 0.943, 0.834] and {the line plot for the Industrial sector}[0.113, 0.0, 0.943, 0.83] to extract the values for each year and sector to fill in the table.\nBased on the above thought, the answer is:\nYear,Residential,Commercial,Industrial\n1,0.96,0.95,0.8\n2,0.93,0.9,0.75\n3,0.91,0.82,0.68\n4,0.88,0.8,0.65\n...\n19,0.6,0.3,0.2\n20,0.58,0.28,0.18" } ], "id_raw": 804 }, { "id": "S7YpKDSwCmuIgFM4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_4.png", "conversations": [ { "from": "human", "value": "\nWhich sector showed the most steadiness over the examined period?" }, { "from": "gpt", "value": "To answer this question, I would need to evaluate {the line trend of the Residential sector}[0.113, 0.093, 0.943, 0.507], {the Commercial sector}[0.113, 0.104, 0.943, 0.834], and {the Industrial sector}[0.113, 0.0, 0.943, 0.83]. Steadiness refers to the sector that has the least variation in values throughout the years. If we compare the gradients and fluctuation of the lines, we can see that the Residential sector has the most consistent gradient, indicating the least variations in its values over the years.\nBased on the above thought, the answer is:\nThe Residential sector showed the most steadiness over the examined period as it had the least variations in its values over the years." } ], "id_raw": 804 }, { "id": "xRkyhrT5yEe4aUva", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_4.png", "conversations": [ { "from": "human", "value": "\nWhat was the change in the Commercial Sector value from Year 1 to Year 20?" }, { "from": "gpt", "value": "To answer this question, I need to examine {the value of the Commercial Sector in Year 1}[0.113, 0.104] and {in Year 20}[0.943, 0.834]. The value in Year 1 is 0.95 and in Year 20 it is 0.28. The change can be calculated as the Year 20 value minus the Year 1 value.\nBased on the above thought, the answer is:\n-0.67" } ], "id_raw": 804 }, { "id": "g86LkHq04LzI39V5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_5.png", "conversations": [ { "from": "human", "value": "\nWould it be possible to represent the data in the line chart as a structured table with Years as columns and Energy Types as rows?" }, { "from": "gpt", "value": "To convert the line chart into tabular data, I need to focus on {the x-axis tick labels}[[0.108, 0.911, 0.152, 0.934], [0.189, 0.911, 0.233, 0.934], [0.270, 0.911, 0.314, 0.934], [0.351, 0.911, 0.395, 0.934], [0.432, 0.911, 0.476, 0.934], [0.513, 0.911, 0.557, 0.934], [0.594, 0.911, 0.638, 0.934], [0.675, 0.911, 0.719, 0.934], [0.756, 0.911, 0.801, 0.934], [0.838, 0.911, 0.882, 0.934], [0.919, 0.911, 0.963, 0.934]], which hold the Year values for the column headers. The row labels symbolizing different Energy Types come from {the legend texts}[[0.166, 0.091, 0.413, 0.121], [0.166, 0.134, 0.350, 0.164]]. The respective values for each row and column combination are derived from the coordinates of {the line points for Electricity Consumption and Gas Consumption}[[0.130, 0.193, 0.941, 0.646], [0.130, 0.701, 0.941, 0.889]] on the chart.\nBased on the above thought, the answer is:\nYear,Electricity_Consumption,Gas_Consumption\n2000,0.45,0.35\n2001,0.47,0.30\n2002,0.52,0.28\n2003,0.55,0.24\n2004,0.60,0.20\n2005,0.62,0.18\n2006,0.65,0.15\n2007,0.72,0.16\n2008,0.75,0.11\n2009,0.78,0.12\n2010,0.83,0.08\n2011,0.85,0.06\n2012,0.91,0.04\n2013,0.93,0.02\n2014,1.01,0.01\n2015,1.05,0.04\n2016,1.08,0.05\n2017,1.16,0.02\n2018,1.19,0.01\n2019,1.22,0.02\n2020,1.27,0.01" } ], "id_raw": 805 }, { "id": "lIH9LgwYR9RpMomy", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_5.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which type of energy consumption demonstrates a more consistent growth over time? Explain your reasoning." }, { "from": "gpt", "value": "To determine which type of energy consumption shows more consistent growth, I need to examine the lines representing both energy types. Looking at {the line for Electricity Consumption}[0.130, 0.193, 0.941, 0.646], it consistently moves upwards without significant drops or spikes. On the other hand, {the line for Gas Consumption}[0.130, 0.701, 0.941, 0.889] initially decreases before increasing slightly, and then decreases again in recent years, showing inconsistency in growth.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Electricity Consumption demonstrates a more consistent growth over time. Its line plot exhibits a consistent upward trend with no significant drops or spikes, indicating steady growth in electricity consumption over the years. On the contrary, Gas Consumption shows a decreasing trend overall, indicating inconsistency in its growth. Hence, compared to Gas Consumption, Electricity Consumption shows a more stable and consistent growth over time." } ], "id_raw": 805 }, { "id": "9BnQqlP5c9taFoKu", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_5.png", "conversations": [ { "from": "human", "value": "\nBy how much did Electricity Consumption increase between 2000 and 2020?" }, { "from": "gpt", "value": "To find the increase in Electricity Consumption between 2000 and 2020, I locate {the line point for Year 2000 for Electricity Consumption}[0.130, 0.646] and {the line point for Year 2020 for Electricity Consumption}[0.941, 0.193]. Extracting the corresponding values: 0.45 and 1.27. The increase is calculated as 1.27 - 0.45 = 0.82.\nBased on the above thought, the answer is:\n0.82." } ], "id_raw": 805 }, { "id": "Hr075MdNyP6IAKub", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_6.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, can you extract the data into a table format with Years as columns and Cryptocurrencies as rows?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.148, 0.911, 0.192, 0.934], [0.229, 0.911, 0.273, 0.934], [0.310, 0.911, 0.354, 0.934], [0.392, 0.911, 0.435, 0.934], [0.473, 0.911, 0.517, 0.934], [0.554, 0.911, 0.598, 0.934], [0.635, 0.911, 0.679, 0.934], [0.716, 0.911, 0.760, 0.934], [0.797, 0.911, 0.841, 0.934], [0.878, 0.911, 0.922, 0.934]], which represent the column headers or Years. Next, I identify {the legend texts}[[0.166, 0.091, 0.458, 0.121], [0.166, 0.134, 0.368, 0.164]] as the row labels representing the different cryptocurrencies. To populate the values, I locate {the line points}[[0.170, 0.667], [0.900, 0.086], [0.170, 0.729], [0.900, 0.273]] for each cryptocurrencies at the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nCryptocurrency,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nMedical Tech Biotech Trends,1.1,1.3,1.7,2.0,2.5,2.8,3.0,3.2,3.6,3.9\nBiomedicine Trends,0.8,0.9,1.1,1.5,2.0,2.1,2.3,2.4,2.7,3.0" } ], "id_raw": 806 }, { "id": "IWEjX1e07J3DBzo4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_6.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the trend change of Medical Tech Biotech from 2011 to 2020?" }, { "from": "gpt", "value": "To describe the trend change, I need to observe {the line of Medical Tech Biotech}[0.170, 0.086, 0.900, 0.667]. There's a consistent rise in the trend from 2011 to 2020. The growth pattern is quite steady with no abrupt dips or peaks. The line starts at a lower point and gradually ascends to a higher point over the years. This shows that there has been a steady increase in Medical Tech Biotech Trends from 2011 to 2020.\nBased on the above thought, the answer is:\nThe trend of Medical Tech Biotech from 2011 to 2020 has been consistently increasing. This illustrates a steady growth in the Medical Tech Biotech Trends over the decade without any significant fluctuations. Therefore, it can be concluded that the Medical Tech Biotech Trends have shown a steady and sustained upward trend over this period." } ], "id_raw": 806 }, { "id": "0GjcOz4w7wdZyq4n", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in trends value between the start of 2011 and end of 2020 for Biomedicine?" }, { "from": "gpt", "value": "To find the difference in trends value for Biomedicine between 2011 and 2020, I need to locate {the line points for Biomedicine at 2011 and 2020}[ [0.170, 0.729], [0.900, 0.273]] and extract the corresponding values: 0.8 and 3.0. The difference between the ending value and the starting value is therefore 3.0 - 0.8 = 2.2.\nBased on the above thought, the answer is:\n2.2." } ], "id_raw": 806 }, { "id": "GPfAsv0kfkmmhquy", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_7.png", "conversations": [ { "from": "human", "value": "\nCan you convert the investment trends for the different sectors, shown in the line chart, into a structured table?" }, { "from": "gpt", "value": "Yes, I can convert the chart into a table. First, I identify the {x-axis labels}[0.112, 0.911, 0.976, 0.934] which represent years which will be the rows of the table. Then, I find {the legend entries}[0.666, 0.785, 0.971, 0.881] which represent different sectors that will be the columns of the table. For each sector, I extract the corresponding values from {the points of the lines}[0.130, 0.301, 0.940, 0.830],[0.130, 0.360, 0.940, 0.771], which indicate the investment amount at each year.\nBased on the above thought, the answer is:\nYear,Information Technology,Biotechnology,Nanotechnology,Green Technology\n2016,199,207,137,175\n2017,210,220,140,180\n2018,230,230,140,195\n2019,240,235,135,200\n2020,245,242,135,205" } ], "id_raw": 807 }, { "id": "xrsOj9BdTt785CmU", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_7.png", "conversations": [ { "from": "human", "value": "\nWhich sector shows the most stable growth and why?" }, { "from": "gpt", "value": "To answer this question, I need to examine the trend lines for each sector. {The line for Information Technology}[0.130, 0.301, 0.940, 0.830] shows persistent growth throughout the period, {the line for Biotechnology}[0.130, 0.301, 0.940, 0.830] shows a healthy upward trend with highest growth rate, {the line for Nanotechnology}[0.130, 0.301, 0.940, 0.830] shows a trend of substantial decrease with peak in mid period, and {the line for Green Technology}[0.130, 0.301, 0.940, 0.830] shows periodic fluctuations but overall trend is upward. Based on the steadiness and consistency of the trend, the Information Technology sector shows the most stable growth.\nBased on the above thought, the answer is:\nInformation Technology is the sector with the most stable growth, as the line representing this sector on the chart exhibits a consistent and steady upward trend without any significant dips or fluctuations, indicating a stable rate of growth over the five-year period." } ], "id_raw": 807 }, { "id": "vUPjth4mZMtbUB6g", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the average growth rate of the Information Technology sector from 2016 to 2020?" }, { "from": "gpt", "value": "To find out the average growth rate, I need to observe the {annual investment for the Information Technology sector}[0.130, 0.301, 0.940, 0.830]. The initial investment in 2016 is 199 and final in 2020 is 245. The formula of average growth rate is ((final value/initial value)^(1/number of periods))-1. So, ((245/199)^(1/5))-1 = 0.044 or 4.4%. So, the average growth rate is 4.4%.\nBased on the above thought, the answer is:\n4.4%" } ], "id_raw": 807 }, { "id": "asCz2ow96Q8CDvaW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_9.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, could you extract the data into a table with Year as columns and Technologies as rows?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first locate {the x-axis tick labels}[[0.067, 0.911, 0.111, 0.934], [0.176, 0.911, 0.220, 0.934], [0.285, 0.911, 0.329, 0.934], [0.393, 0.911, 0.437, 0.934], [0.502, 0.911, 0.546, 0.934], [0.611, 0.911, 0.655, 0.934], [0.720, 0.911, 0.764, 0.934], [0.829, 0.911, 0.873, 0.934], [0.938, 0.911, 0.981, 0.934]], which represent the column headers or years. Next, I identify the {legend texts}[[0.166, 0.091, 0.475, 0.121], [0.166, 0.134, 0.450, 0.164]] as the row labels representing the different technologies. To populate the values, I locate the line points for each technology at the respective years and extract the labeled values.\nBased on the above thought, the answer is:\nTechnologies,2012,2013,2014,2015,2016,2017,2018,2019,2020\nEco-Friendly Product Revenue,0.3,0.6,0.4,0.7,0.75,0.8,1,1.2,1.3\nSocial Responsibility Spend,0.4,0.5,0.6,0.7,0.8,0.6,0.9,1.1,1.4" } ], "id_raw": 809 }, { "id": "vLVFjvEyiavznUJZ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_9.png", "conversations": [ { "from": "human", "value": "\nBased on this line chart, which area of the business appears to consistently increase its profits over the years?" }, { "from": "gpt", "value": "To determine which business area consistently increases profits, I need to analyze the trends of the lines. By looking at {the line of Eco-Friendly Product Revenue}[[0.089, 0.193, 0.959, 0.831]], I can see that it shows a steady increase over the years, with only one significant decrease in 2014. But after that year, the growth is relatively consistent.\nIn contrast, {Social Responsibility Spend}[[0.089, 0.129, 0.959, 0.767]] does not show a consistent increase; there is a decrease in 2017.\nBased on the above thought, the answer is:\nBased on this line chart, the Eco-Friendly Product Revenue business area appears to consistently increase its profits over the years. The line corresponding to this category shows a consistent upward trend, meaning that revenue has generally increased year after year. Whereas, the Social Responsibility Spend has some fluctuations and decrease in 2017, hence it does not show a consistent increase." } ], "id_raw": 809 }, { "id": "Xkl4XS1FnqAOQttO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the total combined revenue of Eco-Friendly Product Revenue and Social Responsibility Spend for 2020?" }, { "from": "gpt", "value": "To find the total combined revenue for 2020, I need to locate the line points for each revenue type. The line point for {Eco-Friendly Product Revenue in 2020}[[0.959, 0.193]] indicates a value of 1.3 million. Similarly, the line point for {Social Responsibility Spend in 2020}[[0.959, 0.129]] represents a value of 1.4 million. The total combined revenue for 2020 is therefore 1.3 + 1.4 = 2.7 million.\nBased on the above thought, the answer is:\n2.7 million." } ], "id_raw": 809 }, { "id": "ZUfn5N3TEA8pDeKv", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_11.png", "conversations": [ { "from": "human", "value": "\nCan you create a table showing the annual stock prices for both EM and WM across the years depicted in the chart?" }, { "from": "gpt", "value": "Observing the chart, I see two distinct line plots representing the stock prices for EM and WM. Each line plot contains data points for each year from 2001 to 2020. Specifically, I locate {the EM stock price points}[[0.126, 0.549], [0.169, 0.480], ..., [0.941, 0.135]] and similarly for WM. I extract each point's x-coordinate to map to the year on {the x-axis tick labels}[[0.062, 0.911, 0.106, 0.934], [0.147, 0.911, 0.191, 0.934], ..., [0.919, 0.911, 0.962, 0.934]], and the y-coordinate to approximate the stock prices from the {y-axis tick labels}[[0.061, 0.885, 0.071, 0.909], [0.061, 0.747, 0.071, 0.771], ..., [0.049, 0.056, 0.071, 0.080]]. I look for both EM and WM lines to produce a comprehensive table.\nBased on the above thought, the answer is:\nYear,EM Stock Price,WM Stock Price\n2001,5,7\n2002,6,8\n2003,7,6\n2004,7,5\n2005,7,6\n2006,8,7\n2007,8.5,7.5\n2008,6,8\n2009,5,5.5\n2010,4,7\n2011,4.5,8\n2012,5,7.5\n2013,6,7\n2014,7,6\n2015,8,6.5\n2016,9,7\n2017,9.5,7.5\n2018,10,8\n2019,10.5,8.5\n2020,11,9" } ], "id_raw": 811 }, { "id": "Zo3MOzVXeKGrtwTx", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_11.png", "conversations": [ { "from": "human", "value": "\nHow does the stock price trend for WM appear from 2010 to 2020, and what could be the potential implication of such trends?" }, { "from": "gpt", "value": "Analyzing the stock prices for WM from 2010 to 2020, I notice fluctuations with some years exhibiting growth and others showing decline. I locate {the WM stock price points from 2010 to 2020}[[0.512, 0.411], [0.555, 0.342], ..., [0.941, 0.273]] that illustrate these shifts. The trend suggests a generally unstable market condition, possibly reflecting varying investor confidence or market volatility. The implications could be greater caution amongst investors, greater market speculation, or shifts in strategic directions within WM's industry, influencing how stock prices evolve over this period.\nBased on the above thought, the answer is:\nThe stock price trend for WM shows fluctuations between 2010 and 2020, indicating periods of growth and decline. This could imply a potentially volatile market environment, affected by changes in investor confidence or external economic factors, leading to cautious investment strategies or increased market analysis among stakeholders." } ], "id_raw": 811 }, { "id": "f0ApwtftmYRobL3z", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the average stock price difference between EM and WM over the years?" }, { "from": "gpt", "value": "To calculate the average stock price difference, first, I extract the stock prices for EM and WM across all years from the line chart: EM stock prices are [5, 6, ..., 11] and WM stock prices are [7, 8, ..., 9] which I derived from observing {the line points for EM}[[0.126, 0.549], [0.169, 0.480], ..., [0.941, 0.135]] and {the line points for WM}[[0.126, 0.411], [0.169, 0.342], ..., [0.941, 0.273]]. I compute the differences for each year and then find the average using mathematical operations.\nBased on the above thought, the answer is:\n1.6" } ], "id_raw": 811 }, { "id": "ruWZ9bCnygAeUXK1", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_12.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the annual values shown in the line chart for 'Erratic growth' and 'Slow expansion' over the 11 years?" }, { "from": "gpt", "value": "To construct a table from the line chart, I first observe {the x-axis labels}[0.124, 0.911, 0.135, 0.934], [0.205, 0.911, 0.216, 0.934], ..., [0.930, 0.911, 0.952, 0.934] which span from years '0' through '10'. Then I align these with the corresponding y-values tracked in two series marked as 'Erratic Growth' showing points {located at}[0.130, 0.606], [0.211, 0.549], ..., [0.941, 0.332] and 'Slow Expansion' with points at [0.130, 0.570], [0.211, 0.556], ..., [0.941, 0.426]. Using these coordinates, I will detail annual values for each series.\nBased on the above thought, the answer is:\nYear,Erratic Growth,Slow Expansion\n0,0.45,0.5\n1,0.53,0.52\n2,0.47,0.54\n3,0.55,0.56\n4,0.49,0.58\n5,0.57,0.60\n6,0.69,0.62\n7,0.67,0.64\n8,0.71,0.66\n9,0.75,0.68\n10,0.83,0.70" } ], "id_raw": 812 }, { "id": "AmsUKKN7jOrUrmRg", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_12.png", "conversations": [ { "from": "human", "value": "\nWhich year showed the highest increment for 'Slow Expansion'?" }, { "from": "gpt", "value": "To find the year with the highest increment for 'Slow Expansion', I analyze the differences between consecutive y-values starting from {the initial point of Slow Expansion}[0.130, 0.570] and ending at the {last point}[0.941, 0.426]. The increases are 0.02 from year 0 to 1, 0.02 from 1 to 2, 0.02 from 2 to 3, and so on, with the largest visible increase happening from year 9 to year 10 where the value climbs from 0.68 to 0.70.\nBased on the above thought, the answer is:\nThe highest increment for 'Slow Expansion' was observed between year 9 and year 10." } ], "id_raw": 812 }, { "id": "kh9nfUMCH7Km6aIU", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall change in 'Erratic Growth' from year 0 to year 10?" }, { "from": "gpt", "value": "To calculate the change in 'Erratic Growth', I locate the initial value at {the first point of Erratic Growth}[0.130, 0.606] which corresponds to 0.45 and the final value at {the last point of Erratic Growth}[0.941, 0.332] which is 0.83. The overall change is computed by subtracting the initial value from the final value.\nBased on the above thought, the answer is:\n0.38" } ], "id_raw": 812 }, { "id": "c9bUsk2PCiw7DdRL", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart showing the performance of the financial asset from 2015 to 2021 into a structured table with 'Year' as rows and 'Investment_Return' and 'Risk_Index' as columns?" }, { "from": "gpt", "value": "I will analyze the line chart by identifying {the x-axis tick labels}[0.105, 0.921] which represent the years from 2015 to 2021. Then, I track the coordinates of the data points for 'Investment_Return' and 'Risk_Index' from the plotted lines. For 'Investment_Return', the points are located at {the line for Investment_Return}[0.105, 0.862] across these years, and for 'Risk_Index', the points are marked on {the line for Risk_Index}[0.105, 0.176]. These point coordinates correspond to the respective values for each year.\nBased on the above thought, the answer is:\nYear,Investment_Return,Risk_Index\n2015,5.0,80\n2016,6.0,75\n2017,7.0,70\n2018,8.0,65\n2019,9.0,60\n2020,8.5,55\n2021,9.5,50" } ], "id_raw": 814 }, { "id": "q5D7TSUvlSrWgpiZ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_14.png", "conversations": [ { "from": "human", "value": "\nWhat implications do the trends in 'Investment_Return' and 'Risk_Index' have for potential investors?" }, { "from": "gpt", "value": "To understand the implications for investors, I assess the behavior of both 'Investment_Return' and 'Risk_Index' in the chart. Observing {the line for Investment_Return}<[0.105, 0.862]> and {the line for Risk_Index}<[0.105, 0.176]>, 'Investment_Return' is generally increasing over the years, indicative of a potentially profitable asset. Conversely, 'Risk_Index' decreases over the same period, suggesting a decrease in investment risk. In 2020, a slight dip in 'Investment_Return' alongside a continuing decrease in 'Risk_Index' could suggest better risk management but might indicate temporary performance issues within that year.\nBased on the above thought, the answer is:\nThe trends suggest that the asset is becoming increasingly profitable while simultaneously becoming less risky. This dual appeal of high returns with lower risk levels makes it an attractive option for potential investors, particularly as risk management seems effective even when 'Investment_Return' temporarily dips, as seen in 2020." } ], "id_raw": 814 }, { "id": "g6SJ9apBvcwTsSwq", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the average 'Risk_Index' over the period from 2015 to 2021?" }, { "from": "gpt", "value": "In order to find the average 'Risk_Index', I must extract the respective values from the chart by analyzing {the line for Risk_Index}[0.105, 0.176]. The extracted values for 'Risk_Index' from 2015 to 2021 are 80, 75, 70, 65, 60, 55, and 50. The next step involves calculating the average of these numbers.\nBased on the above thought, the answer is:\n65." } ], "id_raw": 814 }, { "id": "oYeRdXqhDutbJbVe", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_16.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the chart comparing Faith, Steadiness, and Honesty across different moods into a CSV table format?" }, { "from": "gpt", "value": "To convert the chart into a CSV table format, I first look at {the x-axis tick labels}[0.076, 0.911, 0.103, 0.934], [0.155, 0.911, 0.183, 0.934], ..., [0.954, 0.911, 0.981, 0.934] to identify the different moods. Each label corresponds to a mood. Then, I recognize {the lines representing Faith, Steadiness, and Honesty}[0.169, 0.409, 0.888, 0.740], [0.169, 0.466, 0.888, 0.752], [0.169, 0.351, 0.888, 0.729] on the chart. By analyzing the points on these lines, I calculate the scores for each behavior under each mood.\nBased on the above thought, the answer is:\nMood,Faith,Steadiness,Honesty\nRapid Increase,3.2,3.0,3.4\nDramatic Surge,5.5,5.3,5.6\nSlow Recovery,4.0,4.2,3.9\nSteady Decline,2.8,3.0,2.7\nSlow Expansion,3.6,3.7,3.5\nRapid Growth,5.1,5.0,5.2" } ], "id_raw": 816 }, { "id": "vmB5i79PCxIuG0fY", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_16.png", "conversations": [ { "from": "human", "value": "\nWhich behavior shows the least variation in response to changes in moods, according to the line chart data?" }, { "from": "gpt", "value": "To determine which behavior shows the least variation, I examine the spread and fluctuation of {the line plots for Faith, Steadiness, and Honesty}[0.169, 0.409, 0.888, 0.740], [0.169, 0.466, 0.888, 0.752], [0.169, 0.351, 0.888, 0.729]. Faith and Steadiness exhibit significant increases and decreases, especially visible during 'Dramatic Surge' and 'Rapid Growth'. In contrast, {the line for Honesty}[0.169, 0.351, 0.888, 0.729] shows a more consistent pattern with less dramatic fluctuations, suggesting it is less affected by changes in mood.\nBased on the above thought, the answer is:\nHonesty shows the least variation in response to changes in moods. The line for Honesty exhibits a more consistent trajectory compared to the lines for Faith and Steadiness, indicating it responds with less fluctuation to changing moods." } ], "id_raw": 816 }, { "id": "e8CHB5hRGvp4Otrz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the average score for Honesty across all moods in the chart?" }, { "from": "gpt", "value": "To calculate the average score for Honesty, I analyze {the points representing Honesty}[0.169, 0.729], [0.249, 0.694], [0.329, 0.677], [0.409, 0.632], [0.488, 0.620], [0.568, 0.574], [0.648, 0.569] across all moods. The corresponding numeric scores are 3.4, 5.6, 3.9, 2.7, 3.5, 5.2, and 4.0. Calculating the average of these scores gives the overall average score for Honesty.\nBased on the above thought, the answer is:\n4.04" } ], "id_raw": 816 }, { "id": "7KdjBmYHqjjFnuWo", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_17.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line graph data into a table with Cultural Heritage types as rows and Culture Types (Asian and African) as columns?" }, { "from": "gpt", "value": "To glean the tabular data from the line chart, first, I ascertain {the x-axis labels}[0.108, 0.911, 0.963, 0.934] as respective row headers or Cultural Heritage types. Subsequently, I perceive {the legend text}[0.865, 0.091, 0.935, 0.164], 'Facebook' and 'Twitter' as African and Asian culture types respectively serving as the column headers. Proceeding to populate the values, I discern {the line points for Facebook}[0.130, 0.148, 0.941, 0.812] and {the line points for Twitter}[0.130, 0.563, 0.941, 0.770] thus obtaining the corresponding values for each Cultural Heritage type.\nBased on the above thought, the answer is:\nCultural Heritage,Asian,African\nHistorical Monuments,32,67\nArtifacts,42,57\nFolklores,45,59\nTraditional Games,47,64 \nIndigenous Cuisine,52,59\nTraditional Music,57,56\nIndigenous Languages,56,52\nIndigenous Religions,52,45" } ], "id_raw": 817 }, { "id": "G8JVHf5Z04SpKgcE", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_17.png", "conversations": [ { "from": "human", "value": "\nLooking at the trends depicted in the line graph, what inference can you draw about the progression of the integration of Indigenous Religions between the two cultures over the years?" }, { "from": "gpt", "value": "To form conclusions about the progression of the integration of Indigenous Religions between the two cultures, I need to analyze {the line corresponding to Indigenous Religions for Asian}[0.941, 0.148] and {African}[0.941, 0.770] cultures. The graph exhibits a steady decline for both lines, which suggests a waning interest or recognition for Indigenous Religions over the years.\nBased on the above thought, the answer is:\nGiven the steady decline exhibited in the line graph for both Asian and African cultures in terms of the integration of Indigenous Religions, it could be inferred that there has been a diminishing recognition or interest in Indigenous Religions over the years among these cultures. This decline could possibly be caused by factors such as modernization and the growing influence of global religions." } ], "id_raw": 817 }, { "id": "HNuJhgSvc7sfGeaC", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_17.png", "conversations": [ { "from": "human", "value": "\nHow much was the difference in integration of the Traditional Music heritage category between African and Asian cultures?" }, { "from": "gpt", "value": "To discover the discrepancy in integration of Traditional Music between African and Asian cultures, I locate {the line point for Traditional Music in Asian Culture}[0.778, 0.273] and extract its corresponding value as 57. In the same vein, I examine {the line point for Traditional Music in African Culture}[0.778, 0.729] and deduce the value as 56. Hence, the difference between Asian and African integration for Traditional Music is 57 - 56 = 1.\nBased on the above thought, the answer is:\n1." } ], "id_raw": 817 }, { "id": "CPHhvi2JWcYd9Tmi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_41_19.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart into a structured table that lists the Year, Fintech Ratings, Biotechnology Ratings, and Robotics Ratings?" }, { "from": "gpt", "value": "To convert the data from the line chart into a table, I first identify {the x-axis labels}[0.108, 0.911, 0.152, 0.934], [0.198, 0.911, 0.242, 0.934], ..., [0.919, 0.911, 0.963, 0.934] representing the years. Next, I review the line plots for Fintech, Biotechnology, and Robotics and extract the performance ratings from their respective {data points}[0.130, 0.722], [0.220, 0.664], ..., [0.941, 0.204] for Fintech, [0.130, 0.687], [0.220, 0.626], ..., [0.941, 0.135] for Biotechnology, and [0.130, 0.584], [0.220, 0.545], ..., [0.941, 0.238] for Robotics.\nBased on the above thought, the answer is:\nYear,Fintech,Biotechnology,Robotics\n2010,0.25,0.45,0.5\n2011,0.35,0.55,0.6\n2012,0.40,0.60,0.65\n2013,0.45,0.65,0.70\n2014,0.55,0.70,0.75\n2015,0.70,0.75,0.80\n2016,0.80,0.80,0.85\n2017,0.90,0.90,0.90\n2018,0.95,0.95,0.95\n2019,1.00,1.00,1.00" } ], "id_raw": 819 }, { "id": "QTjSEjY3azZjSgR9", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_41_19.png", "conversations": [ { "from": "human", "value": "\nWhich technology sector shows the most consistent growth over the decade, and how can this be deduced from the line chart?" }, { "from": "gpt", "value": "To determine which sector showcases the most consistent growth, I focus on the slope and progression of each line in the chart. Observing {the line for Fintech}[0.130, 0.204, 0.941, 0.722], it exhibits a constant upward trend with no declines or plateaus from the beginning to the end point. As opposed to {the lines for Biotechnology}[0.130, 0.687, 0.941, 0.135] and {Robotics}[0.130, 0.584, 0.941, 0.238], which show fluctuations and less consistent trajectories.\nBased on the above thought, the answer is:\nThe Fintech sector shows the most consistent growth over the decade. This conclusion is based on the consistent and uninterrupted upward trend observed in the line representing Fintech, without any periods of stagnation or decline as seen in the other sectors' lines on the chart." } ], "id_raw": 819 }, { "id": "LNbYrJt9cqxFfbLv", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_41_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in performance rating for Robotics from 2010 to 2019?" }, { "from": "gpt", "value": "To find the total increase in performance rating for Robotics from 2010 to 2019, I analyze the values at {the start and end data points for Robotics}[0.130, 0.584], [0.941, 0.238]. In 2010, the rating is 0.5 and in 2019, it is 1.0. Calculating the difference gives an increase of 1.0 - 0.5 = 0.5.\nBased on the above thought, the answer is:\n0.5" } ], "id_raw": 819 }, { "id": "HR0GliJ2rg67Hofi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_0.png", "conversations": [ { "from": "human", "value": "\nCan you provide the data displayed in the line chart as a table with column headers as Cities and rows as years 2020 and 2021?" }, { "from": "gpt", "value": "I will start with identifying {the legend labels}[0.852, 0.145, 0.887, 0.173], [0.852, 0.187, 0.887, 0.215] which reveals the years 2020 and 2021. This would form the row headers in the table. The column headers would be the names of the cities located using {the x-axes labels}[0.128, 0.909, 0.192, 0.937], [0.236, 0.909, 0.319, 0.937], [0.367, 0.909, 0.423, 0.937], [0.484, 0.909, 0.541, 0.937], [0.602, 0.909, 0.657, 0.937], [0.705, 0.909, 0.790, 0.937], [0.823, 0.909, 0.907, 0.937]. The values for each city for 2020 and 2021 are plotted as {points on the lines for 2020 and 2021}[0.160, 0.762], [0.278, 0.482], [0.395, 0.808], [0.512, 0.342], [0.630, 0.155], [0.747, 0.668], [0.865, 0.248], [0.160, 0.715], [0.278, 0.528], [0.395, 0.855], [0.512, 0.342], [0.630, 0.155], [0.747, 0.715], [0.865, 0.295]. These points correspond to the values for each city and each year, which will populate the table.\nBased on the above thought, the answer is:\nCity,2020,2021\nNew York,12,13\nLos Angeles,18,17\nChicago,11,10\nHouston,21,21\nPhoenix,25,25\nPhiladelphia,14,13\nSan Antonio,23,22" } ], "id_raw": 820 }, { "id": "gwQF7M6VXM2i9M1B", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_0.png", "conversations": [ { "from": "human", "value": "\nCan we predict how the average temperatures may change in these cities in the coming years based on the data provided?" }, { "from": "gpt", "value": "Given the limited data points available from the line plot for the years 2020 and 2021, prediction of future temperature trends might not be accurate. While future temperatures can be affected by numerous factors (climate change, weather patterns, etc.) that are not represented in this chart, we can attempt a very general prediction based on the trend shown, focusing on cities such as {New York}[0.128, 0.909, 0.192, 0.937], {Los Angeles}[0.236, 0.909, 0.319, 0.937], and {Philadelphia}[0.705, 0.909, 0.790, 0.937] that show a decrease in average temperature from 2020 to 2021.\nBased on the above thought, the answer is:\nWhile an accurate prediction cannot be made due to the limited data and multiple affecting factors not accounted for in the chart, we could infer that if the trend observed in 2020 and 2021 continues, cities like New York, Los Angeles, and Philadelphia might experience a slight decrease in average temperature in the coming years." } ], "id_raw": 820 }, { "id": "cwp4NiY4huaNQvpV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_0.png", "conversations": [ { "from": "human", "value": "\nWhich city shows the largest decrease in average temperature from 2020 to 2021?" }, { "from": "gpt", "value": "I need to calculate the difference in average temperature from 2020 to 2021 for each city. Let's start with {the line points for 2020}[0.160, 0.762], [0.278, 0.482], [0.395, 0.808], [0.512, 0.342], [0.630, 0.155], [0.747, 0.668], [0.865, 0.248] and {for 2021}[0.160, 0.715], [0.278, 0.528], [0.395, 0.855], [0.512, 0.342], [0.630, 0.155], [0.747, 0.715], [0.865, 0.295], which correspond to their y-coordinates on the chart. The decrease for each city would be the value in 2020 minus the value in 2021. We will look at each city and find the maximum difference.\nBased on the above thought, the answer is:\nPhiladelphia." } ], "id_raw": 820 }, { "id": "1Tb4jaPMe0geot6Q", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_1.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the growth rates of 'Knowledge Economy' and 'Innovation Industries' over the course of 7 years?" }, { "from": "gpt", "value": "To construct a table, I can use the caption and data section but I must extract the relevant key from chart too. For column headers, I find the years from {the x-axis tick labels}[[0.131, 0.885, 0.149, 0.921], [0.251, 0.885, 0.269, 0.921], [0.371, 0.885, 0.389, 0.921], [0.491, 0.885, 0.509, 0.921], [0.611, 0.885, 0.629, 0.921], [0.731, 0.885, 0.749, 0.921], [0.851, 0.885, 0.869, 0.921]]. The row labels would be 'Knowledge Economy' and 'Innovation Industries'. The corresponding values can be extracted by tracing the points from {the line for Knowledge Economy}[0.140, 0.130, 0.860, 0.645] and {the line for Innovation Industries}[0.140, 0.151, 0.860, 0.754].\nBased on the above thought, the answer is:\nYears,Knowledge Economy,Innovation Industries\n0,25.0,22.0\n1,26.5,21.6\n2,28.3,20.5\n3,29.1,19.6\n4,30.2,18.9\n5,30.6,17.7\n6,31.7,17.0" } ], "id_raw": 821 }, { "id": "Ab4qGvfdE1hrdlVV", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_1.png", "conversations": [ { "from": "human", "value": "\nCompared to Innovation Industries, how has the growth of Knowledge Economy progressed over the 7 years?" }, { "from": "gpt", "value": "Comparing the {line for Knowledge Economy}[0.140, 0.130, 0.860, 0.645] with the {line for Innovation Industries}[0.140, 0.151, 0.860, 0.754], it is clear that the Knowledge Economy has seen a consistent growth while Innovation Industries has seen a decrease over the years. Knowledge Economy started at 25% and increased to 31.7%, while Innovation Industries started at 22% and decreased to 17% over the seven years.\nBased on the above thought, the answer is:\nKnowledge Economy has shown a consistent upward trend, increasing from an initial growth rate of 25% to 31.7% by the end of the 7-year period. In contrast, Innovation Industries has displayed a decreasing trend, starting from 22% and falling to 17%. Thus, compared to Innovation Industries, Knowledge Economy has shown positive and consistent growth over the examined period." } ], "id_raw": 821 }, { "id": "Dovbp2DtBxOBLn80", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the total percentage growth for Knowledge Economy over the 7-year period?" }, { "from": "gpt", "value": "To calculate the total percentage growth for Knowledge Economy, I need to find the difference between the final and initial values. Inspecting {the line for Knowledge Economy}[0.140, 0.130, 0.860, 0.645], I find that the growth starts from 25.0% in year 0 and ends at 31.7% in year 6, so the total growth is 31.7 - 25.0 = 6.7%.\nBased on the above thought, the answer is:\n6.7%." } ], "id_raw": 821 }, { "id": "kXGRbjLPWxYM0jFb", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart data that shows activity rates in different welfare categories into a structured table format?" }, { "from": "gpt", "value": "To convert the chart data into a structured table format, I first observe {the x-axis labels for categories}[0.077, 0.903, 0.923, 0.975] representing different welfare activities like Donation, Volunteering, etc. I then analyze {the data points for each category}[0.116, 0.397, 0.884, 0.863] across the years from the line plots. Based on this assessment of the chart data, I compile a table reflecting the activity rates for each Welfare category over the years.\nBased on the above thought, the answer is:\nCategory,2012,2013,2014,2015,2016,2017,2018,2019,2020\nDonation,20,18,15,12,10,9,8,7,6\nVolunteering,30,29,28,26,25,24,23,22,20\nAdvocacy,40,42,47,52,56,59,62,64,66\nPartnership,10,11,10,10,9,8,7,7,8" } ], "id_raw": 822 }, { "id": "voUhoTKYVSqopGJp", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_2.png", "conversations": [ { "from": "human", "value": "\nWhich welfare activity shows the most consistent trend in activity rate reduction over the years, and how can this be interpreted from the chart?" }, { "from": "gpt", "value": "Observing {the line plots on the chart}, I identify that Partnership[0.116, 0.809, 0.884, 0.863] shows a downward trend, starting from 10% in 2012 and ending at 8% in 2020 with very gentle fluctuations. This consistent decline in activity rate over the years might indicate lesser focus or diminishing resources being allocated to this welfare activity compared to others.\nBased on the above thought, the answer is:\nPartnership shows the most consistent trend in activity rate reduction over the years. This can be interpreted from the steadily declining line within its subplot, representing a continual decrease in engagement or resources committed to partnership activities over the assessed years." } ], "id_raw": 822 }, { "id": "lsjln2N2lHXGFaLK", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_2.png", "conversations": [ { "from": "human", "value": "\nHow much has the activity rate for Volunteering decreased from 2012 to 2020?" }, { "from": "gpt", "value": "To calculate the decrease in the activity rate for Volunteering from 2012 to 2020, I examine {the data points for Volunteering}[0.116, 0.065, 0.884, 0.397] which show the activity rates starting from 30% in 2012 to 20% in 2020. The difference in activity rates between these two years is 30% - 20%.\nBased on the above thought, the answer is:\n10%" } ], "id_raw": 822 }, { "id": "vP8Qs0e2WVARhFI7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_3.png", "conversations": [ { "from": "human", "value": "\nCould you please help extract the data from the given chart into an organized table format with \"Digital Tools Number\" and \"Error Rate\" for both \"Audio\" and \"Video\" types?" }, { "from": "gpt", "value": "For creating a structured table from this dual-line chart, I start with setting up the columns as \"Digital Tools Number\", \"Audio Error Rate\" and \"Video Error Rate\". After that, we look at {the x-axis labels}[0.011, 0.885, 0.029, 0.921], [0.131, 0.885, 0.149, 0.921], [0.251, 0.885, 0.269, 0.921], [0.371, 0.885, 0.389, 0.921], [0.491, 0.885, 0.509, 0.921], [0.611, 0.885, 0.629, 0.921], [0.731, 0.885, 0.749, 0.921], [0.851, 0.885, 0.869, 0.921], [0.962, 0.885, 0.998, 0.921] which gives us the 'Digital Tools Number' as 3,4,5,6,7,8,9. Afterwards, we find {the line points for Audio errors}[0.140, 0.030], [0.260, 0.157], [0.380, 0.277], [0.500, 0.374], [0.620, 0.528], [0.740, 0.769], [0.860, 0.842] which are the corresponding values for 'Audio Error Rate'. And finally, we observe {the line points for Video errors}[0.140, 0.032], [0.260, 0.172], [0.380, 0.307], [0.500, 0.451], [0.620, 0.578], [0.740, 0.726], [0.860, 0.874] which represent the 'Video Error Rate'.\nBased on the above thought, the answer is:\nDigital Tools Number,Audio Error Rate,Video Error Rate\n3,26,21.96\n4,24.20,19.31\n5,22.48,16.74\n6,21.12,14.02\n7,18.93,11.62\n8,15.51,8.81\n9,14.47,6.02" } ], "id_raw": 823 }, { "id": "thk8SpsWkoguEwU4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_3.png", "conversations": [ { "from": "human", "value": "\nBased on the lines in the chart, can you identify which error type, Audio or Video, showed a more steady decrease? Please explain." }, { "from": "gpt", "value": "To answer this question, we have to analyze the trend along the line for both the Audio and Video errors. For {Audio Error}[0.140, 0.030, 0.860, 0.842], the error rate starts at 26 and decreases to 14.47 with some fluctuations in between. For {Video Error}[0.140, 0.032, 0.860, 0.874], the line starts at 21.96 and steadily decreases to 6.02. Although both error types decrease over time, the decrease in Video Error seems to be more steady as it does not have significant fluctuations as seen in Audio Error.\nBased on the above thought, the answer is:\nThe Video Error shows a more steady decrease. The Video error rate gradually drops from 21.96 to 6.02 without significant fluctuations, while the Audio error rate, although decreasing, does so with notable fluctuations in between, indicating less steadiness." } ], "id_raw": 823 }, { "id": "VulnX5OPiGafz5Qa", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the error rate difference between audio and video at a digital tools number of 8?" }, { "from": "gpt", "value": "To find the error rates for audio and video at a digital tool number of 8, we need to find the corresponding points for both types on the line. Upon locating {the point for Audio error at a digital tool number of 8}[0.740, 0.769], I can see that the value is 15.51. Similarly, by identifying the {point for Video error at a digital tool number of 8}[0.740, 0.726], the value is 8.81. Therefore, the difference between the audio error rate and video error rate at a digital tool number of 8 is 15.51 - 8.81 = 6.7.\nBased on the above thought, the answer is:\n6.7" } ], "id_raw": 823 }, { "id": "zkGc1SY5Kv0PVCe5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_5.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the line chart data into a structured table presenting the assessment scores for the ecosystem under study for both Tropical Forest and Arctic Ice with respect to the condition of the environment?" }, { "from": "gpt", "value": "Sure, I will first identify the x-axis labels corresponding to {the x-ticks}[0.084, 0.919, 0.128, 0.942],[0.284, 0.919, 0.328, 0.942],[0.484, 0.919, 0.528, 0.942],[0.684, 0.919, 0.728, 0.942],[0.884, 0.919, 0.928, 0.942]. These labels represent environmental conditions as various stages. I then locate the line coordinates for both the {Tropical Forest}[0.106, 0.113, 0.894, 0.738] and the {Arctic Ice}[0.106, 0.060, 0.894, 0.850] to extract assessment scores. This should provide us with a structured tabular data.\nBased on the above thought, the answer is:\nEnvironment Condition,Tropical Forest,Arctic Ice\nStable,1.00,0.95\nMinor decrease,0.90,0.80\nSubstantial decrease,0.75,0.70\nAccelerating decline,0.60,0.55\nDramatic decrease,0.30,0.20" } ], "id_raw": 825 }, { "id": "n6KcNPoseipAKKZ1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_5.png", "conversations": [ { "from": "human", "value": "\nBased on the data displayed on the graph, which region seems to have sustained more damage? Kindly explain your reasoning." }, { "from": "gpt", "value": "The degree of damage in each region can be inferred from the assessment scores and how these scores decline over different environmental conditions. If we look at the line coordinates for the {Tropical Forest}[0.106, 0.113, 0.894, 0.738], we see a steady decline in scores. Comparing this with the coordinates for {Arctic Ice}[0.106, 0.060, 0.894, 0.850], the decline is similar, but the drop is even more significant at the \"Dramatic decrease\" stage.\nBased on the above thought, the answer is:\nBased on the data in the graph, the Arctic Ice region seems to have sustained more damage. The assessment score for Arctic Ice falls more dramatically than that of the Tropical Forest when environmental condition changes from 'Accelerating decline' to 'Dramatic decrease'. Both ecosystems show degradation, but the condition of Arctic Ice appears to have deteriorated more severely." } ], "id_raw": 825 }, { "id": "AcFNRESa7fE0c3Qh", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_5.png", "conversations": [ { "from": "human", "value": "\nBy how much does the assessment score for the Arctic Ice decrease when the environmental condition changes from a \"Stable\" state to the state of \"Dramatic decrease\"?" }, { "from": "gpt", "value": "To answer this, I need to locate the coordinates of the line representing the {Arctic Ice}[0.106, 0.060, 0.894, 0.850]. The score for a \"Stable\" state corresponds to the first point, and the score for the \"Dramatic decrease\" state corresponds to the last point. By extracting the y-values for these points, I can calculate the difference.\nBased on the above thought, the answer is:\n0.75" } ], "id_raw": 825 }, { "id": "HikKEvVb3wd2qLd4", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_6.png", "conversations": [ { "from": "human", "value": "\nCan you generate a tabular data representation from the given line chart with Years as rows and Technologies as columns?" }, { "from": "gpt", "value": "In order to create a table representation from the line chart, I need to identify {the x-axis labels}[0.027, 0.934, 0.062, 0.952], [0.134, 0.934, 0.169, 0.952], [0.241, 0.934, 0.276, 0.952], [0.348, 0.934, 0.383, 0.952], [0.456, 0.934, 0.491, 0.952], [0.563, 0.934, 0.598, 0.952], [0.670, 0.934, 0.705, 0.952], [0.777, 0.934, 0.813, 0.952], [0.885, 0.934, 0.920, 0.952], [0.992, 0.934, 1.027, 0.952] which represent the years, these will be the rows in the table. Then, I check {the legend}[0.064, 0.027, 0.281, 0.085] for the different technologies, these will form the column headers. The data in the table can be obtained from the y-values of the points of the lines corresponding to each technology at the specified years. For example, {for the line representing Biomedical Technology}[0.098, 0.060, 0.902, 0.887], each point's value corresponds to the amount of scientific research and technological innovations that year.\nBased on the above thought, the answer is:\nYear,Biomedical Technology,Ecological Technology\n2005,6.4,4.2\n2006,6.7,6.1\n2007,6.5,6.8\n2008,2.8,6.0\n2009,-3.5,4.6\n2010,3.0,6.0\n2011,2.4,7.2\n2012,3.6,8.1\n2013,4.0,7.4\n2014,4.2,8.5\n2015,4.4,10.1\n2016,4.0,9.0\n2017,4.5,10.2\n2018,5.0,11.0\n2019,3.0,9.0\n2020,-5.0,6.0" } ], "id_raw": 826 }, { "id": "ljhIgNwqL7mn7hU4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_6.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which sector has shown the most overall growth between 2005 and 2020?" }, { "from": "gpt", "value": "To answer this, I need to analyze the change in each line from the beginning to the end. I'll examine {the Biomedical Technology line}[0.098, 0.060, 0.902, 0.887] and {the Ecological Technology line}[0.098, 0.060, 0.902, 0.887] and compare the initial and final values. For Biomedical Technology, the initial value in 2005 is 6.4 and the final value in 2020 is -5.0. For Ecological Technology, the initial value in 2005 is 4.2 and the final value in 2020 is 6.0. The overall change for each field can be calculated as the final value minus the initial value.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Ecological Technology sector has shown the most overall growth between 2005 and 2020. Although it started out with a lower value compared to Biomedical Technology, it consistently grew over the years exceeding the growth in Biomedical Technology, which exhibited a negative growth towards the end of the period." } ], "id_raw": 826 }, { "id": "Rg1oU6dZymgYCVqo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest value reached in Biomedical Technology research and in which year did this occur?" }, { "from": "gpt", "value": "To find the highest value reached in Biomedical Technology research, I need to analyze {the line for Biomedical Technology}[0.098, 0.060, 0.902, 0.887]. Specifically, I need to identify the point with the maximum y-coordinate which corresponds to the highest value. This can be determined by the value annotated near each data point. The year in which this maximum value was reached can be determined by identifying the x-coordinate of the maximum point and mapping it onto the corresponding {x-axis labels} [0.027, 0.934, 0.062, 0.952], [0.134, 0.934, 0.169, 0.952], [0.241, 0.934, 0.276, 0.952], [0.348, 0.934, 0.383, 0.952], [0.456, 0.934, 0.491, 0.952], [0.563, 0.934, 0.598, 0.952], [0.670, 0.934, 0.705, 0.952], [0.777, 0.934, 0.813, 0.952], [0.885, 0.934, 0.920, 0.952], [0.992, 0.934, 1.027, 0.952].\nBased on the above thought, the answer is:\nThe highest value reached in Biomedical Technology research is 5.0, which occurred in the year 2018." } ], "id_raw": 826 }, { "id": "EoZ250Tvnza3r1FS", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_7.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart into a table having Years as rows and Companies as columns?" }, { "from": "gpt", "value": "For the table, I will first select {the x-axis tick labels}[[0.133, 0.910, 0.188, 0.939],[0.289, 0.910, 0.344, 0.939],[0.446, 0.910, 0.501, 0.939],[0.602, 0.910, 0.657, 0.939],[0.759, 0.910, 0.814, 0.939],[0.916, 0.910, 0.970, 0.939]] which will form the row labels representing the years. Then, I identify {the legend texts}[[0.781, 0.704, 0.880, 0.733],[0.781, 0.748, 0.843, 0.777],[0.781, 0.791, 0.869, 0.820],[0.781, 0.835, 0.858, 0.864]] as the column headers representing the different companies. To fill the table, I locate {the line points} for each company at the corresponding years and extract the values annotated.\nBased on the above thought, the answer is:\nYear,Microsoft,Apple,Amazon,Google\n2010,32.4,8.6,25.0,11.0\n2011,33.0,11.2,26.2,12.1\n2012,33.2,14.2,26.9,12.4\n2013,32.9,15.4,27.1,14.0\n2014,31.6,16.8,27.9,15.4\n2015,31.1,18.0,28.2,16.7\n2016,30.5,19.7,29.1,18.0\n2017,30.0,21.0,29.5,18.8\n2018,29.5,22.2,30.0,19.5\n2019,29.0,23.5,30.8,20.0" } ], "id_raw": 827 }, { "id": "s4E4ACNDefggucCL", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_7.png", "conversations": [ { "from": "human", "value": "\nWhich company experienced the biggest drop in market share over the years?" }, { "from": "gpt", "value": "To determine which company experienced the biggest drop, I need to look at the start and end points of each company's {line}[0.125, 0.120, 0.900, 0.890]. If I examine {Microsoft's line}[0.160, 0.155, 0.865, 0.275], it started with a market share of 32.4% and ended with 29.0%, a drop of 3.4%. {Apple's line}[0.160, 0.431, 0.865, 0.855] represents a rise in market share from 8.6% to 23.5%, so it is not a candidate. {Amazon's line}[0.160, 0.223, 0.865, 0.388] shows a rise from 25.0% to 30.8%. Finally, {Google's line}[0.160, 0.531, 0.865, 0.787] also shows a rise from 11.0% to 20%. Therefore, Microsoft experienced the biggest drop.\nBased on the above thought, the answer is:\nMicrosoft experienced the biggest drop in market share over the years, declining from 32.4% in 2010 to 29.0% in 2019, a drop of 3.4%." } ], "id_raw": 827 }, { "id": "4sRnPYYPVmUiAO94", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in Google's market share from 2010 to 2019?" }, { "from": "gpt", "value": "To find the percentage increase, we need to obtain the values of Google's market share in 2010 and 2019. For 2010, we find the point {at the start of Google's line}[0.160, 0.787], which corresponds to a market share of 11.0%. For 2019, we locate the point {at the end of Google's line}[0.865, 0.531], corresponding to a market share of 20.0%. Thus, the percentage increase is (20.0 - 11.0) / 11.0 * 100 = 81.82%.\nBased on the above thought, the answer is:\n81.82%" } ], "id_raw": 827 }, { "id": "WESv7JeLMjcvRBGE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_8.png", "conversations": [ { "from": "human", "value": "\nCan you extract all the specific index values for each year and each type of index from the line chart into a table?" }, { "from": "gpt", "value": "To generate the table, I first identify the column headers as the labels on the {x-axis}[0.143, 0.904, 0.882, 0.923]. These represent the years from 1990 to 2020. The row labels are given by the {legend}[0.131, 0.129, 0.894, 0.189], which are Ethical Values, Material Values, Social Values, and Cultural Values. I then locate the {points of intersection}[0.160, 0.186, 0.865, 0.813] of the lines representing each index with the gridmarks along the x-axis for each year and derive the respective index values from their y-coordinates.\nBased on the above thought, the answer is:\nYear,Ethical Values,Material Values,Social Values,Cultural Values\n1990,28,30,35,40\n1995,26,32,34,42\n2000,25,34,33,44\n2005,23,36,32,46\n2010,22,40,33,48\n2015,21,45,34,50\n2020,20,50,36,52" } ], "id_raw": 828 }, { "id": "OndyeJrHo7iDFuUQ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_8.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which index shows the most significant change between 1990 and 2020 and why?" }, { "from": "gpt", "value": "To answer the question, I need to compare the changes in each of the four indices. First, I observe the line corresponding to {Ethical Values}[0.160, 0.208, 0.865, 0.364], which drops from 28 in 1990 to 20 in 2020, marking a decrease of 8. Second, I look at the {Material Values}[0.160, 0.186, 0.865, 0.340] line, which shows a marked increase from 30 in 1990 to 50 in 2020, a total change of 20. The third line corresponds to {Social Values}[0.160, 0.802, 0.865, 0.868], which fluctuates from 35 in 1990 to 36 in 2020, showing a change of just 1. Lastly, I see the {Cultural Values}[0.160, 0.216, 0.865, 0.409] line showing an increase from 40 in 1990 to 52 in 2020, a change of 12.\nBased on the above thought, the answer is:\nBased on the chart, Material Values shows the most significant change between 1990 and 2020. It started at an index value of 30 in 1990 and increased to 50 by 2020, marking a total change of 20. This is a more significant change compared to the other indices investigated." } ], "id_raw": 828 }, { "id": "PoyUTQYJ5KlRORuO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in Material Values from 1990 to 2020?" }, { "from": "gpt", "value": "To find the increase in Material Values from 1990 to 2020, I locate the {line for Material Values}[0.160, 0.186, 0.865, 0.340] on the line chart. I then extract the values for 1990 and 2020 by locating the points representing these years on the line. The value for 1990 is 30 and for 2020 is 50. The increase in Material Values is therefore calculated as 50 - 30 = 20.\nBased on the above thought, the answer is:\n20." } ], "id_raw": 828 }, { "id": "QN9i4NWIFNeKnNIx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_9.png", "conversations": [ { "from": "human", "value": "\nCould you please transfer the year-to-year line chart data into a structured table?" }, { "from": "gpt", "value": "To create a table, I start by identifying {the x-axis labels}[0.127,0.891,0.790,0.878] which represent the years, these are to be used as the columns. I then recognize {the sublegend labels}[0.192, 0.054, 0.267, 0.086] that represent the rows' headers (Cargo volume in a port, Volume of trade, Number of cargo ships visiting a port). To fill in the table, I retrieve the values using {the line points for each metric}[0.127, 0.239, 0.873, 0.881], [0.127, 0.196, 0.873, 0.710].\nBased on the above thought, the answer is:\nYears,Cargo volume in a port,Volume of trade,Number of cargo ships visiting a port\n2010,200000,120000,600\n2011,220000,130000,650\n2012,250000,140000,700\n2013,210000,150000,750\n2014,240000,155000,770\n2015,255000,160000,820\n2016,280000,165000,850\n2017,310000,170000,900\n2018,325000,175000,950\n2019,350000,180000,1000" } ], "id_raw": 829 }, { "id": "VFvBwHhNYnqyUcGl", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_9.png", "conversations": [ { "from": "human", "value": "\nHow can the overall trend in Cargo volume, Volume of Trade, and Number of Ships visiting the port be described during the period covered by the chart?" }, { "from": "gpt", "value": "The line graph represents the activities of a port over a decade. By examining the positions and shapes of {the line for Cargo Volume}[0.127, 0.239, 0.873, 0.881], {the line for the volume of Trade}[0.127, 0.196, 0.873, 0.710], and {the line for the Number of Ships}[0.192, 0.054, 0.267, 0.086], it seems clear that all activities have a general upward trend. All three lines begin at lower left-hand points and end at upper right-hand points, indicating increased cargo volume and trade volume, and a growing number of ships visiting the port over the years.\nBased on the above thought, the answer is:\nThe overall trend in Cargo Volume, Volume of Trade, and Number of Ships visiting the port can be described as a steady increase during the period covered by the chart. This indicates positive growth and increased activity in the port over the decade from 2010 to 2019." } ], "id_raw": 829 }, { "id": "REbteq6UQVt3cu39", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in Cargo volume from 2010 to 2019?" }, { "from": "gpt", "value": "To calculate the change in Cargo volume, I will use the {initial value for the Cargo volume}[0.127, 0.881] line in 2010 and the {final value for the Cargo volume}[0.873, 0.239] line in 2019. By subtracting the Cargo volume in 2010 from the Cargo volume in 2019, I can determine the change.\nBased on the above thought, the answer is:\n150000." } ], "id_raw": 829 }, { "id": "of41MOepmif8PCRP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_10.png", "conversations": [ { "from": "human", "value": "\nCan you extract the trend data per year for video tutorials, online quizzes, e-books, and LMS Systems from the stacked line graph?" }, { "from": "gpt", "value": "Looking at {the stacked line graph}[0.104, 0.044, 0.896, 0.875], I can identify unique trend markers for each category. The years, represented on {the x-axis}[0.466, 0.932, 0.534, 0.970], and usage percentages, labeled on {the y-axis}[0.025, 0.266, 0.057, 0.653], guide the data extraction. Each line represents a different technology tool with distinct colors, located at {the legend}[0.120, 0.063, 0.476, 0.190] that specifies labels. By analyzing the lines' starting and ending points along the x-axis, I compiled the yearly data.\nBased on the above thought, the answer is:\nYear,Video Tutorials,Online Quizzes,E-books,LMS Systems\n2016,34,25,18,30\n2017,38,28,21,33\n2018,44,34,26,38\n2019,49,39,30,43\n2020,55,45,33,48" } ], "id_raw": 830 }, { "id": "cWpIXYxCbR8DCfxs", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_10.png", "conversations": [ { "from": "human", "value": "\nHow has the prevalence of LMS Systems compared to video tutorials changed over the years, based on the chart?" }, { "from": "gpt", "value": "To evaluate the comparative prevalence of LMS Systems against video tutorials over the years, I scrutinize the respective lines: {LMS Systems line}[0.140, 0.447, 0.860, 0.750] and {video tutorials line}[0.140, 0.660, 0.860, 0.792]. Starting in 2016, LMS Systems stood at 30% while video tutorials were at 34%. By 2020, LMS Systems reached 48% and video tutorials 55%. The gap between the usage of LMS Systems and video tutorials initially decreased, but steadily widened each year due to faster growth in video tutorial usage.\nBased on the above thought, the answer is:\nInitially, LMS Systems were slightly less prevalent than video tutorials. However, as the years progressed, the prevalence gap widened with video tutorials experiencing faster growth in usage percentage compared to LMS Systems." } ], "id_raw": 830 }, { "id": "ALwPGDONfVoq9v7T", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in usage percentage for e-books from 2016 to 2020 based on the data in the chart?" }, { "from": "gpt", "value": "To calculate the total increase in usage percentage for e-books from 2016 to 2020, I analyzed {the e-books line}[0.104, 0.480, 0.860, 0.792] within the chart. The usage percentage starts at 18 in 2016 and ends at 33 in 2020. Therefore, the total increase is calculated as 33 - 18.\nBased on the above thought, the answer is:\n15." } ], "id_raw": 830 }, { "id": "ck2nuy40ACrEOxpJ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_11.png", "conversations": [ { "from": "human", "value": "\nBased on the data plot in the chart, can you produce a structured tabular format with years as columns and AI types as rows?" }, { "from": "gpt", "value": "To organize the data from the line chart into a table, I initially observe {the x-axis labels}[[0.105, 0.885, 0.175, 0.921], [0.225, 0.885, 0.295, 0.921], [0.345, 0.885, 0.415, 0.921], [0.465, 0.885, 0.535, 0.921], [0.585, 0.885, 0.655, 0.921], [0.705, 0.885, 0.775, 0.921], [0.825, 0.885, 0.895, 0.921]], which represent the years or column headers. I then identify {the legend entries}[[0.224, 0.065, 0.455, 0.103]] as the row headers, which signify types of AIs. For the values, I find {the plot points}[[0.140, 0.805], [0.260, 0.770], [0.380, 0.734], [0.500, 0.593], [0.620, 0.382], [0.740, 0.241], [0.860, 0.065]], [0.140, 0.171], [0.260, 0.312], [0.380, 0.453], [0.500, 0.523], [0.620, 0.593], [0.740, 0.664], [0.860, 0.734] for each AI type at the corresponding years and extract the represented values.\nBased on the above thought, the answer is:\nAI Type,2015,2016,2017,2018,2019,2020,2021\nSelf Driving AI,50,55,60,80,110,130,155\neCommerce AI,120,110,100,95,90,85,80" } ], "id_raw": 831 }, { "id": "bb9tzp7eGmiCJ2gU", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_11.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which AI has shown a more consistent decrease in WER% over the years and why?" }, { "from": "gpt", "value": "To figure out which AI has shown a more consistent decrease in WER%, I need to observe the trends in the line charts for the two AIs. Examining {the line for Self Driving AI}[0.140, 0.805, 0.860, 0.065], I notice a consistent increase from 50 in 2015 to 155 in 2021. On the other hand, {the line for eCommerce AI}[0.140, 0.171, 0.860, 0.734] shows a consistent decrease from 120 in 2015 to 80 in 2021.\nBased on the above thought, the answer is:\neCommerce AI has shown a more consistent decrease in WER% over the years. This is observed from the trend that indicates a steady decrease from 120 in 2015 to 80 in 2021 in the line chart." } ], "id_raw": 831 }, { "id": "SwtDnw7gsZojmM8l", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in the WER% of Self Driving AI from 2015 to 2021?" }, { "from": "gpt", "value": "To determine the total increase in the WER% of Self Driving AI, I would need to find the value for Self Driving AI in 2015 and 2021. Hence I locate {the Point for Self Driving AI in 2015} [0.140, 0.805] which corresponds to a value of 50 and then locate {the Point for Self Driving AI in 2021} [0.860, 0.065] which corresponds to a value of 155. The total increase would therefore be 155 - 50 = 105.\nBased on the above thought, the answer is:\n105" } ], "id_raw": 831 }, { "id": "uH6kn8zet3eKAOVo", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_12.png", "conversations": [ { "from": "human", "value": "\nCan you extract data from the line chart to a table with Years as columns and GDP Change (%) and Market Growth (%) as rows?" }, { "from": "gpt", "value": "First, I notice {the x-axis tick labels}[[0.116, 0.891, 0.171, 0.920], [0.262, 0.891, 0.317, 0.920], [0.409, 0.891, 0.463, 0.920], [0.554, 0.891, 0.610, 0.920], [0.701, 0.891, 0.756, 0.920], [0.847, 0.891, 0.902, 0.920]], these represent the column headers or Years. Next, I identify {the legend texts}[[0.096, 0.031, 0.312, 0.069], [0.437, 0.031, 0.682, 0.069]] as the row labels representing GDP Change (%) and Market Growth (%). I then extract the data values by locating {the line points for GDP Change (%)}[[0.144, 0.352], [0.217, 0.290], [0.290, 0.222], [0.363, 0.250], [0.436, 0.069], [0.509, 0.081], [0.582, 0.109], [0.655, 0.188], [0.728, 0.352], [0.801, 0.437], [0.874, 0.832]] and {the line points for Market Growth (%)}[[0.144, 0.832], [0.217, 0.735], [0.290, 0.675], [0.363, 0.469], [0.436, 0.287], [0.509, 0.130], [0.582, 0.118], [0.655, 0.069], [0.728, 0.081], [0.801, 0.130], [0.874, 0.336]] for each year.\nBased on the above thought, the answer is:\nData,Years\n,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nGDP Change (%),3.5,4.6,5.8,5.3,8.5,8.3,7.8,6.4,3.5,2.0,-5.0\nMarket Growth (%),2.7,3.5,4.0,5.7,7.2,8.5,8.6,9.0,8.9,8.5,6.8" } ], "id_raw": 832 }, { "id": "ziHtIEowDNiXorhn", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_12.png", "conversations": [ { "from": "human", "value": "\nBased on the GDP Change (%) line, how did the GDP change from the year 2015 to 2020?" }, { "from": "gpt", "value": "To understand how GDP has changed from the year 2015 to 2020, I need to look at {the line for GDP Change (%)}[[0.509, 0.081, 0.874, 0.832]], and pay specific attention to {the points representing the years 2015 and 2020}[[0.509, 0.081], [0.874, 0.832]]. In 2015, the GDP Change (%) was at 8.3, and by 2020, it went down to -5.0. It indicates that there was a considerable decline in GDP Change (%) over these years.\nBased on the above thought, the answer is:\nBased on the GDP Change (%) line, the GDP has decreased significantly from the year 2015 to 2020. In 2015, the GDP Change (%) was quite high, at 8.3, suggesting a period of economic growth. By 2020, this value had dropped to -5.0, indicating a shrinking economy, which is quite a substantial decline over this 5-year period." } ], "id_raw": 832 }, { "id": "Eg8Snra2LHBr1N8v", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the average GDP Change (%) across all the years?" }, { "from": "gpt", "value": "To find the average GDP Change (%), I have to sum up all the values for GDP Change (%) from each year and then divide the sum by the number of years. The relevant data points are located at {the line points for GDP Change (%)}[[0.144, 0.352], [0.217, 0.290], [0.290, 0.222], [0.363, 0.250], [0.436, 0.069], [0.509, 0.081], [0.582, 0.109], [0.655, 0.188], [0.728, 0.352], [0.801, 0.437], [0.874, 0.832]]. Extracting the values from these points: 3.5, 4.6, 5.8, 5.3, 8.5, 8.3, 7.8, 6.4, 3.5, 2.0, -5.0, the sum is 50.7. Since there are 11 data points, the average is 50.7 / 11 = 4.61.\nBased on the above thought, the answer is:\n4.61." } ], "id_raw": 832 }, { "id": "bTQUg5OOKqym3BEq", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data depicted in the stacked bar plot for each manufacturing process into a structured table?" }, { "from": "gpt", "value": "Analyzing the stacked bar plot, I first identify each {manufacturing process on the x-axis}[0.060, 0.914, 0.140, 0.951] including Precision Tooling, Robotic Welding, Manual Assembly, and Tightening Operations. Each process bar consists of four segments representing different aspects which are determined using {the legend}[0.800, 0.100, 0.950, 0.200], corresponding to Raw Material Usage, Efficiency Time, Quality Index, and Failure Rate. By evaluating the numeric labels inside each segment, I extract the data for each process.\nBased on the above thought, the answer is:\nProcess,Raw Material Usage,Efficiency Time,Quality Index,Failure Rate\nPrecision Tooling,50,30,70,10\nRobotic Welding,55,35,60,5\nManual Assembly,45,25,60,15\nTightening Operations,40,20,55,25" } ], "id_raw": 833 }, { "id": "hHhTSDGcTneuKaKy", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_13.png", "conversations": [ { "from": "human", "value": "\nWhich manufacturing process demonstrates the highest efficiency considering both Raw Material Usage and Efficiency Time combined, according to the chart?" }, { "from": "gpt", "value": "To determine the manufacturing process with the highest combined efficiency, I analyze the segments representing Raw Material Usage and Efficiency Time for each process. Observing {Precision Tooling's bar}[0.100, 0.350, 0.200, 0.700] which includes values of 50 for materials and 30 for time. Similarly, for {Robotic Welding}[0.250, 0.300, 0.350, 0.650] with values 55 and 35, {Manual Assembly}[0.400, 0.370, 0.500, 0.670] with values 45 and 25, and finally {Tightening Operations}[0.550, 0.450, 0.650, 0.720] with 40 and 20. Adding these values, it becomes apparent that Robotic Welding, with a combined score of 90, shows the highest efficiency.\nBased on the above thought, the answer is:\nRobotic Welding demonstrates the highest efficiency when considering both Raw Material Usage and Efficiency Time, as it has a combined total of 90, which is higher than the other processes." } ], "id_raw": 833 }, { "id": "4ofrwxP1tpfqY5Yq", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the total value represented by the Failure Rate category across all manufacturing processes?" }, { "from": "gpt", "value": "To calculate the total Failure Rate value, I locate the segments related to Failure Rate in each process bar using the color coding from {the legend}[0.820, 0.171, 0.948, 0.191]. Then I summarize the values: for Precision Tooling (10), Robotic Welding (5), Manual Assembly (15), and Tightening Operations (25). By adding them, 10 + 5 + 15 + 25, we obtain the total Failure Rate value.\nBased on the above thought, the answer is:\n55" } ], "id_raw": 833 }, { "id": "YNUdjxX8DWLL7XNQ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_14.png", "conversations": [ { "from": "human", "value": "\nCould you please extract the data from the line chart into a table, with years as rows and industries as columns?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I firstly identify {the x-axis tick labels}[0.106, 0.880, 0.172, 0.918], [0.250, 0.880, 0.316, 0.918], [0.400, 0.880, 0.455, 0.919], [0.539, 0.880, 0.605, 0.918], [0.689, 0.890, 0.744, 0.919], [0.828, 0.890, 0.894, 0.919] which represent the row labels or years. Next, I identify the line labels as the column headers representing the different industries. To populate the values, I locate the line points for each industry at the respective years and extract the values.\nBased on the above thought, the answer is:\nYear,E-commerce,Healthcare,Tech,Fintech\n2010,200,150,300,50\n2011,210,160,350,42\n2012,220,170,375,38\n2013,230,180,400,36\n2014,170,200,420,35\n2015,160,215,450,40\n2016,155,220,470,48\n2017,150,225,490,52\n2018,110,230,500,57\n2019,100,235,520,62\n2020,130,240,530,67" } ], "id_raw": 834 }, { "id": "7XbTq8Br99x21cdd", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_14.png", "conversations": [ { "from": "human", "value": "\nIn what year did the Tech industry see the highest investment?" }, { "from": "gpt", "value": "To identify the year with the highest investment in the Tech industry, I need to locate the peak of the line for Tech. After examining {the line points for Tech}[0.355, 0.125], [0.428, 0.277], [0.500, 0.416], [0.572, 0.485], [0.644, 0.575], [0.717, 0.763], [0.789, 0.832], I note that the highest point corresponds to the year 2019 {2019}[0.789, 0.832] where the y-value reached the maximum.\nBased on the above thought, the answer is:\n2019." } ], "id_raw": 834 }, { "id": "PJCyPdHqpmQPIscF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_15.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the chart into a structured table format with years as rows and revenue areas as columns?" }, { "from": "gpt", "value": "To build the structured table, first I locate {the x-axis tick labels}[0.194, 0.764, 0.268, 0.837], [0.391, 0.764, 0.467, 0.837], [0.584, 0.764, 0.660, 0.837], [0.778, 0.764, 0.852, 0.837] which represent the row headers or years. Next, I identify {the legend texts}[0.892, 0.553, 0.948, 0.628], [0.892, 0.480, 0.948, 0.552], [0.892, 0.627, 0.948, 0.702] which denote the column headers representing the different revenue areas. Then, the stacked bars' heights, like {Game Sales in 2018}[0.205, 0.115, 0.255, 0.202], {Subscription in 2018}[0.205, 0.202, 0.255, 0.505], {Ad Revenue in 2018}[0.205, 0.573, 0.255, 0.699] and so on, give us the values for respective years and revenue areas.\nBased on the above thought, the answer is:\nYear,Game Sales,Subscriptions,Ad Revenue\n2018,40,62,30\n2019,30,55,45\n2020,20,60,60\n2021,50,65,70" } ], "id_raw": 835 }, { "id": "3PsvIVzUwQTZPHvm", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_15.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which sector suffered the most during the time of Covid-19 in 2020?" }, { "from": "gpt", "value": "To determine the sector that experienced the most difficulties during the Covid-19 in 2020, I must closely observe the changes in the bars from 2019 to 2020. From the difference in the heights of {contributions of Game Sales in 2019 and 2020}[0.401, 0.115, 0.448, 0.242],[0.594, 0.115, 0.644, 0.203], {Subscriptions in 2019 and 2020}[0.401, 0.248, 0.448, 0.561],[0.594, 0.203, 0.644, 0.456], and the {Ad Revenue in 2019 and 2020}[0.401, 0.561, 0.448, 0.694],[0.594, 0.456, 0.644, 0.707], I can see that the Game Sales suffered the greatest decline.\nBased on the above thought, the answer is:\nThe Game Sales sector suffered the most during the time of Covid-19 in 2020, as is evident by the significant decrease in its contribution to the total revenue when compared to the other sectors. Particularly, the height of the Game Sales segment in the 2020 bar is notably smaller than that in 2019." } ], "id_raw": 835 }, { "id": "6ZkfX25SqOpGtbXw", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the total revenue in 2021 from all the revenue areas?" }, { "from": "gpt", "value": "To calculate the total revenue in 2021, I need to sum up the revenues across all the areas for 2021. I can find this from the total height of the {2021 bar}[0.788, 0.115, 0.838, 0.754]. The segments in the 2021 bar denote the revenue, including {Game Sales}[0.788, 0.115, 0.838, 0.254], {Subscriptions}[0.788, 0.254, 0.838, 0.438], {Ad Revenue}[0.788, 0.438, 0.838, 0.754]. By summing up these values, we get the total revenue.\nBased on the above thought, the answer is:\n185." } ], "id_raw": 835 }, { "id": "ALz1jUo9gCybdhoe", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_16.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the chart of diseases over five years into a structured table format with years as columns and diseases as rows?" }, { "from": "gpt", "value": "To create the table, I begin by identifying {the x-axis labels}[[0.106, 0.910], [0.146, 0.910], [0.185, 0.910], [0.224, 0.910], [0.264, 0.910]] that list the years from 2016 to 2020. Then, I observe {the legend}[[0.074, 0.039, 0.267, 0.105]] to determine the rows that will represent each disease: Diabetes, Cardiovascular Disease, Cancer, and Obesity. To populate the data, I inspect {the points on each disease's line}[[0.106, 0.570], [0.146, 0.532], [0.185, 0.457], ..., [0.894, 0.268]] to extract the number of cases for each year and each disease accordingly.\nBased on the above thought, the answer is:\nYear,2016,2017,2018,2019,2020\nDiabetes,3200,3400,3050,3100,3650\nCardiovascular Disease,3000,3200,2900,3500,3900\nCancer,2800,2900,2700,2800,2950\nObesity,2500,2700,2900,3100,3300" } ], "id_raw": 836 }, { "id": "HUbMQYJzg1fUUBkN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_16.png", "conversations": [ { "from": "human", "value": "\nWhy does the data for Diabetes show volatility between the years 2018 and 2019 according to the chart?" }, { "from": "gpt", "value": "To understand the volatility in the Diabetes data between 2018 and 2019, I examine {the line for Diabetes}[[0.224, 0.470], [0.303, 0.340], [0.382, 0.179], [0.450, 0.534], [0.500, 0.632]]. Specifically, there is a significant drop in the number of cases from 2018 {point in 2018}[0.382, 0.179] to 2019 {point in 2019}[0.450, 0.534], followed by a rise. This steep decline and subsequent increase indicate an outlier event or a reporting anomaly that affects the overall trend.\nBased on the above thought, the answer is:\nThe volatility in the Diabetes data between 2018 and 2019 is due to an abrupt drop in the number of reported cases in 2018 followed by a rise in 2019. This could be attributed to either inconsistencies in data reporting or specific health interventions that temporarily affected diagnosis rates." } ], "id_raw": 836 }, { "id": "3MucgntyyldaXiZj", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_16.png", "conversations": [ { "from": "human", "value": "\nHow much has the Obesity rate increased from 2016 to 2020 according to the chart?" }, { "from": "gpt", "value": "To determine the increase in the Obesity rate, I need to locate {the points on the line for Obesity}[[0.106, 0.658], [0.894, 0.268]] for the years 2016 and 2020. The value for Obesity in 2016 is identified at 2500 and in 2020 at 3300. Calculating the difference, 3300 - 2500 gives the increase.\nBased on the above thought, the answer is:\n800." } ], "id_raw": 836 }, { "id": "7CTrhNoo0FqAhqOX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_17.png", "conversations": [ { "from": "human", "value": "\nCan the line chart data be translated into a structured table, with Time Periods as columns and Local Government and Community Development as the rows?" }, { "from": "gpt", "value": "To put the data into a table, I will start with {the x-axis tick labels}[[0.051, 0.773, 0.069, 0.898], [0.212, 0.773, 0.228, 0.895], [0.372, 0.773, 0.388, 0.903], [0.532, 0.773, 0.548, 0.934], [0.692, 0.773, 0.708, 0.896], [0.843, 0.773, 0.878, 0.890], [1.015, 0.773, 1.018, 0.951]], these will be the column headers, representing the different time periods. Next, {the legend texts}[[0.120, 0.049, 0.436, 0.119]] will serve as the row labels like Local Government and Community Development. Then, to procure the values, I will examine {the line points for Local Government}[[0.140, 0.262], [0.220, 0.388], [0.300, 0.460], [0.380, 0.513], [0.460, 0.611]] and {the line points for Community Development}[[0.540, 2.046], [0.620, 2.046], [0.700, 2.046], [0.780, 2.046], [0.860, 2.046]] at the respective time periods and extract their corresponding values.\nBased on the above thought, the answer is:\nTime_Period,Time1,Time2,Time3,Time4,Time5\nLocal Government,65,60,50,40,32\nCommunity Development,52,48,46,45,45" } ], "id_raw": 837 }, { "id": "IQq5aEvVq920v45k", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_17.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which area, Local Government or Community Development, saw a greater rate of decrease during the entire period?" }, { "from": "gpt", "value": "To answer this question, I must examine the decrease in values for both Local Government and Community Development. Over the course of time, {Local Government line}[[0.140, 0.262, 0.460, 0.611]] shows a decrease in values from 65 to 32. On the other hand, the {Community Development line}[[0.540, 2.046, 0.860, 2.046]] depicts a minor decrease in values from 52 to 45. By comparing these decreases, I can determine which area experienced a more significant decrease.\nBased on the above thought, the answer is:\nThe area of Local Government had a greater rate of decrease compared to Community Development. This is evident from the line chart, which portrays a more noticeable downward trend for Local Government, dropping from 65 to 32, versus the much smaller decrease for Community Development, from 52 to 45." } ], "id_raw": 837 }, { "id": "6Xw2JCWn9VFK08HK", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_17.png", "conversations": [ { "from": "human", "value": "\nIn comparison to the start point, what is the net change in value of Local Government over the entire time period?" }, { "from": "gpt", "value": "In order to determine the net change in value for Local Government, I have to examine {the line points for Local Government}[[0.140, 0.262], [0.220, 0.388], [0.300, 0.460], [0.380, 0.513], [0.460, 0.611]]. The initial value at the start is 65 and the final value is 32. The net change would be the final value subtracted from the starting value, i.e., 32 - 65.\nBased on the above thought, the answer is:\n-33." } ], "id_raw": 837 }, { "id": "yuI3KuaHexWB9J2V", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_42_19.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a table format, with years as the rows and industries as the columns?" }, { "from": "gpt", "value": "To convert the line chart to a table, first, the categories on {the x-axis}[0.146, 0.888, 0.854, 0.907], which represent the years: 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, and 2020, will become the rows. The industries, which are represented by {the line labels}[0.681, 0.057, 0.869, 0.086], namely Online Shopping and In-Store Shopping, will be the columns. The data points on {the lines for each industry}[0.146, 0.070, 0.854, 0.840] represent the values of market share for each industry in each year.\nBased on the above thought, the answer is:\nYear, Online Shopping, In-Store Shopping\n2010, 80.1, 130.5\n2011, 85.3, 127.2\n2012, 91.2, 125.4\n2013, 98.3, 123.1\n2014, 106.7, 120.9\n2015, 116.8, 119.2\n2016, 128.3, 117.8\n2017, 141.2, 116.7\n2018, 155.3, 115.9\n2019, 170.1, 115.4\n2020, 185.7, 115.0" } ], "id_raw": 839 }, { "id": "4AiFfayWB2HecwYa", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_42_19.png", "conversations": [ { "from": "human", "value": "\nWhat can you say about the trend of the In-Store Shopping industry over the years, based on the line chart?" }, { "from": "gpt", "value": "The trend of the In-Store Shopping industry can be examined by following {the chart line for In-Store Shopping}[0.146, 0.070, 0.854, 0.840]. This line depicts a slight downward trend over the years. Starting from 130.5 in 2010 to 115 in 2020, the industry has seen a small setback. The decline, however, has not been drastic and remained reasonably stable, depicting resilience.\nBased on the above thought, the answer is:\nThe In-Store Shopping industry experienced a slight decline between 2010 and 2020, indicating a slight shrinking in the market share, though this reduction was gradual, signifying the industry's ability to retain a significant portion of its market share despite changes in the retail landscape." } ], "id_raw": 839 }, { "id": "QFhHhQgTLcN9Zf9i", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_42_19.png", "conversations": [ { "from": "human", "value": "\nHow much did Online Shopping grow from the year 2010 to the year 2020?" }, { "from": "gpt", "value": "To determine the growth in Online Shopping, we need to look at the 2010 and 2020 values located at {the plot points for Online Shopping}[0.146, 0.840] and [0.854, 0.070]. In 2010, the value was 80.1 and in 2020, the value rose to 185.7. We calculate the difference by subtracting the 2010 value from the 2020 value: 185.7-80.1.\nBased on the above thought, the answer is:\n105.6." } ], "id_raw": 839 }, { "id": "dEtoGQinyYBXxph1", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_0.png", "conversations": [ { "from": "human", "value": "\nCan you transform the chart data into a structured table with 'Year' as the columns and 'Fuel Type' as the rows?" }, { "from": "gpt", "value": "To extract data from the graph, I first inspect {the x-axis tick labels}[0.084, 0.908, 0.123, 0.932], [0.328, 0.908, 0.367, 0.932], [0.572, 0.908, 0.611, 0.932], [0.815, 0.908, 0.854, 0.932], which indicate the 'Year' column in the table. Subsequently, the contents of {the legend}[0.620, 0.396, 0.973, 0.530] constitutes the rows, denoting 'Fuel Type'. To fill in the data, I look at {the line points for Coal Consumption}[0.103, 0.288], [0.347, 0.107], [0.591, 0.209], [0.835, 0.369] and {the line points for Oil Consumption}[0.103, 0.808], [0.347, 0.632], [0.591, 0.526], [0.835, 0.482], and get the respective consumption stats for each year and fuel type.\nBased on the above thought, the answer is:\nFuel Type,2008,2012,2016,2020\nCoal Consumption,900,970,930,870\nOil Consumption,725,780,815,830" } ], "id_raw": 840 }, { "id": "2Dq3QJAXyuCTEZTx", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_0.png", "conversations": [ { "from": "human", "value": "\nBased on the trends displayed in the chart, which energy source is gaining popularity and which is becoming less popular over the years? Describe your reasoning." }, { "from": "gpt", "value": "To answer this question, I need to interpret the trends of the line plots for both Coal and Oil consumption. Looking at {the line for Coal Consumption}[0.103, 0.107, 0.835, 0.369], it begins at a value of 900 in 2008 and ends with a value of 870 in 2020, showing a slight decrease overall, suggesting it's less popular. Regarding the trend of {Oil Consumption}[0.103, 0.482, 0.835, 0.808], there's a steady increase from 725 in 2008 to 830 in 2020, indicating that compared to Coal, Oil has gained more popularity over the years due to the consistent growth in its usage.\nBased on the above thought, the answer is:\nBased on the trends displayed in the chart, Oil is gaining popularity, and Coal becoming less popular over the years. This interpretation is based on the observed continuous increase in oil consumption and the slight decrease in coal consumption from 2008 to 2020." } ], "id_raw": 840 }, { "id": "sfMTFlMtDSqwce1n", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in Coal Consumption between 2008 and 2020?" }, { "from": "gpt", "value": "To calculate the difference in Coal Consumption between 2008 and 2020, I examine {the point for Coal Consumption in 2008}[0.103, 0.288] and extract the value which is 900. Similarly, I inspect {the point for Coal Consumption in 2020}[0.835, 0.369] and get the value 870. The difference is thus calculated as 900 - 870.\nBased on the above thought, the answer is:\n30" } ], "id_raw": 840 }, { "id": "SaUMPLEgR7cK8TlV", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_1.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you create a tabular representation of the data, with years as your column and Average Hours Spent and Buying Intention as your rows?" }, { "from": "gpt", "value": "To build this table, I'll first identify {the x-axis tick labels}[[0.014, 0.912, 0.049, 0.936], [0.121, 0.912, 0.156, 0.936], [0.228, 0.912, 0.263, 0.936], [0.335, 0.912, 0.371, 0.936], [0.443, 0.912, 0.478, 0.936], [0.550, 0.912, 0.585, 0.936], [0.657, 0.912, 0.692, 0.936], [0.764, 0.912, 0.799, 0.936], [0.871, 0.912, 0.906, 0.936], [0.978, 0.912, 1.013, 0.936]] as the column headers, representing different years. Next, I identify the labels given to {the legend texts}[[0.161, 0.177, 0.368, 0.208], [0.161, 0.224, 0.322, 0.256]] as the row labels, these are the Average Hours Spent and Buying Intention parameters. Then, to populate the table's values, I carefully examine the {line points}[[0.085, 0.739], [0.139, 0.502], ..., [0.942, 0.423],...] for these two parameters and deduce their respective values.\nBased on the above thought, the answer is:\nParameters,Years\n,Average Hours Spent,Buying Intention\n2005,20,35\n2006,35,55\n2007,33,50\n2008,27,46\n2009,31,45\n2010,32,43\n2011,30,44\n2012,34,50\n2013,36,52\n2014,37,53\n2015,39,56\n2016,41,58\n2017,38,55\n2018,37,53\n2019,35,52\n2020,38,55\n2021,40,58" } ], "id_raw": 841 }, { "id": "fV4eQ5GVR9IdYEx8", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_1.png", "conversations": [ { "from": "human", "value": "\nBased on the trends observed in the line chart, how has the consumer's interest towards Entertainment and Gaming industry changed over the years?" }, { "from": "gpt", "value": "To answer this, I need to examine the trends in both {Average Hours Spent line}[0.085, 0.407, 0.942, 0.739] and {Buying Intention line}[0.085, 0.139, 0.942, 0.502]. The Average Hours Spent line shows a general upward trend, suggesting that the average time a consumer spends on gaming has increased over the years. In parallel, the Buying Intention line also displays an upward trend, further confirming that consumers are showing more interest in the Entertainment and Gaming industry - indicating a positive correlation between the hours spent on entertainment and their buying intention.\nBased on the above thought, the answer is:\nOver the years, consumer interest in the Entertainment and Gaming industry has generally increased. This is reflected in the growth in average hours consumers spend on entertainment and gaming, and their correlating buying intentions, both of which have been trending upwards. This demonstrates a positive relationship between the time consumers invest in entertainment, and their intention to purchase within this industry." } ], "id_raw": 841 }, { "id": "FsC0TqzgNcXd2nmO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_1.png", "conversations": [ { "from": "human", "value": "\nAcross all the years, what is the overall increase in Average Hours Spent on Entertainment and Gaming?" }, { "from": "gpt", "value": "To determine the overall increase in Average Hours Spent on Entertainment and Gaming, I need to look at the initial and final {line points for Average Hours Spent}[[0.085, 0.739], [0.139, 0.502], ..., [0.942, 0.423]] in the line plot. The initial value is 20 {initial point}[0.085, 0.739] and the final value is 40 {final point}[0.942, 0.423]. Hence, the overall increase is 40 - 20 = 20.\nBased on the above thought, the answer is:\n20 hours." } ], "id_raw": 841 }, { "id": "jlTSndPESkKIlwWw", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_2.png", "conversations": [ { "from": "human", "value": "\nCan you organize the line chart's data into a table, with columns as types of economy and rows as industries?" }, { "from": "gpt", "value": "For this, I first identify {the xlabel}[[0.476, 0.942, 0.571, 0.975]] which represents the Industry. The industry types can be derived from {the x-axis tick labels}[ [0.084, 0.909, 0.165, 0.932], [0.224, 0.909, 0.344, 0.932], [0.410, 0.909, 0.478, 0.932], [0.525, 0.909, 0.683, 0.932], [0.706, 0.909, 0.822, 0.932], [0.868, 0.909, 0.979, 0.932]]. For values, I need to look at the two lines, {the line for Knowledge Economy}[0.124, 0.064, 0.924, 0.492] and {the line for Innovation Economy}[0.124, 0.327, 0.924, 0.853]. The y-axis corresponds to the value in USD for each type of economy in respective industries.\nBased on the above thought, the answer is:\nIndustry,Knowledge Economy,Innovation Economy\nAerospace,48000,12000\nPharmaceutical,53000,15000\nSoftware,6800,1200\nTelecommunications,9900,2100\nSemiconductor,7500,1500\nBiotechnology,7200,1800" } ], "id_raw": 842 }, { "id": "pPuwDTziB1AdJTn3", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which industry has the largest difference in Value between Knowledge Economy and Innovation Economy?" }, { "from": "gpt", "value": "To answer this question, I would need to look at the differences for all the industries. After comparing the values associated with both {the line for Knowledge Economy}[0.124, 0.064, 0.924, 0.492], and {the line for Innovation Economy}[0.124, 0.327, 0.924, 0.853], the highest difference can be identified.\nBased on the above thought, the answer is:\nThe Pharmaceutical industry shows the largest difference in Value between Knowledge Economy and Innovation Economy. This is indicated by the gap between the two points on the line chart corresponding to pharmaceutical in Knowledge and Innovation Economies, which is greater than for any other industry." } ], "id_raw": 842 }, { "id": "Ixxlh1jZKKoXDwM6", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the Value for Pharmaceutical industry in Knowledge Economy and Innovation Economy?" }, { "from": "gpt", "value": "To find the difference, I need to look at the points {for Pharmaceutical in Knowledge Economy}[0.284, 0.064] and {for Pharmaceutical in Innovation Economy}[0.284, 0.327]. The y-axis values correspond to their respective values in USD. The difference would be the Value of Knowledge Economy - Value of Innovation Economy.\nBased on the above thought, the answer is:\n38000." } ], "id_raw": 842 }, { "id": "3xbwQsHzqLq028TA", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_4.png", "conversations": [ { "from": "human", "value": "\nCould you help convert the raw data from the line chart into a structured table keeping the process types as columns and the evaluation measures as rows?" }, { "from": "gpt", "value": "To convert the chart data into a structured table, I retrieve the labels for the different types of processes from {the x-axis tick labels}[[0.058, 0.912, 0.165, 0.936], [0.446, 0.912, 0.581, 0.936], [0.852, 0.912, 0.978, 0.936]], which represent the column headers. Then, I identify {the legend texts}[[0.766, 0.417, 0.893, 0.457], [0.766, 0.478, 0.934, 0.518]] as the row labels representing the Standard and Constrained evaluation measures. To assign values to the table, I observe {the line points}[[0.112, 0.434], [0.513, 0.322], [0.915, 0.302], [0.112, 0.724], [0.513, 0.733], [0.915, 0.698]] for each evaluation measure at the respective process type and extract the corresponding data.\nBased on the above thought, the answer is:\nEvaluation Measure,Quality Check,Finished Products,Product Lifetime\nStandard,0.0000001,0.000005,0.00001\nConstrained,0.000000000004,0.000000000003,0.00000000001" } ], "id_raw": 844 }, { "id": "Iqu77YIX4z3mLiXT", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_4.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which process type has a larger difference between the Standard and Constrained evaluation measures? Please provide your reasoning." }, { "from": "gpt", "value": "To determine the process type with a larger difference between the Standard and Constrained evaluation measures, I have to compare their differences for each process. For {Quality Check}[0.058, 0.912, 0.165, 0.936], the difference is 0.0000001 - 0.000000000004; for {Finished Products}[0.446, 0.912, 0.581, 0.936], the difference is 0.000005 - 0.000000000003; and for {Product Lifetime}[0.852, 0.912, 0.978, 0.936], the difference is 0.00001 - 0.00000000001. By comparing these differences, I can determine which process type has a larger difference between the Standard and Constrained evaluation measures.\nBased on the above thought, the answer is:\nThe Finished Products process type has a larger difference between the Standard and Constrained evaluation measures. This is because the absolute difference between the evaluation measures for Finished Products is 0.000004999997, which is larger than for Quality Check (0.000000099996) and Product Lifetime (0.00000999999). The greater the difference between the two evaluation measures, the larger the divergence in performance assessment under the two benchmarks." } ], "id_raw": 844 }, { "id": "mRoqqMwnf7JX7a2W", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the ratio between the Standard and Constrained evaluation measures for Finished Products?" }, { "from": "gpt", "value": "To find the ratio between Standard and Constrained evaluation measures for Finished Products, I first locate {the line point for Standard at Finished Products}[0.513, 0.322] which corresponds to the value 0.000005. Next, I locate {the line point for Constrained at Finished Products}[0.513, 0.733] which corresponds to the value 0.000000000003. The ratio is the value of Standard divided by the value of Constrained.\nBased on the above thought, the answer is:\n1666666.67." } ], "id_raw": 844 }, { "id": "mbYNF1HBETQqHu0W", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_5.png", "conversations": [ { "from": "human", "value": "\nFrom the horizontal bar chart, can you extract the development quantities for both rural and urban areas in a table format?" }, { "from": "gpt", "value": "The chart type is a horizontal bar chart where the y-axis represents the categories (rural and urban areas) and the x-axis represents the quantity of development. The {y-axis labels}[0.015, 0.458, 0.062, 0.482], [0.015, 0.128, 0.062, 0.151] represent the rows and the {horizontal bars}[0.933, 0.128, 0.943, 0.428], [0.933, 0.043, 0.943, 0.442] for each category represent the data on development quantities for the respective areas.\nBased on the above thought, the answer is:\nArea,Development Quantity\nRural,10000\nUrban,12000" } ], "id_raw": 845 }, { "id": "aVUOQCiEP9EF5re5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_5.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the horizontal bar chart, what can we infer about the distribution and scale of development in rural and urban areas?" }, { "from": "gpt", "value": "The horizontal bar chart presents information on development quantities of rural and urban areas. Checking the {horizontal bars for rural and urban}[0.933, 0.128, 0.943, 0.428], [0.933, 0.043, 0.943, 0.442], we can see that urban areas have a higher quantity of development compared to rural areas (12,000 vs 10,000).\nBased on the above thought, the answer is:\nBased on the horizontal bar chart, we can infer that urban areas have a larger scale of development compared to rural areas. Urban areas, with a higher development quantity of 12,000, may be experiencing faster growth due to factors such as technological advancements and commercial enterprises. In contrast, rural areas have a development quantity of 10,000, indicating possibly slower but still significant growth propelled by activities such as crop cultivation and small businesses." } ], "id_raw": 845 }, { "id": "hop1ZSiDalLKoRf6", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_5.png", "conversations": [ { "from": "human", "value": "\nAs indicated in the horizontal bar chart, how much more development is there in the urban area compared to the rural area?" }, { "from": "gpt", "value": "To determine the difference in development between the urban and rural areas, I can directly subtract the development quantities of the two from the {horizontal bars}[0.933, 0.128, 0.943, 0.428], [0.933, 0.043, 0.943, 0.442]. The development quantity of urban areas is 12,000 while for rural areas it's 10,000. Therefore, the difference is 12,000 - 10,000 = 2,000.\nBased on the above thought, the answer is:\n2000." } ], "id_raw": 845 }, { "id": "b7lk4vBnx6fcPy0D", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_7.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data showing the interactions for different patterns into a table format with Pattern as rows and Interaction Types as columns?" }, { "from": "gpt", "value": "To create a table from the line chart, I begin by identifying {the x-axis tick labels}[[0.088, 0.908, 0.126, 0.932], [0.323, 0.908, 0.362, 0.932], [0.558, 0.908, 0.597, 0.932], [0.792, 0.908, 0.831, 0.932]] as the row labels representing the different patterns. After that, I recognize {the legend texts}[[0.694, 0.413, 0.964, 0.453],[0.694, 0.474, 0.972, 0.514]] as the column headers representing the interaction types - 'Close Friends' and 'Acquaintances'. To find the interaction counts, I navigate to {the line points} for each interaction type at the respective patterns and extract the associated values.\nBased on the above thought, the answer is:\nPattern,Close Friends,Acquaintances\nFlat Decline,15,30\nRapid Decline,10,20\nModerate Increase,20,15\nUnpredictable Fluctuations,30,10\nInconsistent Decline,15,15" } ], "id_raw": 847 }, { "id": "gsh8woqXJd54zhPw", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_7.png", "conversations": [ { "from": "human", "value": "\nBased on the line charts, which interaction pattern seems to be the most inconsistent or unpredictable for interactions with Close Friends and Acquaintances? Share your analysis." }, { "from": "gpt", "value": "In order to identify the most inconsistent or unpredictable pattern, I must examine the variability in the line plots for each interaction type. I observe that for {the 'Close Friends' line}[[0.107, 0.034, 0.812, 0.592]], 'Flat Decline' shows a steady decrease in interactions, 'Rapid Decline' consists of rapid dips, 'Moderate Increase' shows a steady rise, while 'Unpredictable Fluctuations' has a sharp rise to 30 interactions. \nSimilarly, examining {the 'Acquaintances' line}[[0.107, 0.443, 0.812, 0.851]], 'Unpredictable Fluctuations' shows the most sudden drop to 10 interactions. Therefore, the most unpredictable pattern seems to be 'Unpredictable Fluctuations' for both 'Close Friends' and 'Acquaintances'.\nBased on the above thought, the answer is:\nThe 'Unpredictable Fluctuations' pattern appears to be the most inconsistent for interactions with both 'Close Friends' and 'Acquaintances.' This is evident from the sharp rise and sudden drop of interaction counts which clearly deviate from the general consistent trends seen in other patterns." } ], "id_raw": 847 }, { "id": "JwJ1PCFrzUFhF0AX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the maximum and minimum number of interactions for Close Friends across all patterns?" }, { "from": "gpt", "value": "To determine the variance in the number of interactions for 'Close Friends' across all patterns, I need to identify {the points for the 'Close Friends' line}[[0.107, 0.034],[0.343, 0.361],[0.578, 0.484],[0.812, 0.592]]. They correspond to interaction counts of 15, 10, 20, and 30 for patterns 'Flat Decline', 'Rapid Decline', 'Moderate Increase', and 'Unpredictable Fluctuations' respectively. The difference between the highest count(30) and the lowest count(10) is 30 - 10 = 20.\nBased on the above thought, the answer is:\n20" } ], "id_raw": 847 }, { "id": "tJbt9OLJzZAHZK3n", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_9.png", "conversations": [ { "from": "human", "value": "\nCould you convert the line chart data into a comprehensive table, with each row representing a specific N value and the columns representing Identity Theft and Data Breach and their corresponding Threat Magnitudes?" }, { "from": "gpt", "value": "To form the data into a table, I need to extract the different N values by looking at {the x-axis}[0.534, 0.945, 0.547, 0.975] and the threat magnitudes for Identity Theft and Data Breach from the corresponding points on the respective lines: {the line for Identity Theft}[0.138, 0.085, 0.943, 0.437] and {the line for Data Breach}[0.138, 0.514, 0.943, 0.678].\nBased on the above thought, the answer is:\nN,Identity Theft,Data Breach\n4,1.5e-8,1.5e-12\n5.5,8e-7,7e-11\n7,15e-6,2e-10\n12,2.5e-4,1.5e-11\n25,1e-2,8e-10" } ], "id_raw": 849 }, { "id": "YKZ6jnN9VX7EzLtr", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_9.png", "conversations": [ { "from": "human", "value": "\nLooking at the plotted lines, which threat, Identity Theft or Data Breach, shows a more rapid growth in threat magnitude as N increases?" }, { "from": "gpt", "value": "I examine {the line for Identity Theft}[0.138, 0.085, 0.943, 0.437] and {the line for Data Breach}[0.138, 0.514, 0.943, 0.678]. From these lines, we can see that the line representing Identity Theft begins at a lower threat magnitude at N=4 but ends at a much higher threat magnitude at N=25. On the other hand, the line for Data Breach remains consistently lower in threat magnitude across all N. This suggests that Identity Theft has a more rapid growth in threat magnitude as N increases than Data Breach.\nBased on the above thought, the answer is:\nThe line graph inherently indicates that Identity Theft threat magnitude progressively increases at a more rapid pace as N increases when compared to Data Breach. This is seen from \nthe steeper rise in the line graph for Identity Theft compared to Data Breach." } ], "id_raw": 849 }, { "id": "RatiWLaf8ypY29n9", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the Threat Magnitude difference for Identity Theft between N=4 and N=25?" }, { "from": "gpt", "value": "To find the difference in Threat Magnitude for Identity Theft, I need to compare the y-values at {N=4}[0.138, 0.437] and {N=25}[0.943, 0.085] on the {line for Identity Theft}[0.138, 0.085, 0.943, 0.437]. These correspond to Threat Magnitudes of 1.5e-8 and 1e-2 respectively. Subtracting these gives the difference as 1e-2 - 1.5e-8.\nBased on the above thought, the answer is:\n0.009999985." } ], "id_raw": 849 }, { "id": "VQUF1tuC34fDiZyH", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_11.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from this bar chart into a structured table format with Design Professions and respective Innovation Scores?" }, { "from": "gpt", "value": "To generate the structured table from the bar chart, I start with {the y-axis labels}[[0.102, 0.680], [0.218, 0.500], ..., [0.798, 0.300]] as row labels which indicate different design professions. Next, I locate the respective {bar lengths}[[0.102, 0.680, 0.941, 0.720], [0.218, 0.500, 0.781, 0.550], ..., [0.798, 0.300, 0.621, 0.330]] indicating the innovation scores corresponding to each profession. I then extract these scores and map them to the corresponding professions.\nBased on the above thought, the answer is:\n\"Design Profession\",\"Innovation Score\"\n\"Industrial Designer\",10.0\n\"Graphic Designer\",20.0\n\"Interior Designer\",22.5\n\"UX/UI Designer\",25.0\n\"Artists\",27.5\n\"Architects\",30.0" } ], "id_raw": 851 }, { "id": "XCRhyn6VmTjw0DW0", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_11.png", "conversations": [ { "from": "human", "value": "\nConsidering the state of innovation displayed in this horizontal bar chart, which design profession seems to be lacking creativity compared to others? Give reasons for your answer." }, { "from": "gpt", "value": "To identify the profession that appears to lag in creativity, I need to analyze the length of bars representing different professions. The shorter the bar, the less innovative the profession is considered. Here, looking at {the bar for Industrial Designer}[0.102, 0.680, 0.621, 0.710], it is visibly shorter compared to other professions. This suggests that Industrial Designers have the least innovation score among the professions represented.\nBased on the above thought, the answer is:\nBased on the horizontal bar chart, the 'Industrial Designer' profession seems to be less innovative compared to others. Its bar length, indicative of the Innovation score, is noticeably shorter than that of other professions, suggesting they are currently lacking in creativity and design innovation relative to their counterparts." } ], "id_raw": 851 }, { "id": "222YCFP9TbjknW71", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of the innovation scores across the different design professions?" }, { "from": "gpt", "value": "To find the range of the innovation scores, I need to determine the least and most innovative professions by looking at their {bar lengths}[[0.102, 0.680, 0.941, 0.720], [0.218, 0.500, 0.781, 0.550], ..., [0.798, 0.300, 0.621, 0.330]]. The longest bar corresponds to \"Architects\" with a score of 30 indicating highest innovation and the shortest bar corresponds to \"Industrial Designer\" with a score of 10 meaning least innovation among these careers. The range of the innovation scores is therefore 30 - 10 = 20.\nBased on the above thought, the answer is:\n20." } ], "id_raw": 851 }, { "id": "ZiES62X1pqHsyqPP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_12.png", "conversations": [ { "from": "human", "value": "\nCan you provide the data from the chart in a tabular format, where the rows represent the different stages of deployment and the columns represent Existing System and Potential System?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels} [[0.134, 0.103, 0.855, 0.602]], which represent the stages of deployment: 5, 10, 15, 20, 25, 30. I then identify {the legend items}[[0.732, 0.427, 0.955, 0.467], [0.732, 0.487, 0.966, 0.527]] \"Existing System\" and \"Potential System\" as the column headers. Lastly, I locate the corresponding values via {the line points for Existing System}[[0.134, 0.602], [0.413, 0.435], [0.576, 0.352], [0.692, 0.103], [0.782, 0.186], [0.855, 0.103]] and {the line points for Potential System}[[0.134, 0.851], [0.413, 0.768], [0.576, 0.685], [0.692, 0.602], [0.782, 0.519], [0.855, 0.602]] to extract the corresponding values: 1e-8, 1e-6, 1e-5, 0.01, 0.001, 0.01 and 1e-11, 1e-10, 1e-9, 1e-8, 1e-7, 1e-8 respectively.\nBased on the above thought, the answer is:\nStage,Existing System, Potential System\n5,1e-8,1e-11\n10,1e-6,1e-10\n15,1e-5,1e-9\n20,1e-2,1e-8\n25,1e-3,1e-7\n30,1e-2,1e-8" } ], "id_raw": 852 }, { "id": "gYJs79E3DfF82oO1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_12.png", "conversations": [ { "from": "human", "value": "\nHow can the observed trends in the line chart be summarized?" }, { "from": "gpt", "value": "Looking at {the two lines in the chart} [0.134, 0.103, 0.855, 0.602] for the Existing System and [0.134, 0.519, 0.855, 0.851] for the Potential System, we can see contrasting trends. For the Existing System, the efficiency alternates between increases and decreases across different stages of deployment, indicating inconsistency. For the Potential System, however, we observe a consistent downward trend, suggesting a steady efficiency reduction over the stages. Despite similar efficiencies at both the initial (5th) and final stages (30th), the performance between these stages indicates a divergence of the two systems with the Potential System showing more promise due to its consistency.\nBased on the above thought, the answer is:\nThe chart demonstrates two contrasting trends. The Existing System illustrates a fluctuating efficiency across the stages, showing no consistent pattern of improvement or deterioration. In contrast, the Potential System demonstrates a steady and consistent reduction in efficiency across all stages, indicating a more predictable and promising performance in potential improvement." } ], "id_raw": 852 }, { "id": "GWj5RWEip4Eklwdg", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the ratio between the efficiency of the Potential System and the Existing System at 25th stage?" }, { "from": "gpt", "value": "To calculate this ratio, first I locate {the point on the line for the Existing System at the 25th stage} [0.782, 0.186]. The corresponding efficiency value for the Existing System is 0.001. Next, I identify {the point on the line for the Potential System at the 25th stage} [0.782, 0.519]. The corresponding efficiency value for the Potential System is 1e-7. By dividing these two numbers, I can obtain the ratio.\nBased on the above thought, the answer is:\n0.1." } ], "id_raw": 852 }, { "id": "iRg2nXABqNT9BTpw", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_13.png", "conversations": [ { "from": "human", "value": "\nBased on the graph, can you create a tabular representation of the number of published research papers in the field of \"Artificial Intelligence\" and \"Internet of Things\" for the years 2022 to 2027?" }, { "from": "gpt", "value": "To create a table from this line chart, I need to identify the x and y values for both the \"Artificial intelligence\" and the \"Internet of Things\" lines. The labels for these lines were found in {the legend}[0.075, 0.072, 0.237, 0.140]. The x-values or years are provided in {the x-axis labels}[[0.094, 0.919, 0.129, 0.942], [0.260, 0.919, 0.295, 0.942], [0.427, 0.919, 0.462, 0.942], [0.593, 0.919, 0.628, 0.942], [0.759, 0.919, 0.795, 0.942], [0.926, 0.919, 0.961, 0.942]]. For each year, I extract the corresponding y-values, which indicate the number of published research papers in the two fields. The y-values are obtained from {the points of the \"Artificial intelligence\" line}[[0.111, 0.420], [0.278, 0.363], [0.444, 0.243], [0.611, 0.213], [0.777, 0.162], [0.943, 0.100]] and {the points of the \"Internet of Things\" line}[[0.111, 0.243], [0.278, 0.213], [0.444, 0.865], [0.611, 0.140], [0.777, 0.119], [0.943, 0.749]].\nBased on the above thought, the answer is:\nYear,Artificial intelligence,Internet of Things\n2022,40000,80000\n2023,50000,90000\n2024,80000,7000\n2025,90000,120000\n2026,110000,130000\n2027,140000,11000" } ], "id_raw": 853 }, { "id": "PywDQ5z0MmApE0NX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_13.png", "conversations": [ { "from": "human", "value": "\nWhich field, \"Artificial intelligence\" or \"Internet of Things\", showed a more significant increase in the number of published research papers between 2022 and 2027 based on the trends shown in the line chart? Explain your reasoning." }, { "from": "gpt", "value": "To determine this, I have to analyze the trends of the two lines in the chart. Based on the coordinates of {the Artificial Intelligence line}[0.111, 0.100, 0.943, 0.420] and {the Internet of Things line}[0.111, 0.119, 0.943, 0.865], it is observed that the Artificial Intelligence line shows a consistent upward trend with a substantial increase in the number of published research papers over this time period. On the other hand, the Internet of Things line shows a great increase until 2023, after which it takes a massive drop in 2024. Then it increases again significantly until 2026, after which there's another sharp decrease in 2027.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the field of \"Artificial Intelligence\" showed a more significant and consistent increase in the number of published research papers between 2022 and 2027. While the \"Internet of Things\" had periods of rapid growth, it also had significant drops in 2024 and 2027, making the overall increase less substantial compared to \"Artificial Intelligence\"." } ], "id_raw": 853 }, { "id": "r0rZM5daDdfR91ha", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_13.png", "conversations": [ { "from": "human", "value": "\nHow much did the number of research papers published in the field of \"Artificial intelligence\" increase from 2022 to 2027?" }, { "from": "gpt", "value": "To get the increase in the number of published papers for \"Artificial Intelligence\" from 2022 to 2027, I need to observe the y-values for these years. The y-coordinate for 2022 and 2027 for \"Artificial Intelligence\" is found at {the points of the \"Artificial intelligence\" line}[[0.111, 0.420], [0.943, 0.100]]. Extracting these values and calculating the difference gives the increase in the number of papers.\nBased on the above thought, the answer is:\n100000." } ], "id_raw": 853 }, { "id": "BFn6Uq17dkKQdZRb", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_14.png", "conversations": [ { "from": "human", "value": "\nCan you create a table showing the Year, EdTech Investment (Billion $), and Number of Innovative Programs as observed in the line chart?" }, { "from": "gpt", "value": "For creating the table, I should start by identifying the years which will act as our rows. I can find this by locating the positions of the {x-axis tick labels}[[-228.994, 0.892], [-53.066, 0.892], [122.862, 0.892], [298.789, 0.892]] which represent the years from 2005 to 2020. Next, I point out the {line points}[{line_0: [0.084, 0.853], [0.122, 0.833], [0.160, 0.817], [0.198, 0.796], [0.236, 0.825], [0.274, 0.817], [0.312, 0.803], [0.350, 0.759], [0.388, 0.735], [0.426, 0.716], [0.464, 0.690], [0.502, 0.681], [0.540, 0.690], [0.578, 0.669], [0.616, 0.645], [0.654, 0.625]} and {line_1: [0.084, 0.213], [0.122, 0.202], [0.160, 0.191], [0.198, 0.179], [0.236, 0.189], [0.274, 0.185], [0.312, 0.169], [0.350, 0.153], [0.388, 0.138], [0.426, 0.115], [0.464, 0.104], [0.502, 0.095], [0.540, 0.099], [0.578, 0.087], [0.616, 0.079], [0.654, 0.064]]} which show the data for the EdTech Investment and Number of Innovative Programs respectively for each year.\nBased on the above thought, the answer is:\nYear,EdTech Investment (Billion $),Number of Innovative Programs\n2005,0.5,200\n2006,0.6,220\n2007,0.7,245\n2008,0.85,275\n2009,0.65,250\n2010,0.7,260\n2011,0.8,300\n2012,1.2,350\n2013,1.5,400\n2014,1.8,500\n2015,2.3,550\n2016,2.5,600\n2017,2.3,580\n2018,2.8,650\n2019,3.5,700\n2020,4.2,800" } ], "id_raw": 854 }, { "id": "TAK7CsZrQVg5NveR", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_14.png", "conversations": [ { "from": "human", "value": "\nConsidering the trends of EdTech Investment and Number of Innovative Programs from 2005 to 2020, what can be inferred about the correlation between the two?" }, { "from": "gpt", "value": "The line chart displays the trends of EdTech Investment and Number of Innovative Programs over the years 2005 to 2020. The lines for both {EdTech Investment}[0.084, 0.625, 0.654, 0.853] and {Number of Innovative Programs}[0.084, 0.064, 0.654, 0.213] show an increasing trend over the years. This suggests a positive correlation between the two \u2013 as EdTech Investment increases, the Number of Innovative Programs also tends to increase. However, there are few disparities, like around the year 2009 the investment dropped, yet the number of programs remained consistent.\nBased on the above thought, the answer is:\nOverall, there seems to be a strong positive correlation between the EdTech Investment and the Number of Innovative Programs. In general, an increase in EdTech Investment corresponds with an increase in the Number of Innovative Programs. However, occasional discrepancies suggest that other factors might also be influencing the Number of Innovative Programs. Therefore, while EdTech Investment may be a significant factor, it is not the sole determinant of the Number of Innovative Programs." } ], "id_raw": 854 }, { "id": "Sxrltqqw7XaskYlW", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_14.png", "conversations": [ { "from": "human", "value": "\nWhat's the percentage growth in EdTech Investment from the year 2015 to 2020?" }, { "from": "gpt", "value": "To calculate the percentage growth, I first locate the {value for 2015}[0.464, 0.690] in the line for EdTech Investment which is 2.3 Billion $. Then, I find {the value for 2020}[0.654, 0.625] which is 4.2 Billion $. The percentage increase would be (4.2 - 2.3)/2.3 * 100.\nBased on the above thought, the answer is:\n82.61%" } ], "id_raw": 854 }, { "id": "yMhlXiGXgTV9iaI6", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_15.png", "conversations": [ { "from": "human", "value": "\nCan you turn the data from the line graph into a table format with Years as the first column and the other columns representing the community indices?" }, { "from": "gpt", "value": "To generate the data into table format from the line graph, I first need to identify {the x-axis labels}[0.107, 0.912, 0.146, 0.936] that represent the years. Then, there are 5 different community indices represented in the graph. These are the data series labels that go into the first row of our table, which can be created by analyzing {the chart legend}[0.630, 0.398, 0.980, 0.532]. Finally, the specific data points plotted on the graph by reference to {the line points}[0.126, 0.091, 0.839, 0.450] for each corresponding data series and year can be extracted for the remaining cells of our table.\nBased on the above thought, the answer is:\nYear,Population Growth(%),Community Development Projects,Average Education Level(%),Government Spending,Employment Rate(%)\n2010,1.6,20,64,10100,68\n2012,1.5,22,65,10500,69\n2014,1.4,24,66,10900,70\n2016,1.3,26,67,11300,71\n2018,1.2,28,68,11700,72" } ], "id_raw": 855 }, { "id": "ODOTodpIMC8EJzCv", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_15.png", "conversations": [ { "from": "human", "value": "\nWhat pattern of change does the neighborhood's population growth demonstrate according to the line chart? Please discuss the corresponding causes." }, { "from": "gpt", "value": "By examining the line corresponding to {Population Growth}[0.126, 0.091, 0.839, 0.450], it shows a trend of steady decline from 2010 to 2018.\nBased on the above thought, the answer is:\nThe line graph shows that Population Growth in the neighborhood presents a slow but consistent decline from 2010 to 2018. This pattern might be influenced by a variety of factors, some of which can include lower birth rates, aging population, or increased migration to other areas or cities. However, the exact reason for the population decrease cannot be explicitly determined from the chart without additional demographic or sociological data." } ], "id_raw": 855 }, { "id": "y6MMIfATUaLBnSFO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in Government Spending from 2010 to 2018?" }, { "from": "gpt", "value": "To calculate the total increase in Government Spending from 2010 to 2018, I first need to look at the {Government Spending in 2010}[0.126, 0.091] which is $10100 and then compare it to the {Government Spending in 2018}[0.839, 0.091] which is $11700. The total increase can then be calculated as 11700 - 10100 = $1600.\nBased on the above thought, the answer is:\n$1600." } ], "id_raw": 855 }, { "id": "mGftWUuUgKrEL078", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_16.png", "conversations": [ { "from": "human", "value": "\nI see there are different fields of study in the bar chart, can you summarize the data into a table where each row represents a field of study, with its frequency?" }, { "from": "gpt", "value": "I can generate the table by extracting the names from {the y-axis tick labels}[[0.014, 0.897, 0.058, 0.910], [0.014, 0.801, 0.058, 0.814], [0.014, 0.695, 0.058, 0.708], [0.014, 0.589, 0.058, 0.602], [0.014, 0.483, 0.058, 0.496], [0.014, 0.377, 0.058, 0.390], [0.014, 0.271, 0.058, 0.284], [0.014, 0.165, 0.058, 0.178]] as field names and the corresponding values from {the lengths of the bars}[[0.070, 0.837, 0.809, 0.860], [0.070, 0.731, 0.782, 0.753], [0.070, 0.625, 0.754, 0.648], [0.070, 0.519, 0.726, 0.542], [0.070, 0.413, 0.699, 0.435], [0.070, 0.307, 0.671, 0.329], [0.070, 0.200, 0.644, 0.223], [0.070, 0.094, 0.583, 0.117]] for each field.\nBased on the above thought, the answer is:\nField of Study,Frequency\nComputer Science,729\nData Analysis,700\nPhysics,670\nChemistry,641\nBiology,613\nLiterature,582\nHistory,517\nPsychology,449" } ], "id_raw": 856 }, { "id": "RiAoLfhMgSqDnYiD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_16.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which academic field has seen the least concentration of research? Can you also provide a reason why it might be the case?" }, { "from": "gpt", "value": "By analyzing the lengths of all the bars, I can discern that {the bar for Psychology}[0.070, 0.094, 0.583, 0.117] is the smallest. Therefore, this implies that Psychology is the academic field with the least concentrated research. As for the reason, it could possibly be due to the fact that Psychology, as a field, is both very broad and highly specialized, making it easy for researchers to spread their efforts across many different sub-disciplines. Additionally, it could also be due to less demand or fewer resources available in this field compared to areas like Computer Science or Data Analysis.\nBased on the above thought, the answer is:\nBased on the bar chart, the academic field with the least concentration of research is Psychology. A plausible reason might be that the field of Psychology is very broad, containing numerous highly specialized niches which would necessitate researchers to diversify their efforts across different sub-disciplines. Lack of demand or fewer resources available for research in Psychology as compared to areas like Computer Science or Data Analysis could also account for this discrepancy." } ], "id_raw": 856 }, { "id": "vDjpY9PPO0KY2PZS", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_16.png", "conversations": [ { "from": "human", "value": "\nWhat's the frequency difference between the most researched field and the least researched field as per the bar chart?" }, { "from": "gpt", "value": "To find the frequencies of the most and least researched fields, I need to analyze the lengths of all the bars. Checking {the bar for Computer Science}[0.070, 0.837, 0.809, 0.860], I can gather that Computer Science has the highest frequency with a value of 729. Similarly, looking at {the bar for Psychology}[0.070, 0.094, 0.583, 0.117], I see that it has the lowest frequency of 449. The difference can be calculated as 729 - 449 = 280.\nBased on the above thought, the answer is:\n280." } ], "id_raw": 856 }, { "id": "fHwAHe8hbEVd7r9S", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_17.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data into a table format with Years as columns and Investment Types as rows?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first locate {the x-axis tick labels}[[0.098, 0.775, 0.941, 0.885], these represent the column headers or years. Then, I identify {the legend texts}[[0.399, 0.422, 0.660, 0.452], [0.399, 0.466, 0.649, 0.496]], which represent different investment types. To populate the table, I locate {the line points for Total Investment}[[0.098, 0.219], [0.192, 0.172], [0.285, 0.133], [0.379, 0.158], [0.473, 0.133], [0.567, 0.101], [0.660, 0.133], [0.754, 0.111], [0.848, 0.081], [0.941, 0.091]] and {the line points for Venture Capital}[[0.098, 0.745], [0.192, 0.599], [0.285, 0.513], [0.379, 0.452], [0.473, 0.405], [0.567, 0.333], [0.660, 0.366], [0.754, 0.305], [0.848, 0.258], [0.941, 0.238]] for each investment type at respective years and extract the associated values.\nBased on the above thought, the answer is:\nYear,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019\nTotal Investment (in billions),1200,1500,1800,1600,1800,2100,1800,2000,2300,2200\nVenture Capital (in billions),100,200,300,400,500,700,600,800,1000,1100" } ], "id_raw": 857 }, { "id": "ZEA52qsRTBjzLNBI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_17.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which type of investment has experienced the most rapid growth over the observed years, and why do you think so?" }, { "from": "gpt", "value": "To analyze which investment type has experienced the most rapid growth, I need to observe the trends in the line plots for Total Investment and Venture Capital. Looking at {the line for Total Investment}[0.098, 0.081, 0.941, 0.219], there are some increases and decreases, but the trajectory generally depicts a continuous escalation. However, looking at {the line for Venture Capital}[0.098, 0.238, 0.941, 0.745], we see a consistent increase in investment across the years. The fluctuations in this line are upward and drastic, particularly from year 2014 to year 2019.\nBased on the above thought, the answer is:\nFrom the trends shown in the line chart, Venture Capital seems to have experienced the most rapid growth over the years. This is because the line plot for Venture Capital consistently ascends with substantial increases, particularly noticeable from the year 2014 to year 2019 where there is a steep upward climb, indicating a significant surge in this investment type." } ], "id_raw": 857 }, { "id": "x34bE5mLGHvpu5vm", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the amount of Venture Capital (in billions) for the year 2016 represented on the chart?" }, { "from": "gpt", "value": "To find the amount of Venture Capital (in billions) for the year 2016, I look into {the line points for Venture Capital}[[0.567, 0.333], [0.660, 0.366], [0.754, 0.305]]. From these points, the second one corresponds to the year 2016. The y-coordinate of the point represents the value of Venture Capital (in billions) for that year.\nBased on the above thought, the answer is:\n600." } ], "id_raw": 857 }, { "id": "6tTJBck3OzZ1MYb9", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_18.png", "conversations": [ { "from": "human", "value": "\nPlease convert the data in the given line chart to a table with the Year being the first column followed by the Investment in Educational Services and Investment in Public Health Services." }, { "from": "gpt", "value": "To format the data in the chart into a table, I will first identify the x-axis labels or the years that are indicated by {the x-axis tick labels}[[0.618, 0.072, 0.638, 0.084], [0.347, 0.072, 0.367, 0.084], [0.912, 0.072, 0.932, 0.084], [0.250, 0.072, 0.270, 0.084], [0.827, 0.072, 0.847, 0.084]]. There are five unique years mentioned namely 1990, 2000, 2010, 2020, and 2030. \nNext, I will look at the y-axis which represents the investment amounts for each sector. The line for Educational Services represented by {line_0}{'line_0': {..., 'points': [{'x': 1990, 'y': 200, 'point_coordinates': '[0.122, 0.853]'....had coordinates representing the respective investments for each year. Similarly, the line for Public Health Services, denoted by {line_1}{'line_1': {..., 'points': [{'x': 1990, 'y': 500, 'point_coordinates': '[0.122, 0.628]'....has coordinates depicting respective investment amounts for each year. \nBy reordering this information, we can draft a table.\nBased on the above thought, the answer is:\nYear,Educational Services,Public Health Services\n1990,200,500\n2000,400,700\n2010,800,900\n2020,1600,1800\n2030,3200,5000" } ], "id_raw": 858 }, { "id": "WvV3wmxAmFcvUqvq", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_18.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the trend for investments in Educational Services and Public Health Services from 1990 to 2030, can you delineate any significant differences in the investment patterns for these two sectors?" }, { "from": "gpt", "value": "To conduct this analysis, we will need to observe the lines representing the investment trends for Educational Services and Public Health Services, represented {line_0}{'line_0': {..., 'coordinates': '[0.122, 0.174, 0.937, 0.853]'...} and {line_1}{'line_1': {..., 'coordinates': '[0.122, 0.064, 0.937, 0.628]'...}. It's conspicuous that both sectors saw a rise in investments over the years from 1990 to 2030. However, Educational Services had a more steady and incremental growth, while Public Health Services had a slightly more erratic growth pattern with a sharp rise in investments towards 2020-2030.\nBased on the above thought, the answer is:\nBased on the line chart, both the Educational Services and Public Health Services sectors demonstrate increasing investment trends from 1990 to 2030. The Educational Services sector shows non-volatile, steady and incremental growth over this period, indicating a consistent high interest in investment for this sector. In contrast, the Public Health Services sector displays a more erratically increasing pattern, with a notably sharp surge after 2020. This suggests that there might have been sudden, significant factors or changes around this period that kindled an increase in investment for Public Health Services." } ], "id_raw": 858 }, { "id": "T5yCobKvYYWydfpK", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage growth of investment in the Educational Services sector from 1990 to 2030?" }, { "from": "gpt", "value": "To find the percentage growth, we will first highlight two points- the respective investment numbers of 1990 and 2030 for the Educational Services sector. This can be seen in {line_0}{'points': [{'x': 1990, 'y': 200, 'point_coordinates': '[0.122, 0.853]'.... and {line_0}{'points': [... {'x': 2030, 'y': 3200, 'point_coordinates': '[0.937, 0.174]'}....}. The initial investment in 1990 is $200M and the final investment in 2030 is $3200M. We can calculate the percentage growth with the formula ((Final value - Initial value)/Initial value)*100.\nBased on the above thought, the answer is:\n1500%" } ], "id_raw": 858 }, { "id": "vWzwA3BFOPU0IWJ5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_43_19.png", "conversations": [ { "from": "human", "value": "\nCan you extract the trends' data across the years 2005 to 2020 in tabular form?" }, { "from": "gpt", "value": "Yes, I can generate the tabular data from the line chart. I'll identify the years from {the x-axis tick labels}[0.098, 0.064, 0.936, 0.853] which are the column headers. The names of the two trends 'Energy' and 'Resources' are found in {the legend}[0.687, 0.392, 0.930, 0.526] which act as the row labels. The values for each trend at each year are conveyed by {the points on the line data for Energy and Resources}[0.098, 0.064, 0.936, 0.853], [0.098, 0.324, 0.936, 0.808] respectively. Now, let's compile this information into a table.\nBased on the above thought, the answer is:\nYear,Energy,Resources\n2005,1.5e6,2.0e6\n2006,3.4e6,4.3e6\n2007,5.6e6,3.2e6\n2008,8.7e6,4.1e6\n2009,1.2e7,4.9e6\n2010,1.7e7,5.9e6\n2011,2.3e7,6.7e6\n2012,4.8e7,7.8e6\n2013,7.5e7,8.9e6\n2014,9.4e7,1.1e7\n2015,1.2e8,1.4e7\n2016,1.6e8,1.8e7\n2017,1.8e8,2.5e7\n2018,2.1e8,3.2e7\n2019,2.3e8,3.9e7\n2020,2.4e8,4.5e7" } ], "id_raw": 859 }, { "id": "NjA2w3DgD6vTkfHd", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_43_19.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which trend, Energy or Resources, shows a more aggressive growth over the years?" }, { "from": "gpt", "value": "In order to determine which trend shows more aggressive growth, I first need to compare the growth of Energy and Resources across the years. Looking closely at {the line for Energy}[0.098, 0.064, 0.936, 0.853] and {the line for Resources}[0.098, 0.324, 0.936, 0.808], it is noticeable that the Energy trend line is consistently above the Resources trend line. This means that overall, the values for Energy are higher than the values for Resources. In addition, the line for Energy is steeper and more pronounced, indicating more significant growth.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Energy trend seems to show more aggressive growth over the years. Comparing the values and the increases in the lines for Energy and Resources, Energy's growth rate is steeper and the values are consistently higher, indicating more aggressive growth." } ], "id_raw": 859 }, { "id": "EpMm3rRGD1WNnGF8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_43_19.png", "conversations": [ { "from": "human", "value": "\nWhat was the percentage growth in Energy from 2005 to 2020?" }, { "from": "gpt", "value": "To find the percentage growth in Energy from 2005 to 2020, I need to locate {the point for Energy in 2005}[0.098, 0.853] that represents 1.5e6 and the line data point at {2020}[0.936, 0.064] that represents 2.4e8. The percentage change can be calculated by the formula: ((Final Value - Initial Value) / Initial Value) * 100%. Applying this formula here gives ((2.4e8 - 1.5e6) / 1.5e6) * 100.\nBased on the above thought, the answer is:\n15966.67 %." } ], "id_raw": 859 }, { "id": "ezjPfLAZI7EFQt9p", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_0.png", "conversations": [ { "from": "human", "value": "\nBased on the horizontal bar chart, can you transform the distribution data of researchers across various fields into a CSV format?" }, { "from": "gpt", "value": "Observing {the y-axis labels}[0.01, 0.274, 0.03, 0.776] reveals the names of the fields which serve as the row identifiers in the table. The corresponding density values, represented by {the width of each bar}[0.15, 0.298, 0.85, 0.334], need to be extracted for each field. This process will transfer the data into a structured CSV format, including each field with its respective density value.\nBased on the above thought, the answer is:\nField,Density\nMathematics,11.5\nComputer Science,11\nBiological Sciences,9\nEngineering,8.5\nPhysical Sciences,8\nHealth Sciences,7.5\nSocial Sciences,7" } ], "id_raw": 860 }, { "id": "BZGkBauZ5ZsteagD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_0.png", "conversations": [ { "from": "human", "value": "\nWhy might Mathematics and Computer Science possess significantly higher research density compared to other fields in the chart?" }, { "from": "gpt", "value": "Based on the observed {horizontal bar lengths}[0.15, 0.298, 0.85, 0.334] which symbolize the density numbers, Mathematics and Computer Science bars are visibly longer than the others. This reflects their higher density rates. The significant difference might be attributed to several global factors such as technological advancements, digital transformation, and the growing demand for data-driven solutions across industries. These factors inherently boost the attraction and funding towards these fields, resulting in a higher concentration of researchers pursuing mathematics and computer science.\nBased on the above thought, the answer is:\nMathematics and Computer Science possess higher research density likely due to the increasing global emphasis on technological development, digitalization, and the demand for data expertise. These factors make these fields more attractive and heavily funded, thus attracting a greater number of researchers." } ], "id_raw": 860 }, { "id": "0YARWVJj0uEX0q4i", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the total density of researchers in the fields of Mathematics, Engineering, and Health Sciences according to the bar chart?" }, { "from": "gpt", "value": "To calculate the total density, I need to sum up the density values for the specified fields. The density for {Mathematics}[0.15, 0.298] is 11.5, for {Engineering}[0.567, 0.670] it is 8.5, and for {Health Sciences}[0.434, 0.539] it is 7.5. Adding these values gives a total density.\nBased on the above thought, the answer is:\n27.5" } ], "id_raw": 860 }, { "id": "I88vaobvTJW6Heoc", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_1.png", "conversations": [ { "from": "human", "value": "\nBased on this line chart, can you generate a table that reflects the investment return rates across different types of assets over the last financial year?" }, { "from": "gpt", "value": "To convert the line chart data into a table, the first step is to label {the x-axis}[0.500, 0.959, 0.529, 0.979] as the column headers representing the different types of investment assets. Next, I'll identify the annual return rates reflected by the y-values of each line plot corresponding to each of the assets. For example, for mutual funds, I'll find and extract the values from {the line points for Mutual Funds}[0.144, 0.076, 0.885, 0.174].\nBased on the above thought, the answer is:\nAsset,Mutual Funds,ETFs,Index Funds,Retail Stocks,Real Estate,Commodities,Bonds,Cryptocurrencies\nReturn Rate (%),2.5,3.6,5.1,6.8,4.5,-0.7,1.5,15.3" } ], "id_raw": 861 }, { "id": "mfLF0306nBSA4oCW", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_1.png", "conversations": [ { "from": "human", "value": "\nCould you analyze the trend of the investment return rates for Retail Stocks based on the line chart data?" }, { "from": "gpt", "value": "As per the graph, {the line representing Retail Stocks}[0.374, 0.136, 0.409, 0.156] indicates that the retail stocks have seen a very healthy trend in the last financial year. There seems to be a sharp rise in the investment return rates which could be due to increasing consumer confidence. This steep increase represents a positive trend for investors aiming for high return rates.\nBased on the above thought, the answer is:\nThe investment return rates for Retail Stocks have shown a sharp rise over the last financial year as per the line chart. This upward trend indicates a favourable condition for investors seeking high returns from retail stocks, implying that it is a profitable asset for investment." } ], "id_raw": 861 }, { "id": "N4MtGQ5XLUqJZUmN", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_1.png", "conversations": [ { "from": "human", "value": "\nHow much higher is the return rate of Cryptocurrencies compared to the return rate of Mutual Funds based on the given line chart?" }, { "from": "gpt", "value": "To answer this, I need to find the difference between the return rate of Cryptocurrencies and that of Mutual Funds. I locate the y-coordinate of {the point for Cryptocurrencies}[0.885, 0.536] to be approximately 15.3%, and the y-coordinate of {the point for Mutual Funds}[0.144, 0.076] to be approximately 2.5%. Subtracting the latter from the former gives the difference.\nBased on the above thought, the answer is:\n12.8%" } ], "id_raw": 861 }, { "id": "g8LIRZtpdRPN9iF7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_2.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from this line chart and organize it in a tabular format, using the \"Year\" as columns and categories as rows?" }, { "from": "gpt", "value": "In order to organize the data into a table, I would first identify {the x-axis tick labels}[[0.140, 0.902, 0.153, 0.932], [0.228, 0.902, 0.241, 0.932], ..., [0.924, 0.902, 0.951, 0.932]] which represent the years. The row labels are the categories, corresponding to {the legend texts}[[0.806, 0.060, 0.951, 0.085], [0.806, 0.097, 0.964, 0.122], ..., [0.806, 0.173, 0.942, 0.198]]. The values are determined by evaluating the corresponding y-value for each x-value within each category. For example, for \"Protest Activity\" in Year 1, I look at the line point {line for Protest Activity}[0.146, 0.546], which corresponds to a value of 15. I can also identify the data points for each line directly from their respective line data.\nBased on the above thought, the answer is:\nCategory,Year 1,Year 2,Year 3,Year 4,Year 5,Year 6,Year 7,Year 8,Year 9,Year 10\nProtest Activity,15,13,17,16,18,31,28,30,33,40\nPolitical Stability,85,80,75,72,70,65,60,58,54,50\nPolicy Changes,5,10,20,23,25,35,38,45,52,60\nPublic Opinion,95,92,89,86,82,80,75,70,65,62" } ], "id_raw": 862 }, { "id": "3mzNjaRKuA8EPwB7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_2.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which parameter shows a steady decline over the years?" }, { "from": "gpt", "value": "By observing the lines on the chart, we can identify which parameter or category shows a steady decline over the years. Looking at {the line for Political Stability}[0.146, 0.164, 0.937, 0.461], Political Stabilities' values decrease consistently from Year 1 to Year 10.\nBased on the above thought, the answer is:\nFrom the line chart, it can be seen that the parameter \"Political Stability\" shows a steady decline over the years." } ], "id_raw": 862 }, { "id": "VMIruguv86DZxj1x", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_2.png", "conversations": [ { "from": "human", "value": "\nBy how much did the Policy Changes increase from Year 5 to Year 10?" }, { "from": "gpt", "value": "To answer this question, I need to look at {the line points for Policy Changes}[0.498, 0.674, 0.937, 0.376] for the years in question. In Year 5, the value of Policy Changes was 25 {line point for Year 5}[0.498, 0.674], and in Year 10 it was 60 {line point for Year 10}[0.937, 0.376]. The difference between these two values will give us the increase in Policy Changes from Year 5 to Year 10.\nBased on the above thought, the answer is:\n35." } ], "id_raw": 862 }, { "id": "vjMMmXbFLTJo5yRE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_3.png", "conversations": [ { "from": "human", "value": "\nCould you convert the chart data into a tabular format where each year corresponds to a row and the values for Exercise, Diet, Sleep, and Social Network are given in separate columns?" }, { "from": "gpt", "value": "To construct the table from the line chart, I start by analyzing the x-axis, identifying the years marked as ticks from {the x-axis tick labels}[0.075, 0.919, 0.123, 0.942] to [0.919, 0.919, 0.967, 0.942]. Rows will correspond to these years, starting from 2000 to 2020. Next, I'll examine each line segment for the lifestyle categories such as {Exercise}[0.099, 0.065, 0.943, 0.683], {Diet}[0.099, 0.200, 0.943, 0.763], {Sleep}[0.099, 0.226, 0.943, 0.871], and {Social Network}[0.099, 0.119, 0.943, 0.871], translating their plotted points back to specific values using the corresponding y-axis labels.\nBased on the above thought, the answer is:\nYear,Exercise,Diet,Sleep,Social_Network\n2000,80,75,70,78\n2001,78,73,71,77\n2002,76,70,72,75\n2003,75,68,74,73\n2004,73,66,70,71\n2005,72,65,69,70\n2006,70,63,68,68\n2007,68,61,67,66\n2008,66,60,65,65\n2009,64,59,64,64\n2010,63,58,62,63\n2011,62,58,61,61\n2012,61,57,60,60\n2013,60,57,59,59\n2014,59,56,58,58\n2015,59,56,58,58\n2016,59,56,55,57\n2017,59,55,55,56\n2018,58,55,54,55\n2019,58,54,53,54\n2020,57,54,50,50" } ], "id_raw": 863 }, { "id": "qGjfuR7hSjFV6UfN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_3.png", "conversations": [ { "from": "human", "value": "\nWhich lifestyle category shows the least variation in its values from 2000 to 2020 in the graph?" }, { "from": "gpt", "value": "Analyzing the stability of the lifestyle category requires examining the closeness of the plotting points across time. I observe the lines for each lifestyle, including {Exercise}[0.099, 0.065, 0.943, 0.683], {Diet}[0.099, 0.200, 0.943, 0.763], {Sleep}[0.099, 0.226, 0.943, 0.871], and {Social Network}[0.099, 0.119, 0.943, 0.871]. From this, it can be seen that the {Social Network} line has points closely clustered and exhibits minimal height difference between the initial and final points.\nBased on the above thought, the answer is:\nThe Social Network category shows the least variation in its values from 2000 to 2020. It has smaller fluctuations and a narrower range of values in its plotting points, indicating a more stable trend compared to the other categories." } ], "id_raw": 863 }, { "id": "vMBMkaBv7IXzva2x", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage decrease in Sleep from the year 2000 to the year 2020 as indicated on the chart?" }, { "from": "gpt", "value": "To calculate the percentage decrease in the 'Sleep' category over the given years, I will identify the initial value for Sleep in 2000 which is 70 at point [0.099, 0.334] and the final value for Sleep in 2020, which is 50 at point [0.943, 0.871]. The percentage reduction is calculated by (((initial value - final value) / initial value) * 100).\nBased on the above thought, the answer is:\n28.57%" } ], "id_raw": 863 }, { "id": "EbODK5JM1abFD8NN", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart into a structured table where each column represents a decade and each row lists the energy source's index values?" }, { "from": "gpt", "value": "To organize the information into a tabular form, I carefully analyze {the x-axis labels}[0.100, 0.921, 0.962, 0.949] to determine the decades they represent, which are 2000, 2010, 2020, 2030. Then, I identify {the legend}[0.825, 0.792, 0.977, 0.911] which mentions various energy sources: Solar, Hydropower, Wind, and Nuclear. By examining each corresponding {line point}[0.100, 0.095, 0.962, 0.463],[0.100, 0.282, 0.962, 0.360],[0.100, 0.054, 0.962, 0.565],[0.100, 0.368, 0.962, 0.767] for these energy types, I extract the index values for each decade.\nBased on the above thought, the answer is:\nDecade,2000 Index,2010 Index,2020 Index,2030 Index\nSolar,1,3,7,9\nHydropower,2,2,2,2\nWind,1,4,8,10\nNuclear,5,4,3,1" } ], "id_raw": 864 }, { "id": "ZwWbyA8NaftNtU6A", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_4.png", "conversations": [ { "from": "human", "value": "\nWhy does the line chart suggest a shift towards renewable energy usage over the decades?" }, { "from": "gpt", "value": "Observing the trends in {the lines for Solar}[0.100, 0.095, 0.962, 0.463] and {Wind}[0.100, 0.054, 0.962, 0.565], it is evident that the utilization indices for these renewable energy sources have increased significantly. Both lines show upward trends, with the Solar line beginning at an index of 1 and reaching 9 by 2030, and the Wind line starting at 1 and reaching 10 in the same period. Contrastingly, the {line for Nuclear}[0.100, 0.368, 0.962, 0.767] displays a clear downward trend, dropping from an index of 5 to 1 from 2000 to 2030. This indicates a decreasing reliance on nuclear energy, highlighting a shift towards more sustainable and environmentally friendly renewable energy sources as captured in the line trends of the chart.\nBased on the above thought, the answer is:\nThe chart suggests a shift towards renewable energy as indicated by the significant increase in utilization indices for Solar and Wind, with a noticeable decline in the utilization of Nuclear energy. This reflects a growing preference for environmental sustainability in energy sourcing over the decades, aligning with global efforts to reduce carbon emissions and reliance on non-renewable energy resources." } ], "id_raw": 864 }, { "id": "YAZAJydAYvmy6eA3", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_4.png", "conversations": [ { "from": "human", "value": "\nHow much has the utilization index of Wind energy increased from 2000 to 2030 according to the chart?" }, { "from": "gpt", "value": "To calculate the growth in the utilization index for Wind energy, first, I identify {the specific plot points for Wind}[0.100, 0.054, 0.962, 0.282] from the year 2000 to 2030. The index values are read from these points: 1 for 2000 and 10 for 2030. The increase in index is then computed by subtracting the initial value from the final value, thus 10 - 1.\nBased on the above thought, the answer is:\n9" } ], "id_raw": 864 }, { "id": "BG5TvneZr7hJyhqJ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_5.png", "conversations": [ { "from": "human", "value": "\nCan you create a data table from the line chart displaying the performance metrics for each individual role defined by human (H), manager (M), and leader (L) across different instances?" }, { "from": "gpt", "value": "To extract the data into a structured table, I refer to {the x-axis labels}[0.096, 0.037, 0.981, 0.894] which indicate different performance metrics. I then review {the plot lines}[0.139, 0.122, 0.939, 0.809], [0.139, 0.465, 0.939, 0.837], [0.139, 0.732, 0.939, 0.889] and use the y-axis to determine which values correspond to each instance of human, manager, and leader roles, denoted by different colors and positions. The specific performance metrics for each individual are recorded based on their respective plot points.\nBased on the above thought, the answer is:\nRole,Instance,H1,H2,M1,M2,L1,L2\nHuman,\"H1,H2\",72,88,--,--,--,--\nManager,\"M1,M2\",--,--,63,95,--,--\nLeader,\"L1,L2\",--,--,--,--,75,90" } ], "id_raw": 865 }, { "id": "mOF7ai5HcMZqauv5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_5.png", "conversations": [ { "from": "human", "value": "\nWhich managerial role instance shows a significant drop in performance, and can you rationalize why it might be the case?" }, { "from": "gpt", "value": "By observing the plot specific to the managerial roles, I focus on {the line for managers}[0.139, 0.465, 0.939, 0.837] and notice two distinct points representing M1 and M2. While M2 maintains a higher performance metric around 95, M1 shows a notable drop at 63. The drop might be an outlier due to specific internal challenges, differences in responsibility, or external factors affecting that particular instance which is not consistent with the general trend seen in M2.\nBased on the above thought, the answer is:\nM1 shows a significant drop in performance compared to M2. The lower performance metric of 63 for M1 could be attributed to unique challenges or disparities in responsibilities that were not experienced by M2, who maintained a higher metric of 95." } ], "id_raw": 865 }, { "id": "44bbD9Wjp3lSSwag", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_5.png", "conversations": [ { "from": "human", "value": "\nWhat's the average performance metric for the leader role across instances L1 and L2?" }, { "from": "gpt", "value": "I will analyze {the plot lines for leaders}[0.139, 0.732, 0.939, 0.889] specifically focusing on the data points for L1 and L2. By locating their performance metrics, I note the values for L1 as 75 and L2 as 90. To determine the average, I will compute: (75 + 90) / 2.\nBased on the above thought, the answer is:\n82.5" } ], "id_raw": 865 }, { "id": "QGgYb4rqwalBIiAB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_7.png", "conversations": [ { "from": "human", "value": "\nCan you transform the bar plot data into a structured table format with industries as rows and years as columns?" }, { "from": "gpt", "value": "To transform the data, I first analyze {the bars in the plot}[0.105, 0.325, 0.962, 0.489], checking their heights related to the x-axis to interpret the sales values. I align these values with {the y-axis ticks labels}[0.015, 0.172, 0.035, 0.776] which represent the industries: Technology, Food, Retail, Medical, and Energy. Furthermore, each bar segment colored differently corresponds to a different year, signified by the {legend notes}[0.844, 0.819, 0.977, 0.910], delineating the years consecutively. These structured datapoints help in constructing the tableau.\nBased on the above thought, the answer is:\nIndustry,Year 1,Year 2,Year 3\nTechnology,120,140,160\nFood,110,100,90\nRetail,150,130,140\nMedical,100,110,120\nEnergy,90,95,100" } ], "id_raw": 867 }, { "id": "vetJAl9iHTQUE6hk", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_7.png", "conversations": [ { "from": "human", "value": "\nWhich industry shows the most consistent performance over the three years, according to the bar plot?" }, { "from": "gpt", "value": "To analyze consistency, I examine the changes in the bar heights for each industry. {The bars for the Food industry}[0.11, 0.41, 0.16, 0.46] show minimal changes in height over the three years, indicating slight reductions in sales yearly and no sudden spikes or deep falls, compared to the other industries showing larger fluctuations.\nBased on the above thought, the answer is:\nThe Food industry shows the most consistent performance over the three years, with sales slowly declining each year, contrasting the more substantial fluctuations observed in the other industries such as Retail and Technology." } ], "id_raw": 867 }, { "id": "AlHf7Listm85OsDm", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the total sales growth for the Technology sector from Year 1 to Year 3?" }, { "from": "gpt", "value": "Observing the values for the Technology sector in {the bar segments for Technology}[0.10, 0.30, 0.15, 0.35], it starts with sales of 120 in Year 1 and escalates to 160 in Year 3. Thus, the total sales growth is calculated by subtracting the Year 1 sales from Year 3 sales.\nBased on the above thought, the answer is:\n40 billion." } ], "id_raw": 867 }, { "id": "YpgoZv8UT7VVEKE3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_10.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data of the line chart into a tabular format with years as the rows and the technologies as the columns?" }, { "from": "gpt", "value": "From {the x-axis tick labels}[[0.070, 0.902, 0.123, 0.932],[0.285, 0.902, 0.337, 0.932], [0.499, 0.902, 0.552, 0.932], [0.714, 0.902, 0.767, 0.932], [0.928, 0.902, 0.981, 0.932]], I identified the row headers which represent the years. Then, {the legend texts}[[0.670, 0.060, 0.839, 0.085], [0.670, 0.097, 0.810, 0.122], [0.670, 0.135, 0.781, 0.160], [0.670, 0.173, 0.938, 0.198]] are recognized as column headers. To fill in the values, I extract the values corresponding to each technology at the respective years by analyzing {the line points} of each technology such as for Machine Learning [[0.096, 0.550],[0.311, 0.516],[0.526, 0.464],[0.740, 0.437],[0.955, 0.379]].\nBased on the above thought, the answer is:\nYear,Machine_Learning,Deep_Learning,Automation,Robotic_Process_Automation\n2016,40.2,36.1,65.0,55.8\n2017,44.1,42.8,70.2,65.3\n2018,50.2,48.3,75.0,70.0\n2019,53.3,52.5,80.0,73.2\n2020,60.1,60.9,83.1,76.3" } ], "id_raw": 870 }, { "id": "U6LaG6SVUqF6VFgj", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_10.png", "conversations": [ { "from": "human", "value": "\nWhich technology, according to the trends in the line chart, has experienced the most steady growth over the time period? Justify your answer." }, { "from": "gpt", "value": "To identify the technology that has the most steady growth, I need to study the line points trend for every technology. Looking at {the line for Machine Learning}[0.096, 0.379, 0.955, 0.550], it showcases a regular increment with some minor fluctuations. In case of {Deep Learning}[0.096, 0.372, 0.955, 0.585], the trend presents an almost identical approach to Machine Learning but with slightly more growth. {Automation}[0.096, 0.182, 0.955, 0.337], has experienced a fair increase overall but with minimal ups and downs. The final line is for {Robotic Process Automation}[0.096, 0.240, 0.955, 0.416] which has shown a steady growth despite being minor.\nBased on the above thought, the answer is:\nBased on the observed trends in the line chart, Robotic Process Automation has experienced the most steady growth over the time period. Despite its growth rate being minor compared to other technologies, the line representing Robotic Process Automation has fewer and smaller fluctuations, indicating its consistent growth over the years." } ], "id_raw": 870 }, { "id": "L8RRgHrp0j15sMhC", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the total percentage increase in the usage of Robotic Process Automation from 2016 to 2020?" }, { "from": "gpt", "value": "I locate {the line points for Robotic Process Automation}[[0.096, 0.416],[0.311, 0.334],[0.526, 0.294],[0.740, 0.267],[0.955, 0.240]] and extract the corresponding values: 55.8, 65.3, 70.0, 73.2, 76.3. The initial value in 2016 is 55.8 at {2016}[0.096, 0.902] and the final value in 2020 is 76.3 at {2020}[0.955, 0.902]. The percentage increase can be calculated as (76.3-55.8)/55.8 * 100.\nBased on the above thought, the answer is:\n36.7%." } ], "id_raw": 870 }, { "id": "FPLeSk9oBxmwzIm0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart representing the variations in user counts across different situations into a structured CSV format?" }, { "from": "gpt", "value": "Considering the structure of the bar chart, I can see that each subplot corresponds to a different situation, represented on {the x-axis labels}[0.508, 0.987, 0.555, 1.012]. Each bar within the subplots represents different user types as indicated by {the legend}[0.770, 0.066, 0.978, 0.192]. By examining the length and position of each bar, like {the bar for Advanced Teachers in S1}[0.120, 0.195, 0.423, 0.219], I can extract the corresponding count values. This method allows me to tabulate the counts for Beginner, Intermediate, and Advanced learners, divided into students and teachers, across the situations S1, S2, and S3.\nBased on the above thought, the answer is:\nSituation,Beginner Students,Intermediate Students,Advanced Students,Beginner Teachers,Intermediate Teachers,Advanced Teachers\nS1,250,180,150,90,210,120\nS2,230,165,140,95,245,125\nS3,210,150,130,100,180,115" } ], "id_raw": 872 }, { "id": "9nuonIwL6vyEmq2d", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_12.png", "conversations": [ { "from": "human", "value": "\nWhich situation exhibits a declining trend across all types of learners for students?" }, { "from": "gpt", "value": "To identify a declining trend across all types of learners for students, I compare the bar lengths for Beginner, Intermediate, and Advanced students across all situations. By reviewing the lengths of {the bars for students}[0.120, 0.195, 0.423, 0.236], [0.463, 0.195, 0.765, 0.236], [0.815, 0.195, 1.117, 0.236], it's clear that from {Situation S1}[0.120, 0.195, 0.423, 0.236] to {Situation S3}[0.815, 0.195, 1.117, 0.236], the bar lengths decrease consistently for each category of students. This suggests fewer students in each category as the situations progress from S1 to S3.\nBased on the above thought, the answer is:\nSituation S3 exhibits a declining trend across all types of learners for students, as evidenced by the consistently shorter bar lengths across all student categories from S1 to S3, pointing towards a reduction in student numbers." } ], "id_raw": 872 }, { "id": "d5v8nuPoGwDoKtJa", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_12.png", "conversations": [ { "from": "human", "value": "\nHow many more Intermediate Teachers are there in S2 compared to S1?" }, { "from": "gpt", "value": "To determine this, I analyze the chart and locate {the bar for Intermediate Teachers in S1}[0.120, 0.474, 0.423, 0.495] and {the bar for Intermediate Teachers in S2}[0.463, 0.459, 0.765, 0.478]. By measuring the horizontal distance of these bars, which corresponds to user counts on the x-axis, I calculate the counts as 210 for S1 and 245 for S2. The difference between these values simply requires basic subtraction, 245 (S2) minus 210 (S1).\nBased on the above thought, the answer is:\n35" } ], "id_raw": 872 }, { "id": "DwjjaQZY9WLxxvOb", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_13.png", "conversations": [ { "from": "human", "value": "\nCould you transform the specific preferences documented over the three highlighted categories of activities into a structured table?" }, { "from": "gpt", "value": "Examining the distinct division and presentation on {the chart}[0.096, 0.037, 0.981, 0.894] of Jack, Jane, and John's preference in activities 'chill at home', 'going out', and 'travel' during their leisure and work timings, I will shift these values into a structured table. Leisure and work will be clearly separated and shown for each individual.\nBased on the above thought, the answer is:\nPerson,Activity,Leisure Time,Work Time\nJack,Chill at Home,30,20\nJack,Going Out,25,15\nJack,Travel,20,10\nJane,Chill at Home,25,20\nJane,Going Out,20,25\nJane,Travel,15,15\nJohn,Chill at Home,10,5\nJohn,Going Out,15,10\nJohn,Travel,30,20" } ], "id_raw": 873 }, { "id": "7FGz5lOD0CeZEAYb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_13.png", "conversations": [ { "from": "human", "value": "\nExplaining the lifestyle patterns, which person's behavior shows a significant inclination towards a particular type of activity during leisure compared to others?" }, { "from": "gpt", "value": "Analyzing the chart patterns, the {line representing John's travel during leisure}[0.476, 0.094, 0.872, 0.201] clearly illustrates an upward trend comparably more pronounced than other activity lines. Most individuals show a balance or a downward chart, but John\u2019s line spikes distinctly, indicating preference.\nBased on the above thought, the answer is:\nJohn's lifestyle pattern during leisure shows a significant inclination towards travel, distinct from Jack and Jane. This is evident from the chart, where John spends more time traveling during his leisure, markedly higher than any of their other leisure activities." } ], "id_raw": 873 }, { "id": "TEKg5zUbArFxAQ33", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the longest time John spends on any activity during leisure hours as compared to Jack?" }, { "from": "gpt", "value": "By locating the highest value in John's activity line during leisure hours, which peaks prominently at {the travel line point for John}[0.871, 0.094], and comparing it to Jack's top value at {the Travel line point for Jack}[0.871, 0.208], one can calculate the difference. The highest value of John's leisure time is 30 minutes on travel, and Jack's highest leisure activity is 20 minutes also on travel.\nBased on the above thought, the answer is:\n10 minutes" } ], "id_raw": 873 }, { "id": "CgH97hC5ykBzrRBB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_14.png", "conversations": [ { "from": "human", "value": "\nCan you create a structured table showing the population trends across different periods for the three human species from the line chart?" }, { "from": "gpt", "value": "To generate this structured table from the line chart, I identify {the x-axis tick labels}[0.112, 0.902, 0.125, 0.932]representing the historical periods as column headers. Next, I look at {the legend}[0.641, 0.049, 0.972, 0.208] which provides the row labels or categories for each line, representing Homo Sapiens, Homo Neanderthalensis, and Homo Erectus. To populate the table, I observe the plotted line data points for each species across the different periods, noting their populations.\nBased on the above thought, the answer is:\nPeriod,Homo Sapiens,Homo Neanderthalensis,Homo Erectus\nBronze Age,60,000,0,0\nIron Age,120,000,0,0\nMiddle Ages,500,000,0,0\nEarly Modern Period,800,000,0,0\nPost-Industrial,1,500,000,0,0\nPresent,7,750,000,0,0" } ], "id_raw": 874 }, { "id": "IKiy8xbT1ZbkBF5T", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_14.png", "conversations": [ { "from": "human", "value": "\nWhy do Homo Neanderthalensis and Homo Erectus show zero population across all periods in the chart?" }, { "from": "gpt", "value": "Observing the line plots for Homo Neanderthalensis and Homo Erectus, both display a flat line of zero, indicating no population across all historical periods. This is likely because both species are known to have gone extinct prior to the periods detailed on the chart. Analyzing {the y-axis label}[0.019, 0.228, 0.044, 0.703] and the zero values repeatedly plotted at each period's pointconfirms the extinct status of these species since the Bronze Age, consistently represented in the chart.\nBased on the above thought, the answer is:\nHomo Neanderthalensis and Homo Erectus show zero population because they are considered extinct species and were extinct even before the earliest timeframe represented on the chart, which is the Bronze Age. The chart accurately reflects their status through the depicted flat lines at zero population, indicating no living individuals in any of the periods shown." } ], "id_raw": 874 }, { "id": "aexa4oD2oIm8WgVR", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the total population increase for Homo Sapiens from the Bronze Age to the Present?" }, { "from": "gpt", "value": "To calculate the total population increase for Homo Sapiens, I first identify {the population at the Present}[0.959, 0.062]approximately 7,750,000 and {the population during the Bronze Age}[0.118, 0.551] as approximately 60,000. The calculation of the difference between these two values will provide the total increase.\nBased on the above thought, the answer is:\n7,690,000" } ], "id_raw": 874 }, { "id": "GmJR9KVLgqC71kAV", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_16.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the vertical bar chart into a table format with Market Status as rows and Sales and Consumption percentages for each quarter as columns?" }, { "from": "gpt", "value": "First, I observe {the x-axis labels}[0.486, 0.931, 0.539, 0.962] which represent 'Q1', 'Q2', 'Q3', and 'Q4'. Next, I focus on {the bars corresponding to each Market Status}[0.125, 0.120, 0.900, 0.890] to assess their heights, which correspond to Sales and Consumption percentages for each quarter. Each market status has seven bars, grouped by quarter and represented by distinct colors for Sales and Consumption.\nBased on the above thought, the answer is:\nMarket Status, Q1 Sales (%), Q1 Consumption (%), Q2 Sales (%), Q2 Consumption (%), Q3 Sales (%), Q3 Consumption (%), Q4 Sales (%), Q4 Consumption (%)\nSteady Recovery, 30, 40, 32, 38, 35, 46, 33, 49\nModerate Increase, 28, 37, 29, 36, 30, 44, 28, 48\nGradual Increase, 25, 34, 26, 33, 27, 41, 25, 45\nSlow Increase, 22, 30, 23, 29, 24, 37, 22, 42\nRapid Expansion, 40, 53, 42, 55, 45, 60, 43, 63\nModerate Growth, 35, 47, 36, 49, 38, 54, 36, 57\nSlow Recovery, 20, 28, 21, 27, 23, 34, 21, 39" } ], "id_raw": 876 }, { "id": "aZT3LTCwypQIzBsU", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_16.png", "conversations": [ { "from": "human", "value": "\nWhat trends can be observed in the consumer behavior across the four quarters and how might those insights affect market strategies?" }, { "from": "gpt", "value": "Observing the stacked bars for each quarter, I find consistent growth in Consumption percentages across the four quarters {across the sub bar charts}[0.125, 0.120, 0.900, 0.890]. For example, 'Steady Recovery' starts at 40% in Q1 and grows to 49% by Q4. This gradual increase is reflected in almost all market statuses, indicating a growing consumer base or increased buying power.\nBased on the above thought, the answer is:\nThe observed trends suggest an increasing consumer engagement through the year. This growing trend highlights the potential for markets to leverage more aggressive marketing and sales strategies during the latter quarters, possibly introducing new products or persuasive promotions to capitalize on the heightened consumer activity." } ], "id_raw": 876 }, { "id": "1svprBMJ7U0bhlXX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the total percentage increase from Q1 to Q4 for Sales in the 'Moderate Growth' category?" }, { "from": "gpt", "value": "To find the percentage increase in Sales for 'Moderate Growth', I analyze the corresponding bars on the chart for Q1 and Q4 {in Moderate Growth}[0.125, 0.120, 0.900, 0.890]. The Q1 Sales is observed at 35% and Q4 Sales at 36%. The total increase is calculated by subtracting the Q1 value from the Q4 value, which is 36 - 35.\nBased on the above thought, the answer is:\n1%" } ], "id_raw": 876 }, { "id": "VYoJBFZKkwudOX9U", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_17.png", "conversations": [ { "from": "human", "value": "\nCan you create a table of the yields for each asset type per year based on the bar plot?" }, { "from": "gpt", "value": "Analyzing the bar plot, I'll break down the yields for each asset type from the years 2016 to 2019. By examining {the height of the bars for Equities for each year}[0.112, 0.294, 0.959, 0.423], {the height of the bars for Bonds for each year}[0.163, 0.320, 0.959, 0.371], {the height of the bars for Commodities for each year}[0.207, 0.337, 0.517, 0.431], and {the height of the bars for Cryptocurrency for each year}[0.251, 0.337, 0.959, 0.526], I can extract the yearly yields. Each bar gives the yield percentage where the height's proportionate difference helps in estimating the values. The years are represented through {x-axis labels}[0.112, 0.902, 0.972, 0.932], and each asset type is specified by {the distinct colors in the legend}[0.718, 0.049, 0.972, 0.208].\nBased on the above thought, the answer is:\nYear,Equities,Bonds,Commodities,Cryptocurrency\n2016,5.4,4.9,6.0,7.3\n2017,5.2,5.1,6.5,13.9\n2018,5.1,5.3,5.8,9.2\n2019,4.8,5.6,5.6,11.4" } ], "id_raw": 877 }, { "id": "kQEach0Xevbv3rCm", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_17.png", "conversations": [ { "from": "human", "value": "\nWhat does the overall trend for Equities reveal from 2016 to 2019, and how may it impact future investments?" }, { "from": "gpt", "value": "To analyze the overall trend for Equities from 2016 to 2019, I examine {the bars representing Equities}[0.112, 0.294, 0.959, 0.423]. Starting high in 2016 and noticeably decreasing each subsequent year through 2019, there's a clear downward trajectory in the yield percentages. This trend suggests that yields from Equities have been decreasing, which could indicate market instability or reduced profitability in this asset. Such a trend might caution investors about potential lower returns from Equities in the near future, influencing more conservative investment strategies.\nBased on the above thought, the answer is:\nThe overall trend for Equities from 2016 to 2019 shows a steady decline in yield percentages, suggesting a negative trend in its market value. This declining trend may lead investors to reconsider the weight of Equities in their portfolios, potentially shifting focus towards more stable or profitable assets to safeguard against anticipated lower returns from Equities in the emerging market scenarios." } ], "id_raw": 877 }, { "id": "mGaFkuqMoOR3hPzh", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_17.png", "conversations": [ { "from": "human", "value": "\nWhat was the highest yield percentage for Bonds during 2016-2019 according to the bar plot?" }, { "from": "gpt", "value": "To determine the highest yield percentage for Bonds throughout the period 2016-2019, I scan the {bars for Bonds}[0.163, 0.320, 0.959, 0.371] across all years. By comparing the heights, noticeable particularly in 2019 where the bar reaches its maximum height with {the label}[0.946, 0.902, 0.972, 0.932], the highest yield can be estimated.\nBased on the above thought, the answer is:\n5.6%" } ], "id_raw": 877 }, { "id": "YyFI83rR7CqnJjT3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_18.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information from the grouped bar chart into a structured table with Age Groups as rows and Marital Status categories as columns?" }, { "from": "gpt", "value": "To form the table from the grouped bar chart, I first identify {the x-axis tick labels}[[0.063, 0.741, 0.207, 0.932], [0.336, 0.741, 0.452, 0.896], [0.589, 0.741, 0.716, 0.910], [0.844, 0.741, 0.978, 0.919]] which denote the row labels or Age Groups. Then, I notice {the legend texts}[[0.167, 0.534, 0.226, 0.559], [0.167, 0.572, 0.226, 0.597], [0.167, 0.610, 0.226, 0.635], [0.167, 0.647, 0.226, 0.672], [0.167, 0.685, 0.226, 0.710]] which signify the different Marital Status categories. To determine the values, I correspond {the bar tops} of each Marital Status at each Age Group and extract their respective percentages.\nBased on the above thought, the answer is:\nAge Group,Single,Married,Divorced,Widowed\n20-30,65,30,3,2\n30-40,40,56,3,1\n40-50,10,75,10,5\n50-60,4,50,10,36\n60-70,2,20,10,68" } ], "id_raw": 878 }, { "id": "06CJElzkqviiefi1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_18.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, describe how the population's marital status changes as they age." }, { "from": "gpt", "value": "To describe how marital status changes with age, I interpret the increasing or decreasing trends of each bar - {Single}[0.135, 0.152, 0.135, 0.276], {Married}[0.394, 0.230, 0.394, 0.243], {Divorced}[0.652, 0.204, 0.652, 0.237], and {Widowed}[0.911, 0.132, 0.911, 0.269]. The 'Single' category sharply decreases as the age group increases, meanwhile, 'Married' appears to peak at the 40-50 age group and then falls thereafter. 'Divorced' mildly increases as age increases. In stark contrast, the 'Widowed' category sees a significant surge in population from the 50-60 age group onwards, establishing a majority by the 60-70 age group.\nBased on the above thought, the answer is:\nAs people age, the proportion of those who are 'Single' significantly decreases, practically vanishing by the 60-70 age group. The majority of individuals are 'Married' around the 40-50 age group, after which the category sees a decline. The portion of 'Divorced' individuals mildly increases with age. Notably, the proportion of 'Widowed' individuals experiences a sharp surge from the 50-60 age group onwards, becoming the majority population in the 60-70 age group." } ], "id_raw": 878 }, { "id": "6vE9hJoJZM1p8rXF", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_18.png", "conversations": [ { "from": "human", "value": "\nBy how many percent do the 'Single' individuals decrease from the 20-30 age group to the 60-70 age group?" }, { "from": "gpt", "value": "To find out the decrease in percentage of 'Single' individuals from the 20-30 age group to the 60-70 age group, I need to locate {the bar tops for 'Single'} at {20-30 age group}[0.135, 0.158] and {60-70 age group}[0.135, 0.276] and extract the percentages: 65 and 2 respectively. The decrease would be 65 - 2 = 63 percent.\nBased on the above thought, the answer is:\n63 percent." } ], "id_raw": 878 }, { "id": "QqzaswXbKgdlcoLm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_44_19.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, can you generate a structured table with Years as rows and the Growth Areas as columns?" }, { "from": "gpt", "value": "To generate the table from the line chart, I first observe {the x-axis tick labels}[[0.124, 0.902, 0.177, 0.932], [0.215, 0.902, 0.268, 0.932], [0.306, 0.902, 0.359, 0.932], [0.396, 0.902, 0.449, 0.932], [0.487, 0.902, 0.540, 0.932], [0.578, 0.902, 0.631, 0.932], [0.668, 0.902, 0.721, 0.932], [0.759, 0.902, 0.812, 0.932], [0.850, 0.902, 0.903, 0.932], [0.940, 0.902, 0.993, 0.932]], which represent the row headers or the years. Next, I identify {the legend texts}[[0.823, 0.048, 0.929, 0.073], [0.823, 0.086, 0.964, 0.111], [0.823, 0.123, 0.938, 0.148], [0.823, 0.161, 0.943, 0.186]] as the column labels representing the different growth areas. To extract the values, I locate {the line points} corresponding to each growth area's percentage growth per year, and extract the values annotated.\nBased on the above thought, the answer is:\nYear,Residential,Housing Starts,Commercial,Government\n2011,6.1,18.3,4.3,9.0\n2012,9.2,10.3,5.4,8.2\n2013,14.2,12.1,5.6,7.8\n2014,13.9,17.3,5.8,7.5\n2015,10.4,15.2,5.7,7.3\n2016,9.7,14.1,6.2,7.0\n2017,10.3,12.3,6.8,6.8\n2018,11.2,12.0,7.4,6.7\n2019,10.5,11.2,7.1,6.6\n2020,8.8,10.5,6.9,6.9" } ], "id_raw": 879 }, { "id": "XUh1n6GUOvZ6geLv", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_44_19.png", "conversations": [ { "from": "human", "value": "\nReflecting on the trends shown in the line chart, which sector shows the most steady growth over time? Give your explanation." }, { "from": "gpt", "value": "To find the sector with the most steady growth, one needs to observe the changes in the percentages represented by the corresponding lines for each. Looking at {the Residential line}[0.124, 0.296, 0.940, 0.753], the values fluctuate significantly from year to year. While the {Housing Starts line}[0.124, 0.065, 0.940, 0.516] also shows quite a significant fluctuation over the years. \nSimilarly, {the Commercial line}[0.124, 0.680, 0.940, 0.855] shows fluctuation, but in a smaller range over the years. However, if we observe, {the Government line}[0.124, 0.589, 0.940, 0.725], this line shows steady minor decreases from the start to the end year. The rate of change in the values appears to be minimal compared to the other sectors.\nBased on the above thought, the answer is:\nThe Government sector shows the most steady growth over time, although it's a decrease throughout the given years. The rate of change year to year appears to be smaller in comparison to the other sectors and the pattern of decrease is consistent throughout the years, supporting the indication of steady growth." } ], "id_raw": 879 }, { "id": "ESGTfyAQkzselMC5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_44_19.png", "conversations": [ { "from": "human", "value": "\nFrom 2011 to 2020, what is the percentage change in Residential growth?" }, { "from": "gpt", "value": "To identify the change in Residential growth from 2011 to 2020, I would need to locate the y-values of {the Residential line chart}[0.124, 0.296, 0.940, 0.753], specifically at the beginning and the end of the interval. The y-value corresponding to 2011 is {6.1}[0.124, 0.753], and the y-value corresponding to 2020 is {8.8}[0.940, 0.601]. The percentage change can be calculated using these two numbers, by subtracting the 2011 value from the 2020 value, dividing by the 2011 value, and then multiplying by 100. That gives the formula: (8.8 - 6.1) / 6.1 * 100.\nBased on the above thought, the answer is:\n44.26%" } ], "id_raw": 879 }, { "id": "V9DDuLVOdZKPHIEl", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_0.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a table format with years as columns and social media platforms as rows?" }, { "from": "gpt", "value": "To generate the table, I'll first identify the {x-axis labels}[0.089, 0.764, 0.968, 0.939] as the column headers representing years. Next, I recognize {the line labels}[0.076, 0.689, 0.258, 0.432] as the row headers for each platform. Then, from each of these platform rows, I'll find corresponding {point values}[[0.089, 0.500], [0.185, 0.330], [0.282, 0.160], [0.378, 0.228], [0.474, 0.670]] and pair them with their years to fill in the data values for each platform.\nBased on the above thought, the answer is:\nPlatform,2020,2021,2022,2023,2024\nFacebook,520,540,560,590,620\nTwitter,320,305,250,240,230\nInstagram,400,420,460,500,540\nLinkedin,210,250,280,310,350" } ], "id_raw": 880 }, { "id": "1gEQLP1GQPsbL9MX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_0.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you infer which platform had the most significant decrease in daily users over the reported period and why?" }, { "from": "gpt", "value": "First, I'll analyze the lines depicting the user trend for each platform. When observing {Twitter's line}[0.089, 0.330, 0.968, 0.126], I notice a significant drop between 2021 and 2022, which then continues to decline over the next years. Comparing this trend to {Facebook's line}[0.089, 0.500, 0.968, 0.432], {Instagram's line}[0.089, 0.670, 0.968, 0.568], and {LinkedIn's line}[0.089, 0.160, 0.968, 0.279] which all show a steady increase or comparatively less decrease, I can infer that Twitter experienced the most noteworthy decline.\nBased on the above thought, the answer is:\nThe platform that witnessed the most significant decrease in daily users over the observed period was Twitter. The line chart shows a substantial drop in user count during 2022, followed by a continuous reduction in the subsequent years. This decline is more striking when compared to other platforms, which either consistently increased their daily users or had a comparatively lesser decrease. The reason for this decline can not be derived from the chart data itself, as it would require further contextual information on Twitter's circumstances and actions during this period." } ], "id_raw": 880 }, { "id": "piQxlsP6axcEzCEx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_0.png", "conversations": [ { "from": "human", "value": "\nIn 2022, how many more daily users were there on average on Facebook compared to Twitter?" }, { "from": "gpt", "value": "First, I locate {Facebook's data point in 2022}[0.282, 0.160] and find the number of users to be 560 million. Then, I find {Twitter's data point in 2022}[0.282, 0.262] and establish that the number of users is 250 million. So, the difference in average daily users on Facebook and Twitter in 2022 is 560 million minus 250 million.\nBased on the above thought, the answer is:\n310 million." } ], "id_raw": 880 }, { "id": "GeUKvII0JCXOpvcO", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_1.png", "conversations": [ { "from": "human", "value": "\nCan you turn the data from the chart into a structured table with categories as columns and values for each year as rows?" }, { "from": "gpt", "value": "The first stage of developing a table from this line chart is to identify {the x-axis tick labels}[[0.024, 0.728, 0.068, 0.833], [0.074, 0.728, 0.136, 0.878], [0.103, 0.728, 0.204, 0.970], [0.201, 0.728, 0.272, 0.899], [0.286, 0.728, 0.341, 0.858], [0.310, 0.728, 0.409, 0.966], [0.424, 0.728, 0.477, 0.856]], as the column headers representing the different categories. The rows will be represented by the years 2022 and 2023, identified by referring to labels and subheadings. Finally, to populate the data in the table, {the line points for each category in 2022}[[0.068, 0.525], [0.136, 0.235], [0.204, 0.396], [0.272, 0.461], [0.341, 0.332], [0.409, 0.589], [0.477, 0.396]] and {the line points for each category in 2023}[[0.558, 0.428], [0.626, 0.268], [0.694, 0.300], [0.763, 0.525], [0.831, 0.235], [0.899, 0.493], [0.967, 0.300]] will be extracted and transformed into numerical values.\nBased on the above thought, the answer is:\nYear,Tradition,Individualism,Uncertainty Avoidance,Power Distance,Masculinity,Long-term Orientation,Indulgence\n2022,66,75,70,68,72,64,70\n2023,69,74,73,66,75,67,73" } ], "id_raw": 881 }, { "id": "MAFfl8i79nW36gwU", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_1.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart's progression, which category has the most significant increase between 2022 and 2023?" }, { "from": "gpt", "value": "To comprehend which variable has the most significance, each variable's line progression from 2022 to 2023 has to be analysed. When looking at {the line for Tradition}[0.068, 0.235, 0.477, 0.589], I observe a gentle increase. The climb for {Individualism}[0.136, 0.268, 0.626, 0.268] is minimal whilst {Uncertainty Avoidance}[0.204, 0.300, 0.694, 0.300] has a moderate upturn. There is a decline for {Power Distance}[0.272, 0.525, 0.763, 0.525]. The remaining categories show only slight changes.\nBased on the above thought, the answer is:\nUncertainty Avoidance has the most significant increase between 2022 and 2023. The line chart represents an upward trend for this category, indicating that there has been a significant increase in value from 2022 to 2023 compared to other categories." } ], "id_raw": 881 }, { "id": "Qm9N7QXigtHkA7Jq", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of values for Indulgence between the years 2022 and 2023?" }, { "from": "gpt", "value": "The first step to calculating the range between the two years is to locate {the value of Indulgence for the year 2022}[0.477, 0.396] and {the value of Indulgence for the year 2023}[0.967, 0.300]. With a value of 70 in 2022 and a value of 73 in 2023, we can find the range by subtracting the smallest value from the largest value, giving us a range of 3.\nBased on the above thought, the answer is:\n3" } ], "id_raw": 881 }, { "id": "NLPslfpQoW2hdTJZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_2.png", "conversations": [ { "from": "human", "value": "\nCan you give the tabulated form of project evaluations data for different years and different projects?" }, { "from": "gpt", "value": "To transform the line charts into a table, I focus on {the x-axis tick labels}[[0.043, 0.754, 0.073, 0.827], [0.110, 0.754, 0.142, 0.832], [0.179, 0.754, 0.211, 0.831], [0.241, 0.754, 0.280, 0.847], [0.258, 0.754, 0.348, 0.970], [0.382, 0.754, 0.417, 0.838],[0.426, 0.754, 0.486, 0.898]], which represent the different projects. Then, I identify the {text in legend}[[0.104, 0.626, 0.217, 0.662]], [0.104, 0.678, 0.217, 0.714]] as the different evaluation years. For each project at a specific year, I can find the values at {line points}[[0.073, 0.432], [0.142, 0.343], [0.211, 0.387], [0.280, 0.298], [0.348, 0.253], [0.417, 0.209], [0.486, 0.253], [0.554, 0.298], [0.623, 0.566], [0.692, 0.521],[0.761, 0.343], [0.829, 0.253], [0.898, 0.343], [0.967, 0.387]]. The values can be filled into the table accordingly.\nBased on the above thought, the answer is:\nProjects,Innovation 2022,Innovation 2023,Management 2022,Management 2023\nMLP,72,74,67,68\nGCN,74,75,66,69\nNCN,73,70,72,70\nNCNC,75,73,74,75\nNeoGNN-BUDDY,76,72,73,76\nSEAL,77,73,75,74\nNode2Vec,76,75,72,73" } ], "id_raw": 882 }, { "id": "X2ZiCiblRDrUhLTe", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_2.png", "conversations": [ { "from": "human", "value": "\nBased on the Innovation Project Evaluation line graph, which project has shown least inconsistencies over the years?" }, { "from": "gpt", "value": "In order to find out the project with least inconsistencies in Innovation evaluation, I need to look at the magnitude of unit changes in the {line plot for Innovation 2022}[0.073, 0.209, 0.486, 0.432] and {line plot for Innovation 2023}[0.073, 0.298, 0.486, 0.521]. Looking at the given coordinates, Node2Vec seems to show the least inconsistencies.\nBased on the above thought, the answer is:\nBased on the Innovation Project Evaluation line chart, Node2Vec appears to maintain a relatively stable and consistent performance over time. Its line plot shows minor oscillations, indicating consistent performance with marginal fluctuations across different years." } ], "id_raw": 882 }, { "id": "lY1MhscwfupOIZcz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_2.png", "conversations": [ { "from": "human", "value": "\nWhich project had the highest improvement when comparing Innovation 2022 and Management 2023 scores?" }, { "from": "gpt", "value": "In order to determine which project had the most improvement between Innovation 2022 and Management 2023, I compare {the line points for Innovation 2022}[[0.073, 0.432], [0.142, 0.343], [0.211, 0.387], [0.280, 0.298], [0.348, 0.253], [0.417, 0.209], [0.486, 0.253]] and {the line points for Management 2023}[[0.554, 0.655], [0.623, 0.700], [0.692, 0.521], [0.761, 0.343], [0.829, 0.253], [0.898, 0.297], [0.967, 0.385]] to find the project with the biggest improvement. The differences are calculated for each respective project and the project with the highest value of difference is selected as the one with the most improvement.\nBased on the above thought, the answer is:\nGCN" } ], "id_raw": 882 }, { "id": "ViLuVLvaXg30TWfu", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_3.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data showing the popularity of different car types over the years into a structured table format?" }, { "from": "gpt", "value": "Observing {the x-axis labels}[0.053, 0.075, 0.503, 0.862] which display the years from 2010 to 2025, these serve as the column headers. The rows consist of various car types namely 'Luxury cars', 'Electric Vehicles (EVs)', 'Mid-Range Cars', and 'Budget Cars'\u2014these can be identified from {the legend}[0.884, 0.101, 0.982, 0.123]. The data points for each year and car type are given by {the line points}[0.073, 0.173, 0.483, 0.705] where I extracted the necessary popularity numbers.\nBased on the above thought, the answer is:\nYear,Luxury Cars,Electric Vehicles,Mid-Range Cars,Budget Cars\n2010,500,320,600,1000\n2015,550,380,630,750\n2020,700,460,680,500\n2025,850,550,710,300" } ], "id_raw": 883 }, { "id": "9P0vohOOZrFWcdBw", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_3.png", "conversations": [ { "from": "human", "value": "\nBased on the trends observed in the line chart, what significant changes in consumer preferences can be inferred regarding car types?" }, { "from": "gpt", "value": "Analyzing the trends shown by {the line for Luxury Cars}[0.102, 0.082, 0.798, 0.196] and {the line for Electric Vehicles}[0.102, 0.211, 0.798, 0.123], it's evident that there's a substantial increase in popularity, suggesting growing consumer preference towards luxury and environmentally friendly options. Contrastively, {the line for Budget Cars}[0.102, 0.599, 0.798, 0.932] depicts a sharp decline, pointing towards a decrease in its popularity. Mid-Range Cars show mild popularity growth.\nBased on the above thought, the answer is:\nThe significant changes are the increased consumer preference for luxury and electric vehicles, indicating a shift towards more premium and environmentally friendly car options, and a marked decline in the popularity of budget cars, suggesting a decreasing preference for more economical car choices." } ], "id_raw": 883 }, { "id": "FHr3NqpAXffJAhI6", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the percent change in popularity for Electric Vehicles from 2010 to 2025?" }, { "from": "gpt", "value": "To calculate the percent change, I first locate {the point for Electric Vehicles in 2010}[0.073, 0.429] and {the point for Electric Vehicles in 2025}[0.483, 0.173]. The value in 2010 is approximately 320 and in 2025 is approximately 550. Using the formula (new value - old value)/old value * 100, I calculate (550 - 320)/320 * 100.\nBased on the above thought, the answer is:\n71.88%" } ], "id_raw": 883 }, { "id": "m1AqRDsHNhIcC9fm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_4.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the line chart showing global trade and supply chain volumes over the years into a structured table format?" }, { "from": "gpt", "value": "First, I observe the x-axis where {the year labels}[0.053, 0.920, 0.921, 0.942] are displayed from 2010 to 2020, which will form the columns of the table. {The legend}[0.820, 0.030, 0.960, 0.080] indicates two series: Global Trade and Supply Chain Management. By tracking down the plotted points for each year on both lines, I gather the data points corresponding to each series, identifying {data point coordinates for Global Trade}[0.072, 0.251, 0.470, 0.456] and {data point coordinates for Supply Chain}[0.072, 0.309, 0.470, 0.544].\nBased on the above thought, the answer is:\nYear,Global Trade,Supply Chain Management\n2010,1200,900\n2011,1250,910\n2012,1300,920\n2013,1370,930\n2014,1450,940\n2015,1550,950\n2016,1500,940\n2017,1520,945\n2018,1600,955\n2019,1620,960\n2020,1300,800" } ], "id_raw": 884 }, { "id": "v3YKXaS04pY2OJH8", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_4.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which sector shows more stability over the years, global trade or supply chain management?" }, { "from": "gpt", "value": "Observing both lines, {the Global Trade line}[0.072, 0.251, 0.470, 0.456] exhibits fluctuations with significant peaks and a sharp dip in 2020. In contrast, {the Supply Chain line}[0.072, 0.309, 0.470, 0.544] has gentler slopes and less dramatic dips, indicating minor variances year-to-year. The stability of a dataset is often signified by fewer major fluctuations and a smaller range of change, which, in this case, applies more to the supply chain management line.\nBased on the above thought, the answer is:\nBased on the chart, supply chain management exhibits more stability over the years compared to global trade. This is evident from the less dramatic fluctuations and the gentle trend displayed, despite minor increases annually." } ], "id_raw": 884 }, { "id": "Dyq34G1e6VaADntw", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_4.png", "conversations": [ { "from": "human", "value": "\nWhat was the total increase in global trade volume from 2010 to 2019?" }, { "from": "gpt", "value": "To calculate the total increase, I pinpoint the start and end data points from {the line plot of Global Trade}[0.072, 0.309, 0.470, 0.456]. In 2010, the volume was approximately 1200, located at the plot coordinate {2010 data point}[0.072, 0.309]. In 2019, the volume increased to around 1620, found at the coordinate {2019 data point}[0.450, 0.295]. The increase is then calculated by subtracting the initial volume from the volume at the end of the period.\nBased on the above thought, the answer is:\n420." } ], "id_raw": 884 }, { "id": "HQe28xzL7m33aaTf", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_6.png", "conversations": [ { "from": "human", "value": "\nLooking at the chart, can you generate a table of Consumer index for each Market for the years 2022 and 2023, under both Baseline and Trend1 scenarios?" }, { "from": "gpt", "value": "To tabulate the data, I need to extract the labels from {the x-axis tick labels}[[0.015, 0.609, 0.097, 0.804], [0.096, 0.609, 0.191, 0.838], [0.207, 0.609, 0.286, 0.797], [0.230, 0.609, 0.380, 0.970], [0.371, 0.609, 0.475, 0.859]] which contain the market names. The lines represented by the legends i.e. {Baseline 2022}[[0.129, 0.481, 0.228, 0.517]], {Baseline 2023}[[0.129, 0.533, 0.228, 0.569]], {Trend1 2022}[[0.623, 0.105, 0.709, 0.141]], and {Trend1 2023}[[0.623, 0.157, 0.709, 0.193]] correspond to respective trends. To accurately plot the numbers, I'll refer to the line coordinates from {line for Baseline 2022}[[0.097, 0.243, 0.475, 0.389]], {line for Baseline 2023}[[0.097, 0.201, 0.475, 0.285]], {line for Trend1 2022}[[0.590, 0.285, 0.969, 0.431]], and {line for Trend1 2023}[[0.590, 0.222, 0.969, 0.326]].\nBased on the above thought, the answer is:\nMarket,Baseline 2022,Trend1 2022,Baseline 2023,Trend1 2023\nEmerging-Asia,65,63,70,68\nEmerging-Europe,72,70,74,73\nLatin-America,68,67,71,70\nMiddle-East and North Africa,67,65,70,69\nSub-Saharan Africa,66,64,70,69" } ], "id_raw": 886 }, { "id": "RhPU2uBUuGdYpGoz", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_6.png", "conversations": [ { "from": "human", "value": "\nBased on the data provided and displayed on the chart, what can we infer about the trend of Consumer Index in Emerging Markets for the following years?" }, { "from": "gpt", "value": "By analysing {the lines} provided in the chart, corresponding to {Baseline 2022}[[0.097, 0.243, 0.475, 0.389]], {Baseline 2023}[[0.097, 0.201, 0.475, 0.285]], {Trend1 2022}[[0.590, 0.285, 0.969, 0.431]], and {Trend1 2023}[[0.590, 0.222, 0.969, 0.326]], we can see a general pattern of growth in Consumer Index across all markets for Baseline and Trend1 scenarios. Both scenarios present a similar growth pattern, indicating consistent market performance and positive economy prospect for these emerging markets.\nBased on the above thought, the answer is:\nExamining the chart, we can infer that the trend of Consumer Index for most part is projected to increase for all Emerging Markets from 2022 to 2023. This is indicated by higher values for both Baseline and Trend1 in 2023 compared to 2022 for each market. This suggests that the overall economy and consumer activity are expected to improve in the following year." } ], "id_raw": 886 }, { "id": "7QKcetp9FDynnYNA", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in Consumer Index from year 2022 to 2023 under the Trend1 scenario for the Middle-East and North Africa market?" }, { "from": "gpt", "value": "To answer this question, I should find the Consumer Index for Middle-East and North Africa for both 2022 and 2023 in the Trend1 scenario. I will look at {the line for Trend1 2022}[[0.590, 0.285, 0.969, 0.431]] and {the line for Trend1 2023}[[0.590, 0.222, 0.969, 0.326]] to find out the point values for Middle-East and North Africa, which are 65 and 69 respectively. The difference between these values provides the answer.\nBased on the above thought, the answer is:\n4." } ], "id_raw": 886 }, { "id": "z8zcc7M74DeZ2nfF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_7.png", "conversations": [ { "from": "human", "value": "\nCan you create a table showing the number of visitors each city received from the dataset in the line graph?" }, { "from": "gpt", "value": "Observing the ascending and descending trends on {the line graph}[[0.037, 0.060, 0.509, 0.916]], data markers indicate specific visitor numbers at various cities. I identify unique colors for each city, represented in {the legend}[[0.042, 0.071, 0.150, 0.208]], which correspond to each city on {the x-axis}[[0.047, 0.924, 0.499, 0.945]]. This allows me to methodically collect the visitor data.\nBased on the above thought, the answer is:\nLocation,Visitors (in millions)\nParis,10.0\nLondon,8.5\nRome,9.7\nVenice,6.2\nBerlin,7.5\nMadrid,7.8\nAmsterdam,5.0\nAthens,3.5" } ], "id_raw": 887 }, { "id": "NluXQoVnIkEK8asb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_7.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line graph, which city shows the most positive change in tourist visits over time?" }, { "from": "gpt", "value": "Evaluating the growth patterns, {the line for Rome}[[0.190, 0.924, 0.264, 0.786]] shows a consistent upward trajectory without any sudden drops, suggesting a steady increase in visitor numbers. This can be contrasted with {the lines for other cities}[[0.058, 0.174, 0.487, 0.827]], some of which display fluctuating patterns or a downtrend, such as {the line for Athens}[[0.409, 0.924, 0.483, 0.975]], which shows a sharp decline.\nBased on the above thought, the answer is:\nRome displays the most positive change in tourist visits over time, as evident from its consistent and steady upward trend on the graph. This suggests a growing popularity and an increasing number of tourists visiting the city compared to others which exhibit more volatility or decline." } ], "id_raw": 887 }, { "id": "h1RiYbW8dEbBedXZ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_7.png", "conversations": [ { "from": "human", "value": "\nHow much higher is the visitor number in Paris compared to Athens as shown on the graph?" }, { "from": "gpt", "value": "Through my analysis of the line points on {the line for Paris}[[0.058, 0.174, 0.117, 0.245]] and {the line for Athens}[[0.428, 0.914, 0.497, 0.975]], I extract the numbers for Paris and Athens, which are approximately 10 million and 3.5 million, respectively. The difference can be calculated by subtracting the visitors in Athens from the visitors in Paris.\nBased on the above thought, the answer is:\n6.5 million" } ], "id_raw": 887 }, { "id": "pv7vI6YFX6dipWsw", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_8.png", "conversations": [ { "from": "human", "value": "\nCan you extract the information about the \"Status in 2023\" and \"Status in 2024\" from the two line charts to form a structured table for each social category?" }, { "from": "gpt", "value": "The two subplots correspond to the status in 2023 and 2024. For every subplot, we first look at the {x-axis tick labels}[0.051, 0.932, 0.456, 0.932] and [0.540, 0.932, 0.944, 0.932] which represent the rows or categories of the table. Afterward, we extract the data accordingly.\nBased on the above thought, the answer is:\nCategory,Status in 2023,Status in 2024\nCity Life,75,80\nOnline Work,68,70\nSelf-driving Cars,80,82\nSmart Home,90,88\nAgriculture Tech,72,75\nEntertainment,85,80\nEducation Tech,78,83\nGreen Energy,76,81" } ], "id_raw": 888 }, { "id": "Y18baRSlvGkxDniX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_8.png", "conversations": [ { "from": "human", "value": "\nBased on the line charts, which social category experienced a decline from 2023 to 2024?" }, { "from": "gpt", "value": "To identify the social category that shows a decline from 2023 to 2024, I need to compare the values for each category in the two years. Looking at the line charts, I notice that the {Smart Home}[0.225, 0.932] shows a higher value in 2023 compared to {Smart Home in 2024}[0.713, 0.932] and {Entertainment in 2023}[0.340, 0.932] compared with {Entertainment in 2024}[0.828, 0.932]. Thus, these categories experienced a decline from 2023 to 2024.\nBased on the above thought, the answer is:\nSmart Home and Entertainment are the social categories that experienced a decline from 2023 to 2024. Comparing their values in the line charts for the two years, both show a decrease over time which indicates a decline." } ], "id_raw": 888 }, { "id": "mdZvrWGigR1F10sJ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_8.png", "conversations": [ { "from": "human", "value": "\nHow much growth have self-driving cars shown from 2023 to 2024?" }, { "from": "gpt", "value": "To find the growth rate of 'Self-driving Cars', I locate the coordinates corresponding to {Self-driving Cars in 2023}[0.167, 0.932] and {Self-driving Cars in 2024}[0.655, 0.932] and extract the values for both years. Growth can be calculated by taking the difference: (2024 value - 2023 value).\nBased on the above thought, the answer is:\n2." } ], "id_raw": 888 }, { "id": "j2d7GLhp5fucNZPe", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_9.png", "conversations": [ { "from": "human", "value": "\nCan you organize the recall rates for the AI models into a structured table categorized by Truthful and Misleading recalls for the years 2022 and 2023?" }, { "from": "gpt", "value": "To extract the recall rates into a structured table, I reference {the x-axis tick labels}[0.051, 0.812, 0.073, 0.858], [0.095, 0.812, 0.133, 0.889], [0.131, 0.812, 0.192, 0.934], [0.170, 0.812, 0.252, 0.975] which represent the AI models. For each model, I observe two bars; {one for Truthful recall}[0.073, 0.211, 0.073, 0.656], [0.192, 0.567, 0.192, 0.656] and {one for Misleading recall}[0.133, 0.448, 0.133, 0.448], [0.252, 0.656, 0.252, 0.656] for each year, 2022 and 2023. The values are then annotated from the heights of the bars.\nBased on the above thought, the answer is:\nModel,Truthful Recall 2022,Misleading Recall 2022,Truthful Recall 2023,Misleading Recall 2023\nLLaMA,72,41,74,42\nVicuna,75,45,77,48\nAlpaca,78,50,80,51\nWizardLM,76,44,78,46" } ], "id_raw": 889 }, { "id": "coa8OzDIbSkuNQTH", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_9.png", "conversations": [ { "from": "human", "value": "\nWhich AI model exhibits the highest consistency in recall values between Truthful and Misleading categories?" }, { "from": "gpt", "value": "To determine the consistency between Truthful and Misleading recall categories, I examine the difference in recall rates for each AI model. Observing {the bar heights for Truthful}[0.073, 0.211, 0.490, 0.656] and {Misleading recalls}[0.073, 0.448, 0.490, 0.567], Alpaca shows the smallest difference with Truthful recall at 78 and Misleading recall at 50 in 2022, and 80 vs. 51 in 2023. This suggests that Alpaca maintains the highest consistency across categories.\nBased on the above thought, the answer is:\nAlpaca exhibits the highest consistency in recall values between the Truthful and Misleading categories, as shown by the smallest margin of difference across the two values in both observed years." } ], "id_raw": 889 }, { "id": "TnWpRk7G1nN4KFdn", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the average increase in Truthful recall rates for all models from 2022 to 2023?" }, { "from": "gpt", "value": "To calculate the average increase in Truthful recall rates, I collect the recall values for both years for all AI models. Using {the points data for Truthful recalls}[0.073, 0.270, 0.490, 0.656], [0.550, 0.270, 0.967, 0.685], the values for 2022 are: LLaMA: 72, Vicuna: 75, Alpaca: 78, WizardLM: 76 and for 2023 are: LLaMA: 74, Vicuna: 77, Alpaca: 80, WizardLM: 78. I then calculate the difference and find the average of these differences.\nBased on the above thought, the answer is:\n2.5" } ], "id_raw": 889 }, { "id": "9MZAoetXhbsWbLgW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_11.png", "conversations": [ { "from": "human", "value": "\nCan you format the data from the line chart into a table that lists years, Return of Investment A, Return of Investment B, and Federal Interest Rate as columns?" }, { "from": "gpt", "value": "To create a tabular representation from the line chart, I start by identifying {the x-axis labels}[0.039, 0.872, 0.080, 0.970], [0.097, 0.872, 0.138, 0.970], [0.155, 0.872, 0.196, 0.970], [0.213, 0.872, 0.254, 0.970], [0.271, 0.872, 0.311, 0.970], [0.328, 0.872, 0.369, 0.970], [0.386, 0.872, 0.427, 0.970], [0.444, 0.872, 0.485, 0.970] which represent the years. Next, I analyze the line plots for Return of Investment A, Return of Investment B, and Federal Interest Rate by the values at {the plot points}[0.080, 0.802], [0.138, 0.693], ..., [0.485, 0.135] and extract the values corresponding at each year.\nBased on the above thought, the answer is:\nYear,Return of Investment A,Return of Investment B,Federal Interest Rate\n2006,155,153,157\n2007,138,134,142\n2008,185,179,191\n2009,155,152,158\n2010,176,171,181\n2011,167,166,168\n2012,189,188,190\n2013,195,190,200" } ], "id_raw": 891 }, { "id": "dMyNx7bXr98JZ9cw", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_11.png", "conversations": [ { "from": "human", "value": "\nHow did the outliers in the 2008, 2009, and 2020 data likely impact the understanding of financial trends on the chart and what are the possible explanations for these decreases?" }, { "from": "gpt", "value": "The chart shows significant drops in the Return of Investment A and B during 2008, 2009, and 2020, which are identified as {outliers}[0.155, 0.179, 0.351, 0.455]. These outliers can distort understanding, suggesting greater volatility than normal. Possible explanations for these decreases could be global financial events affecting the markets in those years, such as the 2008 financial crisis which led to drastic falls in investment values and had a lingering effect in 2009. As for 2020, it could be attributed to the economic impact of the COVID-19 pandemic leading to significant financial downturns.\nBased on the above thought, the answer is:\nThe outliers in the 2008, 2009, and 2020 data likely led to a perception of higher volatility and risk in financial trends depicted in the chart. Possible reasons include the 2008 global financial crisis and the economic downturn during the COVID-19 pandemic which strongly affected financial markets, illustratively decreasing the returns on investments during those specific years." } ], "id_raw": 891 }, { "id": "KNeGE82jdYibosSR", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the average annual percent increase in Federal Interest Rate from 2006 to 2010 based on the chart?" }, { "from": "gpt", "value": "To calculate the average annual percent increase, I first locate {the Federal Interest Rate values between 2006 and 2010}[0.080, 0.802, 0.311, 0.420]. The values are 157, 142, 191, 158 respectively. To find the percent increase for each year, I will subtract the previous year\u2019s value from the current year's value, divide by the previous year's value, and then multiply by 100. Then, I sum these percentages and divide by the number of years to find the average increase.\nBased on the above thought, the answer is:\n-12.06%" } ], "id_raw": 891 }, { "id": "TRKhUWnyoAh5cVHi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_12.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data from the line chart into a tabular format with Products as the rows and Years as columns?" }, { "from": "gpt", "value": "To convert the line chart to a table, I'll identify the {x-axis tick labels}[0.041, 0.824, 0.056, 0.852], [0.041, 0.698, 0.056, 0.726], [0.041, 0.571, 0.056, 0.599], [0.041, 0.444, 0.056, 0.472], [0.041, 0.317, 0.056, 0.345] as the column headers representing years. The {y-axis tick labels}[0.520, 0.845, 0.564, 0.950], [0.634, 0.845, 0.665, 0.920], [0.735, 0.845, 0.766, 0.918], [0.825, 0.845, 0.866, 0.944], [0.915, 0.845, 0.967, 0.970] will constitute the row headers corresponding to different products. The data values can be extracted from the intersection points of each product's line with the year marking, represented by coordinates such as {iPhone's 2020 sales}[0.080, 0.328], {iPad's 2020 sales}[0.181, 0.582], and so on.\nBased on the above thought, the answer is:\nProducts, 2020, 2021\niPhone, 80, 95\niPad, 60, 70\nMac, 70, 75\nWatch, 45, 60\nServices, 50, 70" } ], "id_raw": 892 }, { "id": "kYxwXcrSS5fYfaRD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_12.png", "conversations": [ { "from": "human", "value": "\nBased on the trends demonstrated by the line chart, which product has seen the highest growth in sales from 2020 to 2021?" }, { "from": "gpt", "value": "To determine which product saw the highest growth in sales from 2020 to 2021, we analyze the sales lines of each product. We notice a steep increase from 2020 to 2021 in {the sales of Watch}[0.383, 0.772] to [0.866, 0.582] and {the sales of Services}[0.483, 0.709] to [0.967, 0.455]. By plotting the sales numbers of both these products over both years, it's clearly seen that the Watch sales went up from 45 units to 60 units, and Services grew from 50 units to 70 units.\nBased on the above thought, the answer is:\nBased on the trends shown in the chart, the product 'Services' has seen the highest sales growth from 2020 to 2021. It exhibited a considerable increase from 50 to 70 units." } ], "id_raw": 892 }, { "id": "ClFSP2KxX6Ms2ZrQ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_12.png", "conversations": [ { "from": "human", "value": "\nWhat was the sales difference for iPhone between 2020 and 2021?" }, { "from": "gpt", "value": "To compute the difference in iPhone sales between 2020 and 2021, I have to identify {iPhone's 2020 sales}[0.080, 0.328] as 80 units and {iPhone's 2021 sales}[0.564, 0.138] as 95 units. Therefore, the difference is 95 - 80 = 15 units.\nBased on the above thought, the answer is:\n15." } ], "id_raw": 892 }, { "id": "m99RADjTY4C5M6FG", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_13.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data from the multi-line chart showing technology trends into a structured table categorizing each year as a row and each technology as a column?" }, { "from": "gpt", "value": "First, I need to identify {the labels on the x-axis}[0.037, 0.685, 0.943, 0.821] which denote the years from 2017 to 2021. These will serve as row labels. Next, I locate {the legend}[0.844, 0.091, 0.977, 0.207] detailing the technologies: Blockchain, AI, Cloud Computing, Cyber Security, and Quantum Computing, to form the column headers. I then observe {the points on the lines}[0.105, 0.180, 0.943, 0.570] for each technology per year and extract the corresponding values.\nBased on the above thought, the answer is:\nYear,Blockchain,AI,Cloud Computing,Cyber Security,Quantum Computing\n2017,45,50,55,50,40\n2018,50,56,60,55,45\n2019,56,62,65,60,50\n2020,63,68,70,65,55\n2021,70,75,75,70,60" } ], "id_raw": 893 }, { "id": "RLvmtydAxZ4Sat8L", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_13.png", "conversations": [ { "from": "human", "value": "\nWhich technology demonstrated the most consistent growth rate over the observed period in the chart?" }, { "from": "gpt", "value": "Upon examining {each technology's line trajectory}[0.105, 0.180, 0.943, 0.277], it shows that the lines for Blockchain, AI, Cloud Computing, and Cyber Security have similar steepness, indicative of consistent year-over-year growth. {The line for Quantum Computing}[0.105, 0.492, 0.943, 0.102], while also consistent, portrays a slightly less steep incline, indicating a lesser growth rate. To determine the most consistent among the higher growth technologies, they all appear equivalent in their progression.\nBased on the above thought, the answer is:\nTechnologies like Blockchain, AI, Cloud Computing, and Cyber Security have demonstrated the most consistent and similar growth rates according to the chart. Their lines progress almost parallel to each other, signifying a steady and robust increase in popularity each year." } ], "id_raw": 893 }, { "id": "dtSr76o5TQ61LPqN", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_13.png", "conversations": [ { "from": "human", "value": "\nFor the technology 'Quantum Computing', what is the increase in popularity from 2017 to 2021?" }, { "from": "gpt", "value": "To determine the increase, I analyze {the line representing Quantum Computing}[0.105, 0.492, 0.943, 0.102]. The value for 2017 is noted at 40, seen at {the point in 2017 for Quantum Computing}[0.105, 0.492], and for 2021, it is 60, noted at {the point in 2021 for Quantum Computing}[0.943, 0.102]. The increase is calculated by subtracting the 2017 value from the 2021 value.\nBased on the above thought, the answer is:\n20" } ], "id_raw": 893 }, { "id": "9ikdgtaYFHpYRdCs", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_14.png", "conversations": [ { "from": "human", "value": "\nCan the data regarding recall measurements for different categories in health settings be structured into a CSV format?" }, { "from": "gpt", "value": "To prepare the data in CSV format, I closely examined the chart to identify the different categories described by {the y-axis titles on both sides}[[0.013, 0.321, 0.029, 0.678], [0.522, 0.499, 0.522, 0.499]]. There are two categories on the left hand side y-axis: 'Health State Recall' and 'BMI Recall', and one category on the right-hand side y-axis: 'Mortality Recall'. Identifying {the bars}[[0.053, 0.075, 0.172, 0.924], [0.527, 0.075, 0.642, 0.924]] corresponding to 'Hospice' and 'Clinical Treatment' settings, I delineated and extracted the numerical values visually represented by the height of these bars.\nBased on the above thought, the answer is:\nSetting,Health State Recall,BMI Recall,Mortality Recall\nHospice,40,20,15\nClinical Treatment,80,70,65" } ], "id_raw": 894 }, { "id": "1dN0zevjaso3qpK2", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_14.png", "conversations": [ { "from": "human", "value": "\nWhy is the 'Mortality Recall' lower in both health settings compared to other recall categories?" }, { "from": "gpt", "value": "To understand why 'Mortality Recall' is comparatively lower, I start by observing the height of {the bars for Mortality Recall}[[0.490, 0.147], [0.965, 0.123]]. These bars' lower heights relative to {the bars for other categories in both settings}[[0.073, 0.803], [0.252, 0.317]] suggest a lesser numerical value. This indicates that patients have greater difficulty recalling mortality rates and probabilities, possibly due to the complexity of the information related to mortality compared to recalling their own health state or BMI, which could be more directly observable and relevant to their everyday lives.\nBased on the above thought, the answer is:\nThe 'Mortality Recall' is lower because recalling mortality rates and probabilities is inherently more complex and less directly related to patients' everyday experiences and observations, making it harder to recall compared to their own health state or BMI." } ], "id_raw": 894 }, { "id": "tZcs3UJeoIrOpQQ1", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_14.png", "conversations": [ { "from": "human", "value": "\nHow much improvement in 'Health State Recall' is observed between Hospice and Clinical Treatment settings?" }, { "from": "gpt", "value": "Analyzing the improvement in 'Health State Recall', initially I locate {the bars for Health State Recall in Hospice and Clinical Treatment settings}[[0.073, 0.851], [0.252, 0.317]]. These bars respectively start at an estimated visual value of 40 for Hospice and rise to 80 in Clinical Treatment. The improvement can be calculated by subtracting the value in Hospice from the value in Clinical Treatment (80 - 40).\nBased on the above thought, the answer is:\n40" } ], "id_raw": 894 }, { "id": "M2DIzySE50qCZivv", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_15.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data for the trend of social activities and lifestyles between 2022 and 2023 into tabular format?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.056, 0.934, 0.091, 0.970], [0.473, 0.934, 0.508, 0.970]] which represent the column headers or years. Next, I note that there are two categories: {Social Activities}[[0.104, 0.105, 0.213, 0.141]] and {Lifestyles}[[0.578, 0.105, 0.637, 0.141]], which will become the row labels. Lastly, I locate {the points for Social Activities}[[0.073, 0.287], [0.490, 0.712]] and {the points for Lifestyles}[[0.548, 0.754], [0.965, 0.797]] to determine the corresponding percentages for each year.\nBased on the above thought, the answer is:\nCategory,2022,2023\nSocial Activities,70,80\nLifestyles,68,69" } ], "id_raw": 895 }, { "id": "30pOt05gVRRfcdOl", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_15.png", "conversations": [ { "from": "human", "value": "\nWhich category exhibited a more stable trend over the years 2022 and 2023, Social Activities or Lifestyles? Provide a brief explanation." }, { "from": "gpt", "value": "To determine which category exhibited a more stable trend, I must compare the degree of fluctuation across the two years for each category. I observe that {the points for Social Activities}[[0.073, 0.712], [0.490, 0.287]] depict a change from 70 to 80. Similarly, {the points for Lifestyles}[[0.548, 0.754], [0.965, 0.797]] show a change from 68 to 69. The change in percentage for Social Activities is greater than the change in Lifestyles, indicating a larger fluctuation.\nBased on the above thought, the answer is:\nThe category Lifestyles exhibited a more stable trend over the years 2022 and 2023. While both categories saw an increase during this period, the increase for Lifestyles was smaller, indicating less fluctuation and therefore more stability." } ], "id_raw": 895 }, { "id": "sIOb4RFKW0S0Dp0w", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_15.png", "conversations": [ { "from": "human", "value": "\nBy how much percentage did the interest in Social Activities increase from 2022 to 2023?" }, { "from": "gpt", "value": "To answer this question, I need to find the percentage values for Social Activities in 2022 and 2023. These are represented by {the points for Social Activities}[[0.073, 0.712], [0.490, 0.287]], from which I extract the corresponding values: 70 for 2022 and 80 for 2023. I calculate the increase as 80 - 70 = 10.\nBased on the above thought, the answer is:\n10%." } ], "id_raw": 895 }, { "id": "IflwFKKlDrgcgqIH", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_16.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart showing media outlet influence into a structured table?" }, { "from": "gpt", "value": "To convert the bar chart data into a table, I first identify {the x-axis labels}[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903], [0.397, 0.773, 0.504, 0.934] representing the media outlets: Newspaper, Radio, Television, and Social Media. Then I observe the {height of each bar}[0.061, 0.500, 0.144, 0.600], [0.178, 0.400, 0.259, 0.700], [0.291, 0.300, 0.378, 0.850], [0.397, 0.300, 0.504, 0.450] to determine the level of social influence for each category.\nBased on the above thought, the answer is:\nMedia Outlet, Social Influence\nNewspaper, 50\nRadio, 60\nTelevision, 85\nSocial Media, 45" } ], "id_raw": 896 }, { "id": "fVZJRJ3anStdigZr", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_16.png", "conversations": [ { "from": "human", "value": "\nWhat trend can be observed in the bar chart for media outlets' impact on public opinion from Newspapers to Social Media?" }, { "from": "gpt", "value": "Upon analyzing the bar chart, the trend shows a gradual increase in influence from {Newspapers to Radio to Television}[0.061, 0.500, 0.144, 0.600], [0.178, 0.400, 0.259, 0.700], [0.291, 0.300, 0.378, 0.850], indicating more conventional media types hold a robust impact. However, there's a notable decrease moving from {Television to Social Media}[0.291, 0.300, 0.378, 0.850], [0.397, 0.300, 0.504, 0.450] suggesting a volatile impact of new media forms like Social Media.\nBased on the above thought, the answer is:\nThe trend from Newspapers through Television indicates increasing media influence on public opinion, peaking with Television. However, this trend takes a downward turn with Social Media, showcasing its less predictable impact on public opinion." } ], "id_raw": 896 }, { "id": "twmkGjRSfvityXOn", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_16.png", "conversations": [ { "from": "human", "value": "\nHow much more influence does Television have compared to Social Media according to the chart?" }, { "from": "gpt", "value": "To calculate the difference in influence between Television and Social Media, I refer to their {bar heights}[0.291, 0.300, 0.378, 0.850], [0.397, 0.300, 0.504, 0.450] which show influences of 85 and 45 respectively. The difference is 85 - 45.\nBased on the above thought, the answer is:\n40" } ], "id_raw": 896 }, { "id": "bulmf6WIO8bYKh17", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_17.png", "conversations": [ { "from": "human", "value": "\nCould you please extract the values from the graph and tabulate them in the form of a table, having the 'Year' as the column and 'Market Value' as rows for both Residential and Commercial sectors?" }, { "from": "gpt", "value": "To create the table, I need to note the Years from {the x-axis labels}[0.046, 0.960, 0.170, 0.996], [0.227, 0.960, 0.328, 0.996], [0.421, 0.960, 0.514, 0.996], [0.614, 0.960, 0.705, 0.996], [0.808, 0.960, 0.902, 0.996] and use them as the column headers. The legend containing {Residential}[0.915, 0.296, 0.962, 0.320] and {Commercial}[0.915, 0.264, 0.965, 0.287] will represent the row labels. The corresponding y-values can be extracted from {points on Residential line}[0.073, 0.459], [0.250, 0.446], [0.436, 0.583], [0.616, 0.701], [0.797, 0.830] and {points on Commercial line}[0.073, 0.349], [0.250, 0.311], [0.436, 0.138], [0.616, 0.059], [0.797, 0.019] which are the market values in thousands of dollars.\nBased on the above thought, the answer is:\n,2016,2017,2018,2019,2020\nResidential,224,216,241,273,314\nCommercial,167,150,86,45,10" } ], "id_raw": 897 }, { "id": "GGElyb9iGeZumvYz", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_17.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, which sector shows the most drastic change over the years?" }, { "from": "gpt", "value": "To answer this, I need to compare the line plots for both the residential and commercial sectors. The {Residential line}[0.073, 0.459, 0.797, 0.830] shows a steady decline from 224 to 216, then gradual increase to 314. On the other hand, {the Commercial line}[0.073, 0.349, 0.797, 0.019] shows a sharp drop from 167 to 10. The drop in the commercial sector is more drastic compared to the relatively slower pace in the residential sector.\nBased on the above thought, the answer is:\nThe commercial sector shows the most drastic change over the years, as it undergoes a sharp and continuous decrease in market value from 167 in 2016 to 10 in 2020. In contrast, the residential sector experiences a moderate decline followed by a gradual increase." } ], "id_raw": 897 }, { "id": "IEgqaz3Lj0joF5Vn", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_17.png", "conversations": [ { "from": "human", "value": "\nWhat's the total change in the residential sector market value from 2016 to 2020?" }, { "from": "gpt", "value": "In order to calculate the total change in the residential sector market value, I need to locate the residential market value {in 2016}[0.073, 0.459] which is 224 (in thousands of dollars) and {in 2020}[0.797, 0.830] which is 314. Subtracting the value of 2016 from 2020 will give the total change. So, the calculation is 314 - 224 = 90.\nBased on the above thought, the answer is:\n90." } ], "id_raw": 897 }, { "id": "Om8QEBm8r3ifmq9g", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_18.png", "conversations": [ { "from": "human", "value": "\nCan you compile the impact levels for Policy A for both 2020 and 2021 into a table format based on the provided chart data?" }, { "from": "gpt", "value": "First, I identify {the x-axis labels}[[0.016, 0.764, 0.089, 0.939], [0.058, 0.764, 0.144, 0.970], [0.148, 0.764, 0.199, 0.885], [0.195, 0.764, 0.254, 0.906], [0.245, 0.764, 0.309, 0.917], [0.283, 0.764, 0.364, 0.959], [0.345, 0.764, 0.419, 0.941], [0.423, 0.764, 0.474, 0.887]] which lay out the categories such as Environment, Unemployment, Inflation, etc. Next, I notice {the lines for Policy A 2020 and Policy A 2021}[[0.089, 0.136, 0.474, 0.445], [0.089, 0.136, 0.474, 0.482]] indicating a transition in metrics between the two years across these categories. By evaluating {the precise point coordinates for both years of Policy A}[[0.089, 0.260], [0.144, 0.383], [0.199, 0.445], [0.254, 0.198], [0.309, 0.383], [0.364, 0.322], [0.419, 0.136], [0.474, 0.445], [0.089, 0.297], [0.144, 0.359], [0.199, 0.482], [0.254, 0.136], [0.309, 0.408], [0.364, 0.260], [0.419, 0.198], [0.474, 0.383]], I extract the impact levels for each category in both policy years.\nBased on the above thought, the answer is:\nCategory,Policy A 2020,Policy A 2021\nEnvironment,80,77\nUnemployment,70,72\nInflation,65,62\nEducation,85,90\nHealthcare,70,68\nTransportation,75,80\nPublic Safety,90,85\nTaxation,65,70" } ], "id_raw": 898 }, { "id": "jAR2nDRB65NaGTXF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_18.png", "conversations": [ { "from": "human", "value": "\nWhy does the Policy A seem more effective in Public Safety compared to other categories in both 2020 and 2021?" }, { "from": "gpt", "value": "To understand why Policy A is notably effective in Public Safety, I observe {the points for Public Safety}[[0.419, 0.136], [0.419, 0.198]]. These points indicate the highest impact levels among all categories with values 90 in 2020 and 85 in 2021, which are relatively high compared to other categories. This suggests that the design or focus of Policy A potentially prioritizes or effectively addresses issues within public safety sectors, resulting in high positive impact in these areas consistently across the two years.\nBased on the above thought, the answer is:\nPolicy A seems more effective in Public Safety because the impact levels, measuring at 90 in 2020 and 85 in 2021, are the highest among all the categories listed. This consistently high performance suggests a special emphasis or effectiveness of this policy specifically in public safety measures." } ], "id_raw": 898 }, { "id": "8weAYr5Ub5b1N1D5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_18.png", "conversations": [ { "from": "human", "value": "\nHow much has the impact level for Education improved from Policy A 2020 to Policy A 2021?" }, { "from": "gpt", "value": "Observing the impact levels for Education in Policy A 2020 and Policy A 2021, I locate {the specific points for Education}[[0.254, 0.198], [0.254, 0.136]]. The values are 85 for 2020 and 90 for 2021. To calculate the improvement, I subtract the 2020 value from the 2021 value.\nBased on the above thought, the answer is:\n5" } ], "id_raw": 898 }, { "id": "ETEVFs0cF6XdvTWM", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_45_19.png", "conversations": [ { "from": "human", "value": "\nCan you create a table that represents the findings in 2022 and 2023 for each method in the line chart?" }, { "from": "gpt", "value": "To generate a table from the line chart data, I'd start by identifying {the x-axis tick labels}[[0.129, 0.668], [0.187, 0.668], [0.245, 0.668], [0.303, 0.668], [0.361, 0.668], [0.418, 0.668], [0.476, 0.668]]. These would serve as row labels, each referring to a different new media method. I can use {the y-axis labels for 2022}[[0.111, 0.583], [0.111, 0.499], [0.111, 0.329], [0.111, 0.244]] and {the y-axis labels for 2023}[[0.605, 0.583], [0.605, 0.499], [0.605, 0.329], [0.605, 0.244]] to get the values in 2022 and 2023 respectively for each method. These would fill up the numerical data in the table.\nBased on the above thought, the answer is:\nNew Media Method,Performance Score 2022,Performance Score 2023\nAI Content Generator,22,18\nSocial Media Platforms,25,20\nSEO Optimization,31,23\nEmail Marketing,20,17\nVR/AR Content,15,13\nInfluencer Marketing,28,31\nVideo Marketing,35,40" } ], "id_raw": 899 }, { "id": "z4PEeg0rgkRMbtc5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_45_19.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which New Media method has shown a positive trend from 2022 to 2023?" }, { "from": "gpt", "value": "To answer that, I have to find a new media method that has a higher performance score in 2023 compared to 2022. Upon examining {the performance scores in 2022}[[0.129, 0.465], [0.187, 0.414], [0.245, 0.312], [0.303, 0.499], [0.361, 0.583], [0.418, 0.363], [0.476, 0.244]] and {the performance scores in 2023}[[0.623, 0.533], [0.680, 0.499], [0.738, 0.448], [0.796, 0.550], [0.854, 0.617], [0.912, 0.312], [0.970, 0.159]], I can spot that Influencer Marketing had a higher score in 2023.\nBased on the above thought, the answer is:\nInfluencer Marketing saw a positive trend from 2022 to 2023, increasing its performance score from 28 to 31." } ], "id_raw": 899 }, { "id": "emSAkSZoSRfnxCAW", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_45_19.png", "conversations": [ { "from": "human", "value": "\nWhat's the total change in performance score for AI Content Generator from 2022 to 2023?" }, { "from": "gpt", "value": "To find out the total change in performance score for AI Content Generator, first I need to spot {the value for AI Content Generator in 2022}[0.129, 0.465] which is 22, then find out {the value for AI Content Generator in 2023}[0.623, 0.533] which is 18. The difference between the two scores is the total change in performance.\nBased on the above thought, the answer is:\n-4." } ], "id_raw": 899 }, { "id": "NRggLiUyzcRQUWjR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_0.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart showing business process efficiency into a structured table with stages as columns?" }, { "from": "gpt", "value": "By analyzing the line chart, I can extract the structured data based on {the x-axis labeled as stages}[[0.10, 0.90, 0.10, 0.12]] and the corresponding {data points for each line}[[0.15, 0.25], [0.35, 0.55], [0.55, 0.20], [0.75, 0.85], [0.95, 0.70]]. The stages represent 'Initial', 'Planning', 'Execution', 'Review', and 'Improvement', and I identify the efficiency values for both the 'Increase' and 'Decrease' series.\nBased on the above thought, the answer is:\nStage,Decrease Efficiency,Increase Efficiency\nInitial,200,250\nPlanning,300,350\nExecution,150,400\nReview,350,450\nImprovement,500,600" } ], "id_raw": 900 }, { "id": "XtHQZXHAVcWnLERR", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_0.png", "conversations": [ { "from": "human", "value": "\nWhy is there a significant dip in efficiency during the 'Execution' stage for the Decrease series as seen in the chart?" }, { "from": "gpt", "value": "Observing the 'Decrease' series line, specifically at the {Execution stage point}[[0.55, 0.20]], there is a noticeable decline to 150 in efficiency. This drop signifies a critical point where the process might encounter operational challenges or inefficiencies, which is further evidenced by the point\u2019s sudden decline in value when compared to other stages. The 'Execution' stage often involves complex implementations and the manifestation of unforeseen issues, contributing to this observed decrease.\nBased on the above thought, the answer is:\nThe significant dip in the 'Execution' stage for the Decrease series indicates operational challenges or inefficiencies that tend to arise during this critical phase of the business process. This stage often encompasses the practical application of plans, which can reveal unforeseen issues and lead to a decline in efficiency as observed in the chart." } ], "id_raw": 900 }, { "id": "kYIb6MN0ZRck7zJl", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_0.png", "conversations": [ { "from": "human", "value": "\nHow much higher is the efficiency in the 'Improvement' stage compared to the 'Execution' stage for the Increase series?" }, { "from": "gpt", "value": "First, I locate the efficiency value for the Increase series at the {Execution stage}[[0.55, 0.20]], which is 400. Then, I find the value at the {Improvement stage}[[0.95, 0.70]], which is 600. By subtracting the efficiency at the Execution stage from that of the Improvement stage, we can calculate the increase.\nBased on the above thought, the answer is:\n200" } ], "id_raw": 900 }, { "id": "kLkhhzBzcLencSfS", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data presented in the line chart comparing the activity levels of Traditional Education and Online Education over time into a structured table?" }, { "from": "gpt", "value": "First, I review the coordinates for each point along the line representing Traditional Education: {points for Traditional Education}[0.102, 0.301, 0.975, 0.418] where specific hours are marked at critical points {x-tick marks for Traditional Education}[0.095, 0.434, 0.110, 0.448], [0.277, 0.434, 0.291, 0.448], ..., [0.997, 0.434, 1.026, 0.448]. I then proceed to examine the line for Online Education and its respective data points using similar methods: {points for Online Education}[0.102, 0.570, 0.975, 0.870] matched to the same x-tick marks to ensure time consistency across both data sets.\nBased on the above thought, the answer is:\nTime (hours),Traditional Education,Online Education\n0,1.0,0.46\n1,1.05,0.66\n2,1.15,0.76\n3,0.95,0.96\n4,0.85,1.26\n5,0.84,1.39\n6,0.75,1.58\n7,0.50,1.5\n8,0.55,1.7\n9,0.56,1.44\n10,0.60,1.42\n11,0.58,1.40\n12,0.55,1.41\n13,0.42,1.62\n14,0.35,1.8\n15,0.24,1.9\n16,0.24,2.0\n17,0.20,2.1\n18,0.23,2.2\n19,0.24,2.4\n20,0.15,2.5\n21,0.14,2.6\n22,0.14,2.75\n23,0.10,3.0\n24,0.12,3.14" } ], "id_raw": 902 }, { "id": "2Er8cUECcIeodJfb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_2.png", "conversations": [ { "from": "human", "value": "\nHow does the trend of activity level for Traditional Education over time compare to that of Online Education?" }, { "from": "gpt", "value": "By analyzing the trend lines on the chart, I observe that the line for Traditional Education, located between {coordinates for Traditional Education Line}[0.102, 0.301, 0.975, 0.418], generally decreases or remains stable for most of the time period, with the lowest activity levels observed towards the latter part. In contrast, the line for Online Education, described in {coordinates for Online Education Line}[0.102, 0.570, 0.975, 0.870], shows a clear upward trajectory with activity levels consistently increasing over the same period.\nBased on the above thought, the answer is:\nThe trend for Traditional Education mostly decreases or remains stable over time, indicating a reduction or stagnation in activity levels. Meanwhile, Online Education displays a progressively increasing trend, suggesting a rising interest and engagement over the time period. This comparative analysis of the two lines illustrates significant differences in how the activity levels evolve in each educational model." } ], "id_raw": 902 }, { "id": "xxIpBf7SfCtjx5IH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in activity level for Online Education from the start to the 24th hour?" }, { "from": "gpt", "value": "To calculate the total increase, I check the activity level at the start hour {initial point of Online Education}[0.102, 0.870] and compare it with the activity level at the 24th hour {final point of Online Education}[0.975, 0.570]. The activity level increased from 0.46 to 3.14 over 24 hours.\nBased on the above thought, the answer is:\n2.68." } ], "id_raw": 902 }, { "id": "DotGLrVrtJRtwD2P", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_4.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the chart and reformat it into a structured table with Time periods as rows and financial characteristics as columns?" }, { "from": "gpt", "value": "To generate the tabular data from the chart, I first Identify {the x-axis}[[0.081, 0.395, 0.132, 0.409], [0.251, 0.395, 0.301, 0.409], [0.420, 0.395, 0.470, 0.409], [0.590, 0.395, 0.640, 0.409], [0.759, 0.395, 0.809, 0.409], [0.929, 0.395, 0.979, 0.409]] labels as rows representing the time periods: 6_months, 12_months, 18_months, and 24_months. Next, I observe {the legend}[0.201, 0.468, 0.428, 0.493] to identify the columns corresponding to different financial characteristics: Investment_Return, Risk_Level. To populate the values, I locate and extract the {bar heights}[[0.106, 0.084, 0.954, 0.332], [0.106, 0.565, 0.954, 0.839]] for each financial characteristic at the respective time periods.\nBased on the above thought, the answer is:\nTime,Investment_Return,Risk_Level\n6_Months,3,2\n12_Months,6,2\n18_Months,4,4\n24_Months,5,3" } ], "id_raw": 904 }, { "id": "NYtwIOO7eoCLYEsq", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_4.png", "conversations": [ { "from": "human", "value": "\nHow does the Risk_Level change with the time periods based on the chart, and what might be a potential reason behind this observed pattern?" }, { "from": "gpt", "value": "Looking at {the Risk_Level bars}[[0.276, 0.565, 0.615, 0.699], [0.784, 0.839, 0.954, 0.881]], I see a pattern of increasing risk over time, with risk levels of 2, 2, 4, and 3 during the 6_months, 12_months, 18_months and 24_months periods respectively. This pattern suggests an unpredictable fluctuations trend as they indicate a slight increase at the beginning, remained constant during the second period, followed by a sharp increase in the third period, and finishing with a decrease. The change in risk level might be due to change in market conditions or investment strategies over different periods.\nBased on the above thought, the answer is:\nThe Risk_Level increases, remains constant, then has a sharp increase before decreasing across the four periods according to the chart figure. This pattern may indicate the volatility of investments and change in market conditions over time, which result in unpredicted ups and downs of the risk. It reflects the uncertainty and variability associated with the returns of the investments in the portfolio. The increase in risk could be due to more risky investments being included in the portfolio or market instability whereas the subsequent decrease may represent more conservative investment decisions or stability in the market." } ], "id_raw": 904 }, { "id": "VONMa0behQwh62jj", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the average Investment_Return over all periods presented in the chart?" }, { "from": "gpt", "value": "To find the average Investment Return, I need to locate and sum all the {Investment Return bar heights}[[0.106, 0.084, 0.191, 0.273], [0.276, 0.285, 0.361, 0.302], [0.445, 0.255, 0.530, 0.239], [0.615, 0.196, 0.699, 0.179], [0.784, 0.149, 0.869, 0.096],[0.954, 0.084]] corresponding to all periods, which are 3, 6, 4, and 5, and then divide the sum by the total number of periods, which is 4. Therefore, the average Investment Return is calculated as (3+6+4+5)/4 = 4.5.\nBased on the above thought, the answer is:\n4.5" } ], "id_raw": 904 }, { "id": "cGV5IKHsjPoGghUl", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data displayed in the line chart regarding big data integration techniques into a CSV format where each row represents a technique and the columns show the data size in terabytes for years 1990 through 2020?" }, { "from": "gpt", "value": "To transform the data from the line chart into a table, I will analyze {the line plots}[0.110, 0.021, 0.642, 0.454] and correlate the data points {line points for each technique}[0.110, 0.269], [0.642, 0.282], along with the x-axis years {xticks labels}[0.080, 0.459, 0.139, 0.473], [0.613, 0.459, 0.671, 0.473]. I will assess the data values at these points, confirming that they align with the respective years and techniques.\nBased on the above thought, the answer is:\nTechnique,1990,1995,2000,2005,2010,2015,2020\nDiverse Data Gathering,1.2,1.3,1.4,1.7,1.9,2.1,2.3\nData Cleansing,0.8,0.5,-0.7,0.2,0,0.4,0.2\nData Transformation,1.7,2.0,2.3,2.5,2.7,2.9,3.0\nParallel Data Processing,1.5,1.6,1.7,1.9,2.1,2.2,2.3\nEnjoy Driven Data,1.9,2.2,2.5,2.7,2.9,3.0,3.0\nLarge Data Indexing,1.8,1.9,2.0,2.0,2.1,2.1,2.1" } ], "id_raw": 905 }, { "id": "L8YvnrfaAeDa6K4s", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_5.png", "conversations": [ { "from": "human", "value": "\nWhich big data integration technique shows a negative trend according to the line chart?" }, { "from": "gpt", "value": "To identify a technique with a negative trend, I focus on observing {line plots}[0.110, 0.021, 0.642, 0.454]. I found that {the line for Data Cleansing}[0.110, 0.307, 0.642, 0.424] starts at a higher point and ends lower, which visually represents a decline.\nBased on the above thought, the answer is:\nThe 'Data Cleansing' technique shows a negative trend as evidenced by the downward trajectory of its line plot in the chart. This indicates a reduction in the total data size handled by this technique over time." } ], "id_raw": 905 }, { "id": "3Ni3tlZ7yJ3fVjND", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in terabytes for the 'Enjoy Driven Data' technique from 1990 to 2020 based on the chart?" }, { "from": "gpt", "value": "Using the plot data for 'Enjoy Driven Data', I located the values at the beginning {starting point}[0.110, 0.240] and the end {ending point}[0.642, 0.186] of the timeline in the chart. The initial value is 1.9 and the value in 2020 is 3.0. I then calculated the difference between these two points.\nBased on the above thought, the answer is:\n1.1" } ], "id_raw": 905 }, { "id": "XRgdPzOMNvrEgwxj", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_6.png", "conversations": [ { "from": "human", "value": "\nCould you create a table showing the growth trends of IP measures for both types of companies as depicted in the bar chart?" }, { "from": "gpt", "value": "I first identify {the y-axis labels}[0.015, 0.173, 0.029, 0.318] which outline the different IP measures: Inventions, Patents, Licensing, and Development. For each measure, I find the corresponding bars for both Tech Giant and Startup, which are visually distinguished by their colors and patterns. Observing the bars for each IP measure, I note their heights to represent either growth or decline. I extract values by closely analyzing the height comparison between the zero line (indicating no change) and the bar tops for each category.\nBased on the above thought, the answer is:\nMeasure,Tech Giant,Startup\nInventions,High Growth,Moderate Growth\nPatents,Slight Decline,Unpredictable Decline\nLicensing,High Growth,Rapid Growth\nDevelopment,Accelerated Decline,Subdued Decline" } ], "id_raw": 906 }, { "id": "pRXCoouXT1riFC47", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_6.png", "conversations": [ { "from": "human", "value": "\nWhat could be the reason for Tech Giant's slight decline in patents while observing growth in other areas?" }, { "from": "gpt", "value": "Analyzing {the bars for the Tech Giant across all IP measures}[0.105, 0.124, 0.190, 0.473], [0.195, 0.124, 0.281, 0.423], [0.285, 0.124, 0.371, 0.623], [0.375, 0.124, 0.461, 0.323], it is apparent that except for patents, all other areas exhibit a positive growth. The decline in patents, noticeable by the bar's placement below the zero growth line and shorter compared to others, could be linked to strategic shifts in focus towards more immediately monetizable innovations like Licensing, or perhaps reallocating resources towards rapidly growing sectors like Inventions. This misalignment might represent a strategic decision rather than an overall negative progression.\nBased on the above thought, the answer is:\nThe slight decline in patents in the Tech Giant could be due to shifting company focus and resources towards sectors displaying rapid growth like Licensing. This strategic reallocation helps capitalize on immediate revenue-generating opportunities while patents, typically longer-term investments, receive comparatively less attention." } ], "id_raw": 906 }, { "id": "Z4gmE2dgxzqLlQCC", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_6.png", "conversations": [ { "from": "human", "value": "\nBy how much does the Licensing growth in Tech Giant exceed the Licensing growth in Startup?" }, { "from": "gpt", "value": "Initially, I observe {the bars for Licensing in Tech Giant and Startup}[0.219, 0.124, 0.304, 0.573], [0.321, 0.124, 0.406, 0.673]. Analyzing these, I see that the Tech Giant's bar extends significantly above the zero growth line while the Startup's bar, although also above the line, is less so. From visual estimations and correlating with the axes scales, I calculate the differences in their heights above zero, attributing to their growth.\nBased on the above thought, the answer is:\n10%" } ], "id_raw": 906 }, { "id": "tf4FDgfupWg6ZfT5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_7.png", "conversations": [ { "from": "human", "value": "\nCan you convert the growth data from the line graph to a structured table format with Time Periods as columns and Fields as rows?" }, { "from": "gpt", "value": "Observing the line graph, I analyze the x-axis which contains {time periods labels}[0.110, 0.454, 0.926, 0.454] representing the different Time Periods. The y-axis gives values for each field observed at these time periods {the line data points}[0.110, 0.371, 0.926, 0.947]. The field names are illustrated by the legend, segmented as \"Education Technology\" and \"Training Software\" {the legend}[0.121, 0.044, 0.502, 0.069]. Gathering this visual data, I can construct a table.\nBased on the above thought, the answer is:\nTime Periods, Education Technology, Training Software\nPeriod 1, 2.0, 1.5\nPeriod 2, 3.6, 2.0\nPeriod 3, 3.1, 4.7\nPeriod 4, 2.3, 1.6" } ], "id_raw": 907 }, { "id": "blIuqCd5xMok6HH5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_7.png", "conversations": [ { "from": "human", "value": "\nConsidering the trends depicted, which field demonstrates a more stable progression and why?" }, { "from": "gpt", "value": "To ascertain which field manifests a more stable progression, I scrutinize the lines representing each field within the graph. For \"Education Technology\" {line for Education Technology}[0.110, 0.371, 0.926, 0.947] the plot shows sharp peaks and troughs indicating high volatility.\nComparatively, the \"Training Software\" {line for Training Software}[0.110, 0.947, 0.926, 0.572] though it also showcases peaks and declines, has periods of slower incline and decline that tend to stabilize before rising sharply and then declining similarly. Still, this is relatively more balanced than the extreme sudden changes observed in the \"Education Technology\".\nBased on the above thought, the answer is:\n\"Training Software\" demonstrates a more stable progression compared to \"Education Technology\". Despite its fluctuations, Training Software has sections of slower increase and decrease which add a degree of steadiness, in contrast to the abrupt changes seen in the progression of Education Technology." } ], "id_raw": 907 }, { "id": "3HqBTOBQkWNL1J7C", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest growth rate recorded for Training Software and during which time period was it observed?" }, { "from": "gpt", "value": "To determine the highest growth rate for Training Software, I examine {the line points for Training Software}[0.110, 0.947, 0.926, 0.572] spanning different periods. I can see the values plotted across the graph with the highest visibly at the third time period. The x-axis {time labels}[0.450, 0.454] confirms that this corresponds to the third segment. Checking the value, it is around 4.7 during \"Period 3\".\nBased on the above thought, the answer is:\n4.7 during Period 3." } ], "id_raw": 907 }, { "id": "MFu53UbK1w3pzRB1", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_8.png", "conversations": [ { "from": "human", "value": "\nCan you convert the growth data of different sectors from the line chart into a structured table, listing the years as rows and sectors as columns?" }, { "from": "gpt", "value": "To transform the line chart data into a table, I start by analyzing key placement markers. By observing {the x-axis tick labels}[[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903], [0.397, 0.773, 0.504, 0.934], [0.525, 0.773, 0.607, 0.896], [0.643, 0.773, 0.721, 0.890], [0.739, 0.773, 0.857, 0.951], [0.843, 0.773, 0.959, 0.941], [0.937, 0.773, 1.053, 0.951]], which represent the years ranging from 2010 to 2020. Next, I locate {the legend texts}[[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156], [0.884, 0.167, 0.946, 0.189], [0.884, 0.200, 0.933, 0.222]] to identify the sectors: Software, Hardware, E-commerce, and ISP. Subsequently, data points are identified by locating {the line points for each sector}[[0.102, 0.470], [0.218, 0.082], ..., [0.798, 0.243]] within the plot to extract relative growth values annotated by year and sector.\nBased on the above thought, the answer is:\nYear,Software,Hardware,E-commerce,ISP\n2010,5,8,10,6\n2011,6,9,11,7\n2012,7,10,12,8\n2013,8,11,8,8\n2014,9,12,9,8\n2015,5,14,7,8\n2016,3,15,4,8\n2017,4,16,5,8\n2018,6,17,6,8\n2019,5,18,7,7\n2020,4,19,6,7" } ], "id_raw": 908 }, { "id": "nrOTIFbn4FrsySYd", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_8.png", "conversations": [ { "from": "human", "value": "\nHow does the trend of growth in the E-commerce sector compare to that in the ISP sector throughout the decade as per the line chart?" }, { "from": "gpt", "value": "To compare the growth trends of the E-commerce and ISP sectors from the line chart, I review the paths of {the line for E-commerce}[0.102, 0.179, 0.798, 0.632] and {the line for ISP}[0.102, 0.211, 0.798, 0.729]. E-commerce starts at a higher value with strong growth initially, then experiences a decline before slightly recovering. In contrast, {the ISP line} follows a more stable path with minimal fluctuations and generally maintains a stable growth rate throughout the decade. This comparative analysis suggests that while both sectors started closely, E-commerce was subject to more significant changes in growth, whereas ISP remained more steady and less volatile.\nBased on the above thought, the answer is:\nThe E-commerce sector experienced more fluctuations in growth throughout the decade compared to the ISP sector. Initially, E-commerce saw a rapid increase, followed by a notable decline and a mild recovery, indicating its volatile nature. On the other hand, the ISP sector demonstrated steady growth with slight declines, showcasing its stability over the period from 2010 to 2020. This stability could suggest a consistent market demand or effective management stabilizing its growth rate against potential declines." } ], "id_raw": 908 }, { "id": "gCnkLbS5SGqRI6Qc", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the average annual growth for the Hardware sector from 2010 to 2020?" }, { "from": "gpt", "value": "To calculate the average annual growth for the Hardware sector over the period from 2010 to 2020, I need to locate {the line data points for Hardware}[[0.102, 0.375], [0.218, 0.291], ..., [0.798, 0.110]] and extract the corresponding values across all years: {8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19}. The sum of these values is 139, and there are 11 years in total. To find the average, I divide the sum by the number of years: 139 / 11.\nBased on the above thought, the answer is:\n12.63" } ], "id_raw": 908 }, { "id": "fhjbdvlScoX98aek", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_9.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table showing the changes in scores for each social media platform across the different constraint words?" }, { "from": "gpt", "value": "In analyzing the line chart, I first examine {the x-axis labels}[0.057, 0.459, 0.066, 0.473], [0.224, 0.459, 0.259, 0.473], [0.405, 0.459, 0.440, 0.473], [0.585, 0.459, 0.620, 0.473], [0.765, 0.459, 0.800, 0.473], [0.941, 0.459, 0.985, 0.473] which correspond to constraint words from '3' to '10'. Then, I observe {the lines representing each social media platform}[0.061, 0.530, 0.963, 0.947] to extract the score points for Facebook, Twitter, Instagram, and LinkedIn along these constraints. The scores are directly extracted from points plotted on the graph for each social media.\nBased on the above thought, the answer is:\nConstraint Words,Facebook,Twitter,Instagram,LinkedIn\n3,10000,8000,1000,900\n4,8000,6000,2000,1500\n5,4000,10000,3000,2000\n6,3000,4000,4000,2500\n7,2000,3000,6000,3500\n8,1000,2000,8000,5000\n9,500,1000,9000,7500\n10,1000,800,10000,10000" } ], "id_raw": 909 }, { "id": "4kXnVIqhisz4Q4Az", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_9.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform shows a rising trend despite an initial drop, as per the chart?" }, { "from": "gpt", "value": "Observing the chart, I analyze the trend lines of different social media platforms. For this assessment, I focus on the lines for {Facebook}[0.061, 0.530, 0.963, 0.947] and {Instagram}[0.061, 0.530, 0.963, 0.947]. Facebook shows a general downward trend from '3' to '10' constraint words, while Instagram initially has a lower score but gradually increases, leading to a rapid expansion in scores after passing the midpoint of constraint '5', showing the most significant rise towards the end.\nBased on the above thought, the answer is:\nInstagram exhibits a rising trend despite an initial drop. Initially starting with lower scores at constraint word '3', Instagram's score gradually grows exponentially towards the high peak by constraint word '10', marking a striking upward trajectory in the graph." } ], "id_raw": 909 }, { "id": "tSE9zBqCAT7JkPBo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the peak score for Instagram according to the graph data?" }, { "from": "gpt", "value": "To identify the peak score for Instagram, I need to analyze {the line for Instagram}[0.061, 0.530, 0.963, 0.947] across all constraint words. By examining the particular points where Instagram line peaks, especially towards the end, I need to find the coordinate with the highest y-value along the line, indicating the highest score.\nBased on the above thought, the answer is:\n10000." } ], "id_raw": 909 }, { "id": "WRV0xsenfiivXPnK", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_10.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data from the line chart showing product life cycle stages and investments into a structured table with Stages as columns and Investment types as rows?" }, { "from": "gpt", "value": "To organize the data from the line chart into a table, I begin by identifying {the x-axis tick labels}[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903], [0.397, 0.773, 0.504, 0.934], [0.525, 0.773, 0.607, 0.896], [0.643, 0.773, 0.721, 0.890], [0.739, 0.773, 0.857, 0.951] which indicate the different stages of the product life cycle. I also identify {the legend}[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156], [0.884, 0.167, 0.946, 0.189] marking categories such as Product Design, Inventory Management, and Customer Service. After locating {the data points on the lines}[0.102, 0.470], [0.218, 0.082], [0.334, 0.179], [0.450, 0.534], [0.566, 0.632], [0.682, 0.437], [0.798, 0.243] for each investment type and stage, I can extract and tabulate the corresponding figures.\nBased on the above thought, the answer is:\nStage,Initial,Growth,Establishment,Maturity,Expansion,Decline\nProduct Design,5,12,23,32,29,20\nInventory Management,3,9,16,22,21,15\nCustomer Service,7,15,24,35,33,25" } ], "id_raw": 910 }, { "id": "fDtxWi66czMJjD9S", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_10.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the provided chart, which investment category appears to have the most consistent growth throughout the product life cycle stages?" }, { "from": "gpt", "value": "Inspecting {the line for Customer Service}[0.102, 0.470], [0.218, 0.082], [0.334, 0.179], [0.450, 0.534], [0.566, 0.632], [0.682, 0.437], [0.798, 0.243] across all stages illustrates a consistent upward trend, despite the outlier at Maturity stage. In contrast, both {the line for Product Design}[0.102, 0.082, 0.798, 0.696] and {Inventory Management}[0.102, 0.179, 0.798, 0.632] don't exhibit a consistent rate of increase as they have fluctuations and plateau periods. Therefore, Customer Service shows the most consistent growth trend.\nBased on the above thought, the answer is:\nCustomer Service shows the most consistent growth throughout all product life cycle stages. This consistency is marked by a steady increase from the Initial to Decline stages, even with a markable peak during the Maturity stage." } ], "id_raw": 910 }, { "id": "5IRifyF4kxJK5ABR", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_10.png", "conversations": [ { "from": "human", "value": "\nHow much does the peak investment in Customer Service at the Maturity stage differ from its initial investment?" }, { "from": "gpt", "value": "Reviewing the peak value in Customer Service data, I focus on {the peak point for Customer Service}[0.397, 0.773, 0.504, 0.934] during the Maturity stage where the investment is 35. I then compare it to {the initial point for Customer Service}[0.061, 0.773, 0.144, 0.898] at the Initial stage with an investment of 7. The difference is calculated as 35 - 7.\nBased on the above thought, the answer is:\n28" } ], "id_raw": 910 }, { "id": "dnxqE7v3dOSdwPyq", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart showing technology company market shares over four quarters into a structured table format?" }, { "from": "gpt", "value": "To create the table from the line chart, I note down the headers from {the x-axis tick labels}[0.088, 0.434, 0.102, 0.448], [0.271, 0.434, 0.286, 0.448], [0.447, 0.434, 0.476, 0.448], [0.630, 0.434, 0.660, 0.448] which denote the quarters Q1 through Q4. For the rows, I identify the labels from {the legend}[0.402, 0.476, 0.668, 0.501] which represent the company names - Apple, Samsung, Huawei, and Google. Visualizing the trends of market share from {the lines corresponding to each company}[0.095, 0.116, 0.975, 0.369], I summarize the values for each quarter based on their market share positions in the chart.\nBased on the above thought, the answer is:\nQuarter,Apple,Samsung,Huawei,Google\nQ1,24,21,18,10\nQ2,21,18,15,12\nQ3,18,15,6,15\nQ4,19,14,6,18" } ], "id_raw": 911 }, { "id": "EhWvTRYHV6bYGsvC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_11.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which company showed the most stability in market share over the four quarters?" }, { "from": "gpt", "value": "To find out which company had the most stable market share, I need to examine the fluctuation of the lines on the chart. {Apple's line}[0.095, 0.116, 0.975, 0.369] and {Samsung's line}[0.095, 0.182, 0.975, 0.248] both show notable declines. {Huawei's line}[0.095, 0.194, 0.975, 0.357] has a sharp drop, particularly noticeable in Q3. However, {Google's line}[0.095, 0.560, 0.975, 0.369] displays a consistent upward trend with less variability compared to others.\nBased on the above thought, the answer is:\nGoogle demonstrated the most stability in market share over the four quarters, as evidenced by its consistent upward trajectory and lesser fluctuations in the market share levels compared to the other companies." } ], "id_raw": 911 }, { "id": "fBkXg5j47TnUlWWe", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_11.png", "conversations": [ { "from": "human", "value": "\nHow much did Google's market share increase from Q1 to Q4?" }, { "from": "gpt", "value": "The market share of Google at the beginning of the year can be determined from the first point on {Google's line}[0.095, 0.560], and the end of the year share from the last point on the same line[0.975, 0.369]. Initially, Google's market share is approximately 10 and it rises to around 18 by Q4. Calculating the increase involves subtracting the Q1 share from the Q4 share.\nBased on the above thought, the answer is:\n8" } ], "id_raw": 911 }, { "id": "s2KwbVAPdtulkDKI", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the provided data on migration trends into a table with time as columns and type of migration as rows?" }, { "from": "gpt", "value": "To formulate a table, I first identify {the x-axis tick labels}[0.114, 0.395, 0.946, 0.409] which represent different years, acting as columns for the table. {The legend}[0.133, 0.468, 0.394, 0.493] and [0.133, 0.961, 0.404, 0.986] for 'Rural Migration' and 'Urban Migration' respectively, will act as row headers. For each of these categories, I locate their corresponding {line points}[0.122, 0.305, 0.960, 0.390] and [0.122, 0.539, 0.960, 0.706] to extract the data values.\nBased on the above thought, the answer is:\nTime,Rural Migration, Urban Migration\n0,-0.5,0.8\n1,-0.45,0.85\n2,-0.4,0.92\n3,-0.35,0.96\n4,-0.3,1.02\n5,-0.26,1.1\n6,-0.2,1.12\n7,-0.15,1.25\n8,-0.12,1.33\n9,-0.1,1.4\n10,-0.08,1.45\n11,-0.06,1.5\n12,-0.05,1.52\n13,-0.04,1.6\n14,-0.03,1.65\n15,-0.02,1.7\n16,-0.01,1.75\n17,0,1.79\n18,0.01,1.82\n19,0.02,1.86\n20,0.03,1.9\n21,0.05,1.93\n22,0.07,1.95\n23,0.09,1.99\n24,0.11,2.01\n25,0.13,2.03" } ], "id_raw": 912 }, { "id": "HI0w6aiguZ0kuY31", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_12.png", "conversations": [ { "from": "human", "value": "\nWhich migration trend shows a consistently positive movement, and what does this indicate about urban versus rural trends?" }, { "from": "gpt", "value": "Observing the {line for Rural Migration}[0.122, 0.305, 0.960, 0.390], the trend moves from negative values towards zero, indicating a gradual reduction in negative migration or a decrease in loss. Conversely, the {line for Urban Migration}[0.122, 0.539, 0.960, 0.706] starts from a positive value and continues to rise, showing consistently positive growth. This suggests that Urban areas might be experiencing growth due to factors like better job opportunities or living conditions, attracting populations from rural areas or possibly other urban areas, whereas Rural areas are seeing a slowdown or reversal in out-migration, potentially due to improving local conditions or decreased attractiveness of urban offerings.\nBased on the above thought, the answer is:\nUrban Migration shows a consistently positive movement. This indicates a robust attraction towards urban areas, likely due to better opportunities or amenities compared to rural areas, resulting in a net population increase in urban spots gradually influencing a balancing act in rural migration trends." } ], "id_raw": 912 }, { "id": "ByJlVzkBzGM0XGJg", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in Urban Migration from year 5 to year 10?" }, { "from": "gpt", "value": "Using the {Urban Migration line points}[0.289, 0.673], [0.457, 0.640], I extract the migration values at year 5 and year 10, which are 1.046 and 1.292 respectively. The increase is calculated by subtracting the value at year 5 from the value at year 10, resulting in 1.292 - 1.046 = 0.246.\nBased on the above thought, the answer is:\n0.246" } ], "id_raw": 912 }, { "id": "hycI7TRysyx1ouZf", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_13.png", "conversations": [ { "from": "human", "value": "\nHow can you transform the dataset from the chart into a structured table showing \"Time (Hours)\" as columns and \"Trends\" as rows?" }, { "from": "gpt", "value": "First, I observe {the x-axis ticks}[0.073, 0.459, 0.088, 0.473], [0.249, 0.459, 0.264, 0.473], [0.418, 0.459, 0.447, 0.473], [0.594, 0.459, 0.623, 0.473], [0.770, 0.459, 0.799, 0.473], [0.946, 0.459, 0.975, 0.473] clearly labeled from 0 to 25 hours representing the columns, and the plot legends from {the subplot legends}[0.688, 0.422, 0.949, 0.447], [0.092, 0.537, 0.298, 0.562] indicating the \"Self-confidence\" and \"Innovation\" trends for the rows. I then collect the numerical data corresponding to each time point for both trends from identified {data points}[0.080, 0.395], [0.116, 0.406], ..., [0.925, 0.299], [0.080, 0.798], ..., [0.925, 0.542].\nBased on the above thought, the answer is:\nTime (Hours),Self-confidence,Innovation\n0,1.0,2.5\n1,0.8,2.7\n2,0.65,3.0\n3,0.4,3.2\n4,0.5,3.3\n5,0.36,3.1\n6,0.3,3.8\n7,0.5,4.0\n8,0.6,4.3\n9,0.7,4.0\n10,0.8,4.5\n11,0.9,4.6\n12,1.0,4.9\n13,1.1,4.8\n14,1.3,5.0\n15,1.5,5.2\n16,1.6,5.5\n17,1.8,5.6\n18,1.9,5.9\n19,2.0,5.8\n20,2.1,6.0\n21,2.3,6.1\n22,2.4,6.4\n23,2.5,6.6\n24,2.6,6.8" } ], "id_raw": 913 }, { "id": "YwVyXMzQfVDzRMbP", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_13.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the trends, which one appears to have a more steady growth over time, Self-confidence or Innovation, and why?" }, { "from": "gpt", "value": "Observing the trends, I assess the {Self-confidence line}[0.080, 0.395, 0.925, 0.299] and {Innovation line}[0.080, 0.798, 0.925, 0.542]. The Self-confidence shows fluctuations with an increase and decrease in levels between data points. In contrast, the Innovation, despite some fluctuations, shows a general upward trend with higher end values and fewer dips below its trend line. This indicates a more steady overall growth for Innovation compared to the more variable Self-confidence.\nBased on the above thought, the answer is:\nInnovation demonstrates a more steady growth over time compared to Self-confidence. The Innovation trend, compared to Self-confidence, exhibits a consistent rise with generally increasing values, showing fewer and less significant drops, indicating a steady upward progression." } ], "id_raw": 913 }, { "id": "XBLsMZU7NmjHo89i", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the average increase in \"Self-confidence\" from hour 0 to hour 24?" }, { "from": "gpt", "value": "I will determine the average increase by comparing the initial and final {data points for Self-confidence}[0.080, 0.395], [0.925, 0.299] which show values from 1.0 at hour 0 to 2.6 at hour 24. To calculate the average increase: (2.6 - 1.0) / 24 = 0.0667.\nBased on the above thought, the answer is:\n0.0667." } ], "id_raw": 913 }, { "id": "WX93ohmAzR3JhdFc", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_14.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured data table from the bar chart showing the number of individuals in different professions across various age groups?" }, { "from": "gpt", "value": "Observing the bar chart, I see three sets of bars grouped across the X-axis that represent the different age groups, starting from {the first age group label}(r>[0.05, 0.95, 0.1, 0.97]) to {the last age group label}(r>[0.9, 0.95, 0.95, 0.97]). Each group contains bars for {Entrepreneurs}(r>[0.06, 0.2, 0.08, 0.7]), {Professionals}(r>[0.09, 0.2, 0.11, 0.5]), and {Trades}(r>[0.12, 0.2, 0.14, 0.4]), represented by distinct colors. By decoding these bars' heights against the Y-axis that measures counts starting from labels at {the beginning of the Y-axis}(r>[0.0, 0.9, 0.03, 0.95]) to {the end of the Y-axis}(r>[0.0, 0.1, 0.03, 0.15]), we can tabulate the data.\nBased on the above thought, the answer is:\nAge Group, Entrepreneurs, Professionals, Trades\n20-30, 150, 120, 130\n30-40, 180, 105, 115\n40-50, 160, 90, 80\n50-60, 140, 75, 60\n60-70, 100, 60, 40\n70-80, 85, 50, 30" } ], "id_raw": 914 }, { "id": "KzB1zjDEjMcROpCD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_14.png", "conversations": [ { "from": "human", "value": "\nWhich profession shows a continuous decrease as age increases according to the chart?" }, { "from": "gpt", "value": "To analyze the trends for each profession, I look at how each profession's bar heights vary from the youngest to the oldest age groups. Tracking down the bars for {Professionals}(r>[0.09, 0.2, 0.11, 0.5]), I observe a consistent reduction in height from {the youngest age group's bar}(r>[0.06, 0.2, 0.08, 0.7]) to {the oldest age group's bar}(r>[0.12, 0.2, 0.14, 0.4]), which indicates a decrease in numbers. This consistent trend is not observed with the same regularity in the other professions based on their respective bar groups across age segments.\nBased on the above thought, the answer is:\nProfessionals show a continuous decrease in their number as age increases according to the data represented in the chart. This trend is deduced from the consistently diminishing bar heights across the age groups for this profession." } ], "id_raw": 914 }, { "id": "Dxy4l2Hy7axmSxJf", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the total number of individuals across all professions in the age group 30-40?" }, { "from": "gpt", "value": "First, I locate the bars for the age group 30-40 which are defined within {the range on the x-axis}(r>[0.15, 0.95, 0.2, 0.97]) on the chart. Inside this grouping, I find the specific bars for {Entrepreneurs}(r>[0.16, 0.2, 0.18, 0.9]), {Professionals}(r>[0.19, 0.2, 0.21, 0.6]), and {Trades}(r>[0.22, 0.2, 0.24, 0.55]). The values for these bars are 180, 105, and 115 respectively. Adding these values together provides the total number of individuals in this age group.\nBased on the above thought, the answer is:\n400" } ], "id_raw": 914 }, { "id": "A7bkxzo8IVcmEO8z", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_15.png", "conversations": [ { "from": "human", "value": "\nCan you generate a structured table from the line chart showing the effectiveness of each action plan across different policies?" }, { "from": "gpt", "value": "From my analysis of the line chart, I started by identifying {the x-axis tick labels}[0.124, 0.773, 0.227, 0.898], {0.291, 0.773, 0.378, 0.903], and {0.397, 0.773, 0.504, 0.934] representing 'Policy Enforcement', 'Social Welfare', and 'Security Boost' respectively. I then looked at {the legend}[0.792, 0.132, 0.893, 0.208] identifying Action Plans - Act1, Act2, Act3. Corresponding points for these action plans were extracted across policies: Act1{[0.152, 0.470], [0.318, 0.582], [0.482, 0.696]}, Act2{[0.152, 0.540], [0.318, 0.439], [0.482, 0.348]}, Act3{[0.152, 0.643], [0.318, 0.729], [0.482, 0.815]}.\nBased on the above thought, the answer is:\nPolicies,Act1,Act2,Act3\nPolicy Enforcement,150,90,170\nSocial Welfare,130,140,160\nSecurity Boost,120,150,150" } ], "id_raw": 915 }, { "id": "7cF2jKilaey7MyUO", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_15.png", "conversations": [ { "from": "human", "value": "\nWhich action plan showed the most improvement from 'Policy Enforcement' to 'Security Boost'?" }, { "from": "gpt", "value": "Analyzing the effectiveness from 'Policy Enforcement' to 'Security Boost':\nFor Act1, the values change from 150 to 120 displaying a decrease; for Act2, values go from 90 to 150 showing significant improvement; for Act3, the effectiveness goes from 170 to 150, observing a slight decrease. Therefore, {Act2}[0.152, 0.540], [0.482, 0.348] shows the most significant improvement, moving towards a higher effectiveness score.\nBased on the above thought, the answer is:\nAct2 showed the most improvement moving from a lower effectiveness score in 'Policy Enforcement' to a higher score in 'Security Boost'." } ], "id_raw": 915 }, { "id": "iCcpNUaycgIblWcj", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_15.png", "conversations": [ { "from": "human", "value": "\nHow much did the average effectiveness differ between the highest and lowest performing action plans in 'Security Boost'?" }, { "from": "gpt", "value": "To answer this, I examine the data points for 'Security Boost' for the different action plans. Act1 records an effectiveness of 120 at {0.482, 0.696]}, Act2 shows 150 at [0.482, 0.348] and Act3 also has 150 at [0.482, 0.815]. Therefore, the maximum value is 150 (for both Act2 and Act3) and the minimum value for Act1 is 120. The difference is calculated as 150 - 120 = 30.\nBased on the above thought, the answer is:\n30." } ], "id_raw": 915 }, { "id": "xfqZaaWt0y9vJbCP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_16.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data displayed in the line chart regarding 'Organic Produce', 'GM Crops', and 'Artificial Fertilizers' from 2010 to 2020 into a structured table format?" }, { "from": "gpt", "value": "Firstly, I observe {the x-axis labels}[0.124, 0.459, 0.793, 0.960, 0.947] representing the years from 2010 to 2020. I then identify the three lines for 'Organic Produce', 'GM Crops', and 'Artificial Fertilizers' by their distinct colors and extract values from each point plotted across the years. The {points on the Organic Produce line}[0.124, 0.454, 0.927, 0.454] {for GM Crops}[0.124, 0.454, 0.927, 0.454] and {for Artificial Fertilizers}[0.124, 0.454, 0.927, 0.454] provide the data for the table.\nBased on the above thought, the answer is:\nYear,Organic Produce (000s),GM Crops (000s),Artificial Fertilizers (000s)\n2010,580,670,720\n2011,610,650,700\n2012,630,630,680\n2013,640,610,660\n2014,650,600,640\n2015,640,605,630\n2016,630,610,620\n2017,620,620,610\n2018,610,630,600\n2019,600,640,590\n2020,590,650,580" } ], "id_raw": 916 }, { "id": "XSMyYMSOrunnTe3U", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_16.png", "conversations": [ { "from": "human", "value": "\nWhich of the variables presented had the most stable trend over the 2010 to 2020 period?" }, { "from": "gpt", "value": "Upon analyzing the stability of the variables, I look into {the line for Organic Produce}[0.124, 0.454, 0.927, 0.454], {the line for GM Crops}[0.124, 0.454, 0.927, 0.454], and {the line for Artificial Fertilizers}[0.124, 0.454, 0.927, 0.454]. Organic Produce and Artificial Fertilizers show considerable variability and significant peaks and troughs throughout the period. In contrast, GM Crops, although showing decline, does not have as wide fluctuations as the other two.\nBased on the above thought, the answer is:\n'GM Crops' exhibited the most stable trend overall with lesser fluctuations compared to 'Organic Produce' and 'Artificial Fertilizers', discerned by narrower movement between high and low points throughout the decade." } ], "id_raw": 916 }, { "id": "gtjTMfAlQvoLlxRo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_16.png", "conversations": [ { "from": "human", "value": "\nOver the period 2010 to 2020, by how much did 'Artificial Fertilizers' production decrease from its highest to its lowest value?" }, { "from": "gpt", "value": "Reviewing the line denoting 'Artificial Fertilizers' between {starting point}[0.124, 0.454] and {ending point}[0.927, 0.454], I extract the peak and the lowest values represented at these points. As seen, the highest value observed is 720 in 2010 and the lowest is 580 in 2020. The difference calculation would be 720 - 580.\nBased on the above thought, the answer is:\n140 thousand units." } ], "id_raw": 916 }, { "id": "vcdxjAAOPRUPqkQY", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_17.png", "conversations": [ { "from": "human", "value": "\nCan you generate a data table that categorizes the data by years and policy type based on the line chart?" }, { "from": "gpt", "value": "To convert the data displayed in the line chart into a table format, I start by identifying {the x-axis tick labels}[0.122, 0.960, 0.122, 0.960] that indicate the years from 2015 to 2019. Then, I note {the legend items}[0.377, 0.476, 0.705, 0.501] which classify the lines as 'Fiscal Policy' and 'Monetary Policy'. By analyzing {the line for Fiscal Policy}[0.122, 0.168, 0.927, 0.410] and {the line for Monetary Policy}[0.122, 0.549, 0.927, 0.791], I extract the specific policy values for each year.\nBased on the above thought, the answer is:\nYear,Fiscal Policy (Billion Dollars),Monetary Policy (Billion Dollars)\n2015,5900,4355\n2016,6100,5820\n2017,5800,4860\n2018,5600,5235\n2019,5500,5575" } ], "id_raw": 917 }, { "id": "NITNjGWLc7HJDf5o", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_17.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the chart, which policy showed more stability over the five-year period and why?" }, { "from": "gpt", "value": "To assess which policy displayed more stability, I examine the line plots for both policies. Observing {the line for Fiscal Policy}[0.122, 0.168, 0.927, 0.410], it indicates a mostly declining trend with minor fluctuations and no abrupt changes. In contrast, the {line for Monetary Policy}[0.122, 0.549, 0.927, 0.791] shows noticeable ups and downs, especially the rapid rise in 2016 followed by a sharp decline in 2017 and subsequent fluctuations. This suggests that the Fiscal Policy had a steadier decline over the years compared to the Monetary Policy which exhibited more volatility.\nBased on the above thought, the answer is:\nThe Fiscal Policy demonstrated more stability over the five-year period as evidenced by its steadily declining line, showing fewer fluctuations and more predictability compared to the Monetary Policy, which exhibited significant volatility with abrupt increases and decreases over the same period." } ], "id_raw": 917 }, { "id": "33M13GetXXv6kzHX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the total change in Fiscal Policy value from 2015 to 2019?" }, { "from": "gpt", "value": "To calculate the total change in Fiscal Policy value, I first identify {the point for Fiscal Policy in 2015}[0.122, 0.410] which is 5900 Billion and {the point for Fiscal Policy in 2019}[0.927, 0.320] which is 5500 Billion. The difference is calculated by subtracting the value in 2015 from the value in 2019, giving 5900 - 5500.\nBased on the above thought, the answer is:\n400 Billion Dollars." } ], "id_raw": 917 }, { "id": "9YwJT7kso6GeRhjO", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_18.png", "conversations": [ { "from": "human", "value": "\nCan you frame the information on this chart into a tabular format with years as rows and market shares as columns?" }, { "from": "gpt", "value": "To generate this table, I'll start by observing {the x-axis' labels}[0.066, 0.459, 0.946, 0.473] which represent the rows for the various years ranging from 2008 to 2020. Then I identify {the legend texts}[0.568, 0.044, 0.934, 0.069][0.568, 0.537, 0.934, 0.562] as the column headers, signifying the Traditional and Innovative market shares. \nFor the cell values, I take into account {line points for Traditional Market}[0.095, 0.287, 0.946, 0.379] and those {for Innovative Market}[0.095, 0.572, 0.946, 0.863] from the plotted lines and extract the corresponding data.\nBased on the above thought, the answer is:\nYear,Market Share Traditional,Market Share Innovative\n2008,20,10\n2009,22,12\n2010,21,15\n2011,19,17\n2012,18,21\n2013,16,23\n2014,15,28\n2015,14,32\n2016,13,34\n2017,11,38\n2018,10,41\n2019,10,43\n2020,9,45" } ], "id_raw": 918 }, { "id": "pH9Bc8aC1CXccjbr", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_18.png", "conversations": [ { "from": "human", "value": "\nBased on the respective trends in this line chart, could you tell me which market share had a more significant increase over the years?" }, { "from": "gpt", "value": "For analyzing which of the market shares had a more significant increase over the years, I need to compare {the line for the Traditional Market Share}[0.095, 0.271, 0.946, 0.379] and {the line for the Innovative Market Share}[0.095, 0.572, 0.946, 0.863]. \nThe Traditional Market Share decreased from 20% in 2008 to 9% in 2020, showing a reduction in adoption. On the other hand, The Innovative Market recorded a steady increase in the same period, from 10% to 45%.\nBased on the above thought, the answer is:\nThe Innovative Market Share has a more significant increase over the years. The data shows its adoption rate goes up from 10% in 2008 to 45% in 2020, while that of the Traditional Market Share decreased in the same period." } ], "id_raw": 918 }, { "id": "krXgUL00v0sTFQI0", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_18.png", "conversations": [ { "from": "human", "value": "\nBased on the information in the chart, by how much did the Traditional Market Share decrease from 2009 to 2019?" }, { "from": "gpt", "value": "To find the decrease in Traditional Market Share from 2009 to 2019, I should look at {the line points for Traditional Market}[0.095, 0.287, 0.946, 0.379]. The value for 2009 is 22 based on the point at {2009 marker}[0.166, 0.271] and the value for 2019 is 10 based on the point at {2019 marker}[0.875, 0.371]. The decrease from 2009 to 2019 is therefore 22 - 10 = 12.\nBased on the above thought, the answer is:\n12." } ], "id_raw": 918 }, { "id": "IQdgQO2iHfAxiYvo", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_46_19.png", "conversations": [ { "from": "human", "value": "\nCan you convert the sales data from the line chart into a structured table detailing the sales over different hours for both Standard and Innovative vehicles?" }, { "from": "gpt", "value": "First, I observe the sales data based on {the x-axis labels}[0.061, 0.459, 0.070, 0.473], [0.244, 0.459, 0.252, 0.473], [0.421, 0.459, 0.439, 0.473], [0.603, 0.459, 0.621, 0.473], [0.785, 0.459, 0.803, 0.473], [0.968, 0.459, 0.985, 0.473] which denote the time in hours starting from 0 to 25 at intervals of 5 hours. I then locate the sales data for both {Standard Sales line}[0.066, 0.294, 0.940, 0.385] and {Innovative Sales line}[0.066, 0.585, 0.940, 0.863] and extract the respective unit sales values from their {data points}[0.066, 0.385], [0.102, 0.371], [0.139, 0.374], [0.175, 0.367], ..., [0.940, 0.294] for Standard and [0.066, 0.863], [0.102, 0.842], [0.139, 0.822], ..., [0.940, 0.585] for Innovative vehicles.\nBased on the above thought, the answer is:\nTime (Hours),Standard Sales (units),Innovative Sales (units)\n0,200,220\n5,240,350\n10,260,410\n15,285,500\n20,310,545\n25,330,620" } ], "id_raw": 919 }, { "id": "ZiTXJn0PHyVD1ERK", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_46_19.png", "conversations": [ { "from": "human", "value": "\nWhich vehicle type shows a more consistent sales performance over the observed time interval based on the chart?" }, { "from": "gpt", "value": "Observing the sales trends of the vehicles, I analyze the fluctuations in the sales lines. The {Standard Sales line}[0.066, 0.294, 0.940, 0.385] presents smaller fluctuations within a confined range, indicating a more consistent performance over time with gradual increments in sales values. On the other hand, the {Innovative Sales line}[0.066, 0.585, 0.940, 0.863] exhibits a broader range of sales values, particularly notable between hour 5 and 25, suggesting a less consistent, though more aggressive growth pattern.\nBased on the above thought, the answer is:\nThe Standard Vehicle type shows a more consistent sales performance over the observed time interval as indicated by the smaller fluctuations and more gradual increases in its sales data line compared to the Innovative Vehicle, which exhibited a more rapid growth but with larger fluctuations." } ], "id_raw": 919 }, { "id": "P28rxHIz4AuvJ12A", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_46_19.png", "conversations": [ { "from": "human", "value": "\nHow much do the Innovative Vehicle sales increase from hour 0 to hour 25?" }, { "from": "gpt", "value": "Based on the plotted data, I first locate the sales value for Innovative Vehicles at hour 0 with {the point coordinate}[0.066, 0.863] and then locate the sales value at hour 25 with {the last point coordinate}[0.940, 0.585]. Subtracting the initial value (220) from the final value (620) will give the total increase.\nBased on the above thought, the answer is:\n400 units." } ], "id_raw": 919 }, { "id": "EmOFZTSxmbAkRvx5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_0.png", "conversations": [ { "from": "human", "value": "\nCan you extract and create a tabular representation indicating the yearly development for Rural and Urban areas from the line chart?" }, { "from": "gpt", "value": "First, I locate {the x-axis tick labels}[0.070, 0.919, 0.130, 0.942], [0.168, 0.919, 0.228, 0.942], ..., [0.854, 0.919, 0.914, 0.942] to determine the years from 2000 to 2020. Then, I identify {the line points for Rural Development}[0.100, 0.871], [0.139, 0.852], ..., [0.884, 0.065] and {the line points for Urban Development}[0.100, 0.815], [0.139, 0.676], ..., [0.884, 0.065] to obtain the development values for each year. Managing this data extraction and comparing the coordinates to the respective year, I can create a table.\nBased on the above thought, the answer is:\nYear,Rural Development,Urban Development\n2000,3.5,6.7\n2001,3.6,7.2\n2002,3.8,7.0\n2003,4.1,6.5\n2004,4.3,6.9\n2005,4.5,7.3\n2006,4.8,7.5\n2007,4.9,7.8\n2008,5.2,7.7\n2009,5.5,7.6\n2010,5.8,7.6\n2011,6.0,7.4\n2012,6.2,7.9\n2013,6.8,8.3\n2014,6.3,8.4\n2015,6.7,8.7\n2016,6.9,8.3\n2017,7.2,8.9\n2018,7.3,9.0\n2019,7.5,9.0\n2020,7.7,9.4" } ], "id_raw": 920 }, { "id": "OPnfSYOin5DBDjrI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_0.png", "conversations": [ { "from": "human", "value": "\nHow does the trend in Urban Development differ from Rural Development over the years?" }, { "from": "gpt", "value": "Considering the line trends for both developments, {the line for Rural Development}[0.100, 0.065, 0.884, 0.871] shows a steady increase with minor fluctuations from a value of 3.5 in 2000 to a value of 7.7 in 2020. {The line for Urban Development}[0.100, 0.065, 0.884, 0.871] also demonstrates a general upward trend but with larger variability and peaking at a value of 9.4 in 2020 compared to the less steep rural development. The peaks and troughs are more defined in the urban data reflecting greater volatility, particularly noticeable in years after 2015.\nBased on the above thought, the answer is:\nUrban Development exhibits a significantly more volatile trend compared to Rural Development, with higher peaks indicating rapid growth periods and pronounced troughs signaling sharper declines in specific years. Over the same timeframe, Rural Development's growth is more gradual and stable with less pronounced fluctuations, culminating in a consistent upward trend with minor yearly deviations." } ], "id_raw": 920 }, { "id": "rDgDz8tuOU190i1p", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the average urban development value over the years 2015 to 2020?" }, { "from": "gpt", "value": "To calculate the average, I need to extract the urban development values for the years 2015 to 2020. These values are obtained from {the points corresponding to Urban Development from 2015 to 2020}[0.688, 0.260], [0.727, 0.371], [0.766, 0.204], [0.806, 0.176], [0.845, 0.176], [0.884, 0.065]. The values for these years are 8.7, 8.3, 8.9, 9.0, 9.0, 9.4. The sum is 53.3, and there are 6 years, therefore, the average is 53.3/6 = 8.88.\nBased on the above thought, the answer is:\n8.88" } ], "id_raw": 920 }, { "id": "bKXg3HMYrKPaXY0Y", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_1.png", "conversations": [ { "from": "human", "value": "\nCould you transform the data from the bar chart showing investor confidence vs. the number of startups funded into a structured table format?" }, { "from": "gpt", "value": "By analyzing the visual elements in the bar chart, I can discern distinct bars representing various investor confidence trends. I first identify {the x-axis labels}[0.122, 0.766, 0.176, 0.788], [0.236, 0.766, 0.290, 0.788], [0.350, 0.766, 0.404, 0.788], [0.464, 0.766, 0.518, 0.788], [0.578, 0.766, 0.632, 0.788], [0.692, 0.766, 0.746, 0.788] and determine they are corresponding to 'Stable Growth', 'Subdued Decline', 'Abrupt Increase', 'Persistent Rise', 'Steep Decline', and 'Steady Recovery'. Then I measure {the bar heights}[0.130, 0.400, 0.155, 0.766], [0.244, 0.500, 0.269, 0.766], [0.358, 0.200, 0.383, 0.766], [0.472, 0.350, 0.497, 0.766], [0.586, 0.600, 0.611, 0.766], [0.700, 0.450, 0.725, 0.766] that correlate with the amount of funding for startups under each category.\nBased on the above thought, the answer is:\nInvestor Confidence,Number of Startups Funded\nStable Growth,1100\nSubdued Decline,800\nAbrupt Increase,1700\nPersistent Rise,1300\nSteep Decline,400\nSteady Recovery,950" } ], "id_raw": 921 }, { "id": "kwTh2YTUJNMn2Jpr", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_1.png", "conversations": [ { "from": "human", "value": "\nWhich category of investor confidence showed the lowest funding for startups and why might this be the case?" }, { "from": "gpt", "value": "After analyzing the chart, it is apparent that the category 'Steep Decline' in investor confidence has the lowest bar height observed at {the bar for 'Steep Decline'}[0.586, 0.600, 0.611, 0.766], indicating the least number of startups funded, adding up to 400. This low funding in the 'Steep Decline' category could be attributed to decreased investor optimism and hesitation to invest in new ventures during times perceived as financially unstable or risky, reflecting the investor's lack of confidence.\nBased on the above thought, the answer is:\nThe 'Steep Decline' category showed the lowest funding for startups. This may be because during a sharp decline in investor confidence, investors are likely to be more risk-averse and reluctant to allocate resources to new or existing ventures, therefore resulting in the diminished funding observed." } ], "id_raw": 921 }, { "id": "W0bdtweOA5EhWDj6", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the total number of startups funded across all categories of investor confidence?" }, { "from": "gpt", "value": "To calculate the total number of startups funded across different categories of investor confidence, I add up the values corresponding to each bar height in the chart: 1100 (Stable Growth) + 800 (Subdued Decline) + 1700 (Abrupt Increase) + 1300 (Persistent Rise) + 400 (Steep Decline) + 950 (Steady Recovery). These values are extracted by analyzing {the bar heights}[0.130, 0.400, 0.155, 0.766], [0.244, 0.500, 0.269, 0.766], [0.358, 0.200, 0.383, 0.766], [0.472, 0.350, 0.497, 0.766], [0.586, 0.600, 0.611, 0.766], [0.700, 0.450, 0.725, 0.766].\nBased on the above thought, the answer is:\n6250" } ], "id_raw": 921 }, { "id": "8A3VFF04T8iM0kuU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_2.png", "conversations": [ { "from": "human", "value": "\nCan you organize the data points from the chart showing trends in Total Crypto Users, Crypto Startups Values, and Bitcoin Prices over the years into a table?" }, { "from": "gpt", "value": "To populate the tabular data, I analyze the chart and use the {x-axis labels}[0.081, 0.919, 0.092, 0.942] for the years and gather data labels from {the legend}[0.886, 0.077, 0.914, 0.119] defining each data series distinctly. I follow this by noting down each point's value from the y-values plotted.\nBased on the above thought, the answer is:\nYear, Total Crypto Users, Crypto Startups Value, Bitcoin Price\n2016, 90, 5, 968\n2017, 450, 120, 13765\n2018, 780, 200, 3643\n2019, 2400, 320, 7845\n2020, 4100, 480, 19143\n2021, 6000, 650, 47765" } ], "id_raw": 922 }, { "id": "nXjyEOUQdWe9SCp0", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_2.png", "conversations": [ { "from": "human", "value": "\nWhat insights can you derive from the trends in the chart considering the technologies represented by Crypto Startups Values and Total Crypto Users?" }, { "from": "gpt", "value": "Observing the trends, {Crypto Startups Values}[0.115, 0.104, 0.885, 0.873] show a consistent rise, indicating growing investments or valuations in crypto startups from 2016 to 2021. Similarly, {Total Crypto Users}[0.115, 0.104, 0.885, 0.873] has an exponential growth curve, suggesting a rapidly increasing user base in the cryptocurrency market. These trends together suggest a significant expansion and acceptance of cryptocurrency technologies, attracting both users and investors progressively.\nBased on the above thought, the answer is:\nThe trends demonstrate significant and consistent growth in both the user base and the economic valuation of cryptocurrency technologies, indicating robust market expansion and increased acceptance. These might suggest a maturing industry with growing trust and investment from the general populace and investors alike." } ], "id_raw": 922 }, { "id": "dVe2f62SbCWPGXkC", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in Bitcoin Price from 2016 to 2021?" }, { "from": "gpt", "value": "To compute the percentage increase, I locate and extract the data points for {Bitcoin Price in 2016 and 2021}[0.115, 0.873, 0.885, 0.258]. The price rose from 968 in 2016 to 47765 in 2021. The increase is calculated as ((47765 - 968) / 968) * 100.\nBased on the above thought, the answer is:\n4831.89%" } ], "id_raw": 922 }, { "id": "PhiFMqhnFuqVGTQl", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_3.png", "conversations": [ { "from": "human", "value": "\nCan you construct a table that represents the change in Artificial Intelligence and Virtual Reality trend over the years?" }, { "from": "gpt", "value": "To construct the table, firstly, I identify {the x-axis labels}[0.002, 0.919, 0.046, 0.942], [0.175, 0.919, 0.219, 0.942], [0.348, 0.919, 0.392, 0.942], [0.521, 0.919, 0.565, 0.942], [0.694, 0.919, 0.739, 0.942], [0.868, 0.919, 0.912, 0.942], [1.041, 0.919, 1.085, 0.942] as the years from 2011 to 2020. The {line for Artificial Intelligence}[0.110, 0.226, 0.890, 0.810] and the {line for Virtual Reality}[0.110, 0.615, 0.890, 0.810] represent the trend of each technology over the years, respectively. The specific value for each year can be extracted from the {line points}[0.110, 0.810], [0.197, 0.784], [0.283, 0.733], [0.370, 0.674], [0.457, 0.615], [0.543, 0.497], [0.630, 0.421], [0.717, 0.378], [0.803, 0.302], [0.890, 0.226] for Artificial Intelligence, and [0.110, 0.717], [0.197, 0.666], [0.283, 0.632], [0.370, 0.615], [0.457, 0.632], [0.543, 0.683], [0.630, 0.725], [0.717, 0.759],[0.803, 0.784], [0.890, 0.810] for Virtual Reality based on their position on the y-axis, and by correlating these with the corresponding x-axis points, which represent years.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Virtual Reality\n2011,1.2,2.3\n2012,1.5,2.9\n2013,2.1,3.3\n2014,2.8,3.5\n2015,3.5,3.3\n2016,4.9,2.7\n2017,5.8,2.2\n2018,6.3,1.8\n2019,7.2,1.5\n2020,8.1,1.2" } ], "id_raw": 923 }, { "id": "TsAhOhVlmXG44YNB", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_3.png", "conversations": [ { "from": "human", "value": "\nHow can the trends of Artificial Intelligence and Virtual Reality over the years be interpreted?" }, { "from": "gpt", "value": "I can analyze the trends by examining the {line for Artificial Intelligence}[0.110, 0.226, 0.890, 0.810] and the {line for Virtual Reality}[0.110, 0.615, 0.890, 0.810]. The line for Artificial Intelligence shows an overall upward trend which means there is a consistent increase in the application or use of Artificial Intelligence during these years. On the other hand, the line for Virtual Reality depicts a downward trend, showing that the application or use of Virtual Reality has been steadily decreasing over the same period.\nBased on the above thought, the answer is:\nThe trends of Artificial Intelligence and Virtual Reality between 2011 to 2020 highlight the contrasting dynamics of these two technologies. Artificial Intelligence has seen a continuous increase in its application or use over the years, suggesting its growing significance and integration in various sectors. In contrast, the application or use of Virtual Reality has witnessed a decline, pointing towards a possible decrease in its acceptance or utility over this period." } ], "id_raw": 923 }, { "id": "TCRquB4eh7UmgQET", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_3.png", "conversations": [ { "from": "human", "value": "\nCalculate the total increase in the trend of Artificial Intelligence from 2011 to 2020." }, { "from": "gpt", "value": "To calculate the total increase in the trend of Artificial Intelligence, I need to focus on the {line points for Artificial Intelligence}[0.110, 0.810], [0.197, 0.784], [0.283, 0.733], [0.370, 0.674], [0.457, 0.615], [0.543, 0.497], [0.630, 0.421], [0.717, 0.378], [0.803, 0.302], [0.890, 0.226]. The points represent the values of Artificial Intelligence trend from 1.2 in 2011 to 8.1 in 2020. The total increase will be the value in 2020 minus the value in 2011, which is 8.1 - 1.2 = 6.9.\nBased on the above thought, the answer is:\n6.9." } ], "id_raw": 923 }, { "id": "PhaWJMCIl3EJG8xx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_4.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data presented in the line chart into a structured tabular format with each type of energy as columns and years as rows?" }, { "from": "gpt", "value": "First, I identify {the x-axis tick labels}[[0.093, 0.919, 0.137, 0.942], [0.219, 0.919, 0.263, 0.942], [0.346, 0.919, 0.390, 0.942], [0.472, 0.919, 0.516, 0.942], [0.599, 0.919, 0.643, 0.942], [0.726, 0.919, 0.769, 0.942], [0.852, 0.919, 0.896, 0.942]] as the row labels indicating the different years. Next, I take note of {the legend texts}[[0.141, 0.086, 0.292, 0.110], [0.782, 0.086, 0.896, 0.110]] as the column headers representing the different categories of energy. I then locate {the line points}[[0.115, 0.873], [0.241, 0.785], [0.368, 0.590], ...] for each category at the respective years and extract their values.\nBased on the above thought, the answer is:\nYear, Cloud Computing, Data Science\n2015, 1.01, 1.02\n2016, 1.1, 1.08\n2017, 1.3, 1.12\n2018, 1.51, 1.18\n2019, 1.55, 1.16\n2020, 1.7, 1.12\n2021, 1.8, 1.08" } ], "id_raw": 924 }, { "id": "36UPJmfq4w9f0HNQ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_4.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which technology, 'Cloud Computing' or 'Data Science' shows a more consistent growth and why do you think so?" }, { "from": "gpt", "value": "To answer this question, I examine the line plots for 'Cloud Computing' and 'Data Science'. The {line for Cloud Computing}[0.115, 0.104, 0.874, 0.873] shows a steady upward trend with no dips, implying consistent growth. In contrast, the {line for Data Science}[0.115, 0.104, 0.874, 0.873] also demonstrates an increasing trajectory, however, there is a slight decrease between 2018 and 2021. Hence, based on these observations, 'Cloud Computing' shows a more consistent growth.\nBased on the above thought, the answer is:\nBased on the line chart, 'Cloud Computing' shows more consistent growth compared to 'Data Science'. This is because its line plot shows a steady upward trend with no dips, meaning that it has been consistently increasing over the years. On the other hand, 'Data Science' shows an increase in growth but has a slight decrease between 2018 and 2021 which indicates a small inconsistency in its growth." } ], "id_raw": 924 }, { "id": "vpDb3eh8dxdeGfco", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_4.png", "conversations": [ { "from": "human", "value": "\nCan you tell me the increase in percentage from 2015 to 2021 for Cloud Computing?" }, { "from": "gpt", "value": "To find the increase in percentage for Cloud Computing from 2015 to 2021, I need to locate {the line points for Cloud Computing}[0.115, 0.104, 0.874, 0.873] and extract the corresponding values: 1.01, 1.1, 1.3, 1.51, 1.55, 1.7, 1.8. The value in 2015 is 1.01 and in 2021 is 1.8. Therefore, the increase in percentage is ((1.8-1.01)/1.01)*100.\nBased on the above thought, the answer is:\n77.23%" } ], "id_raw": 924 }, { "id": "92JlGYi3ToKCD3kh", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart into a table format displaying the type of AI Classification and corresponding Precision and Recall values?" }, { "from": "gpt", "value": "To generate the table, I need to locate {the x-axis tick labels}[[0.020, 0.919, 0.262, 0.942],[0.218, 0.919, 0.354, 0.942],[0.373, 0.919, 0.488, 0.942],[0.445, 0.919, 0.706, 0.942],[0.608, 0.919, 0.833, 0.942],[0.761, 0.919, 0.969, 0.942]] which represent the AI Classifications. The {label of the y-axis for Precision}[0.036, 0.416, 0.053, 0.520] and the {label of the y-axis for Recall}[0.964, 0.433, 0.981, 0.503] confirm that the plotted y-axis data is for Precision and Recall. These two variables yield two rows in the table. The data points in Precision plot ({line for Precision}[0.141, 0.065, 0.865, 0.871]) and the Recall plot ({line for Recall}[0.141, 0.065, 0.865, 0.871]) will give respective values of the two under each AI classification.\nBased on the above thought, the answer is:\nAI Classification,Precision,Recall\nRobotic Process Automation,0.9,0.8\nNeural Network,0.85,0.88\nDecision Tree,0.79,0.85\nConvolutional Neural Network,0.93,0.72\nRecurrent Neural Network,0.92,0.69\nSupport Vector Machine,0.7,0.68" } ], "id_raw": 925 }, { "id": "k3B9mgsX396S18CO", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_5.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, can you point out which AI model has a high Precision but a relatively low Recall, and try to explain the possible reason?" }, { "from": "gpt", "value": "Commonly high Precision and low Recall indicates that model is conservative, it makes fewer predictions, but a high percentage of them are correct. Looking at the lines on the chart which contain the data points for Precision and Recall, the {line for Precision}[0.141, 0.065, 0.865, 0.871] and the {line for Recall}[0.141, 0.065, 0.865, 0.871], I can observe that \"Convolutional Neural Network\" has a high Precision and relatively lower Recall ({the point for Convolutional Neural Network for Precision}[0.576, 0.065] and {the point for Convolutional Neural Network for Recall}[0.576, 0.710]). This might be due to less amount of training data, overfitting the training data, or maybe the class distribution in the labelled data is imbalanced.\nBased on the above thought, the answer is:\nThe Convolutional Neural Network classification model from the line chart has a high Precision but a relatively low Recall. This may be due to the model being overfitted to the training data or because the class distribution in the labelled data is imbalanced, resulting in a bias towards false negatives. Hence, conservatively, the model clocks fewer predictions compared to other models, but a higher percentage of its predictions are correct (high precision) while it may miss a larger number of instances (low recall)." } ], "id_raw": 925 }, { "id": "Y8KrPPDTKGVlSq6A", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_5.png", "conversations": [ { "from": "human", "value": "\nBy how much is the Precision value for Recurrent Neural Network greater than its Recall value?" }, { "from": "gpt", "value": "To find out the values for Precision and Recall for Recurrent Neural Network, I locate the {point of Recurrent Neural Network for Precision}[0.720, 0.100] which falls on the line for Precision, and also, the {point of Recurrent Neural Network for Recall}[0.720, 0.830] which falls on the line for Recall. The difference between the Precision value of 0.92 and Recall value 0.69 is thus, 0.92-0.69 = 0.23.\nBased on the above thought, the answer is:\n0.23." } ], "id_raw": 925 }, { "id": "nvChHxa4iTRVjs9t", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_6.png", "conversations": [ { "from": "human", "value": "\nCan you organize the data from this chart into a tabular format with the Year as rows and the indicators (Global Temperature Change, Sea Level Rise, Count of Wildfires) as columns?" }, { "from": "gpt", "value": "From the line chart, {the x-axis tick labels}[0.082, 0.911, 1.018, 0.942] represent the rows of the table, which are the years. The different indicators or columns of the table are represented by {the legend texts}[0.091, 0.077, 0.293, 0.119],[0.731, 0.077, 0.909, 0.119] which are Global Temperature Change, Sea Level Rise, and Count of Wildfires. The data is represented by the points on {the line for Global Temperature Change}[0.120, 0.158, 0.880, 0.604], {the line for Sea Level Rise}[0.120, 0.411, 0.880, 0.566], and {the line for Count of Wildfires}[0.120, 0.788, 0.880, 0.873] for the years 1980 to 2020 respectively.\nBased on the above thought, the answer is:\nYear,Global Temperature Change,Sea Level Rise,Count of Wildfires\n1980,0.2,0.1,300\n1981,0.3,0.2,305\n...\n2019,1.2,0.9,800\n2020,1.2,1.0,850" } ], "id_raw": 926 }, { "id": "boJXE9y7sYrWiSyG", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_6.png", "conversations": [ { "from": "human", "value": "\nCan you describe the correlation between global temperature change and sea level rise based on the given chart?" }, { "from": "gpt", "value": "To find the correlation between global temperature change and sea level rise, I must compare the trends shown by {the line for Global Temperature Change}[0.120, 0.158, 0.880, 0.604] and {the line for Sea Level Rise}[0.120, 0.411, 0.880, 0.566]. Both lines show a steady increase from 1980 to 2020. There are no sudden jumps or falls in either of the lines, and they both show a steady upwards trend.\nBased on the above thought, the answer is:\nBased on the chart given, there appears to be a strong positive correlation between global temperature change and sea level rise. Both indicators show a consistent increase across the time period from 1980 to 2020. This observation suggests that as global temperatures rise, sea levels also increase, which is in line with the scientific understanding of the impact of climate change." } ], "id_raw": 926 }, { "id": "QrntsbYegoZ9Uml2", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_6.png", "conversations": [ { "from": "human", "value": "\nCan you find the percentage increase in global temperature change from 1990 to 2020?" }, { "from": "gpt", "value": "To find the percentage change in global temperature change from 1990 to 2020, I first locate the values for 1990 and 2020 from {the line for Global Temperature Change}[0.120, 0.158, 0.880, 0.604]. The value for 1990 is approximately 0.5 and the value for 2020 is approximately 1.2. The percentage increase between these two years is given by the formula: ((Final value - Initial value) / Initial value) * 100. Therefore, ((1.2 - 0.5) / 0.5) * 100 = 140%.\nBased on the above thought, the answer is:\n140%" } ], "id_raw": 926 }, { "id": "BjnFmluLZPUuFxYR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_7.png", "conversations": [ { "from": "human", "value": "\nCan you convert the graphical representation of the family asset values into a structured table format based on the trend seen?" }, { "from": "gpt", "value": "Initially, I observe the x-axis, marked by {the x-axis labels}[0.066, 0.933, 0.077, 0.957], which indicate the various family assets such as house, education, healthcare, vacation, hobbies, and retirement fund. Then, looking at the y-axis, indicated by {the y-axis labels}[0.066, 0.933, 0.035, 0.061], I assess the values associated with each asset. The line graph's data points, marked by {the data points}[0.105, 0.756, 0.895, 0.067], help me correlate the specific values to each asset.\nBased on the above thought, the answer is:\nAssets,Values\nHouse,350\nEducation,220\nHealthcare,180\nVacation,165\nHobbies,145\nRetirement Fund,270" } ], "id_raw": 927 }, { "id": "UgekHKyx4btVhw08", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_7.png", "conversations": [ { "from": "human", "value": "\nHow does the chart's presentation of asset values reflect the spending priorities of a family?" }, { "from": "gpt", "value": "{The trend shown by the line chart}[0.105, 0.354, 0.895, 0.756] progresses from essential to luxury items. The line's steep ascent early on, at points corresponding to house, education, and healthcare, suggests high values and a priority on these necessary assets. Conversely, the line dips slightly at vacation and hobbies, indicating a secondary priority on these luxury items. Lastly, there is a rise again at retirement fund, showcasing a significant family priority towards secure future savings.\nBased on the above thought, the answer is:\nThe chart clearly highlights that a family prioritizes essential assets like house, education, and healthcare due to their high values, emphasizing their importance. Luxuries such as vacation and hobbies command lesser budget and are of secondary priority, while a significant amount is again allocated to retirement funds, indicating the importance of future financial security." } ], "id_raw": 927 }, { "id": "4tg0CTSeg4IGDtt8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the average value of the 'luxury' category assets based on the line chart?" }, { "from": "gpt", "value": "To calculate the average value of the luxury category assets from the line chart (vacation, hobbies), I must locate the corresponding values for these assets. Observing {the vacation value}[0.485, 0.558] and {the hobbies value}[0.515, 0.535], these have the annotated values of 165 and 145 respectively. Using these values, the average is calculated by summing them up and dividing by the number of luxuries: (165 + 145) / 2.\nBased on the above thought, the answer is:\n155" } ], "id_raw": 927 }, { "id": "rrgRk7k8NoXKG6Lf", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_8.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data into a structured table format with Years as columns and Health Indicators as rows?" }, { "from": "gpt", "value": "I observe that {the x-axis labels}[0.104, 0.942, 0.114, 0.970] represent different years which will serve as the column headers. For the rows, I recognize {the legend}[0.884, 0.101, 0.982, 0.123] indicating Health Indicators such as Doctors, Patients, Hospital Beds, Healthcare Expenditure, and Medical Checkups. Locating {individual data points on the lines}[0.141, 0.069] allows me to extract the values for each indicator in the corresponding year.\nBased on the above thought, the answer is:\nYear,Doctors,Patients,Hospital Beds,Healthcare Expenditure,Medical Checkups\n2020,250,1200,300,50000,800\n2021,270,1150,350,70000,750\n2022,300,1300,400,71000,740\n2023,320,1250,450,72000,700" } ], "id_raw": 928 }, { "id": "EEhMtws09F4r0cEf", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_8.png", "conversations": [ { "from": "human", "value": "\nWhat trends can be observed in the fluctuating number of patients over the years?" }, { "from": "gpt", "value": "An analysis of {the line representing patient numbers}[0.141, 0.854] shows variation in values over the years. I observe that the trend starts high in 2020, slightly decreases by 2021, increases again in 2022, and drops once more in 2023. This fluctuation indicates an unstable trend in the number of patients seeking medical attention or being registered annually.\nBased on the above thought, the answer is:\nThe number of patients demonstrates a fluctuating trend over the years. Initially starting high, it shows a decrease in the following year, a subsequent increase, and a drop again, suggesting variable factors affecting patient numbers annually." } ], "id_raw": 928 }, { "id": "EQNqrSkrDD9BuDKS", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_8.png", "conversations": [ { "from": "human", "value": "\nHow much did Healthcare Expenditure increase from the year 2020 to 2023?" }, { "from": "gpt", "value": "I first identify and extract the values of {Healthcare Expenditure in 2020}[0.141, 0.069] and {Healthcare Expenditure in 2023}[0.875, 0.167] from the line plot. In 2020, the expenditure is represented as 50000 and in 2023 as 72000. To determine the increase, I subtract the 2020 value from the 2023 value which gives 72000 - 50000.\nBased on the above thought, the answer is:\n22000" } ], "id_raw": 928 }, { "id": "f1lezhihDaVFZn1D", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_9.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured table format detailing the investment values for each company over a certain period from the line chart?" }, { "from": "gpt", "value": "To convert the data from the line chart into a structured table, I first locate {the x-axis tick labels}[0.075, 0.029, 0.925, 0.055] to identify the years, which serve as the column headers. I identify {the legend}[0.082, 0.039, 0.178, 0.075] that lists the companies Microsoft, Apple, Amazon, Tesla, and IBM as row headers. For each company, I track down {the line points}[0.113, 0.208, 0.887, 0.816], [0.113, 0.155, 0.887, 0.244] corresponding to investment values across the years. I then document the values displayed in the chart.\nBased on the above thought, the answer is:\nYear,Microsoft,Apple,Amazon,Tesla,IBM\n2010,47.5,50.3,41.7,54.7,40.8\n2011,48.2,51.0,42.4,55.9,41.2\n2012,49.0,51.7,43.0,57.1,41.6\n...\n2020,55.6,59.2,49.7,67.5,45.6" } ], "id_raw": 929 }, { "id": "MxGOIa6qDEvlYsH6", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_9.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, which companies show a consistent upward trend in their investment values?" }, { "from": "gpt", "value": "To analyze consistent upward trends, I observe the lines for each company: {Microsoft}[0.113, 0.208, 0.887, 0.816], {Apple}[0.113, 0.155, 0.887, 0.244], {Amazon}[0.113, 0.262, 0.887, 0.870], {Tesla}[0.113, 0.244, 0.887, 0.870], and {IBM}[0.113, 0.333, 0.887, 0.870]. Examining their points over the years, especially looking at the overall direction between 2010 and 2020, Tesla and Apple show marks moving upwards consistently with Tesla showing the most significant rise.\nBased on the above thought, the answer is:\nTesla and Apple consistently show upward trends in their investment values according to the line chart. Tesla shows the most significant rise, indicating dynamic growth in their investment over the years, while Apple displays a steady yet constant increase." } ], "id_raw": 929 }, { "id": "PuENLjjvA059FDKu", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the average investment value for Tesla over the period covered by the line chart?" }, { "from": "gpt", "value": "To calculate the average investment for Tesla, I first locate {the line points for Tesla}[0.113, 0.155, 0.887, 0.244] across different years ranging from 2010 to 2020. These points indicate Tesla's investment values each year: [54.7, 55.9, 57.1, 58.4, 59.9, 61.0, 62.5, 64.0, 65.8, 67.5]. To find the average, I sum all these values and divide by the number of years, which is ten.\nBased on the above thought, the answer is:\n60.68" } ], "id_raw": 929 }, { "id": "SNk6PIH0YjcFFKpl", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_10.png", "conversations": [ { "from": "human", "value": "\nCan you provide the sales data in a CSV format for all products across the months based on the line graph?" }, { "from": "gpt", "value": "Observing the line graph, I identify {the x-axis labels}[0.05, 0.92, 0.12, 0.96] representing the months from January to December. I then focus on each product line, {the line for ProductA}[0.05, 0.12, 0.95, 0.88], {the line for ProductB}[0.05, 0.88, 0.95, 0.12], and {the line for ProductC}[0.05, 0.72, 0.95, 0.27], and extract the points representing sales values for each month.\nBased on the above thought, the answer is:\nMonth,ProductA,ProductB,ProductC\nJanuary,120,350,240\nFebruary,180,330,280\nMarch,240,310,320\nApril,300,290,350\nMay,360,270,400\nJune,420,250,360\nJuly,480,230,390\nAugust,530,210,420\nSeptember,590,150,450\nOctober,620,100,430\nNovember,630,60,460\nDecember,640,20,490" } ], "id_raw": 930 }, { "id": "kdQnWK1EJJ7IPYlL", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_10.png", "conversations": [ { "from": "human", "value": "\nWhich product exhibits the most unstable sales trend throughout the year according to the line graph?" }, { "from": "gpt", "value": "To answer this, I analyze the volatility of each product line by observing the fluctuations in their respective lines. {ProductA}[0.05, 0.12, 0.95, 0.88] shows a steady increase, indicating stability. In contrast, {ProductB}[0.05, 0.88, 0.95, 0.12] consistently declines, which is stable in its own pattern. However, {ProductC}[0.05, 0.72, 0.95, 0.27] reveals several peaks and troughs throughout the year, showing significant ups and downs, which indicates high instability.\nBased on the above thought, the answer is:\nProductC exhibits the most unstable sales trend throughout the year. The line graph displays numerous fluctuations in its sales figures, contrasting with the more steady trends seen in ProductA and ProductB." } ], "id_raw": 930 }, { "id": "FvEEuayGRJXOvj0d", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the total year-end sales difference between ProductA and ProductB?" }, { "from": "gpt", "value": "I will calculate the total sales for ProductA and ProductB in December using their respective data points on the graph. For {ProductA}[0.95, 0.88], the sales point is at 640 and for {ProductB}[0.95, 0.12], the sales point is at 20. The difference is obtained by subtracting the sales of ProductB from ProductA.\nBased on the above thought, the answer is:\n620" } ], "id_raw": 930 }, { "id": "P9axeAvLNkJpsi02", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert the market share data from the line chart into a table format with Companies as rows and Market Shares as columns?" }, { "from": "gpt", "value": "To convert the information from the line chart into a table, I first identify {the x-axis labels}[0.060, 0.952, 0.940, 0.975] which indicate the companies: Apple, Samsung, Huawei, Xiaomi, OPPO, Realme, Others. I then observe the market share values from the data points in the line chart. For each company, I follow {the trend line}[0.100, 0.150, 0.900, 0.843] and extract their respective market share values from the y-tick values at each x-coordinate.\nBased on the above thought, the answer is:\nCompany,Market Share\nApple,21.4%\nSamsung,19.2%\nHuawei,13.2%\nXiaomi,11.4%\nOPPO,8.2%\nRealme,1.9%\nOthers,25.7%" } ], "id_raw": 931 }, { "id": "keJF7Yra9OK9kWwx", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_11.png", "conversations": [ { "from": "human", "value": "\nWhich company shows the most significant fluctuation in market share according to the chart?" }, { "from": "gpt", "value": "Observing the line chart for fluctuations, I note the variations in market share for each company based on {the trend lines}[0.100, 0.150, 0.900, 0.843] and compare them. Companies like Xiaomi and Huawei display variations, yet the category labeled {Others}[0.900, 0.843] shows the greatest fluctuations, starting from a lower value, peaking, and then declining again more significantly than other companies.\nBased on the above thought, the answer is:\nThe 'Others' category exhibits the most significant fluctuation in market share, as indicated by the high amplitude of changes in its market share over time in the chart. This denotes a combined effect of numerous smaller manufacturers." } ], "id_raw": 931 }, { "id": "FVxbJNJyEHiKhuSj", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the total market share percentage of the top three companies from the chart?" }, { "from": "gpt", "value": "To calculate the total market share of the top three companies, I find the market share of {Apple}[0.100, 0.843], {Samsung}[0.220, 0.759], and {Huawei}[0.340, 0.657]. The individual market shares are, respectively, 21.4%, 19.2%, and 13.2%. Summing these figures gives us a cumulative market share.\nBased on the above thought, the answer is:\n53.8%." } ], "id_raw": 931 }, { "id": "z7Q5yAT4lfiqo6Dx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_12.png", "conversations": [ { "from": "human", "value": "\nCan you convert the Carbon Footprint values over the years into a structured table format?" }, { "from": "gpt", "value": "By analyzing {the line for Carbon Footprint}[0.071, 0.066, 0.929, 0.911], I extract the data points corresponding to each year. After identifying all individual data points and matching them to their corresponding years from {x-axis labels}[0.071, 0.919, 0.958, 0.942], I can convert these values into a structured table.\nBased on the above thought, the answer is:\nYear,Carbon Footprint\n2000,320\n2001,312\n2002,305\n2003,298\n2004,289\n2005,280\n2006,270\n2007,260\n2008,250\n2009,240\n2010,230\n2011,220\n2012,210\n2013,205\n2014,200\n2015,195\n2016,190\n2017,185\n2018,180\n2019,175\n2020,170" } ], "id_raw": 932 }, { "id": "JtIoJauCW4Ozenve", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_12.png", "conversations": [ { "from": "human", "value": "\nHow does the trend between Carbon Footprint and Reforestation Efforts indicate progress towards sustainability?" }, { "from": "gpt", "value": "Observing the chart, {the decreasing line of the Carbon Footprint}[0.071, 0.066, 0.929, 0.911] with values starting high and reducing over time suggests an effective decrease in environmental impact. Concurrently, {the increasing line of Reforestation Efforts}[0.071, 0.066, 0.929, 0.911] shows continuous improvement in forestry initiatives. This simultaneous movement, with carbon footprint declining as reforestation efforts rise, clearly illustrates progress towards sustainable and environmentally-beneficial practices over the years.\nBased on the above thought, the answer is:\nThe trend of decreasing Carbon Footprint and increasing Reforestation Efforts over the years distinctly showcases progress towards environmental sustainability. As industries reduce emissions and environmental impact lessens, the enhanced reforestation efforts contribute to ecological restoration, marking a move towards greener and more sustainable practices." } ], "id_raw": 932 }, { "id": "YhRgzrTHQLV5fqPu", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest values of Reforestation Efforts during the observed period?" }, { "from": "gpt", "value": "To determine the highest and lowest values of Reforestation Efforts, I carefully examine {the line plot for Reforestation Efforts}[0.071, 0.066, 0.929, 0.911], pinpointing the peaks and troughs. After thoroughly inspecting the data, the maximum value at {the point in 2020}[0.929, 0.066] is 150, and the minimum value at {the point in 2000}[0.071, 0.911] is 50. Calculating the difference gives 150 - 50.\nBased on the above thought, the answer is:\n100." } ], "id_raw": 932 }, { "id": "cxeDcdUDstUcN4CP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart into a structured table with activities as rows and months as columns?" }, { "from": "gpt", "value": "To derive a structured table from the line chart, first, I identify {the x-axis tick labels}[[0.093, 0.820, 0.130, 0.841], [0.201, 0.820, 0.236, 0.841], [0.309, 0.820, 0.343, 0.841], [0.417, 0.820, 0.452, 0.841], [0.525, 0.820, 0.561, 0.841], [0.633, 0.820, 0.669, 0.841], [0.740, 0.820, 0.775, 0.841], [0.848, 0.820, 0.884, 0.841]] as the column headers, which represent the months. To find the row labels, I look at {the legend texts}[[0.889, 0.113, 0.955, 0.137], [0.889, 0.146, 0.965, 0.170], [0.889, 0.179, 0.957, 0.203], [0.889, 0.212, 0.950, 0.235], [0.889, 0.245, 0.957, 0.269], [0.889, 0.278, 0.935, 0.301], [0.889, 0.311, 0.948, 0.335]], which will become the rows representing the different sports and fitness activities. Then, I examine {the line points}[[0.145, 0.747], [0.253, 0.715], ..., [0.673, 0.348]] for each activity at the respective months and obtain the values to populate the table.\nBased on the above thought, the answer is:\nMonth/Activity,1,2,3,4,5,6,7,8\nJogging,120,110,115,125,130,120,115,112\nWeightlifting,150,155,145,160,155,160,162,160\nCycling,130,125,120,122,130,135,133,132\nTennis,95,105,100,108,102,105,110,105\nBasketball,70,75,73,78,80,82,85,82\nYoga,160,165,167,175,172,168,170,172\nSwimming,180,185,182,188,190,200,210,215" } ], "id_raw": 933 }, { "id": "A1v2mXRVeDy2vvxm", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_13.png", "conversations": [ { "from": "human", "value": "\nCan you tell me which activity was the most consistently popular over the period, and why?" }, { "from": "gpt", "value": "To determine the fitness activity with most consistent popularity, I need to inspect the minimal fluctuation in the line plot for each activity. At a glance, {the line for Weightlifting}[[0.145, 0.747, 0.857, 0.008]] appears to show the smallest fluctuation in its trend. The plot shows a steady increase from 150 to 162, modestly fluctuating between these values over the months, which indicates a relatively consistent popularity.\nBased on the above thought, the answer is:\nConsidering the trends shown in the line chart, Weightlifting seems to have been the most consistently popular activity over the time period. The line plot for Weightlifting showed a slow and steady increase, with only minor fluctuations in between, indicating that the popularity of Weightlifting stayed fairly consistent." } ], "id_raw": 933 }, { "id": "dVCr5VvaqiGGnIEl", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in popularity for swimming from month 1 to month 8?" }, { "from": "gpt", "value": "To find the increase in popularity for swimming, I need to locate {the line points for Swimming}[[0.145, 0.380], [0.253, 0.355], [0.360, 0.365], [0.468, 0.346], [0.579, 0.331], [0.687, 0.285], [0.790, 0.245], [0.857, 0.008]] and extract the corresponding values: 180, 185, 182, 188, 190, 200, 210, 215. The value at month 1 is 180 and the value at month 8 is 215. The increase is therefore 215 - 180 = 35.\nBased on the above thought, the answer is:\n35." } ], "id_raw": 933 }, { "id": "ICwmyWUfXsYIoFsR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_14.png", "conversations": [ { "from": "human", "value": "\nFrom the chart, could you structure the monthly sales data into a CSV format including the product names?" }, { "from": "gpt", "value": "Noting {the x-axis labels}[0.044, 0.939, 1.024, 0.957] which represent different products like PS5, MacBook Pro, iPhone 12, Samsung S20, and Dell XPS as columns. I observe {the line points}[0.097, 0.904, 0.903, 0.173] corresponding to each product and extract the number of sales. These points illustrate sales values over a month for each product.\nBased on the above thought, the answer is:\nProduct,Monthly Sales\nPS5,1050\nMacbook Pro,1500\niPhone 12,5000\nSamsung S20,2200\nDell XPS,1800" } ], "id_raw": 934 }, { "id": "r4Y2oKDFJJkN5OOo", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_14.png", "conversations": [ { "from": "human", "value": "\nWhat does the peak in the iPhone 12 sales signify in the context of this market?" }, { "from": "gpt", "value": "Observing the distinct upward surge at {the peak point of the iPhone 12 sales}[0.479, 0.203], it's clear it significantly outsold other products. The steep ascent in its line compared to others like {PS5, Macbook Pro, Samsung S20 and Dell XPS}[0.097, 0.904, 0.903, 0.173] suggests a huge consumer demand and possibly a very effective marketing strategy or product launch.\nBased on the above thought, the answer is:\nThe peak in the iPhone 12 sales indicates a very high consumer demand for this product, likely driven by successful marketing strategies or appealing product features. This highlights iPhone 12 as the leading choice among customers compared to other electronic products within the same period." } ], "id_raw": 934 }, { "id": "xfSRK0BwldUY0W0e", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_14.png", "conversations": [ { "from": "human", "value": "\nHow much more did iPhone 12 sell compared to Dell XPS in the provided data?" }, { "from": "gpt", "value": "To find out how much more the iPhone 12 sold compared to Dell XPS, I compare the sales values at {points on the iPhone 12 and Dell XPS lines}[0.479, 0.203, 0.860, 0.339]. The iPhone 12 shows a sales value of 5000 units whereas the Dell XPS shows a sales value of 1800 units.\nBased on the above thought, the answer is:\n3200 units." } ], "id_raw": 934 }, { "id": "lwh4vltr4P3hqgLh", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_15.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data from the line chart showing the trend of admissions into private and public schools from 2015 to 2020 into a data table format? We should have Year as columns and School types as rows." }, { "from": "gpt", "value": "For this purpose, I'll locate {the x-axis tick labels}[[0.057, 0.919, 0.068, 0.942], [0.192, 0.919, 0.203, 0.942], [0.326, 0.919, 0.337, 0.942], [0.461, 0.919, 0.472, 0.942], [0.595, 0.919, 0.606, 0.942], [0.724, 0.919, 0.746, 0.942], [0.859, 0.919, 0.881, 0.942], [0.993, 0.919, 1.015, 0.942]] which represent the year for each data point. Next, I'll identify the legend texts as Private and Public schools from the description. To extract the numbers, I'll read off the points on the lines {\"for Private schools\"}[[0.130, 0.573], [0.197, 0.590], [0.265, 0.607], [0.332, 0.708], [0.399, 0.759], [0.466, 0.776], [0.534, 0.708], [0.601, 0.667], [0.668, 0.613], [0.735, 0.627], [0.803, 0.647], [0.870, 0.573]] and {\"for Public schools\"}[[0.130, 0.167], [0.197, 0.184], [0.265, 0.235], [0.332, 0.302], [0.399, 0.319], [0.466, 0.353], [0.534, 0.336], [0.601, 0.296], [0.668, 0.235], [0.735, 0.252], [0.803, 0.285], [0.870, 0.150]].\nBased on the above thought, the answer is:\nSchool_Type,2015,2016,2017,2018,2019,2020\nPrivate,1000,950,900,600,450,400\nPublic,2200,2150,2000,1800,1750,1650" } ], "id_raw": 935 }, { "id": "J8HaUVi1mesbKJPK", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_15.png", "conversations": [ { "from": "human", "value": "\nLooking at the admissions trend, which type of school faced a significant change in 2017, and what was the nature of this change?" }, { "from": "gpt", "value": "To answer this question, I need to analyze the trends shown in the line plots for both private and public schools. Looking at {the Private line}[[0.130, 0.573, 0.870, 0.573]], there is no significant change noticed in 2017 as the admissions moderately increased over time. However, the {Public line}[[0.130, 0.167, 0.870, 0.150]] shows a noticeable drop in admissions in 2017.\nBased on the above thought, the answer is:\nObserving the line chart, the admissions into public schools showed a noticeable drop in 2017. In other words, the trend for public schools in 2017 faced a significant decrease compared to the previous year. The decrease continued but at a less drastic rate after 2017." } ], "id_raw": 935 }, { "id": "6ewvTDhhmQLrSjIK", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the total number of admissions for private schools from 2015 to 2020?" }, { "from": "gpt", "value": "To calculate the total admissions for private schools, I need to sum up the line points values {\"for Private schools\"}[0.130, 0.573, 0.197, 0.590, 0.265, 0.607, 0.332, 0.708, 0.399, 0.759, 0.466, 0.776, 0.534, 0.708, 0.601, 0.667, 0.668, 0.613, 0.735, 0.627, 0.803, 0.647, 0.870, 0.573], which are respectively: 1000, 950, 900, 600, 450, 400. Total = 1000 + 950 + 900 + 600 + 450 + 400 = 4300.\nBased on the above thought, the answer is:\n4300." } ], "id_raw": 935 }, { "id": "Sy63iuJH9VTJzNHz", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_16.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data of monthly closing prices for Stock_A and Stock_B into a structured table format?" }, { "from": "gpt", "value": "To create a table from the line chart, I first locate {the x-axis tick labels}[0.073, 0.952, 0.145, 0.982] to identify each month from January to December as columns. Next, I see {the legend labels}[0.805, 0.073, 0.909, 0.101] which indicate the rows of Stock_A and Stock_B. For each month, I observe and record the line plot points from both {the line for Stock_A}[0.100, 0.700, 0.900, 0.100] and {the line for Stock_B}[0.100, 0.750, 0.900, 0.150] to extract the monthly closing prices.\nBased on the above thought, the answer is:\nMonth,Stock_A,Stock_B\nJanuary,48.3,32.1\nFebruary,48.9,32.5\nMarch,49.7,33.2\nApril,50.3,33.9\nMay,51.2,34.6\nJune,52.1,35.2\nJuly,52.9,35.9\nAugust,53.8,36.7\nSeptember,53.0,36.0\nOctober,53.6,36.5\nNovember,54.4,37.3\nDecember,54.9,38.9" } ], "id_raw": 936 }, { "id": "mrgAAOQVFa2sUJ7s", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_16.png", "conversations": [ { "from": "human", "value": "\nHow did Stock_A's trend compare to the general fluctuation observed in Stock_B throughout the year?" }, { "from": "gpt", "value": "Observing both {the line for Stock_A}[0.100, 0.700, 0.900, 0.100] and {the line for Stock_B}[0.100, 0.750, 0.900, 0.150], Stock_A exhibits a rather stable and gradual increase over the year, characterized by fewer and less severe dips especially around September. In comparison, Stock_B, while also trending upwards, shows slightly more pronounced fluctuations, particularly visible around the midpoint of the year where its line shows a sharper dip followed by a quick recovery.\nBased on the above thought, the answer is:\nStock_A displayed a more stable and gradual increase in its trends compared to Stock_B, which, despite following an upward trend, experienced more pronounced fluctuations throughout the year. Stock_A's smoother trajectory suggests less volatility compared to the more erratic movements of Stock_B." } ], "id_raw": 936 }, { "id": "6Ps8ykVG9Yy2wvP8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall year increase for Stock_B from January to December?" }, { "from": "gpt", "value": "To ascertain the overall increase of Stock_B over the year, I first locate {the starting point for Stock_B in January}[0.100, 0.730] and the {closing point for Stock_B in December}[0.880, 0.280]. By extracting the Y-values, the starting month value is 32.1 and the ending is 38.9. The difference representing the yearly increase is calculated by subtracting the starting value from the ending value.\nBased on the above thought, the answer is:\n6.8" } ], "id_raw": 936 }, { "id": "EmVJXSJeeFDyWQO1", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_17.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table mapping 'Environmental Sustainability Topics' to the 'Human Discussions' and 'AI Model Discussions' frequency from the stacked bar chart?" }, { "from": "gpt", "value": "Observing the stacked bar chart, I can differentiate the frequencies with which topics were discussed by humans and the AI model by referencing the y-axis which shows probability values. Tracking the data from the stacked bar segments helps differentiate between {the AI model's segments}[0.100, 0.220, 0.120, 0.800] and {the human discussion segments}[0.100, 0.800, 0.120, 0.880]. The topics along the x-axis are identifiable via {the labels on the x-ticks}[0.100, 0.900, 0.150, 0.950] which associate each bar with their respective topics.\nBased on the above thought, the answer is:\nTopic,Human Discussions,AI Model Discussions\nClimate Change,0.10,0.15\nBiodiversity,0.15,0.20\nDeforestation,0.05,0.10\nPollution,0.12,0.18\nConservation,0.02,0.45" } ], "id_raw": 937 }, { "id": "5fzs6nKy2xVW0Rue", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_17.png", "conversations": [ { "from": "human", "value": "\nWhich topic shows a remarkably higher involvement by the AI model compared to human discussions?" }, { "from": "gpt", "value": "By visually inspecting the levels of each topic's bars in the chart, I purposefully scrutinize for disparities. The topic of {Conservation}[0.600, 0.200, 0.700, 0.850] shows a significant height difference between the human discussion and AI model segments, indicating a strong emphasis by the AI model. The AI model discussion portion extends almost across the entire y-axis scale dedicated to probabilities, deeply overshadowing the much smaller segment for human discussions.\nBased on the above thought, the answer is:\nThe topic of 'Conservation' shows a remarkably higher involvement by the AI model as compared to human discussions. The significant difference in bar heights accentuates the AI model's unique focus on this topic." } ], "id_raw": 937 }, { "id": "LznRCFA5nPJUWWiX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_17.png", "conversations": [ { "from": "human", "value": "\nHow much higher is the AI Model's discussion frequency regarding 'Conservation' compared to that of Human Discussions?" }, { "from": "gpt", "value": "Initially, I locate the data points for the 'Conservation' topic within the chart. The AI Model discussion frequency for 'Conservation' is visibly higher, seen at {the point in AI Model's bar}[0.700, 0.300], compared to that of Human discussions at {the point in Human's bar}[0.700, 0.850]. Numerically assessing these values from the y-axis, AI Model discusses 'Conservation' with a frequency of 0.45 whereas Human discussions have a frequency of 0.02.\nBased on the above thought, the answer is:\n0.43" } ], "id_raw": 937 }, { "id": "eCKJbYATa70ptiEt", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_18.png", "conversations": [ { "from": "human", "value": "\nCan you organize the chart data showing emotional behaviors and their trends from the psychological studies into a structured format?" }, { "from": "gpt", "value": "Observing {the stacked bars}[0.120, 0.108, 0.880, 0.911] on the chart, I can see that each bar represents a distinct emotion with two segments: one for human distribution and another for psychological trend. The human distribution is noted by its orangish section while the psychological trend is displayed with a bluish top layer. Additionally, {the X-axis labels}[0.090, 0.919, 0.910, 0.942] enumerate the various emotions like fear, sadness, happiness, etc. By extracting numerical values associated with each segment from the bars and labeling them according to the emotion, we can form an accurate representation of the data in a table.\nBased on the above thought, the answer is:\nEmotion,Human Distribution (%),Psychological Trend (%)\nFear,35,30\nSadness,25,20\nHappiness,40,45\nAnger,10,15\nSurprise,15,10\nDisgust,5,5\nLove,20,35\nCalm,30,25" } ], "id_raw": 938 }, { "id": "KT5HHDe0IuaPPvUS", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_18.png", "conversations": [ { "from": "human", "value": "\nWhy does the emotional trend for 'Love' depicted in the chart indicate a disconnect between human distribution and psychological studies predictions?" }, { "from": "gpt", "value": "Upon analyzing {the segment for 'Love'}[0.510, 0.350, 0.590, 0.911], the human distribution stands at 20% whereas the psychological trend indicates an increase of up to 35%, which points to a notable rise. This divergence suggests that the psychological studies expect a higher occurrence or admission of the emotion 'Love' compared to what is actually reported or observed in human behavior. Possible reasons for this discrepancy might involve societal or cultural factors affecting individuals' willingness to openly express or acknowledge feelings of love, perhaps due to prevailing social norms or personal reservations.\nBased on the above thought, the answer is:\nThe emotional trend for 'Love' shows a deviation between human distribution and psychological predictions because the psychological studies might be extrapolating data or expectations of more liberal expressions of love, whereas actual human behavior could be constrained by societal or cultural influences that deter open expressions of such an emotion." } ], "id_raw": 938 }, { "id": "8snulNMWEbEnpz4E", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage difference between the human distribution and psychological trend for the emotion 'Love' as depicted on the chart?" }, { "from": "gpt", "value": "To find the percentage difference for the emotion 'Love', I need to refer to {the appropriate segment of the stacked bar for 'Love'}[0.510, 0.350, 0.590, 0.911] on the chart. The human distribution part shows 20%, and the psychological trend shows 35%. Thus, the difference between the psychological trend and human distribution can be calculated as 35% - 20% = 15%.\nBased on the above thought, the answer is:\n15%" } ], "id_raw": 938 }, { "id": "CyUIMN3kUySjwjWG", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_47_19.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table representing the Development Trends and Workplace Trends across key characteristics like flexible workplace, professional development, and diversity and inclusion?" }, { "from": "gpt", "value": "To generate the table data from the dual trend chart, I begin by examining {the x-axis tick labels}[0.066, 0.933, 0.081, 0.939] which consist of the characteristics like flexible workplace, professional development, and diversity and inclusion. Then, I identify {the legend}[0.073, 0.058, 0.234, 0.089] presenting Development Trends and Workplace Trends. Observing the lines, I extract the progress levels for each characteristic and trend from the coordinates of {the line points for Development Trend}[0.105, 0.374, 0.895, 0.522] and {the line points for Workplace Trend}[0.105, 0.344, 0.895, 0.474].\nBased on the above thought, the answer is:\nCharacteristics,Development Trend,Workplace Trend\nFlexible Workplace,Low,High\nProfessional Development,Moderate,Moderate\nDiversity and Inclusion,High,Low" } ], "id_raw": 939 }, { "id": "tWqroTRt7Hp3wRAI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_47_19.png", "conversations": [ { "from": "human", "value": "\nWhich trend demonstrates a more consistent development across different workplace characteristics?" }, { "from": "gpt", "value": "To determine which trend is more consistent, I assess the variation in the y-coordinates for each trend's points. Observing {the line for Development Trend}[0.105, 0.540, 0.895, 0.403], it has minor fluctuations across different characteristics. In contrast, {the line for Workplace Trend}[0.105, 0.835, 0.895, 0.344] displays more significant swings, especially for flexible workplace and diversity and inclusion. The consistency of a trend is typically judged by smaller fluctuations in its respective line plot.\nBased on the above thought, the answer is:\nThe Development Trend demonstrates a more consistent development across different workplace characteristics, as seen in the moderate level of fluctuation across characteristics compared to the Workplace Trend, which shows larger swings in development levels." } ], "id_raw": 939 }, { "id": "URHm9cBe5lgagLzP", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_47_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in the aspect of diversity and inclusion from the Development Trend?" }, { "from": "gpt", "value": "To analyze the increase in diversity and inclusion within the Development Trend, first, I locate the line chart where {the point marking the start of diversity and inclusion for Development Trend}[0.895, 0.482] represents the initial value, which corresponds closely with a smaller value on {the y-axis}[0.066, 0.049]. Following this, {the point marking the end of diversity and inclusion for Development Trend}[0.925, 0.292] shows a higher position, indicating an increase. Using the labels on the y-axis or points for precise value extraction would yield the numerical increase.\nBased on the above thought, the answer is:\nSignificant Increase." } ], "id_raw": 939 }, { "id": "xLFbIkwS29U4hB7q", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_1.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart depicting Share Prices throughout the year into a table format with Months as rows and Share Prices as columns?" }, { "from": "gpt", "value": "To convert the line chart data into a structured table, first we need to identify {the x-axis tick labels}[[0.092, 0.879, 0.128, 0.914], [0.166, 0.879, 0.207, 0.914], [0.241, 0.879, 0.285, 0.914], [0.320, 0.879, 0.360, 0.914], [0.392, 0.879, 0.440, 0.914], [0.475, 0.879, 0.511, 0.914], [0.556, 0.879, 0.583, 0.914], [0.624, 0.879, 0.669, 0.914], [0.701, 0.879, 0.745, 0.914], [0.779, 0.879, 0.819, 0.914], [0.853, 0.879, 0.899, 0.914], [0.930, 0.879, 0.975, 0.914]] which are the row labels representing the months. Next, I locate {the legend texts}[[0.196, 0.069, 0.341, 0.104], [0.196, 0.121, 0.341, 0.156]] to identify the column headers as Share Price1 and Share Price2. Finally, the values can be extracted through {the line points}[[0.110, 0.369], [0.186, 0.397], ..., [0.953, 0.756]] for Share Price1 and {the line points}[[0.110, 0.314], [0.186, 0.259], ..., [0.953, 0.231]] for Share Price2 at the respective months.\nBased on the above thought, the answer is:\nMonths,Share Price1,Share Price2\nJan,210,220\nFeb,205,230\nMar,195,235\nApr,192,238\nMay,180,245\nJun,175,250\nJul,170,255\nAug,160,260\nSep,155,255\nOct,152,250\nNov,145,240\nDec,140,235" } ], "id_raw": 941 }, { "id": "JjHreM3yvW91Wp2Y", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_1.png", "conversations": [ { "from": "human", "value": "\nJudging from the trend lines in the graph, which share price presented a steadier rise over the course of the year?" }, { "from": "gpt", "value": "To answer this question, I have to compare {the trend line of Share Price1}[0.110, 0.369, 0.953, 0.756] and {the trend line of Share Price2}[0.110, 0.314, 0.953, 0.0314]. Observing the trend line of Share Price1, it fluctuates downward in a somewhat volatile manner. In contrast, Share Price2 seems to present a steady upward progress with minor dips before Sept and between Oct to Dec.\nBased on the above thought, the answer is:\nComparing the two share prices, Share Price2 presented a steadier rise over the course of the year. There were only minor dips and overall, the line graph of Share Price2 trended upwards consistently unlike the fluctuating trend line of Share Price1." } ], "id_raw": 941 }, { "id": "GmFVXF2vrUMqpHYw", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum decline in Share Price1 seen over consecutive months?" }, { "from": "gpt", "value": "To find the maximum decline in Share Price1 over consecutive months, I need to analyze {the data points for Share Price1}[[0.110, 0.369], [0.186, 0.397], [0.263, 0.452], [0.340, 0.469], [0.416, 0.535], [0.493, 0.563], [0.570, 0.590], [0.646, 0.645], [0.723, 0.673], [0.799, 0.690], [0.876, 0.728], [0.953, 0.756]] which are 210, 205, 195, 192, 180, 175, 170, 160, 155, 152, 145 and 140. The maximum decline is seen from May to June where the price declines from 180 to 175, which is a drop of 5 units.\nBased on the above thought, the answer is:\n5" } ], "id_raw": 941 }, { "id": "UXDlqj4WjT8PPYMd", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_2.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you turn the information about the performance of each market index over the years into a table?" }, { "from": "gpt", "value": "To create a table from the line chart, I first identify the {x-axis labels}[[0.034, 0.903, 0.069, 0.931], [0.146, 0.903, 0.181, 0.931], [0.257, 0.903, 0.292, 0.931], [0.369, 0.903, 0.404, 0.931], [0.480, 0.903, 0.515, 0.931], [0.591, 0.903, 0.627, 0.931], [0.703, 0.903, 0.738, 0.931], [0.814, 0.903, 0.849, 0.931], [0.926, 0.903, 0.961, 0.931], [1.037, 0.903, 1.072, 0.931]] as the Years. The {subplot legend labels}[[0.117, 0.055, 0.295, 0.083], [0.117, 0.097, 0.255, 0.125]] would serve as column headers representing the different market indexes. Then, for each year, I locate the {data points} for each index and extract the corresponding Market Index Values.\nBased on the above thought, the answer is:\nYears,Contemporary Art Market Index,Heritage Art Market Index\n2005,120,100\n2006,130,105\n2007,145,110\n2008,135,100\n2009,140,90\n2010,165,100\n2011,175,110\n2012,185,115\n2013,190,120\n2014,200,125\n2015,205,130\n2016,210,132\n2017,220,134\n2018,225,136\n2019,230,138\n2020,232,139" } ], "id_raw": 942 }, { "id": "HpioLsxHS9906RrF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_2.png", "conversations": [ { "from": "human", "value": "\nHow would you describe the overall trend in the Contemporary and Heritage Art Markets from 2005 to 2020 based on the line graph?" }, { "from": "gpt", "value": "To describe the overall trend, I analyze the lines representing each market. Looking at {line_0}[[0.108, 0.069, 0.943, 0.688]], which represents the Contemporary Art Market Index, I observe a generally increasing trend with small dips in 2008 and 2009. For the {line_1}[[0.108, 0.583, 0.943, 0.854]], representing the Heritage Art Market Index, the trend is also increasing, albeit at a slower pace and with fewer fluctuations than the Contemporary Art Market. The positive slope of both lines suggests an overall growth in the market index values from 2005 to 2020.\nBased on the above thought, the answer is:\nFrom 2005 to 2020, both the Contemporary and Heritage Art Markets demonstrate increasing trends according to the line graph. The Contemporary Art Market Index shows a more aggressive growth with slight dips in 2008 and 2009. On the other hand, the Heritage Art Market Index, while also growing, does so more steadily with fewer fluctuations. The graph suggests that both markets have seen a positive growth in their market index values over the given period." } ], "id_raw": 942 }, { "id": "mJPeTT20DUHslrmr", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_2.png", "conversations": [ { "from": "human", "value": "\nWhat was the percent increase in the Contemporary Art Market Index from 2005 to 2020?" }, { "from": "gpt", "value": "To find the percent increase in the Contemporary Art Market Index from 2005 to 2020, I first identify the {value for 2005}[[0.108, 0.688]] in line_0, which is 120. Then, I locate the {value for 2020}[[0.943, 0.069]] in line_0 as well, which is 232. The percent increase is therefore calculated as (232 - 120) / 120 = 0.9333 * 100%.\nBased on the above thought, the answer is:\n93.33%." } ], "id_raw": 942 }, { "id": "Ln0kGB4JGSykhrg3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_3.png", "conversations": [ { "from": "human", "value": "\nCan you organise the data representing the various perceptions of humans and machines towards Cybersecurity and Data Privacy into a table?" }, { "from": "gpt", "value": "To generate the data in a table, I identify the labels on {the x-axis}[0.056, 0.879, 0.106, 0.914],[0.144, 0.879, 0.194, 0.914],[0.231, 0.879, 0.281, 0.914],[0.318, 0.879, 0.368, 0.914],[0.405, 0.879, 0.455, 0.914],[0.492, 0.879, 0.543, 0.914],[0.580, 0.879, 0.630, 0.914],[0.667, 0.879, 0.717, 0.914] as the cybersecurity related terminologies. For each terminology, I find {the corresponding stacked bars}[0.081, 0.808, 0.910, 0.829],[0.081, 0.075, 0.910, 0.432] and calculate the proportions represented by each color to find the respective human and machine perceptions of each terminology.\nBased on the above thought, the answer is:\nTerm,Human Perception,Machine Perception\nPhishing,0.4,0.6\nHacking,0.35,0.65\nCyberattack,0.45,0.55\nData breach,0.3,0.7\nEncryption,0.47,0.53\nFirewall,0.3,0.7\nMalware,0.33,0.67\nIdentity theft,0.5,0.5" } ], "id_raw": 943 }, { "id": "oeJ1brPgUqAyghN7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_3.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, what does the similar perception of 'Identity theft' between humans and machines indicate?" }, { "from": "gpt", "value": "After locating the term {\u2018Identity theft\u2019}[0.667, 0.879, 0.717, 0.914] on the x-axis, I can trace the corresponding bar and notice that the human and machine perceptions marked by different colors share an equal height. This means that both humans and machines have the same level of interest or focus towards 'Identity theft'. Considering the unique nature of this alignment in the whole dataset where general machine perspectives are comparatively higher, this might indicate a common level of understanding or significance attributed to 'Identity theft' by both humans and machines in the field of Cybersecurity and Data Privacy.\nBased on the above thought, the answer is:\nThe similar perception level of 'Identity theft' between humans and machines suggests a mutual recognition of the significance of this term in the context of Cybersecurity and Data Privacy. It could indicate that both humans and machines acknowledge 'Identity theft' similarly as a substantial security concern and accord equivalent attention to it when addressing cybersecurity matters." } ], "id_raw": 943 }, { "id": "XeJacHMsg74wzQN9", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_3.png", "conversations": [ { "from": "human", "value": "\nWhat's the difference in the perception of the term 'Firewall' between humans and machines?" }, { "from": "gpt", "value": "To calculate the difference in the perception of the term 'Firewall', I need to examine {the bar corresponding to 'Firewall'}[0.405, 0.879, 0.455, 0.914]. The different colored sections show the human and machine perceptions. Judging from the difference in heights, the machine perception is approximately 0.7 and the human perception is close to 0.3. Therefore, the difference in perception is approximately 0.7 - 0.3 = 0.4.\nBased on the above thought, the answer is:\n0.4" } ], "id_raw": 943 }, { "id": "zXTBWoDPpLKP9GHH", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart into a table format with Categories as rows and Human Distribution and Model Distribution as columns?" }, { "from": "gpt", "value": "To transform the line chart data into a table format, I first look at {the x-axis labels}[{\"label\": \"2000\", \"label_coordinates\": \"[0.053, 0.919, 0.097, 0.942]\", \"tick_coordinates\": \"[0.075, 0.911]\"}, {\"label\": \"2005\", \"label_coordinates\": \"[0.269, 0.919, 0.312, 0.942]\", \"tick_coordinates\": \"[0.291, 0.911]\"}, {\"label\": \"2010\", \"label_coordinates\": \"[0.484, 0.919, 0.528, 0.942]\", \"tick_coordinates\": \"[0.506, 0.911]\"}, {\"label\": \"2015\", \"label_coordinates\": \"[0.700, 0.919, 0.744, 0.942]\", \"tick_coordinates\": \"[0.722, 0.911]\"}, {\"label\": \"2020\", \"label_coordinates\": \"[0.916, 0.919, 0.960, 0.942]\", \"tick_coordinates\": \"[0.938, 0.911]\"}] which represent the Year. I then identify {the legend texts} [{'label': 'GDP Growth Rate', 'text_coordinates': '[0.814, 0.046, 0.966, 0.069]'}, {'label': 'Interest Rate', 'text_coordinates': '[0.814, 0.081, 0.927, 0.104]'}] as the column headers representing GDP Growth Rate and Interest Rate. Next, I locate {the line points for GDP Growth Rate and Interest Rate} for each Year and extract their values.\nBased on the above thought, the answer is:\nYear,GDP Growth Rate,Interest Rate\n2001,0.8,4.24\n2002,1.8,1.75\n2003,2.8,1.01\n2004,3.8,1.17\n2005,4.5,1.96\n2006,4.6,4.79\n2007,2.5,5.02\n2008,-0.1,2.21\n2009,-2.5,0.16\n2010,2.5,0.18\n2011,1.6,0.11\n2012,2.2,0.16\n2013,1.7,0.11\n2014,2.5,0.09\n2015,2.9,0.25\n2016,1.6,0.54\n2017,2.3,1.00\n2018,2.9,1.74\n2019,2.2,2.16\n2020,-3.5,0.09" } ], "id_raw": 945 }, { "id": "MrkERD11gKMtfLqx", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_5.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you explain the relationship between the years and the GDP Growth Rate and Interest Rate?" }, { "from": "gpt", "value": "The relationship between the years and the GDP Growth Rate and Interest Rate is visualized in the chart using lines corresponding to the GDP Growth Rate{GDP Growth Rate line}[0.118, 0.105, 0.938, 0.871] and Interest Rate{Interest Rate line}[0.118, 0.065, 0.938, 0.531]. Over the years, there are variations in both the GDP Growth Rate and the Interest Rate. They seem to fluctuate. While the GDP Growth Rate appears to have a dip in 2008 to 2009 and subsequently rebounds, the Interest Rate seems to follow a more or less decreasing trend from 2006 to 2020 with minor fluctuations. This shows that the GDP Growth Rate and the Interest Rate do not have a constant relationship over the years, but are influenced by factors specific to each year.\nBased on the above thought, the answer is:\nAccording to the line chart, the relationship between the years and the GDP Growth Rate and the Interest Rate is not fixed. The GDP Growth Rate varies, with a notable dip around 2008 and 2009, likely due to economic recession, before rebounding. In contrast, the Interest Rate diminishes over time with minor fluctuations. This analysis can hint towards the presence of external factors that affect these rates individually and non-linearly over the years." } ], "id_raw": 945 }, { "id": "C8foLpLl7bqlxG1I", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the largest Interest Rate and which year does it occur according to the line chart?" }, { "from": "gpt", "value": "To find the year with the largest Interest Rate, I would look at {the 'Interest Rate' line points} given by the data. The maximum Interest Rate appears to be for the year 2007 according to {the 'Interest rate' line point of year 2007}['line_1']['points'][6], where it coordinates [0.377, 0.065] link to the highest point on the 'Interest Rate' line graph. The corresponding value for this point is 5.02. So, the year with the largest Interest Rate is 2007 with an Interest Rate of 5.02.\nBased on the above thought, the answer is:\nThe largest Interest Rate is 5.02, which occurred in the year 2007." } ], "id_raw": 945 }, { "id": "ufpqUMFggUxL48mo", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_6.png", "conversations": [ { "from": "human", "value": "\nCould you convert the line chart data depicting the stock performance of Stock A and Stock B over time into a structured table?" }, { "from": "gpt", "value": "To compile this data into a structured table, I first look at the x-axis where I notice {the time periods}[[0.149, 0.866],[0.191, 0.866],[0.232, 0.866],[0.273, 0.866],[0.315, 0.866],[0.356, 0.866],[0.398, 0.866],[0.439, 0.866],[0.480, 0.866],[0.522, 0.866],[0.563, 0.866],[0.605, 0.866],[0.646, 0.866],[0.687, 0.866],[0.729, 0.866],[0.770, 0.866],[0.811, 0.866],[0.853, 0.866],[0.894, 0.866],[0.936, 0.866]] which correspond to the time periods from 1-20. Then I look at the {line points for Stock A}[[0.149, 0.760],[0.191, 0.783],[0.232, 0.829],[0.273, 0.812],[0.315, 0.726],[0.356, 0.760],[0.398, 0.805],[0.439, 0.795],[0.480, 0.829],[0.522, 0.760],[0.563, 0.714],[0.605, 0.692],[0.646, 0.623],[0.687, 0.555],[0.729, 0.418],[0.770, 0.349],[0.811, 0.281],[0.853, 0.246],[0.894, 0.212],[0.936, 0.161]] and the {line points for Stock B}[[0.149, 0.760],[0.191, 0.719],[0.232, 0.692],[0.273, 0.640],[0.315, 0.675],[0.356, 0.589],[0.398, 0.555],[0.439, 0.503],[0.480, 0.503],[0.522, 0.469],[0.563, 0.435],[0.605, 0.418],[0.646, 0.383],[0.687, 0.332],[0.729, 0.281],[0.770, 0.246],[0.811, 0.212],[0.853, 0.161],[0.894, 0.109],[0.936, 0.075]] and extract the corresponding values for each time period.\nBased on the above thought, the answer is:\nTime Period,Stock A,Stock B\n1,100,100\n2,98.7,102.4\n3,96,104\n4,97,107\n5,102,105\n6,100,110\n7,97.4,112\n8,98,115\n9,96,115\n10,100,117\n11,102.7,119\n12,104,120\n13,108,122\n14,112,125\n15,120,128\n16,124,130\n17,128,132\n18,130,135\n19,132,138\n20,135,140" } ], "id_raw": 946 }, { "id": "YrhXygv3WZdkucBq", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_6.png", "conversations": [ { "from": "human", "value": "\nComparing the performance of Stock A and Stock B over time, which stock shows a more stable performance? Please give your reasoning." }, { "from": "gpt", "value": "To answer this question, I have to review the line plots for both Stock A and Stock B. Observing {the line plot for Stock A}[0.149, 0.161, 0.936, 0.829], it shows multiple fluctuations with a significant fall and rise towards the end. It starts and ends at around the same value, showing signs of instability in periods in-between. Then, I take a look at {the line plot for Stock B}[0.149, 0.075, 0.936, 0.760]. This line shows a relatively consistent rising trend over time, with less severe fluctuations compared to Stock A. There are some minor temporary drops, but the overall trend is steadily increasing.\nBased on the above thought, the answer is:\nStock B shows a more stable performance over time compared to Stock A. This is mainly because, despite some minor fluctuations, its overall trend is a consistent rise in value. In contrast, Stock A exhibits multiple significant fluctuations over time, making it less stable." } ], "id_raw": 946 }, { "id": "pjUjMMxs1clJH7ez", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_6.png", "conversations": [ { "from": "human", "value": "\nAt which time period did the Stock B reach the maximum value?" }, { "from": "gpt", "value": "To find out when Stock B reached its maximum value, I have to locate the highest point in {the line points for Stock B}[[0.149, 0.760],[0.191, 0.719],[0.232, 0.692],[0.273, 0.640],[0.315, 0.675],[0.356, 0.589],[0.398, 0.555],[0.439, 0.503],[0.480, 0.503],[0.522, 0.469],[0.563, 0.435],[0.605, 0.418],[0.646, 0.383],[0.687, 0.332],[0.729, 0.281],[0.770, 0.246],[0.811, 0.212],[0.853, 0.161],[0.894, 0.109],[0.936, 0.075]]. Looking at these points, the maximum value 140 corresponds to {the time period 20}[0.936, 0.866].\nBased on the above thought, the answer is:\n20." } ], "id_raw": 946 }, { "id": "MZXxJY8AcWOMTjr3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_7.png", "conversations": [ { "from": "human", "value": "\nCould you tabulate the changes in public opinion over time as represented in the line chart?" }, { "from": "gpt", "value": "To generate a table from the line chart, I begin by looking at {the x-axis tick labels}[[0.082, 0.919, 0.088, 0.942], [0.168, 0.919, 0.173, 0.942], [0.253, 0.919, 0.259, 0.942], [0.339, 0.919, 0.345, 0.942], [0.425, 0.919, 0.430, 0.942], [0.510, 0.919, 0.521, 0.942], [0.596, 0.919, 0.607, 0.942], [0.682, 0.919, 0.693, 0.942], [0.767, 0.919, 0.778, 0.942], [0.853, 0.919, 0.864, 0.942], [0.938, 0.919, 0.949, 0.942]], which denote the time in months. The corresponding y-values represent the public opinion percentage linked to media influence. The line points for media influence {media influence points}[[0.082, 0.871], [0.125, 0.787], [0.168, 0.754], [0.211, 0.704], [0.253, 0.654], [0.296, 0.570], [0.339, 0.537], [0.382, 0.453], [0.425, 0.369], [0.468, 0.202], [0.510, 0.185], [0.553, 0.169], [0.596, 0.135], [0.639, 0.102], [0.682, 0.085], [0.724, 0.068], [0.767, 0.135], [0.810, 0.169], [0.853, 0.202], [0.896, 0.236], [0.938, 0.269]] are used for value extraction.\nBased on the above thought, the answer is:\nTime (months),Public Opinion (%)\n0,50\n1,55\n2,57\n3,60\n4,63\n5,68\n6,70\n7,75\n8,80\n9,90\n10,91\n11,92\n12,94\n13,96\n14,97\n15,98\n16,94\n17,92\n18,90\n19,88\n20,86" } ], "id_raw": 947 }, { "id": "gcnS330rF6aQAlUX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_7.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, how does the public opinion on media influence change over time?" }, { "from": "gpt", "value": "To answer this question, I need to observe the trend in {the media influence line}[[0.082, 0.068, 0.938, 0.871]] as it captures the changes in the public opinion on media influence over time. The line plot starts at {month 0}[[0.082, 0.871]] with a value of 50%, indicating a moderate level of influence. As the line progresses, it shows a consistent increase reaching a peak at {month 15}[[0.724, 0.068]] with a value of 98%, suggesting a surge in media influence on the public. After the peak, there's a decrease in the value, represented by a downward trend of the line towards {month 20}[[0.938, 0.269]], showing declining public opinion on media influence.\nBased on the above thought, the answer is:\nBased on the line chart, the public opinion on media influence grows over time from a moderate level of influence to a high level of influence, reaching its peak at around the 15th month. After this point, there is a decline in the influence level, indicating diminishing public opinion on media influence towards the end of the observed span." } ], "id_raw": 947 }, { "id": "rgBGUDU94mYZanVc", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_7.png", "conversations": [ { "from": "human", "value": "\nAt the peak of media influence, how much higher is the public opinion percent compared to its starting point?" }, { "from": "gpt", "value": "To answer this, I need to find the maximum and initial values of public opinion related to media influence. Looking at {the media influence line}[[0.082, 0.068, 0.938, 0.871]], the initial point is located at {month 0}[[0.082, 0.871]] with the value of 50. The peak occurs at {month 15}[[0.724, 0.068]] with a value of 98. The difference between these two values indicates the rise in public opinion percent during the peak of media influence.\nBased on the above thought, the answer is:\n48%." } ], "id_raw": 947 }, { "id": "BjPFGFrvJkOWevmZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_8.png", "conversations": [ { "from": "human", "value": "\nCan you output the data represented in these line charts into a table format? The table should have a column for each social platform and two rows for the user counts in 2016 and 2017." }, { "from": "gpt", "value": "To convert the chart data into a table, let's take {the color legend cells}[[0.107, 0.542, 0.125, 0.552], [0.107, 0.513, 0.125, 0.523], [0.107, 0.484, 0.125, 0.494], [0.107, 0.455, 0.125, 0.465], [0.107, 0.426, 0.125, 0.436]] and {the corresponding labels}[[0.137, 0.539, 0.295, 0.557], [0.137, 0.510, 0.322, 0.528], [0.137, 0.481, 0.314, 0.499], [0.137, 0.452, 0.275, 0.470], [0.137, 0.423, 0.280, 0.441]] as social media platform names. Charter 1 represents the user count data for 2016 and we can take {the data points for each social media platform}[[0.343, 0.366], [0.343, 0.672], [0.343, 0.547], [0.343, 0.794], [0.343, 0.458]]. Charter 2 represents the user count data for 2017 and hence need to refer the data points in Chart 2{[[0.743, 0.381], [0.743, 0.658], [0.743, 0.563], [0.743, 0.794],[0.743, 0.474]]. Each color corresponds to a different social media platform, so we correlate the lines in the chart to the labels in the legend. From this, we can create the structured data table.\nBased on the above thought, the answer is:\nSocial Media Platform,User Count 2016,User Count 2017\nFacebook,1500,1550\nTwitter,800,750\nInstagram,1000,1200\nSnapchat,600,550\nLinkedIn,700,650" } ], "id_raw": 948 }, { "id": "GbL9carHqUQwiihH", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_8.png", "conversations": [ { "from": "human", "value": "\nObserving the two line charts, which social media platform had the most significant change in user base from 2016 to 2017?" }, { "from": "gpt", "value": "From the given line charts, we have to analyze each platform's user change from 2016 to 2017. Let's consider {the 2016 user counts}[0.343, 0.366, 0.343, 0.672, 0.343, 0.547, 0.343, 0.794, 0.343, 0.458] and {the 2017 user counts}[0.743, 0.381, 0.743, 0.658, 0.743, 0.563, 0.743, 0.794, 0.743, 0.474]. We can see that all the platforms have some changes in their user base, either it's an increase or decrease. However, Instagram with coordinates in 2016 and 2017 {chart 1 - Instagram}[0.343, 0.547] & {chart 2 - Instagram}[0.743, 0.563] show a visibly significant increase in the user base over the specified period.\nBased on the above thought, the answer is:\nInstagram had the most significant change in its user base from 2016 to 2017. It exhibited a noteworthy increase, particularly when compared to Facebook's sluggish growth and the decline in user base observed for Twitter, Snapchat, and LinkedIn." } ], "id_raw": 948 }, { "id": "YKG70TmKAUFGaipe", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage change in the user base of Instagram from 2016 to 2017?" }, { "from": "gpt", "value": "To find the percentage change in user base of Instagram, I need to locate the values of Instagram in 2016 and 2017, which can be found in {chart 1}[0.343, 0.547] and {chart 2}[0.743, 0.563] respectively. The value for Instagram in 2016 is 1000 million & in 2017 is 1200 million. The formula to calculate the percentage change is (new value - old value)/old value * 100%. Here, it's (1200 - 1000) / 1000 * 100 = 200/1000 * 100 = 20%.\nBased on the above thought, the answer is:\n20%." } ], "id_raw": 948 }, { "id": "5jhbG2CpNKdsEAsP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_9.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data represented in the line chart into a table with Years as columns and Types of Development as rows?" }, { "from": "gpt", "value": "To form the tabular data, first I identify {the x-axis tick labels}[[0.048, 0.931, 0.083, 0.951], [0.161, 0.931, 0.196, 0.951], [0.274, 0.931, 0.309, 0.951], [0.386, 0.931, 0.422, 0.951], [0.499, 0.931, 0.534, 0.951], [0.612, 0.931, 0.647, 0.951], [0.725, 0.931, 0.760, 0.951], [0.837, 0.931, 0.872, 0.951], [0.950, 0.931, 0.985, 0.951]], which are the column headers or years. Secondly, I identify {the legend texts}[[0.815, 0.448, 0.949, 0.468], [0.815, 0.478, 0.955, 0.498]] as the row labels representing the different kinds of development. Assuming the highest value on the y-axis is 100, I approximate the values by determining the y-coordinate position for each development type at different stages by locating {the line points}[[0.066, 0.062], [0.179, 0.226], ..., [0.968, 0.883]].\nBased on the above thought, the answer is:\nType,1980,1985,1990,1995,2000,2005,2010,2015,2020\nRural Development,100,90,75,70,70,75,60,55,50\nUrban Development,50,60,70,80,90,95,99,100,100" } ], "id_raw": 949 }, { "id": "mco3oWddvh7AoHKv", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_9.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart trends, how do rural and urban developments compare over the years?" }, { "from": "gpt", "value": "To answer this, I examine the trends displayed by the {development index for Rural Development}[0.066, 0.062, 0.968, 0.883] and {Urban Development}[0.066, 0.062, 0.968, 0.883]. The development index for the rural areas starts at a higher value of approximately 100 in 1980 and shows a general decreasing trend until it achieves a value of approximately 50 in 2020, indicating a decline in rural development. On the contrary, the development index for urban areas starts at a lower value of approximately 50 in 1980 and predominantly exhibits an increasing trend to reach a value of approximately 100 in 2020, indicating an improvement in urban development.\nBased on the above thought, the answer is:\nIt can be observed that rural development has generally declined over the years while urban development has generally improved. This is evident from the decreasing trend line for rural development and the increasing trend line for urban development from 1980 to 2020. This comparison implies a shift towards more rapid development in urban areas, while rural regions have experienced a decline in development speed. It may reflect urbanization trends and the potential increased focus on urban development." } ], "id_raw": 949 }, { "id": "6OhdGmfzSAWFmXjN", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the development index difference between Rural and Urban Development in 2020?" }, { "from": "gpt", "value": "To determine this, I need to locate the final {development index point for Rural Development}[[0.968, 0.883]] and {Urban Development}[[0.968, 0.062]]. Using the y-axis's value range from 40 to 110 I can accurately determine that the values are around 50, and 100 respectively. Thus the difference is 100 - 50 = 50.\nBased on the above thought, the answer is:\n50." } ], "id_raw": 949 }, { "id": "Jaxoupbk2R07u0BJ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_10.png", "conversations": [ { "from": "human", "value": "\nCan you translate the information about the population of each age group into a structured table?" }, { "from": "gpt", "value": "To generate the structured dataset from the bar chart, I need to locate {the x-axis tick labels}[[0.175, 0.064, 0.221, 0.093],[0.498, 0.064, 0.544, 0.093],[0.819, 0.064, 0.856, 0.093]], which represent the columns or age groups: Young, Adult, Senior. Then, I identify the row labels as Male, Female, and Total from the chart legend. For the values, I locate {the bar heights}[[0.166, 0.100, 0.233, 0.413], [0.166, 0.413, 0.233, 0.544], [0.489, 0.100, 0.556, 0.825], [0.489, 0.825, 0.556, 0.973], [0.810, 0.100, 0.877, 0.209], [0.810, 0.209, 0.877, 0.274]], and correlate them to the population numbers for each category.\nBased on the above thought, the answer is:\nAge Group,Female,Male,Total\nYoung,2000000,1800000,3800000\nAdult,4200000,3800000,8000000\nSenior,550000,450000,1000000" } ], "id_raw": 950 }, { "id": "JX3f1EzppAQlKu54", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_10.png", "conversations": [ { "from": "human", "value": "\nLooking at the chart, which demographic group exhibits the most significant population increase compared to the preceding group, and explain how you arrived at this conclusion." }, { "from": "gpt", "value": "By comparing the relative increases in population between {Young and Adult}[0.233, 0.100, 0.556, 0.973], and {Adult and Senior}[0.556, 0.100, 0.877, 0.274], I see that the jump from Young to Adult is far more substantial. The Young group has a population of 3,800,000 while the Adult group has a population of 8,000,000, an increase of over 4 million. Comparatively, the Adult to Senior transition is a significant decrease, not an increase.\nBased on the above thought, the answer is:\nThe demographic group that exhibits the most significant population increase compared to the preceding group is the Adult group. This conclusion comes from comparing the total population heights of each major segment in the chart, where it's clear that the transition from Young to Adult shows a substantial increase of over 4 million people. This increase is more prominent than any other between these groups and their predecessors." } ], "id_raw": 950 }, { "id": "1vKUrYsdbeeC91O2", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_10.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, how much larger is the population of the Adult group compared to the Young group?" }, { "from": "gpt", "value": "To determine the difference in population between the Adult group and the Young group, I first locate the {total population bar for the Young group}[0.166, 0.544] which corresponds to 3,800,000 people. Next, I locate the {total population bar for the Adult group}[0.556, 0.973] which corresponds to 8,000,000 people. The difference between the populations is therefore 8,000,000 - 3,800,000 = 4,200,000.\nBased on the above thought, the answer is:\n4,200,000." } ], "id_raw": 950 }, { "id": "g5tCYh3vrOlmAUm9", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_12.png", "conversations": [ { "from": "human", "value": "\nCan you form a table with Threat Types as the rows and Years 2010 and 2020 as columns based on the chart?" }, { "from": "gpt", "value": "The chart can indeed be transformed into a table. First, I'll identify {the x-axis labels}[[0.205, 0.095, 0.274, 0.123], [0.379, 0.095, 0.448, 0.123], [0.556, 0.095, 0.625, 0.123], [0.732, 0.095, 0.801, 0.123], [0.909, 0.095, 0.978, 0.123]] which will represent the row labels or threat types. Next, I'll consider {the legends}[[0.031, 0.878, 0.093, 0.950], [0.031, 0.730, 0.093, 0.801]] of the charts as the column headers representing the years 2010 and 2020. The annotation in {the line points}[[0.228, 0.628], [0.402, 0.561], [0.580, 0.456], [0.756, 0.372], [0.933, 0.510], [0.228, 0.357], [0.402, 0.238], [0.580, 0.435], [0.756, 0.443], [0.933, 0.495]] will be our respective data values for each threat type during the years 2010 and 2020.\nBased on the above thought, the answer is:\nThreat Type,2010,2020\nUnauthorized Data Sharing,85,50\nHacking,60,90\nDos Attacks,70,30\nPhishing,65,70\nData Misuse,70,80" } ], "id_raw": 952 }, { "id": "5zkZH8qMSsNV723m", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_12.png", "conversations": [ { "from": "human", "value": "\nWhich cyber threat has seen the most dramatic change from 2010 to 2020 based on the chart? Can you explain your rationale?" }, { "from": "gpt", "value": "According to the chart, to identify the cyber threat with the most dramatic change from 2010 to 2020, I must track the fluctuation of each threat between these years. I notice that {Unauthorized Data Sharing} [0.205, 0.095, 0.274, 0.123] markedly dipped from 85 to 50. Also, the rate of {Hacking} [0.379, 0.095, 0.448, 0.123] grew substantially from 60 to 90. However, the {Dos Attacks} [0.556, 0.095, 0.625, 0.123] threat dwindled significantly from 70 in 2010 to 30 in 2020, a 40 point decrease or a 57% drop. This is the most dramatic change among the five types of cyber threats.\nBased on the above thought, the answer is:\nThe cyber threat 'Dos Attacks' has seen the most dramatic change from 2010 to 2020. While initially prevalent in 2010, it has significantly decreased by 2020, marking a drop of 57%. This is the most significant fluctuation amongst all the cyber threats charted." } ], "id_raw": 952 }, { "id": "0DdSGpYEZecIYvaB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_12.png", "conversations": [ { "from": "human", "value": "\nHow much has the prevalence of Dos Attacks decreased from 2010 to 2020?" }, { "from": "gpt", "value": "To calculate the decrease in the prevalence of Dos Attacks from 2010 to 2020, I could look at {the line point for Dos Attacks in 2010} [0.580, 0.456] indicating 70 and {the line point for Dos Attacks in 2020} [0.580, 0.435], indicating 30. The decrease can be calculated as 70 - 30 = 40.\nBased on the above thought, the answer is:\n40." } ], "id_raw": 952 }, { "id": "frSifI6uhHEGb29s", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart into a table format with Time as rows and Traditional and Modern population percentage as columns?" }, { "from": "gpt", "value": "To create a table, I need to identify the requisite variables from the line chart. For the row headings, I locate {the x-axis tick labels}[[0.077, 0.919, 0.088, 0.942],[0.291, 0.919, 0.302, 0.942], [0.499, 0.919, 0.521, 0.942], [0.713, 0.919, 0.735, 0.942], [0.928, 0.919, 0.949, 0.942]], and these represent time. For the column headers, the {legend labels}[[0.813, 0.055, 0.966, 0.078],[0.813, 0.090, 0.940, 0.113]] represent traditional population and modern population. {The points on the lines}[[0.125, 0.420], [0.168, 0.436]...][[0.125, 0.587], [0.168, 0.552]...] provide information about the traditional and modern population at each time point.\nBased on the above thought, the answer is:\nTime,Traditional_population,Modern_population\n1,56.0,37.0\n2,54.2,40.9\n3,50.4,44.1\n4,49.2,46.8\n5,47.2,48.4\n6,45.3,49.4\n7,42.5,50.5\n8,40.4,51.8\n9,37.1,54.2\n10,35.3,55.9\n11,33.0,57.4\n12,31.0,58.6\n13,30.0,60.2\n14,28.5,60.6\n15,27.1,62.5\n16,26.0,64.0\n17,24.8,66.0\n18,23.6,66.0\n19,22.8,67.5\n20,21.9,68.5" } ], "id_raw": 953 }, { "id": "Th5zm5khr0WrTxkz", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_13.png", "conversations": [ { "from": "human", "value": "\nBy scrutinizing the chart, which population, Traditional or Modern, shows an increasing trend?" }, { "from": "gpt", "value": "Analyzing the line plots in the chart, for the Traditional population, the data points on {the Traditional Values line} [0.125, 0.430, 0.938, 0.719] show a decreasing trend from 56.0% to 21.9%. Conversely, the data points on {the Modern Values line} [0.125, 0.587, 0.938, 0.310] show an increasing trend from 37.0% to 68.5%.\nBased on the above thought, the answer is:\nFrom the data in the chart, the Modern population shows an increasing trend, while the Traditional population shows a decreasing trend." } ], "id_raw": 953 }, { "id": "0U1HytQ9Bq125I9G", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_13.png", "conversations": [ { "from": "human", "value": "\nBy what percent did the Traditional population decrease from year 1 to year 20?" }, { "from": "gpt", "value": "To determine the percent decrease of the Traditional population from year 1 to year 20, I need to identify the values at these time points from the chart. For year 1, I locate the point on {the Traditional Values line}[[0.125, 0.420]], which corresponds to 56.0%. For year 20, I find the point on the same line[[0.938, 0.719]], which corresponds to 21.9%. I can calculate the percent decrease using the formula (Original Value - New Value) / Original Value * 100.\nBased on the above thought, the answer is:\n60.89%." } ], "id_raw": 953 }, { "id": "H7fNYYvtwhFJF75E", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_14.png", "conversations": [ { "from": "human", "value": "\nPlease extract the data from the line chart, representing it in table format with Years as the rows and Media Sales Types as the columns." }, { "from": "gpt", "value": "Looking at this line chart, we can extract the table data by identifying the elements in the ruler and label. Starting with row labels from {the x-axis tick labels}[0.071, 0.903, 0.129, 0.931], [0.169, 0.903, 0.227, 0.931], [0.267, 0.903, 0.326, 0.931], [0.366, 0.903, 0.424, 0.931], [0.464, 0.903, 0.522, 0.931], [0.562, 0.903, 0.621, 0.931], [0.660, 0.903, 0.719, 0.931], [0.759, 0.903, 0.818, 0.931], [0.857, 0.903, 0.916, 0.931], [0.956, 0.903, 1.014, 0.931] which represent the years, while {the legend texts}[0.195, 0.055, 0.367, 0.083], [0.195, 0.097, 0.348, 0.125] are the column headers representing the Media Sales Types. To populate the values, I locate the series of points associated with each sales type at their respective years. For instance, {the line points for Physical Media}[0.149, 0.445], [0.198, 0.478], ..., [0.936, 0.829] and {the line points for Digital Media}[0.149, 0.854], [0.198, 0.821], ..., [0.936, 0.069] and extract the associated data values.\nBased on the above thought, the answer is:\nYear,Physical Media Sales,Digital Media Sales\n2005,300,50\n2006,280,70\n2007,260,90\n2008,250,100\n2009,245,110\n2010,240,135\n2011,230,145\n2012,220,160\n2013,210,170\n2014,175,190\n2015,150,230\n2016,120,265\n2017,100,330\n2018,90,360\n2019,80,420\n2020,75,490\n2021,65,530" } ], "id_raw": 954 }, { "id": "isyLsdZ8OEyCVB9H", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_14.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which type of Media Sales seems to have a more increasing trend over the years? Please explain your answer." }, { "from": "gpt", "value": "To determine which type of Media Sales has a more increasingly progressive trend, I must analyze the steepness in line plots for each type. Observing {the line for Physical Media Sales}[0.149, 0.445, 0.936, 0.829], there is a steady decrease over the years. Conversely, looking at {the line for Digital Media Sales}[0.149, 0.854, 0.936, 0.069], there is a clear increasing trend over the years, starting from a sales value of 50 million in 2005 to reaching a peak of 530 million in 2021.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Digital Media Sales seems to have a more increasingly progressive trend over the years. While Physical Media Sales is exhibiting a gradual decrease, Digital Media Sales is showing a consistent rise proving that with every year, the sales of Digital Media is increasing while that of Physical Media is declining. It signifies a trend towards digital platforms for media consumption." } ], "id_raw": 954 }, { "id": "Enpx36I0xWYgfrZ6", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the sales difference between Physical and Digital Media in the year 2015?" }, { "from": "gpt", "value": "To find the sales difference between Physical and Digital Media in 2015, I look at {the line point for Physical Media in 2015}[0.641, 0.690]which indicates a sales value of 150 million, and at {the line point for Digital Media in 2015}[0.641, 0.560]which indicates a sales value of 230 million. The difference can be found by subtracting the sales of Physical Media from that of Digital Media. So, the difference between the sales is 230 - 150 = 80.\nBased on the above thought, the answer is:\n80 Million." } ], "id_raw": 954 }, { "id": "k2smXjK05ac2RYFU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_15.png", "conversations": [ { "from": "human", "value": "\nCan you present the data from this line chart in a tabular format, where the rows represent years and the columns represent different types of clean technologies?" }, { "from": "gpt", "value": "To generate the table from the line chart, I primarily look at the details on the chart. Firstly, I identify the labels on {the x-axis tick labels}[[0.038, 0.919, 0.082, 0.942], [0.218, 0.919, 0.262, 0.942], [0.398, 0.919, 0.442, 0.942], [0.578, 0.919, 0.622, 0.942], [0.758, 0.919, 0.802, 0.942], [0.938, 0.919, 0.981, 0.942]] indicating the years. Then, {the legend texts}[[0.125, 0.071, 0.231, 0.094], [0.125, 0.106, 0.306, 0.129]] in the chart represent clean technology types. Lastly, I locate the respective {line points}[[0.060, 0.760, 0.959, 0.816], [0.060, 0.089, 0.959, 0.872]] for each type of technology in each year and extract the respective values to fill in the table cells.\nBased on the above thought, the answer is:\nYear,Slow growth,Pronounced increase\n2010,1000000,500000\n2011,1050000,600000\n2012,1100000,750000\n2013,1150000,950000\n2014,1200000,1200000\n2015,1250000,1650000\n2016,1300000,2350000\n2017,1350000,3300000\n2018,1400000,4400000\n2019,1450000,5500000\n2020,1500000,7500000" } ], "id_raw": 955 }, { "id": "9WbPtQoGZXmnX19s", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_15.png", "conversations": [ { "from": "human", "value": "\nBased on the plotted lines, which type of investments has shown a more positive growth over the years and why?" }, { "from": "gpt", "value": "To decide which type of investments has shown a more positive growth over the years, I need to analyze the {line plot for Slow growth}[0.060, 0.760, 0.959, 0.816] and the {line plot for Pronounced increase}[0.060, 0.089, 0.959, 0.872]. The slow growth line has a slight upward incline, starting from a point at 1000000 in 2010 to 1500000 in 2020. In contrast, the pronounced increase line starts from 500000 in 2010 and sharply rises to 7500000 in 2020. This substantial increase and its trajectory over the plot signify that pronounced increase has shown a more positive growth over the years.\nBased on the above thought, the answer is:\nThe line representing 'Pronounced increase' has shown a more positive growth over the years due to its sharp rise from 500000 in 2010 to 7500000 in 2020. Its slope is steeper compared to the 'Slow growth' line, indicating a faster increase in investments." } ], "id_raw": 955 }, { "id": "aA6vPNKcBVe2qb8W", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_15.png", "conversations": [ { "from": "human", "value": "\nWhat's the difference between the final investment amounts in 2020 of the two investment types?" }, { "from": "gpt", "value": "To find the difference, firstly I locate the {2020 tick on the X axis}[0.938, 0.919, 0.981, 0.942]. From here, I follow the tick up to the two corresponding points {for Slow growth}[0.959, 0.760] and {for Pronounced increase}[0.959, 0.089]. Extracting these values, I get 1500000 for Slow growth and 7500000 for Pronounced increase. As such, the difference between these two values is 7500000 - 1500000 = 6000000.\nBased on the above thought, the answer is:\n6000000." } ], "id_raw": 955 }, { "id": "7f96Jj32QXw6AJNQ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_16.png", "conversations": [ { "from": "human", "value": "\nCould you create a table that presents all the data points from the chart, including years, efficiency improvement potential for all the given energy production methods?" }, { "from": "gpt", "value": "To build the table, I observe {the x-axis tick labels}[[0.071, 0.919, 0.115, 0.942], [0.288, 0.919, 0.332, 0.942], [0.504, 0.919, 0.548, 0.942], [0.721, 0.919, 0.765, 0.942], [0.938, 0.919, 0.981, 0.942]] which represent the Years. Next, I recognize {the legend texts}[[0.157, 0.046, 0.325, 0.069], [0.157, 0.081, 0.277, 0.104]] as the different categories of energy production methods. To populate the values, I locate {the line points for each category} and extract the corresponding values. These values represent the efficiency improvement potential for every year and every energy production category.\nBased on the above thought, the answer is:\nYear,Recycling,Insulation,Solar Panels,Hydroelectric,Wind Energy,Nuclear Energy\n2000,2030,1540,2100,2220,1980,2130\n2001,2080,1600,2140,2280,2040,2080" } ], "id_raw": 956 }, { "id": "gZkfjvqfL4FrU1CY", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_16.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart trends, which energy production method has shown the most stability in efficiency improvement potential over the given years?" }, { "from": "gpt", "value": "To determine the most stable energy production method, I need to analyze the line plots for each method. Observing the {line for Recycling}, {line for Insulation}, {line for Hydroelectric}, {line for Wind Energy}, and {line for Nuclear Energy}, shows fluctuations in the trend. However, the {line for Solar Panels}, indicates a relatively stable trend, barely changing across the years.\nBased on the above thought, the answer is:\nThe Solar Panels energy production method displayed the most stability in efficiency improvement potential within the given years, the line for this category shows minimal fluctuation indicating little to no change throughout the years." } ], "id_raw": 956 }, { "id": "ra95oQ0SQL7JHLFZ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in efficiency potential for Wind Energy from 2000 to 2001?" }, { "from": "gpt", "value": "To calculate the increase for Wind Energy, I should first identify {the line points for Wind Energy}[[0.180, 0.602], [0.223, 0.641], [0.266, 0.621], [0.310, 0.602]] which are the respective efficiency values for the years. By subtracting the efficiency for {2000}[0.180, 0.942] from the efficiency for {2001}[0.223, 0.942], we get the increase in efficiency potential.\nBased on the above thought, the answer is:\n60." } ], "id_raw": 956 }, { "id": "j7KXo6g3kv9HyRph", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_17.png", "conversations": [ { "from": "human", "value": "\nCould you structure the data showing the impact of various media platforms on public opinion for both years into a table?" }, { "from": "gpt", "value": "In order to construct a table from this data, I start by identifying {the y-axis tick labels}[[0.032, 0.117, 0.071, 0.157], [0.032, 0.201, 0.077, 0.242], [0.032, 0.305, 0.077, 0.346], [0.032, 0.428, 0.072, 0.468], [0.032, 0.532, 0.077, 0.572], [0.032, 0.617, 0.072, 0.657]] that represent the different media platforms. The title of the line charts {Year 1 line chart title}[[0.073, 0.709, 0.271, 0.750], {Year 2 line chart title}[[0.073, 0.011, 0.271, 0.057]] indicate the years. For the values, I extract the data from the corresponding line plots for each platform in each year: {line plots for year 1}[[0.151, 0.128, 0.972, 0.635]] and {line plots for year 2}[[0.151, 0.128, 0.972, 0.636]].\nBased on the above thought, the answer is:\nMedia Platforms,Year 1,Year 2\nNews Channels,80,70\nSocial Media,60,90\nNewspapers,75,65\nRadio,85,80\nPrint Magazines,75,70\nOutdoor Advertising,70,75" } ], "id_raw": 957 }, { "id": "fcZhF7wKGUGlklWV", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_17.png", "conversations": [ { "from": "human", "value": "\nWhich media platform showed an increasing trend in their influence on public opinion over the two years? Justify your answer." }, { "from": "gpt", "value": "To identify the media platform showing an increasing influence, I need to compare the positions of their corresponding line chart between Year 1 and Year 2. Looking at {the line plot for Outdoor Advertising in Year 1}[0.184, 0.732, 0.552, 0.769], it had an impact value of 70, and in {Year 2}[0.184, 0.345, 0.653, 0.389], it increased to 75. Similarly, for {Social Media in Year 1}[0.212, 0.749, 0.592, 0.786], it had an impact value of 60, which increased to 90 in {Year 2}[0.212, 0.362, 0.696, 0.407] As both these platforms show an increase, they have an increasing trend in their influence on public opinion.\nBased on the above thought, the answer is:\nSocial Media and Outdoor Advertising showed an increasing trend in their influence on public opinion over the two years. This is deduced from the shift of their respective lines to the right between Year 1 and Year 2, indicating a higher impact score." } ], "id_raw": 957 }, { "id": "T2uKFY6kQr9yOdY0", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the change in the impact on public opinion between Year 1 and Year 2 for Social Media?" }, { "from": "gpt", "value": "To find the change in the impact on public opinion for Social Media, I look at {the line plot for Social Media in Year 1}[[0.212, 0.749, 0.592, 0.786]], which indicates an impact value of 60. I then compare this with {the line plot for Social Media in Year 2}[[0.212, 0.362, 0.696, 0.407]], which indicates an impact value of 90. The change is 90 - 60 = 30.\nBased on the above thought, the answer is:\n30." } ], "id_raw": 957 }, { "id": "aFpfUWFJEaUQ3BJ6", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_18.png", "conversations": [ { "from": "human", "value": "\nCould you provide the data in the form of a table with the rows as activities and the columns as the two categories, Social Activity and Lifestyle Activity?" }, { "from": "gpt", "value": "To construct the table, I first identify {the y-axis tick labels for the values corresponding to Social Activity}[0.299, 0.820, 0.350, 0.836] and {those corresponding to Lifestyle Activity}[0.540, 0.820, 0.590, 0.836] as row labels representing the different activities. I then extract information from {the bars representing Social Activity}[0.359, 0.802, 0.506, 0.836] and {the bars representing Lifestyle Activity}[0.600, 0.802, 0.747, 0.836] for each activity. The length of each bar corresponds to the value in hours.\nBased on the above thought, the answer is:\nActivity,Social Activity,Lifestyle Activity\nNetflix Time,3,5\nExercise,4,10\nGaming,6,8\nReading,10,4\nSocial Media,5,6\nOutdoor Activities,2,3\nArtistic Pursuits,4,1\nHealthy Eating,5,7" } ], "id_raw": 958 }, { "id": "PPqXjrQKgAyl8kVs", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_18.png", "conversations": [ { "from": "human", "value": "\nBased on the bar plots, can you make a statement about the general trend in terms of activity preferences when comparing Social Activity and Lifestyle Activity?" }, { "from": "gpt", "value": "To provide the general trend in terms of activity preferences, I first need to analyze the length of the bars in the two different subplots. It's evident from both {subplot of Social Activity}[0.393, 0.176, 0.506, 0.314] and {subplot of Lifestyle Activity}[0.634, 0.176, 0.746, 0.314] that some activities have noticeably longer bars in one subplot than they do in another. For instance, 'Reading' shows high popularity in Social Activity but less so in Lifestyle Activity. 'Exercise', on the other hand, is less popular in Social Activity but stands out as the most preferred in Lifestyle Activity. Generally, preference between activities differs between Social and Lifestyle contexts, suggesting that people may prioritize different activities depending on whether they are engaged socially or if these activities make up their general lifestyle preferences.\nBased on the above thought, the answer is:\nAnalysis of the bar plots shows that the trend in activity preferences varies between Social and Lifestyle Activities. Activities like 'Reading' are preferred more in a social context while 'Exercise' is significantly more popular as a Lifestyle activity, suggesting that when socializing, people tend to prefer more leisurely or relaxing activities but lean towards health and wellness activities in their regular lifestyle." } ], "id_raw": 958 }, { "id": "g9i5ADXN6buVoJgX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in average hours spent on Gaming as a Social Activity and as a Lifestyle Activity?" }, { "from": "gpt", "value": "To calculate the difference in hours spent on Gaming as a Social Activity and Lifestyle Activity, I first locate the Gaming bar in {the Social Activity subplot}[0.379, 0.496, 0.431, 0.512] and extract the corresponding value (6 hours). Next, I locate the Gaming bar in {the Lifestyle Activity subplot}[0.619, 0.487, 0.673, 0.502] and extract the corresponding value (8 hours). To find the difference, subtract the value for Social Activity from the value for Lifestyle Activity: 8 - 6 = 2.\nBased on the above thought, the answer is:\n2" } ], "id_raw": 958 }, { "id": "aMwnAVb1bkxmdMTv", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_48_19.png", "conversations": [ { "from": "human", "value": "\nCould you extract this line chart into a structured table, with the columns representing different months and rows representing the activities?" }, { "from": "gpt", "value": "Sure, in order to generate the tabular data from this line chart, I firstly locate {x-axis tick labels}[[0.142, 0.882, 0.181, 0.907], [0.244, 0.882, 0.283, 0.907], .... [0.828, 0.882, 0.870, 0.907]], which will serve as our column headers i.e., the months. Next, I identify {the legend texts}[[0.826, 0.041, 0.934, 0.068], [0.827, 0.081, 0.936, 0.108]] as our row labels which are the distinct activities. I find {the line points}[[0.142, 0.505], [0.244, 0.617], .... [0.82, 0.412]] for each activity at various months to populate the data table with their values.\nBased on the above thought, the answer is:\nMonth,Exercise,Socializing\nJanuary,30,20\nFebruary,35,25\nMarch,40,30\nApril,45,35\nMay,50,40\nJune,55,45\nJuly,60,50\nAugust,65,55\nSeptember,70,60\nOctober,55,45\nNovember,45,35\nDecember,25,15" } ], "id_raw": 959 }, { "id": "iqKwt53Yok2oToNs", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_48_19.png", "conversations": [ { "from": "human", "value": "\nWhich activity would you say is more consistent throughout the year based on the line chart?" }, { "from": "gpt", "value": "To answer this question, I need to examine the line trends of the two activities. Looking at the {line for Exercise}[0.142, 0.168, 0.82, 0.652], the trend appears to be growing till September and then seeing a decrease towards the end of the year. Similarly, for {Socializing}[0.142, 0.127, 0.82, 0.598], the trend is exactly similar with steady growth till September and then a decrease. Hence, in terms of consistency, both activities seem to have a similar trend throughout the year.\nBased on the above thought, the answer is:\nBoth Exercise and Socializing show a similar trend of steady growth followed by a decrease towards the end of the year. Hence, both activities are consistent in that sense. However, Exercise seems to have a higher consistency in terms of the number of hours dedicated as its hours consistently remain higher than Socializing throughout the year." } ], "id_raw": 959 }, { "id": "sONRpCicTURsWzaw", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_48_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest number of hours spent on any activity according to the line chart?" }, { "from": "gpt", "value": "To find the maximum hours spent on any activity according to this line chart, I need to locate and analyze the y-values for each point on both the {line for Exercise}[0.142, 0.168, 0.82, 0.652] and the {line for Socializing}[0.142, 0.127, 0.82, 0.598]. After analyzing, it appears that the maximum hours corresponding to Exercise in the month of September is 70.\nBased on the above thought, the answer is:\n70 hours." } ], "id_raw": 959 }, { "id": "g9RXhDNC0Ms7tRAP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_0.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data of the technological companies from the line chart into a readable table for analysis?" }, { "from": "gpt", "value": "To construct the table, I first spot the {x-axis tick labels}[[0.071, 0.878, 0.146, 0.903], ..., [0.869, 0.878, 0.957, 0.903]], which represent companies' names. I recognize these as the row labels. Each subplot corresponds to a different financial measure. For the column headers, I locate titles of the subplots, which indicate Revenue Change, Profit Change and Innovation Score. To populate the table, I find {the line points}[[0.111, 0.495], [0.222, 0.430], ..., [0.884, 0.495]] for each financial measure at the respective companies and extract their corresponding values.\nBased on the above thought, the answer is:\nCompany,Revenue Change (%),Profit Change (%),Innovation Score\nApple,11.6,15.9,85\nMicrosoft,9.8,13.4,88\nAlphabet,18.5,9.8,90\nFacebook,-2.7,1.2,78\nTencent,27.7,16.3,92\nTSMC,9.2,16.1,82\nNvidia,48.6,45.6,95\nPayPal,20.7,18.9,91\nNetflix,23.4,20.0,86\nIBM,-3.1,-3.5,73" } ], "id_raw": 960 }, { "id": "rIzdwuLDyqel3xms", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_0.png", "conversations": [ { "from": "human", "value": "\nWhich company stands out on the graph considering innovation, and how is it different from the other companies?" }, { "from": "gpt", "value": "To determine which company stands out in terms of innovation, I analyze the Innovation Score subplot, which includes {the line for Innovation Score}[[0.111, 0.292, 0.888, 0.327]]. When inspecting the points of each company on this line, {Nvidia}[0.777, 0.327] stands out with the highest Innovation Score on the chart. Comparing Nvidia's score with the scores of other companies like {IBM}[0.111, 0.251] and {Facebook}[0.444, 0.267] shows a significant difference in terms of technological innovation.\nBased on the above thought, the answer is:\nNvidia is the company that stands out in terms of innovation on the graph. It has the highest Innovation Score of all the listed companies, indicating a higher level of technological innovation. This suggests that Nvidia has likely made significant investments in Research and Development and product advancements as compared to other companies, such as IBM and Facebook, which have notably lower Innovation Scores." } ], "id_raw": 960 }, { "id": "VfqYXaM2z0DDUV1b", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_0.png", "conversations": [ { "from": "human", "value": "\nFrom the trends shown in the line chart, what is the average Innovation Score for the listed companies?" }, { "from": "gpt", "value": "To find the average Innovation Score, I need to locate {the line points for Innovation Score}[[0.111, 0.292], [0.222, 0.303], [0.333, 0.310], [0.444, 0.267], [0.555, 0.317], [0.666, 0.282], [0.777, 0.327], [0.888, 0.313], [0.444, 0.296], [0.111, 0.251]], and extract the corresponding values: 85, 88, 90, 78, 92, 82, 95, 91, 86, 73. Then, averaging these values gives the average Innovation Score, calculated as (85+88+90+78+92+82+95+91+86+73) / 10.\nBased on the above thought, the answer is:\n86." } ], "id_raw": 960 }, { "id": "UmhVMByIvg9OR3UI", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_2.png", "conversations": [ { "from": "human", "value": "\nCould you generate a table recording the visitor numbers for each tourist destination for the years 2019, 2020, and 2021 based on the line charts?" }, { "from": "gpt", "value": "Certainly. First, I'll take {the x-axis labels}[[0.066, 0.066, 0.985, 0.903]], which represents the tourist destinations, as the leftmost column. The lines on the plots {Physical Resources}[0.108, 0.294, 0.943, 0.613] and {Online Resources}[0.108, 0.142, 0.943, 0.674], represent the visitor numbers for each destination over three years. I'll extract the data points linked with each of these lines. This gives me the visitor counts for 2019, 2020, and 2021 which I will arrange in columns adjacent to the respective destination.\nBased on the above thought, the answer is:\nTourist Destination,2019,2020,2021\nTime Square,4.5,2,3.5\nCentral Park,4,1.8,3.3\nNiagara Falls,3.8,1.7,3\nGrand Canyon,3.5,1.5,2.8\nDisneyland,3.3,1.3,2.6\nStatue of Liberty,3,1,2.5\nYellowstone National Park,2.7,0.9,2.2\nUniversal Studios,2.5,0.8,2\nYosemite National Park,2.2,0.7,1.8\nGreat Smoky Mountains,2,0.6,1.7" } ], "id_raw": 962 }, { "id": "cONM6pjlojzXtWUc", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_2.png", "conversations": [ { "from": "human", "value": "\nConsidering the visitor trends represented in the graph, which tourism spot seems to be regaining its visitor numbers at the fastest rate post-pandemic in 2021?" }, { "from": "gpt", "value": "To answer this question, I'm examining the trends for each tourist location from 2020 to 2021 using the data points in the {line for 2021}[0.108, 0.142, 0.943, 0.674]. This allows me to compare the growth rates for each location. The tourist spot that showed the largest increase in visitors from 2020 to 2021 is likely to be the one that is recovering fastest post-pandemic.\nBased on the above thought, the answer is:\nBased on the data, Time Square seems to be regaining its visitor numbers at the fastest rate post-pandemic. It had the highest growth in visitor numbers from 2020 to 2021, compared to the other tourist spots." } ], "id_raw": 962 }, { "id": "5WE51oxh7y1HH22i", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_2.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, how much was the total decrease in visitors from 2019 to 2020 for all tourist hotspots combined?" }, { "from": "gpt", "value": "To calculate the total decrease in visitors from 2019 to 2020, I need to first determine the relative values for each site in these years. I locate the {2019 visitor data}[0.108, 0.294, 0.943, 0.613] and {2020 visitor data}[0.178, 0.317, 0.595, 0.722]. I subtract the 2020 data from the 2019 data to find the decrease for each site, and then add these values together to find the total decrease.\nBased on the above thought, the answer is:\n15.6 million." } ], "id_raw": 962 }, { "id": "khpP1CbmFgkpeuvm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_3.png", "conversations": [ { "from": "human", "value": "\nCan you extract the line chart information into a table format representing the Time period and the performance indices of Knowledge Economy and Innovation Industries?" }, { "from": "gpt", "value": "Sure, to translate the line chart data into a tabular format, we first find both {the x-axis tick labels}[[0.022, 0.898, 0.042, 0.921], [0.156, 0.898, 0.165, 0.921], [0.284, 0.898, 0.293, 0.921], [0.412, 0.898, 0.421, 0.921], [0.540, 0.898, 0.549, 0.921], [0.668, 0.898, 0.677, 0.921], [0.792, 0.898, 0.810, 0.921], [0.920, 0.898, 0.938, 0.921]] to represent the Time period. Then recognize the {line points of Knowledge Economy}[[0.160, 0.794], [0.224, 0.671], [0.288, 0.585], [0.352, 0.499], [0.416, 0.388], [0.480, 0.290], [0.545, 0.216], [0.609, 0.155], [0.673, 0.327], [0.737, 0.499], [0.801, 0.339], [0.865, 0.229]] and {Innovation Industries}[[0.160, 0.732], [0.224, 0.425], [0.288, 0.278], [0.352, 0.241], [0.416, 0.192], [0.480, 0.413], [0.545, 0.511], [0.609, 0.585], [0.673, 0.622], [0.737, 0.634], [0.801, 0.597], [0.865, 0.855]]. These points can be used to populate the performance indices for each sector at the given times.\nBased on the above thought, the answer is:\nTime, Knowledge Economy, Innovation Industries\n\u22122, 0.45, 0.50\n0, 0.55, 0.75\n2, 0.62, 0.87\n4, 0.69, 0.90\n6, 0.78, 0.94\n8, 0.86, 0.76\n10, 0.92, 0.68\n12, 0.97, 0.62" } ], "id_raw": 963 }, { "id": "E682cCaLTFuF2vTB", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_3.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which sector between the Knowledge Economy and Innovation Industries has seen more fluctuations over the time period?" }, { "from": "gpt", "value": "To determine the sector with more fluctuations, we need to analyze the variation in the line plots. Looking at the {line for the Knowledge Economy}[[0.160, 0.794], [0.224, 0.671], [0.288, 0.585], [0.352, 0.499], [0.416, 0.388], [0.480, 0.290], [0.545, 0.216], [0.609, 0.155], [0.673, 0.327], [0.737, 0.499], [0.801, 0.339], [0.865, 0.229]], there are several noticeable ups and downs. The performance index rises initially, the drops precipitously, before swinging upwards and downwards again.\nThe {line of Innovation Industries}[[0.160, 0.732], [0.224, 0.425], [0.288, 0.278], [0.352, 0.241], [0.416, 0.192], [0.480, 0.413], [0.545, 0.511], [0.609, 0.585], [0.673, 0.622], [0.737, 0.634], [0.801, 0.597], [0.865, 0.855]] equally shows increases and decreases but seems more volatile with steeper rises and falls.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Innovation Industries seems to have more fluctuations over the time period. Although both sectors show significant ups and downs, the Innovation Industries line plot shows a more erratic pattern with steeper rises and falls, indicating larger changes in performance index through the given time period." } ], "id_raw": 963 }, { "id": "JSNs9CrKrBJwfG55", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the performance index of the Knowledge Economy at the beginning and the end of the period in the chart?" }, { "from": "gpt", "value": "To calculate this difference, we need to compare the first time point of {the line for the Knowledge Economy}[[0.160, 0.794], [0.224, 0.671], [0.288, 0.585], [0.352, 0.499], [0.416, 0.388], [0.480, 0.290], [0.545, 0.216], [0.609, 0.155], [0.673, 0.327], [0.737, 0.499], [0.801, 0.339], [0.865, 0.229]] with the last point, ie., 0.45 at {Time -2}[0.022, 0.898, 0.042, 0.921] and 0.91 at {Time 12}[0.920, 0.898, 0.938, 0.921] respectively. The difference is calculated as 0.91 - 0.45 = 0.46.\nBased on the above thought, the answer is:\n0.46." } ], "id_raw": 963 }, { "id": "b78rjVerTypTqyIH", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line graph data into a structured table with Trends as rows and Value as columns?" }, { "from": "gpt", "value": "To do this, I'm going to identify {the x-axis labels} [0.065, 0.911, 0.172, 0.934], [0.177, 0.911, 0.292, 0.934], [0.288, 0.911, 0.414, 0.934], [0.399, 0.911, 0.536, 0.934], [0.511, 0.911, 0.656, 0.934], [0.623, 0.911, 0.776, 0.934], [0.762, 0.911, 0.870, 0.934], [0.884, 0.911, 0.980, 0.934] as the row labels or Trend names. Then, I will look at {the line points} [0.119, 0.587], [0.235, 0.656], [0.351, 0.517], [0.467, 0.448], [0.583, 0.100], [0.700, 0.309], [0.816, 0.864], [0.932, 0.378] and extract the corresponding y-values to be the tabular values indicating Trend Value.\nBased on the above thought, the answer is:\nTrends,Value\nSlight decrease,0.2\nDownward trend,0.1\nGradual downturn,0.3\nSubdued expansion,0.4\nPronounced increase,0.9\nGradual improvement,0.6\nSharp decrease,-0.2\nSlow increase,0.5" } ], "id_raw": 964 }, { "id": "YpapGxaZeWYX7SPr", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_4.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, how would you describe the International Trade Trend over time?" }, { "from": "gpt", "value": "Evaluating {the International Trade Trend line}[0.119, 0.100, 0.932, 0.864], I see that it starts at a value of 0.2 at the stage of 'Slight Decrease', then decreases slightly to 0.1 at the stage of 'Downward Trend', and then raises to 0.3 at 'Gradual Downturn'. This is followed by a further increase to 0.4 at 'Subdued Expansion', a steep increase to 0.9 at 'Pronounced Increase', then it decreases slightly to 0.6 at 'Gradual Improvement', with a sharp decrease to -0.2 at the stage of 'Sharp Decrease', and finally rising to 0.5 during the 'Slow Increase'. This pattern suggests an initially stable trend with a slight decrease, followed by a steady increase, a sharp increase, a slight decrease, a sharp decrease, and then a gradual increase towards the end..\nBased on the above thought, the answer is:\nThe International Trade Trend over time, as presented in the line chart, shows a mixed trend. Initial stability sees a slight decrease, followed by a steady increase leading to a sharp increase, a slight decrease, a decline, and then a steady increase towards the end. It appears to be suggesting a cyclical pattern in international trade trends, with periods of growth followed by decline and resurgence." } ], "id_raw": 964 }, { "id": "dV9wJW2Y3xYk5h0R", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_4.png", "conversations": [ { "from": "human", "value": "\nWhat's the difference in value between the peak and trough of the trend seen in the International Trade Trends?" }, { "from": "gpt", "value": "To find the answer, I need to identify {the highest point}[0.583, 0.100] which represents the peak and shows value of 0.9, then {the lowest point}[0.816, 0.864] which represents the trough and shows a value of -0.2. Subtracting the lowest value from the highest value gives the answer.\nBased on the above thought, the answer is:\n1.1." } ], "id_raw": 964 }, { "id": "SF1MTEQi6JIVbjIG", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the provided information on Active and New Users in CSV format according to the line chart?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first locate {the x-axis tick marks}[[0.104, 0.911, 0.113, 0.934], [0.243, 0.911, 0.252, 0.934], [0.382, 0.911, 0.391, 0.934], [0.521, 0.911, 0.530, 0.934], [0.660, 0.911, 0.669, 0.934], [0.795, 0.911, 0.813, 0.934], [0.934, 0.911, 0.952, 0.934]] for the months and {the line points for Active Users}[[0.108, 0.100], [0.178, 0.206], ..., [0.943, 0.100]] and {New Users}[[0.108, 0.817], [0.178, 0.841], ..., [0.943, 0.347]], which represent data values for the subsequent table formation.\nBased on the above thought, the answer is:\nMonth,Active Users,New Users\n0,100000,20000\n1,120000,18000\n2,122000,16000\n3,119800,18000\n4,120000,24000\n5,125000,35000\n6,120000,40000\n7,118000,50000\n8,113000,48000\n9,108000,45000\n10,115000,50000\n11,125000,55000\n12,135000,60000" } ], "id_raw": 965 }, { "id": "bADDZKdCrzg8fZs2", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_5.png", "conversations": [ { "from": "human", "value": "\nWhat can you infer about the trends of Active and New Users based on the given line chart?" }, { "from": "gpt", "value": "Looking at the {line for Active Users}[0.108, 0.100, 0.943, 0.347], the trend is slightly increasing, implying that the platform has been able to retain more users over time. It reaches a peak around month 5, dips slightly in the next couple of months, and then surges back and reaches its highest peak at the last month.\nStudying the {line for New Users}[0.108, 0.347, 0.943, 0.864], it shows a consistent increase indicating a steady stream of new users joining the platform across months. The notable surge starting from month 5 suggests an effective user acquisition strategy.\nBased on the above thought, the answer is:\nThe trends for both Active and New Users appear to be positive over the year. For Active Users, the platform has managed to retain more users over time, signifying the platform's appeal and user engaging content. The surge in New Users hints at successful campaigns or strategies to attract new users. However, the rising trend in New Users emphasizes the need for the platform to strategize effective user retention mechanisms to convert these new users to active ones." } ], "id_raw": 965 }, { "id": "Lx4gmcW3nP0dhIXa", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_5.png", "conversations": [ { "from": "human", "value": "\nWhat was the highest increase in Active Users between consecutive months?" }, { "from": "gpt", "value": "To find the highest increase in Active Users between consecutive months, I need to locate the {change in Active Users}[[0.108, 0.100, 0.178, 0.206], [0.178, 0.206, 0.247, 0.192], ..., [0.874, 0.171, 0.943, 0.100]]. Mapping these to the points in the table and calculating the difference between each set of consecutive months will yield the result.\nBased on the above thought, the answer is:\n5000." } ], "id_raw": 965 }, { "id": "gmSzoJAbosvHR30w", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_6.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart into a compact table, considering months as rows and economic indicators as columns?" }, { "from": "gpt", "value": "To extract the data from the line graph into a table, I'll first consider {the x-axis labels}[[0.091, 0.911, 0.100, 0.934],...,[0.934, 0.911, 0.952, 0.934]] which represent the row headers or months. Next, I'll identify {the legend texts}[[0.115, 0.087, 0.183, 0.117],[0.115, 0.131, 0.243, 0.161]] as the column headers that represent the different economic indicators. Lastly, each {value point}[[0.096, 0.835],...,[0.943, 0.829],[0.096, 0.491],...,[0.943, 0.124]] will be placed into the corresponding cell of the table.\nBased on the above thought, the answer is:\nMonth,Inflation,Unemployment\n0,2.05,4.96\n1,3.24,4.85\n2,1.90,5.12\n3,3.54,5.30\n4,2.98,5.50\n5,4.50,6.03\n6,3.24,5.84\n7,2.55,6.68\n8,2.40,7.09\n9,3.02,7.46\n10,3.55,7.80\n11,2.10,8.06" } ], "id_raw": 966 }, { "id": "DlC9T680yZTzxqhf", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_6.png", "conversations": [ { "from": "human", "value": "\nCould you explain the correlations observed between the trend of Inflation and Unemployment over the given period?" }, { "from": "gpt", "value": "To establish the correlation between Inflation and Unemployment, we need to analyze the information presented in the line plots. The {Inflation plot}[0.096, 0.545, 0.943, 0.853] shows fluctuations, with values rising and dropping over time. Similarly, the {Unemployment plot}[0.096, 0.124, 0.943, 0.504] exhibits a steady increase over time. Even though both lines show variation, the trend in Inflation does not seem to directly correspond to the trend in Unemployment. While Inflation varies, Unemployment consistently increases.\nBased on the above thought, the answer is:\nFrom the line plot, there doesn't appear to be a direct correlation between Inflation and Unemployment. While Inflation fluctuates, with periods of both increases and decreases, Unemployment steadily rises throughout the given period. A direct correlation would imply that the two measures rise and fall together, which does not consistently happen in this dataset." } ], "id_raw": 966 }, { "id": "hVoPJOJGKYY0seXg", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_6.png", "conversations": [ { "from": "human", "value": "\nHow does the value of Inflation change in the third month compared to the first month?" }, { "from": "gpt", "value": "To answer this question, I need to find the Inflation values for the first and third months. I see that the {Inflation value for the first month} has coordinates [0.096, 0.835] representing 2.05, while the {Inflation value for the third month} has coordinates [0.250, 0.853] and stands for 1.9. To calculate the change, I subtract the first month's value from the third month's value.\nBased on the above thought, the answer is:\n-0.15" } ], "id_raw": 966 }, { "id": "yAiiAhoOYW3cUHBh", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_7.png", "conversations": [ { "from": "human", "value": "\nCan you structure the performance indices of Financial Management and Risk Control over time into a tabular format?" }, { "from": "gpt", "value": "To construct the table, I first look at {the x-axis ticks}[[0.104, 0.911, 0.113, 0.934], [0.243, 0.911, 0.252, 0.934], [0.382, 0.911, 0.391, 0.934], [0.521, 0.911, 0.530, 0.934], [0.660, 0.911, 0.669, 0.934], [0.795, 0.911, 0.813, 0.934], [0.934, 0.911, 0.952, 0.934]] which represent the time in months. Next, I track {the line points for Financial Management}[[0.108, 0.633], [0.178, 0.722], [0.247, 0.793], [0.317, 0.847], [0.387, 0.864], [0.456, 0.776], [0.526, 0.705], [0.595, 0.633], [0.665, 0.545], [0.734, 0.491], [0.804, 0.438], [0.874, 0.367], [0.943, 0.314]] and {the line points for Risk Control}[[0.108, 0.100], [0.178, 0.189], [0.247, 0.314], [0.317, 0.438], [0.387, 0.545], [0.456, 0.598], [0.526, 0.509], [0.595, 0.491], [0.665, 0.456], [0.734, 0.402], [0.804, 0.278], [0.874, 0.189], [0.943, 0.136]] to extract the values for each time.\nBased on the above thought, the answer is:\nTime (Months),Financial Management,Risk Control\n0,0.6,0.9\n1,0.55,0.85\n2,0.51,0.78\n3,0.48,0.71\n4,0.47,0.65\n5,0.52,0.62\n6,0.56,0.67\n7,0.6,0.68\n8,0.65,0.7\n9,0.68,0.73\n10,0.71,0.8\n11,0.75,0.85\n12,0.78,0.88" } ], "id_raw": 967 }, { "id": "98QpT87iMeUgTdMF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_7.png", "conversations": [ { "from": "human", "value": "\nFrom the trends in the line chart, which index showed more improvement over time? Describe your analysis." }, { "from": "gpt", "value": "To infer which index showed more improvement, I have to examine the slopes of the respective lines. The line for {Financial Management}[0.108, 0.314, 0.943, 0.864] shows a consistent upwards trend over time, indicating steady improvement in the index. For {Risk Control}[0.108, 0.100, 0.943, 0.598], the line also shows an increase over time but to a lesser degree compared to Financial Management until month 10 where Risk Control surpassed Financial Management. Hence, Financial Management showed a steeper slope and therefore, more improvement over time until month 10.\nBased on the above thought, the answer is:\nThe Financial Management index showed more improvement over time. Its value consistently increased over the period from the start until month 10. Although the Risk Control index also increased over the observation period, the rate of increase was slower compared to that of Financial Management until time point 10 when the Risk Control surpassed the Financial Management." } ], "id_raw": 967 }, { "id": "fOULQ9G7agRbO3Xd", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_7.png", "conversations": [ { "from": "human", "value": "\nAt what time does the Risk Control performance index first exceed the Financial Management performance index?" }, { "from": "gpt", "value": "To answer this question, I have to compare the points on the line for Financial Management {Financial Management}[0.108, 0.314, 0.943, 0.864] and Risk Control {Risk Control}[0.108, 0.100, 0.943, 0.598] at each time on the x-axis. Observing the coordinates, I can see that at time 4 and onwards, the Risk Control index values are higher than those for Financial Management.\nBased on the above thought, the answer is:\n4." } ], "id_raw": 967 }, { "id": "uubUxxJradJA9fQx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_8.png", "conversations": [ { "from": "human", "value": "\nCan you present the data from the line graph in a tabular format, with 'Time' as the rows and 'AI Automation' and 'Industrial Automation' as the columns?" }, { "from": "gpt", "value": "To convert the line chart to a table, I firstly identify {the x-axis ticks}[[0.062, 0.911, 0.071, 0.934], [0.138, 0.911, 0.147, 0.934], [0.214, 0.911, 0.223, 0.934], ..., [0.967, 0.911, 0.985, 0.934]] that represent the Time periods (0 to 12). Secondly, I locate {the line for AI Automation}[[0.067, 0.365, 0.976, 0.714]] and {the line for Industrial Automation}[[0.067, 0.449, 0.976, 0.646]] and extract the associated point values for each 'Time' period (AI Automation: 0.45 to 1.22 and Industrial Automation: 0.65 to 0.99).\nBased on the above thought, the answer is:\nTime, AI Automation, Industrial Automation\n0, 0.45, 0.65\n1, 0.60, 0.78\n2, 0.75, 0.68\n3, 0.67, 0.72\n4, 0.78, 0.61\n5, 0.82, 0.73\n6, 0.88, 0.76\n7, 0.93, 0.85\n8, 1.05, 0.88\n9, 1.12, 0.92\n10, 1.20, 0.96\n11, 1.28, 1.08\n12, 1.22, 0.99" } ], "id_raw": 968 }, { "id": "RwCS1MPh6QY9fKPM", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_8.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the trends in AI and Industrial Automation over time, which field showed a more consistent improvement? Explain your reasoning." }, { "from": "gpt", "value": "To determine which field showed a more consistent improvement, we need to analyze the direction and magnitude of changes in the line plots over time. Looking at the {AI Automation line}[[0.067, 0.365, 0.976, 0.714]], we see a general upward trend, indicating consistent improvement, although there are some minor fluctuations. On the other hand, {Industrial Automation line}[[0.067, 0.449, 0.976, 0.646]] also exhibits a growth trend over time, with pronounced fluctuations particularly between times 1 and 5, and 7 and 9.\nBased on the above thought, the answer is:\nAI Automation shows a more consistent improvement over time. Its line plot indicates a steady incline, suggesting continuous growth with minor fluctuations. In contrast, though Industrial Automation also has an upward trend, its progression is peppered with larger fluctuations, which may indicate periods of stagnation or slower growth, hence suggesting less consistency." } ], "id_raw": 968 }, { "id": "1yBrI4lEHEXlDF1x", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_8.png", "conversations": [ { "from": "human", "value": "\nAt which time point did AI Automation reach its peak performance index?" }, { "from": "gpt", "value": "To find the peak performance index for AI Automation, I need to inspect the line plot for AI Automation ({the AI Automation line and points}[[0.067, 0.365, 0.976, 0.714]]). The peak performance occurs at the point with the maximum Y-coordinate, corresponding to a performance index of 1.28 at time 11 ({Time 11 point}[0.900, 0.365]).\nBased on the above thought, the answer is:\n11." } ], "id_raw": 968 }, { "id": "LBbeX7yFHkfTirJC", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_9.png", "conversations": [ { "from": "human", "value": "\nCan you turn the information from the line chart into a csv file format with companies as columns and time as rows?" }, { "from": "gpt", "value": "Sure! The first step is to use {the x-axis labels}[0.028, 0.911, 0.037, 0.934], ..., [0.934, 0.911, 0.952, 0.934] which represent the row headers, or time periods. Then, {the legend entries}[0.795, 0.076, 0.977, 0.172] serve as the column headers, symbolizing the companies in this case. Finally, for each company at a specific time period, I identify {the point on the line} for that combination and record the corresponding values, such as {these points}[0.108, 0.755], [0.184, 0.646], ..., [0.943, 0.100].\nBased on the above thought, the answer is:\nTime,AR Technology,VR Technology\n1,0.5,0.4\n2,0.6,0.42\n3,0.7,0.44\n4,0.9,0.46\n5,1.0,0.48\n6,1.1,0.5\n7,0.9,0.52\n8,0.8,0.54\n9,0.7,0.56\n10,0.9,0.58\n11,1.0,0.6\n12,1.1,0.62" } ], "id_raw": 969 }, { "id": "8kxDjwbz7hgpDpPI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_9.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which technology, AR or VR, has shown a more rapid adoption rate increase over time? Explain your reasoning." }, { "from": "gpt", "value": "In order to determine which technology has had a more rapid increase in adoption rate, I need to evaluate {the line for AR Technology}[0.108, 0.100, 0.943, 0.755] and {the line for VR Technology}[0.108, 0.624, 0.943, 0.864]. For AR Technology, the adoption rate points move from 0.5 to a maximum of 1.1, reflecting a significant upward trend. On the other hand, the VR Technology's points move from 0.4 to a maximum of 0.62, also showing an upward trend but at a less steep angle compared to AR Technology. Therefore, the adoption rate of AR Technology seems to have increased at a faster rate.\nBased on the above thought, the answer is:\nAR Technology seems to have shown a more rapid increase in adoption rate over time compared to VR Technology. Looking at the line chart, AR Technology's line shows a steeper upward slope, representing a quicker rise in adoption rate compared to the somewhat less steep line for VR Technology." } ], "id_raw": 969 }, { "id": "BRpqN1g72e6ad6zz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the maximum and minimum adoption rates for AR Technology over the time period?" }, { "from": "gpt", "value": "To find this, I will locate {the line points for AR Technology}[0.108, 0.755], [0.184, 0.646], ..., [0.943, 0.100] and extract their corresponding values: 0.5, 0.6, 0.7, 0.9, 1.0, 1.1, 0.9, 0.8, 0.7, 0.9, 1.0, 1.1. The maximum value is 1.1 and the minimum value is 0.5. The difference between the maximum and minimum is 1.1 - 0.5 = 0.6.\nBased on the above thought, the answer is:\n0.6" } ], "id_raw": 969 }, { "id": "CtXlitpMg21YNDus", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_10.png", "conversations": [ { "from": "human", "value": "\nCan you transform the chart data into a table where columns represent years and rows represent types of inequality?" }, { "from": "gpt", "value": "To build the table, I have to go through the data from each line plot. For the column headers, I will go through {the x-axis ticks}[[0.061, 0.912, 0.070, 0.936], [0.109, 0.912, 0.118, 0.936], ..., [0.967, 0.912, 0.985, 0.936]] which represent the years. For row labels, I will look at {the plot legend}[[0.117, 0.083, 0.202, 0.106], [0.117, 0.118, 0.246, 0.141]], which represent the types of inequalities. Lastly, for the actual data, I will extract the y-values from {the AI Efficiency line points}[[0.065, 0.828], ..., [0.976, 0.828]] and {the Automation Speed line points}[[0.065, 0.751], ..., [0.976, 0.139]] and align them respectively with the line plot type and the corresponding year.\nBased on the above thought, the answer is:\nYear,AI Efficiency,Automation Speed\n1,0.45,0.5\n2,0.5,0.54\n3,0.56,0.59\n4,0.61,0.63\n5,0.64,0.66\n6,0.67,0.68\n7,0.7,0.7\n8,0.71,0.71\n9,0.71,0.72\n10,0.7,0.74\n11,0.68,0.76\n12,0.65,0.77\n13,0.61,0.78\n14,0.56,0.8\n15,0.55,0.82\n16,0.53,0.83\n17,0.51,0.84\n18,0.49,0.86\n19,0.47,0.88\n20,0.45,0.9" } ], "id_raw": 970 }, { "id": "3ppCuMS0frIX8ElA", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_10.png", "conversations": [ { "from": "human", "value": "\nBased on the line plots, can you outline any common trends or patterns observed in AI Efficiency and Automation Speed over the 20 months period?" }, { "from": "gpt", "value": "On observing {the AI Efficiency line}[[0.065, 0.430, 0.976, 0.828]] and {the Automation Speed line}[[0.065, 0.139, 0.976, 0.751]], both trends appear to increase initially though Automation Speed kept on increasing whereas the AI Efficiency trend began to decrease after reaching a peak around the 9th month. The Automation speed almost always maintained a higher value as compared to AI efficiency with the gap between them increasing during the later months.\nBased on the above thought, the answer is:\nBoth AI Efficiency and Automation Speed demonstrate an overall upward trend over the 20 months depicted. AI Efficiency steadily increased until approximately the 9th month, after which the trend started to descend. In contrast, Automation maintained a steady rising trend throughout the period. There was also a noticeable efficiency gap with Automation Speed largely outpacing AI Efficiency, which became especially noticeable towards the later months." } ], "id_raw": 970 }, { "id": "aW7dRC2zYDD5XbfL", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_10.png", "conversations": [ { "from": "human", "value": "\nWhat was the maximum Automation Speed value achieved and in what year did it happen?" }, { "from": "gpt", "value": "To find the year with the maximum Automation Speed value, I have to go through {the Automation Speed line points}[[0.065, 0.751], ..., [0.976, 0.139]]. The maximum value achieved according to the line plot was 0.9. Next, I correspond this value to a timestep in {the x-axis ticks}[[0.061, 0.912, 0.070, 0.936], ..., [0.967, 0.912, 0.985, 0.936]]. The maximum value seems to correspond to the last point, meaning it happened in the 20th month.\nBased on the above thought, the answer is:\nThe maximum Automation Speed value was 0.9 and it was achieved in the 20th month." } ], "id_raw": 970 }, { "id": "GCwRGE8vd2qn4U7L", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_11.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured table from the line chart showing the progression over time for Risk Management, Financial Planning, and Growth Investment?" }, { "from": "gpt", "value": "To create this structured table, I first identify the {x-axis tick labels}[0.108, 0.903, 0.117, 0.934], [0.243, 0.911, 0.252, 0.934], [0.382, 0.911, 0.391, 0.934], [0.521, 0.911, 0.530, 0.934], [0.660, 0.911, 0.669, 0.934] on the chart that denote the different time points. I next note the presence of three categories represented on the chart with three distinct colors through {the legend}[0.794, 0.076, 0.977, 0.216] where the respective data points are linked to each category: Risk Management, Financial Planning, and Growth Investment, through {line tags}[0.108, 0.145, 0.943, 0.145], [0.108, 0.323, 0.943, 0.536], [0.108, 0.500, 0.943, 0.820]. Reviewing the lines we can extract the values at different time points thus filling the table with the proper data scores.\nBased on the above thought, the answer is:\nTime, Risk Management, Financial Planning, Growth Investment\n10, 0.2, 0.45, 0.7\n20, 0.3, 0.5, 0.6\n30, 0.35, 0.55, 0.65\n40, 0.4, 0.6, 0.55\n50, 0.5, 0.65, 0.45" } ], "id_raw": 971 }, { "id": "4NSmgjvFZnlvsp5O", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_11.png", "conversations": [ { "from": "human", "value": "\nHow does the trend in Financial Planning compare with Risk Management over time in the chart?" }, { "from": "gpt", "value": "To analyze and compare the trends, I carefully observe both {the line for Financial Planning}[0.108, 0.323, 0.943, 0.536] and {the line for Risk Management}[0.108, 0.145, 0.943, 0.145]. Following these lines across the timeline, I can see that Financial Planning starts at a score of {0.45}[0.108, 0.323] and gently increases reaching {0.65}[0.660, 0.536], showing consistent growth. In contrast, Risk Management starts at a somewhat lower value of {0.2}[0.108, 0.145] but accelerates through the timeline ending slightly higher at {0.5}[0.943, 0.145]. These observations indicate that though both categories show an upward trend, Risk Management exhibits a sharper increase as compared to the more gradual growth of Financial Planning.\nBased on the above thought, the answer is:\nThe trend in Financial Planning shows a steady and gradual increase over time. Conversely, Risk Management starts at a lower point but increases more sharply, finishing slightly ahead of Financial Planning by the end of the observed time period." } ], "id_raw": 971 }, { "id": "FTBEUYzzAuJMTIUk", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the average score for Growth Investment over the indicated time points in the chart?" }, { "from": "gpt", "value": "To calculate the average score for Growth Investment, I locate the related data from {the line for Growth Investment}[0.108, 0.500, 0.943, 0.820]. The scores at different time points are extracted from the line points at {Time 10}[0.108, 0.7], {Time 20}[0.243, 0.6], {Time 30}[0.382, 0.65], {Time 40}[0.521, 0.55], and {Time 50}[0.660, 0.45]. Adding these scores (0.7, 0.6, 0.65, 0.55, 0.45) gives a total of 2.95, and dividing by the number of time points (5), the average is calculated.\nBased on the above thought, the answer is:\n0.59" } ], "id_raw": 971 }, { "id": "3OX4rZhDvIorp0Do", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_12.png", "conversations": [ { "from": "human", "value": "\nCould you summarize the shown line chart data into a structured table with each product category as a column and the months as rows?" }, { "from": "gpt", "value": "To form the required table, I would utilize the x-axis as row indices, illustrated by {the x-axis tick labels}[0.049, 0.911, 0.058, 0.934], [0.126, 0.911, 0.135, 0.934], ... , [0.967, 0.911, 0.985, 0.934] which symbolize the months. Next, the {legend}[0.062, 0.076, 0.296, 0.172] provides the column headers indicating different product categories. To capture the values, I observe the respective coordinates of {the plot points of each line}[0.053, 0.264, 0.976, 0.735], [0.053, 0.264, 0.976, 0.701] and note down the specific sales data for each product in the corresponding month.\nBased on the above thought, the answer is:\nMonth,Standard Production,High-tech Production\n0,5.0,6.0\n1,5.8,6.5\n2,6.9,7.2\n3,7.1,6.8\n4,6.6,5.8\n5,6.0,5.2\n6,5.9,5.7\n7,6.6,6.8\n8,7.4,7.8\n9,7.8,7.5\n10,7.6,7.0\n11,7.0,6.6\n12,6.5,6.4" } ], "id_raw": 972 }, { "id": "jMzNZMwromTsRLxC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_12.png", "conversations": [ { "from": "human", "value": "\nWhich production method has shown a consistent growth trend over the first six months?" }, { "from": "gpt", "value": "To answer this question, I need to study the trend of performance across the first six months for both Standard and High-tech Productions. Observing {the line for Standard Production}[0.053, 0.264, 0.515, 0.583], I see a general increase over the first six months, from an efficiency rate of 5.0 to 6.0. Meanwhile, examining {the line for High-tech Production}[0.053, 0.264, 0.515, 0.617], there is also an upward trend starting from an efficiency rate of 6.0 to 5.2. Thus, both production methods have shown a consistent growth trend over the first six months.\nBased on the above thought, the answer is:\nBoth production methods, Standard Production and High-tech Production, have shown a consistent growth trend over the first six months." } ], "id_raw": 972 }, { "id": "DVeIaDOnn75GPu5l", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in production efficiency between Standard Production and High-tech Production in the 9th month?" }, { "from": "gpt", "value": "To find the difference in the 9th month, I need to locate {the 9th point on the Standard Production line}[0.745, 0.264] which corresponds to 7.8. Then, I locate {the 9th point on the High-tech Production line}[0.745, 0.314] which corresponds to 7.5. To get the difference, I would subtract 7.5 from 7.8.\nBased on the above thought, the answer is:\n0.3." } ], "id_raw": 972 }, { "id": "F5NvKoPuDxgrVk7S", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_13.png", "conversations": [ { "from": "human", "value": "\nExtract the data from the line chart into a structured table, with the months as columns and the type of purchase as rows." }, { "from": "gpt", "value": "To turn the chart data into table format, first, I identify {the x-axis tick labels}[[0.112,0.143,0.943,0.355], [0.188,0.338,0.943,0.355], ..., [0.868,0.151,0.794,0.315], [0.943,0.142,0.794,0.315]] as column headers representing the months. Next, each line represents a type of purchase, {Regular Purchase}[0.112, 0.143, 0.943, 0.355] and {Panic Buying}[0.112, 0.100, 0.943, 0.270], which serve as row labels. Finally, I gather the values for each type of purchase at each month by looking at {the data points for Regular Purchase}[[0.112, 0.355], [0.188, 0.338], ..., [0.868, 0.151], [0.943, 0.142]] and {the data points for Panic Buying}[[0.112, 0.270], [0.188, 0.270], ..., [0.868, 0.151], [0.943, 0.100]].\nBased on the above thought, the answer is:\nType,Month 1,Month 2,Month 3,Month 4,Month 5,Month 6,Month 7,Month 8,Month 9,Month 10,Month 11,Month12\nRegular Purchase,300,310,320,330,340,350,360,380,400,415,420,425\nPanic Buying,350,350,365,370,385,375,350,360,375,400,420,450" } ], "id_raw": 973 }, { "id": "MnzfrU2iup8sGNAp", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_13.png", "conversations": [ { "from": "human", "value": "\nBased on the trends in the line chart, which behavior shows a general increase in purchase volume over time? Describe your observations." }, { "from": "gpt", "value": "To answer the question, I'll analyze the trends exhibited by each purchase behavior. Looking at the line for {Regular Purchase}[0.112, 0.143, 0.943, 0.355], we can see a steady increase in the purchase volume from 300 at the 1st month to 425 at the 12th month. However, when we look at the line for {Panic Buying}[0.112, 0.100, 0.943, 0.270], while there are increases and decreases, Panic Buying also ends at a higher volume, going from 350 to 450 from the 1st month to the 12th.\nBased on the above thought, the answer is:\nBoth Regular Purchase and Panic Buying shows a general increase in purchase volume over time. Regular Purchase shows a steady uphill trend throughout the year, starting at a purchase volume of 300 in the 1st month and ending with a volume of 425 in the 12th month. Panic Buying, while showing more fluctuation throughout the year, also demonstrates a general increase as it starts at a volume of 350 in the 1st month and increases to 450 in the 12th month." } ], "id_raw": 973 }, { "id": "w0O5iGrwKISByV9N", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_13.png", "conversations": [ { "from": "human", "value": "\nCalculate the difference between the maximum and minimum purchase values for Panic Buying?" }, { "from": "gpt", "value": "To calculate this, I will first need to find the maximum and minimum values related to Panic Buying. I will do this by analyzing {the data points for Panic Buying}[[0.112, 0.270], [0.188, 0.270], ..., [0.868, 0.151], [0.943, 0.100]]. \nThe maximum Panic Buying value, 450, matches to {the data point to month 12}[0.943, 0.100]. The minimum Panic Buying value, 350, matches to {the data point to month 1}[0.112, 0.270] and to {the data point to month 2}[0.188, 0.270]. Calculating the difference gives 450 - 350.\nBased on the above thought, the answer is:\n100." } ], "id_raw": 973 }, { "id": "Vfrr7OddjpccDRH3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line graph data of the manufacturing production efficiency of the two factories across the eight different periods into a table?" }, { "from": "gpt", "value": "To construct the table, I first look at {the x-axis tick labels}[[0.104, 0.911, 0.113, 0.934], [0.243, 0.911, 0.252, 0.934], [0.382, 0.911, 0.391, 0.934], [0.521, 0.911, 0.530, 0.934], [0.660, 0.911, 0.669, 0.934], [0.795, 0.911, 0.813, 0.934], [0.934, 0.911, 0.952, 0.934]], which represent the column headers or periods. The row labels representing the factories can be identified from {the legend texts}[[0.809, 0.050, 0.963, 0.080], [0.809, 0.094, 0.970, 0.124]]. The production efficiency values can be deduced from the respective {line points (Factory A)}[[0.108, 0.278], [0.178, 0.288], ..., [0.943, 0.863]] and {line points (Factory B)}[[0.108, 0.065], [0.178, 0.107], ..., [0.943, 0.224]] that map to each period.\nBased on the above thought, the answer is:\nPeriod,Factory A,Factory B\nUnpredictable decline,75,80\nSustained decline,49,70\nIncremental growth,68,65\nDramatic surge,62,40\nSporadic growth,70,50\nStable growth,70,60\nPeriodic fluctuations,66,70\nErratic growth,66,75" } ], "id_raw": 974 }, { "id": "HJL2CBDI3YwbTbzX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_14.png", "conversations": [ { "from": "human", "value": "\nBy observing the line chart, what can be deduced about the performance stability between Factory A and Factory B?" }, { "from": "gpt", "value": "Factory A's performance, represented as {the line for Factory A}[0.108, 0.278, 0.943, 0.863], fluctuates throughout the periods, with several peaks and troughs indicating a degree of instability. Meanwhile, Factory B's performance, represented by {the line for Factory B}[0.108, 0.065, 0.943, 0.224], also fluctuates, but its trend seems to be more steady especially after it recovers from the dramatic surge period. Therefore, it seems to be slightly more stable than Factory A.\nBased on the above thought, the answer is:\nFrom the line chart, Factory B appears to have a more stable performance compared to Factory A, especially in the periods after the dramatic surge. While both factories exhibit fluctuations in their manufacturing production efficiency, the fluctuation amplitude of Factory B seems to be less volatile after its recovery from the lowest efficiency value." } ], "id_raw": 974 }, { "id": "N07XieP0G5H9cEpD", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum efficiency gap between Factory A and Factory B across all periods?" }, { "from": "gpt", "value": "For this, I need to find the maximum difference in production efficiency between the two factories. I need to locate each pair of matching {points for Factory A}[[0.108, 0.278], [0.178, 0.288], ..., [0.943, 0.863]] and {points for Factory B}[[0.108, 0.065], [0.178, 0.107], ..., [0.943, 0.224]], subtract the value of Factory A from that of Factory B for each period, and find the maximum absolute difference from all the computed results.\nBased on the above thought, the answer is:\n30" } ], "id_raw": 974 }, { "id": "BYOoBjgNsUc9DjMq", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_16.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the line chart that represents user activity for social networks like Facebook and Instagram over a period of 12 months into a tabular form?" }, { "from": "gpt", "value": "Sure, I first need to identify {the x-axis labels that represent months}[0.062, 0.911, 0.985, 0.934] as column headers. The {legend labels}[0.766, 0.087, 0.961, 0.161] indicate row labels of Facebook User Activity and Instagram User Activity. The values are determined by tracing the line points of {Facebook User Activity}[0.067, 0.215, 0.976, 0.521] and {Instagram User Activity}[0.067, 0.139, 0.976, 0.444] across these months and identifying the corresponding user activity.\nBased on the above thought, the answer is:\nTime (Months),Facebook User Activity,Instagram User Activity\n0,0.5,0.6\n1,0.6,0.7\n2,0.65,0.75\n3,0.7,0.8\n4,0.75,0.85\n5,0.8,0.9\n6,0.85,0.95\n7,0.9,1.0\n8,0.88,0.97\n9,0.86,0.9\n10,0.84,0.85\n11,0.82,0.8\n12,0.8,0.75" } ], "id_raw": 976 }, { "id": "UuU6RLpxttNmta0V", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_16.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which social network, Facebook or Instagram, recorded a higher rate of user activity overall and why so?" }, { "from": "gpt", "value": "To determine which social network recorded a higher overall user activity, I need to analyse both the {line for Facebook User Activity}[0.067, 0.215, 0.976, 0.521] and the {line for Instagram User Activity}[0.067, 0.139, 0.976, 0.444]. It's clear that the Instagram User Activity line is consistently above the Facebook User Activity line throughout the time period, indicating that Instagram had a higher rate of user activity overall.\nBased on the above thought, the answer is:\nBased on the line chart, Instagram recorded a higher rate of user activity overall. This conclusion is drawn from the fact that the line representing Instagram User Activity is consistently higher than the line representing Facebook User Activity, indicating a higher rate of user engagement on Instagram throughout the 12 month period." } ], "id_raw": 976 }, { "id": "ReB6bGQKMAnfyPeB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_16.png", "conversations": [ { "from": "human", "value": "\nIn what month do the user activities of Facebook and Instagram coincide?" }, { "from": "gpt", "value": "To find when Facebook and Instagram User Activities coincide, I need to locate the points on the line for each where the y-values or user activity values are the same. Comparing the points on the lines of {Facebook User Activity}[0.067, 0.215, 0.976, 0.521] and {Instagram User Activity}[0.067, 0.139, 0.976, 0.444], I find that they intersect at the 5th month.\nBased on the above thought, the answer is:\n5" } ], "id_raw": 976 }, { "id": "BdLphCyM75aPAeGV", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_17.png", "conversations": [ { "from": "human", "value": "\nCan you provide the month by month market values for all types of investments in a table?" }, { "from": "gpt", "value": "First, I'll locate and extract the label {Month} on the x-axis[0.495, 0.943, 0.556, 0.975]these will act as the row identifiers. Next, both the types of investments, namely {Private Investment} and {Public Venture Capital} are extracted from the legend[0.734, 0.076, 0.977, 0.172], these will act as column headers. Afterward, the corresponding market values for each type of investment over each month are extracted by analyzing {the points on the line for Private Investment}[0.108, 0.100, 0.943, 0.864] and {the points on the line for Public Venture Capital}[0.108, 0.190, 0.943, 0.864].\nBased on the above thought, the answer is:\nMonth,Private Investment,Public Venture Capital\n0,1.0,1.0\n1,1.08,1.15\n2,1.15,1.27\n3,1.21,1.36\n4,1.25,1.42\n5,1.27,1.44\n6,1.3,1.45\n7,1.35,1.47\n8,1.4,1.5\n9,1.48,1.51\n10,1.53,1.52\n11,1.6,1.53" } ], "id_raw": 977 }, { "id": "aZcA8fwshnb6wsEB", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_17.png", "conversations": [ { "from": "human", "value": "\nWhich investment type is showing a stronger growth trend and why?" }, { "from": "gpt", "value": "Comparing the two lines, {the line for Private Investment}[0.108, 0.100, 0.943, 0.864] and {the line for Public Venture Capital}[0.108, 0.190, 0.943, 0.864], I can see that they both start at a value of 1.0 at month 0. By the end at month 11, the Private Investment has grown to 1.6, while the Public Venture Capital has grown to 1.53. The growth rate is determined by the increase of the value over time. The Private Investment line trend shows more significant steepness upward in comparison to the Public Venture Capital line.\nBased on the above thought, the answer is:\nThe stronger growth trend is shown by the Private Investment. This is because it starts at the same initial value as Public Venture Capital, but it ends with a higher value. This demonstrates a greater overall increase in value for Private Investment over the timeframe of 11 months." } ], "id_raw": 977 }, { "id": "J8LA2PaQOrwgFxzf", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_17.png", "conversations": [ { "from": "human", "value": "\nHow much did the Private Investment value change between months 0 and 11?" }, { "from": "gpt", "value": "I need to find the difference between the Private Investment values at month 0 and month 11. I can find these values by checking {the points on the line for Private Investment}[0.108, 0.100, 0.943, 0.864]. The value at month 0 is 1.0 and at month 11 is 1.6, so the difference is 1.6 - 1.0 = 0.6.\nBased on the above thought, the answer is:\n0.6" } ], "id_raw": 977 }, { "id": "mOkRqMvJ48NE552s", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_18.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data shown in the line graph about cultural artifacts and geographic regions into a CSV format?" }, { "from": "gpt", "value": "To convert the data from the line graph into a CSV format, I first identify {the x-axis labels}[0.06, 0.95, 0.1, 0.98] which are the cultural artifacts (Statue, Painting, Manuscript, Architecture). Then, I review {the legend}[0.771, 0.076, 0.977, 0.172] to identify each geographic region (Global, European, African, Asian, American). To compile the data, I extract values from each {line point}[0.108, 0.482, 0.943, 0.864] that correspond to the artifacts and geographic regions.\nBased on the above thought, the answer is:\nArtifact,Global,European,African,Asian,American\nStatue,200,300,150,250,180\nPainting,500,600,400,550,450\nManuscript,350,450,300,400,350\nArchitecture,400,500,350,450,320" } ], "id_raw": 978 }, { "id": "BvsjNRXWIucWYpbI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_18.png", "conversations": [ { "from": "human", "value": "\nWhich cultural region shows the most significant increase in embracing artifacts according to the chart?" }, { "from": "gpt", "value": "To determine which cultural region shows the most significant increase, I analyze {the lines representing each region}[0.108, 0.482, 0.943, 0.864]. I observe {the European line}[0.108, 0.350, 0.943, 0.100], noting its significant upward trend compared to other lines, which suggests a larger increase in embracing artifacts over time.\nBased on the above thought, the answer is:\nThe European region shows the most significant increase in embracing artifacts according to the chart. The line representing European culture demonstrates a pronounced upward trend, more so than the lines for other regions, indicating an increased cultural embrace of artifacts in this region." } ], "id_raw": 978 }, { "id": "zr99iKVQ8kzwQiqg", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum value represented on the y-axis in the chart?" }, { "from": "gpt", "value": "To find the maximum value represented on the y-axis, I look at the {highest tick on the y-axis}[0.040, 0.091, 0.062, 0.115]. I analyze the numerical labels along the y-axis and determine the highest value indexed.\nBased on the above thought, the answer is:\n1.4" } ], "id_raw": 978 }, { "id": "DE0zQMyw0u21VoPx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_49_19.png", "conversations": [ { "from": "human", "value": "\nCan we break down the time-series data of the transportation trends into table format with months as columns and transport methods as rows?" }, { "from": "gpt", "value": "To map the line chart's data into a table, first, we need to take note of the time range from {the x-axis tick labels}[[0.121, 0.911, 0.129, 0.934], [0.195, 0.911, 0.204, 0.934], ..., [0.935, 0.911, 0.953, 0.934]], which will be the column headers. Then, the {legend texts}[[0.772, 0.087, 0.958, 0.117], [0.772, 0.131, 0.970, 0.161]] will be the row headers representing different transport methods. We obtain the values by tracking {the line points for each transport method}[[0.125, 0.482, ..., 0.944, 0.062], [0.125, 0.377,..., 0.944, 0.509]] through the respective months and extract their corresponding values.\nBased on the above thought, the answer is:\nMonth, Standard Performance, Innovative Performance\n0,0.8,1.0\n1,0.9,0.75\n2,1.1,1.02\n3,1.2,0.85\n4,1.3,1.08\n5,1.4,0.89\n6,1.45,1.10\n7,1.2,1.05\n8,1.1,1.20\n9,1.3,1.15\n10,1.5,1.3\n11,1.6,1.35" } ], "id_raw": 979 }, { "id": "wwRmnfYl9FfmuRNa", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_49_19.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which performance evaluation type shows a more erratic trend over time?" }, { "from": "gpt", "value": "I need to analyze the volatility in the line plots for both performance evaluation types. Reviewing the {line for Standard Performance}[0.125, 0.482, 0.944, 0.062], the values vary between 0.8 to 1.6. However, the changes don't appear drastic or unexpected given the overall upward trend.\nWhen I examine the {line for Innovative Performance}[0.125, 0.377, ..., 0.944, 0.194], the values range from 0.75 to 1.35. Although the range is narrower, the line displays more sudden jumps and falls, indicating a more erratic trend in comparison.\nBased on the above thought, the answer is:\nThe Innovative Performance evaluation type shows a more erratic trend over time, as its line plot exhibits more sudden rises and falls compared to the steady growth observed in the Standard Performance type. Even though the range of values is smaller for Innovative Performance, its trend seems to be less predictable." } ], "id_raw": 979 }, { "id": "y98uI2Gat8uZc0Gv", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_49_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in Innovative Performance from Month 0 to Month 11?" }, { "from": "gpt", "value": "To obtain the percentage increase in Innovative Performance, I need to locate {the line points for Innovative Performance}[[0.125, 0.377, ..., 0.944, 0.194]]. The performance index at Month 0 is 1.0 (at {point}[0.125, 0.377]) and at Month 11 is 1.35 (at {point}[0.944, 0.194]). The percentage increase can then be calculated as ((1.35 - 1.0) / 1.0) * 100%.\nBased on the above thought, the answer is:\n35%." } ], "id_raw": 979 }, { "id": "ZSOBtvbZ3wvVzaZn", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_50_6.png", "conversations": [ { "from": "human", "value": "\nCan you format the information on the trends of 'Prediction Accuracy' and 'Number of Training Samples' for 'Application Case 1' and 'Application Case 2' into a structured text format?" }, { "from": "gpt", "value": "{The legend}[0.696, 0.046, 0.920, 0.123] indicates two cases 'Application Case 1' and 'Application Case 2'. The x-axis labels at {the x-axis tick labels}[0.019, 0.919, 0.190, 0.942], [0.731, 0.919, 0.977, 0.942] represent 'Prediction Accuracy' and 'Number of Training Samples' respectively. The line points for {Application Case 1}[0.104, 0.911], [0.854, 0.765] indicate that the values are 0.5 and 1000, respectively. The line points for {Application Case 2}[0.104, 0.911], [0.854, 0.180] indicate that the values are 0.6 and 5000, respectively.\nBased on the above thought, the answer is:\nFields,Application Case 1,Application Case 2\nPrediction Accuracy,0.5,0.6\nNumber of Training Samples,1000,5000" } ], "id_raw": 986 }, { "id": "Tvp0TOCNEA9ZnxXi", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_50_6.png", "conversations": [ { "from": "human", "value": "\nBased on the line graph, which application case had a better performance with respect to 'Number of Training Samples' and 'Prediction Accuracy'?" }, { "from": "gpt", "value": "I can determine the performance by analyzing both the 'Prediction Accuracy' and 'Number of Training Samples'. For 'Application Case 1', the 'Prediction Accuracy' is located at {the Prediction Accuracy for Application Case 1}[0.104, 0.911] with a value of 0.5 and 'Number of Training Samples' is located at {the Number of Training Samples for Application Case 1}[0.854, 0.765] with a value of 1000. For 'Application Case 2', the 'Prediction Accuracy' is located at {the Prediction Accuracy for Application Case 2}[0.104, 0.911] with a value of 0.6 and 'Number of Training Samples' is located at {the Number of Training Samples for Application Case 2}[0.854, 0.180] with a value of 5000. Both the 'Prediction Accuracy' and 'Number of Training Samples' are higher in 'Application Case 2'.\nBased on the above thought, the answer is:\nBased on the line graph, 'Application Case 2' had better performance than 'Application Case 1'. This conclusion is based on both, the higher 'Prediction Accuracy' and the larger 'Number of Training Samples' observed for 'Application Case 2'." } ], "id_raw": 986 }, { "id": "s3kvS9BUY6SWSM2M", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_50_6.png", "conversations": [ { "from": "human", "value": "\nHow many more 'Number of Training Samples' are there in 'Application Case 2' compared to 'Application Case 1'?" }, { "from": "gpt", "value": "To find the difference in the number of Training Samples, I need to locate the points for 'Application Case 1' and 'Application Case 2'. The y-value for the 'Number of Training Samples' for {Application Case 1}[0.104, 0.765] is 1000 and for {Application Case 2}[0.854, 0.180] is 5000. The difference can be calculated as 5000 - 1000.\nBased on the above thought, the answer is:\n4000" } ], "id_raw": 986 }, { "id": "HmavzmqV00i9KHgX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_50_8.png", "conversations": [ { "from": "human", "value": "\nCan you arrange the data from the line chart which shows trends across years for different sectors into a tabular format grouped by year and sector?" }, { "from": "gpt", "value": "Observing {the x-axis labels}[0.101, 0.919, 0.145, 0.942] to [0.919, 0.919, 0.962, 0.942] which denote the years from 2010 to 2020, and identifying {the legend items}[0.147, 0.727, 0.273, 0.751] to [0.147, 0.867, 0.335, 0.890] which represent sectors like Manufacturing, Logistics, Marketing, Services, and Product Development, I extracted the corresponding values from the chart. The values are tracked at each year's position for each sector using their specific data lines.\nBased on the above thought, the answer is:\nYear,Manufacturing,Logistics,Marketing,Services,Product Development\n2010,73.1,50.2,90.3,33.7,80.2\n2011,68.5,40.1,85.2,35.8,78.2\n2012,64.0,32.1,78.3,40.0,76.2\n2013,61.3,35.0,75.1,43.0,80.3\n2014,60.0,38.8,73.5,45.8,85.0\n2015,59.0,39.8,72.0,47.5,90.0\n2016,57.8,45.5,70.0,50.0,95.0\n2017,57.0,50.0,68.5,52.0,95.2\n2018,56.3,52.3,68.0,55.0,100.0\n2019,56.0,55.0,68.3,58.2,105.2\n2020,56.0,58.5,70.0,60.0,110.0" } ], "id_raw": 988 }, { "id": "9SyLLUcbC96FlW1y", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_50_8.png", "conversations": [ { "from": "human", "value": "\nWhich sector shows the most consistent decrease in value over the years according to the line chart?" }, { "from": "gpt", "value": "Observing each sector's line in the chart, I note the trend of {the line for Manufacturing}[0.123, 0.371, 0.940, 0.498] consistently moving downward year over year from a value of 73.1 in 2010 to 56.0 in 2020. This consistent descending trajectory indicates a decrease in value every year.\nBased on the above thought, the answer is:\nThe Manufacturing sector shows the most consistent decrease in value over the years. The line representing Manufacturing in the chart consistently moves downward, indicating a regular decline in its values from the year 2010 to 2020." } ], "id_raw": 988 }, { "id": "bq1qKXabk1w4M5G4", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_50_8.png", "conversations": [ { "from": "human", "value": "\nHow much increase is observed in the Product Development sector from 2010 to 2020?" }, { "from": "gpt", "value": "Starting from the value of Product Development in 2010, which is 80.2 at {point for 2010 in Product Development}[0.123, 0.319], and concluding at the value in 2020 which is 110.0 at {point for 2020 in Product Development}[0.940, 0.099], I calculate the increase by subtracting the initial value from the final value.\nBased on the above thought, the answer is:\n29.8" } ], "id_raw": 988 }, { "id": "kSHSEonmehqdFCRG", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_50_11.png", "conversations": [ { "from": "human", "value": "\nCan you convert the efficiency data for each section from the line chart into a table format?" }, { "from": "gpt", "value": "To create the table from the line chart, I first identify {the x-axis labels}[0.086, 0.939, 0.121, 0.957], [0.254, 0.939, 0.289, 0.957], [0.422, 0.939, 0.457, 0.957] which list the different sections: Lighting, HVAC, and Plug Load. I analyze {the lines}[0.104, 0.415, 0.943, 0.631], [0.104, 0.631, 0.943, 0.890] depicting both potential and achieved efficiencies respectively. The visual cues at each section intersection give the corresponding efficiency values for potential and achieved.\nBased on the above thought, the answer is:\nSection,Potential Efficiency (%),Achieved Efficiency (%)\nLighting,30,15\nHVAC,45,30\nPlug Load,20,10" } ], "id_raw": 991 }, { "id": "ZAZnFFSA39Uaq0l3", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_50_11.png", "conversations": [ { "from": "human", "value": "\nWhy does the chart suggest that all sections have room for improvement in efficiency?" }, { "from": "gpt", "value": "Analyzing the chart, specifically observing {the potential efficiency line}[0.104, 0.415, 0.943, 0.631] and {the achieved efficiency line}[0.104, 0.631, 0.943, 0.890], it is clear that the potential efficiency line is consistently above the achieved efficiency line across all sections {Lighting}[0.104, 0.501], {HVAC}[0.254, 0.174], and {Plug Load}[0.422, 0.457]. This indicates that the actual efficiencies are lower than what could potentially be achieved, suggesting a gap and therefore room for improvement.\nBased on the above thought, the answer is:\nThere is room for improvement in all sections as depicted by the higher potential efficiency line compared to the achieved efficiency line for each section, signaling that the current achievements are below the identified potential." } ], "id_raw": 991 }, { "id": "GOTg7SRpPDw1RhU7", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_50_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage difference between potential and achieved efficiency for HVAC as depicted in the chart?" }, { "from": "gpt", "value": "Observing {the line points for HVAC}[0.254, 0.174], [0.254, 0.533], the potential efficiency is indicated as 45% and the achieved efficiency is 30%. To calculate the percentage difference, the formula is (Potential - Achieved) / Potential * 100. Substituting the values, it is (45 - 30) / 45 * 100.\nBased on the above thought, the answer is:\n33.33%" } ], "id_raw": 991 }, { "id": "l5JCL4mHGDwUOVx7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_50_12.png", "conversations": [ { "from": "human", "value": "\nConsidering the line chart, can you bring out the data in table format with Years as columns and different technological fields as rows?" }, { "from": "gpt", "value": "To extract the data for the tabular view from the line chart, {the x-axis labels}[0.160, 0.890, 0.865, 0.921] can be identified as columns i.e. the years 2012 to 2021. The rows are represented by the {legend labels}[0.176, 0.141, 0.276, 0.269], which include the various technological fields AI, Quantum Computing, Blockchain, and Biotechnology. Finally, the respective values for each year and technological field can be obtained by tracking {the line points}[0.160, 0.155, 0.865, 0.855] for each technological field in respective years.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Quantum Computing,Blockchain,Biotechnology\n2012,5.8,1.2,3.6,1.1\n2013,6.9,1.5,4.1,1.2\n2014,7.8,2.0,4.7,1.4\n2015,9.0,2.6,5.1,1.7\n2016,10.6,3.3,5.3,2.0\n2017,12.5,4.3,5.4,2.5\n2018,14.8,5.5,5.6,3.0\n2019,17.4,7.0,5.8,3.7\n2020,20.5,8.7,6.0,4.5\n2021,23.9,10.6,6.4,5.4" } ], "id_raw": 992 }, { "id": "gQYmOt64sMAU0NnJ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_50_12.png", "conversations": [ { "from": "human", "value": "\nConsidering the growth trends presented in the line chart, which technological field seems to have experienced the most significant progress over time? Please elaborate." }, { "from": "gpt", "value": "To evaluate which technological field experienced the most significant progress, I investigate {the starting and ending points of each line}[0.160, 0.711, 0.865, 0.155], [0.160, 0.852, 0.865, 0.563], [0.160, 0.778, 0.865, 0.692], [0.160, 0.855, 0.865, 0.723]. I compare the progress i.e., difference between the y-coordinates (which represent scaled values) at the starting and ending points of each field. It's apparent that the line representing Artificial Intelligence shows the greatest increase in value, from approximately 5.8 in 2012 to around 23.9 by 2021.\nBased on the above thought, the answer is:\nConsidering the depicted trends in the line chart, Artificial Intelligence appears to have experienced the most significant progress over the observed timeline. This is marked by a substantial increase in its value from about 5.8 in 2012 to nearly 23.9 by 2021. The proportional increase for Artificial Intelligence seems to be larger than for the other fields, making it the field with the most significant progress over time." } ], "id_raw": 992 }, { "id": "HgQtjjliKQKhatKR", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_50_12.png", "conversations": [ { "from": "human", "value": "\nHow much progress did the field of Quantum Computing make from 2012 to 2021?" }, { "from": "gpt", "value": "To determine the progress made by Quantum Computing from 2012 to 2021, I look at {the points for Quantum Computing}[0.160, 0.852, 0.865, 0.563]. I observe that the value in 2012 was 1.2 and in 2021 it was 10.6. Therefore, the progress made by Quantum Computing is 10.6 - 1.2 = 9.4.\nBased on the above thought, the answer is:\n9.4." } ], "id_raw": 992 }, { "id": "NQhlr2xNMpAiaO5b", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_50_19.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a table format with Years as columns and Market Types as rows?" }, { "from": "gpt", "value": "To prepare the table, I will start by identifying {the x-axis tick labels}[[0.083, 0.919, 0.143, 0.942], [0.289, 0.919, 0.350, 0.942], [0.496, 0.919, 0.556, 0.942], [0.703, 0.919, 0.763, 0.942], [0.910, 0.919, 0.970, 0.942]] as column headers which represent the years. Then, I note that {the legend texts}[[0.810, 0.423, 0.959, 0.446], [0.810, 0.457, 0.948, 0.481], [0.810, 0.492, 0.925, 0.516], [0.810, 0.527, 0.966, 0.551]] symbolize the different areas of the financial market which will serve as row labels. Now, to pull the values, I can determine {the line points}[[0.113, 0.872], [0.319, 0.872], ..., [0.940, 0.871]] for each market type in the corresponding years and extract the recognized values.\nBased on the above thought, the answer is:\nMarket type,2000,2005,2010,2015,2020\nLiving Conditions,0.40,0.45,0.50,0.55,0.60\nLife Expectancy,75.65,77.65,79.54,81.72,83.56\nHousing Cost,0.55,0.65,0.70,0.80,0.85\nEmployment Rate,0.85,0.88,0.89,0.90,0.92" } ], "id_raw": 999 }, { "id": "UIuFCAmMaQRRFhtd", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_50_19.png", "conversations": [ { "from": "human", "value": "\nWhich of the given factors demonstrated the greatest improvement over the 20 year period and why?" }, { "from": "gpt", "value": "To determine which factor demonstrated the greatest improvement, I need to analyze the change in the line plots for each factor. Looking at the rate of change in {the Living Conditions line}[0.113, 0.871, 0.940, 0.872], it increases from 0.40 to 0.60. For {the Life Expectancy line}[0.113, 0.101, 0.940, 0.174], there's a considerable jump from 75.65 to 83.56. Assessing the {Housing Cost line}[0.113, 0.868, 0.940, 0.871], it increased gradually from 0.55 to 0.85. Finally, observing the {Employment Rate line}[0.113, 0.868, 0.940, 0.868], the improvement is steady but slight, from 0.85 to 0.92.\nBased on the above thought, the answer is:\nLife expectancy demonstrated the greatest improvement over the 20-year period. The line chart shows a significant increase from 75.65 to 83.56. This represents a large absolute change relative to the other factors and signifies major improvements in lifespan over this period." } ], "id_raw": 999 }, { "id": "CSbIzZFPvqWj7cYK", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_50_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in life expectancy between the years 2000 and 2020?" }, { "from": "gpt", "value": "To find the difference in life expectancy between the years 2000 and 2020, I need to locate points on the Life Expectancy line representing these years. The corresponding values are {for the year 2000}[0.113, 0.174], value is 75.65 and {for the year 2020}[0.940, 0.101], value is 83.56. The difference between these two values is therefore 83.56 - 75.65 = 7.91.\nBased on the above thought, the answer is:\n7.91" } ], "id_raw": 999 }, { "id": "xtkTM3SYHAtWGgBh", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_0.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data from the line chart demonstrating the trends of cultural heritage and cultural integration into a well-structured table?" }, { "from": "gpt", "value": "Observing the plot, each data point relating to 'cultural heritage' and 'cultural integration' can be documented. The x-axis seems to represent the year and the y-axis the percentage. Each point marked for 'cultural heritage' and 'cultural integration' is noted from 2010 to 2019 with {the value for cultural heritage at year 2010}[0.155, 0.891] being 2.3% and for {cultural integration at year 2010}[0.155, 0.141] being 2.7% respectively. The points for subsequent years are also located in a similar manner along the lines for each category until the year 2019 with coordinates captured as such.\nBased on the above thought, the answer is:\nYear,Cultural Heritage (%),Cultural Integration (%)\n2010,2.3,2.7\n2011,2.5,2.8\n2012,2.8,3.0\n2013,3.2,3.5\n2014,3.9,4.1\n2015,4.6,4.7\n2016,5.4,5.5\n2017,6.3,6.6\n2018,7.1,6.7\n2019,7.9,7.8" } ], "id_raw": 1000 }, { "id": "4E9achMSpJQdtf7W", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_0.png", "conversations": [ { "from": "human", "value": "\nWhich of the two series, cultural heritage or cultural integration, shows greater overall growth from 2010 to 2019?" }, { "from": "gpt", "value": "To answer this question, I would review the data points on the line chart for both 'cultural heritage' and 'cultural integration' from the year 2010 to 2019. For 'cultural heritage', the percentage begins at 2.3% {at year 2010 for cultural heritage}[0.155, 0.891] and climbs to 7.9% {at year 2019 for cultural heritage}[0.942, 0.892], while 'cultural integration' starts from 2.7% {at year 2010 for cultural integration}[0.155, 0.141] and increases to 7.8% {at year 2019 for cultural integration}[0.942, 0.054]. Comparing these values, 'cultural heritage' shows a slightly greater increase from 2.3% to 7.9% (a difference of 5.6%) compared to 'cultural integration' which grew from 2.7% to 7.8% (a difference of 5.1%).\nBased on the above thought, the answer is:\nCultural heritage shows a greater overall growth from 2010 to 2019 compared to cultural integration, as evidenced by a slightly larger percentage increase over this period." } ], "id_raw": 1000 }, { "id": "0lGApkriMYit8M7R", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in cultural heritage from the year 2010 to 2019?" }, { "from": "gpt", "value": "Initially, I will locate the values of cultural heritage in the year 2010 and 2019 from the graph. The value for {2010 in cultural heritage}[0.155, 0.891] is 2.3% and for {2019 in cultural heritage}[0.942, 0.892] is 7.9%. To determine the increase, I will subtract the initial value from the final value: 7.9% - 2.3% = 5.6%.\nBased on the above thought, the answer is:\n5.6%" } ], "id_raw": 1000 }, { "id": "rCAamVmE43k3JuWF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_1.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart into a table format with Fuel Economy (mpg) as rows, and the different models as columns?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first locate the {x-axis tick labels}[[0.012, 0.904, 0.058, 0.934], [0.112, 0.904, 0.158, 0.934], [0.213, 0.904, 0.259, 0.934], [0.314, 0.904, 0.360, 0.934], [0.414, 0.904, 0.460, 0.934]], which represent the row headers or Fuel Economy (mpg). The legend texts {Lambda-S, Lambda-S+, DeepTraffic, Our Model}[[0.864, 0.747, 0.951, 0.770], [0.864, 0.782, 0.966, 0.805], [0.864, 0.817, 0.963, 0.840], [0.864, 0.852, 0.953, 0.875]] serve as the column headers representing the different models. I analyze each model's line points, for example {Lambda-S}[[0.135, 0.212, 0.941, 0.365]], to fill in the corresponding table cells.\nBased on the above thought, the answer is:\nFuel Economy(mpg),Lambda-S,Lambda-S+,DeepTraffic,Our Model\n20,67.21,29.68,80.58,29.85\n25,60.98,38.93, 86.94, 38.74\n30,57.32,20.72, 91.39,47.24\n35,50.09,34.86, 94.56,52.61\n40,42.97,19.55, 99.83, 61.31" } ], "id_raw": 1001 }, { "id": "OCIB9Ol7diSvbQca", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_1.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which model seems to have the highest Classification Accuracy (%) at a Fuel Economy of 40 mpg? Explain your reasoning." }, { "from": "gpt", "value": "To determine which model has the highest Classification Accuracy (%) at a Fuel Economy of 40 mpg, I need to analyze the y-values of all model lines at x equals 40 mpg. By examining the coordinates of each model line - {Lambda-S}[0.135, 0.212, 0.941, 0.365], {Lambda-S+}[0.135, 0.568, 0.941, 0.728], {DeepTraffic}[0.135, 0.070, 0.941, 0.181], and {Our Model}[0.135, 0.357, 0.941, 0.552] - at the 40 mpg mark, I see that the y-value of the DeepTraffic model is the highest.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the DeepTraffic model seems to have the highest Classification Accuracy (%) at a Fuel Economy of 40 mpg. This is evident from the points plotted on the chart, where the line representing the DeepTraffic model reaches the highest y-value at the 40 mpg fuel economy mark, compared to the lines representing the other models." } ], "id_raw": 1001 }, { "id": "xOmDRwDf7ZHjmqKS", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_1.png", "conversations": [ { "from": "human", "value": "\nWhat's the increase in Classification Accuracy (%) of Our Model between 20 mpg and 40 mpg?" }, { "from": "gpt", "value": "The 'Our Model' line coordinates are {Our Model}[0.135, 0.357, 0.941, 0.552]. The y-value at 20 mpg corresponds to 29.85, found at coordinates [0.135, 0.454]. The y-value at 40 mpg corresponds to 61.31, found at coordinates [0.941, 0.552]. To find the increase, I subtract the initial value from the final value, which is 61.31 - 29.85.\nBased on the above thought, the answer is:\n31.46" } ], "id_raw": 1001 }, { "id": "DDCGUqDBpXQB8jA9", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_2.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the bar chart data in a table format using \u2018Media Sources\u2019 as the rows and \u2018Elections\u2019 and \u2018Immigration' as the columns?" }, { "from": "gpt", "value": "To generate the tabular data from the bar chart, I first identify the labels on {the x-axis}[0.117, 0.927, 0.942, 0.951] as the row labels or 'Media Sources'. Next, looking at {the legend}[0.083, 0.058, 0.218, 0.115], I identify 'Elections' and 'Immigration' as the column headers. Then, I analyze {the blue bar points}[0.117, 0.384, 0.942, 0.593] and {the orange bar points}[0.117, 0.300, 0.942, 0.693] for each media source in both categories and extract the corresponding values.\nBased on the above thought, the answer is:\nMedia Source,Elections,Immigration\nCNN,65,50\nFOX,80,75\nBBC,73,60\nREUTERS,78,58\nYAHOO,65,45\nMSNBC,83,28\nTWITTER,60,55\nFACEBOOK,62,45\nGOOGLE,58,50\nINSTAGRAM,50,60\nSNAPCHAT,40,55" } ], "id_raw": 1002 }, { "id": "8L61HpN9ffTH6TKZ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_2.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which media source seems to exert the most influence on public opinion regarding Elections?" }, { "from": "gpt", "value": "To determine which media source is most influential for Elections, I need to find the media source with the highest percentage in the {Elections series}[0.117, 0.233, 0.942, 0.593]. From the data points, I can see that the point with the lowest y-coordinate corresponds to MSNBC.\nBased on the above thought, the answer is:\nBased on the chart, MSNBC appears to exert the most influence on public opinion regarding Elections. It has the highest percentage among all the media sources tracked in the Elections series." } ], "id_raw": 1002 }, { "id": "HuybyGtB3lq2vSb5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the average influence of each media source on public opinion about Elections and Immigration?" }, { "from": "gpt", "value": "To calculate the average influence, I locate the series of data points for both {Elections}[0.117, 0.233, 0.942, 0.593] and {Immigration}[0.117, 0.300, 0.942, 0.693]. Next, I extract the values corresponding to each media source across both these categories and calculate the average.\nBased on the above thought, the answer is:\nThe average influence of each media source on public opinion about Elections and Immigration, rounded to the nearest whole number, is 67%." } ], "id_raw": 1002 }, { "id": "3NgqQ4qZGodo1nZ5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_3.png", "conversations": [ { "from": "human", "value": "\nCould you generate a table from the data presented in the line chart with Trends as columns and categories (Innovation, Creativity) as rows?" }, { "from": "gpt", "value": "To generate the table data from the line chart, I first look at {the x-axis tick labels}[[0.063, 0.703, 0.206, 0.894], [0.233, 0.703, 0.349, 0.857], [0.395, 0.703, 0.499, 0.841], [0.517, 0.703, 0.690, 0.934], [0.691, 0.703, 0.829, 0.887], [0.856, 0.703, 0.977, 0.865]], which represent the column headers or Trends. Next, I identify {the legend texts}[[0.848, 0.046, 0.940, 0.069], [0.848, 0.081, 0.933, 0.104]] as the row labels representing Innovation and Creativity. To populate the values, I locate {the line points for Innovation}[[0.134, 0.153], [0.291, 0.344], [0.447, 0.440], [0.604, 0.121], [0.760, 0.280], [0.917, 0.503]] and {Creativity}[[0.134, 0.216], [0.291, 0.471], [0.447, 0.567], [0.604, 0.089], [0.760, 0.248], [0.917, 0.248]] at the respective Trends and extract the values annotated.\nBased on the above thought, the answer is:\nCategory,Moderate decline,Steep decline,Abrupt drop,Consistent expansion,Subdued decline,Erratic decline\nInnovation,85,55,40,90,65,30\nCreativity,75,35,20,95,70,70" } ], "id_raw": 1003 }, { "id": "4vMcqheApOd7v7fh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_3.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the trends of Innovation and Creativity from the line chart?" }, { "from": "gpt", "value": "To analyse the trends of Innovation and Creativity, I focus on {the line for Innovation}[0.134, 0.121, 0.917, 0.503] and {the line for Creativity}[0.134, 0.089, 0.917, 0.567]. The Innovation line illustrates a decrease from 85 to 40, suggesting a decline in innovation during the first three trends. However, it then increases to 90 during the Consistent Expansion trend before slightly decreasing and finishing at 30 in Erratic Decline. This shows a significant fluctuation in the trend of Innovation. On the other hand, the Creativity line starts at 75, decreases to 20 in the middle of the period, recovers slowly to 70 and stabilizes from the steepest decline to the end. This shows a more stable pattern for Creativity compared to Innovation.\nBased on the above thought, the answer is:\nThe line chart suggests that the trend of Innovation underwent significant fluctuations, with a severe decline in the first three trends, followed by a brief recovery and a second decline. On the other hand, Creativity had a more stable pattern, with a decrease initially, gradual recovery, and then stagnation. Overall, the patterns suggest volatility for Innovation, while Creativity tends to be more resilient and steady after initial changes." } ], "id_raw": 1003 }, { "id": "0rZLlDmJbsTfYXnu", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest scores for Creativity?" }, { "from": "gpt", "value": "To find the highest and lowest values for Creativity, I need to locate {the line points for Creativity}[[0.134, 0.216], [0.291, 0.471], [0.447, 0.567], [0.604, 0.089], [0.760, 0.248], [0.917, 0.248]] and extract the corresponding values: 75, 35, 20, 95, 70, 70. The maximum value is 95 corresponding to {Consistent expansion}[0.604, 0.703], and the minimum value is 20 corresponding to {Abrupt drop}[0.447, 0.703]. The difference between the maximum and minimum is therefore 95 - 20 = 75.\nBased on the above thought, the answer is:\n75." } ], "id_raw": 1003 }, { "id": "Qn876nuiS7Byz7iB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_4.png", "conversations": [ { "from": "human", "value": "\nCould you provide the data from the line graph in tabular format, with 'SNR' values representing columns and 'Classification Accuracy (%)' values as rows?" }, { "from": "gpt", "value": "To make the line graph into a table, I need to look at {the x-axis tick labels}[[0.104, 0.928, 0.130, 0.951], [0.324, 0.928, 0.342, 0.951], [0.531, 0.928, 0.549, 0.951], [0.737, 0.928, 0.756, 0.951], [0.944, 0.928, 0.962, 0.951]] as columns, which represent 'SNR' values. Then, I treat {the legend texts}[[0.825, 0.810, 0.957, 0.828], [0.825, 0.836, 0.946, 0.854], [0.825, 0.863, 0.962, 0.880], [0.825, 0.889, 0.973, 0.906]] as rows, which correspond to different categories of 'Classification Accuracy (%)'. Then, I extract from {the line points}[[0.117, 0.533], [0.324, 0.503], [0.531, 0.533], [0.737, 0.475], [0.944, 0.613], ..., [0.944, 0.222]] the values for each category at each 'SNR'.\nBased on the above thought, the answer is:\nSNR,Startups Incubated,Research Centers,Ecosystem Partners,Policies Implemented\n5,45.26,36.65,52.87,58.96\n10,48.73,32.78,56.01,63.78\n15,45.23,29.74,47.12,65.25\n20,52.00,24.40,57.55,77.01\n25,35.87,22.98,48.93,81.37" } ], "id_raw": 1004 }, { "id": "pbCEOyoaBOCmFErt", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_4.png", "conversations": [ { "from": "human", "value": "\nBased on the trends in the line graph, which category showed a continuous decline in the 'Classification Accuracy (%)' with increasing 'SNR'?" }, { "from": "gpt", "value": "To determine which category shows a continuous decline, I need to consider the slope of the lines for each category. Observing {the line for Startups Incubated}[0.117, 0.475, 0.944, 0.613], it's clear that it doesn't display a continuous decline as it goes up and down. The {line for Research Centers}[0.117, 0.607, 0.944, 0.724] shows a continuous decline from 'SNR' of 5 to 'SNR' of 25. Similarly, the {line for Ecosystem Partners}[0.117, 0.427, 0.944, 0.517] doesn't exhibit a continuous decline. The {line for Policies Implemented}[0.117, 0.222, 0.944, 0.415] shows an increase over the 'SNR' values.\nBased on the above thought, the answer is:\nThe \"Research Centers\" category shows a continuous decline in 'Classification Accuracy (%)' with increasing 'SNR' values. The line plot for this category consistently slopes downwards from 'SNR' of 5 to 'SNR' of 25, indicating a sustained decrease in 'Classification Accuracy (%)'." } ], "id_raw": 1004 }, { "id": "rq6Jh4FomRYCYDtP", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_4.png", "conversations": [ { "from": "human", "value": "\nWhat's the maximum 'Classification Accuracy (%)' that the Research Centers obtained across all given SNR values?" }, { "from": "gpt", "value": "To obtain the maximum 'Classification Accuracy (%)' for Research Centers, I have to examine {the line points for Research Centers}[[0.117, 0.607], [0.324, 0.640], [0.531, 0.666], [0.737, 0.712], [0.944, 0.724]]. The corresponding 'Classification Accuracy (%)' values are 36.65, 32.78, 29.74, 24.4, 22.98 respectively. The maximum value among these is 36.65 at 'SNR' of 5.\nBased on the above thought, the answer is:\n36.65" } ], "id_raw": 1004 }, { "id": "Ldoug885cM5AfM1O", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the provided line chart data into a table with Years as columns and Technology Types as rows?" }, { "from": "gpt", "value": "I first locate {the x-axis tick labels}[[0.096, 0.904, 0.149, 0.934], [0.260, 0.904, 0.313, 0.934], [0.423, 0.904, 0.476, 0.934], [0.587, 0.904, 0.640, 0.934], [0.750, 0.904, 0.803, 0.934], [0.914, 0.904, 0.967, 0.934]], which represent the individual years. Next, I identify {the legend texts}[[0.146, 0.057, 0.346, 0.081], [0.146, 0.092, 0.268, 0.116], [0.146, 0.127, 0.324, 0.151], [0.146, 0.162, 0.240, 0.186]] as the row headers representing different technology types. To find the values for each technology at the respective years, I identify the {line points} for each technology and extract the values annotated.\nBased on the above thought, the answer is:\nTechnology,2015,2016,2017,2018,2019,2020\nMultimedia Technology,70,72,73,77,80,83\nVirtual Reality,55,60,65,68,70,75\nArtificial Intelligence,40,42,50,55,60,70\nBlockchain,30,35,40,45,50,60" } ], "id_raw": 1005 }, { "id": "EP8rC57q92Dy8ZD1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_5.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which technology has seen the most consistent growth over time from 2015 to 2020, and why do you think so?" }, { "from": "gpt", "value": "To figure out which technology has experienced the most consistent growth, I need to analyze the lines representing each technology. Looking at {the line for Multimedia Technology}[0.123, 0.123, 0.940, 0.282], the line has a slight positive slope indicating a stable but limited growth. The second line representing {Virtual Reality}[0.123, 0.221, 0.940, 0.466] shows a trend of relatively constant growth while {Artificial Intelligence}[0.123, 0.282, 0.940, 0.650] and {Blockchain}[0.123, 0.405, 0.940, 0.773] both demonstrate sharp inclines over the years, indicating accelerated growth. However, among these, Artificial Intelligence's growth appears more steady and consistent across the years as compared to Blockchain.\nBased on the above thought, the answer is:\nArtificial Intelligence has seen the most consistent growth over time from 2015 to 2020. While all technology types have shown growth, the line representing Artificial Intelligence demonstrates a regular and steady upward trend, suggesting consistent annual growth in the adaption rate of this technology." } ], "id_raw": 1005 }, { "id": "Y8BsfL01huKaZwnV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the growth rate of Blockchain from 2015 to 2020?" }, { "from": "gpt", "value": "To find the growth rate of Blockchain, I need to locate {the line points for Blockchain}[[0.123, 0.773], [0.286, 0.712], [0.450, 0.650], [0.613, 0.589], [0.777, 0.528], [0.940, 0.405]] and extract the corresponding values: 30, 35, 40, 45, 50, 60. The initial value in 2015 is 30 and the final value in 2020 is 60. The growth rate is therefore (60 - 30)/30 = 100%.\nBased on the above thought, the answer is:\n100%." } ], "id_raw": 1005 }, { "id": "2c8o7hzD7WXMRH7d", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_6.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table format based on the information from the line chart, listing different energy sources as columns with their corresponding usage values over the years?" }, { "from": "gpt", "value": "To create a table from the line chart data, I analyze the position of each line, corresponding to different energy sources. First, I consider {the points for Coal Consumption}[0.118, 0.301], [0.944, 0.447] and note down the values at each year mark from 2000 to 2020. I follow the same steps for {Oil Consumption}[0.118, 0.357], [0.944, 0.639], {Natural Gas Consumption}[0.118, 0.385], [0.944, 0.041], and {Renewable Energy Consumption}[0.118, 0.864], [0.944, 0.075]. These positions on the y-axis correspond to the energy usage, from which I extract and list the values.\nBased on the above thought, the answer is:\nYear,Coal,Oil,Natural Gas,Renewables\n2000,500,480,470,300\n2001,510,470,472,310\n2002,515,460,470,320\n2003,520,458,475,340\n2004,540,470,480,350\n2005,560,472,485,355\n2006,565,470,495,370\n2007,560,468,506,390\n2008,570,470,520,400\n2009,560,450,520,430\n2010,550,440,530,440\n2011,540,430,540,460\n2012,530,425,550,480\n2013,502,420,552,500\n2014,500,410,560,520\n2015,490,405,565,530\n2016,470,400,570,540\n2017,460,395,572,550\n2018,450,390,580,560\n2019,448,385,590,570\n2020,450,380,592,580" } ], "id_raw": 1006 }, { "id": "ZqtQyNYAQsvtFEx7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_6.png", "conversations": [ { "from": "human", "value": "\nBased on the graph, which energy source shows the most consistent upward trend over the period?" }, { "from": "gpt", "value": "To determine the most consistent upward trend, I analyze the line trajectories for each energy source closely. By observing {the line for Renewable Energy}[0.118, 0.864], [0.944, 0.075], I note its starting and ending values at the highest and lowest positions respectively, indicating a consistent increase. This is in contrast to other sources like coal and oil that show fluctuations or a decrease in values at certain points.\nBased on the above thought, the answer is:\nRenewable energy demonstrates the most consistent upward trend over the period based on the graph, as its line shows a progressive and steady increase from start to finish among the energy sources depicted." } ], "id_raw": 1006 }, { "id": "MJe5sHNnhMlMqG9z", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage change in oil consumption from the year 2000 to 2020 according to the chart?" }, { "from": "gpt", "value": "To find the percentage change, I first identify {the oil consumption points in 2000 and 2020}[0.118, 0.357], [0.944, 0.639] which are marked at 480 and 380 respectively. The formula for percentage change is [(New Value - Old Value) / Old Value] * 100. Substituting the values, [(380 - 480) / 480] * 100, provides the percentage change.\nBased on the above thought, the answer is:\n-20.83%" } ], "id_raw": 1006 }, { "id": "vb4VD5OWpRdjcD4h", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_7.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart showing the trend of each type over the years into a table format?" }, { "from": "gpt", "value": "Yes, I can extract the data from the line chart. To do this, I first recognize {the x-axis tick labels}[[0.087, 0.927, 0.129, 0.949], [0.296, 0.927, 0.338, 0.949], [0.505, 0.927, 0.547, 0.949], [0.713, 0.927, 0.756, 0.949], [0.922, 0.927, 0.964, 0.949]], which correspond to the years and will serve as row headers in the table. I identify {the legend texts}[[0.118, 0.043, 0.229, 0.061], [0.118, 0.069, 0.187, 0.087], [0.118, 0.095, 0.216, 0.113], [0.118, 0.122, 0.234, 0.139], [0.118, 0.148, 0.208, 0.165]] as column headers representing the different types of trends. To populate the values in the table, I will trace {the line points} for each trend at different years and extract the corresponding values.\nBased on the above thought, the answer is:\nYear,Immunotherapy,Radiology,AI Diagnostics,DNA Sequencing,Telemedicine\n2000,50.122,78.754,32.612,60.444,38.982\n2005,55.33,80.406,35.972,66.485,47.71\n2010,58.471,81.664,49.315,70.486,59.438\n2015,65.664,83.355,68.935,74.551,75.714\n2020,71.113,86.328,79.673,79.122,89.624" } ], "id_raw": 1007 }, { "id": "3th2UtnGCOq9AxVN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_7.png", "conversations": [ { "from": "human", "value": "\nAccording to the trends shown in the line chart, which type has the steepest growth over the period 2000 to 2020?" }, { "from": "gpt", "value": "To answer this question, I need to inspect the line plots for different types, focus particularly on the year 2000 and 2020. I review the plot for {Immunotherapy}[0.108, 0.356, 0.943, 0.644], {Radiology}[0.108, 0.147, 0.943, 0.251], {AI Diagnostics}[0.108, 0.238, 0.943, 0.885], {DNA Sequencing}[0.108, 0.246, 0.943, 0.502], and {Telemedicine}[0.108, 0.101, 0.943, 0.797] during this period. Analyzing their slope which indicates the rate of change, the plot for Telemedicine has the steepest growth, since it rose from the lowest point in 2000 to one of the highest points in 2020.\nBased on the above thought, the answer is:\nAccording to the trends shown in the line chart, Telemedicine shows the steepest growth from 2000 to 2020, as it demonstrates the most substantial increase in value over this period relative to the other types. The line for Telemedicine started at the bottom among all categories in 2000 but rose significantly and outperformed most of the other categories by 2020. This steep upward trajectory indicates a high rate of growth." } ], "id_raw": 1007 }, { "id": "oreGVePjSYmccPsz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_7.png", "conversations": [ { "from": "human", "value": "\nCan you calculate the increase of AI Diagnostics from 2005 to 2015?" }, { "from": "gpt", "value": "To find the increase of AI Diagnostics from 2005 to 2015, I need to look at the AI Diagnostics line plot and locate the points corresponding to the years 2005 and 2015. These correspond to {the point for AI Diagnostics in 2005} [0.317, 0.839] and {the point for AI Diagnostics in 2015} [0.735, 0.386]. Reviewing these points, I extract the corresponding values of 35.972 for 2005 and 68.935 for 2015. The increase in AI Diagnostics from 2005 to 2015 is then calculated as 68.935 minus 35.972, which equates to 32.963.\nBased on the above thought, the answer is:\n32.963" } ], "id_raw": 1007 }, { "id": "sWsaz6aA4xg0iAH6", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_8.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you create a table where the rows represent the years and the columns represent the different sectors?" }, { "from": "gpt", "value": "Starting with the extraction of information, I identify {the x-axis labels}['xticks'][{'label': '2010', 'label_coordinates': '[0.134, 0.904, 0.187, 0.934]', 'tick_coordinates': '[0.160, 0.896]'}, {'label': '2012', 'label_coordinates': '[0.290, 0.904, 0.343, 0.934]', 'tick_coordinates': '[0.317, 0.896]'}, {'label': '2014', 'label_coordinates': '[0.447, 0.904, 0.500, 0.934]', 'tick_coordinates': '[0.473, 0.896]'}, {'label': '2016', 'label_coordinates': '[0.603, 0.904, 0.656, 0.934]', 'tick_coordinates': '[0.629, 0.896]'}, {'label': '2018', 'label_coordinates': '[0.759, 0.904, 0.812, 0.934]', 'tick_coordinates': '[0.786, 0.896]'}, {'label': '2020', 'label_coordinates': '[0.916, 0.904, 0.969, 0.934]', 'tick_coordinates': '[0.942, 0.896]'}] representing years. Meanwhile, {the legend labels}['sublegend']['items'][{'label': 'Modern Art', 'text_coordinates': '[0.186, 0.046, 0.282, 0.069]'}, {'label': 'Ethnic Art', 'text_coordinates': '[0.186, 0.081, 0.271, 0.104]'}, {'label': 'Performing Art', 'text_coordinates': '[0.186, 0.116, 0.312, 0.139]'}, {'label': 'Artisan Crafts', 'text_coordinates': '[0.186, 0.151, 0.304, 0.174]'}] denote different sectors. Furthermore, {the data points}['lines']['line_0']['points'], ['line_1']['points'], ['line_2']['points'], ['line_3']['points'] offer the actual revenue for each sector in each year.\nBased on the above thought, the answer is:\nYear,Modern Art,Ethnic Art,Performing Art,Artisan Crafts\n2010,14582,13295,12562,13500\n2011,16000,15200,15200,15800\n2012,17500,13500,15800,16500\n2013,19347,25000,12800,9300\n2014,21470,21500,21400,9100\n2015,24000,24000,15300,9800\n2016,26750,10000,12800,10200\n2017,29000,15200,13500,10846\n2018,32000,24500,14000,14500\n2019,36000,27200,15300,15000\n2020,42000,30000,15500,17000" } ], "id_raw": 1008 }, { "id": "SuLqcyB1nx7hsNTr", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_8.png", "conversations": [ { "from": "human", "value": "\nCan you describe the trend of the Ethnic Art sector over the years based on the line chart?" }, { "from": "gpt", "value": "To comprehend the trend for the Ethnic Art sector, I need to analyze {the line plot for Ethnic Art}['lines']['line_1']['coordinates']. Initially, there's a steady increase from 13295 in 2010 to 15200 in 2011. However, there's a dip in 2012 to 13500, followed by a drastic increase to 25000 in 2013. From 2014 to 2015, the revenue remains steady around the 24000 mark. Surprisingly, there's a drop in 2016 to 10000. It then rises to 15200 in 2017 and sharply increases reaching 27200 in 2019. Thankfully, there is stability followed by a gentle increase till 2020 where it stands at 30000. This indicates quite the volatility in the Ethnic Art sector.\nBased on the above thought, the answer is:\nThe revenue in the Ethnic Art sector sees several fluctuations over the years. Initially, it increases slightly, experiences a slight drop, then drastically increases, and remains steady for a while. Following this, it significantly drops and then rises, before finally stabilizing followed by a gentle increase." } ], "id_raw": 1008 }, { "id": "zSM5oeipaJ4nehjh", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_8.png", "conversations": [ { "from": "human", "value": "\nIn which year did Performing Arts sector get the maximum revenue?" }, { "from": "gpt", "value": "To identify the year corresponding to the maximum revenue for the Performing Art sector, I need to scan {the data points for Performing Art}['lines']['line_2']['points'][{'x': 2010, 'y': 12562, 'point_coordinates': '[0.160, 0.653]'}, {'x': 2011, 'y': 15200, 'point_coordinates': '[0.239, 0.602]'}, {'x': 2012, 'y': 15800, 'point_coordinates': '[0.317, 0.590]'}, {'x': 2013, 'y': 12800, 'point_coordinates': '[0.395, 0.648]'}, {'x': 2014, 'y': 21400, 'point_coordinates': '[0.473, 0.482]'}, {'x': 2015, 'y': 15300, 'point_coordinates': '[0.551, 0.600]'}, {'x': 2016, 'y': 12800, 'point_coordinates': '[0.629, 0.648]'}, {'x': 2017, 'y': 13500, 'point_coordinates': '[0.708, 0.635]'}, {'x': 2018, 'y': 14000, 'point_coordinates': '[0.786, 0.625]'}, {'x': 2019, 'y': 15300, 'point_coordinates': '[0.864, 0.600]'}, {'x': 2020, 'y': 15500, 'point_coordinates': '[0.942, 0.596]'}]. The maximum value I see here is 21400 which corresponds to the year 2014.\nBased on the above thought, the answer is:\n2014" } ], "id_raw": 1008 }, { "id": "PtAjPxkN9XfxrPcM", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_9.png", "conversations": [ { "from": "human", "value": "\nCan you please extract the data from the line chart into a table with Years as columns and Activity Type as rows?" }, { "from": "gpt", "value": "To create a table, I identify {the x-axis tick labels}[[0.129, 0.904, 0.142, 0.934], [0.330, 0.904, 0.343, 0.934], [0.532, 0.904, 0.545, 0.934], [0.733, 0.904, 0.746, 0.934], [0.934, 0.904, 0.948, 0.934]] as the column headers representing the Years. Next, I determine {the legend texts}[[0.838, 0.747, 0.933, 0.770], [0.838, 0.782, 0.925, 0.805]] as the row labels representing the different Activity Types. I then find the line points respectively for each activity in each year to fill the table with appropriate frequency (Reading: {line_0}[[0.135, 0.555, 0.941, 0.642]]; Gardening: {line_1}[[0.135, 0.221, 0.941, 0.352]]).\nBased on the above thought, the answer is:\nActivity Type,Year 1,Year 2,Year 3,Year 4\nReading,45,48,50,47\nGardening,35,32,30,28" } ], "id_raw": 1009 }, { "id": "CCd0t88ln4TF7wD8", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_9.png", "conversations": [ { "from": "human", "value": "\nWhich activity, Reading or Gardening, portrays a more stable trend?" }, { "from": "gpt", "value": "To determine which activity portrays a more stable trend, I need to assess the fluctuations in the Reading and Gardening lines ({line_0}[[0.135, 0.555, 0.941, 0.642]] and {line_1}[[0.135, 0.221, 0.941, 0.352]], respectively). From 2018 to 2019 and 2019 to 2020, Reading shows a consistent growth, however, it dips slightly in 2021. In contrast, Gardening displays a steady and consistent decline over the years.\nBased on the above thought, the answer is:\nGardening depicts a more stable trend. Although both activities show changes over the years, the consistency of decline in Gardening, as opposed to the irregular behavior of Reading, suggests greater stability. The predictable decrease in Gardening points to stable, albeit negative, growth." } ], "id_raw": 1009 }, { "id": "NzB846sr70gI7nfW", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the total change in frequency for the Reading activity from 2018 to 2021?" }, { "from": "gpt", "value": "To find the total change in the Reading activity, I need to locate the start and end points or coordinates for the Reading line - {line_0}[[0.135, 0.555, 0.941, 0.642]], and extract the corresponding values, i.e. start frequency in 2018 - 45 and final frequency in 2021 - 47. The total change in frequency therefore is 47 - 45 = 2.\nBased on the above thought, the answer is:\n2." } ], "id_raw": 1009 }, { "id": "6iSAfTCfMbePKwy4", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_10.png", "conversations": [ { "from": "human", "value": "\nCan you organize the line chart data into a structured table showing the years as columns and different cultural aspects as rows?" }, { "from": "gpt", "value": "Sure, to obtain the data table from this line chart, I identify {the x-axis tick labels} [[0.096, 0.904, 0.149, 0.934], [0.301, 0.904, 0.354, 0.934], [0.505, 0.904, 0.558, 0.934], [0.710, 0.904, 0.762, 0.934], [0.914, 0.904, 0.967, 0.934]] as the headers, i.e., the years. The {legend texts} [[0.748, 0.747, 0.927, 0.770], [0.748, 0.782, 0.916, 0.805]] refer to the different cultural aspects. The values for each year and aspect are extracted from the line points {for Language Diversity} [[0.123, 0.585], [0.327, 0.460], [0.532, 0.398], [0.736, 0.274], [0.940, 0.087]] and {for Cuisine Diversity} [[0.123, 0.772], [0.327, 0.709], [0.532, 0.585], [0.736, 0.485], [0.940, 0.398]].\nBased on the above thought, the answer is:\nAspect,2018,2019,2020,2021\nLanguage Diversity,65,70,75,90\nCuisine Diversity,77,72,75,79" } ], "id_raw": 1010 }, { "id": "CZsmLIiMIvmqdbZm", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_10.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which aspects of cultural heritage and integration showed more consistent growth over the years? Justify your answer." }, { "from": "gpt", "value": "I need to compare the line plots for Language Diversity and Cuisine Diversity to answer this. {The line for Language Diversity}[0.123, 0.087, 0.940, 0.585] shows a consistent upward trend from 2018 to 2021, with all points falling above the previous ones, indicating steady growth. In contrast, {the line for Cuisine Diversity}[0.123, 0.398, 0.940, 0.772] shows a decline from 2018 to 2019 before climbing back up, implying more variation and less consistent growth overall.\nBased on the above thought, the answer is:\nAccording to the line chart, Language Diversity showed more consistent growth over the years. Its line plot suggests a steady, consistent growth, with no downturns observed. Contrastingly, Cuisine Diversity shows an inconsistent pattern where it initially declines before rising again, indicating less consistency in growth compared to Language Diversity." } ], "id_raw": 1010 }, { "id": "hsW7x9EkvUXgNJgr", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_10.png", "conversations": [ { "from": "human", "value": "\nHow much did Language Diversity increase from 2018 to 2021?" }, { "from": "gpt", "value": "To find the increase in Language Diversity, I need to identify the start and end points of the line plot for Language Diversity. The points for 2018 and 2021 in {the line points for Language Diversity}[[0.123, 0.585], [0.327, 0.460], [0.532, 0.398], [0.736, 0.274], [0.940, 0.087]] correspond to values of 65 and 90 respectively. Therefore, the increase from 2018 to 2021 is 90 - 65 = 25.\nBased on the above thought, the answer is:\n25" } ], "id_raw": 1010 }, { "id": "SsogoJDFLv2yqn3b", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_11.png", "conversations": [ { "from": "human", "value": "\nCan you structure all the data in the line chart into a table with years and different fitness trends as columns?" }, { "from": "gpt", "value": "To structure the data into a table, I first locate {the x-axis tick labels}[[0.086, 0.928, 0.128, 0.951], [0.253, 0.928, 0.296, 0.951], [0.420, 0.928, 0.463, 0.951], [0.588, 0.928, 0.630, 0.951], [0.755, 0.928, 0.797, 0.951], [0.922, 0.928, 0.964, 0.951]], which represent years, and these may be used as column headers. Next, I identify the {legend texts}[[0.850, 0.810, 0.921, 0.828], [0.850, 0.836, 0.959, 0.854], [0.850, 0.863, 0.962, 0.880], [0.850, 0.889, 0.973, 0.906]] as fitness trends and place these as subsequent column headers. Then it's about populating the values. The respective coordinates of the points in the chart lines representing {Bootcamp}[[0.107, 0.771], [0.274, 0.696], [0.442, 0.621], [0.609, 0.666], [0.776, 0.711], [0.943, 0.681]], {Outdoor Fitness}[[0.107, 0.546], [0.274, 0.500], [0.442, 0.591], [0.609, 0.696], [0.776, 0.802], [0.943, 0.847]], {Smart Bracelets}[[0.107, 0.094], [0.274, 0.124], [0.442, 0.169], [0.609, 0.199], [0.776, 0.245], [0.943, 0.275]], and {Gym Membership}[[0.107, 0.245], [0.274, 0.290], [0.442, 0.335], [0.609, 0.380], [0.776, 0.425], [0.943, 0.470]] allow us to estimate their values for each year.\nBased on the above thought, the answer is:\nYear,Bootcamp,Outdoor Fitness,Smart Bracelets,Gym Membership\n2015,50,65,95,85\n2016,55,68,93,82\n2017,60,62,90,79\n2018,57,55,88,76\n2019,54,48,85,73\n2020,56,45,83,70" } ], "id_raw": 1011 }, { "id": "ayU3sXU6ZYiRzXZ6", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_11.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which fitness trend experienced the most significant decrease over the years? Justify your answer." }, { "from": "gpt", "value": "To determine which fitness trend experienced the most significant decrease, I need to compare the drops in line plots for each fitness trend in the given span of years. Looking at {the line for Bootcamp}[0.107, 0.943], there is a general decreasing slope with a little increase towards the end. \nFor {Outdoor Fitness}[0.107, 0.943], there is a clear decreasing slope representing a constant decrease in value from the year 2015 to 2020. \nThe line for {Smart Bracelets}[0.107, 0.943] also has a continuous decrease, but the values are not decreasing as sharply as Outdoor Fitness. \nFinally, {Gym Membership}[0.107, 0.943] shows a steady decrease, comparable to Smart Bracelets but less than Outdoor Fitness.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Outdoor Fitness seems to have experienced the most significant decrease over the years. Its line plot shows a clear and almost constant downward trend from the year 2015 to 2020, indicating a continuous decrease in its popularity rate. Compared to Bootcamp, Smart Bracelets, and Gym Membership, the decrease in Outdoor Fitness rate is the most pronounced." } ], "id_raw": 1011 }, { "id": "qNgBkhkDbP472B6O", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_11.png", "conversations": [ { "from": "human", "value": "\nHow much has the popularity rate of smart bracelets decreased from 2015 to 2020?" }, { "from": "gpt", "value": "To calculate the decrease in the popularity rate of smart bracelets from 2015 to 2020, I need to look at the respective values at these years. From the line plot for {smart bracelets}[0.107, 0.943], the subject is represented with green color and \"X\" markers. I notice that at 2015, represented by the point {at}[0.107, 0.094], the value is 95, while at 2020, represented by the point {at}[0.943, 0.275], the value is 83. Therefore, the decrease would be 95-83=12.\nBased on the above thought, the answer is:\n12." } ], "id_raw": 1011 }, { "id": "AK54dPnyus0EY40f", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_12.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data into a table format with years as the row labels and the allocation types as the columns?" }, { "from": "gpt", "value": "To create a table from the line chart data, I need to examine the {x-axis tick labels}[[0.096, 0.904, 0.149, 0.934], [0.301, 0.904, 0.354, 0.934], [0.505, 0.904, 0.558, 0.934], [0.710, 0.904, 0.762, 0.934], [0.914, 0.904, 0.967, 0.934]] which represent the row labels or years. Then, I need to look at the {legend's labels}[[0.146, 0.046, 0.300, 0.069], [0.146, 0.081, 0.309, 0.104], [0.146, 0.116, 0.260, 0.139], [0.146, 0.151, 0.290, 0.174]] which represent the column headers or allocation types. I can populate the values by finding {the points of each line}[[0.123, 0.460], [0.327, 0.429], [0.532, 0.386], [0.736, 0.355], [0.940, 0.324], ..., [0.123, 0.087], [0.327, 0.212], [0.532, 0.181], [0.736, 0.112], [0.940, 0.087]] for each allocation type at the respective years and extracting the corresponding values.\nBased on the above thought, the answer is:\nYear,Public Institutions,Private Institutions,Home School,Online_Programs\n2016,35,42.5,22.5,50\n2017,37.5,44,23.5,55\n2018,41,45.5,24.5,57.5\n2019,43.5,47,26,63\n2020,46,49.5,27.5,65" } ], "id_raw": 1012 }, { "id": "xn3WtW5AaGiGEqfX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_12.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which education type seems to have the highest increase in allocation percentage over time?" }, { "from": "gpt", "value": "To determine which education type has the highest increase in allocation percentage, I need to analyze the trends in the line plots for each type. Looking at {the line for Public Institutions}[0.123, 0.324, 0.940, 0.460], it shows an increase from 35 to 46. This is a difference of 11.\nLooking at {the line for Private Institutions}[0.123, 0.280, 0.940, 0.367], it shows an increase from 42.5 to 49.5. This is a difference of 7.\nInspecting {the line for Home School}[0.123, 0.554, 0.940, 0.616], it shows an increase from 22.5 to 27.5. This is a difference of 5.\nFinally, looking at {the line for Online Programs}[0.123, 0.087, 0.940, 0.274], it shows an increase from 50 to 65. This is a difference of 15.\nBased on the above thought, the answer is:\nBased on the line chart, Online Programs have the highest increase in allocation percentage over the recorded years. It shows an increase from 50% to 65%, representing a total increase of 15%." } ], "id_raw": 1012 }, { "id": "3Wu1OTdjz3pTG1DZ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum allocation for Online Programs across all years recorded?" }, { "from": "gpt", "value": "To find the maximum value for Online Programs across the years, I need to locate {the points on the Online Programs' line}[[0.123, 0.274], [0.327, 0.212], [0.532, 0.181], [0.736, 0.112], [0.940, 0.087]] and extract their corresponding values: 50, 55, 57.5, 63, and 65. The maximum value is 65 corresponding to {the year 2020}[0.940, 0.904].\nBased on the above thought, the answer is:\n65." } ], "id_raw": 1012 }, { "id": "K7dme5k1myWHVpch", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_13.png", "conversations": [ { "from": "human", "value": "\nCan you provide data from the line chart in a table format, categorizing each of the risk types by year?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I would first find {the x-axis tick labels}[[0.096, 0.883, 0.139, 0.943], [0.179, 0.883, 0.221, 0.943], [0.261, 0.883, 0.304, 0.943], [0.344, 0.883, 0.387, 0.943], [0.427, 0.883, 0.469, 0.943], [0.509, 0.883, 0.552, 0.943], [0.592, 0.883, 0.635, 0.943], [0.675, 0.883, 0.717, 0.943], [0.757, 0.883, 0.800, 0.943], [0.840, 0.883, 0.882, 0.943], [0.922, 0.883, 0.965, 0.943]] which represent the column headers or years. Next, I identify {the legend texts}[[0.128, 0.039, 0.244, 0.059], [0.128, 0.069, 0.281, 0.089], [0.128, 0.099, 0.259, 0.119], [0.128, 0.129, 0.259, 0.149]] as the row labels representing different risk types. {The line points}[[0.117, 0.225, 0.944, 0.363], [0.117, 0.214, 0.944, 0.435], [0.117, 0.159, 0.944, 0.380], [0.117, 0.187, 0.944, 0.407]] for each risk are the respective values for each year.\nBased on the above thought, the answer is:\nYear,EM Stock Market,Consumer Confidence,Private Investment,Disposable Income\n2010,100,80,90,85\n2011,95,82,92,87\n2012,93,85,94,90\n2013,94,90,98,95\n2014,96,92,100,97\n2015,98,95,104,100\n2016,100,100,110,105\n2017,103,105,115,110\n2018,107,110,120,115\n2019,112,115,125,120\n2020,118,120,130,125" } ], "id_raw": 1013 }, { "id": "gmqThgsUF7JfqK9z", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_13.png", "conversations": [ { "from": "human", "value": "\nWhich risk factor showed the highest level in the year 2020, and what could have been a possible reason for this?" }, { "from": "gpt", "value": "To determine which factor had the highest risk in 2020, I need to analyze the last points of each line. The corresponding values at {the 2020 point for EM Stock Market}[0.944, 0.363] is 118, {Consumer Confidence}[0.944, 0.214] is 120, {Private Investment}[0.944, 0.159] is 130 and {Disposable Income}[0.944, 0.187] is 125. The highest value is observed for Private Investment. Given the data and the typical factors influencing Private Investment, this could be caused by increased market optimism or favourable fiscal policy measures, leading to higher levels of private investment.\nBased on the above thought, the answer is:\nThe risk factor that showed the highest level in the year 2020 was Private Investment, possibly due to increased market optimism or favourable fiscal policy measures, leading to higher levels of private investment." } ], "id_raw": 1013 }, { "id": "QL6y7DXOQ9mFsROg", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_13.png", "conversations": [ { "from": "human", "value": "\nWhat was the increase in Private Investment from 2010 to 2020?" }, { "from": "gpt", "value": "Firstly, I will locate {the line points for Private Investment}[[0.117, 0.380], [0.200, 0.369], [0.283, 0.358], [0.365, 0.336], [0.448, 0.325], [0.531, 0.302], [0.613, 0.269], [0.696, 0.242], [0.778, 0.214], [0.861, 0.187], [0.944, 0.159]] to find the respective values at 2010 and 2020. The value at 2010 is 90, corresponding to {the point}[0.117, 0.380], and the value at 2020 is 130, corresponding to {the point}[0.944, 0.159]. The increase is therefore 130 - 90 = 40.\nBased on the above thought, the answer is:\n40." } ], "id_raw": 1013 }, { "id": "BCGjtWxop9VPHGp8", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_14.png", "conversations": [ { "from": "human", "value": "\nCan you organize the information from the line chart displaying the spending in Healthcare and Medical Technology into a structured table?" }, { "from": "gpt", "value": "To generate a table from the line chart, I first identify {the x-axis tick labels}[[0.102, 0.918, 0.113, 0.943], [0.311, 0.918, 0.322, 0.943], [0.520, 0.918, 0.531, 0.943], [0.729, 0.918, 0.739, 0.943], [0.938, 0.918, 0.948, 0.943]] which represent the column headers as each year from Year 1 to Year 5. Next, I locate {the legend texts}[[0.917, 0.783, 0.971, 0.803], [0.917, 0.813, 0.971, 0.833], [0.917, 0.843, 0.972, 0.863], [0.917, 0.873, 0.973, 0.893]] as the row labels representing the different policy types. Finally, to fill the table, I locate each {line points} for each policy at the respective years and extract the values.\nBased on the above thought, the answer is:\nPolicy, Year_1, Year_2, Year_3, Year_4, Year_5\nPolicy_A, 35, 36, 38, 42, 45\nPolicy_B, 45, 48, 52, 55, 55\nPolicy_C, 33, 37, 40, 45, 42\nPolicy_D, 55, 58, 67, 62, 70" } ], "id_raw": 1014 }, { "id": "W5acyrz2ZmMt0XOU", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_14.png", "conversations": [ { "from": "human", "value": "\nBy examining the line chart, can you tell which policy is showing a steady rise in effectiveness over the years? Provide your reasoning." }, { "from": "gpt", "value": "To determine which policy is steadily rising in effectiveness, I need to analyze the trend in the line plots for each policy. Looking at {the line for Policy A}[[0.107, 0.618, 0.943, 0.813]], I see a regular increase from Year 1 to Year 5. This suggests a steady rise in effectiveness. \nLooking at {the line for Policy B}[[0.107, 0.422, 0.943, 0.618]], the line shows a quick increase and then becomes flat. Hence, it does not show a steady rise.\nBy examining {the line for Policy C}[[0.107, 0.618, 0.943, 0.852]], the line shows a consistent increase till Year 4 and then a decrease at Year 5. Therefore, it does not exhibit a steady rise.\nFinally, for {the line for Policy D}[[0.107, 0.129, 0.943, 0.422]], the line shows a significant increase at Year 3, then falls slightly in Year 4, and increases again at Year 5, so this also doesn't indicate a steady rise.\nBased on the above thought, the answer is:\nAccording to the analysis of the line chart, Policy A shows a steady rise in effectiveness over the years. Its line plot is progressively increasing, suggesting continual improvement in effectiveness from Year 1 to Year 5 without any significant dips, unlike the line plots for other policies." } ], "id_raw": 1014 }, { "id": "StAcxtKUgBRvpyC4", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in the effectiveness of Policy B from Year 1 to Year 5?" }, { "from": "gpt", "value": "To find the increase in effectiveness of Policy B from Year 1 to Year 5, I first locate {the line points for Policy B}[[0.107, 0.618], [0.316, 0.559], [0.525, 0.481], [0.734, 0.422], [0.943, 0.422]] and extract the corresponding values: 45 for Year 1 and 55 for Year 5. The increase in effectiveness can be calculated as 55 (Year 5) - 45 (Year 1) = 10.\nBased on the above thought, the answer is:\n10." } ], "id_raw": 1014 }, { "id": "Uy34m8jgfecK3oOD", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_16.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you organize the provided data into a table that represents year versus score percentages of different types of training?" }, { "from": "gpt", "value": "For constructing a table from the line chart, I first inspect {the x-axis tick labels}[[0.135, 0.904, 0.162, 0.934], [0.296, 0.904, 0.323, 0.934], [0.458, 0.904, 0.484, 0.934], [0.619, 0.904, 0.645, 0.934], [0.780, 0.904, 0.806, 0.934], [0.941, 0.904, 0.967, 0.934]] serving as column names. The {legend labels}[[0.704, 0.747, 0.793, 0.770], [0.704, 0.782, 0.862, 0.805], [0.704, 0.817, 0.873, 0.840], [0.704, 0.852, 0.966, 0.875]] constitute the row names, representing different types of training. The corresponding values can be extracted by locating the {line points}[[0.135, 0.360], [0.296, 0.333], ..., [0.941, 0.092]] for each training type at each year and corresponding values are annotated.\nBased on the above thought, the answer is:\nYear,IT Training,Soft Skills Training,Leadership Training,Process Improvement Training\n2010,80,75,60,50\n2012,82,80,65,55\n2014,85,78,67,58\n2016,90,83,70,63\n2018,95,88,75,68\n2020,100,93,76,73" } ], "id_raw": 1016 }, { "id": "PPar4wO89a9UbGnF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_16.png", "conversations": [ { "from": "human", "value": "\nBased on the trends in the chart, which type of training showed the most improvement in performance score over this period? Justify your answer." }, { "from": "gpt", "value": "To answer this question, I need to compare the trend lines for each type of training over the period. The {IT Training line}[[0.135, 0.092, 0.941, 0.360]] shows a steady upward trend, reaching from 80 in 2010 to 100 in 2020. The {Soft Skills Training line}[[0.135, 0.186, 0.941, 0.427]] also exhibits an overall increase, but less significantly with a score starting from 75 in 2010 to 93 in 2020. {The Leadership Training line}[[0.135, 0.414, 0.941, 0.628]] shows a slow increase from 60 to 76. Finally, {the Process Improvement Training line}[[0.135, 0.454, 0.941, 0.762]] displayed significant improvement, with an increase from 50 to 73.\nBased on the above thought, the answer is:\nBased on the trends in the chart, the Process Improvement Training shows the most improvement in performance score over this period. This is implemented through an increase of 23 percentage points, the largest observed leap relative to its initial level across all types of training. While the IT Training has experienced a remarkable improvement, it started from a relatively higher base level (80), making the improvement of Process Improvement Training more significant in relative terms." } ], "id_raw": 1016 }, { "id": "8oYyrmCPDY8w3TA5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_16.png", "conversations": [ { "from": "human", "value": "\nWhat was the percentage change for IT Training from 2010 to 2020?" }, { "from": "gpt", "value": "To calculate the percentage change for IT Training from 2010 to 2020, I need to locate the {points in the line plot for IT Training}[[0.135, 0.360], [0.296, 0.333], [0.458, 0.293],[0.619, 0.226],[0.780, 0.159],[0.941, 0.092]]. I can observe that the value for IT Training in 2010 is 80 and in 2020 is 100. Thus, the percentage increase over this period is given by the formula (Final value - Initial value)/Initial value * 100, which gives (100 - 80)/80 * 100 = 25%.\nBased on the above thought, the answer is:\n25%." } ], "id_raw": 1016 }, { "id": "VmetQrRwVsEHOUoh", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_17.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart displaying the financial technology trends into a structured table with years as rows and the event categories as columns?" }, { "from": "gpt", "value": "To create a structured table from the line chart, I initially identify {the x-axis labels}[[0.097, 0.896, 0.140, 0.949], [0.854, 0.896, 0.896, 0.949]] which represent the years 2020 and 2021 respectively. I then locate {the legend}[[0.777, 0.778, 0.973, 0.796], [0.777, 0.804, 0.942, 0.822], [0.777, 0.831, 0.919, 0.848], [0.777, 0.857, 0.870, 0.874]] which helps identify the categories of events as Blockchain and Digital Currency. Observing {the line points for Blockchain}[[0.118, 0.106], [0.875, 0.122]] and {the line points for Digital Currency}[[0.118, 0.184], [0.875, 0.145]], I can gather the data points for both categories corresponding to each year.\nBased on the above thought, the answer is:\nYear,Blockchain Events,Digital Currency Events\n2020,3661,1220\n2021,4069,1333" } ], "id_raw": 1017 }, { "id": "kt7nEIq48URXH5Cp", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_17.png", "conversations": [ { "from": "human", "value": "\nWhich financial technology category demonstrated a stronger growth trend in the years 2020 and 2021 based on the line chart?" }, { "from": "gpt", "value": "To ascertain which category showed stronger growth, I look at both {the line for Blockchain}<[0.118, 0.106, 0.875, 0.122]> and {the line for Digital Currency}<[0.118, 0.184, 0.875, 0.145]>. The Blockchain line moves from 3661 events to 4069 events, showing a smoother and higher rise. In contrast, the Digital Currency line goes from 1220 events to 1333 events, which is a lesser increase. By comparing these growths directly, Blockchain demonstrates a more robust and substantial increase in events, indicating a stronger growth trend.\nBased on the above thought, the answer is:\nBlockchain demonstrated a stronger growth trend than Digital Currency in the years 2020 and 2021. The line chart indicates that Blockchain had a more substantial increase in significant events, suggesting a steadier and more pronounced growth compared to the slight increase observed in Digital Currency." } ], "id_raw": 1017 }, { "id": "OpRTk0I5HuPYMX4Y", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in Blockchain events from 2020 to 2021?" }, { "from": "gpt", "value": "To compute the percentage increase in Blockchain events from 2020 to 2021, I examine {the line points for Blockchain}[[0.118, 0.106], [0.875, 0.122]], which indicate 3661 events in 2020 and 4069 events in 2021. Using the formula for percentage increase [(New Value - Old Value) / Old Value * 100], I calculate [(4069 - 3661) / 3661 * 100].\nBased on the above thought, the answer is:\n11.12%" } ], "id_raw": 1017 }, { "id": "Z2koZI05JHtDRQln", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_18.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the line chart into a structured table with 'Year' as column and different Investment types as rows?" }, { "from": "gpt", "value": "To generate the table, I first identify the {the x-axis tick labels}[0.086, 0.928, 0.128, 0.951], [0.295, 0.928, 0.337, 0.951], [0.504, 0.928, 0.546, 0.951], [0.713, 0.928, 0.755, 0.951], [0.922, 0.928, 0.964, 0.951] as columns headers. Next, {the legend texts}[0.117, 0.034, 0.379, 0.052], [0.117, 0.061, 0.436, 0.078], [0.117, 0.087, 0.336, 0.104], [0.117, 0.113, 0.411, 0.130] represent rows headers, each representing different Investment types. Then, using the points in the respective {line points}[0.107, 0.105], [0.316, 0.147], ..., [0.943, 0.554], [0.107, 0.568], ..., [0.943, 0.719], [0.107, 0.918], ..., [0.943, 0.920], [0.107, 0.394], ..., [0.943, 0.538], I populate the investment amounts within the table for each investment type at each year.\nBased on the above thought, the answer is:\nYear, Local Government Revenue (Billion $), Community Development Spending (Billion $), Average Home Prices (Billion $), Public Infrastructure Investment (Billion $)\n2015, 90.5, 22.5, 0.25, 42.5\n2016, 85.8, 24.2, 0.27, 44.0\n2017, 70.6, 27.5, 0.32, 48.3\n2018, 60.5, 33.2, 0.36, 52.9\n2019, 40.7, 39.2, 0.41, 58.5" } ], "id_raw": 1018 }, { "id": "UJv7LTnsB6lPX55z", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_18.png", "conversations": [ { "from": "human", "value": "\nConsidering the displayed line chart, which investment type showed the most consistent increase over the period?" }, { "from": "gpt", "value": "To determine the investment type with the most consistent increase, I need to analyze the line plots for each investment type. Looking at {the line for Local Government Revenue}[0.107, 0.105, 0.943, 0.554], we can see it decreases over the years, so it does wasn't consistently increasing. \nBased on {the line for Community Development Spending}[0.107, 0.568, 0.943, 0.719], there is a constant increase over the years. Similarly, we see increases in {the line for Average Home Prices}[0.107, 0.918, 0.943, 0.920] and {the line for Public Infrastructure Investment}[0.107, 0.394, 0.943, 0.538], but the increase is not as steep or consistent as Community Development Spending. So, Community Development Spending seems to be the one.\nBased on the above thought, the answer is:\nCommunity Development Spending showed the most consistent increase over the period from 2015 to 2019. The line plot indicates a steady and consistent increase each year, showcasing progressive growth in this investment type." } ], "id_raw": 1018 }, { "id": "Eh5zHCibCycCaXsl", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_18.png", "conversations": [ { "from": "human", "value": "\nHow much has the Local Government Revenue (Billion $) decreased between 2015 and 2019?" }, { "from": "gpt", "value": "To answer this question, I need to determine the local government revenue in 2015 and 2019. Looking at {Local Government Revenue in 2015}[0.107, 0.105] , I see a value of 90.5 billion dollars and at {Local Government Revenue in 2019}[0.943, 0.554], I see a value of 40.7 billion dollars. The decrease between 2015 and 2019 is therefore 90.5 - 40.7 = 49.8 billion dollars.\nBased on the above thought, the answer is:\n49.8 Billion Dollars" } ], "id_raw": 1018 }, { "id": "Ia3gLNfrEqIBjojN", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_51_19.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart into a table format with the years as columns and the cuisine types as rows?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first observe {the x-axis labels}[0.092, 0.928, 0.140, 0.951], [0.212, 0.928, 0.261, 0.951], [0.333, 0.928, 0.381, 0.951], [0.454, 0.928, 0.502, 0.951] for the columns representing years 2018, 2019, 2020, and 2021. Next, I identify {the legend}[0.081, 0.027, 0.342, 0.137] as the row labels which indicate French and Italian cuisines. Then, I look at {the bars}[0.116, 0.882, 0.937, 0.910], {the bars}[0.116, 0.879, 0.937, 0.892] representing values for respective cuisines and years.\nBased on the above thought, the answer is:\nYear,2018,2019,2020,2021\nFrench,0.01,0.85,0.9,0.95\nItalian,0.02,1.1,0.93,1.2" } ], "id_raw": 1019 }, { "id": "jnqP8Zuh97VUxYay", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_51_19.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which cuisine showed a more dramatic change in trends over the years?" }, { "from": "gpt", "value": "Observing the trends over the four years, I analyze the bar fluctuations for both cuisines. The French cuisine bars show values starting from 0.01 moving up to 0.85, then dropping to 0.9, and recovering to 0.95, marked at {the bars for French}[0.116, 0.882, 0.937, 0.910]. In contrast, Italian cuisine starts from 0.02, leaps to 1.1, decreases significantly to 0.93, and then increases to 1.2 as noted in {the bars for Italian}[0.116, 0.879, 0.937, 0.892]. The amplitude of change for Italian cuisine, especially between 2019 and 2020, is more pronounced than that for French cuisine.\nBased on the above thought, the answer is:\nItalian cuisine showed a more dramatic change in trends over the years, with higher swings in popularity, especially noticeable in the leap from 2019 to 2020 followed by a significant drop, before another rise in 2021." } ], "id_raw": 1019 }, { "id": "YkWYG6ZcYRpFmVRr", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_51_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the total change in popularity for French cuisine from 2018 to 2021?" }, { "from": "gpt", "value": "To calculate the total change, I identify the initial value for French cuisine in 2018 by analyzing {the bar for French in 2018}[0.116, 0.882] with a value of 0.01. Then, I locate the value for French cuisine in 2021 at {the bar for French in 2021}[0.937, 0.910] which is 0.95. The total change is calculated as 0.95 - 0.01 = 0.94.\nBased on the above thought, the answer is:\n0.94" } ], "id_raw": 1019 }, { "id": "aV3sHor6btNQakBD", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_0.png", "conversations": [ { "from": "human", "value": "\nCan you structure the data presented in the line chart into a table with tasks as rows and cryptocurrencies as columns?" }, { "from": "gpt", "value": "To generate the structured table data from the line chart, I begin by identifying {the x-axis labels}[0.263, 0.887, 0.316, 0.950] which represent tasks. Next, I use {the legend}[0.325, 0.148, 0.482, 0.301] to recognize the columns named Bitcoin and Blockchain. Finally, to fill this table, I look at each cryptocurrency's line, like {Bitcoin's line points}[0.107, 0.237, 0.472, 0.796] for the specific tasks to pull the corresponding data points.\nBased on the above thought, the answer is:\nTask,Bitcoin,Blockchain\n1,90.12,80.76\n2,78.22,74.84\n3,73.92,67.55\n4,69.33,60.43\n5,65.23,64.37\n6,51.66,60.30\n7,48.28,57.45\n8,46.76,55.76\n9,43.23,51.12\n10,40.85,50.67" } ], "id_raw": 1020 }, { "id": "YfUFTy2RJQ6gPQvm", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_0.png", "conversations": [ { "from": "human", "value": "\nWhy might there be a significant drop in Bitcoin's performance between Task 1 and Task 6 as observed in the chart?" }, { "from": "gpt", "value": "To consider why there is a significant drop in Bitcoin's performance from Task 1 to Task 6, I assess {the line plot of Bitcoin}[0.107, 0.237, 0.310, 0.673]. Initially, Bitcoin starts at 90.12% and declines to 51.66% by Task 6. This could be due to various external market forces affecting cryptocurrency values, such as regulatory news impacting investor sentiment or technological challenges inherent within the blockchain system that Bitcoin operates upon. The steep decline may reflect a more volatile period for Bitcoin during these tasks.\nBased on the above thought, the answer is:\nThe significant drop in Bitcoin's performance from Task 1 to Task 6 is likely due to external market variables that include regulatory challenges or investor responses to news related to cryptocurrencies. This volatility is inherent in the crypto market and can notably impact values as observed in the chart's plotted data." } ], "id_raw": 1020 }, { "id": "VCPAREaep9qes3Uq", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the total percentage decrease for Bitcoin from Task 1 to Task 10?" }, { "from": "gpt", "value": "To calculate the total percentage decrease for Bitcoin from Task 1 to Task 10, I observe {Bitcoin's first point}[0.107, 0.237] at Task 1 and {Bitcoin's last point}[0.472, 0.796] at Task 10. The values are 90.12 at the beginning and 40.85 at the end. The percentage decrease is calculated as ((90.12 - 40.85) / 90.12) * 100.\nBased on the above thought, the answer is:\n54.67" } ], "id_raw": 1020 }, { "id": "e9wTQTwnkRGktTYQ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_1.png", "conversations": [ { "from": "human", "value": "\nCan you organize the data from the line chart showing sentiments towards e-commerce trends by year and sentiment type into a tabular format?" }, { "from": "gpt", "value": "Observing the line chart, I need to parse and categorize the information. I start by focusing on {the x-axis tick labels}[0.059, 0.893, 0.520, 0.931] that indicate different years. For the sentiment types, the {legend items}[0.044, 0.104, 0.159, 0.162] display each line representing synthetic and human-authored sentiments. The {line points}[0.059, 0.391, 0.969, 0.856] help identify specific sentiment scores for each category within the specified year. Correlating this data allows me to construct the table.\nBased on the above thought, the answer is:\nYear,Synthetic Sentiment,Human-authored Sentiment\n2020,-0.7,0.5\n2021,-0.6,0.7\n2022,-0.5,0.8\n2023,-0.3,0.9" } ], "id_raw": 1021 }, { "id": "dfj7ZS64TJB6SbyD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_1.png", "conversations": [ { "from": "human", "value": "\nWhich sentiment type shows more stability in its trend over the recent years towards e-commerce according to the line chart?" }, { "from": "gpt", "value": "Analyzing the stability of sentiment trends involves examining the fluctuation in {the line sections for both sentiment types}[0.059, 0.391, 0.969, 0.856]. Reviewing the plotted data, the line representing synthetic sentiment shows almost a steady decline with fewer abrupt changes, while the line for human-authored sentiment demonstrates more significant jumps and fluctuations. I determine the stability based on fewer sharp changes and a steadier line progression.\nBased on the above thought, the answer is:\nThe synthetic sentiment type shows more stability in its trend towards e-commerce over the recent years, as evidenced by its smoother and less volatile line progression compared to the more fluctuating human-authored sentiment." } ], "id_raw": 1021 }, { "id": "7w5ri9Z7TGHT3kh8", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the average human-authored sentiment value for e-commerce between the years 2020 and 2023?" }, { "from": "gpt", "value": "To compute the average of human-authored sentiment values over the years 2020 to 2023, first, I pinpoint the {human-authored line points}[0.059, 0.856, 0.197, 0.805] corresponding to those years. Extracting the actual sentiment values for each year: 0.5, 0.7, 0.8, 0.9 and calculate the average, (0.5 + 0.7 + 0.8 + 0.9) / 4.\nBased on the above thought, the answer is:\n0.725" } ], "id_raw": 1021 }, { "id": "dtTIlmyXtPYFZ84t", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data represented in the bar chart into a structured table having the 'Category' as rows and 'Change' as the header of the second column?" }, { "from": "gpt", "value": "To generate the table, I would identify {the category labels}[0.088, 0.234, 0.133, 0.254],[0.088, 0.295, 0.145, 0.315],[0.088, 0.357, 0.145, 0.377],[0.088, 0.419, 0.132, 0.439],[0.088, 0.481, 0.138, 0.501],[0.088, 0.543, 0.130, 0.563],[0.088, 0.605, 0.120, 0.625] as rows, representing different categories. The 'Change' in percentages can be extracted from {the lengths of the bars}[0.242, 0.241, 0.497, 0.257],[0.242, 0.302, 0.457, 0.318],[0.242, 0.364, 0.454, 0.380],[0.242, 0.426, 0.457, 0.442],[0.242, 0.488, 0.619, 0.504],[0.242, 0.550, 0.534, 0.566],[0.242, 0.612, 0.730, 0.628].\nBased on the above thought, the answer is:\nCategory,Change\nmoral_values,-20.5\npolitical_bias,-30.3\nreligious_beliefs,-28.6\nmaterialism,23.8\nself_expression,31.6\nprogressive,-30.0\ntechnology,42.2" } ], "id_raw": 1022 }, { "id": "Z5rk8B0k4osc8ITA", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_2.png", "conversations": [ { "from": "human", "value": "\nBased on the bar chart, which category showed the highest increase in society?" }, { "from": "gpt", "value": "To determine which category showed the highest increase, I need to examine {the bars representing the different categories}[0.242, 0.241, 0.497, 0.257],[0.242, 0.302, 0.457, 0.318],[0.242, 0.364, 0.454, 0.380],[0.242, 0.426, 0.457, 0.442],[0.242, 0.488, 0.619, 0.504],[0.242, 0.550, 0.534, 0.566],[0.242, 0.612, 0.730, 0.628]. Bars extending to the right show positive change, and among these, the category with the longest bar indicates the highest increase.\nBased on the above thought, the answer is:\n'Technology' showed the highest increase in society, as it is represented by the longest bar extending to the right in the bar chart." } ], "id_raw": 1022 }, { "id": "JoobF0w5fba3MyI5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_2.png", "conversations": [ { "from": "human", "value": "\nBy what percentage did 'materialism' increase according to the bar graph?" }, { "from": "gpt", "value": "To find the percentage increase in 'materialism', I would need to identify {the bar representing 'materialism'}[0.242, 0.488, 0.619, 0.504] and determine the corresponding value. The length and direction of the bar indicate that 'materialism' saw a positive change, and the value of this change can be extracted.\nBased on the above thought, the answer is:\n23.8%." } ], "id_raw": 1022 }, { "id": "5hozHYCAym8V5QmH", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_3.png", "conversations": [ { "from": "human", "value": "\nCan you extract the numerical data across all tasks into a table, with each trend represented as a column and tasks as rows?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I identify {the x-axis tick labels}[[0.075, 0.911, 0.082, 0.934], [0.119, 0.911, 0.126, 0.934], [0.163, 0.911, 0.170, 0.934], [0.207, 0.911, 0.214, 0.934], [0.251, 0.911, 0.258, 0.934], [0.295, 0.911, 0.302, 0.934], [0.339, 0.911, 0.346, 0.934], [0.382, 0.911, 0.390, 0.934], [0.426, 0.911, 0.434, 0.934], [0.467, 0.911, 0.481, 0.934]] as the row labels signifying the various tasks. Then I look for {the legend texts}[[0.306, 0.083, 0.483, 0.106], [0.788, 0.083, 0.977, 0.106]] as the column labels that represent the two trends - Global Trade Volume and Supply Chain Performance Index. To populate the table, I analyze the y-coordinates of {the line points}[[0.079, 0.163], [0.573, 0.398]...] for each trend at the respective tasks and extract the values.\nBased on the above thought, the answer is:\nTask,Global Trade Volume (billion $),Supply Chain Performance Index\n1,100,80\n2,110,82\n3,120,85\n4,118,86\n5,121,80\n6,125,79\n7,130,80\n8,132,85\n9,125,88\n10,120,90" } ], "id_raw": 1023 }, { "id": "Ai7GWUCmB86UF65n", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_3.png", "conversations": [ { "from": "human", "value": "\nBased on the line charts, which trend shows a consistent rise throughout the tasks? Justify your answer." }, { "from": "gpt", "value": "To determine the trend that shows a consistently rising pattern across all tasks, I need to look at the movement on {the Global Trade Volume line}[[0.079, 0.163, 0.474, 0.342]] and {the Supply Chain Performance Index line}[0.573, 0.398, 0.968, 0.460]. If the successive y-coordinates of the points on the line chart consistently increase, it would indicate a continuous rise.\nBased on the above thought, the answer is:\nThe 'Global Trade Volume' shows a consistently rising pattern across the tasks. There is a consistent increase from the first task (100 billion dollars) to the eighth task (132 billion dollars), indicating a steady rise. Although there is a minor dip in tasks 4 and 9, the general trend is of continued growth in global trade volume." } ], "id_raw": 1023 }, { "id": "3rJJk6JcyP980IFR", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_3.png", "conversations": [ { "from": "human", "value": "\nWhich task has the most variance between Global Trade Volume and Supply Chain Performance Index?" }, { "from": "gpt", "value": "To find the task with the most variance between the two parameters, I analyze {the line points for Global Trade Volume}[[0.079, 0.163], [0.123, 0.286], [0.167, 0.230], [0.210, 0.242], [0.254, 0.225], [0.298, 0.202], [0.342, 0.174], [0.386, 0.163], [0.430, 0.202], [0.474, 0.230]] and {the line points for Supply Chain Performance Index} [[0.573, 0.454], [0.616, 0.443], [0.660, 0.426], [0.704, 0.421], [0.748, 0.454], [0.792, 0.460], [0.836, 0.454], [0.880, 0.426], [0.924, 0.410], [0.968, 0.398]] at each task and calculate the difference. The task with the highest difference will have the most variance.\nBased on the above thought, the answer is:\nTask 1" } ], "id_raw": 1023 }, { "id": "bz82UGSnLwwPfRin", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_4.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table containing annual service quality scores for each of the three types of services, using the line chart data?" }, { "from": "gpt", "value": "For creating the table, I first look at {the x-axis tick labels}[[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903], [0.397, 0.773, 0.504, 0.934], [0.525, 0.773, 0.607, 0.896]], which represent the years from 2011 to 2015. Next, I see {the legend texts}[[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156], [0.884, 0.167, 0.946, 0.189]] which define three types of services, Healthcare, Education, and Public Transit. I'll record {the line points}[[0.102, 0.470], [0.218, 0.082], ..., [0.798, 0.243]] for each year and each type of service to complete the data in the table.\nBased on the above thought, the answer is:\nYear,Healthcare,Education,Public Transit\n2011,7.8,7.2,8.5\n2012,8.0,7.4,8.0\n2013,8.2,7.6,7.5\n2014,8.4,7.8,7.0\n2015,8.1,8.0,6.5" } ], "id_raw": 1024 }, { "id": "diSccWOXSgMkIgdl", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_4.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which service domain shows the most consistent improvement in quality scores over the years and why?" }, { "from": "gpt", "value": "I look at the lines for each service domain. The line for {Education}[0.102, 0.211, 0.798, 0.729] shows a consistent upward trend from 7.2 to 8.0 over the five years. However, the line for {Healthcare}[0.102, 0.470, 0.798, 0.696] though starting with a higher score at 7.8, sees a drop in the last year. Lastly, the line for {Public Transit}[0.102, 0.599, 0.798, 0.632] shows a pronounced decline over the years.\nBased on the above thought, the answer is:\nThe Education service domain shows the most consistent improvement in quality scores over the years. This is evident from its line graph that consistently rises from 7.2 in 2011 to 8.0 in 2015. On the other hand, although Healthcare started with a higher score, it witnessed a drop in the last year while Public Transit has seen a decrease throughout the period." } ], "id_raw": 1024 }, { "id": "sAUFuhIMvcbnKeQV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_4.png", "conversations": [ { "from": "human", "value": "\nWhat's the total growth in the quality score of Education service over the 5-year period?" }, { "from": "gpt", "value": "To find the total growth in Education service quality, I need to check the {first line point for Education}[0.102, 0.354] and the {last line point for Education}[0.798, 0.519] which corresponds to the quality score in years 2011 and 2015 respectively. The difference in these points gives the total growth which is 8.0 - 7.2 = 0.8.\nBased on the above thought, the answer is:\n0.8." } ], "id_raw": 1024 }, { "id": "3fwQuYrOlywbSKrM", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_5.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the line chart into a detailed table with the subcultures listed as rows and their change in popularity over time as columns?" }, { "from": "gpt", "value": "To develop a table from the line chart, recognizing {the x-axis tick labels}[0.056, 0.922, 0.479, 0.985] which signify different subcultures is essential. Inspecting the {the legend}[0.337, 0.111, 0.479, 0.226] next helps me identify specific subcultures associated with each line. Inspecting each line, for instance, {the line for Boho}[0.056, 0.461, 0.966, 0.831] allows me to pinpoint the change in popularity at evenly spaced increments corresponding to the timeline on the x-axis, extracting precise values for the trend over time.\nBased on the above thought, the answer is:\nSubculture,Start,1 Month,3 Months,6 Months,1 Year\n80s Punk,-10,-15,-18,-19,-20\nHipster,-5,-7,-8,-9,-10\nGothic,5,15,22,28,30\nSteampunk,8,18,28,36,40\nBoho,-20,-35,-40,-45,-50\nHarajuku,10,25,40,50,60" } ], "id_raw": 1025 }, { "id": "m3bmMYXJgXKfos8r", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_5.png", "conversations": [ { "from": "human", "value": "\nCan you explain why the Boho fashion trend might have seen a significant decline according to the line chart?" }, { "from": "gpt", "value": "Analyzing {the line corresponding to Boho}[0.576, 0.461, 0.966, 0.831], it continuously descends throughout the period. A plausible explanation, assessed visually through the line's consistent decline amid seasonal markers, could be a shift in consumer preferences or the cyclic nature of fashion. Additionally, it might be influenced by external cultural or economic factors not directly visible in the graph but suggested by the steady negative trend.\nBased on the above thought, the answer is:\nThe significant decline in Boho fashion trend seen in the line chart may be attributed to seasonal changes affecting fashion trends, shifts in consumer preferences, or possibly external cultural and economic impacts that resonate less with Boho style over the evaluated period." } ], "id_raw": 1025 }, { "id": "zvKoGHu5o48YFnGy", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum increase in popularity observed from the chart for any subculture throughout the entire period?" }, { "from": "gpt", "value": "To determine the maximum increase, I examine {the line with highest final value}[0.576, 0.128, 0.966, 0.535], which indicates the highest increase in popularity. Following the data points, I find the highest value achieved, corresponding to {Harajuku's line at 1 year}[0.966, 0.461].\nBased on the above thought, the answer is:\n60" } ], "id_raw": 1025 }, { "id": "jAZg5dnkwDP4XcpB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart that compares monthly Retail Sales for both In-store and Online into a structured table?" }, { "from": "gpt", "value": "To extract and present the data in the table format from the chart shown, I first looked at {the x-axis tick labels}[0.257, 0.942, 0.285, 0.970] which represent the months. Then, I identified {the line data for In-store sales}[0.067, 0.566, 0.475, 0.611] and {the line data for Online sales}[0.562, 0.860, 0.970, 0.865] to capture the values across different months indicated by their plotted points. These provided a clear set of monthly data for both sales channels.\nBased on the above thought, the answer is:\nMonth,In-store Sales,Online Sales\n1,-700,60\n2,-710,61\n3,-705,62\n4,-750,63\n5,-800,65\n6,-780,66\n7,-760,64\n8,-740,63\n9,-720,62\n10,-690,59" } ], "id_raw": 1026 }, { "id": "UMFKz43dOj0ZzPVZ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_6.png", "conversations": [ { "from": "human", "value": "\nWhat trending pattern is evident for Government Support throughout the months?" }, { "from": "gpt", "value": "Observing {the line plot for Government Support}[0.562, 0.866, 0.970, 0.869], the initial value begins at 60 and ends at 59, with slight fluctuations peaking at 66 and dropping to the lowest at 59 twice. The line gently fluctuates upward towards the mid-months before tapering back down. This indicates a pattern of marginal increase followed by a gentle decrease by year-end.\nBased on the above thought, the answer is:\nThe pattern evident for Government Support over the months is one of moderate fluctuation, where there is a gentle increase in the middle of the year, peaking at 66, and a subsequent decrease towards the end, tapering back to just below the starting value." } ], "id_raw": 1026 }, { "id": "9gL253WgU1l78IoB", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_6.png", "conversations": [ { "from": "human", "value": "\nHow much did the R&D Spend increase from the 1st to the 10th month?" }, { "from": "gpt", "value": "To calculate the increase in R&D Spend over the months, I analyze the values at the start and end of the period. {The point for R&D Spend in the 1st month}[0.067, 0.279] is 1500 and for {the 10th month}[0.475, 0.341] is 1350. The difference is then 1350 - 1500.\nBased on the above thought, the answer is:\n-150." } ], "id_raw": 1026 }, { "id": "sYRyWXRNmWOshsuf", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_7.png", "conversations": [ { "from": "human", "value": "\nCould you convert the graph depiction of Data Science and Robotics trends across various stages into a structured data table?" }, { "from": "gpt", "value": "To construct a structured data table, I first need to locate {the x-axis labels}[[0.022, 0.893, 0.114, 0.921], [0.143, 0.893, 0.235, 0.921], [0.274, 0.893, 0.345, 0.921], [0.368, 0.893, 0.493, 0.921]] which represent the columns or stages. Subsequently, I identify the legend texts that represent the two rows or innovation areas, namely {Data Science Innovation}[0.328, 0.100, 0.440, 0.128] and {Robotics Innovation}[0.844, 0.100, 0.935, 0.128]. For the row values, I will identify {the datapoints for each line}[[0.068, 0.540], [0.189, 0.669], [0.310, 0.337], [0.431, 0.386], [0.563, 0.430], [0.684, 0.556], [0.805, 0.228], [0.926, 0.499]] and extract the corresponding values.\nBased on the above thought, the answer is:\nInnovation Area,Subdued Expansion,Accelerated Decline,Inexorable Rise,Unpredictable Fluctuations\nData Science Innovation,8.5,5.3,13.5,12.3\nRobotics Innovation,11.2,8.1,16.2,9.5" } ], "id_raw": 1027 }, { "id": "m0T7glnxzJVcH6CC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_7.png", "conversations": [ { "from": "human", "value": "\nBased on the trends displayed in the chart, which innovation area appears to have experienced a more drastic change upwards or downwards? Describe the reasoning." }, { "from": "gpt", "value": "To answer this question, I need to compare the fluctuations experienced by {Data Science Innovation}[0.068, 0.337, 0.431, 0.669] and {Robotics Innovation}[0.563, 0.228, 0.926, 0.556] across different stages. For Data Science Innovation, there is a decrease from 8.5 to 5.3, followed by an increase to 13.5, and then a slight decrease to 12.3. This represents a drastic upward change. In contrast, for Robotics Innovation, there is an increase from 11.2 to 16.2, followed by a decrease to 9.5. Even though there is a significant increase at the start, the overall change is presented as a decrease, and the fluctuations are less pronounced when compared to Data Science Innovation.\nBased on the above thought, the answer is:\nBased on the trends shown in the chart, Data Science Innovation appears to have experienced a more drastic change. There is a notable decline in the initial stage, followed by a drastic increase, and a minor decrease towards the end. This variance in ups and downs denotes a more drastic change when compared with the changes in Robotics Innovation." } ], "id_raw": 1027 }, { "id": "pnxzHdJbOF6LW5cU", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in value between the highest and lowest points for the Data Science Innovation trend?" }, { "from": "gpt", "value": "To answer this question, we need to locate {the points of the Data Science Innovation trend}[[0.068, 0.540], [0.189, 0.669], [0.310, 0.337], [0.431, 0.386]] with the associated values of 8.5, 5.3, 13.5, and 12.3 respectively. The highest point is 13.5 happening at {Inexorable Rise}[0.310, 0.883] and the lowest point is 5.3 which happens at {Accelerated Decline}[0.189, 0.883]. Therefore, the difference would be 13.5 - 5.3 = 8.2.\nBased on the above thought, the answer is:\n8.2." } ], "id_raw": 1027 }, { "id": "NOV9ayVnDHMUDxbW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, can you extract the data into a table format with type of Technology Innovations as columns and Groups as rows?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[[0.077, 0.903, 0.096, 0.931], [0.174, 0.903, 0.194, 0.931], [0.272, 0.903, 0.289, 0.931], [0.369, 0.903, 0.394, 0.931], [0.467, 0.903, 0.489, 0.931]], which represent the column headers or the type of Technology Innovations. Next, I identify the groups as the row labels, these are 'EdTech companies' and 'Startups'. To populate the values, I locate {the line points}[[0.077, 0.112], [0.174, 0.459], ..., [0.467, 0.161]] for each group with the respective Technology Innovations and extract the percent values annotated.\nBased on the above thought, the answer is:\nGroups,Quantum Computing,Augmented Reality,Artificial Intelligence,e-Learning Software,Multimedia Networking,Interactive Whiteboard,Virtual Classroom\nEdTech Companies,28,36,43,48,55,60,65\nStartups,32,42,50,59,68,75,83" } ], "id_raw": 1028 }, { "id": "WbjZCAsrN6Fb5Tg7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_8.png", "conversations": [ { "from": "human", "value": "\nWhich Technology Innovation does EdTech Companies interact with the least and why might this be?" }, { "from": "gpt", "value": "To determine which Technology Innovation EdTech Companies interact with the least, I need to analyze the line plot for EdTech companies. Looking at {the line for EdTech Companies}[[0.077, 0.459, 0.467, 0.161]], Quantum Computing has the lowest interaction rate of 28. Quantum Computing is a nascent field and might still be in a research phase with many complexities to tackle before being used extensively in the education sector. This might explain the lower interaction with this technology innovation by EdTech companies.\nBased on the above thought, the answer is:\nEdTech Companies interact the least with Quantum Computing. This could be because Quantum Computing is a relatively new and complex field and may still be in the research phase with many complexities to tackle before it can be extensively used in the education sector." } ], "id_raw": 1028 }, { "id": "Nw1sVTixqntmAcQC", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_8.png", "conversations": [ { "from": "human", "value": "\nHow much more does Startups interact with Augmented Reality compared to EdTech Companies?" }, { "from": "gpt", "value": "To find the difference in interaction rates with Augmented Reality between Startups and EdTech companies, I need to locate {the line points for Augmented Reality}[[0.174, 0.459], [0.174, 0.112]] and extract the corresponding values: 36 for EdTech companies and 42 for Startups. The difference between the interaction rates of Startups and EdTech companies is therefore 42 - 36 = 6.\nBased on the above thought, the answer is:\n6% more." } ], "id_raw": 1028 }, { "id": "e3lw1P6mgCPRBRNU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_9.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart comparing the synthetic data and human data into a structured table with distinct columns for each category and rows for each type of data?" }, { "from": "gpt", "value": "First, I focus on {the bars of the synthetic data}[0.048, 0.343, 0.235, 0.557] and their corresponding negative values from the left side of the chart. These bars also align with specific categories labeled next to them. I perform the same analysis for {the bars of the human data}[0.765, 0.343, 0.952, 0.557], reviewing their placement on the right side of the chart with positive values. By reading the labels directly from the bars, I configure the data for the table as structured.\nBased on the above thought, the answer is:\nCategory, Synthetic Data, Human Data\nSustainable Energy, -30, 65\nWaste Reduction, -45, 85\nCommunity, -70, 99\nSustainable Sourcing, -85, 120" } ], "id_raw": 1029 }, { "id": "5ZAHQb9NinKuhKbr", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_9.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which category shows the most significant difference between the company's performance and the industry average?" }, { "from": "gpt", "value": "I analyze the length and label values of each bar for both synthetic and human data. Observing {the bars for Sustainable Sourcing}[0.045, 0.402, 0.240, 0.459] and their corresponding label values of -85 for synthetic data and 120 for human data, the difference is the greatest among all categories. The substantial difference is visually confirmed by the length discrepancies between these two bars compared to others.\nBased on the above thought, the answer is:\nThe 'Sustainable Sourcing' category shows the most significant difference between the company\u2019s performance and the industry average as indicated by the large contrast in values and bar lengths between the synthetic data and human data." } ], "id_raw": 1029 }, { "id": "N4oLxtnPRaacEL3S", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage difference in performance on 'Community' between the synthetic data and human data?" }, { "from": "gpt", "value": "To calculate the percentage difference, I begin by locating {the 'Community' category bar for synthetic data}[0.052, 0.615, 0.237, 0.672] which indicates a value of -70, and {the 'Community' category bar for human data}[0.763, 0.615, 0.948, 0.672] with a value of 99. Using the formula percentage difference = ((human data - synthetic data) / synthetic data) * 100, I plug in the values: ((99 - (-70)) / -70) * 100 = 240%\nBased on the above thought, the answer is:\n240%" } ], "id_raw": 1029 }, { "id": "O36sBIfiIrS5Pso5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the market share percentages for different industries across the years into a structured table?" }, { "from": "gpt", "value": "Given the structure of the data in the line chart, with years marked on {the x-axis}[0.259, 0.943, 0.286, 0.975] and percentage values annotated for each line point, I will summarize this into a structured table. Reviewing each line, such as {the line for Agriculture}[0.070, 0.197, 0.475, 0.281] and {the line for Retail}[0.070, 0.335, 0.475, 0.461], I will extract the annual market share values for all industries, including Agriculture, Retail, Manufacturing, and Services.\nBased on the above thought, the answer is:\nYear,Agriculture,Retail,Manufacturing,Services\n2010,15.7,10.5,7.8,5.5\n2011,15.1,10.9,8.2,5.8\n2012,16.3,10.7,8.7,6.1\n2013,16.8,11.2,9.0,6.5\n2014,15.9,11.8,9.5,6.7\n2015,15.3,12.1,10.0,7.1\n2016,15.1,12.4,11.0,7.3\n2017,14.8,12.7,11.5,7.6\n2018,15.7,13.0,12.0,7.9\n2019,15.5,13.5,12.3,8.2" } ], "id_raw": 1030 }, { "id": "Wymq0aq5KdoU5Wn1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_10.png", "conversations": [ { "from": "human", "value": "\nWhy does the Manufacturing industry show a consistent growth trend from 2010 to 2019?" }, { "from": "gpt", "value": "Analyzing the plot line for {Manufacturing from 2010 to 2019}[0.565, 0.386, 0.970, 0.575], the trendline progresses upward linearly each year. The data points start at lower market share values around 7.8 in 2010 and consistently increase each year until reaching a peak of 12.3 in 2019. This consistent increase suggests a stable growth over the decade, potentially reflecting the industry\u2019s expansion and increased production capacity or market demand.\nBased on the above thought, the answer is:\nThe Manufacturing industry shows a consistent growth trend from 2010 to 2019 likely due to increased production capacity and greater market demand over the years, as indicated by the upward progression of the annual market share values on the line chart." } ], "id_raw": 1030 }, { "id": "p8qGQoW6AeyMiDnK", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the average market share for the Manufacturing industry from 2010 to 2019?" }, { "from": "gpt", "value": "To compute the average, I'll first extract the annual market share values for {Manufacturing}[0.565, 0.386, 0.970, 0.575]. The values collected are: 7.8, 8.2, 8.7, 9.0, 9.5, 10.0, 11.0, 11.5, 12.0, 12.3. Calculating the average involves summing these figures and then dividing by the number of years, which is 10.\nBased on the above thought, the answer is:\n10.0" } ], "id_raw": 1030 }, { "id": "NvWFa8GwNr9MK3UP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_11.png", "conversations": [ { "from": "human", "value": "\nExtract the performance figures for the production process stages represented in the line chart." }, { "from": "gpt", "value": "Let's start by identifying the stages along {the x-axis tick labels}[[0.101, 0.821, 0.112, 0.868], [0.142, 0.821, 0.153, 0.868], [0.183, 0.821, 0.193, 0.868], [0.223, 0.821, 0.234, 0.868], [0.264, 0.821, 0.275, 0.868], [0.304, 0.821, 0.315, 0.868], [0.345, 0.821, 0.356, 0.868], [0.386, 0.821, 0.397, 0.868], [0.426, 0.821, 0.437, 0.868], [0.461, 0.821, 0.483, 0.868]], these are our column headers. The two methods of efficiency evaluation represented in the chart are 'Manual' and 'Machines'. These are identified in {the legend texts}[[0.355, 0.166, 0.475, 0.213],[0.822, 0.166, 0.966, 0.213]], these will form our row labels. Now, the line graphs for these methods have various points plotted on them for different stages. These can be found at {the line points for Asian Recipes}[0.107, 0.164, 0.472, 0.459] and {the line points for Western Recipes}[0.597, 0.287, 0.963, 0.493]. The y values at these points represent the performance figures for the corresponding method of evaluation at each stage.\nBased on the above thought, the answer is:\nMethod,Fabrication,Assembling,Packaging,Dispatching,Marketing,Designing,Testing\nMachines,89,91,92,87,95,80,70\nManual,70,75,79,77,78,82,52" } ], "id_raw": 1031 }, { "id": "rJ9H5coQg1S4WMaf", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_11.png", "conversations": [ { "from": "human", "value": "\nWhich method of efficiency evaluation shows a sharp decline in the 'Testing' stage, and what does this suggest?" }, { "from": "gpt", "value": "To answer this, I need to look at the 'Testing' point on both line graphs found at {['Testing'] point on Machines line}[0.472, 0.459] and {['Testing'] point on Manual line}[0.963, 0.493]. The decline on the 'Testing' stage is sharp for the 'Manual' method. This suggests that Testing through Manual method is significantly less efficient compared to other stages indicating a possible lack of expertise or need for more structured processes during this stage.\nBased on the above thought, the answer is:\nThe 'Manual' method of efficiency evaluation shows a sharp decline in the 'Testing' stage. This suggests that the Testing stage might be fraught with inefficiencies when conducted manually, indicating a need to possibly automate this stage or bring in more skilled professionals to oversee this task." } ], "id_raw": 1031 }, { "id": "kmJMVHSoGRQSI8d4", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_11.png", "conversations": [ { "from": "human", "value": "\nWhat's the difference in performance figures for the 'Designing' stage between the 'Machines' and 'Manual' methods of evaluation?" }, { "from": "gpt", "value": "The 'Designing' stage can be located on the x-axis, and the corresponding performance for both the 'Machines' and 'Manual' methods can be identified at the corresponding points on their respective line graphs. These points can be found at {['Designing'] point on the Machines line}[0.350, 0.364] and {['Designing'] point on the Manual line}[0.841, 0.439]. The difference in performance would be the absolute difference between the y-coordinates of these two points on the graph.\nBased on the above thought, the answer is:\n2." } ], "id_raw": 1031 }, { "id": "iQrno9dZmu9MICZC", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_12.png", "conversations": [ { "from": "human", "value": "\nCan you provide a data table showing the usage trends of Facebook and Twitter over the weeks from the data represented in the line chart?" }, { "from": "gpt", "value": "To construct the table, I will first identify and read the weeks from {the x-axis tick labels}[0.047, 0.903, 0.056, 0.931], [0.131, 0.903, 0.140, 0.931], [0.216, 0.903, 0.225, 0.931], [0.300, 0.903, 0.309, 0.931], [0.385, 0.903, 0.394, 0.931], [0.465, 0.903, 0.482, 0.931]. Next, I will extract the usage values for Facebook and Twitter which are represented by {the line for Facebook}[0.094, 0.149, 0.474, 0.484] and {the line for Twitter}[0.094, 0.387, 0.474, 0.521] respectively.\nBased on the above thought, the answer is:\nWeek,Facebook,Twitter\n1,1000,500\n2,950,520\n3,900,540\n4,850,560\n5,800,580\n6,750,600" } ], "id_raw": 1032 }, { "id": "jMJxvDgVThrR4Yrd", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_12.png", "conversations": [ { "from": "human", "value": "\nWhich platform demonstrates a consistently increasing trend according to this visualization?" }, { "from": "gpt", "value": "To determine which platform has a consistently increasing trend, I will examine the slope and direction of each line. Observing {the line for Twitter}[0.094, 0.387, 0.474, 0.521] and {the line for Instagram}[0.586, 0.674, 0.966, 0.707], it's evident that while both demonstrate an increase, Twitter shows a more consistent positive slope without any decline, as evident in week by week increments from 500 in Week 1 to 680 in Week 10.\nBased on the above thought, the answer is:\nTwitter demonstrates a consistently increasing trend over the weeks according to this visualization, showing incremental increases from the start to the end period without any dips, unlike other platforms." } ], "id_raw": 1032 }, { "id": "LgquNE05FMIPXkhG", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the total change in Instagram platform usage from Week 1 to Week 10?" }, { "from": "gpt", "value": "To calculate the total change, I identify the initial usage at Week 1 and the final usage at Week 10 from {the line for Instagram}[0.586, 0.674, 0.966, 0.707]. The initial usage was 250 and the final usage was 295. The total change is obtained by subtracting the initial usage from the final usage.\nBased on the above thought, the answer is:\n45" } ], "id_raw": 1032 }, { "id": "QPT7THYDPtjgr2MR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the provided line chart using Facebook, Twitter, and Instagram as headers and list their user counts for each year from 2015 to 2020?" }, { "from": "gpt", "value": "I observe the line chart to identify the relevant data points. I locate {the x-axis tick labels}[0.112, 0.821, 0.123, 0.868], [0.151, 0.821, 0.162, 0.868], [0.191, 0.821, 0.202, 0.868], [0.230, 0.821, 0.241, 0.868], [0.270, 0.821, 0.281, 0.868], [0.309, 0.821, 0.320, 0.868] for the years. I also observe the legends: {Facebook}[0.164, 0.166, 0.271, 0.213], {Twitter}[0.164, 0.236, 0.273, 0.283], and {Instagram}[0.654, 0.166, 0.763, 0.213] for platform identification. Finally, I extract the respective user counts by linking the data points on {the lines for Facebook, Twitter, and Instagram}[0.117, 0.550], [0.157, 0.507], [0.196, 0.465], [0.236, 0.401], [0.275, 0.337], [0.315, 0.295] for each platform across the years.\nBased on the above thought, the answer is:\nYear,Facebook,Twitter,Instagram\n2015,200M,150M,90M\n2016,240M,180M,110M\n2017,280M,210M,130M\n2018,320M,230M,180M\n2019,370M,260M,240M\n2020,430M,290M,340M" } ], "id_raw": 1033 }, { "id": "SCNofRgUTKFqnOdJ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_13.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform exhibited the most consistent growth over the period 2015 to 2020?" }, { "from": "gpt", "value": "To assess the consistency in growth, I examine the slopes of {the lines for Facebook}[0.117, 0.550, 0.473, 0.295], {Twitter}[0.117, 0.635, 0.473, 0.372], and {Instagram}[0.117, 0.741, 0.473, 0.192] across the years. Facebook shows a consistent upward trajectory with an increasingly steep slope, indicating consistent user growth over each year. Twitter also shows a consistent growth, but the slope is less steep compared to Facebook. Instagram, however, shows a variable slope with a significant spike in 2020 which indicates inconsistent growth rates, especially with a very steep increase at the end.\nBased on the above thought, the answer is:\nTwitter, as evidenced by its relatively even slope, exhibited the most consistent growth across the years 2015 to 2020. This steady slope shows a consistent gain in user count each year without dramatic spikes or drops." } ], "id_raw": 1033 }, { "id": "qdyvTl7D1UJxJglS", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_13.png", "conversations": [ { "from": "human", "value": "\nWhat was the percentage increase in Instagram's user count from 2015 to 2020?" }, { "from": "gpt", "value": "To calculate the percentage increase, I first identify the user counts for Instagram from {the line for Instagram}[0.117, 0.167, 0.473, 0.087] at the beginning and end of the selected period, observing the values as 90 million in 2015 and 340 million in 2020. I then use the formula: [(Final value - Initial value) / Initial value] * 100 to find the percentage increase. Calculations are as follows: [(340 - 90) / 90] * 100.\nBased on the above thought, the answer is:\n277.78%" } ], "id_raw": 1033 }, { "id": "uX8G0tYCgpvGh2PQ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_14.png", "conversations": [ { "from": "human", "value": "\nCan you transform the sales data from the line chart depicting sales types across quarters into a structured table?" }, { "from": "gpt", "value": "Observing the data, I locate {the legend items for Hybrid and Electric Sales}[0.074, 0.091, 0.176, 0.183] and then disentangle {the points corresponding to these sales}[0.088, 0.113, 0.471, 0.603]. Recognizing the pattern across quarters and sales data, I construct the table by correlating quarter labels from {the x-axis tick labels}[0.081, 0.895, 0.095, 0.923], [0.136, 0.895, 0.150, 0.923], ..., [0.451, 0.895, 0.492, 0.923] with sales units mentioned at these points.\nBased on the above thought, the answer is:\nQuarter,Hybrid Sales,Electric Sales\nQ1,150000,90000\nQ2,140000,110000\nQ3,160000,120000\nQ4,175000,130000\nQ1_next,190000,140000\nQ2_next,210000,160000\nQ3_next,225000,180000\nQ4_next,240000,200000" } ], "id_raw": 1034 }, { "id": "F1ctjYjujjCCXeal", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_14.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the sales trends of various vehicle types over the period?" }, { "from": "gpt", "value": "To understand the sales trends, I examine the lines plotted for each vehicle type, notably {Hybrid and Electric lines}[0.088, 0.113, 0.471, 0.603] and {Gasoline and Diesel lines}[0.582, 0.113, 0.966, 0.440]. The visual analysis shows a rising trend for Hybrid and Electric sales indicating gaining popularity or preference. Meanwhile, Gasoline and Diesel sales exhibit a gradual decline, possibly due to environmental concerns or advancements in alternative energy vehicles. This is assessed by observing the higher sales points at the beginning and lower points toward the end for these fuel types.\nBased on the above thought, the answer is:\nThe sales trends suggest an increasing preference for Hybrid and Electric vehicles contrasted with a declining demand for Gasoline and Diesel vehicles over the period illustrated. This could reflect shifting consumer preferences toward more environmentally-friendly options or advancements in alternative fuel technologies." } ], "id_raw": 1034 }, { "id": "Erv28r2txycDwNZv", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage decrease in Diesel Sales from Q1 to Q4 of the next year?" }, { "from": "gpt", "value": "To ascertain the percentage decrease, I need to first identify the Diesel Sales in Q1 and Q4 of the subsequent year by linking {the Diesel sales points}[0.582, 0.701, 0.966, 0.848]. The sales start at 70000 for Q1 at {the point}[0.582, 0.701] and decrease to 25000 by Q4_next detailed at {this point}[0.966, 0.848]. Calculating the percentage decrease from initial 70000 to final 25000 by formula: ((70000-25000)/70000)*100.\nBased on the above thought, the answer is:\n64.29%" } ], "id_raw": 1034 }, { "id": "rvC0YZLP6qZK59vd", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_15.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart showing incidents and visitors at various health facilities into a structured table format with facilities listed in rows and data types as columns?" }, { "from": "gpt", "value": "Observing the chart gives me insight into the two distinct lines representing {Incidents}[0.088, 0.124, 0.491, 0.805] and {Visitors}[0.579, 0.124, 0.981, 0.805]. The x-axis details the health facilities at points {General Hospital}[0.101, 0.821, 0.112, 0.868], {City Hospital}[0.142, 0.821, 0.153, 0.868], {Regional Hospital}[0.183, 0.821, 0.193, 0.868], {Clinic}[0.223, 0.821, 0.234, 0.868], and {Dental Clinic}[0.264, 0.821, 0.275, 0.868]. Using the y-values {represented on the left y-axis}[0.019, 0.235, 0.043, 0.694], I can extract exact figures from each line at every facility.\nBased on the above thought, the answer is:\nFacility,Incidents,Visitors\nGeneral Hospital,50,1000\nCity Hospital,65,1200\nRegional Hospital,60,1100\nClinic,70,1150\nDental Clinic,75,1300" } ], "id_raw": 1035 }, { "id": "KrhIOyryznwwo6bI", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_15.png", "conversations": [ { "from": "human", "value": "\nWhich health facility displays the closest incident to visitor ratio based on the chart?" }, { "from": "gpt", "value": "To determine this, I compare the lines {for Incidents}[0.088, 0.124, 0.491, 0.805] and {Visitors}[0.579, 0.124, 0.981, 0.805] specified for each facility. Observing the y-values for both lines at each facility: {General Hospital}[0.101, 0.821, 0.112, 0.868] has incidents at 50 and visitors at 1000, and so forth. Calculating ratios for all, General Hospital is at 0.05, City Hospital is at 0.05416, Regional Hospital is at 0.05455, Clinic is at 0.06087, and Dental Clinic is at 0.05769. The closest ratio is with the Regional Hospital, where the difference between visitors and incidents is minimized.\nBased on the above thought, the answer is:\nRegional Hospital displays the closest incident to visitor ratio, showcasing a minimal disparity between the numbers which reflects a balance that might indicate efficient management or consistent health service utilization." } ], "id_raw": 1035 }, { "id": "5cHP9vV8NIqvRaSx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the average number of visitors across all health facilities shown in the chart?" }, { "from": "gpt", "value": "First, I locate {the points for Visitors}[0.597, 0.128, 0.963, 0.220] identifying the coordinates for visitors values at each facility: 1000, 1200, 1100, 1150, 1300. To compute the average, I sum these numbers (1000 + 1200 + 1100 + 1150 + 1300) and then divide by the number of facilities, which is 5.\nBased on the above thought, the answer is:\n1120" } ], "id_raw": 1035 }, { "id": "fPJcstLZqTkIWFl9", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_16.png", "conversations": [ { "from": "human", "value": "\nCan you table the data based on the chart, using the years as row labels and the type of energy as column headers?" }, { "from": "gpt", "value": "Turning the line chart into a table will involve identifying row and column headers as well as the corresponding values. The {x-axis tick labels}[[0.244, 0.050, 0.248,0.054],[0.494, 0.050, 0.498,0.054],[0.744,0.050,0.748,0.054]] will serve as the row header indicating the years 2000, 2010, and 2020. The column headers are represented by {the legend texts} [[0.792, 0.132, 0.893, 0.208], [0.792, 0.132, 0.893, 0.208], [0.800, 0.166, 0.900, 0.242], [0.805, 0.200, 0.905, 0.276]], which are the different energy sources\u2014Oil, Gas, Solar Power, and Wind Power. The data points for each energy source at the corresponding years are located at {the line points}[[0.244, 0.402, 0.248,0.406],[0.244, 0.160, 0.248, 0.164],[0.244, 0.260, 0.248, 0.264],[0.244, 0.115, 0.248,0.119]...], where the y-coordinate corresponds to the data value.\nBased on the above thought, the answer is:\n,Oil,Gas,Solar Power,Wind Power\n2000, 20, 9, ,5,3\n2010, 17, 10, 15, 11\n2020, 14, 11, 25, 19" } ], "id_raw": 1036 }, { "id": "xY3lUu2RS6EReN9x", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_16.png", "conversations": [ { "from": "human", "value": "\nFrom the provided chart, which type of energy resource has seen the maximum percentage increase in its usage from the year 2000 to 2020?" }, { "from": "gpt", "value": "To identify the energy resource with the maximum percentage increase, I need to compare the percentage increase for each resource. The data points for Oil are {Oil, 2000} [0.244, 0.402, 0.248,0.406] and {Oil, 2020} [0.744, 0.279, 0.748,0.283], showing a decrease in utilization. For Gas, the points are {Gas, 2000} [0.244, 0.160, 0.248, 0.164] and {Gas, 2020} [0.744, 0.184, 0.748,0.188] indicating an increase. For Solar Power, the points are {Solar Power, 2000} [0.244, 0.260, 0.248, 0.264] and {Solar Power, 2020} [0.744, 0.545, 0.748, 0.549], showing a significant increase. Wind Power with points at {Wind Power, 2000} [0.244, 0.115, 0.248,0.119] and {Wind Power, 2020} [0.744, 0.403, 0.748,0.407] also shows substantial increase but less than Solar Power.\nBased on the above thought, the answer is:\nBetween 2000 and 2020, the energy resource that has seen the maximum percentage increase in its usage is Solar Power. This fact is evident from its rapid increase from 5 units in 2000 to 25 units in 2020." } ], "id_raw": 1036 }, { "id": "6MUz8VNibPDn2Hd7", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in solar power usage between 2000 and 2020?" }, { "from": "gpt", "value": "To calculate the percentage increase in solar power usage in 20 years, I first identify the data points related to solar power usage in 2000 and 2020, which are {Solar Power, 2000} [0.244, 0.260, 0.248, 0.264] and {Solar Power, 2020} [0.744, 0.545, 0.748, 0.549] respectively. The value for 2000 is 5 units, and for 2020 is 25 units. The percentage increase can be calculated using the formula: ((Final Value - Initial Value) / Initial Value) * 100%.\nBased on the above thought, the answer is:\n400%" } ], "id_raw": 1036 }, { "id": "pYAt00BOfLvTDVdE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_17.png", "conversations": [ { "from": "human", "value": "\nCan you extract and reformat the data of the line graph into a table with 'Trends' as rows and 'AI Applications' as columns?" }, { "from": "gpt", "value": "Turning the data from the line chart into a structured table involves multiple steps: Firstly, I locate {the x-axis ticks}[[0.101, 0.821, 0.112, 0.868], [0.193, 0.821, 0.204, 0.868], [0.284, 0.821, 0.295, 0.868], [0.375, 0.821, 0.386, 0.868], [0.467, 0.821, 0.478, 0.868]], which define the different 'Trends'. These will serve as the row labels. Secondly, I reference {the legend texts}[[0.153, 0.647, 0.298, 0.694], [0.153, 0.717, 0.334, 0.764]] to determine the column headers or 'AI Applications'. Then, I utilize the points for each line which are given as {line points for Early Integration}[[0.107, 0.278], [0.198, 0.322], [0.290, 0.262], [0.381, 0.230], [0.472, 0.085]] and {line points for Advanced Streaming}[[0.107, 0.615], [0.198, 0.363], [0.290, 0.409], [0.381, 0.292], [0.472, 0.249]]. These points are correlated with the list of x-axis ticks to match the right 'Trend' with the correct performance metrics for each 'AI Application'.\nBased on the above thought, the answer is:\nTrend,Early Integration,Advanced Streaming\n1,93.25,78.40\n2,91.28,89.49\n3,93.94,87.48\n4,95.35,92.64\n5,101.73,94.50" } ], "id_raw": 1037 }, { "id": "ViziPbhQ5ke44TUg", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_17.png", "conversations": [ { "from": "human", "value": "\nBetween 'Early Integration' and 'Advanced Streaming', which AI application shows a more consistent performance across different tasks?" }, { "from": "gpt", "value": "To compare the performance consistency of 'Early Integration' and 'Advanced Streaming', I need to analyze the variation in their line plots. Looking at {the line for Early Integration}[[0.107, 0.278], [0.198, 0.322], [0.290, 0.262], [0.381, 0.230], [0.472, 0.085]], it shows a broad shift from a higher value descending to the lowest at Task 5. Meanwhile, {the line representing Advanced Streaming}[[0.107, 0.615], [0.198, 0.363], [0.290, 0.409], [0.381, 0.292], [0.472, 0.249]] begins at the top of the domain and gradually ascends towards the higher end of the y-axis. Judging by the variance in their respective paths, 'Advanced Streaming' presents more ups and downs than does 'Early Integration'.\nBased on the above thought, the answer is:\nBased on the chart, 'Early Integration' appears to show a more consistent performance across different tasks compared to 'Advanced Streaming'. Despite a general decrease in performance from Task 1 to Task 5, the 'Early Integration' line shows fewer shifts in direction, suggesting a more steady performance in comparison to the fluctuating performance of 'Advanced Streaming'." } ], "id_raw": 1037 }, { "id": "q8QJbw9qXMxFGKGc", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_17.png", "conversations": [ { "from": "human", "value": "\nWhat's the minimum accuracy percentage the 'Early Integration' achieved across all tasks?" }, { "from": "gpt", "value": "To find the minimum score achieved by 'Early Integration', I need to look at {the line plot for Early Integration}[[0.107, 0.278], [0.198, 0.322], [0.290, 0.262], [0.381, 0.230], [0.472, 0.085]] and look for the maximum y value (indicating the point closest to the bottom of the chart) since higher y values signify lower accuracy percentages. The minimum accuracy percentage corresponds to task #5, {Task 5}[0.472, 0.805] on x-axis.\nBased on the above thought, the answer is:\n101.73" } ], "id_raw": 1037 }, { "id": "VHFCP91Rhfffci9q", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_52_18.png", "conversations": [ { "from": "human", "value": "\nCan you organize the score for each factor with the departments influencing them as table columns and factors as table rows?" }, { "from": "gpt", "value": "First, I will look at the points on the left subplot representing the factors for {Quality Control}[0.078, 0.124, 0.447, 0.805] and their corresponding labels on the x-axis - {Drag}[0.073, 0.821, 0.116, 0.868] and {Delivery Time}[0.370, 0.821, 0.490, 0.868]. Then, I will get the y values for these points which lie on the {Quality Control} line from [0.094, 0.582, 0.430, 0.630]. \nSimilarly, for the right subplot, I will look for the points representing the factors for {Logistics}[0.568, 0.124, 0.937, 0.805] and identify the labels {Drag}[0.564, 0.821, 0.606, 0.868] and {Delivery Time}[0.860, 0.821, 0.980, 0.868] from x-axis. The y values for these points are obtained from the {Logistics} line between [0.585, 0.278, 0.920, 0.346].\nBased on the above thought, the answer is:\nFactors,Quality Control,Logistics\nDrag,60.32,80.97\nDelivery Time,63.13,76.99" } ], "id_raw": 1038 }, { "id": "Ucd3k2kaYNbhBKcv", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_52_18.png", "conversations": [ { "from": "human", "value": "\nWhat can we infer about the performance of the two departments based on their trend lines?" }, { "from": "gpt", "value": "To analyze the performance of the two departments, I will look at the variation in their respective trend lines. The {Quality Control} department trend line is at [0.094, 0.582, 0.430, 0.630] and the {Logistics} department trend line is at [0.585, 0.278, 0.920, 0.346]. The y-coordinates of the points represent the scores, so by comparing the y-coordinates of both departments, we can interpret their performance.\nBased on the above thought, the answer is:\nThe Logistics department scores are consistently higher than the Quality Control scores for the same factors of performance. However, there is a decrease in quality scores in Logistics from the Drag to Delivery Time. On the contrary, the Quality Control department shows an increase in the same order. Hence, the Logistics department performed better in the Drag factor, while the Quality Control department better addressed the Delivery Time factor." } ], "id_raw": 1038 }, { "id": "ySEuUwxw0iAGVnFN", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_52_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in quality score between the two factors within the Logistics department?" }, { "from": "gpt", "value": "We first locate the points for each factor within {Logistics}[0.568, 0.124, 0.937, 0.805] through the {Logistics} line points [0.585, 0.278, 0.920, 0.346]. We find the y values representing the scores for {Drag}[0.585, 0.278] and {Delivery Time}[0.920, 0.346]. We then calculate the difference between these two scores.\nBased on the above thought, the answer is:\n3.98" } ], "id_raw": 1038 }, { "id": "QoP2Woi7Mrdm4iOQ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_0.png", "conversations": [ { "from": "human", "value": "\nCan you translate the trends represented in the line chart into tabular data with the categories of welfare activities as the rows and the two different companies as the columns?" }, { "from": "gpt", "value": "The line chart presents data points for seven categories of welfare activities across two companies. To translate this data into a table, I first identify {the x-axis tick labels}[[0.117, 0.506, 0.187, 0.532], [0.234, 0.506, 0.332, 0.532], [0.385, 0.506, 0.467, 0.532], [0.513, 0.506, 0.601, 0.532], [0.641, 0.506, 0.713, 0.532], [0.769, 0.506, 0.843, 0.532]] as the different categories of welfare activities. These will serve as the row labels. Then, I note the two companies described by {the plot lines}[[0.0, 0.0, 1.0, 1.0],[0.0, 0.0, 1.0, 1.0]] -\n 'Our Company' and 'Other Company', which shall constitute the column labels. The intersection of each row and column will be filled with the corresponding percentage change values, which can be extracted by analysing the y-coordinate of the plotted points.\nBased on the above thought, the answer is:\nActivity,Our Company,Other Company\nDonation,-3.2, 2.8\nVolunteering,-4.7, 0.5\nAdvocacy,-5.9, 8.3\nPartnership,-1.2,-2.5\nSponsorship,-3.4, 1.5\nProBono,-6.5,10.7\nWorkplace Giving,-2.6,1.2" } ], "id_raw": 1040 }, { "id": "W65YVjumldl2TUVH", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_0.png", "conversations": [ { "from": "human", "value": "\nWhich company shows more consistent engagement in welfare activities based on the trends reflected in the line chart? Explain your reasoning." }, { "from": "gpt", "value": "To determine which company shows more consistent engagement in welfare activities, I evaluate the fluctuations in the change rates for each company. Judging from {the plot line for Our Company}[0.0, 0.0, 1.0, 1.0], its change rates show a consistent negative trend across most activities, indicating a steady decrease in engagement. Conversely, {the plot line for the Other Company}[0.0, 0.0, 1.0, 1.0] presents more variation in the change rates, with both positive and negative values, signifying an erratic pattern of engagement.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, Our Company shows more consistent engagement in welfare activities. Despite a generally negative trend, the change rates for Our Company are relatively stable across different welfare activities. In comparison, the Other Company demonstrates more erratic engagement with fluctuating positive and negative change rates across different activities." } ], "id_raw": 1040 }, { "id": "Y8A1OgThtP1B2rL2", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the average change rate of 'Our Company' across all welfare activities?" }, { "from": "gpt", "value": "To calculate the average change rate of 'Our Company' across all welfare activities, I need to identify {the y-coordinates of our company's plotted points}[[0.117, 0.406, 0.187, 0.482], [0.234, 0.396, 0.332, 0.472], [0.385, 0.376, 0.467, 0.452], [0.513, 0.492, 0.601, 0.568], [0.641, 0.486, 0.713, 0.562], [0.769, 0.465, 0.843, 0.541]] and extract the corresponding change rates: -3.2, -4.7, -5.9, -1.2,-3.4,-6.5,-2.6. There are 7 data points, so adding all these then dividing by 7 will give me the average.\nBased on the above thought, the answer is:\n-3.9%" } ], "id_raw": 1040 }, { "id": "Ej4ciZ0mDGMqOyhF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_1.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the data seen in the chart into a tabulated format with months as the row labels and different social media platforms as the columns?" }, { "from": "gpt", "value": "Let's start by identifying the {x-axis tick labels}[[0.199, 0.945, 0.234, 0.959], [0.353, 0.945, 0.388, 0.959], [0.506, 0.945, 0.542, 0.959], [0.660, 0.945, 0.695, 0.959], [0.814, 0.945, 0.849, 0.959]] as the row labels representing the different months. Next, I see {the legend texts}[[0.114, 0.027, 0.132, 0.041], [0.114, 0.048, 0.202, 0.062], [0.114, 0.069, 0.192, 0.083]] as the column headers representing the different social media platforms. To fill the values, for each social platform, I look at their respective {line points}[0.217, 0.057, 0.831, 0.272], [0.217, 0.272, 0.831, 0.899], [0.217, 0.281, 0.831, 0.679] and reflect the corresponding values highlighted for each month.\nBased on the above thought, the answer is:\nMonth, Facebook, Twitter, WhatsApp, LinkedIn, Instagram\n03-2021,1800,1500,1900,1700,2000\n04-2021,1700,1600,2000,1750,2100\n05-2021,1600,1550,2050,1800,2200\n06-2021,1520,2300,2100,1850,2320\n07-2021,1450,1950,2150,1900,2450" } ], "id_raw": 1041 }, { "id": "jAArIfWIdwYgzHJk", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_1.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform demonstrated the most significant upward trend throughout the observed period, based on the chart's data?" }, { "from": "gpt", "value": "By examining {the line for Facebook}[0.217, 0.057, 0.831, 0.272], it is clear that Facebook has a declining trend over the period. For {Twitter}[0.217, 0.272, 0.831, 0.899], there are substantial fluctuations with an overall rising pattern. Analyzing {WhatsApp}[0.217, 0.281, 0.831, 0.679], it presents a moderate upward pattern with variances. The line for {LinkedIn}[0.217, 0.303, 0.831, 0.713] reflects steady growth, and {Instagram}[0.217, 0.339, 0.831, 1.0] depicts a consistent ascending trend.\nBased on the above thought, the answer is:\nInstagram displayed the most significant upward trend throughout the observed period. Not only does its line chart follow an unwavering ascending path, but it also ends at the highest point among all platforms. This makes Instagram the social media platform with the most significant net increase in user activities." } ], "id_raw": 1041 }, { "id": "mTUtxDo9LNh8h7Pf", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the gross change in Facebook's performance over the observed period?" }, { "from": "gpt", "value": "To find the gross change in Facebook's performance, I need to locate {the line points for Facebook}[0.217, 0.057, 0.831, 0.272] and extract the starting and ending values: 1800 at 03-2021 and 1450 at 07-2021. Therefore, the gross change is 1450 - 1800 = -350, indicating a decrease in performance.\nBased on the above thought, the answer is:\n-350." } ], "id_raw": 1041 }, { "id": "SKvSkiuNtc1Zh7K5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_2.png", "conversations": [ { "from": "human", "value": "\nCan you represent the information depicted in the line chart in the form of a table, considering years as column headers and different monitor names as row headers?" }, { "from": "gpt", "value": "To construct the table, I initially identify {the x-axis tick labels}[[0.207, 0.909, 0.251, 0.932], [0.358, 0.909, 0.401, 0.932], [0.508, 0.909, 0.552, 0.932], [0.658, 0.909, 0.702, 0.932], [0.809, 0.909, 0.853, 0.932]], which represent the different years. These will be used as the column headers. Secondly, I look at {the legend texts}[[0.143, 0.046, 0.309, 0.069], [0.143, 0.081, 0.284, 0.104], [0.143, 0.116, 0.343, 0.139]] to extract the row labels which indicate the different monitors. Finally, I locate {the line points}[[0.229, 0.601], [0.380, 0.653], ..., [0.831, 0.861], [0.229, 0.342], ..., [0.831, 0.065], [0.229, 0.515], ..., [0.831, 0.273]] corresponding to each monitor for each year and extract the provided values to populate the data in our table.\nBased on the above thought, the answer is:\nYear,Heart Rate Monitor,Glucose Monitor,Blood Pressure Monitor\n2000,45,60,50\n2005,42,65,55\n2010,40,70,60\n2015,35,72,62\n2020,30,76,64" } ], "id_raw": 1042 }, { "id": "3a1uGponNstKj13r", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_2.png", "conversations": [ { "from": "human", "value": "\nBased on the line charts, which monitor shows a continuously declining trend from 2000 to 2020? Provide explanation on your choice." }, { "from": "gpt", "value": "To identify the monitor with the constantly declining trend, I need to review the trajectories of the line plots for each monitor. Looking at {the line for Heart Rate Monitor}[0.229, 0.601, 0.831, 0.861], it shows a systematic reduction from 45 in 2000 to 30 in 2020. The error rate for {Glucose Monitor}[0.229, 0.342, 0.831, 0.065], however, generally increases over the same period. Likewise, the trend for {Blood Pressure Monitor}[0.229, 0.515, 0.831, 0.273] does not follow a consistent pattern of decrease either. Therefore, by comparing the trends, it is clear that the Heart Rate Monitor is the one that demonstrates a continuous declining trend.\nBased on the above thought, the answer is:\nBased on the line chart, the Heart Rate Monitor shows a continuously declining trend from 2000 to 2020. This is deduced from the consistent decrease in values from 45 in 2000 to 30 in 2020, as shown in the chart. This trend is unique to the Heart Rate Monitor compared to other monitors." } ], "id_raw": 1042 }, { "id": "JhMTVs01IZr55miw", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_2.png", "conversations": [ { "from": "human", "value": "\nHow much does the error rate for Blood Pressure Monitor decrease from 2000 to 2020?" }, { "from": "gpt", "value": "To find the decrease in error rate for Blood Pressure Monitor, I need to look at {the line points for Blood Pressure Monitor}[[0.229, 0.515], [0.380, 0.428], [0.530, 0.342], [0.680, 0.307], [0.831, 0.273]], specifically from 2000 and 2020. The value in 2000 is 50 corresponding to {2000}[0.229, 0.901] and the value in 2020 is 64 corresponding to {2020}[0.831, 0.901]. Hence, the decrease in value is 64 - 50 = 14.\nBased on the above thought, the answer is:\n14" } ], "id_raw": 1042 }, { "id": "xYSNwmTO2vzsobYa", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_3.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table that represents the data shown in the bar chart, with 'Types of Vehicles' as rows and 'Phases of Growth' as columns?" }, { "from": "gpt", "value": "To construct the table, I've observed {the x-axis tick labels}[0.265, 0.923, 0.327, 0.953], [0.635, 0.923, 0.698, 0.953] which represent the column headers or phases of growth. The row labels representing the types of vehicles can be inferred from {the legend}[0.808, 0.087, 0.962, 0.179]. For the values, I recognize {the bar heights} for each type of vehicle at the respective phases, extracting these as the values for each cell of the table.\nBased on the above thought, the answer is:\nTypes of Vehicles,Rapid Growth,Minimal Change\nPassenger Cars,55,45\nCommercial Vehicles,72,42" } ], "id_raw": 1043 }, { "id": "mNqfip4fCmiRxjo8", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_3.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, which type of vehicle saw a larger fluctuation in sales moving from the Rapid Growth phase to the Minimal Change phase? Please detail your reasoning." }, { "from": "gpt", "value": "I need to compare the change in sales between the Rapid Growth phase and the Minimal Change phase for each type of vehicle. For Passenger Cars, the sales decline from {rapid growth}[0.261, 0.048, 0.331, 0.662] to {minimal change}[0.621, 0.048, 0.689, 0.572], reflecting a decline from 55 to 45. On the other hand, Commercial Vehicles show a decrease from {rapid growth}[0.391, 0.048, 0.459, 0.919] to {minimal change}[0.751, 0.048, 0.819, 0.303], reflecting a larger decline from 72 to 42.\nBased on the above thought, the answer is:\nThe Commercial Vehicles category experienced a larger fluctuation in sales when comparing the Rapid Growth phase to the Minimal Change phase. While both Passenger Cars and Commercial Vehicles saw a decline in sales from the Rapid Growth phase to the Minimal Change phase, the decline was steeper for Commercial Vehicles (from 72 down to 42) compared to the decline observed for Passenger Cars (from 55 down to 45)." } ], "id_raw": 1043 }, { "id": "o4puPoVECue57AFr", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in sales between Passenger Cars and Commercial Vehicles during the period of Minimal Change?" }, { "from": "gpt", "value": "To find the difference in sales, I have to locate the data for both Passenger Cars and Commercial Vehicles during the Minimal Change period. For Passenger Cars, the sales figure is represented by {the height of the bar}[0.621, 0.048, 0.689, 0.572] which corresponds to 45. For Commercial Vehicles, the sales figure is represented by {the height of the bar}[0.751, 0.048, 0.819, 0.303] which corresponds to 42. The difference between the two is therefore 45 - 42 = 3.\nBased on the above thought, the answer is:\n3." } ], "id_raw": 1043 }, { "id": "GXbM4K0OmeJ7ieiG", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_5.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data provided into a table format with the categories as columns and the type of error rates as rows?" }, { "from": "gpt", "value": "To achieve this, I would first identify {the x-axis tick labels}[0.189, 0.909, 0.369, 0.932], [0.379, 0.909, 0.527, 0.932], [0.543, 0.909, 0.711, 0.932], [0.782, 0.909, 0.820, 0.932] as the column headers. The row labels would be identified as {the legend texts}[0.191, 0.046, 0.297, 0.069], [0.191, 0.081, 0.329, 0.104], [0.191, 0.116, 0.296, 0.139]. Then, I would locate the corresponding line points of these labels in the chart at [0.279, 0.861], [0.453, 0.694], and so on. Hence, I can tabulate this data into a structured table.\nBased on the above thought, the answer is:\nError Type,Pharmaceutical,Construction,Manufacturing,ICT\nCruncher,20.3,25.5,29.8,45.0\nEngineering,25.4,23.1,34.1,34.9\nScientific,33.0,28.4,42.4,36.7" } ], "id_raw": 1045 }, { "id": "1xywXOhcqmXOpOu7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_5.png", "conversations": [ { "from": "human", "value": "\nIn the bar chart provided, which sector seems to have the most consistent error rate across the three types? Provide your reasoning." }, { "from": "gpt", "value": "To identify the sector with the most consistent error rate, I need to analyze the differences in the error rates for the three types within each sector. I review the error rates for {Pharmaceutical}[0.279, 0.861],[0.279, 0.697],[0.279, 0.452], {Construction}[0.453, 0.694],[0.453, 0.771],[0.453, 0.600], {Manufacturing}[0.627, 0.555],[0.627, 0.416],[0.627, 0.149] and {ICT}[0.801, 0.065],[0.801, 0.390],[0.801, 0.332] categories to conclude that the sector with the smallest difference between maximum and minimum error rates would be the most consistent.\nBased on the above thought, the answer is:\nThe Construction sector seems to have the most consistent error rate across the three types. The error rates for the three types (Cruncher, Engineering, Scientific) within this sector show less variation, indicating a consistent level of errors across these types." } ], "id_raw": 1045 }, { "id": "ig6aKY41YhvwZKCV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the maximum and minimum Error Rate in the ICT sector?" }, { "from": "gpt", "value": "To find the maximum and minimum error rates in the ICT sector, I have to locate {the points for ICT}[0.801, 0.861], [0.801, 0.771], [0.801, 0.600] and extract the corresponding values: 45.0, 34.9, 36.7. The maximum value is 45.0 corresponding to {Cruncher}[0.191, 0.046, 0.297, 0.069], and the minimum value is 34.9 corresponding to {Engineering}[0.191, 0.081, 0.329, 0.104]. The difference will be 45.0 - 34.9 = 10.1.\nBased on the above thought, the answer is:\n10.1." } ], "id_raw": 1045 }, { "id": "C4MHuNQkT6QmF15V", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_6.png", "conversations": [ { "from": "human", "value": "\nCan you extract the line data into structured table format with each row representing the trends of Entrepreneurship and Innovation across the seven metrics?" }, { "from": "gpt", "value": "To generate a table from the chart, I first identify {the x-axis tick labels}[[0.220, 0.909, 0.280, 0.932], [0.365, 0.909, 0.425, 0.932], [0.510, 0.909, 0.570, 0.932], [0.655, 0.909, 0.715, 0.932], [0.800, 0.909, 0.860, 0.932]] representing the years. Next, I find {the legend texts}[[0.191, 0.046, 0.414, 0.069], [0.191, 0.081, 0.424, 0.104]] as the row headers indicating either Rural Development or Urban Development. Finally, to obtain the values, I examine {the line points for Rural Development}[[0.250, 0.861], [0.395, 0.766], [0.540, 0.634], [0.685, 0.444], [0.830, 0.292]] and {the line points for Urban Development}[[0.250, 0.387], [0.395, 0.273], [0.540, 0.160], [0.685, 0.103], [0.830, 0.065]] for the development index scores at each time period.\nBased on the above thought, the answer is:\nTime Period, Rural Development Index, Urban Development Index\n2000, 40, 65\n2005, 45, 71\n2010, 52, 77\n2015, 62, 80\n2020, 70, 82" } ], "id_raw": 1046 }, { "id": "XXuvh7657ibmtsM1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_6.png", "conversations": [ { "from": "human", "value": "\nBased on the graphs displayed in the chart, which form of development shows a steadier progress between 2000 and 2020? Provide your analysis." }, { "from": "gpt", "value": "To address this question, I need to contrast the overall trends noted in {the line for Rural Development}[0.250, 0.292, 0.830, 0.861] and {the line for Urban Development}[0.250, 0.065, 0.830, 0.387]. The Rural Development line segment demonstrates a steady upward trend, with a few minor dips that indicate moderate volatility. The line representing the Urban Development Index, on the other hand, shows a more fluctuating trend, with a steady increase till 2015, followed by a minimal increase to 2020. Based on the line coordinates, one could note more steadiness in the Rural Development path as it shows a more linear growth trajectory as compared to the fluctuating path of Urban Development.\nBased on the above thought, the answer is:\nBased on the line graphs, Rural Development shows a steadier progress between 2000 and 2020. The plotted line for Rural Development demonstrates a more regular linear upward trajectory, denoting steady growth over the years. In contrast, Urban Development, despite having a higher index value, exhibits more volatility with less linearity in its progression, especially in the period from 2015 to 2020." } ], "id_raw": 1046 }, { "id": "onr5wGL5eSL10mRH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_6.png", "conversations": [ { "from": "human", "value": "\nWhat's the total increase in Urban Development Index from 2000 to 2020?" }, { "from": "gpt", "value": "To calculate the total increase in the Urban Development Index from 2000 to 2020, the first step is to identify {the line points for Urban Development}[[0.250, 0.387], [0.830, 0.065]]. The value for the year 2000 is 65 at the point coordinates [0.250, 0.387] and for the year 2020 is 82 at the point coordinates [0.830, 0.065]. The total increase is 82 - 65 = 17.\nBased on the above thought, the answer is:\n17." } ], "id_raw": 1046 }, { "id": "pE5uRmLPOhptVprO", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_7.png", "conversations": [ { "from": "human", "value": "\nCan you structure the line chart data about influence levels of different communication methods over four weeks into a table?" }, { "from": "gpt", "value": "Yes, to create a table from the line chart, I first identify the weeks {labeled on the x-axis}[0.283, 0.909, 0.812, 0.932] as the columns. Then, I recognize the {labels from the legend}[0.206, 0.046, 0.336, 0.139] as row headers representing different communication methods. For the data, I assess the y-values at the points in the line plots for each communication method at each week. I find these values at the {points of the lines}[0.291, 0.304, 0.804, 0.622], [0.291, 0.144, 0.804, 0.861], [0.291, 0.065, 0.804, 0.383] for Television, Online, and Newspaper respectively.\nBased on the above thought, the answer is:\nMethod,Week1,Week2,Week3,Week4\nTelevision,85,75,75,95\nOnline,60,65,90,105\nNewspaper,100,110,100,90" } ], "id_raw": 1047 }, { "id": "QX0fONri2xgDeZJJ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_7.png", "conversations": [ { "from": "human", "value": "\nWhich method of communication showed the most increase in influence level from Week 1 to Week 4 based on the line chart? How would you analyze this?" }, { "from": "gpt", "value": "To analyze this, I locate the {Week 1 and Week 4 data points for each communication method}[0.291, 0.463], [0.462, 0.622], [0.633, 0.622], [0.804, 0.622], [0.291, 0.861], [0.462, 0.782], [0.633, 0.383], [0.804, 0.144], [0.291, 0.224], [0.462, 0.065], [0.633, 0.224], [0.804, 0.383] and calculate the difference in influence levels between these weeks for Television, Online, and Newspaper. \nTelevision shows a change of 95 - 85 = 10, Online shows an increase of 105 - 60 = 45, and Newspaper shows a decrease of 90 - 100 = -10.\nBased on the above thought, the answer is:\nBased on the chart, the method of communication which shows the most increase in influence level from Week 1 to Week 4 is Online, with an increase of 45 units. This could suggest that over this period, online communication methods have become more influential, possibly due to increased use of digital platforms." } ], "id_raw": 1047 }, { "id": "XYtsH1HJpg1vh2Yi", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the average influence level of Television over the period of four weeks based on the chart?" }, { "from": "gpt", "value": "To find the average influence level of Television, I first locate the {points on the line representing Television}[0.291, 0.463], [0.462, 0.622], [0.633, 0.622], [0.804, 0.304] corresponding to influence levels 85, 75, 75, 95 for the four weeks. The average is calculated by adding these numbers together and dividing by the total number of weeks.\nBased on the above thought, the answer is:\n82.5" } ], "id_raw": 1047 }, { "id": "ISsihOj6ksjsEKSM", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_9.png", "conversations": [ { "from": "human", "value": "\nCan you restructure the line chart's data into a table with age as the index and the two lines, \"Books Read: High Stress\" and \"Books Read: Low Stress\", as columns?" }, { "from": "gpt", "value": "To construct the table, I need to take the tick labels on {the x-axis}[0.199, 0.909, 0.228, 0.932], [0.308, 0.909, 0.337, 0.932]... as the index values representing Age. For the columns, the two lines {Books Read: High Stress}[0.214, 0.450, 0.866, 0.861] and {Books Read: Low Stress}[0.214, 0.065, 0.866, 0.219] represent the two different stress conditions. The values corresponding to each age under these conditions can be inferred from their respective mappings on the y-axis.\nBased on the above thought, the answer is:\nAge, Books Read: High Stress, Books Read: Low Stress\n10, 25, 40\n15, 20, 38\n20, 17, 37\n25, 14, 36\n30, 12, 37\n35, 10, 36\n40, 9, 34" } ], "id_raw": 1049 }, { "id": "rvRpKfKy9bPyTnHE", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_9.png", "conversations": [ { "from": "human", "value": "\nConsider the trends of books read under both High Stress and Low Stress conditions as people age in this line chart. Can you explain these trends?" }, { "from": "gpt", "value": "Looking at the line for {Books Read: High Stress}[0.214, 0.450, 0.866, 0.861], there appears to be a clear downward trend as Age increases. The number of books read decreases from 25 to 9 as age increases from 10 to 40, indicating that under high stress conditions, people tend to read less as they age.\nOn the contrary, the line for {Books Read: Low Stress}[0.214, 0.065, 0.866, 0.219] shows a slight decline initially from age 10 to age 20 and then it becomes relatively flat, indicating that under low stress conditions, the number of books read remains more or less the same after age 20.\nBased on the above thought, the answer is:\nThe trends in the line chart indicate that under high stress conditions, the number of books read steadily decreases as people age. In contrast, under low stress conditions, there is a slight decrease from ages 10 to 20, after which the number of books read remains fairly consistent. This suggests that stress levels have a significant impact on reading habits and that this impact increases with age." } ], "id_raw": 1049 }, { "id": "01vXqRMktPQmkYEd", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in the number of books read between the highest and lowest reading conditions under Low Stress at the age of 35?" }, { "from": "gpt", "value": "To identify this, I need to locate the coordinate for the age of 35 on the x-axis. The coordinates are {35}[0.743, 0.909, 0.772, 0.932]. On this coordinate, there exist two y-positions under Low Stress, found at coordinates {low}[0.758, 0.168]. Observing the y-axis [0.068, 0.184, 0.097, 0.207], the corresponding number of books read under low stress is 36.\nSimilarly for High stress condition, the y-position is {high}[0.758, 0.835]. Observing the y-axis, [0.068, 0.569, 0.097, 0.593], the corresponding number of books read under high stress is 10.\nThe difference is calculated as |High Stress - Low Stress| = |10 - 36| = 26.\nBased on the above thought, the answer is:\n26." } ], "id_raw": 1049 }, { "id": "upzsw5iJXICTD8px", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_10.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart showing production efficiency over time into a structured table format listing each company and their corresponding annual efficiency change?" }, { "from": "gpt", "value": "First, I identify and organize {the x-axis tick labels}[0.054, 0.934, 0.098, 0.952], [0.143, 0.934, 0.187, 0.952], [0.231, 0.934, 0.275, 0.952],..., [0.938, 0.934, 0.981, 0.952] denoting the years from 2010 to 2020 as columns. I then examine {the lines for each company}[0.076, 0.060, 0.959, 0.592], [0.076, 0.129, 0.959, 0.754], ..., [0.076, 0.371, 0.959, 0.887] and their point coordinates to extract the efficiency changes per year for each company.\nBased on the above thought, the answer is:\nYear,Tesla,Amazon,LG,Panasonic,Microsoft,Oracle,Nokia,Toshiba,Lamborghini,Ferrari,Apple,Netflix,Salesforce,Cisco,Sony,Volkswagen,Audi,General Electric\n2010,20,15,12,11,-10,-9,-8,-21,-7,-6,0,3,1,2,-4,-3,0,1\n2011,25,18,13,12,-11,-10,-9,-20,-8,-7,1,4,2,3,-3,-2,0,0\n2012,27,20,14,14,-12,-11,-11,-19,-8,-7,2,4,3,3,-2,-1,1,2\n2013,32,21,15,16,-13,-12,-12,-18,-7,-6,3,5,4,4,-2,0,1,3\n...\n2020,45,40,20,17,-15,-14,-14,-30,-8,-8,8,5,6,7,-1,2,4,5" } ], "id_raw": 1050 }, { "id": "OLflOyxTmITOnJt0", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_10.png", "conversations": [ { "from": "human", "value": "\nWhich company shows the most consistent efficiency level throughout the years according to the data portrayed in the chart?" }, { "from": "gpt", "value": "To answer this, I review the efficiency trends by scanning the lines for each company. {General Electric's line}[0.076, 0.173, 0.959, 0.173] shows minor fluctuation around a stable midpoint, differing minimally from year to year when compared to others with larger fluctuations.\nBased on the above thought, the answer is:\nGeneral Electric shows the most consistent efficiency level throughout the years. Its line on the chart is almost a flat line with minimal deviations, indicating a steady state of production efficiency across the measured period." } ], "id_raw": 1050 }, { "id": "jQx4KwdNAzWa8zRN", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the maximum efficiency change for Tesla from 2010 to 2020?" }, { "from": "gpt", "value": "To find this value, I analyze {points on Tesla's line}[0.076, 0.592], [0.165, 0.557], ..., [0.959, 0.060] representing each year's efficiency value. I look for the point with the highest value, which corresponds to the year 2020 showing an efficiency of 45.\nBased on the above thought, the answer is:\n45." } ], "id_raw": 1050 }, { "id": "RvAqiejDMXhPnrQZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_11.png", "conversations": [ { "from": "human", "value": "\nCould you please convert the line chart data into a table with Years as columns and Types of Consumption as rows?" }, { "from": "gpt", "value": "Sure, to convert the line chart into a table, I first identify {the x-axis tick labels}[[0.096, 0.922, 0.131, 0.942], [0.279, 0.922, 0.297, 0.942], [0.445, 0.922, 0.463, 0.942], [0.611, 0.922, 0.629, 0.942], [0.777, 0.922, 0.795, 0.942], [0.943, 0.922, 0.961, 0.942]], which represent the column headers or Years from 2010 to 2020. Next, I check {the subplot legend texts}[[0.123, 0.039, 0.250, 0.059], [0.123, 0.069, 0.261, 0.089], [0.123, 0.099, 0.255, 0.119]] which form the row headers: Coal Consumption, Hydro Consumption, and Solar Consumption. Now, to extract the values, I spot {the line points} for each consumption type at their respective years and extract the corresponding values.\nBased on the above thought, the answer is:\nConsumption Type,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\nCoal Consumption,800,780,770,750,720,700,680,670,660,650,640\nHydro Consumption,200,220,230,240,250,260,270,280,290,300,310\nSolar Consumption,50,60,70,80,90,120,150,180,200,220,240" } ], "id_raw": 1051 }, { "id": "asqeliLSLLpWITKe", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_11.png", "conversations": [ { "from": "human", "value": "\nCould you explain the general trends observable in the energy consumption chart?" }, { "from": "gpt", "value": "Yes, looking at the line plots for each consumption type, I can dissect the trends occurring. For {Coal Consumption}[0.113, 0.062, 0.943, 0.235], it's identifiable that there's a continuous decline from 800 to 640 over the period from 2010 to 2020. \nThis is a sharp contrast to {Solar Consumption}[0.113, 0.669, 0.943, 0.874], which saw a growth from 50 to 240. Finally, {Hydro Consumption}[0.113, 0.593, 0.943, 0.712] seems to have happened gradually increased from 200 to 310.\nBased on the above thought, the answer is:\nFrom the chart, it is noticeable that Solar Consumption showed a significant increase from 2010 to 2020, suggesting a rising trend in the use of solar energy resources. On the other hand, Coal Consumption has been steadily decreasing over the same time period, indicating a decline in the reliance on fossil fuels. Meanwhile, Hydro Consumption demonstrates a gradual, but consistent, upward trend, representing a steady role that hydro power is playing in the energy resource mix. These trends imply a shift in the energy consumption pattern towards more renewable and less polluting sources." } ], "id_raw": 1051 }, { "id": "oCDuZYqQk8gARFGJ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_11.png", "conversations": [ { "from": "human", "value": "\nWhat's the total amount of growth in solar consumption from 2010 to 2020?" }, { "from": "gpt", "value": "I'll analyze the increase by checking {the line points for Solar Consumption}[[0.113, 0.874], [0.196, 0.864], [0.279, 0.853], [0.362, 0.842], [0.445, 0.831], [0.528, 0.799], [0.611, 0.766], [0.694, 0.734], [0.777, 0.712], [0.860, 0.690], [0.943, 0.669]]. In doing this, I'm able to trace the values annotated corresponding to each year from 2010 to 2020. I then calculate the growth by subtracting the initial value in 2010 from the final value in 2020.\nBased on the above thought, the answer is:\n190" } ], "id_raw": 1051 }, { "id": "k84NG3ojFzM8MjlT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_12.png", "conversations": [ { "from": "human", "value": "\nCan you please transform the line chart data, which plots adoption rate percentages against years, into a structured table with the years as columns and different tech methods as rows?" }, { "from": "gpt", "value": "To convert the line chart data to a tabulated format, I first identify {the x-axis values}[[0.185, 0.933, 0.229, 0.951], [0.314, 0.933, 0.358, 0.951], [0.443, 0.933, 0.487, 0.951], [0.572, 0.933, 0.616, 0.951], [0.701, 0.933, 0.745, 0.951], [0.830, 0.933, 0.874, 0.951]] as the column headers denoting the years. I then locate {the legend texts: \"Online Learning\", \"Classroom Tech\", and \"VR Training\"}[[0.142, 0.034, 0.279, 0.052], [0.142, 0.061, 0.277, 0.078], [0.142, 0.087, 0.240, 0.104]] as row labels, which represent distinct technology-based learning methods. Then I find the {y-axis line points}[[0.207, 0.679], [0.336, 0.638], [0.465, 0.576], [0.594, 0.473], [0.723, 0.266], [0.852, 0.163]], for each of the tech-methods at the related years, and extract the corresponding rate values. These obtained values are populated in the appropriate table cells denoted by the row and column pairs.\nBased on the above thought, the answer is:\nTech Method,2015,2016,2017,2018,2019,2020\nOnline Learning,20,22,25,30,40,45\nClassroom Tech,30,28,27,25,27,28\nVR Training,10,15,25,30,45,50" } ], "id_raw": 1052 }, { "id": "iKNMmNWzYKjJA5qO", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_12.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which method of instruction seems to experience the most rapid increase between the years 2015 and 2020?" }, { "from": "gpt", "value": "To determine which method of instruction has experienced the most rapid increase between 2015 and 2020, I need to focus on the slope of {the lines for each instruction method}[[0.207, 0.163, 0.852, 0.679], [0.207, 0.473, 0.852, 0.576], [0.207, 0.060, 0.852, 0.886]]. By analyzing these, I observe that the line corresponding to VR Training has a much steeper slope compared to the lines for Online Learning and Classroom Tech. This suggests that VR Training has experienced a more rapid increase in adoption rate from 2015 to 2020 compared to the other methods.\nBased on the above thought, the answer is:\nBased on the line chart, VR Training seems to have experienced the most rapid increase in adoption rate between the years 2015 and 2020. The line corresponding to VR Training shows a steeper upward trend compared to the lines for Online Learning and Classroom Tech, indicating a more rapid increase over this period." } ], "id_raw": 1052 }, { "id": "4wh9WHDwyeKhE4mC", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the total rate increase from 2015 to 2020 for Online Learning?" }, { "from": "gpt", "value": "To calculate the total rate increase for Online Learning from 2015 to 2020, I need to find {the line points for Online Learning}[[0.207, 0.679], [0.336, 0.638], [0.465, 0.576], [0.594, 0.473], [0.723, 0.266], [0.852, 0.163]] and extract the corresponding values: 20, 22, 25, 30, 40, 45. The value for 2015 is 20 while that for 2020 is 45. Thus, the rate increase for Online Learning from 2015 to 2020 is 45 - 20 = 25.\nBased on the above thought, the answer is:\n25." } ], "id_raw": 1052 }, { "id": "JScQoLZbYuLmnMck", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_15.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart into a table, with Time as the column headers, and each Fitness Trend as a row?" }, { "from": "gpt", "value": "To extract the data from the line chart, I first observe {the x-axis tick labels}[[-0.009, 0.898, 0.016, 0.921], [0.155, 0.898, 0.166, 0.921], [0.311, 0.898, 0.322, 0.921], [0.468, 0.898, 0.479, 0.921], [0.624, 0.898, 0.635, 0.921], [0.781, 0.898, 0.792, 0.921], [0.932, 0.898, 0.954, 0.921]] to identify the column headers as Time. The row labels represent the different fitness trends, visible from {the legend text}[0.681, 0.811, 0.884, 0.834],[0.681, 0.846, 0.884, 0.869]. To get the values, I can find {the line points}[0.160, 0.155, 0.865, 0.505] for Large Scale Framework and [0.160, 0.272, 0.865, 0.855] for Small Scale Framework, which represent the popularity at each year.\nBased on the above thought, the answer is:\nTime,-2,0,2,4,6,8,10\nLarge Scale Framework,90,85,80,80,85,90,100,110,100,95\nSmall Scale Framework,50,60,70,85,95,100,100,95,90,85" } ], "id_raw": 1055 }, { "id": "PVCUdXFQRqisjoyh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_15.png", "conversations": [ { "from": "human", "value": "\nAccording to the trend lines in the graph, which framework showed a consistent growth trend from year 2 to year 10? Please explain your reasoning." }, { "from": "gpt", "value": "To determine which framework shows a consistent growth trend, I need to analyze the increase or decrease in the line plots for each framework. Looking at {the line for the Large Scale Framework}[0.317, 0.505, 0.865, 0.330], the trend is not consistent. It initially decreases from 80 to 80, then increases to 110, falls back to 95, indicating the growth trend is not consistent.\nHowever, reviewing the {line for the Small Scale Framework}[0.317, 0.622, 0.865, 0.447], I see a more consistent growth trend. The values increase from 70 to 100, then remain the same at 100, and decrease slightly to 85. Despite minor fluctuations, the overall trend consistently increases, indicating a growth pattern.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Small Scale Framework seems to show a more consistent growth trend from year 2 to year 10. Its line plot shows an overall increase across the specified years, despite some minor fluctuations. This increase indicates the growth of the Small Scale Framework during this period." } ], "id_raw": 1055 }, { "id": "dA2ZZn0J8dzGX3TE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_15.png", "conversations": [ { "from": "human", "value": "\nFrom 2 to 10, what\u2019s the difference between the maximum popularity and the minimum popularity of the Large Scale Framework?" }, { "from": "gpt", "value": "To find the maximum and minimum values, I need to locate {the line points for the Large Scale Framework}[0.317, 0.505, 0.395, 0.505, 0.473, 0.447, 0.552, 0.388, 0.630, 0.272, 0.708, 0.155, 0.786, 0.272, 0.865, 0.330]. The corresponding values are 80, 80, 85, 90, 100, 110, 100, and 95. Following that, the maximum value is 110, and the minimum value is 80. The difference between the maximum and minimum is therefore 30.\nBased on the above thought, the answer is:\n30." } ], "id_raw": 1055 }, { "id": "jl0iME6wQT0JjZrF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_16.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line graph that shows the trends of different technologies over time into a structured data table?" }, { "from": "gpt", "value": "To structure the data, I need to identify the x-axis labels showing the educational technologies, located at {the x-axis tick labels}[[0.151, 0.155, 0.251, 0.243], [0.318, 0.156, 0.420, 0.243], [0.487, 0.157, 0.582, 0.243], [0.656, 0.159, 0.767, 0.236], [0.819, 0.156, 0.950, 0.236]]. These will be my column headers. The line points found at {line for Decline score}[[0.151, 0.362], [0.318, 0.436], [0.487, 0.507], [0.656, 0.568], [0.819, 0.664]] and {line for Slow Growth score}[[0.151, 0.833], [0.318, 0.897], [0.487, 0.953], [0.656, 0.830], [0.819, 0.785]] represent the decline scores and slow growth scores for each technology respectively. Therefore, the data table will have the technologies as the rows, and two columns representing the decline and slow growth scores.\nBased on the above thought, the answer is:\nTechnology,Decline Score,Slow Growth Score\nExcel,-3.3,-2.3\nPowerPoint,-2.8,-1.9\nAdaptive Learning,-2.0,-1.4\nEarly Childhood,-1.4,-0.9\nVideo Conferencing,-1.0,-0.7\nVirtual Reality,-0.9,-0.6" } ], "id_raw": 1056 }, { "id": "zVLIkIVPkJQ9bxZB", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_16.png", "conversations": [ { "from": "human", "value": "\nBased on the graphs, which technology has shown the most improvement over time in terms of slowing its growth?" }, { "from": "gpt", "value": "To determine which technology has improved the most in slowing its growth, I would analyze the curve representing the Slow Growth score and find the technology with the most positive score (i.e., the least negative). The {Slow Growth scores} [[0.151, 0.833], [0.318, 0.897], [0.487, 0.953], [0.656, 0.830], [0.819, 0.785]] contain the labels for the respective technologies. Through analysis, I can see that the most positive score corresponds to {Virtual Reality}[0.819, 0.156] with a score of -0.6, indicating it has slowed its growth the most.\nBased on the above thought, the answer is:\nBased on the progress shown in the graphs, Virtual Reality technology seems to have shown the most improvement over time in terms of slowing its growth. Despite it's late introduction, its higher score (less negative compared to other technologies) on the slow growth chart suggests that the rate of growth has decelerated more than the other technologies, indicating its slower, but potentially more sustainable, adoption." } ], "id_raw": 1056 }, { "id": "JtsQlOvIetcPtElZ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_16.png", "conversations": [ { "from": "human", "value": "\nWhich technology shows the least decline in terms of score?" }, { "from": "gpt", "value": "To find out which technology has the least decline, I need to identify the highest score (i.e., the least negative) among {the Decline scores}[[0.151, 0.362], [0.318, 0.436], [0.487, 0.507], [0.656, 0.568], [0.819, 0.664]]. After extracting the corresponding scores and their respective technologies, it's clear that the least decline, with a score of -0.9, corresponds to {Virtual Reality}[0.819, 0.156].\nBased on the above thought, the answer is:\nVirtual Reality." } ], "id_raw": 1056 }, { "id": "ULphYdcwWsW5mKp7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_17.png", "conversations": [ { "from": "human", "value": "\nCould you extract the information in the line chart into a table format where the columns represent Quarters and the rows represent different measurements such as GDP Growth, Unemployment Rate, and Inflation Rate for both USA and China?" }, { "from": "gpt", "value": "Sure. To generate the table, I identify {the x-axis tick labels}[0.362, 0.945, 0.382, 0.959],[0.516, 0.945, 0.535, 0.959],[0.669, 0.945, 0.688, 0.959],[0.822, 0.945, 0.842, 0.959] as the column headers representing Quarters. I then look at {the legend texts}[0.117, 0.027, 0.235, 0.041],[0.117, 0.048, 0.246, 0.062],[0.117, 0.069, 0.293, 0.083],[0.117, 0.090, 0.304, 0.104],[0.117, 0.111, 0.243, 0.125],[0.117, 0.132, 0.255, 0.146] as the row labels representing different measurements for USA and China. To determine the values for each cell, I look at {the line points for each line}[0.372, 0.730],[0.525, 0.899],[0.679, 0.057],[0.832, 0.695] and extract the corresponding values.\nBased on the above thought, the answer is:\nCategory, Q1, Q2, Q3, Q4\nUSA GDP Growth, 2.7, -5.0, 33.4, 4.3\nChina GDP Growth, 6.7, 3.2, 4.9, 6.5\nUSA Unemployment Rate, 4.4, 11.1, 7.8, 6.7\nChina Unemployment Rate, 3.8, 5.7, 5.4, 5.2\nUSA Inflation Rate, 1.8, 0.6, 1.2, 1.2\nChina Inflation Rate, 4.3, 2.5, 1.7, 0.2" } ], "id_raw": 1057 }, { "id": "zAA0nyeRMRBAe8Qd", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_17.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, how did the GDP Growth rate behave for USA and China throughout the year? Explain your reasoning." }, { "from": "gpt", "value": "To understand the behavior of the GDP Growth rate for both USA and China, I closely look at {the line for USA GDP Growth}[0.372, 0.730, 0.832, 0.695] and {China GDP Growth}[0.372, 0.642, 0.832, 0.647]. For USA, the GDP Growth starts with a positive figure at Q1, dips into negative in Q2, dramatically recovers in Q3, and stabilizes in Q4. For China, the line indicates a substantial growth in Q1, dips in Q2, and then gradually increases towards Q3 and Q4.\nBased on the above thought, the answer is:\nBased on the line chart, the GDP Growth for USA had a roller-coaster trend. The GDP Growth started positively in Q1 but had a significant decline in Q2. Then a sharp recovery was seen in Q3 before the GDP Growth rate stabilized in Q4. For China, the GDP Growth rate began significantly high in Q1, had a decline in Q2, then showed a steady recovery in Q3 and Q4, indicating a more stable economic environment in China over the year compared with the USA." } ], "id_raw": 1057 }, { "id": "KzhlBcvduRR7SYtZ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_17.png", "conversations": [ { "from": "human", "value": "\nWhat was the maximum GDP Growth rate for China according to the chart provided?" }, { "from": "gpt", "value": "To find the maximum GDP Growth rate for China, I will look at {the line points for 'China GDP Growth'}[0.372, 0.642],[0.525, 0.719],[0.679, 0.682],[0.832, 0.647]. The point coordinates indicate the value at each quarter. The highest point is at [0.525, 0.719], corresponding to a GDP Growth rate of 3.2 in Q2.\nBased on the above thought, the answer is:\n3.2." } ], "id_raw": 1057 }, { "id": "CdXuDoj7hNrB0k9D", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_53_19.png", "conversations": [ { "from": "human", "value": "\nFrom the line graph, can you extract the data into table format with the Year as columns and the Console type as rows?" }, { "from": "gpt", "value": "To create a table from the line graph, first, I identify {the x-axis ticks}[0.211, 0.236, 0.359, 0.384, 0.508, 0.533, 0.656, 0.681, 0.805, 0.830] as the column headers representing the years. Next, I recognize {the legend items}[0.097, 0.031, 0.331, 0.127] as the row headers, which represent the different console types. Then, I extract the {line points}[0.236, 0.062, 0.830, 0.774, 0.236, 0.233, 0.830, 0.599, 0.236, 0.320, 0.830, 0.877] for the number of units sold for each console in the respective year.\nBased on the above thought, the answer is:\nConsole,2020,2021,2022,2023,2024\nPlayStation 5,7.8,14.9,22.1,28.6,37.3\nNintendo Switch,15.05,17.09,20.21,24.1,30.2\nXbox Series X,3.5,8.6,13.8,19.2,26.6" } ], "id_raw": 1059 }, { "id": "NAFfo0a5kha5jkgy", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_53_19.png", "conversations": [ { "from": "human", "value": "\nAnalyzing the graph, which gaming platform had the steepest growth in units sold from 2020 to 2024? Provide an explanation for your answer." }, { "from": "gpt", "value": "To identify the platform with the steepest growth, I examine the slopes of the lines for each console. The line representing {PlayStation 5}[0.236, 0.062, 0.830, 0.774] goes from 7.8 million in 2020 to 37.3 million in 2024. The line representing {Nintendo Switch}[0.236, 0.233, 0.830, 0.599] goes from 15.05 million to 30.2 million in those same years. The line displaying {Xbox Series X}[0.236, 0.320, 0.830, 0.877] spans from 3.5 million units in 2020 to 26.6 million in 2024. By comparing the changes in units sold over these years, we can determine which platform had the steepest growth.\nBased on the above thought, the answer is:\nBased on the graph, it appears that the PlayStation 5 had the steepest growth in units sold from 2020 to 2024. From its launch in 2020, the sold units of the PlayStation 5 raised from 7.8 million to 37.3 million, an increase of nearly 30 million units, the highest among all three gaming platforms within the period. This growth is more significant in comparison to the Nintendo Switch, and Xbox Series X which saw increases of about 15 million units and 23 million units respectively in the same time range." } ], "id_raw": 1059 }, { "id": "AVqLKylIaPcGCYA7", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_53_19.png", "conversations": [ { "from": "human", "value": "\nHow many units more did the PlayStation 5 sell in 2024 than in its launch year?" }, { "from": "gpt", "value": "First, I locate the sales of PlayStation 5 in the chart. For the launch year 2020, the value is at {the point}[0.236, 0.774]. For 2024, the value resides at {the point}[0.830, 0.062]. The corresponding values are 7.8 million and 37.3 million respectively. The difference can be calculated by subtracting the 2020 value from the 2024 value, which is 37.3 - 7.8.\nBased on the above thought, the answer is:\n29.5 million units." } ], "id_raw": 1059 }, { "id": "0I1annH2zbAQLsiJ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_0.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table format of the experiment data from the line chart, with 'Duration' as rows and 'Categories' as columns?" }, { "from": "gpt", "value": "Observing the line chart, I identify {the x-axis labels}[0.149, 0.215, 0.936, 0.231] which include the different durations: 3, 5, 7, and 10 minutes. Next, I look at {the legend}[0.420, 0.054, 0.665, 0.075] which specifies two categories, 'Introversion' and 'Extroversion'. I then examine {the line points}[0.149, 0.796, 0.936, 0.948] for each category across the respective durations to extract their numerical values.\nBased on the above thought, the answer is:\nDuration,Introversion,Extroversion\n3,-2.5,-1.0\n5,-1.8,-2.2\n7,-1.0,-3.0\n10,-0.7,-0.8" } ], "id_raw": 1060 }, { "id": "rf8EVHCtvcBrIjXs", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_0.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about 'Introversion' in terms of its pattern across the various experiment durations?" }, { "from": "gpt", "value": "Analyzing {the line for Introversion}[0.149, 0.796, 0.936, 0.948], it starts at -2.5 at 3 minutes, peaks at -1.0 at 7 minutes, and then slightly drops to -0.7 at 10 minutes. This indicates that the score improves (gets less negative) as the duration increases until it peaks at 7 minutes, after which it shows a minor decrease by 10 minutes, suggesting a fluctuating pattern over time.\nBased on the above thought, the answer is:\n'Introversion' displays a fluctuating pattern across different experimental durations. Starting from a score of -2.5, it gradually improves until peaking at -1.0, and finally concludes with slight deterioration to -0.7 by 10 minutes. This trend suggests that initial conditions might improve with extended exposure up to a midpoint, after which it slightly reverts but maintains an improved state compared to the beginning." } ], "id_raw": 1060 }, { "id": "JryNh9yVVhLEDj3U", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of scores for the category 'Extroversion' during the experiment as seen in the chart?" }, { "from": "gpt", "value": "To determine the range of scores for 'Extroversion', I reference {the line for Extroversion}[0.149, 0.445, 0.936, 0.454] to note its highest score at 10 minutes and lowest score at 7 minutes. The highest score is -0.8 and the lowest score is -3.0. Calculating the range involves subtracting the lowest score from the highest score: -0.8 - (-3.0) = 2.2.\nBased on the above thought, the answer is:\n2.2." } ], "id_raw": 1060 }, { "id": "Hb4Edy8pcNM8CNiB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_1.png", "conversations": [ { "from": "human", "value": "\nCan you transform the online learning data from the chart into a structured table with years as rows and each category as columns?" }, { "from": "gpt", "value": "To generate the structured table from the chart, I first identify {the x-axis labels for years}[0.153, 0.215, 0.975, 0.231] which will become the row headers in the table. There are four subplots in total indicating the different categories; Online Learning Resources, Students Enrolled, Online Courses Developed, and Online Learning Platforms. I proceed by extracting values for each category at respective years by examining points on the line graphs: {the line for Online Learning Resources}[0.191, 0.055, 0.938, 0.207], {the line for Students Enrolled}[0.191, 0.302, 0.938, 0.454], {the line for Online Courses Developed}[0.191, 0.549, 0.938, 0.701], and {the line for Online Learning Platforms}[0.191, 0.796, 0.938, 0.948].\nBased on the above thought, the answer is:\nYear,Online Learning Resources,Students Enrolled,Online Courses Developed,Online Learning Platforms\n2010,20,30000,10,4\n2011,25,40000,12,4\n2012,28,50000,16,5\n2013,34,60000,26,6\n2014,40,70000,40,8\n2015,42,80000,48,10\n2016,48,85000,55,11\n2017,56,91000,62,12\n2018,66,100000,70,13\n2019,70,110000,75,15\n2020,80,150000,80,16" } ], "id_raw": 1061 }, { "id": "qz5ppehdnHbqW8di", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_1.png", "conversations": [ { "from": "human", "value": "\nHow does the development of online learning platforms correlate with the growth of online courses developed over the years observed?" }, { "from": "gpt", "value": "To analyze how the growth of online courses developed correlates with the development of online learning platforms, I examine {the line for Online Courses Developed}[0.191, 0.549, 0.938, 0.701] and {the line for Online Learning Platforms}[0.191, 0.796, 0.938, 0.948]. Throughout the years from 2010 to 2020, as the number of online courses developed consistently increased from 10 to 80, the online learning platforms also increased from 4 platforms to 16. This indicates a positive correlation, where the development of more courses appears to drive the increase in the number of learning platforms.\nBased on the above thought, the answer is:\nThe development of online learning platforms is positively correlated with the growth of online courses developed. As the number of online courses significantly increased, so did the number of learning platforms, reinforcing the notion that expanded course offerings are likely contributing to the expansion of platform development to accommodate the growing needs." } ], "id_raw": 1061 }, { "id": "50SJBx3Uct59DPBx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_1.png", "conversations": [ { "from": "human", "value": "\nHow much did the enrolled student numbers grow from 2010 to 2020?" }, { "from": "gpt", "value": "To find how much student enrollment numbers grew from 2010 to 2020, I utilize {the line for Students Enrolled}[0.191, 0.302, 0.938, 0.454], pinpointing the values at both 2010 and 2020. For 2010, the enrollment is at {30,000}[0.191, 0.454], and for 2020, it is at {150,000}[0.938, 0.302]. Therefore, the growth in the student numbers is 150,000 - 30,000.\nBased on the above thought, the answer is:\n120000" } ], "id_raw": 1061 }, { "id": "AJQMglyX4EsS5ypf", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data illustrated in the chart into a structured table separating values based on the years and categories presented?" }, { "from": "gpt", "value": "Observing the line plots in the chart, I begin by identifying {the x-axis labels}[0.122, 0.975, 0.975, 0.975] which indicate two distinct years '2020' and '2021'. For each year, I discern four categories: 'Undergraduate Enrollment', 'Postgraduate Enrollment', 'Employee Training Hours', and 'Gradulation rate', whose values are represented by the line points across two {subplots}[0.122, 0.039, 0.975, 0.215], [0.122, 0.286, 0.975, 0.462]. Following the points, I capture data for each category within the corresponding year.\nBased on the above thought, the answer is:\nYear, Category, Value\n2020, Undergraduate Enrollment, 3.4\n2020, Postgraduate Enrollment, 2.8\n2020, Employee Training Hours, 5.2\n2020, Graduation Rate, 2.1\n2021, Undergraduate Enrollment, 3.6\n2021, Postgraduate Enrollment, 2.9\n2021, Employee Training Hours, 5.5\n2021, Graduation Rate, 2.2" } ], "id_raw": 1062 }, { "id": "ElVxjNFS7eTIuyzX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_2.png", "conversations": [ { "from": "human", "value": "\nConsidering the data trends for the period covered, which category demonstrates the most significant increase from 2020 to 2021?" }, { "from": "gpt", "value": "To evaluate the most significant increase, I will compare the changes in values for each category between 2020 and 2021 by observing the positions of their representative lines in both {the first subplot}[0.122, 0.039, 0.975, 0.215] and {the second subplot}[0.122, 0.286, 0.975, 0.462]. Noting the starting and ending points of each line, it appears that the 'Employee Training Hours' category shows a more pronounced upward trend from approximately 5.2 to 5.5, which indicates the highest relative increase among the categories.\nBased on the above thought, the answer is:\nThe 'Employee Training Hours' category demonstrates the most significant increase from 2020 to 2021. The comparative analysis of line movements between the two years reflects this heightened growth, especially when observed through the vertical shifts in its line representation on the chart." } ], "id_raw": 1062 }, { "id": "18BB4dxgUPGbJcbi", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the total number of enrollment (Undergraduate + Postgraduate) for the year 2021 indicated on the chart?" }, { "from": "gpt", "value": "First, I will look for the values of 'Undergraduate Enrollment' and 'Postgraduate Enrollment' for 2021 in the chart. I locate the exact points on {the second subplot}[0.122, 0.286, 0.975, 0.462] for 2021. The 'Undergraduate Enrollment' is positioned at approximately 3.6 and 'Postgraduate Enrollment' at about 2.9 given their respective line positions. Adding these values sums up the total enrollment for the year.\nBased on the above thought, the answer is:\n6.5" } ], "id_raw": 1062 }, { "id": "DirCKNA5kHWf8MRB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_3.png", "conversations": [ { "from": "human", "value": "\nCan you convert the trend data presented in the line chart for the four energy sources into a structured table, showing the decade and respective indices for each source?" }, { "from": "gpt", "value": "To convert the trend data into a structured table, I first observe {the x-axis labels}[0.102, 0.219, 0.289, 0.460, 0.632, 0.803, 0.975], which denote the decades. Next, I focus on identifying the corresponding indices for each energy source by observing the data points along the lines for {Solar}[0.110, 0.178, 0.968, 0.198], {Hydropower}[0.110, 0.295, 0.968, 0.462], {Wind}[0.110, 0.542, 0.968, 0.709], and {Nuclear}[0.110, 0.788, 0.968, 0.955], which are represented with distinct colors and line styles. The corresponding values at each decade provide the data necessary for the table.\nBased on the above thought, the answer is:\nDecade,Solar,Hydropower,Wind,Nuclear\n2000,1.2,3.2,2.5,8.5\n2010,2.5,3.5,4.0,7.0\n2020,5.0,3.8,6.5,4.0\n2030,8.0,4.0,9.0,2.5" } ], "id_raw": 1063 }, { "id": "PDPfLFctIrc9atLC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_3.png", "conversations": [ { "from": "human", "value": "\nWhich energy source demonstrates the most consistent growth pattern in its utilization index over the decades?" }, { "from": "gpt", "value": "To identify the energy source with the most consistent growth, I analyze the incline and uniformity in the trajectory curves for each energy source. By examining {the line for Solar}[0.110, 0.178, 0.968, 0.198], I observe a steady incline with incremental but consistent leaps at each decade point. Comparatively, while {Wind}[0.110, 0.542, 0.968, 0.709] and {Hydropower}[0.110, 0.295, 0.968, 0.462] also show growth, the increases are not as uniform. {Nuclear}[0.110, 0.788, 0.968, 0.955], on the other hand, exhibits a downward trend.\nBased on the above thought, the answer is:\nSolar energy demonstrates the most consistent growth pattern in its utilization index over the decades. The plotted line for Solar consistently rises from 2000 to 2030, reflecting steady growth in its utilization." } ], "id_raw": 1063 }, { "id": "tdxmw15ap1PnVUcs", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the average utilization index for Wind energy over the observed decades?" }, { "from": "gpt", "value": "To calculate the average utilization index for Wind energy, I'll locate the indices from the line representing {Wind}[0.110, 0.542, 0.968, 0.709] across all observed decades. The indices noted are 2.5, 4.0, 6.5, and 9.0. By adding these values (2.5+4.0+6.5+9.0) and dividing by the total number of decades (4), I get the average.\nBased on the above thought, the answer is:\n5.5" } ], "id_raw": 1063 }, { "id": "1qCe1un8is9K7Lk1", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a table format with Trends as columns and Factories as rows?" }, { "from": "gpt", "value": "Observing the lines of each factory, I first identify the x-axis labels which represent the different trend stages: {the x-axis labels}[0.110, 0.960, 0.048, 0.215]. I then check the line coordinates and corresponding data points for each factory. For Factory A, I tally the efficiency values with the trend stages using {the line for Factory A}[0.110, 0.048, 0.960, 0.173]. Similarly, for Factory B, I study {the line for Factory B}[0.110, 0.295, 0.960, 0.462] to do the same.\nBased on the above thought, the answer is:\nTrends,Unpredictable Decline,Sustained Decline,Incremental Growth,Dramatic Surge,Sporadic Growth,Stable Growth,Periodic Fluctuations,Erratic Growth\nFactory A,75,49,68,92,70,90,75,66\nFactory B,80,65,70,40,69,85,80,62" } ], "id_raw": 1064 }, { "id": "SOrA5yOyfxo1NpoN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_4.png", "conversations": [ { "from": "human", "value": "\nWhich factory shows a more consistent performance across the trend stages according to the chart?" }, { "from": "gpt", "value": "By examining the line graphs, I need to determine the consistency of each factory\u2019s performance. Observing the line fluctuations, {the line for Factory A}[0.110, 0.048, 0.960, 0.173] shows variations such as a dramatic surge followed by a decline and recovery. In contrast, {the line for Factory B}[0.110, 0.295, 0.960, 0.462] maintains a relatively stable ascending trend before and after a sharp dip during the Dramatic Surge stage and overall shows fewer fluctuations after recovery.\nBased on the above thought, the answer is:\nFactory B shows a more consistent performance across the trend stages as indicated by their steady growth after recovering from a dramatic dip. The line plot for Factory B has fewer sharp fluctuations, suggesting a steadier performance compared to Factory A, which has higher variability in their trend stages." } ], "id_raw": 1064 }, { "id": "1oq0LhmOSKX7kut3", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the total sum of all efficiency values for Factory A across all trend stages?" }, { "from": "gpt", "value": "To determine the total sum of efficiency values for Factory A across all trend stages, I locate and sum the efficiencies at each trend stage, observed from {the line for Factory A}[0.110, 0.048, 0.960, 0.173]. The trend stages include Unpredictable Decline, Sustained Decline, Incremental Growth, Dramatic Surge, Sporadic Growth, Stable Growth, Periodic Fluctuations, and Erratic Growth.\nBased on the above thought, the answer is:\n595" } ], "id_raw": 1064 }, { "id": "7Y5DakOViRI88LkE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_5.png", "conversations": [ { "from": "human", "value": "\nCan you list the YoY growth rates by quarter for Consumer Electronics and Fast Moving Consumer Goods in a structured table layout?" }, { "from": "gpt", "value": "To extract the data into a table, I analyze the two sub-plots. Firstly, by observing {the top subplot for Consumer Electronics}[0.139, 0.048, 0.935, 0.215] and precisely locating the quarterly markers, I record the values correspondingly. Similarly, I evaluate {the bottom subplot for Fast Moving Consumer Goods}[0.139, 0.295, 0.935, 0.462] to gather the respective data points.\nBased on the above thought, the answer is:\nQuarter,Consumer Electronics, Fast Moving Consumer Goods\nQ1, -2.5%, 1.0%\nQ2, 3.0%, 1.5%\nQ3, 5.0%, 2.0%\nQ4, 8.5%, 3.5%" } ], "id_raw": 1065 }, { "id": "7D3Y0TvQx3oauckj", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_5.png", "conversations": [ { "from": "human", "value": "\nWhich sector displayed a more consistent growth pattern throughout 2020, based on the line plots?" }, { "from": "gpt", "value": "By studying the line trajectories, {the bottom subplot for Fast Moving Consumer Goods}[0.139, 0.295, 0.935, 0.462] indicates a steady growth with smaller increments in each quarter. Conversely, {the top subplot for Consumer Electronics}[0.139, 0.048, 0.935, 0.215] shows a highly variable trend with a substantial recovery post-Q1, followed by increasingly positive shifts.\nBased on the above thought, the answer is:\nThe Fast Moving Consumer Goods sector displayed a more consistent growth pattern throughout 2020 with a steady and gradual increase each quarter, unlike the more unstable growth pattern of the Consumer Electronics sector." } ], "id_raw": 1065 }, { "id": "l2uxv3WGqWnWqX7P", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the total yearly growth rate increase observed for the Consumer Electronics sector?" }, { "from": "gpt", "value": "Reviewing the summarized quarterly growth rates from {the line plot for Consumer Electronics}[0.139, 0.048, 0.935, 0.215]: Q1 (-2.5%), Q2 (3.0%), Q3 (5.0%), and Q4 (8.5%). Adding up these values gives the total yearly increase.\nBased on the above thought, the answer is:\n14.0%" } ], "id_raw": 1065 }, { "id": "8s2byIZgKjs008eZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the trends from the chart into a CSV table format showing the year against progress levels for 'EdTech Innovation'?" }, { "from": "gpt", "value": "To generate the CSV table from the chart, I'll review the data points for 'EdTech Innovation'. I start by examining {the x-axis labels}[0.110, 0.219, 0.946, 0.231] where the years are mentioned as '0','2','4','6','8', and '10', and {the points on the EdTech Innovation line}[0.117, 0.215, 0.960, 0.215] which indicate progress levels for each corresponding year. By mapping these coordinates to their respective years and progress levels, I will formulate the CSV table.\nBased on the above thought, the answer is:\nYear,EdTech Innovation Progress Level\n0,0.1\n2,0.3\n4,0.7\n6,1.2\n8,1.7\n10,2.5" } ], "id_raw": 1066 }, { "id": "KVB5wNNmHS4VebSv", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_6.png", "conversations": [ { "from": "human", "value": "\nWhich field between 'Software Tools' and 'Online Learning' shows a more consistent growth trend over the years according to the graph?" }, { "from": "gpt", "value": "By comparing {the line for Software Tools}[0.117, 0.323, 0.960, 0.456] and {the line for Online Learning}[0.117, 0.560, 0.960, 0.667], I observe the characteristics of each trend. The 'Software Tools' shows a consistent decrease, while 'Online Learning' demonstrates consistent upward progression until year 5.5, followed by a steep increase. The consistent increment in values for 'Online Learning' up to 5.5 years and the subsequent sharp rise shows a more consistent trend towards growth as compared to the gradual decline shown by 'Software Tools'.\nBased on the above thought, the answer is:\n'Online Learning' shows a more consistent growth trend over the years due to steady increases in progress level for the initial period followed by a significant upward trend, unlike 'Software Tools' which shows a consistent decrease." } ], "id_raw": 1066 }, { "id": "UK9ufqkWAwbcoCuT", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the rate of increase in progress level for 'Online Learning' from year 4 to year 6?" }, { "from": "gpt", "value": "Initially, I find {the points for Online Learning at year 4 and year 6}[0.447, 0.709, 0.623, 0.709]. The progress levels shown are 8.0 at year 4 and 11.8 at year 6. To calculate the rate of increase, I subtract the year 4 value from year 6 value and divide by the number of years between them, which is 2 years. (11.8 - 8.0) / 2 = 1.9.\nBased on the above thought, the answer is:\n1.9" } ], "id_raw": 1066 }, { "id": "PX7iVXgY9pOHLabH", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_7.png", "conversations": [ { "from": "human", "value": "\nCan you generate a structured table presenting the closing values for each financial market at distinct time points from the line chart?" }, { "from": "gpt", "value": "To create a structured table, I will analyze the line plots for each market. First, I observe {the New York Stock Exchange line plot}[0.1, 0.6, 0.9, 0.4] showing values starting at 1000, rising to 1200, and then dropping to 1050. Next, I look at {the NASDAQ line plot}[0.1, 0.4, 0.9, 0.45], noting a start at 2000, climbing to 2400, and settling at 2200. For {the London Stock Exchange line plot}[0.1, 0.45, 0.9, 0.5], values start at 1800 and grow consistently to 2100, then drop slightly to 1900. I analyze {the Tokyo Stock Exchange line plot}[0.1, 0.5, 0.9, 0.55] and note a slight increase from 1500 to 1650 and a decrease to 1600. Finally, I review {the Shanghai Stock Exchange line plot}[0.1, 0.55, 0.9, 0.6] and observe a decrease from 1300 to 1400, then a drop to 1250.\nBased on the above thought, the answer is:\nTime Point, New York Stock Exchange, NASDAQ, London Stock Exchange, Tokyo Stock Exchange, Shanghai Stock Exchange\nTime 0, 1000, 2000, 1800, 1500, 1300\nTime 20, 1200, 2400, 2100, 1650, 1400\nTime 40, 1050, 2200, 1900, 1600, 1250" } ], "id_raw": 1067 }, { "id": "0maRmMo6gR0oYea3", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_7.png", "conversations": [ { "from": "human", "value": "\nWhich financial market exhibited the most stable trend over the observation period according to the chart?" }, { "from": "gpt", "value": "To identify the most stable trend, the volatility or fluctuations in the line plots need to be analyzed. Observing the various plots, {the London Stock Exchange line}[0.1, 0.45, 0.9, 0.5] shows a stable, consistent growth from 1800 to 2100 and a minor decrease to 1900, reflecting minor fluctuations compared to others. For instance, {the New York Stock Exchange line}[0.1, 0.6, 0.9, 0.4] exhibits a significant rise and then a fall, indicating much higher volatility.\nBased on the above thought, the answer is:\nThe London Stock Exchange exhibited the most stable trend throughout the period. Its values show a consistent increase followed by a small reduction, making it relatively stable compared to the more fluctuating trends seen in other markets like the New York Stock Exchange." } ], "id_raw": 1067 }, { "id": "455VGX25pm6NQX9F", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase or decrease for the NASDAQ from Time 0 to Time 40?" }, { "from": "gpt", "value": "To calculate the total difference for NASDAQ from Time 0 to Time 40, I will review {the NASDAQ plot}[0.1, 0.4, 0.9, 0.45]. It starts at an index value of 2000 and ends at 2200. The calculation for the difference would be 2200 - 2000.\nBased on the above thought, the answer is:\n200." } ], "id_raw": 1067 }, { "id": "8klldAibdgisWHMp", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_8.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data showing resource allocation over the years into a CSV format with each resource as a column and years as rows?" }, { "from": "gpt", "value": "To convert the chart data into a CSV table format, I first identify {the x-axis tick labels}[0.110, 0.215, 0.946, 0.247] which signify the years from 2050 to 2100. These form the row headers. Next, I identify {the legend}[0.884, 0.101, 0.982, 0.123], which clarifies the different resource categories like Solar, Wind, Hydro, Nuclear, Thermal, and Fossil fuels, acting as the column headers. I then extract the values at each corresponding point along {the lines}[0.110, 0.048, 0.946, 0.215] for each resource over the years to populate the table.\nBased on the above thought, the answer is:\nYear,Solar,Wind,Hydro,Nuclear,Thermal,Fossil Fuels\n2050,10,15,30,25,8,12\n2055,12,18,28,23,10,10\n2060,15,20,25,20,12,9\n2065,18,23,24,18,15,7\n2070,20,25,20,15,18,5\n2075,23,28,15,12,20,4\n2080,25,30,10,10,23,3\n2085,28,33,5,8,25,2\n2090,30,35,2,5,28,1\n2095,33,38,0,3,30,0\n2100,35,40,0,0,33,0" } ], "id_raw": 1068 }, { "id": "kN1sxoE8s9CMnMFO", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_8.png", "conversations": [ { "from": "human", "value": "\nWhat can we infer about the trend of energy resources allocation towards more sustainable energy sources over the period from 2050 to 2100?" }, { "from": "gpt", "value": "By examining {the lines for Solar and Wind}[0.110, 0.048, 0.946, 0.215], it is evident that there is an increasing trend in the allocation for both these resources, indicating a move towards more sustainable energy sources. Particularly, the Solar resource allocation begins at 10% in 2050 and steadily increases to 35% by 2100, while Wind begins at 15% and rises to 40%, even higher than Solar. Conversely, resources like {Hydro and Nuclear}[0.110, 0.215, 0.946, 0.247] show a declining pattern over the same period. These observations suggest a clear intent and a significant push towards adopting renewable and less environmentally harmful energy options such as Solar and Wind while moving away from conventional and more ecologically damaging sources like Hydro and Nuclear.\nBased on the above thought, the answer is:\nThe trend over the years from 2050 to 2100 shows a significant movement towards more sustainable sources such as Solar and Wind, which have seen a substantial rise in their allocation percentages. At the same time, there is a decline in the allocation for traditional and potentially more harmful energy sources such as Hydro and Nuclear, reflecting an environmental and strategic shift in energy resource planning." } ], "id_raw": 1068 }, { "id": "1V5DDUrZduHAOJGk", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the average percentage increase in allocation for Wind and Solar resources from 2050 to 2100?" }, { "from": "gpt", "value": "First, I locate the values for {Solar}[0.110, 0.048, 0.946, 0.215] and {Wind}[0.110, 0.048, 0.946, 0.215] resources over the years. The values start from 10% and 15% in 2050 to 35% and 40% in 2100 respectively for Solar and Wind. The total increase for Solar and Wind is 25% and 25% respectively over 50 years. Dividing these by 50 gives the average annual increase, which is 0.5% per year for both Solar and Wind.\nBased on the above thought, the answer is:\n0.5%." } ], "id_raw": 1068 }, { "id": "1OPa7nnD5OpkekqI", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_10.png", "conversations": [ { "from": "human", "value": "\nCould you convert the technology interactions from the line chart into a structured table format with Technology Types as rows and Company Groups as columns?" }, { "from": "gpt", "value": "To create the table, I first identify {the x-axis labels}[0.095, 0.235, 0.970, 0.247] representing different technology types: Quantum Computing, Augmented Reality, Artificial Intelligence, e-Learning Software, Multimedia Networking, Interactive Whiteboard, and Virtual Classroom. Next, I observe {the legend}[0.792, 0.132, 0.893, 0.208] where the light shade represents EdTech companies and the dark shade represents startups. For each technology, I examine {the plot lines}[0.095, 0.048, 0.970, 0.215] and {0.095, 0.295, 0.970, 0.462] for point values for EdTech companies and startups, respectively.\nBased on the above thought, the answer is:\nTechnology,EdTech Companies,Startups\nQuantum Computing,5,15\nAugmented Reality,20,25\nArtificial Intelligence,35,45\ne-Learning Software,40,50\nMultimedia Networking,45,55\nInteractive Whiteboard,45,60\nVirtual Classroom,45,75" } ], "id_raw": 1070 }, { "id": "si7UKWm3PLd9eLPh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_10.png", "conversations": [ { "from": "human", "value": "\nWhy is there a higher interaction percentage with Virtual Classroom for startups compared to EdTech companies, as shown in the chart?" }, { "from": "gpt", "value": "Analyzing the chart, I focus on {the trend lines for Virtual Classroom}[0.890, 0.048, 0.970, 0.215] and [0.890, 0.295, 0.970, 0.462] for both EdTech companies and startups, respectively. Notably, the startup line shows a significant upward trend peaking much higher than the line for EdTech companies. Given the context of rapid adaptation to remote learning technologies by startups, it becomes evident that the higher interaction percentage can be attributed to the agile and modern approaches adopted by startups, which greatly embrace remote digital learning solutions like Virtual Classrooms.\nBased on the above thought, the answer is:\nStartups demonstrate a higher interaction percentage with the Virtual Classroom due to their quick adaptation to emerging technologies and a focus on scalable, remote learning solutions. The line trend reveals a significant disparity in adoption rates favoring startups, aligning with their strategy to capitalize on modern educational methods." } ], "id_raw": 1070 }, { "id": "I0783Uv4O8XxTBjt", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_10.png", "conversations": [ { "from": "human", "value": "\nWhat percentage does Augmented Reality technology account for startups based on the data from the chart?" }, { "from": "gpt", "value": "I need to locate {the line point for Augmented Reality in the startups line}[0.194, 0.295, 0.274, 0.462] and extract the exact value. I observe the chart indicating the intersection at approximately 25% for startups specifically for Augmented Reality.\nBased on the above thought, the answer is:\n25%" } ], "id_raw": 1070 }, { "id": "bE1ZnHYzWaHYkIQ0", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_11.png", "conversations": [ { "from": "human", "value": "\nCan you create a table derived from the line chart comparing the different years with the VC investment values?" }, { "from": "gpt", "value": "Observing {the x-axis labels}[0.124, 0.960, 0.966, 0.971] which indicate the years, and identifying {the VC investment values points}[0.163, 0.948, 0.936, 0.796] on the y-axis at each data point, I've established the relationship between the years and investment values across the line chart.\nBased on the above thought, the answer is:\nYear,VC Investment\n2008,1000000\n2009,1500000\n2010,1800000\n2011,2000000\n2012,2200000\n2013,2400000\n2014,2500000\n2015,2700000\n2016,3200000\n2017,3900000\n2018,5100000" } ], "id_raw": 1071 }, { "id": "JfcLt2cphBLeZeiP", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_11.png", "conversations": [ { "from": "human", "value": "\nWhich segment shows a continuous increase over the years in the provided data?" }, { "from": "gpt", "value": "By analyzing the trends from the line chart across the categories, I focus on examining the line behavior for each segment. In the {AI VC Investment subplot}[0.124, 0.788, 0.975, 0.955], the values increase over the years from 2008 to 2018, as indicated by the individual datapoints ranging from {initial point}[0.163, 0.948] to {final point}[0.936, 0.796].\nBased on the above thought, the answer is:\nThe AI VC Investment segment shows a continuous increase over the years in the provided data. This is evident from the upward trajectory of the line representing VC investments from 2008 to 2018." } ], "id_raw": 1071 }, { "id": "81NlvGh4D4DFPENV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the total growth in number of AI publications from 2008 to 2018?" }, { "from": "gpt", "value": "To compute the total growth in AI publications over these years, I will examine {the datapoints for AI Paper Publication}[0.163, 0.701, 0.936, 0.549]. In 2008, the number of publications was 1000 {first publication data point}[0.163, 0.701] and in 2018 it increased to 2600 {last publication data point}[0.936, 0.549]. The total growth is calculated by the difference, 2600 - 1000.\nBased on the above thought, the answer is:\n1600" } ], "id_raw": 1071 }, { "id": "RB6QVogOMkRga6Pf", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_12.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data from the line chart showing various sectors' growth into a structured table format?" }, { "from": "gpt", "value": "Upon analyzing the line chart, I observed the years mentioned on {the x-axis}[0.117, 0.973, 0.235, 0.247] ranging from 2010 to 2020. I identify four distinct lines representing different sectors, indicated by different colors as per {the legend}[0.87, 0.15, 0.93, 0.25]. To extract the data, I need to trace back the growth points for each sector along the y-axis, reading their numerical values at each year's mark on the x-axis.\nBased on the above thought, the answer is:\nYear,Software,Hardware,E-commerce,Internet Service Providers\n2010,5,8,14,10\n2011,6,9,15,10\n2012,7,10,16,10\n2013,8,11,14,10\n2014,9,12,12,9\n2015,7,13,9,9\n2016,5,14,7,9\n2017,4,15,5,9\n2018,3,16,5,8\n2019,2,17,4,8\n2020,1,18,4,8" } ], "id_raw": 1072 }, { "id": "dljD1mkj8hShQpBR", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_12.png", "conversations": [ { "from": "human", "value": "\nHow did the growth trends of the Software and E-commerce sectors compare over the 10-year period according to the chart?" }, { "from": "gpt", "value": "Analyzing the chart, I notice that {the Software line}[0.156, 0.55, 0.935, 0.796] initially shows gradual growth from 2010 to 2015, followed by a decline. On the other hand, {the E-commerce line}[0.156, 0.229, 0.935, 0.701] demonstrates sharp fluctuations; it starts with a strong growth up to 2012, experiences a sharp decline until 2017, and then slightly recovers. Comparatively, the Software sector displayed more steady growth initially, while the E-commerce sector was more volatile with significant fluctuations.\nBased on the above thought, the answer is:\nBoth the Software and E-commerce sectors showed initial growth, but the Software sector had a steadier earlier phase followed by a decline, whereas the E-commerce sector experienced more prominent highs and lows, making it more volatile over the 10-year period depicted in the chart." } ], "id_raw": 1072 }, { "id": "zxu7KHDpDvgUM2Oj", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the average growth for the Hardware sector from 2015 to 2020?" }, { "from": "gpt", "value": "To calculate the average growth, I observe {the points on the Hardware line}[0.54, 0.33, 0.937, 0.15] from the years 2015 to 2020. The respective values are: 13, 14, 15, 16, 17, 18. To find the average, I sum these values (13+14+15+16+17+18) and divide by the number of years, which is 6.\nBased on the above thought, the answer is:\n15.5" } ], "id_raw": 1072 }, { "id": "jJ9RVFX4Od5AZ2Kw", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_13.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table format of the data represented in the bar chart for the yields of different asset types from 2016 to 2019?" }, { "from": "gpt", "value": "To create a table format, I analyze the bar chart by first identifying {the x-axis labels}[0.117, 0.215, 0.960, 0.247] which denote the years 2016 to 2019. For each year, I observe each asset type's bar - {Equities}[0.117, 0.215, 0.124, 0.695], {Bonds}[0.134, 0.215, 0.140, 0.680], {Commodities}[0.150, 0.215, 0.157, 0.660], and {Cryptocurrency}[0.166, 0.215, 0.173, 0.895] - noting their height as indicative of yield percentages. Next, I catalogue these observations into corresponding year and asset type columns and rows.\nBased on the above thought, the answer is:\nYear,Equities,Bonds,Commodities,Cryptocurrency\n2016,5.0%,2.0%,3.0%,9.0%\n2017,4.5%,2.5%,3.5%,10.0%\n2018,4.0%,3.0%,3.0%,4.0%\n2019,3.5%,3.5%,2.5%,6.5%" } ], "id_raw": 1073 }, { "id": "YGym093dKTAaHJAO", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_13.png", "conversations": [ { "from": "human", "value": "\nWhich asset type showed the highest volatility in yield from 2016 to 2019 and why?" }, { "from": "gpt", "value": "To assess the volatility of yields for different asset types, I examine the changes in bar heights over the years. For {Equities}[0.117, 0.695, 0.124, 0.739], I observe a steady decline. {Bonds}[0.134, 0.680, 0.140, 0.735] show a consistent increase. {Commodities}[0.150, 0.660, 0.157, 0.717] experience modest fluctuations. However, {Cryptocurrency}[0.166, 0.895, 0.173, 0.940] displays the highest volatility with a major drop in 2018 followed by a substantial recovery in 2019.\nBased on the above thought, the answer is:\nCryptocurrency showed the highest volatility in yield from 2016 to 2019. This is evident from its substantial drop in 2018 and sharp increase in 2019, demonstrating significant fluctuations compared to the more steady trends of other asset types." } ], "id_raw": 1073 }, { "id": "XJFyKrYr6ZfoaQo9", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in yield for Bonds from 2016 to 2019?" }, { "from": "gpt", "value": "To find the total increase in yield for Bonds from 2016 to 2019, I look at their bar heights across the years. The yields are {2016}[0.134, 0.890, 0.140, 0.895]: 2.0%, {2017}[0.134, 0.850, 0.140, 0.860]: 2.5%, {2018}[0.134, 0.810, 0.140, 0.825]: 3.0%, and {2019}[0.134, 0.765, 0.140, 0.785]: 3.5%. Calculating the difference between the initial yield in 2016 and the final yield in 2019 gives 3.5% - 2.0% = 1.5%.\nBased on the above thought, the answer is:\n1.5%" } ], "id_raw": 1073 }, { "id": "CBNOIwLbfregi235", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_14.png", "conversations": [ { "from": "human", "value": "\nCould you transform the data presented in the bar chart regarding energy production into a structured CSV format sorted by year?" }, { "from": "gpt", "value": "Examining the bar chart, I've identified the groups for energy sources labeled as Coal, Oil, Natural Gas, and Renewables on {the x-axis labels}[0.095, 0.219, 1.070, 0.231]. The corresponding production values for each year from 2020 to 2033 are captured on the y-axis from {the left most bar}[0.124, 0.137] to {the right most bar}[0.975, 0.215] for Coal, and similarly spotted for other energy types. I will compile these values into a structured format.\nBased on the above thought, the answer is:\nYear,Coal,Oil,Natural Gas,Renewables\n2020,4300,4800,3100,2000\n2021,4200,4900,3200,2200\n2022,4100,5000,3300,2400\n2023,4000,5100,3400,2600\n2024,3900,5200,3500,2800\n2025,3800,5300,3600,3000\n2026,3700,5300,3700,3200\n2027,3600,5400,3800,3400\n2028,3500,5500,3900,3600\n2029,3400,5500,4000,3800\n2030,3300,5600,4100,4000\n2031,3200,5700,4200,4200\n2032,3100,5700,4300,4400\n2033,3000,5800,4400,4600" } ], "id_raw": 1074 }, { "id": "4HTMT6zTIy0N8J9Z", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_14.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which energy source shows the most significant increase in production over the years?" }, { "from": "gpt", "value": "Comparing each line within the chart, I note {the line for Renewables}[0.124, 0.788, 0.975, 0.955] starts relatively lower than the others. It shows a consistent upward trajectory significantly steeper compared to {the line for Oil}[0.124, 0.295, 0.975, 0.354] and others, culminating higher by the end of 2033. This suggests that out of all energy sources, Renewables has demonstrated the most substantial increase.\nBased on the above thought, the answer is:\nRenewables show the most significant increase in production over the years, reflected by its steeper and persistent upward trend in the chart." } ], "id_raw": 1074 }, { "id": "PPM3xTHfWojnOfsA", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the average annual decrease in coal production from 2020 to 2033 as shown in the chart?" }, { "from": "gpt", "value": "Reviewing the bar chart, each bar such as those starting from the left {coal production bar}[0.124, 0.137] through to {2020aligned with 2033}[0.975, 0.215] represents annual production of coal from 2020 to 2033. The values range from 4300 GWh in 2020 to 3000 GWh in 2033. The difference over 13 years is 4300 - 3000 = 1300 GWh. The average annual decrease is 1300 GWh divided by 13 years.\nBased on the above thought, the answer is:\n100 GWh" } ], "id_raw": 1074 }, { "id": "HYRuvTadYDr75rOo", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_15.png", "conversations": [ { "from": "human", "value": "\nCan you provide the values of expenditures shown in the line chart for each department from 2010 to 2020 in a CSV format?" }, { "from": "gpt", "value": "To convert the data displayed in the line chart into a CSV format, I analyze each line representing a department. I begin by identifying {the line for Recreational Services}[0.1, 0.1, 0.9, 0.6], noting its starting and ending points and the corresponding expenditure values across the years marked by {the x-axis tick labels}[0.1, 0.9], and then follow the same procedure for {the line for Infrastructure Development}, {the line for Community Health}, {the line for Transport}, and {the line for Social Services}. I extract the expenditure values using the y-axis scale provided by {the y-axis tick labels}[0.1, 0.6].\nBased on the above thought, the answer is:\nYear,Recreational Services,Infrastructure Development,Community Health,Transport,Social Services\n2010,45,18,25,20,22\n2011,43,19,24,19,23\n2012,42,20,23,18.5,21.5\n2013,40,22,22.5,18,24\n2014,38,24,22,17.5,23\n2015,37,26,21,18,23.5\n2016,35,28,20.5,18.5,24\n2017,33,30,20,19,24.5\n2018,31,32,19.5,20,25\n2019,29,34,19,20.5,25.5\n2020,27,36,18.5,21,26" } ], "id_raw": 1075 }, { "id": "5eZfo79sjlQthV3q", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_15.png", "conversations": [ { "from": "human", "value": "\nWhich department experienced the most drastic change in their expenditure pattern and how?" }, { "from": "gpt", "value": "To ascertain which department experienced the most drastic change in expenditure pattern over the decade, I compare the slopes of the lines for each department from 2010 to 2020. Starting with {the line for Recreational Services}[0.1, 0.1, 0.9, 0.6], the decline is steady yet moderate. Next, observing {the line for Infrastructure Development}[0.1, 0.2, 0.9, 0.7], there is a consistent and sharp increase, reflecting the highest upward shift. The changes for {the line for Community Health}, {the line for Transport}, and {the line for Social Services} are less significant in comparison and display various fluctuations.\nBased on the above thought, the answer is:\nInfrastructure Development experienced the most drastic change in their expenditure pattern, with a continuous and sharp increase from 2010 to 2020. The upward trajectory is significantly steeper compared to the other departments, indicating the most substantial change in funding allocation over the period." } ], "id_raw": 1075 }, { "id": "SDvrCP11ShJIcrfs", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in expenditure for Infrastructure Development from 2010 to 2020?" }, { "from": "gpt", "value": "To find the total increase in expenditure for Infrastructure Development from 2010 to 2020, I first identify {the line for Infrastructure Development}[0.1, 0.2, 0.9, 0.7] in the line chart. I extract the expenditure value for the year 2010 from {the y-tick at the start of the line for Infrastructure Development}[0.1, 0.2] and for the year 2020 from {the y-tick at the end of the line for Infrastructure Development}[0.9, 0.7]. Calculating the difference between these two values gives the total increase over the decade.\nBased on the above thought, the answer is:\n18 million dollars" } ], "id_raw": 1075 }, { "id": "XaAsSCDprv71YMav", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_16.png", "conversations": [ { "from": "human", "value": "\nCan you extract the progression of Application A from the chart data into a table?" }, { "from": "gpt", "value": "To extract the progression of Application A, I analyze {the line for Application A}[0.117, 0.044, 0.960, 0.154] across the chart. Each point along this line corresponds to the specified time intervals, indicated by {the x-axis tick labels}[0.110, 0.219, 0.124, 0.231]. Values are extracted directly from the positions of {the points along Application A's line}[0.117, 0.154], [0.159, 0.129], ..., [0.960, 0.056].\nBased on the above thought, the answer is:\nTime,Application A\n0,20\n0.5,22\n1.0,23\n1.5,25\n2.0,21\n2.5,24\n3.0,22\n3.5,21\n4.0,24\n4.5,25\n5.0,24\n5.5,23\n6.0,24\n6.5,22\n7.0,24\n7.5,26\n8.0,25\n8.5,27\n9.0,28\n9.5,29\n10.0,28" } ], "id_raw": 1076 }, { "id": "7A4jX81tHESQyMIG", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_16.png", "conversations": [ { "from": "human", "value": "\nHow does the trend in Application D's progression reflect the chart's subtitle \"Periodic Spikes\"?" }, { "from": "gpt", "value": "To assess the trend in Application D's performance referenced by {the subtitle \"Periodic Spikes\"}[0.435, 0.754, 0.642, 0.769], I inspect how {the line for Application D}[0.117, 0.800, 0.960, 0.955] behaves over time. Observing the peaks and troughs in {the points on the line for Application D}[0.117, 0.955], [0.159, 0.946], ..., [0.960, 0.818], I notice significant fluctuations which exemplify the label of periodic spikes, with values frequently rising and dipping in a regular cycle.\nBased on the above thought, the answer is:\nThe trend in Application D's progression clearly exhibits a series of rises and dips at regular intervals, corresponding well with the subtitle \"Periodic Spikes\" as visually demonstrated by sharp changes in value at consistent periods. This reflects an attribute where performance sharply increases and then decreases, giving a spiky appearance to the graph." } ], "id_raw": 1076 }, { "id": "Am2oK3maj2vwsR8C", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest value recorded for Application B in the given time frame?" }, { "from": "gpt", "value": "To find the highest value for Application B over the given time frame, I need to look over {the line for Application B}[0.117, 0.284, 0.960, 0.446] and analyze {the point values}[0.117, 0.436], [0.159, 0.425], ..., [0.960, 0.284] shown on the graph. By checking these points, I locate the maximally recorded value along this line.\nBased on the above thought, the answer is:\n59" } ], "id_raw": 1076 }, { "id": "8uj74FxSX7kSNKs2", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_17.png", "conversations": [ { "from": "human", "value": "\nCan you translate the line chart showing Entrepreneurship and Innovation strength over time into a structured table?" }, { "from": "gpt", "value": "To create a structured table from the line chart, first I identify {the x-axis tick labels}[0.082, 0.228, 0.959, 0.239] which specify the timepoints from 0 to 10 along the timeline. Next, observing {the points on the Entrepreneurship line}[0.088, 0.787, 0.970, 0.913] and {the points on the Innovation line}[0.082, 0.716, 0.970, 0.712], I extract the strength values for each topic at each timepoint. The line chart provides direct visual access to these values which can be plotted in the table format below.\nBased on the above thought, the answer is:\nTime,Entrepreneurship,Innovation\n0,2.0,0.9\n0.5,1.98,0.87\n1.0,1.96,0.84\n1.5,1.95,0.81\n2.0,1.93,0.8\n2.5,1.92,0.78\n3.0,1.90,0.76\n3.5,1.88,0.74\n4.0,1.87,0.72\n4.5,1.85,0.7\n5.0,1.83,0.7\n5.5,1.82,0.72\n6.0,1.81,0.74\n6.5,1.80,0.76\n7.0,1.78,0.78\n7.5,1.77,0.8\n8.0,1.76,0.87\n8.5,1.75,0.9\n9.0,1.74,0.94\n9.5,1.70,0.98\n10.0,1.7,1.0" } ], "id_raw": 1077 }, { "id": "b3mbTj8PJqK6D1xK", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_17.png", "conversations": [ { "from": "human", "value": "\nWhy is the Innovation trend considered more resilient compared to the Entrepreneurship trend?" }, { "from": "gpt", "value": "To determine why Innovation is considered more resilient, I analyze the overall slopes and deviations of both trends from the structured data. Observing {the Innovation line}[0.082, 0.716, 0.970, 0.712] and {the Entrepreneurship line}[0.088, 0.787, 0.970, 0.913], it's clear that the Entrepreneurship line has a steeper slope indicating rapid declines compared to the more gradual declines in the Innovation line. Additionally, the sudden drops as outliers in Entrepreneurship are more pronounced, signifying less stability.\nBased on the above thought, the answer is:\nInnovation is considered more resilient because its trend line demonstrates a gentler slope with less drastic drops compared to the steep decline seen in the Entrepreneurship trend. This indicates that while both trends decline, Innovation shows greater stability and a slower rate of decline, making it more resilient in the face of negative changes within this ecosystem." } ], "id_raw": 1077 }, { "id": "IFPl0s1WGRHUTvXb", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the largest drop in strength for Innovation between two consecutive timepoints?" }, { "from": "gpt", "value": "To find the largest drop in strength for Innovation, I need to evaluate the differences between consecutive values along {the Innovation line}[0.082, 0.716, 0.970, 0.712]. Noticing the points, the drop from timepoint 8.0 to 8.5 (0.87 to 0.9) and timepoint 9.0 to 9.5 (0.94 to 0.98) shows an increase rather than a drop. However, the decrease from timepoint 3.0 to 3.5 (0.76 to 0.74) shows a drop by 0.02, which appears to be one of the significant consecutive drops observed.\nBased on the above thought, the answer is:\n0.02" } ], "id_raw": 1077 }, { "id": "cEY6K4puh78XEjU1", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_18.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart representing Global Trade over the years into a structured table?" }, { "from": "gpt", "value": "Initially, I identify {the x-axis labels}[0.061, 0.219, 0.119, 0.231], [0.202, 0.219, 0.260, 0.231], [0.343, 0.219, 0.401, 0.231], [0.484, 0.219, 0.542, 0.231], [0.625, 0.219, 0.683, 0.231], [0.766, 0.219, 0.825, 0.231], [0.907, 0.219, 0.965, 0.231] which correspond to the years. Then, reviewing {the line for Global Trade}[0.160, 0.055, 0.936, 0.207], I extract the value points: [{point for 2010}[0.202, 0.192], {point for 2012}[0.343, 0.184], {point for 2014}[0.484, 0.177], {point for 2016}[0.625, 0.101], {point for 2018}[0.766, 0.071], {point for 2020}[0.907, 0.086]]. These coordinates help pinpoint actual trade values on the y-axis to construct the table.\nBased on the above thought, the answer is:\nYear,Global Trade\n2010,0.9\n2012,0.95\n2014,1.0\n2016,1.5\n2018,1.7\n2020,1.6" } ], "id_raw": 1078 }, { "id": "IJZizv5SyYcQmv5P", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_18.png", "conversations": [ { "from": "human", "value": "\nHow much did the Supply Chain Management index change from 2010 to 2020?" }, { "from": "gpt", "value": "To determine the change in the Supply Chain Management index, I need to look at {the line plot for Supply Chain Management}[0.160, 0.302, 0.936, 0.454] and extract the values at {2010}[0.202, 0.378] and {2020}[0.907, 0.454]. The initial value in 2010 is approximately 0.1 and in 2020 it's around 2.6. The change is calculated as 2.6 - 0.1.\nBased on the above thought, the answer is:\n2.5" } ], "id_raw": 1078 }, { "id": "SL81EdAIbXHicxrk", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_54_19.png", "conversations": [ { "from": "human", "value": "\nCan you create a CSV format table showing the distribution of energy types across the years from the line chart?" }, { "from": "gpt", "value": "Firstly, I identify the {x-axis tick labels}[0.080, 0.219, 0.139, 0.231], [0.248, 0.219, 0.306, 0.231], [0.415, 0.219, 0.473, 0.231], [0.582, 0.219, 0.641, 0.231], [0.749, 0.219, 0.808, 0.231], [0.917, 0.219, 0.975, 0.231] which represent different years from 2020 to 2030. Then I examine the {sublegend}[0.668, 0.023, 0.934, 0.044], [0.588, 0.270, 0.934, 0.291], [0.673, 0.517, 0.934, 0.538], [0.636, 0.763, 0.934, 0.784] for the labels 'Efficient Energy', 'Non-renewable Energy', 'Nuclear Energy', and 'Renewable Energy'. To fill in the values, I extract the data from {the line points}[0.110, 0.067, 0.946, 0.195], [0.110, 0.304, 0.946, 0.416], [0.110, 0.622, 0.946, 0.659], [0.110, 0.849, 0.946, 0.943] for respective energy types at each year.\nBased on the above thought, the answer is:\nYear,Efficient Energy,Non-renewable Energy,Nuclear Energy,Renewable Energy\n2020,10,80,30,5\n2021,12,78,32,7\n2022,15,75,33,9\n2023,19,72,34,12\n2024,24,68,35,16\n2025,30,63,35,20\n2026,37,57,34,24\n2027,45,50,33,28\n2028,54,42,31,33\n2029,64,33,28,38\n2030,75,23,24,43" } ], "id_raw": 1079 }, { "id": "wTSRVQVgAGO6De7k", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_54_19.png", "conversations": [ { "from": "human", "value": "\nWhich energy type shows a consistent decrease over the period from 2020 to 2030 according to the line chart?" }, { "from": "gpt", "value": "To identify the energy type with a consistent decrease, I review the trends from all energy types labeled in {the sublegend}[0.668, 0.023, 0.934, 0.044], [0.588, 0.270, 0.934, 0.291], [0.673, 0.517, 0.934, 0.538], [0.636, 0.763, 0.934, 0.784], examining their corresponding {line plots}[0.110, 0.304, 0.946, 0.416], [0.110, 0.622, 0.946, 0.659], [0.110, 0.849, 0.946, 0.943]. Upon evaluating, 'Non-renewable Energy' shows a decline from 80% in 2020 to 23% in 2030, observed through decreasing {line points}[0.110, 0.304], [0.946, 0.416].\nBased on the above thought, the answer is:\nNon-renewable Energy shows a consistent decrease over the period from 2020 to 2030. Each year marks a decline in its allocation percentage, starting from 80% and ending at 23% by 2030." } ], "id_raw": 1079 }, { "id": "5tedCWXvxAzZBjDa", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_54_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in Efficient Energy allocation from 2020 to 2030?" }, { "from": "gpt", "value": "To calculate the total increase in Efficient Energy from 2020 to 2030, I need to extract the data for 'Efficient Energy' from {the line points for Efficient Energy}[0.110, 0.195], [0.193, 0.191], [0.277, 0.185], [0.361, 0.177], [0.444, 0.167], [0.528, 0.156], [0.611, 0.142], [0.695, 0.126], [0.779, 0.108], [0.862, 0.088], [0.946, 0.067]. The values at the beginning (2020) and end (2030) of the period are 10 and 75 respectively. The increase is calculated as 75 - 10.\nBased on the above thought, the answer is:\n65" } ], "id_raw": 1079 }, { "id": "uA6Ubz4Fpea0fXhZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_0.png", "conversations": [ { "from": "human", "value": "\nCan you convert the investment data from the bar plots into a table format with quarters as columns and investment types as rows?" }, { "from": "gpt", "value": "To convert the chart data into a table, I analyze {the first bar plot}[0.042, 0.075, 0.331, 0.893] representing quarter 1 and {the second bar plot}[0.372, 0.075, 0.662, 0.893] representing quarter 2. Each bar corresponds to different investment types and their growth or decline rate, located at specific {y-tick coordinates}[0.485, 0.406, 0.485, 0.511] for each investment type. By observing {the end points of each bar}[0.320, 0.459, 0.650, 0.501], the height reflects the ratings for each quarter.\nBased on the above thought, the answer is:\nType,Quarter 1 Rating,Quarter 2 Rating\nCorporate Bonds,3,3.7\nMutual Funds,3.2,3.9\nETFs,3.4,4.1\nGold,3.9,4.6\nCryptocurrency,4.7,5.3\nStocks,3.6,4.2\nReal Estate,4.0,4.5\nTreasury Bonds,2.5,3.1\nMunicipal Bonds,2.8,3.3\nDerivatives,2.9,3.4" } ], "id_raw": 1080 }, { "id": "rZ2SOyD8C3KPkT9Q", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_0.png", "conversations": [ { "from": "human", "value": "\nWhich investment type showed the most overall stability across both quarters?" }, { "from": "gpt", "value": "Assessing the stability requires analyzing the smallest differences in growth rates between the two quarters for all investment types. Observing {the bars for each investment type}[0.042, 0.075, 0.662, 0.893], I note small changes in the bar lengths between quarters. Corporate Bonds show very minimal change, with the x-values of the bar ends being very close across both quarters, indicating stability.\nBased on the above thought, the answer is:\nCorporate Bonds showed the most overall stability across both quarters, as indicated by the minimal change in their ratings from the first to the second quarter, showing a small growth increase." } ], "id_raw": 1080 }, { "id": "k22rAVlxyEyup3O4", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_0.png", "conversations": [ { "from": "human", "value": "\nHow much higher is the rating for Cryptocurrency in the second quarter compared to the first quarter?" }, { "from": "gpt", "value": "Observing {the bar for Cryptocurrency in the first quarter}[0.488, 0.795, 0.488, 0.803] and {the bar for Cryptocurrency in the second quarter}[0.818, 0.795, 0.818, 0.803], the y-values indicate the height representing ratings. The first quarter rating is approximately 4.7 and the second quarter rating is 5.3. Therefore, the difference is 5.3 - 4.7.\nBased on the above thought, the answer is:\n0.6" } ], "id_raw": 1080 }, { "id": "GXLccPtDbaeDvYal", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data showing service quality scores for Healthcare, Education, and Public Transit from 2011 to 2015 into a table format?" }, { "from": "gpt", "value": "To create a table from the chart data, I identify the axes and line plots: {the x-axis with year labels}[0.047, 0.942, 0.330, 0.970] showing years from 2011 to 2015 and {the y-axis with scores}[0.023, 0.396, 0.044, 0.910] ranging from a score of 0.40 to 0.70. The lines representing Healthcare {line for Healthcare}[0.047, 0.192, 0.330, 0.425], Education {line for Education}[0.377, 0.285, 0.654, 0.425], and Public Transit {line for Public Transit}[0.707, 0.285, 0.984, 0.425] are plotted across these axes. By analyzing the points on each line corresponding to the years and their respective scores, I tabulate the data.\nBased on the above thought, the answer is:\nYear,Healthcare,Education,Public Transit\n2011,0.65,0.55,0.50\n2012,0.67,0.57,0.48\n2013,0.64,0.59,0.45\n2014,0.62,0.61,0.42\n2015,0.60,0.63,0.40" } ], "id_raw": 1082 }, { "id": "kGUT06QzIFkhuLeZ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_2.png", "conversations": [ { "from": "human", "value": "\nWhich service area exhibits a declining trend in quality over the given years?" }, { "from": "gpt", "value": "To analyze the trends shown in the line chart, I scrutinize the lines representing each service area. Observing {the line for Public Transit}[0.707, 0.285, 0.984, 0.425] shows a noticeable decrease from 0.50 in 2011 to 0.40 in 2015, significantly dropping almost every year. Therefore, I can conclude that Public Transit exhibits a consistent declining trend in quality.\nBased on the above thought, the answer is:\nPublic Transit showcases a declining trend in service quality over the years from 2011 to 2015. The line chart clearly depicts a consistent reduction in score each year, confirming this trend." } ], "id_raw": 1082 }, { "id": "OHyZa5oUpWPSH9Zz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the average score for Public Transit over the years 2011 to 2015?" }, { "from": "gpt", "value": "To calculate the average score for Public Transit from the given line chart, I observe {the line for Public Transit}[0.707, 0.285, 0.984, 0.425]. The scores for each year are: 0.50, 0.48, 0.45, 0.42, 0.40. To find the average, I sum these scores and divide by the number of years, which is 5.\nBased on the above thought, the answer is:\n0.45" } ], "id_raw": 1082 }, { "id": "ZMEi4zrjZ6amhpx8", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_3.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured table showing the rate of gain change for 'Data Encryption and Integrity' scenarios using both 'Ours' and 'Saliency-based Sampling' methods?" }, { "from": "gpt", "value": "To construct a table from the bar chart, I first identify {the x-axis labels for Data Encryption and Integrity}[[0.041, 0.903, 0.120, 0.931], [0.135, 0.903, 0.149, 0.931]] that include labels for Data Encryption and Data Integrity. Next, I observe the corresponding bars for each method: 'Ours' is shown with blue bars and 'Saliency-based Sampling' with red bars. The coordinates for these bars, {Ours for Data Encryption}[0.055, 0.415, 0.085, 0.893] and {Saliency-based Sampling for Data Integrity}[0.142, 0.550, 0.172, 0.893] represent the rate of gain change for these scenarios.\nBased on the above thought, the answer is:\nScenario,Method,Rate of Gain Change\nData Encryption,Ours,45%\nData Encryption,Saliency-based Sampling,-30%\nData Integrity,Ours,25%\nData Integrity,Saliency-based Sampling,15%" } ], "id_raw": 1083 }, { "id": "qZtHaO5sPnhlo0bS", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_3.png", "conversations": [ { "from": "human", "value": "\nExplain why the 'Data Protection' scenario under the 'Ours' method is considered an outlier in this dataset." }, { "from": "gpt", "value": "By examining the bar chart, it is clear that the {bar for Data Protection under Ours}[[0.501, 0.134, 0.531, 0.893]] is significantly taller compared to other bars within this method and across the category. In comparison, other bars such as {Data Accessibility under Ours}[[0.487, 0.784, 0.517, 0.893]] are much shorter, marking the rate of gain change at around 10%. The 70% rate of gain change for Data Protection highly deviates from these figures, standing out as an evident anomaly in performance comparisons.\nBased on the above thought, the answer is:\nThe 'Data Protection' scenario under the 'Ours' method is considered an outlier primarily because its rate of gain change of 70% far exceeds the rates observed in other scenarios within the same method, which generally show a decrease or marginal increase. This makes it significantly distinct and higher performing compared to its peer cases within 'Ours' method, establishing it distinctly as an outlier." } ], "id_raw": 1083 }, { "id": "uXqmRJd3k36SFC6z", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in the rate of gain change between 'Ours' and 'Saliency-based Sampling' methods for the 'Data Protection' scenario?" }, { "from": "gpt", "value": "I first identify the {bar for Data Protection under Ours}[[0.501, 0.134, 0.531, 0.893]] and check its height, which corresponds to an unusually high positive value, approximately 70%. Next, I locate the {bar for Data Protection under Saliency-based Sampling}[[0.588, 0.687, 0.618, 0.893]], reflecting a rate of 15%. By subtracting the rate from 'Saliency-based Sampling' from 'Ours', we calculate (70% - 15%) = 55%.\nBased on the above thought, the answer is:\n55%" } ], "id_raw": 1083 }, { "id": "SxJHjzph8aUV6sZW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the yearly data of 'Global Temperature', 'Sea Level Rise', 'CO2 Emission', and 'Forest Cover' from the line chart to a structured table?" }, { "from": "gpt", "value": "To convert the data from the line chart to a table, first, I locate and analyze {the x-axis tick labels}[0.050, 0.930, 0.070, 0.950] for the years spanning from 2000 to 2020. Next, I identify each corresponding line's data points for 'Global Temperature', 'Sea Level Rise', 'CO2 Emission', and 'Forest Cover' which are plotted and marked distinctly with color and symbols. By linking {each tick on the x-axis}[0.050, 0.930, 0.070, 0.950] to the exact position of these points on the graph, I extract the values for each year.\nBased on the above thought, the answer is:\nYear,Global Temperature (\u00b0C),Sea Level Rise (mm),CO2 Emission (million tonnes),Forest Cover (Km2)\n2000,14.62,1.75,5424.88,4030140\n2001,14.77,1.85,5501.12,4022140\n2002,14.89,1.93,5600.15,4011140\n2003,15.03,2.11,5702.33,4003240\n2004,15.14,2.14,5784.44,3999240\n2005,15.20,2.18,5844.33,3991240\n2006,15.31,2.23,5922.70,3978240\n2007,15.45,2.29,6021.45,3965240\n2008,15.53,2.34,6100.12,3955240\n2009,15.57,2.37,6201.33,3941240\n2010,15.65,2.43,6322.50,3930140\n2011,15.72,2.48,6433.88,3921140\n2012,15.85,2.52,6565.12,3910240\n2013,15.90,2.57,6671.65,3899110\n2014,15.97,2.61,6754.22,3888070\n2015,16.09,2.66,6855.54,3876030\n2016,16.20,2.75,7030.45,3865990\n2017,16.32,2.80,7190.88,3852910\n2018,16.45,2.88,7374.33,3847010\n2019,16.56,2.95,7950.60,3843100\n2020,16.65,3.01,8262.56,3840140" } ], "id_raw": 1086 }, { "id": "ZesKkos7BQwggRZN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_6.png", "conversations": [ { "from": "human", "value": "\nWhich environmental factor according to the line chart has shown the most significant absolute change over the last two decades?" }, { "from": "gpt", "value": "I analyze the line chart for {the lines representing 'Global Temperature', 'Sea Level Rise', 'CO2 Emission', 'Forest Cover'}[0.05, 0.2, 0.95, 0.8]. 'Global Temperature' shows an increase from 14.62 to 16.65, 'Sea Level Rise' from 1.75 mm to 3.01 mm, 'CO2 Emission' from 5424.88 to 8262.56 million tonnes, and 'Forest Cover' declines from 4030140 Km2 to 3840140 Km2. Looking at these numbers, 'CO2 Emission' has the most significant absolute increase in the millions.\nBased on the above thought, the answer is:\n'CO2 Emission' has shown the most significant absolute change, increasing by approximately 2837.68 million tonnes. This drastic rise is critical, considering the context of global warming and environmental sustainability." } ], "id_raw": 1086 }, { "id": "E5gixKxDYyf3XiqT", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_6.png", "conversations": [ { "from": "human", "value": "\nHow much has the 'Forest Cover' decreased from 2000 to 2020 based on the line graph data?" }, { "from": "gpt", "value": "Observing the line representing 'Forest Cover' in the line chart, I locate the points for the year 2000 at {the starting line point}[0.1, 0.2] and for 2020 at {the ending line point}[0.9, 0.5]. The values are respectively 4030140 Km2 for 2000 and 3840140 Km2 for 2020. The decrease is calculated by subtracting the 2020 value from the 2000 value, resulting in 4030140 - 3840140.\nBased on the above thought, the answer is:\n190000 Km2." } ], "id_raw": 1086 }, { "id": "Lb0nKdQ3QJHvl6vu", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_7.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the horizontal stacked bar chart into a tabular format categorizing the behavioral aspects under each routine scenario?" }, { "from": "gpt", "value": "To generate the table, I first observe {the x-axis labels}[0.051, 0.030, 0.329, 0.735] for categories like Sleep Quality, Mental Focus, and Physical Activity. I then differentiate each category under two routine scenarios as illustrated by {the y-axis labels}[0.012, 0.344, 0.024, 0.421] named Stable Daily Routine and Erratic Daily Routine. Using {the lengths of the bar segments}[0.064, 0.106, 0.317, 0.420] for each behavior aspect, I calculate the percentage values for Sleep Quality, Mental Focus, and Physical Activity in both scenarios.\nBased on the above thought, the answer is:\nRoutine Behavior, Sleep Quality (%), Mental Focus (%), Physical Activity (%)\nStable Daily Routine, 75, 80, 85\nErratic Daily Routine, 55, 50, 45" } ], "id_raw": 1087 }, { "id": "PH9tPSKlGw5m0ri5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_7.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which routine scenario better supports maintaining a high sleep quality, mental focus, and physical activity?" }, { "from": "gpt", "value": "Analyzing the chart, particularly at the lengths and colors of {the bar segments representing each behavior in both routine scenarios}[0.064, 0.106, 0.317, 0.546], it's observable that the Stable Daily Routine consistently has longer bar segments across all three behaviors compared to the Erratic Daily Routine. This visually suggests higher percentage values for sleep quality, mental focus, and physical activity, indicating a more supportive environment for these behaviors under a stable routine.\nBased on the above thought, the answer is:\nThe Stable Daily Routine better supports maintaining high sleep quality, mental focus, and physical activity according to the comparisons in the chart." } ], "id_raw": 1087 }, { "id": "drgP6GkQTwLoD2cn", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the average Mental Focus score for both routine scenarios combined?" }, { "from": "gpt", "value": "To find the average Mental Focus score, I need to identify and use the values from {the bar segments in Mental Focus}[0.064, 0.317] for both Stable Daily Routine and Erratic Daily Routine. The segment lengths are visually converted to percentages: 80% for Stable Daily Routine and 50% for Erratic Daily Routine. The average is then calculated as (80 + 50) / 2.\nBased on the above thought, the answer is:\n65%" } ], "id_raw": 1087 }, { "id": "IcuyPrQn3z2o9MEr", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_8.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart showing revenue trends across different entertainment segments into a table format?" }, { "from": "gpt", "value": "To convert the data into a table, I focus on the three lines representing Advertising, Box Office, and Music Sales {the lines for Advertising}[0.052, 0.650, 0.964, 0.900], {the lines for Box Office}[0.052, 0.450, 0.964, 0.550], and {the lines for Music Sales}[0.052, 0.300, 0.964, 0.400]. I observe each point on these lines representing yearly data over 2010 to 2020. These points are aligned correspondingly with year labels on {the x-axis}[0.034, 0.950, 0.984, 0.970] and values from each sector are extracted.\nBased on the above thought, the answer is:\nYear,Advertising,Box Office,Music Sales\n2010,4.5B,6.2B,2.7B\n2011,4.8B,6.4B,2.9B\n2012,5.1B,6.5B,3.2B\n2013,5.2B,6.3B,3.4B\n2014,5.3B,6.4B,3.5B\n2015,5.5B,6.1B,3.8B\n2016,5.8B,5.9B,4.0B\n2017,6.0B,5.8B,4.2B\n2018,6.2B,5.7B,4.4B\n2019,6.3B,5.6B,4.6B\n2020,6.5B,3.2B,4.9B" } ], "id_raw": 1088 }, { "id": "rMEWSkIUKbm8A8Ju", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_8.png", "conversations": [ { "from": "human", "value": "\nWhat trends can be observed from the revenue data for the Box Office sector over the years? Explain based on the chart." }, { "from": "gpt", "value": "Reviewing the revenue trends for the Box Office, I scrutinize the changes in {the line for Box Office}[0.052, 0.450, 0.964, 0.550]. The trend starts at a higher revenue marker in 2010 and shows irregular up and down movement, generally maintaining a moderate level until a sharp decline in 2020. This outlier in 2020 might correlate with external factors like the COVID-19 pandemic, significantly impacting the Box Office sector due to a decrease in movie theater operations.\nBased on the above thought, the answer is:\nThe Box Office revenue displayed a fluctuating trend with minor ups and downs from 2010 to 2019, maintaining a generally stable revenue. However, a sharp decline is observed in 2020, which is an anomaly likely due to the global impact of the COVID-19 pandemic on theater closures and restrictions." } ], "id_raw": 1088 }, { "id": "b5u940hOqy3dusT3", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_8.png", "conversations": [ { "from": "human", "value": "\nWhat was the total revenue for Music Sales in the year 2020 compared to 2010?" }, { "from": "gpt", "value": "To compute the total revenue difference for Music Sales between 2020 and 2010, I analyze the plotted points for Music Sales and identify the exact revenue in these two years from {the line points for Music Sales}[0.052, 0.350, 0.964, 0.400]. Observing the positions, I find the values to be 4.9B in 2020 and 2.7B in 2010.\nBased on the above thought, the answer is:\n2.2B USD increase." } ], "id_raw": 1088 }, { "id": "L9EPDl9ADVE8sO9z", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_9.png", "conversations": [ { "from": "human", "value": "\nCan you represent the influence of media types on public opinion over two days in a tabular form?" }, { "from": "gpt", "value": "Observing the line chart, I examine {the x-axis labels}[0.038, 0.893, 0.254, 0.931] where media types (Traditional Media and Social Media) are listed and {the y-axis labels}[0.022, 0.882, 0.035, 0.910] representing influence values. Corresponding to each media type, there are individual points plotted, such as {points for Traditional Media}[0.038, 0.784, 0.254, 0.566] and {points for Social Media}[0.038, 0.457, 0.254, 0.129] allowing me to decode the information for each day.\nBased on the above thought, the answer is:\nMedia Type,Day 1,Day 2\nTraditional Media,0.8,0.6\nSocial Media,0.5,0.75" } ], "id_raw": 1089 }, { "id": "Lh7oXmEFiEP1XltJ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_9.png", "conversations": [ { "from": "human", "value": "\nWhat can be inferred about the trend in public opinion regarding Traditional Media and Social Media over the two days shown?" }, { "from": "gpt", "value": "Examining the chart, I notice the trend lines for {Traditional Media}[0.038, 0.784, 0.254, 0.566] and {Social Media}[0.038, 0.457, 0.254, 0.129]. Traditional Media exhibits a downward trend from 0.8 to 0.6, suggesting a decrease in its influence. Conversely, Social Media shows a significant upward trajectory from 0.5 to 0.75, hinting at an increase in its influence and possibly a stronger shift in public preference towards Social Media.\nBased on the above thought, the answer is:\nThe trend suggests a decreasing influence of Traditional Media and a notable increase for Social Media over the two days. This indicates a shift in public opinion more in favor of the instant, interactive nature of Social Media compared to the more static format of Traditional Media." } ], "id_raw": 1089 }, { "id": "AT1ThO5sZKxPIsUk", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in influence for Social Media from Day 1 to Day 2 according to the chart?" }, { "from": "gpt", "value": "To find the increase in influence for Social Media, I analyze the y-values at {the points for Social Media}[0.038, 0.457, 0.254, 0.129]. The influence on day 1 is approximately 0.5, and on day 2, it rises to approximately 0.75. Calculating the increase involves subtracting the Day 1 influence from the Day 2 influence: 0.75 - 0.5.\nBased on the above thought, the answer is:\n0.25" } ], "id_raw": 1089 }, { "id": "3YRkG9Uq2n7qQqlh", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_10.png", "conversations": [ { "from": "human", "value": "\nCan the data represented on the line chart be listed in a table, where one column represents the years and the other two columns represent 'Global Trade' and 'Supply Chain Management' respectively?" }, { "from": "gpt", "value": "To construct this table, I examine {the x-axis labels}[0.044, 0.075, 0.330, 0.893] which contain the years 2010 to 2020. For the values, I consult {the lines for Global Trade}[0.058, 0.382, 0.316, 0.791] and {the line for Supply Chain Management}[0.058, 0.116, 0.316, 0.382] to gather the respective data points across these years.\nBased on the above thought, the answer is:\nYear,Global Trade,Supply Chain Management\n2010,150,120\n2011,160,122\n2012,170,123\n2013,180,125\n2014,190,127\n2015,210,126\n2016,200,128\n2017,220,130\n2018,230,129\n2019,240,132\n2020,180,120" } ], "id_raw": 1090 }, { "id": "PqI6JdOCfiqIlqbr", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_10.png", "conversations": [ { "from": "human", "value": "\nHow did the global pandemic in 2020 affect the trends in Global Trade and Supply Chain Management as shown in the chart?" }, { "from": "gpt", "value": "Observing {the line for Global Trade}[0.058, 0.382, 0.316, 0.791] and {the line for Supply Chain Management}[0.058, 0.116, 0.316, 0.382], both lines depict a sudden drop in the year 2020. Prior to 2020, Global Trade was showing a strong upward trend, peaking in 2019. Similarly, Supply Chain Management was somewhat stagnant but still showed slight growth until 2019. The notable decline in both lines in 2020 indicates significant disruption caused by the global pandemic.\nBased on the above thought, the answer is:\nThe global pandemic resulted in a noticeable decline in both Global Trade and Supply Chain Management in 2020. This disruption is clearly visible as sharp downturns in both the plotted lines, diverging from their previous trends where Global Trade was increasing and Supply Chain Management was maintaining slight incremental growth." } ], "id_raw": 1090 }, { "id": "csJFzc8BHPdMrMTs", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in volume between Global Trade and Supply Chain Management in the year 2020?" }, { "from": "gpt", "value": "First, I locate the data points for the year 2020 from {the lines}[0.316, 0.382, 0.316, 0.791]. For Global Trade, the volume is at 180, and for Supply Chain Management, it's 120. The difference is calculated by subtracting the volume of Supply Chain Management from Global Trade's.\nBased on the above thought, the answer is:\n60." } ], "id_raw": 1090 }, { "id": "eynZfm9siXahXaVc", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_11.png", "conversations": [ { "from": "human", "value": "\nCan you create a structured table displaying the usage distribution of different online learning tools, segregated between EdTech Company A and Company B?" }, { "from": "gpt", "value": "First, I review {the y-axis labels}[0.075, 0.150, 0.183, 0.203] representing the various online learning tools such as Webinars, Online Quizzes, Video Lectures, Forum Discussions, Online Assignments, and Virtual Labs. These serve as the rows of the table. For each tool, I assess the corresponding {stacked bar segments}[0.100, 0.170, 0.300, 0.203] in the chart that differentiate between Company A and Company B's utilization percentages, which are marked on {the x-axis}[0.085, 0.793, 0.105, 0.807]. Using these segments, values for each company and tool are extracted to construct the table.\nBased on the above thought, the answer is:\nTool,EdTech Company A (%),EdTech Company B (%)\nWebinars,45,30\nOnline Quizzes,25,40\nVideo Lectures,30,30\nForum Discussions,15,35\nOnline Assignments,10,40\nVirtual Labs,5,45" } ], "id_raw": 1091 }, { "id": "WmFHkCIVPypjE6Q7", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_11.png", "conversations": [ { "from": "human", "value": "\nWhich online learning tool has shown a mixed trend of usage between EdTech Company A and Company B? Please explain your analysis." }, { "from": "gpt", "value": "To discern the mixed usage trend, I carefully analyze the growth patterns across the tools by comparing their respective {bar segments}[0.100, 0.170, 0.300, 0.203]. Specifically, I observe {the bars for Video Lectures}[0.100, 0.175, 0.220, 0.195], and note that they maintain similar lengths between {Company A}[0.100, 0.175, 0.160, 0.195] and {Company B}[0.165, 0.175, 0.220, 0.195] indicating a similar usage pattern for this tool contrary to others that show varying trends.\nBased on the above thought, the answer is:\nThe online learning tool, Video Lectures, shows a mixed trend of usage between EdTech Company A and Company B. Both companies exhibit a nearly balanced usage rate, differing from other tools which display more distinct growth or decline patterns. This consistent pattern across both companies indicates a stable preference for Video Lectures as a learning tool." } ], "id_raw": 1091 }, { "id": "S8HmsAH2ASW810Bf", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the total percentage increase for the usage of Virtual Labs from Company A to Company B?" }, { "from": "gpt", "value": "Observing {the Virtual Labs bar for Company A}[0.100, 0.197, 0.115, 0.205] and {the Virtual Labs bar for Company B}[0.120, 0.197, 0.195, 0.205], I determine the lengths of these bars represent the percentages. For Company A, the percentage is marked at 5% and for Company B, it is 45%. To find the total percentage increase, I calculate the difference between Company B's and Company A's percentages.\nBased on the above thought, the answer is:\n40%" } ], "id_raw": 1091 }, { "id": "XaPwMdgBbIhOpsY5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data showing technological development prospects into a structured table format distinguishing each technology for the years 2020, 2022, and 2023?" }, { "from": "gpt", "value": "To create a structured table from the line chart, I begin by examining {the x-axis tick labels}[0.081, 0.861, 0.134, 0.886], [0.318, 0.861, 0.371, 0.886], [0.555, 0.861, 0.608, 0.886] which represent the years 2020, 2022, and 2023 respectively. I then look at {the legend}[0.792, 0.132, 0.893, 0.208] to decipher the lines corresponding to each technology. Following this, I track and extract data points for each technology across the specified years by observing the positions of their respective line points {for AI}[0.101, 0.470], [0.318, 0.351], [0.536, 0.236], {for IoT}[0.101, 0.690], [0.318, 0.672], [0.536, 0.653], and so on for other technologies.\nBased on the above thought, the answer is:\nYear, AI, IoT, Blockchain, Robotics, Quantum Computing\n2020, 8.5, 7.0, 6.5, 7.5, 4.0\n2022, 8.7, 7.2, 6.2, 8.0, 5.5\n2023, 9.0, 7.4, 6.0, 8.5, 7.0" } ], "id_raw": 1093 }, { "id": "hMbNaQ0q8ZgFJBfu", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_13.png", "conversations": [ { "from": "human", "value": "\nWhich technology shows the most consistent growth trend according to the line chart?" }, { "from": "gpt", "value": "By examining the trajectory of the lines in the chart, I observe that {the line for AI}[0.101, 0.470], [0.318, 0.351], [0.536, 0.236] almost steadily descends along the y-axis, indicating a consistent growth in its rating over the years 2020 to 2023. On the other hand, other technologies, while also showing growth, exhibit either more gentle slopes or fluctuations in their curves, suggesting variations in the rate of growth.\nBased on the above thought, the answer is:\nAI exhibits the most consistent growth trend in its development prospects, showing a steady decline in the y-axis values on the line chart, indicative of a continuous and stable increase in its ratings from 2020 to 2023." } ], "id_raw": 1093 }, { "id": "5luVCJiQMWa0qdCe", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in the development prospects rating for Quantum Computing from 2020 to 2023 according to the line chart?" }, { "from": "gpt", "value": "To calculate the percentage increase, I first identify Quantum Computing's ratings for 2020 and 2023 by looking at {the line points for Quantum Computing}[0.101, 0.870], [0.536, 0.420] corresponding to the years 2020 and 2023 respectively. The ratings are 4.0 for 2020 and 7.0 for 2023. The percentage increase is calculated as ((7.0 - 4.0) / 4.0) * 100 = 75%.\nBased on the above thought, the answer is:\n75%" } ], "id_raw": 1093 }, { "id": "08fSclbY5NqB943g", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_14.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table showing the comparison of startups and universities across two states from the bar chart?" }, { "from": "gpt", "value": "To create the table, I first identify the two sets of horizontal bars from the chart. {State S1's bars}[0.200, 0.150, 0.850, 0.250] and {State S2's bars}[0.200, 0.350, 0.870, 0.450] represent startups and universities, respectively. Noting that the length of the bars corresponds to the number for each category, I reference {the x-axis values}[0.100, 0.900, 0.200, 0.950] which help in calculating the quantities proportionately.\nBased on the above thought, the answer is:\nState,Categories,200,350,500,520\nS1,Startups,-,-,+,+\nS1,Universities,-,+,+,-\nS2,Startups,+,+,-,+\nS2,Universities,+,+,-,+" } ], "id_raw": 1094 }, { "id": "52pNUVhNsZBxEhI1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_14.png", "conversations": [ { "from": "human", "value": "\nConsidering the data shown, which state shows more stability in the number of universities according to the bar chart?" }, { "from": "gpt", "value": "Upon examining the comparative data for each state, the bars for universities are seen at {State S1's university bar}[0.215, 0.280, 0.715, 0.380], showing a gradual increase from 200 to 500, indicating stability. In contrast, {State S2's university bar}[0.210, 0.480, 0.870, 0.580] demonstrates irregular growth with fluctuating values between 250 and 520, showcasing more variance and less stability.\nBased on the above thought, the answer is:\nState S1 shows more stability in the number of universities as its changes are more gradual compared to State S2, which shows greater fluctuation." } ], "id_raw": 1094 }, { "id": "ZZyeSnoQ54pcAXxH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in the number of universities from the starting point to the peak in State S2 as shown in the bar chart?" }, { "from": "gpt", "value": "The bar chart shows that the number of universities in State S2 starts from {the beginning of State S2's university bar}[0.200, 0.425, 0.250, 0.475] at 250 and reaches a peak level at {the end of the peak section of State S2's university bar}[0.400, 0.425, 0.520, 0.475] of 520. Simple subtraction of 250 from 520 gives the result.\nBased on the above thought, the answer is:\n270" } ], "id_raw": 1094 }, { "id": "spXrFhjb3gCPpuay", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_15.png", "conversations": [ { "from": "human", "value": "\nHow can the data differences between 'Production Efficiency' and 'Renewable Efficiency' for each region and year be represented in a table format?" }, { "from": "gpt", "value": "Observing the bar subplots for the two regions in the figure, I first identify {the upper chart}[0.041, 0.075, 0.330, 0.445] pertaining to the 'US' data and {the lower chart}[0.041, 0.510, 0.330, 0.890] for the 'UK' data. Within each of these, the respective bars for '2015' and '2020' are discerned and correlated to each horizontal section, colored to represent 'Production Efficiency' and 'Renewable Efficiency'. The legend, {found at the top of the figure}[0.041, 0.025, 0.090, 0.068], helps in defining which color corresponds to which type of efficiency.\nBased on the above thought, the answer is:\nRegion,Year,Production Efficiency,Renewable Efficiency\nUS,2015,68%,25%\nUS,2020,75%,30%\nUK,2015,55%,20%\nUK,2020,65%,28%" } ], "id_raw": 1095 }, { "id": "AhME4nzEbU5XiL4v", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_15.png", "conversations": [ { "from": "human", "value": "\nWhich region displayed a stronger improvement in 'Production Efficiency' from 2015 to 2020 according to the chart?" }, { "from": "gpt", "value": "To determine which region showed stronger improvement, I compare the changes in the 'Production Efficiency' segments from 2015 to 2020 for both regions. For the 'US' section in {the upper chart}[0.041, 0.075, 0.330, 0.445], the 'Production Efficiency' improves from '68%' to '75%', whereas in {the UK's section}[0.041, 0.510, 0.330, 0.890], it increases from '55%' to '65%'. Calculating these increases, the US shows a 7% increase, while the UK shows a 10% increase.\nBased on the above thought, the answer is:\nThe UK displayed a stronger improvement in 'Production Efficiency' from 2015 to 2020. The increase of 10% for the UK is greater compared to the US's 7% increase." } ], "id_raw": 1095 }, { "id": "B7NMWNp1NOpWJbdm", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage point difference in 'Renewable Efficiency' between 2015 and 2020 for the UK based on the chart data?" }, { "from": "gpt", "value": "To determine the difference, I examine the lower chart section in {the chart portion for the UK}[0.041, 0.510, 0.330, 0.890], specifically looking at the segments that represent 'Renewable Efficiency' for '2015' and '2020'. By analyzing the lengths of these segments within the bars, it is clear that the growth in this efficiency category shows an increase from '20%' in 2015 to '28%' in 2020.\nBased on the above thought, the answer is:\n8%" } ], "id_raw": 1095 }, { "id": "LsbFYVztqYBTiLlR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_16.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line graph data showing the percentage change in disease management efforts into a structured table?" }, { "from": "gpt", "value": "Observing the line plot, I began by identifying {the x-axis labels}[0.041, 0.075, 0.326, 0.893] representing various health associations. These serve as the row headers. Next, I located {the data points}[0.085, 0.129, 0.304, 0.784] for each health association which represent the percentage change. Using these located points and labels, I created a structure for the table.\nBased on the above thought, the answer is:\nAssociation,Percentage Change\nAmerican Heart Association,8.5%\nAmerican Diabetes Association,-2.9%\nCancer Research Institute,12.1%\nParkinson's Foundation,-7.3%\nAlzheimer Association,15.2%" } ], "id_raw": 1096 }, { "id": "jgBKYPIrArCQBcWJ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_16.png", "conversations": [ { "from": "human", "value": "\nWhich health association shows the most significant positive change in disease management efforts and why?" }, { "from": "gpt", "value": "Identifying the most significant positive change involves examining the data points on the line graph. By finding {the highest peak}[0.968, 0.126] which indicates the highest percentage change, the associated label for this peak is traced to {the Alzheimer Association}[0.748, 0.030, 0.953, 0.066] showing a rapid expansion at 15.2%. This is considerably higher compared to other associations, making it the most significant positive change.\nBased on the above thought, the answer is:\nThe Alzheimer Association exhibits the most significant positive change in disease management efforts with an increase of 15.2%. This pronounced growth indicates a strong focus and successful measures in managing Alzheimer's disease compared to other health conditions." } ], "id_raw": 1096 }, { "id": "Wh33p2dm2IxOp7Hx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the total range of percentage change across all health associations according to the line graph?" }, { "from": "gpt", "value": "To compute the total range of percentage changes, I isolated {the highest data point}[0.968, 0.126] which corresponds to the Alzheimer Association at 15.2% and {the lowest data point}[0.705, 0.637] corresponding to the Parkinson's Foundation at -7.3%. The range is calculated by subtracting the smallest value from the largest value.\nBased on the above thought, the answer is:\n22.5%" } ], "id_raw": 1096 }, { "id": "CAIK8p3yIPDQCXka", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_17.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table format comparing the production efficiencies for WidgetA and WidgetB under both manufacturing processes?" }, { "from": "gpt", "value": "To create a structured table, I examine the bar charts that represent the manufacturing processes. For WidgetA, the bar for Process 1 starts at {the beginning of the WidgetA process 1 bar}[0.12, 0.35] and ends at {the end of the WidgetA process 1 bar}[0.32, 0.45], marked with a label showing 70.5% efficiency. Similarly, for Process 2, the bar begins at {the beginning of the WidgetA process 2 bar}[0.35, 0.35] and ends at {the end of the WidgetA process 2 bar}[0.55, 0.45] with a label indicating 74.3% efficiency.\n\nFor WidgetB, the first bar starts at {the beginning of the WidgetB process 1 bar}[0.12, 0.55] and extends to {the end of the WidgetB process 1 bar}[0.49, 0.65] showing 89.6% efficiency. The second bar for Process 2 starts at {the beginning of the WidgetB process 2 bar}[0.55, 0.55] and finishes at {the end of the WidgetB process 2 bar}[0.78, 0.65] with a label of 92.1% efficiency.\nBased on the above thought, the answer is:\nProduct,Process 1 (%),Process 2 (%)\nWidgetA,70.5,74.3\nWidgetB,89.6,92.1" } ], "id_raw": 1097 }, { "id": "I7MhpIf7SdyiW1Le", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_17.png", "conversations": [ { "from": "human", "value": "\nWhich product demonstrates a greater relative improvement in production efficiency between the two processes, and why?" }, { "from": "gpt", "value": "To answer this question, I review the change in production efficiencies displayed on the bar charts. WidgetA increases from {the efficiency for WidgetA Process 1}[0.12, 0.35, 0.32, 0.45] at 70.5% to {the efficiency for WidgetA Process 2}[0.35, 0.35, 0.55, 0.45] at 74.3%, which is an increase of approx. 5.39%. On the other hand, WidgetB starts at an efficiency of 89.6% under {the efficiency for WidgetB Process 1}[0.12, 0.55, 0.49, 0.65] and climbs to 92.1% under {the efficiency for WidgetB Process 2}[0.55, 0.55, 0.78, 0.65], an improvement of approx. 2.79%. Despite this, considering the initial high efficiency of WidgetB, maintaining and enhancing efficiency close to the maximum is notably challenging, implying that the 2.79% gain represents a significant achievement.\nBased on the above thought, the answer is:\nWidgetB demonstrates a greater relative improvement between the two processes. Despite a smaller percentage increase compared to WidgetA, enhancing efficiency from an already high baseline near maximum capacity (89.6% to 92.1%) is significantly more challenging and impactful, highlighting impressive advancements in production optimization for WidgetB." } ], "id_raw": 1097 }, { "id": "Gw33wKQaNg5wFECQ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in production efficiency for WidgetA from Process 1 to Process 2?" }, { "from": "gpt", "value": "Observing the bar lengths and labels for WidgetA, {the efficiency value for WidgetA under Process 1}[0.12, 0.35, 0.32, 0.45] indicates 70.5% and {the efficiency value for WidgetA under Process 2}[0.35, 0.35, 0.55, 0.45] shows 74.3%. To find the percentage increase, I calculate ((74.3 - 70.5) / 70.5) x 100 = 5.39%.\nBased on the above thought, the answer is:\n5.39%" } ], "id_raw": 1097 }, { "id": "tLSAjx9wYyf4Cn9m", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_18.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data from the line chart showing various environmental issues into a structured table format with years as columns and environmental factors as rows?" }, { "from": "gpt", "value": "To convert the line chart data into a table, I examine {the x-axis labels}[0.1, 0.9, 0.13, 0.93], which provide the years from 2000 to 2010 as the column headers. I identify {the legend}[0.75, 0.1, 0.95, 0.2] to determine the rows, which correspond to different environmental factors such as CO2 emission, Ozone depletion, and others. By analyzing {the plotted lines for each environmental factor}[0.1, 0.3, 0.9, 0.7], I extract the values at each year's data point for the respective environmental issue.\nBased on the above thought, the answer is:\nYear,CO2_emission,Ozone_depletion,Rising_sea_level,Deforestation_rate,Percentage_of_renewable_energy\n2000,28,12,17,41,7\n2001,30,12.5,17.5,40,7.5\n2002,45,13,18,39,8\n2003,50,13.5,18.5,38,8.5\n2004,55,14,19,37,9\n2005,57,14.5,19.5,36,9.5\n2006,60,15,20,35,10\n2007,62,15.5,20.5,34,10.5\n2008,65,16,21,33,11\n2009,68,16.5,21.5,32,11.5\n2010,70,17,22,31,12" } ], "id_raw": 1098 }, { "id": "uOdAwiNh3pKTeDZb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_18.png", "conversations": [ { "from": "human", "value": "\nWhich environmental factor shows the most significant improvement according to the chart, and how can this improvement be quantitatively described?" }, { "from": "gpt", "value": "To determine the most significant improvement, I examine the slopes of {the lines corresponding to each environmental factor}[0.1, 0.3, 0.9, 0.7]. The line for {Deforestation rate}[0.1, 0.4, 0.9, 0.6] depicts a downward trend, indicating improvement. The values start at 41% deforestation rate in 2000 and decrease to 31% in 2010. This quantitative improvement can be described as a 10% absolute reduction or approximately a 24.39% relative reduction over the decade (calculated as (41 - 31) / 41 * 100).\nBased on the above thought, the answer is:\nThe Deforestation rate shows the most significant improvement. It is quantitatively described as a 24.39% relative reduction over the decade from 2000 to 2010, moving from 41% down to 31%, representing a positive environmental impact." } ], "id_raw": 1098 }, { "id": "ykwHc5C4BQu9EFAr", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in CO2 emissions from the year 2000 to 2010 based on the chart data?" }, { "from": "gpt", "value": "To calculate the percentage increase in CO2 emissions over the decade, I first locate {the points for CO2 emission}[0.1, 0.5, 0.9, 0.65]. The value in 2000 is 28% and in 2010 is 70%. Using the formula (new_value - old_value) / old_value * 100, the percentage increase is calculated as (70 - 28) / 28 * 100.\nBased on the above thought, the answer is:\n150%" } ], "id_raw": 1098 }, { "id": "jNXNw5WPmUiokVTm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_55_19.png", "conversations": [ { "from": "human", "value": "\nCan you transform the data about Video Game Revenue and Esport Viewership into a table for easier comparison over the years?" }, { "from": "gpt", "value": "By visually analyzing the two line charts, I start by noting down the years on the shared y-axis ranging from 2010 to 2020, which will serve as rows. Then, I designate two columns, one for Video Game Revenue and another for Esport Viewership. Finally, I collect the data points from {the line representing Video Game Revenue}[0.05, 0.1, 0.54, 0.9] and {the line representing Esport Viewership}[0.56, 0.1, 0.95, 0.9], matching them with the corresponding years to fill up the table.\nBased on the above thought, the answer is:\nYear,Video Game Revenue (Billion $),Esport Viewership (Million)\n2010,10,50\n2011,10.5,56\n2012,11.2,60\n2013,12,65\n2014,13,70\n2015,14,72\n2016,16,81\n2017,17,90\n2018,20,122\n2019,23,140\n2020,28,160" } ], "id_raw": 1099 }, { "id": "tIQRxSeO6o6Zmcgz", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_55_19.png", "conversations": [ { "from": "human", "value": "\nHow does the trend in Video Game Revenue compare to the fluctuations in Esport Viewership between 2010 and 2020?" }, { "from": "gpt", "value": "To compare the trends, I analyze the line plots for both Video Game Revenue and Esport Viewership. The line for {Video Game Revenue}[0.05, 0.1, 0.54, 0.9] shows a relatively steady upward trend with notable jumps in 2016 and 2018. In contrast, {Esport Viewership}[0.56, 0.1, 0.95, 0.9] displays a more erratic but ascending behavior, with a rapid rise especially evident in the later years from 2018 to 2020. This pattern suggests Video Game Revenue grew consistently with substantial boosts linked to key industry events or innovations, whereas Esport Viewership had a more volatile increase, potentially influenced by breakout popularity and viewer engagement.\nBased on the above thought, the answer is:\nThe trend in Video Game Revenue shows consistent growth with periodic spikes possibly linked to industry developments or new game releases, reflecting long-term steady progress. On the other hand, Esport Viewership exhibits a more erratic rise with significant surges in later years, indicating a dynamic and rapidly expanding viewer base driven by increasing global interest and significant event occurrence." } ], "id_raw": 1099 }, { "id": "4fZaCWFwo5nxwj40", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_55_19.png", "conversations": [ { "from": "human", "value": "\nWhat was the percent increase in Esport Viewership from 2010 to 2020?" }, { "from": "gpt", "value": "To calculate the percent increase, I identify {the viewership values in 2010 and 2020}[0.6, 0.85, 0.75, 0.9] which correspond to 50 million and 160 million respectively. Using the formula (new value - old value) / old value * 100, the percent increase is calculated as (160 - 50) / 50 * 100.\nBased on the above thought, the answer is:\n220%" } ], "id_raw": 1099 }, { "id": "pQbgQEGNrvr5EEjr", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_0.png", "conversations": [ { "from": "human", "value": "\nCould you generate a table with Education Levels as rows and Skill types as columns based on the line chart?" }, { "from": "gpt", "value": "To generate the table from the chart, I first identify {the x-axis labels}<[0.069, 0.849, 0.300, 0.899],[0.460, 0.849, 0.648, 0.899],[0.879, 0.849, 0.968, 0.899]> as the row headers representing different Education Levels. Next, I locate {the legend texts}[0.186, 0.754, 0.360, 0.799],[0.470, 0.754, 0.633, 0.799],[0.743, 0.754, 0.931, 0.799] as the column headers representing different Skills. I then inspect {the line points1}[0.185, 0.717],[0.554, 0.397],[0.924, 0.197], {the line points2}[0.185, 0.637],[0.554, 0.517],[0.924, 0.317], and {the line points3}[0.185, 0.797],[0.554, 0.417],[0.924, 0.137] corresponding to the Performance Ratings of each skill for each Education Level.\nBased on the above thought, the answer is:\nEducation_Level,Innovative,Analytical,Conceptual\nBachelor's,66,70,62\nMaster's,82,76,81\nPhD,92,86,95" } ], "id_raw": 1100 }, { "id": "JA5wzEXIlmuD2FBx", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_0.png", "conversations": [ { "from": "human", "value": "\nLooking at the chart, which skill demonstrated the greatest improvement from Bachelor's to PhD level, and in what way does the granularity and nature of the data illustrate this?" }, { "from": "gpt", "value": "To determine this, I observe the line plots for each skill. For {Innovative}[0.185, 0.717, 0.924, 0.197] the increase from Bachelor's to PhD is from 66 to 92, for {Analytical}[0.185, 0.637, 0.924, 0.317] it is from 70 to 86, and for {Conceptual}[0.185, 0.797, 0.924, 0.137] it is from 62 to 95. Conceptual has the largest improvement of 33. The granularity of the data with each point denoting the percentage values for each level of education and the nature being line charts, illustrates this by providing a clear visual progression of each skill from Bachelor\u2019s to PhD, underscored by the steepness of the line plot.\nBased on the above thought, the answer is:\nThe Conceptual skill demonstrated the greatest improvement from Bachelor's to PhD level. This is illustrated by the steepest upward trajectory among all the lines from Bachelor's to PhD, resulting in the largest increase in percentage points compared to the other skills." } ], "id_raw": 1100 }, { "id": "hqsEsdAFlWz72exv", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_0.png", "conversations": [ { "from": "human", "value": "\nHow much did the score for Innovative skill improve from Bachelor's to PhD level?" }, { "from": "gpt", "value": "To calculate the improvement in Innovative skill score from Bachelor's to PhD, I locate {the Innovative skill point for Bachelor's}[0.185, 0.717] extracting the score as 66, and {the Innovative skill point for PhD}[0.924, 0.197] extracting the score as 92. The improvement is therefore 92 - 66 = 26.\nBased on the above thought, the answer is:\n26." } ], "id_raw": 1100 }, { "id": "m56TgJoPYls24sDp", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a structured table format with dates as rows and review scores for each restaurant type as columns?" }, { "from": "gpt", "value": "First, I identify {the x-axis tick labels} [[0.110, 0.900, 0.211, 1.061], [0.188, 0.900, 0.289, 1.061], [0.266, 0.900, 0.367, 1.061], [0.345, 0.900, 0.446, 1.061], [0.423, 0.900, 0.524, 1.061], [0.501, 0.900, 0.602, 1.061], [0.579, 0.900, 0.680, 1.061], [0.658, 0.900, 0.759, 1.061], [0.736, 0.900, 0.837, 1.061], [0.814, 0.900, 0.915, 1.061]] as the row headers representing the review dates. Next, I identify {the legend texts} [[0.202, 0.150, 0.285, 0.186], [0.202, 0.203, 0.266, 0.239], [0.202, 0.255, 0.288, 0.291]] representing Chinese, Italian, and Mexican restaurants as the column headers. To populate the values, I locate the points on each line for {Chinese restaurant} [[0.160, 0.736], [0.239, 0.659], [0.317, 0.621], [0.395, 0.563], [0.473, 0.467], [0.552, 0.409], [0.630, 0.312], [0.708, 0.351], [0.786, 0.370], [0.865, 0.332]], {Italian restaurant} [[0.160, 0.601], [0.239, 0.582], [0.317, 0.563], [0.395, 0.505], [0.473, 0.447], [0.552, 0.409], [0.630, 0.312], [0.708, 0.274], [0.786, 0.255], [0.865, 0.197]], and {Mexican restaurant} [[0.160, 0.774], [0.239, 0.698], [0.317, 0.659], [0.395, 0.621], [0.473, 0.543], [0.552, 0.505], [0.630, 0.409], [0.708, 0.447], [0.786, 0.467], [0.865, 0.409]] on their respective dates.\nBased on the above thought, the answer is:\nReviewDate,Chinese,Italian,Mexican\n2022-01-01,68,75,66\n2022-01-10,72,76,70\n2022-01-20,74,77,72\n2022-01-30,77,80,74\n2022-02-09,82,83,78\n2022-02-19,85,85,80\n2022-02-29,90,90,85\n2022-03-10,88,92,83\n2022-03-20,87,93,82\n2022-03-30,89,96,85" } ], "id_raw": 1102 }, { "id": "awPcOe8uaoUQk99g", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_2.png", "conversations": [ { "from": "human", "value": "\nWhich restaurant category shows the most consistent improvement in review scores over the period under review?" }, { "from": "gpt", "value": "To answer this, I need to analyze the trends in the line plots for each category. From the {line for Chinese} [[0.160, 0.736], [0.239, 0.659], [0.317, 0.621], [0.395, 0.563], [0.473, 0.467], [0.552, 0.409], [0.630, 0.312], [0.708, 0.351], [0.786, 0.370], [0.865, 0.332]], it shows that the review scores are generally increasing over time with a minor dip toward the end. For {Italian restaurant} [[0.160, 0.601], [0.239, 0.582], [0.317, 0.563], [0.395, 0.505], [0.473, 0.447], [0.552, 0.409], [0.630, 0.312], [0.708, 0.274], [0.786, 0.255], [0.865, 0.197]], the increasing trend is more steady, showing continuous improvement each month. On the other hand, the line for {Mexican restaurant} [[0.160, 0.774], [0.239, 0.698], [0.317, 0.659], [0.395, 0.621], [0.473, 0.543], [0.552, 0.505], [0.630, 0.409], [0.708, 0.447], [0.786, 0.467], [0.865, 0.409]] shows an initially stable trend, followed by a significantly fluctuating pattern.\nBased on the above thought, the answer is:\nThe Italian restaurant category shows the most consistent improvement in review scores over the period under review. The line plot for this category shows a clear, consistent upward trend from January to March, with scores consistently rising each month. This suggests that the quality of the restaurant in the eyes of customers has steadily improved over the period." } ], "id_raw": 1102 }, { "id": "jF1ojjzS7KobtGz1", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the average review score for Chinese and Mexican restaurants in the month of February 2022?" }, { "from": "gpt", "value": "To find the average review score for Chinese and Mexican restaurants in February 2022, I need to locate the points for {Chinese restaurant} [[0.473, 0.467], [0.552, 0.409], [0.630, 0.312]] and {Mexican restaurant} [[0.473, 0.543], [0.552, 0.505], [0.630, 0.409]] which correspond to review scores of 82, 85, 90 for Chinese and 78, 80, 85 for Mexican. The average for the Chinese restaurant is (82+85+90)/3 = 85.67 and for Mexican is (78+80+85)/3 = 81.\nBased on the above thought, the answer is:\nChinese: 85.67, Mexican: 81." } ], "id_raw": 1102 }, { "id": "COncZBw3xjm8QjbW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_3.png", "conversations": [ { "from": "human", "value": "\nBased on the available data, could you please convert this line chart into a structured table with the Health Indicators as columns and Hospitals as rows?" }, { "from": "gpt", "value": "To produce a structured table from the line chart, I first identify {the x-axis tick labels}[[1.102, 0.852, 1.181, 0.915], [6.149, 0.852, 6.227, 0.915]], which correspond to the two Health Indicators - 'Patient Recovery Rate' and 'Patient Admission Rate'. Next, I locate the different {legend texts}[[0.190, 0.758, 0.358, 0.803], [0.468, 0.758, 0.637, 0.803], [0.747, 0.758, 0.916, 0.803]] indicating 'Hospital A', 'Hospital B', and 'Hospital C'. Interpreting the plot, I identify {the line points for Hospital A}[[0.197, 0.740], [0.911, 0.238]], {Hospital B}[[0.197, 0.680], [0.911, 0.339]], and {Hospital C}[[0.197, 0.539], [0.911, 0.539]] for each Health Indicator to derive the corresponding values.\nBased on the above thought, the answer is:\nHospital,Patient Recovery Rate,Patient Admission Rate\nHospital A,0.65,0.9\nHospital B,0.68,0.85\nHospital C,0.75,0.75" } ], "id_raw": 1103 }, { "id": "77MfkAtOkMhe2aPB", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_3.png", "conversations": [ { "from": "human", "value": "\nWhich hospital appears to maintain a more consistent performance across both 'Patient Recovery Rate' and 'Patient Admission Rate'? Provide your reasoning." }, { "from": "gpt", "value": "To answer this question, I need to analyze the trends in both 'Patient Recovery Rate' and 'Patient Admission Rate' across the hospitals. Observing {the line for Hospital A}[0.197, 0.238, 0.911, 0.740], it shows a decrease from recovery rate 0.65 to admission rate 0.90, indicating a decrease in performance. The {line for Hospital B}[0.197, 0.339, 0.911, 0.680] also shows a decrease from recovery rate 0.68 to admission rate 0.85. In contrast, {the line for Hospital C}[0.197, 0.539, 0.911, 0.539] remains consistent at 0.75 across both health indicators, reflecting a stable performance.\nBased on the above thought, the answer is:\nBased on the given chart, Hospital C appears to maintain a more consistent performance across both 'Patient Recovery Rate' and 'Patient Admission Rate'. This is deduced from the fact that the line representing Hospital C's performance across the two health indicators is a straight line with no variation, indicating that both the recovery rate and admission rate remain at a consistent 0.75." } ], "id_raw": 1103 }, { "id": "NXxDMUm7Rh7Uj9Ay", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in the 'Patient Recovery Rate' between Hospital A and Hospital C observed in the chart?" }, { "from": "gpt", "value": "To calculate the difference, I need to identify the 'Patient Recovery Rate' for both Hospital A and Hospital C. I locate {the line point for Hospital A on 'Patient Recovery Rate'}[0.197, 0.740] which indicates a recovery rate of 0.65 for Hospital A. Then, I identify {the line point for Hospital C on 'Patient Recovery Rate'}[0.197, 0.539] which shows a recovery rate of 0.75 for Hospital C. The difference is computed as 0.75 - 0.65 = 0.1.\nBased on the above thought, the answer is:\n0.1." } ], "id_raw": 1103 }, { "id": "IqjAp1gEXTmBr8Un", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_4.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, can you extract the data into a table format with Year as columns and Energy, Resource, and Carbon as rows?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first look at {the x-axis tick labels}[0.088, 0.926, 0.117, 0.942, 0.257, 0.926, 0.285, 0.942, 0.425, 0.926, 0.453, 0.942, 0.593, 0.926, 0.621, 0.942, 0.761, 0.926, 0.789, 0.942, 0.929, 0.926, 0.957, 0.942], which represent the column headers or years. Next, I identify {the legend texts}[0.112, 0.078, 0.257, 0.101, 0.112, 0.113, 0.272, 0.136, 0.112, 0.148, 0.215, 0.171] as the row labels representing the different consumption/output types. To populate the values, I locate {the line points}[0.103, 0.198, 0.943, 0.879, 0.103, 0.266, 0.943, 0.538, 0.103, 0.096, 0.943, 0.708] for each consumption/output type at the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nYear,EnergyConsumption,ResourceConsumption,CarbonOutput\n2000,100,200,150\n2001,105,210,155\n2002,110,228,160\n2003,120,236,165\n2004,130,242,170\n2005,150,244,175\n2006,160,248,180\n2007,180,258,190\n2008,210,262,220\n2009,250,264,260\n2010,300,280,330" } ], "id_raw": 1104 }, { "id": "rNLTaUyuaqNJ3V6k", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_4.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which factor seems to have the most rapid increase over time? Explain your reasoning." }, { "from": "gpt", "value": "To determine which factor has the most rapid increase, I need analyze the slopes of the lines for each factor. Looking at {the line for Energy Consumption}[0.103, 0.198, 0.943, 0.879], I see a steady upward trend indicating an increase. For {Resource Consumption}[0.103, 0.266, 0.943, 0.538], there is a moderate increase, but less steep than the Energy Consumption line. For {Carbon Output}[0.103, 0.096, 0.943, 0.708], it shows the steepest upward trend indicating a rapid increase.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, Carbon Output seems to have the most rapid increase over time. Its line plot exhibits a steep upward trend across the years. The slope of this line plot is more pronounced compared to Energy Consumption and Resource Consumption, indicating a faster rate of increase in Carbon Output." } ], "id_raw": 1104 }, { "id": "7AXVd1wX6nDaKvIo", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the rate of growth of energy consumption between 2000 and 2010?" }, { "from": "gpt", "value": "To find the rate of growth of energy consumption, I need to locate the starting and ending values of {the line points for Energy Consumption}[0.103, 0.879, 0.943, 0.198] which are 100 and 300 respectively. The rate of growth is calculated as (Final - Initial) / Initial * 100%. So the rate of growth is (300 - 100) / 100 * 100% = 200%.\nBased on the above thought, the answer is:\n200%" } ], "id_raw": 1104 }, { "id": "MqhUZwuHLR7yfRA5", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart into a table format, with each row representing a different city and each column a different data category?" }, { "from": "gpt", "value": "To derive a structured table from the line chart, I'll take {the x-axis labels}[[0.108, 0.894], [0.317, 0.894], [0.526, 0.894], [0.735, 0.894], [0.943, 0.894]] as the row labels, corresponding to the cities. The columns are represented by {the legend texts}[[0.763, 0.091, 0.970, 0.121], [0.763, 0.134, 0.956, 0.164], [0.763, 0.178, 0.924, 0.208]], i.e., Tech Investment, University Partnerships, and Startup Growth. Next, to populate the data in the table, I locate the {data points on the lines}[[0.108, 0.434], [0.317, 0.321], [0.526, 0.241], [0.735, 0.354], [0.943, 0.413], [0.108, 0.821], [0.317, 0.848], [0.526, 0.796], [0.735, 0.842], [0.943, 0.832], [0.108, 0.768], [0.317, 0.777], [0.526, 0.739], [0.735, 0.799], [0.943, 0.816]] for each city's record.\nBased on the above thought, the answer is:\nCity,Investment in Tech (bn$),University Partnerships,Startup Growth (%)\nSan Francisco,55.5,8.8,15.3\nNew York,69.2,5.6,14.2\nBoston,78.8,11.9,18.8\nSeattle,65.2,6.3,11.5\nChicago,58.1,7.5,9.4" } ], "id_raw": 1105 }, { "id": "QahxOJk966tukFKz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_5.png", "conversations": [ { "from": "human", "value": "\nBy how much does the highest value of University Partnerships exceed the lowest value?" }, { "from": "gpt", "value": "To answer this question, I need to locate {the University Partnerships line points}[[0.108, 0.821], [0.317, 0.848], [0.526, 0.796], [0.735, 0.842], [0.943, 0.832]] and extract the values: 8.8, 5.6, 11.9, 6.3, 7.5. The maximum value is 11.9 represented by {Boston}[0.526, 0.796], while the minimum value is 5.6 represented by {New York}[0.317, 0.848]. The difference is thus 11.9 - 5.6 = 6.3.\nBased on the above thought, the answer is:\n6.3." } ], "id_raw": 1105 }, { "id": "qRzq8tB1AZS6XrMS", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data presented in the line chart, where the X-axis represents the years and the Y-axis represents the different population categories' growth, into a tabular form?" }, { "from": "gpt", "value": "Sure, to create a table from the line chart, I will identify {the x-axis labels}[[0.066, 0.875, 0.081, 0.907], [0.051, 0.703, 0.081, 0.734], [0.051, 0.530, 0.081, 0.562], [0.051, 0.358, 0.081, 0.389], [0.051, 0.185, 0.081, 0.217]] which represent the years. Each population category will be represented by a row - {the legend texts}[[0.376, 0.745, 0.616, 0.775], [0.376, 0.789, 0.632, 0.819], [0.376, 0.833, 0.549, 0.863], [0.700, 0.745, 0.883, 0.775], [0.700, 0.789, 0.963, 0.819]] indicates these categories. I shall refer to {the line points}[[0.128, 0.543, 0.941, 0.543], [0.128, 0.111, 0.941, 0.629], [0.128, 0.456, 0.941, 0.715], [0.128, 0.629, 0.941, 0.802], [0.128, 0.370, 0.941, 0.715]] to obtain the values associated with each category at each year.\nBased on the above thought, the answer is:\nYear,Current age population,Baby boomer population,Youth population,Senior population,Virtual worker population\n0.01,20,45,25,15,30\n0.1,20,45,15,15,30\n1.0,20,25,25,15,20" } ], "id_raw": 1106 }, { "id": "MA4H2pJmrsfK3jYz", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_6.png", "conversations": [ { "from": "human", "value": "\nWhich population group shows the greatest volatility in population growth from year 0.01 to year 1.0 and why?" }, { "from": "gpt", "value": "To evaluate variability, I have to look at the changes in population growth over time. Considering {Current age population's line}[[0.128, 0.543, 0.941, 0.543]], it stays constant at 20%. For {Baby boomer's population line}[[0.128, 0.111, 0.941, 0.629]], it starts at 45% at year 0.01, drops to 15% at year 0.1, and increases to 25% at year 1.0. This is a significant series of fluctuations. For the {Youth population's line}[[0.128, 0.456, 0.941, 0.715]], it starts at 25%, drops to 15%, and rises back to 25%, showing minor but noticeable fluctuations. {Senior population's line}[[0.128, 0.629, 0.941, 0.802]] stays constant at 15%, and finally, the {Virtual worker's population line}[[0.128, 0.370, 0.941, 0.715]] shows stable growth from 10% to 20%.\nBased on the above thought, the answer is:\nThe baby boomer population shows the greatest volatility in growth from year 0.01 to year 1.0. The line plot for the baby boomer population shows significant fluctuations in the population growth percentage, decreasing from 45% to 15% and then increasing to 25%. The degree of these fluctuations is greater than those seen in the other population groups, indicating a larger amount of volatility for the baby boomer population." } ], "id_raw": 1106 }, { "id": "4C9y37jyYM5yv79I", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the net change in population growth percentage for the Youth Population from year 0.01 to year 1.0?" }, { "from": "gpt", "value": "To calculate the net change, I examine {Youth population line}[[0.128, 0.456, 0.941, 0.715]] which represents the population growth of the Youth population. The population growth at year 0.01 is 25%, located at {the point for Youth population at 0.01}[0.128, 0.715]. The population growth at year 1.0 is also 25%, found at {the point for Youth population at 1.0}[0.941, 0.456]. The net change can be calculated by subtracting the initial population growth from the final population growth, so 25 - 25 = 0.\nBased on the above thought, the answer is:\n0." } ], "id_raw": 1106 }, { "id": "4qNXbWhrKBfODRZ4", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_7.png", "conversations": [ { "from": "human", "value": "\nCan you translate the data from the line chart into a structured data table, with time periods as columns and subculture trends as rows?" }, { "from": "gpt", "value": "To generate the table from the line chart, I first identify {the x-axis tick labels}[[0.121, 0.766, 0.180, 0.884], [0.313, 0.766, 0.372, 0.884], [0.505, 0.766, 0.564, 0.884], [0.697, 0.766, 0.756, 0.884], [0.889, 0.766, 0.948, 0.884]], being the time periods, which will represent the column headers. I then take {the legend texts}[[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156], [0.884, 0.167, 0.946, 0.189]] to generate the row labels representing different subculture trends. Finally, I locate {the line points}[[0.144, 0.570], [0.336, 0.490], ..., [0.920, 0.380]] for each subculture at the respective time periods and extract the values encoded by these points.\nBased on the above thought, the answer is:\nSubculture Trend,2005,2010,2015,2020\nMovement Punk Style,25,30,35,40\nIndie Rock Look,35,33,30,28\nHigh Fashion,35,32,27,22" } ], "id_raw": 1107 }, { "id": "46ZtID5SEQqGoJ6F", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_7.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which subculture trend has shown a consistent increase over the years? Explain your analysis." }, { "from": "gpt", "value": "To find out which trend has shown a consistent increase over the years, I need to examine the trajectory of the lines for each subculture. The line for {Movement Punk Style}[0.120, 0.075, 0.930, 0.488] shows a consistent upward trend from 25 to 40. On the other hand, {the Indie Rock Look line}[0.120, 0.490, 0.930, 0.379] and {the High Fashion line}[0.120, 0.490, 0.930, 0.379] show a declining trend. Hence, 'Movement Punk Style' is the subculture that has displayed consistent growth over the years.\nBased on the above thought, the answer is:\nThe 'Movement Punk Style' trend has shown a consistent increase over the years according to the line chart. Its line indicates a consistent upward trend, climbing from 25 in 2005 to 40 in 2020. This is in contrast with the other two trends 'Indie Rock Look' and 'High Fashion', which show a downward trajectory over the same period. Therefore, 'Movement Punk Style' has remained increasingly popular over the years." } ], "id_raw": 1107 }, { "id": "GcuNLtd4PWHx2ZNn", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall change in the trend for 'High Fashion' from 2005 to 2020?" }, { "from": "gpt", "value": "To find the overall change in trend for 'High Fashion', I need to look at {the line points for High Fashion}[[0.164, 0.354], [0.418, 0.484], [0.671, 0.548], [0.924, 0.807]] and extract the corresponding values: 35, 32, 27 and 22 respectively. The trend in 2005 is 35 and in 2020 it is 22. Therefore, the overall change in trend for 'High Fashion' from 2005 to 2020 is 22 - 35 = -13.\nBased on the above thought, the answer is:\n-13." } ], "id_raw": 1107 }, { "id": "QG9cU9fQPHEKo8OT", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_8.png", "conversations": [ { "from": "human", "value": "\nCould you provide the performance scores of the different trends in a table format, using Value Chain Factors as the columns and the trends as rows?" }, { "from": "gpt", "value": "To form the table from the line chart, I am locating {the x-axis tick labels}[[0.021, 0.681, 0.178, 0.942], [0.212, 0.681, 0.314, 0.850], [0.378, 0.681, 0.476, 0.844], [0.555, 0.681, 0.626, 0.800], [0.684, 0.681, 0.826, 0.918], [0.858, 0.681, 0.979, 0.884]] first, these will serve as the column headers. Next, I can identify {the legend texts}[[0.110, 0.050, 0.212, 0.074], [0.110, 0.085, 0.230, 0.109], [0.110, 0.120, 0.233, 0.144], [0.110, 0.155, 0.221, 0.178], [0.110, 0.190, 0.235, 0.213]] as row labels. The trends represented here are the different rows in the table. For filling the values, I need to examine the respective points on {the line charts}[[0.099, 0.338, 0.918, 0.643], [0.099, 0.237, 0.918, 0.567], [0.099, 0.415, 0.918, 0.453], [0.099, 0.351, 0.918, 0.605], [0.099, 0.059, 0.918, 0.631]] for each trend at each stage in the value chain.\nBased on the above thought, the answer is:\nTrend,Raw Material Acquisition,Product Design,Manufacturing,Marketing,Sales and Distribution,After-sales Service\nSlight increase,2,6,10,14,21,26\nDeclining pattern,34,29,25,19,14,8\nSluggish progress,18,18,17,19,20,20\nErratic behavior,5,15,7,20,14,25\nUpward trajectory,3,9,16,26,36,48" } ], "id_raw": 1108 }, { "id": "291ywoRzBqjMf4f5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_8.png", "conversations": [ { "from": "human", "value": "\nBased on this line chart, which trend seems the most erratic over time? Please provide your reasoning." }, { "from": "gpt", "value": "To identify the most erratic trend, I need to observe how much each trend's performance score fluctuates over different stages of the value chain. When I look at {the line for Slight increase}[0.099, 0.338, 0.918, 0.643], I observe a consistent increase in the scores from 2 to 26. Similarly, for {Declining pattern}[0.099, 0.237, 0.918, 0.567], there is a steady decline from 34 to 8. Moreover, the line for {Sluggish progress}[0.099, 0.415, 0.918, 0.453] maintains scores around the middle of the range, showing little fluctuation. Lastly, looking at {Erratic behavior}[0.099, 0.351, 0.918, 0.605], the trend demonstrates considerable fluctuations between different stages. The scores range from a low of 5 to a high of 25 with numerous ups and downs.\nBased on the above thought, the answer is:\nThe trend that seems most erratic over time is indeed the one labelled \"Erratic behavior\". As the name insinuates, this trend does not follow a consistent pattern. It undergoes significant ups and downs across the value chain, with considerable variation in performance scores between stages, making it the most volatile trend depicted on the graph." } ], "id_raw": 1108 }, { "id": "DpCONQ7VLTvOMPWt", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the highest and lowest performance score for the Erratic behavior trend?" }, { "from": "gpt", "value": "To calculate the difference between the highest and lowest performance scores for the Erratic behavior trend, I need to identify {the various data points associated with Erratic behavior}[[0.099, 0.605], [0.263, 0.478], [0.427, 0.580], [0.591, 0.415], [0.755, 0.491], [0.918, 0.351]] and take their corresponding scores: 5, 15, 7, 20, 14, 25. From these scores, the highest value is 25 at {Sales and Distribution}[0.918, 0.673] and the lowest value is 5 at {Raw Material Acquisition}[0.099, 0.673]. The difference is therefore 25 - 5 = 20.\nBased on the above thought, the answer is:\n20." } ], "id_raw": 1108 }, { "id": "JHsAcxbHSctY6nyE", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_9.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a table with processes as rows and 'Before Intervention' and 'After Intervention' as columns?" }, { "from": "gpt", "value": "To generate a table from the line chart, I first locate {the x-axis tick labels}[[0.059, 0.925, 0.156, 0.934], [0.238, 0.925, 0.337, 0.934], ..., [0.863, 0.925, 0.963, 0.934]]. These labels correspond to the different processes and will form the row labels in the table. I analyze {the legend items}[[0.400, 0.057, 0.522, 0.066], [0.526, 0.057, 0.648, 0.066]] to identify 'Before Intervention' and 'After Intervention' as column headers. The line points on {the 'Before Intervention' line}[[0.113, 0.749], [0.291, 0.646], ..., [0.920, 0.682]] and {the 'After Intervention' line}[[0.162, 0.523], [0.341, 0.420], ..., [0.969, 0.533]] represent the efficiency levels before and after the intervention for each process.\nBased on the above thought, the answer is:\nProcess,Before Intervention,After Intervention\nProcess 1,0.60,1.00\nProcess 2,0.50,0.90\nProcess 3,0.75,1.15\nProcess 4,0.65,0.95\nProcess 5,0.70,1.10\nProcess 6,0.85,0.80" } ], "id_raw": 1109 }, { "id": "b5gUtZZOIn5um9Y4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_9.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which process showed the greatest improvement in efficiency after the intervention, and why?" }, { "from": "gpt", "value": "To identify the process with the greatest improvement in efficiency, I analyze the changes between the {points on the 'Before Intervention' line}[[0.113, 0.749], [0.291, 0.646], ..., [0.599, 0.598], [0.775, 0.618], [0.920, 0.682]] and the {points on the 'After Intervention' line}[[0.162, 0.523], [0.341, 0.420], ..., [0.648, 0.422], [0.825, 0.498], [0.969, 0.533]]. It appears that Process 1 had the most substantial improvement in efficiency, improving from 0.60 to 1.00.\nBased on the above thought, the answer is:\nThe process that showed the greatest improvement in efficiency after the intervention was Process 1. The efficiency of Process 1 increased from 0.60 before the intervention to 1.00 after the intervention, representing a significant improvement." } ], "id_raw": 1109 }, { "id": "ZoAkNkB2zMkpMLaO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_9.png", "conversations": [ { "from": "human", "value": "\nHow much did the efficiency of Process 3 improve after the intervention?" }, { "from": "gpt", "value": "To answer this question, I need to find the efficiency levels of Process 3 before and after the intervention. This involves comparing {the point representing Process 3 on the 'Before Intervention' line}[[0.291, 0.646]] and {the point representing Process 3 on the 'After Intervention' line}[[0.341, 0.420]]. The 'Before Intervention' efficiency was 0.75 and the 'After Intervention' efficiency was 1.15. The improvement can therefore be calculated as 1.15 - 0.75 = 0.40.\nBased on the above thought, the answer is:\n0.40" } ], "id_raw": 1109 }, { "id": "M0yQ3Alp5JiiAUMW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_10.png", "conversations": [ { "from": "human", "value": "\nCan you provide a table summarizing the Returns (%) of the Aggressive and Conservative investment strategies across each Market condition?" }, { "from": "gpt", "value": "To generate a tabular summary from the line chart, I first observe that {the x-axis}<[0.261, 0.912, 0.881, 0.962]> represents the Market conditions and they are marked in a logarithmic scale. The y-axis corresponds to Returns (%). The two investment strategies, {Aggressive}<[0.425, 0.741, 0.607, 0.786]> and {Conservative}<[0.717, 0.741, 0.936, 0.786]>, are differentiated by different colors (#008000 for Aggressive and #ff0000 for Conservative). I note the line points for each investment strategy corresponding to each Market condition and retrieve their Returns (%) values from the y-axis to populate the table.\nBased on the above thought, the answer is:\nMarket condition,Aggressive,Conservative\nRecession,68,62\nBull Market,90,87\nBear Market,45,48" } ], "id_raw": 1110 }, { "id": "fqCqIeupZVmrZtWD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_10.png", "conversations": [ { "from": "human", "value": "\nWhich investment strategy seems to be riskier based on returns in different market conditions and why?" }, { "from": "gpt", "value": "To answer this, I compare the trends and fluctuations in {returns for the Aggressive strategy}<[0.212, 0.181, 0.930, 0.737]> and {the Conservative strategy}<[0.212, 0.218, 0.930, 0.700]>. I notice that the Aggressive strategy yields higher returns in Bull Market but significantly lower returns in Bear Market compared to the Conservative strategy. This shows higher potential for gain but also higher risk of losses, which implies greater risk in the Aggressive strategy.\nBased on the above thought, the answer is:\nThe Aggressive investment strategy seems to be riskier based on the returns under different market conditions. The reasoning for this is that while it produces higher returns during Bull Market, it also shows drastically lower returns during Bear Market compared to the Conservative strategy. This pattern suggests a higher risk-return trade-off, which is typically taken as a measure of riskiness in investment strategies." } ], "id_raw": 1110 }, { "id": "haHsnrVi8WeFkR4A", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_10.png", "conversations": [ { "from": "human", "value": "\nHow much more return does the Aggressive strategy make than the Conservative strategy in Bull Market?" }, { "from": "gpt", "value": "To find this, I locate the {line point for Bull Market in the Aggressive strategy}<[0.571, 0.181]>, which corresponds to a return of 90%. Similarly, for the Conservative strategy, the {line point for Bull Market}<[0.571, 0.218]> corresponds to a return of 87%. The difference is hence 90 - 87 = 3%.\nBased on the above thought, the answer is:\n3%." } ], "id_raw": 1110 }, { "id": "Z5sUPOFAC92I4ucd", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_11.png", "conversations": [ { "from": "human", "value": "\nCan you extract the investment percentage for each investment stage into a table format based on the line chart?" }, { "from": "gpt", "value": "To extract the data into a table, I first look at {the x-axis tick labels}[[0.279, 0.836, 0.382, 0.899], [0.581, 0.836, 0.685, 0.899], [0.896, 0.836, 0.975, 0.899]] which detail the investment stages. I then identify the legend entries {Seed Stage}[0.027, 0.741, 0.217, 0.786], {Early Stage Growth}[0.327, 0.741, 0.647, 0.786], and {Late Stage}[0.757, 0.741, 0.936, 0.786] as the row labels. To populate the values, I locate the {line points for Seed Stage}[[0.330, 0.474], [0.633, 0.649], [0.936, 0.771]], {line points for Early Stage Growth}[[0.330, 0.299], [0.633, 0.474], [0.936, 0.649]], and {line points for Late Stage}[[0.330, 0.125], [0.633, 0.247], [0.936, 0.474]] and extract the annotated values.\nBased on the above thought, the answer is:\nInvestment Stage,Seed,Early Stage Growth,Late Stage\n$10^{-4}$,100,110,120\n$10^{-3}$,90,100,113\n$10^{0}$,83,90,100" } ], "id_raw": 1111 }, { "id": "l8Z1hdoo2I80MaPb", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_11.png", "conversations": [ { "from": "human", "value": "\nWhich investment stage seems to have experienced the greatest decrease in investment percentage according to the line chart? Provide your reasoning." }, { "from": "gpt", "value": "To determine which investment stage has experienced the greatest decrease in investment percentage, I need to observe the relative changes in the lines corresponding to {Seed Stage}[[0.330, 0.474], [0.633, 0.649], [0.936, 0.771]], {Early Stage Growth}[[0.330, 0.299], [0.633, 0.474], [0.936, 0.649]], and {Late Stage}[[0.330, 0.125], [0.633, 0.247], [0.936, 0.474]]. By comparing the end and start points of each line, it appears that the Seed Stage line has descended the most steeply.\nBased on the above thought, the answer is:\nBased on the line chart, the Seed Stage seems to have experienced the greatest decrease in investment percentage. The line associated with Seed Stage descends more steeply compared to the lines for Early Stage Growth and Late Stage, suggesting a greater decrease in investment percentage." } ], "id_raw": 1111 }, { "id": "wVzMvrBBxnUsvMCV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_11.png", "conversations": [ { "from": "human", "value": "\nHow much has the investment percentage for Seed Stage decreased from $10^{-4}$ to $10^{0}$?" }, { "from": "gpt", "value": "To find this, I need to locate {the line points for Seed Stage}[[0.330, 0.474], [0.633, 0.649], [0.936, 0.771]]. The investment percentage at the $10^{-4}$ stage is 100 and at the $10^{0}$ stage is 83. Therefore, the decrease is 100 - 83.\nBased on the above thought, the answer is:\n17." } ], "id_raw": 1111 }, { "id": "qHgZ2dteAz8KJWqP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_12.png", "conversations": [ { "from": "human", "value": "\nThe line chart represents greenhouse gas emissions and climate change impact. Could you extract the data and present it in a structured table with columns representing different years and rows indicating 'GHG Emission' and 'Climate Change Impact'?" }, { "from": "gpt", "value": "In order to configure the tabular data, I first observe {the x-axis tick labels}[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903], [0.397, 0.773, 0.504, 0.934], [0.525, 0.773, 0.607, 0.896], [0.643, 0.773, 0.721, 0.890], [0.739, 0.773, 0.857, 0.951], representing stages, consistent with the years. Then I identify the {legend labels}[0.884, 0.101, 0.982, 0.123] as the row headers displaying the trends of 'GHG Emission' and 'Climate Change Impact'. To fill in the values, I track the {line points for GHG Emission}[0.102, 0.470], [0.218, 0.082], ..., [0.798, 0.243] and the {line points for Climate Change Impact}[0.102, 0.599], [0.218, 0.340], [0.334, 0.179], [0.450, 0.534], [0.566, 0.632], [0.682, 0.437], [0.798, 0.243] for each factor during the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nFactor,2025,2030,2035,2040,2045,2050\nGHG Emission,22,23,24,25,26,30\nClimate Change Impact,21,23,25,27,29,35" } ], "id_raw": 1112 }, { "id": "mZD6wVH5waSDZrsh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_12.png", "conversations": [ { "from": "human", "value": "\nHow does the trend of Climate Change Impact relate to the trend of GHG Emission based on the line chart? Briefly describe your reasoning." }, { "from": "gpt", "value": "To assess the mutual relation between the trend of Climate Change Impact and GHG Emission, I need to scrutinize the trajectories of {the line for GHG Emission}[0.102, 0.082, 0.798, 0.696] and {the line for Climate Change Impact}[0.102, 0.211, 0.798, 0.729]. Both lines consistently show growth over time, with slight dips before increasing again. This shared trend suggests a positive correlation between GHG Emissions and Climate Change Impact, indicating that as GHG Emissions increase, Climate Change Impact tends to follow a similar uptrend. The 2050 points in both trends ends up being significantly higher than the expectations followed by the previous years thus could be perceived as outliers.\nBased on the above thought, the answer is:\nThe trend of Climate Change Impact appears to positively correlate with the trend of GHG Emission based on the line chart. Both lines consistently rise over the years, suggesting that as the GHG Emissions increase, the Impact of Climate Change also increases. The graph provides a strong visual hint of the co-dependency between GHG Emissions and Climate Change Impact, indicating they move in a similar direction. This can be interpreted as an increase in GHG Emissions causing a corresponding increase in Climate Change Impact." } ], "id_raw": 1112 }, { "id": "rMiu67LlDHm5BVva", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_12.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, how much did GHG Emissions increase from 2025 to 2050?" }, { "from": "gpt", "value": "To find the increase in GHG emissions from the year 2025 to 2050, I need to locate the {line points for GHG Emission}[0.102, 0.470], [0.798, 0.243], from which we can gather the corresponding values for these years. In 2025, it was 22 and in 2050, it rose to 30. The increase in GHG emissions between 2025 and 2050 is therefore 30 - 22 = 8 units.\nBased on the above thought, the answer is:\n8 units." } ], "id_raw": 1112 }, { "id": "Y3c0nalJhSC8etWz", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_14.png", "conversations": [ { "from": "human", "value": "\nCan you generate a structured table including \"Year\" as columns and \"Activity Type\" as rows based on the provided line chart?" }, { "from": "gpt", "value": "To generate the table data from the line chart, I first look at {the x-axis tick labels}[[0.195, 0.903, 0.246, 0.931], [0.322, 0.903, 0.372, 0.931], [0.448, 0.903, 0.498, 0.931], [0.574, 0.903, 0.625, 0.931], [0.701, 0.903, 0.751, 0.931], [0.827, 0.903, 0.877, 0.931]], which represent the column headers or Years. Then, I consider {the legend texts}[[0.787, 0.066, 0.913, 0.094], [0.787, 0.108, 0.916, 0.136], [0.787, 0.150, 0.961, 0.178]] as the row labels representing the different Activity Types. To populate the values, I locate {the line points}[[0.221, 0.094, 0.852, 0.201], [0.221, 0.680, 0.852, 0.733], [0.221, 0.787, 0.852, 0.851]] for each activity at the respective years and extract the values annotated.\nBased on the above thought, the answer is:\nActivity Type,2010,2011,2012,2013,2014,2015\nDoctor Visits,210,205,208,202,198,190\nClinical Trials,100,95,90,93,92,90\nMedical Research,80,78,77,76,70,68" } ], "id_raw": 1114 }, { "id": "OdzsUkbMGConLIQc", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which activities have shown a steady decline over the years?" }, { "from": "gpt", "value": "To identify the activities showing a steady decline, I need to examine the trends in the line plots for each activity. Looking at {the line for Doctor Visits}[0.221, 0.094, 0.852, 0.201], I see a steady decrease from 210 to 190 over the year. Similarly, for {Medical Research}[0.221, 0.787, 0.852, 0.851], there is a consistent decrease from 80 to 68. However, {Clinical Trials}[0.221, 0.680, 0.852, 0.733] show a more erratic trend with both ups and downs over the years.\nBased on the above thought, the answer is:\nAccording to the line chart, the activities 'Doctor Visits' and 'Medical Research' have shown a steady decline over the years. For 'Doctor Visits', there is a consistent decrease from 210 in 2010 to 190 in 2015. Similarly, 'Medical Research' shows a decline from 80 in 2010 to 68 in 2015. These trends indicate a downward pattern over the years for these activities." } ], "id_raw": 1114 }, { "id": "sP42chDlmc7kjDNO", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the total percentage difference in 'Doctor Visits' from the year 2010 to 2015 based on the line chart?" }, { "from": "gpt", "value": "To calculate the percentage difference in 'Doctor Visits' from the year 2010 to 2015, I have to locate {the line points for Doctor Visits}[[0.221, 0.094],[0.852, 0.201]] and extract the corresponding values: 210 and 190. I then use the formula for percentage change: ((New value - Old value)/Old value)*100 = ((190-210)/210)*100.\nBased on the above thought, the answer is:\n-9.52%." } ], "id_raw": 1114 }, { "id": "YcCrRt53HgZ3TxK4", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_16.png", "conversations": [ { "from": "human", "value": "\nCan you change this line chart data into a table with years as rows and performance metrics as columns?" }, { "from": "gpt", "value": "To create the table, first, I can see {the labels of the x-axis}[[0.350, 0.879, 0.444, 0.919], [0.458, 0.879, 0.552, 0.919], [0.566, 0.879, 0.660, 0.919], [0.673, 0.879, 0.768, 0.919], [0.781, 0.879, 0.876, 0.919], [0.889, 0.879, 0.983, 0.919]], which represent different years. {The legend}[-0.157, 0.790, 0.949, 0.853] indicates the three different performance metrics. To get the values for each metric in each year, I will look at {the points of each line}[[0.397, 0.732], [0.505, 0.458], [0.613, 0.320], [0.720, 0.595], [0.828, 0.512], [0.936, 0.650],[0.397, 0.375], [0.505, 0.183], [0.613, 0.101], [0.720, 0.348], [0.828, 0.266], [0.936, 0.348],[0.397, 0.540], [0.505, 0.320], [0.613, 0.211], [0.720, 0.430], [0.828, 0.375], [0.936, 0.512]] that denote respective values.\nBased on the above thought, the answer is:\nYear,ProductQuality,CustomerSatisfaction,OperationalEfficiency\n2016,65,78,72\n2017,75,85,80\n2018,80,88,84\n2019,70,79,76\n2020,73,82,78\n2021,68,79,73" } ], "id_raw": 1116 }, { "id": "Szqm8tjaUX7PLFyB", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_16.png", "conversations": [ { "from": "human", "value": "\nAccording to the trends in the line chart, which performance metric showed the most improvement over the years? Give an explanation." }, { "from": "gpt", "value": "To identify the performance metric with the most improvement, I need to study {the lines}[[0.397, 0.320, 0.936, 0.732], [0.397, 0.101, 0.936, 0.375], [0.397, 0.211, 0.936, 0.540]] corresponding to each metric. The line for Product Quality shows a gradual increase until 2018, then a decrease and slight fluctuations. Customer Satisfaction shows an upward trend till 2018, but then sees a drop and remains around the same value. Operational Efficiency demonstrates a steady increase till 2018, and then a slight decrease till 2021. Thus, we see that all metrics have initially increased, but then dropped after the peak in 2018.\nBased on the above thought, the answer is:\nBased on the line trends in the chart, none of the performance metrics showed a consistent improvement over the years. However, Operational Efficiency demonstrated the most noticeable improvement up until 2018, followed by a relatively small drop compared to the others over the subsequent years." } ], "id_raw": 1116 }, { "id": "2h3ZTVXlFT4dzGUC", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the average score of Customer Satisfaction from 2016 to 2021?" }, { "from": "gpt", "value": "To calculate the average score of Customer Satisfaction, I need to locate {the points of the Customer Satisfaction line}[[0.397, 0.375], [0.505, 0.183], [0.613, 0.101], [0.720, 0.348], [0.828, 0.266], [0.936, 0.348]] which represent the scores for each year from 2016 to 2021. The scores are 78, 85, 88, 79, 82, 79. Now I can calculate the average by adding them all together and dividing by the total number of years, which is 6.\nBased on the above thought, the answer is:\n82.17" } ], "id_raw": 1116 }, { "id": "bdUcXptFrIaI9gLB", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_17.png", "conversations": [ { "from": "human", "value": "\nCould you please help generate a table capturing the information about the number of Patent Applications, Approvals and Rejections over the years 2005 to 2015?" }, { "from": "gpt", "value": "To generate the table, I have to use the coordinates from the line points from all the three lines in the chart. Starting with {the line for Patent Application}[0.177, 0.077, 0.930, 0.414], {the line for Patent Approval}[0.177, 0.461, 0.930, 0.629] and {the line for Patent Rejection}[0.177, 0.783, 0.930, 0.875] I can extract the respective values for each year. The years can be determined through the given {x-axis ticks}[0.101, 0.875] to [1.006, 0.875].\nBased on the above thought, the answer is:\nYear,Patent Application,Patent Approval,Patent Rejection\n2005,19000,12000,4000\n2006,21000,12500,4200\n2007,22000,13000,4600\n2008,23000,14000,4700\n2009,24000,14500,4900\n2010,25000,15000,5400\n2011,25500,15500,5700\n2012,26500,16000,6100\n2013,28000,16500,6400\n2014,29000,17000,6800\n2015,30000,17500,7000" } ], "id_raw": 1117 }, { "id": "RzuzkWJdqi4m4AGi", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_17.png", "conversations": [ { "from": "human", "value": "\nAnalyze the trend of Patent Approvals and Rejections from 2005-2015. What do these trends suggest about the selection process for patents?" }, { "from": "gpt", "value": "The trend for Patent Approvals and Rejections can be determined by looking at {the line for Patent Approvals}[0.177, 0.461, 0.930, 0.629] and {the line for Patent Rejections}[0.177, 0.783, 0.930, 0.875], respectively. The Patent Approval line shows a constant upward trend suggesting that the number of patents approved has increased over the year. On the other hand, the Patent Rejection line also shows an increasing trend indicating that there has been a simultaneous rise in the number of patent rejections as well. The increase in the number of both Patent Approvals and Rejections could be a result of an overall increase in the number applications, as shown by the rising trend of the {Patent Application line}[0.177, 0.077, 0.930, 0.414]. The growing competition might have led to a more stringent selection process, leading to more rejections alongside approvals.\nBased on the above thought, the answer is:\nThe trends of Patent Approvals and Rejections from 2005 to 2015 suggest that the selection process for patents has become more rigorous over time. With a remarkable increase in Patent Applications, there has been a corresponding rise in both Approvals and Rejections. This could indicate that while more innovators have been able to gain appreciation for their work as patent approvals have risen, the heightened competition and stricter selection process have simultaneously led to an increase in the number of rejections." } ], "id_raw": 1117 }, { "id": "C6SptzBWIkjoBXbz", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_17.png", "conversations": [ { "from": "human", "value": "\nBy how much did the Patent Applications increase from the year 2013 to 2015?" }, { "from": "gpt", "value": "To find out the increase in Patent Applications from 2013 to 2015, we must look at the {line for Patent Applications}[0.177, 0.077, 0.930, 0.414]. The data for 2013 is at point coordinates [0.780, 0.138], which corresponds to 28000 applications. The data for 2015 at point coordinates [0.930, 0.077] corresponds to 30000 applications. The increase in Patent Applications from 2013 to 2015 is thus 30000-28000 = 2000.\nBased on the above thought, the answer is:\n2000" } ], "id_raw": 1117 }, { "id": "4tB75nmcjMPBHlbW", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_18.png", "conversations": [ { "from": "human", "value": "\nCould you extract the data in a table format from line chart, where each row represents a company and columns represent years from 2019 to 2021?" }, { "from": "gpt", "value": "To extract data from the line chart, I firstly locate {the x-axis tick labels}[[0.237, 0.912, 0.315, 0.963], [0.535, 0.912, 0.586, 0.963], [0.834, 0.912, 0.908, 0.963]] to ascertain columns representing years 2019, 2020, 2021. Next, I can infer the row labels as companies' names by identifying them from {the legend texts}[[0.271, 0.075, 0.405, 0.120], [0.271, 0.141, 0.411, 0.186], [0.271, 0.207, 0.395, 0.252], [0.271, 0.272, 0.353, 0.317], [0.271, 0.338, 0.408, 0.383]]. The points from each line plot {the line points like these for Amazon}[[0.183, 0.359], [0.536, 0.278], [0.888, 0.145]] can then be used to extract the values for each company for these years.\nBased on the above thought, the answer is:\nCompany,2019,2020,2021\nAmazon,23,26,31\nWalmart,20,17,15\nAlibaba,18,17,16\nEbay,15,13,11\nBestBuy,11,10,8" } ], "id_raw": 1118 }, { "id": "GdYaAJqZRq61nXlF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_18.png", "conversations": [ { "from": "human", "value": "\nBased on the trends indicated in the line chart, which company's market share increased the most over the years 2019 to 2021? Can you explain your rationale?" }, { "from": "gpt", "value": "To determine which company's market share has increased the most, I need to examine the changes in line plots for each company. The line plot {for Amazon}[0.183, 0.145, 0.888, 0.359] shows an uptrend from 23% to 31%. The line plot {for Walmart}[0.183, 0.439, 0.888, 0.573] shows a decreasing trend from 20% to 15%. The line plot {for Alibaba}[0.183, 0.492, 0.888, 0.546] shows a slight downward trend from 18% to 16%. The line plot {for Ebay}[0.183, 0.573, 0.888, 0.680] also has a declining trend from 15% to 11%. Lastly, the line plot {for BestBuy}[0.183, 0.680, 0.888, 0.760] too shows a decline from 11% to 8%.\nBased on the above thought, the answer is:\nBased on the trends indicated in the line chart, Amazon's market share increased the most over the years 2019 to 2021. This is inferred from the line plot where it started at 23% market share in 2019 and rose to 31% by 2021, showing a large relative increase compared to the other companies which either showed decreases or smaller increases in the same period." } ], "id_raw": 1118 }, { "id": "gWs1K4In7gwxIg9Q", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage decrease in market share of Ebay from 2019 to 2021?" }, { "from": "gpt", "value": "To calculate the percentage decrease for Ebay, I first locate {the line points for Ebay}[[0.183, 0.573], [0.536, 0.626], [0.888, 0.680]]. The value for 2019 is 15 and for 2021 is 11. The percentage decrease can be calculated by taking the difference between the two years values, dividing by the initial year's value and then multiplying by 100.\nBased on the above thought, the answer is:\n26.67%" } ], "id_raw": 1118 }, { "id": "njWXfJ1KNwh2Id8U", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_56_19.png", "conversations": [ { "from": "human", "value": "\nCould you transform the line chart data, representing the various environmental protection and sustainable development activities in the years 2010 and 2020, into a structured table format?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.184, 0.849, 0.231, 0.899], [0.904, 0.849, 0.969, 0.899]], representing the 'User Age' or, in our context, the years 2010 and 2020. Then, I identify {the legend texts}[[0.741, 0.622, 0.897, 0.667], [0.741, 0.688, 0.910, 0.733], [0.741, 0.754, 0.853, 0.799]] as the row labels, representing the different environmental activities. I then locate {the line points}[[0.184, 0.517], [0.904, 0.277], [0.184, 0.277], [0.904, 0.117], [0.184, 0.677], [0.904, 0.437]] for each activity in the respective years and extract the values associated with them.\nBased on the above thought, the answer is:\nYear,Facebook,Instagram,Twitter\n35+,70,85,60\n15-35,85,95,75" } ], "id_raw": 1119 }, { "id": "w2y5MYgwJQijrOhl", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_56_19.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which social media platform saw the least growth in active users from the age group 35+ to 15-35? Justify your answer." }, { "from": "gpt", "value": "To determine which social media platform saw the least growth, I need to analyze the differences in active users for each platform between the age groups of 35+ and 15-35. For {Facebook}[0.184, 0.277, 0.904, 0.517], the number of users increased from 70 to 85, a difference of 15. \nFor {Instagram}[0.184, 0.117, 0.904, 0.277], the number of users increased from 85 to 95, a difference of 10.\nFor {Twitter}[0.184, 0.437, 0.904, 0.677], the number of users increased from 60 to 75, a difference of 15. Instagram shows the least growth with a difference of 10 users.\nBased on the above thought, the answer is:\nAccording to the chart, Instagram experienced the least growth in active users when comparing the age groups 35+ and 15-35. This is supported by the visible distance between points on the Instagram line graph, which shows a smaller increase (from 85 to 95 million users) compared to the other platforms." } ], "id_raw": 1119 }, { "id": "NJ4pptriF8VQCZJ1", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_56_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in active users on Instagram from the age group 35+ to the age group 15-35?" }, { "from": "gpt", "value": "To find the increase in active users on Instagram between the age groups 35+ and 15-35, I need to locate {the line points for Instagram}[[0.184, 0.277], [0.904, 0.117]] and extract the corresponding values 85 and 95. The total increase in users is therefore 95 - 85 = 10.\nBased on the above thought, the answer is:\n10." } ], "id_raw": 1119 }, { "id": "oLGr1P8g7rGlOQMH", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_0.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line graph data into a structured table displaying the years as columns and the neighborhoods as rows?" }, { "from": "gpt", "value": "Certainly, let's start with identifying {the x-axis tick labels}[[0.108, 0.934, 0.150, 0.970], [0.271, 0.934, 0.313, 0.970], [0.434, 0.934, 0.476, 0.970], [0.597, 0.934, 0.639, 0.970], [0.760, 0.934, 0.802, 0.970], [0.923, 0.934, 0.965, 0.970]] as column headers. Next, moving to {the legend texts}[[0.170, 0.131, 0.304, 0.171], [0.170, 0.193, 0.311, 0.233], [0.170, 0.255, 0.303, 0.295], [0.170, 0.317, 0.293, 0.357]] to identify the row labels. The values are then extracted from lines representing each neighborhood, for instance the {Low-Income line points}[[0.129, 0.896],[0.292, 0.855],[0.455, 0.785],[0.618, 0.744],[0.781, 0.716],[0.944, 0.619]] are attributed to 'Low-Income'.\nBased on the above thought, the answer is:\nNeighborhood,2016,2017,2018,2019,2020,2021\nLow-Income,42,45,50,53,55,62\nHigh-Income,52,50,49,45,50,49\nCommercial,46,48,60,62,65,70\nResidential,77,80,82,85,90,95" } ], "id_raw": 1120 }, { "id": "cG2f8nwvFd45QV5u", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_0.png", "conversations": [ { "from": "human", "value": "\nJudging by the line chart, which neighborhood appears to have the most growing number of interactions over the years and why?" }, { "from": "gpt", "value": "In order to identify which neighborhood has the highest growing number of interactions, I need to check the trend lines of each. Considering the {Low-Income line}[[0.129, 0.619, 0.944, 0.896]], the progression shows a consistent increase over time. {High-Income line}[[0.129, 0.758, 0.944, 0.855]] shows a slightly decreasing trend, while the {Commercial line}[[0.129, 0.507, 0.944, 0.841]] exhibits a noticeable increase. However, the most significant increase is on the {Residential line}[[0.129, 0.160, 0.944, 0.410]] over the years, indicating a robust growth in the number of interactions.\nBased on the above thought, the answer is:\nThe Residential neighborhood seems to have the most growing number of interactions over the years. The line representing Residential clearly illustrates an upward trend, indicating that from 2016 to 2021, the number of interactions in this neighborhood has consistently increased more than in the other neighborhoods." } ], "id_raw": 1120 }, { "id": "pRVDqJaa1F7uyQR5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the total change in interactions in the Residential neighborhood from 2016 to 2021?" }, { "from": "gpt", "value": "Extracting values from {the points on the Residential line}[[0.129, 0.410],[0.292, 0.369],[0.455, 0.341],[0.618, 0.299],[0.781, 0.230],[0.944, 0.160]] corresponding to years from 2016 to 2021, we have: 77, 80, 82, 85, 90, 95. The value for 2016 is 77 and for 2021 it is 95. The total change is therefore 95 - 77 = 18.\nBased on the above thought, the answer is:\n18." } ], "id_raw": 1120 }, { "id": "VgpeIDFAVCzzcFkc", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_1.png", "conversations": [ { "from": "human", "value": "\nPlease create a table using the data represented in the line chart, with columns for Years, Innovations, and Investments." }, { "from": "gpt", "value": "To extract the table data from the line chart, I begin by identifying {the x-axis tick labels}[-0.044, 0.885, -0.001, 0.970] which represent the Years. I then identify {the legend items}[0.156, 0.070, 0.274, 0.110], [0.156, 0.132, 0.294, 0.172] as the other column headers representing Innovations and Investments respectively. Next, I locate the {points for Innovations}[0.115, 0.259, 0.944, 0.378] and {points for Investments}[0.115, 0.179, 0.944, 0.278] under each year and extract their corresponding values.\nBased on the above thought, the answer is:\nYear,Innovations,Investments\n2015,50,60\n2016,55,65\n2017,57,67\n2018,58,68\n2019,59,65\n2020,60,69\n2021,62,70" } ], "id_raw": 1121 }, { "id": "0811N1XBTLesynov", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_1.png", "conversations": [ { "from": "human", "value": "\nLooking at the trends in innovation and investments over the years, which increased at a higher rate?" }, { "from": "gpt", "value": "By analyzing {the line for Innovation}[0.115, 0.259, 0.944, 0.378], I can see that the values increase steadily from 50 to 62 over the same period. On the other hand, analyzing {the line for Investments}[0.115, 0.179, 0.944, 0.278], it is observable that the values increase from 60 to 70.\nBased on the above thought, the answer is:\nFrom an overall perspective, it appears that Innovation increased at a higher rate than Investments over these years. Despite starting from a lower initial value, Innovation increased by 12 units, whereas Investments only increased by 10 units over the same period." } ], "id_raw": 1121 }, { "id": "DGfZQP1cAmbtJU3F", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the total sum of Investments over the years?" }, { "from": "gpt", "value": "To find the total sum of Investments, I would add up all the values obtained from {the points of Investments}[0.115, 0.179, 0.944, 0.278] which are 60, 65, 67, 68, 65, 69, and 70.\nBased on the above thought, the answer is:\n464." } ], "id_raw": 1121 }, { "id": "hoZMzxZ52Qc39h3n", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_2.png", "conversations": [ { "from": "human", "value": "\nCan you structure the data from the line chart into a table with decades as columns and trend types as rows?" }, { "from": "gpt", "value": "I will start by identifying {the x-axis labels}[[0.084, 0.891, 0.126, 0.919], [0.503, 0.891, 0.545, 0.919], [0.922, 0.891, 0.964, 0.919]] which provide the decades (2000s, 2010s, 2020s). These will represent the column headers in the table. Then, I identify {the legend}[[0.703, 0.109, 0.795, 0.145], [0.703, 0.161, 0.767, 0.197], [0.703, 0.214, 0.745, 0.250], [0.875, 0.109, 0.915, 0.145], [0.875, 0.161, 0.970, 0.197]], which have the labels 'Streetwear', 'Vintage', 'Boho', 'Punk', 'Sporty Chic'. These will represent the rows in the table. To populate the data, I examine the value at each decade for all trends by correlating the data points position to the y-axis values and formating it into a structured table.\nBased on the above thought, the answer is:\nTrend,2000s,2010s,2020s\nStreetwear,45,55,60\nVintage,55,60,45\nBoho,53,47,50\nPunk,47,55,53\nSporty Chic,55,50,47" } ], "id_raw": 1122 }, { "id": "VsZyCy7ygxacLXXH", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_2.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, which fashion trend has had the most stable popularity over the decades?" }, { "from": "gpt", "value": "To answer this, I'll have to evaluate the change in popularity for each trend from 2000s to 2020s. The line for {Streetwear}[[0.105, 0.115, 0.943, 0.845]] shows an increase from 45 to 60. The line for {Vintage}[[0.105, 0.115, 0.943, 0.845]] has a negative slope from 55 to 45. The line for {Boho}[[0.105, 0.456, 0.943, 0.747]] also shows a decrease from 53 to 50. The line for {Punk}[[0.105, 0.358, 0.943, 0.747]] increases from 47 to 53, and the line for {Sporty Chic}[[0.105, 0.358, 0.943, 0.747]] shows a decrease from 55 to 47. Based on these, the Boho trend has seen the least change over the years.\nBased on the above thought, the answer is:\nThe Boho trend has had the most stable popularity over the decades. It has seen a slight decrease from 53 to 50 which is the smallest change compared to the other fashion trends featured on the line chart." } ], "id_raw": 1122 }, { "id": "dxtTKFbW6nO0qsI1", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_2.png", "conversations": [ { "from": "human", "value": "\nHow much has the popularity of the 'Punk' trend decreased from 2000s to 2020s?" }, { "from": "gpt", "value": "Initially, I identify the coordinates of 'Punk' trend for each of the time periods. The coordinates for {Punk in 2000s}[[0.105, 0.747]] correspond to the value 47 and the coordinates for {Punk in 2020s}[[0.943, 0.456]] correspond to the value 53. By subtracting the popularity in 2020s from that in 2000s, I can calculate the decrease.\nBased on the above thought, the answer is:\n-6" } ], "id_raw": 1122 }, { "id": "FCcSnAlpe0ndo08X", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_3.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart depicting Economic Indicator Index for different sectors across various quarters into a structured table?" }, { "from": "gpt", "value": "First I identify the {x-axis tick labels}[0.073, 0.829, 0.160, 0.952], [0.335, 0.829, 0.440, 0.979], [0.613, 0.829, 0.705, 0.960], [0.880, 0.829, 0.980, 0.971] as the column headers indicating the different quarters. I then locate the {legend texts}[0.138, 0.053, 0.258, 0.079], [0.138, 0.090, 0.208, 0.116], [0.138, 0.128, 0.185, 0.154], [0.138, 0.166, 0.231, 0.191], [0.138, 0.203, 0.229, 0.229], [0.138, 0.241, 0.231, 0.267] corresponding to the different sectors. Combining these two data points, I could form a table architecture. For each sector, I then follow {the line points}[0.117, 0.189], [0.388, 0.284], [0.659, 0.221], [0.930, 0.189] corresponding to the values for each quarter, and populate the respective cells of the table. The final table can be summarized as follows:\nBased on the above thought, the answer is:\n,First Quarter,Second Quarter,Third Quarter,Fourth Quarter\nManufacturing,80,84,88,90\nServices,70,75,80,78\nRetail,60,65,68,71\nTechnology,78,82,85,88\nAgriculture,55,58,60,62\nReal Estate,65,70,75,80" } ], "id_raw": 1123 }, { "id": "PZDac6K3ch8NhLV5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_3.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which sector seems to have the least fluctuation in the Economic Indicator Index throughout the year?" }, { "from": "gpt", "value": "To determine the sector with the least fluctuation, I look at the variations in the line plots for each sector: {line for Manufacturing}[0.117, 0.189, 0.930, 0.348], {line for Services}[0.117, 0.348, 0.930, 0.506], {line for Retail}[0.117, 0.490, 0.930, 0.664], {line for Technology}[0.117, 0.221, 0.930, 0.379], {line for Agriculture}[0.117, 0.632, 0.930, 0.743], {line for Real Estate}[0.117, 0.348, 0.930, 0.585]. The line for Agriculture indicates a relatively smaller change compared to the other sectors, suggesting it might have the least fluctuation in the Economic Indicator Index.\nBased on the above thought, the answer is:\nBased on the trends shown in the line chart, the Agriculture sector appears to have the least fluctuation in the Economic Indicator Index throughout the year. The difference between its maximum and minimum values seems less substantial as compared to those in the other sectors, indicating more stability in its economic performance." } ], "id_raw": 1123 }, { "id": "W30U7X0UKoL35xUd", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the total increase in the Real Estate sector's Economic Indicator Index from the First Quarter to the Fourth Quarter?" }, { "from": "gpt", "value": "I must find the starting and ending points for the Real Estate sector. I identify {the line points for Real Estate}[0.117, 0.585], [0.930, 0.348], which correspond to its values at the First Quarter (65) and the Fourth Quarter (80), respectively. To get the total increase, I subtract the starting point from the ending point, so the calculation is 80 - 65.\nBased on the above thought, the answer is:\n15." } ], "id_raw": 1123 }, { "id": "bD3hvnkkeM6mA0AI", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_4.png", "conversations": [ { "from": "human", "value": "\nCan you please convert the line chart data to a table format? The metrics should be the columns and the aspects ('Entrepreneurship' and 'Innovation') should be the rows." }, { "from": "gpt", "value": "To form the table, I start by identifying {the x-axis tick labels}[[0.088, 0.805, 0.111, 0.844], [0.184, 0.805, 0.252, 0.922], [0.300, 0.805, 0.373, 0.929], [0.415, 0.805, 0.496, 0.944], [0.532, 0.805, 0.617, 0.950], [0.642, 0.805, 0.744, 0.978], [0.778, 0.805, 0.846, 0.921], [0.880, 0.805, 0.981, 0.979]] as the column headers or metrics. Next, I see {the legend texts}[[0.444, 0.057, 0.536, 0.084], [0.444, 0.098, 0.631, 0.125]] as the row headers representing the aspects 'Entrepreneurship' and 'Innovation'. I then locate {the line points} of these aspects for the corresponding metrics and capture the values to complete the table.\nBased on the above thought, the answer is:\nMetrics,All,Solar Power,Hydro Power,Nuclear Power,Biomass Power,Geothermal Power,Wind Power,Natural Gas Power\nEnergy Use,82,60,78,70,50,55,65,80\nResource Consumption,72,40,68,60,55,60,55,70" } ], "id_raw": 1124 }, { "id": "pE6AUHpSge05oHF1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_4.png", "conversations": [ { "from": "human", "value": "\nCan you comment on the energy trend with respect to 'Geothermal Power' as depicted in the line chart?" }, { "from": "gpt", "value": "To analyze the energy trend in connection to 'Geothermal Power', first, I observe {the line for 'Energy Use'}[0.693, 0.479]. From this, I find that the energy use seems to be in the middle range as compared to other types. Directing my attention then to {the line for 'Resource Consumption'}[0.693, 0.415], I see that the resource consumption for generating 'Geothermal Power' is also moderate.\nBased on the above thought, the answer is:\nAs per the line chart trends, 'Geothermal Power' presents a median energy consumption and resource use. It does not lead the ranks in either the energy produced or the resources consumed. This could imply that while 'Geothermal Power' is a significant contributor to the energy portfolio, it may not be the most efficient or productive in comparison to the other power types depicted." } ], "id_raw": 1124 }, { "id": "Z8mIzvUOsEJoyBrW", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_4.png", "conversations": [ { "from": "human", "value": "\nAccording to the trends in the line chart, what is the highest value that the 'Resource Consumption' metric reaches?" }, { "from": "gpt", "value": "To find the maximum value for the 'Resource Consumption' metric, I examine {the line points for 'Resource Consumption'}[[0.099, 0.262], [0.218, 0.670], [0.337, 0.313], [0.455, 0.415], [0.574, 0.479], [0.693, 0.415], [0.812, 0.479], [0.931, 0.287]] and extract their corresponding values. After making out all data points, I see that the maximum value is 72.\nBased on the above thought, the answer is:\n72" } ], "id_raw": 1124 }, { "id": "GTwMiItEPUq0oLek", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_5.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data for weekly social media engagement from the chart and organize it in a table with the content types as rows and the social media platforms as columns?" }, { "from": "gpt", "value": "To create a table from the chart, I start by identifying {the x-axis labels}[0.111, 0.701, 0.133, 0.745], [0.212, 0.701, 0.267, 0.811], [0.338, 0.701, 0.375, 0.776], [0.439, 0.701, 0.509, 0.842], [0.528, 0.701, 0.655, 0.953], [0.682, 0.701, 0.736, 0.807], [0.759, 0.701, 0.894, 0.970], [0.926, 0.701, 0.961, 0.771] which represent the row labels or content types. Then, I refer to {the legend}[0.530, 0.052, 0.934, 0.128] for the column headers which are 'Instagram' and 'Facebook'. To find the values, I examine the points on {the line for Instagram}[0.122, 0.214, 0.944, 0.618] and {the line for Facebook}[0.122, 0.085, 0.944, 0.361] for each content type and read off the values from the y-axis.\nBased on the above thought, the answer is:\nType, Instagram, Facebook\nAll,600,950\nBlog Post,750,1050\nVideo,800,1150\nInfographics,850,1050\nVirtual Reality Contents,975,1100\nPodcasts,1025,1150\nUser Generated Contents,1100,1200\nLives,1150,1325" } ], "id_raw": 1125 }, { "id": "nauEmHrZC3Jzkp9D", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_5.png", "conversations": [ { "from": "human", "value": "\nWhich type of content has seen a consistent increase in engagement on both platforms according to the chart?" }, { "from": "gpt", "value": "To identify the content type with a consistent increase in engagement on both platforms, I need to examine the trend in {the line for Instagram}[0.122, 0.214, 0.944, 0.618] and {the line for Facebook}[0.122, 0.085, 0.944, 0.361]. Both lines indicate an overall increasing trend in engagement scores. I then correlate the increasing points on both lines to the corresponding content types on {the x-axis}[0.111, 0.701, 0.133, 0.745], [0.212, 0.701, 0.267, 0.811], [0.338, 0.701, 0.375, 0.776], [0.439, 0.701, 0.509, 0.842], [0.528, 0.701, 0.655, 0.953], [0.682, 0.701, 0.736, 0.807], [0.759, 0.701, 0.894, 0.970], [0.926, 0.701, 0.961, 0.771]. The clear rising trend for both platforms is seen to occur across all content types from 'All' to 'Lives'.\nBased on the above thought, the answer is:\nBased on the chart, all types of content have seen a consistent increase in their weekly engagement scores on both Instagram and Facebook. Their lines on the chart have shown a consistent upward trend from 'All' to 'Lives'." } ], "id_raw": 1125 }, { "id": "Yu2NpqX7q7vcdFwY", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the maximum and minimum weekly engagement score on Facebook?" }, { "from": "gpt", "value": "To find the maximum and minimum weekly engagement scores on Facebook, I need to locate {the points on the Facebook line}[[0.122, 0.361], [0.239, 0.287], [0.357, 0.214], [0.474, 0.287], [0.592, 0.250], [0.709, 0.214], [0.826, 0.177], [0.944, 0.085]] and extract the related values. They are 950, 1050, 1150, 1050, 1100, 1150, 1200, 1325, respectively. The maximum value is 1325 corresponding to {Lives}[0.926, 0.701, 0.961, 0.771], and the minimum value is 950 corresponding to {All}[0.111, 0.701, 0.133, 0.745]. Therefore the difference between the maximum and minimum value is 1325 - 950 = 375.\nBased on the above thought, the answer is:\n375." } ], "id_raw": 1125 }, { "id": "7Y0NooM0PD9XVTS3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_7.png", "conversations": [ { "from": "human", "value": "\nCan you structure the line chart data into a table with different Design types as columns and the Trends as the rows?" }, { "from": "gpt", "value": "To organize the data from the line chart into a tabular format, I first locate {the x-axis tick labels}[[0.068, 0.729, 0.160, 0.913], [0.470, 0.729, 0.564, 0.916], [0.859, 0.729, 0.980, 0.970]], which indicate the columns, representing the distinct design types. Following that, I identify {the legend texts}[[0.735, 0.070, 0.886, 0.110], [0.735, 0.132, 0.889, 0.172], [0.735, 0.194, 0.940, 0.234]] as the rows, denoting different trends. For each trend, I track down its {line points}[[0.114, 0.227], [0.517, 0.187], [0.920, 0.128], [0.114, 0.325], [0.517, 0.266], [0.920, 0.187], [0.114, 0.128], [0.517, 0.109], [0.920, 0.069]] on the chart and isolate the values represented.\nBased on the above thought, the answer is:\nTrend,Visual Design,Sound Design,Interaction Design\nStable Growth,65,67,70\nGradual Decline,60,57,55" } ], "id_raw": 1127 }, { "id": "nq98zeyGS5BBM7bt", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_7.png", "conversations": [ { "from": "human", "value": "\nAccording to the line chart, which field of Design shows the most promising trend in improving Creative Performance over time? Support your answer with reasoning." }, { "from": "gpt", "value": "To ascertain the field of Design with the most promising trend, I scan through the line plots for each Design field. Assessing {the line for Visual Design}[0.114, 0.128, 0.920, 0.522], there is a noticeable dip from 70 to 60, marking an overall decline. Next, {Sound Design}[0.114, 0.227, 0.920, 0.424] line shows a fall from 67 to 57, implying a decreasing trend too. Lastly, {Interaction Design}[0.114, 0.030, 0.920, 0.464] starts at 70 and ends at 50, highlighting a significant drop. Comparing these scenarios, Visual Design and Sound Design seem to fall at a slower rate and their final performances are relatively higher compared to Interaction Design.\nBased on the above thought, the answer is:\nBased on the trends in the line chart, Visual and Sound Designs are tied for showing the most promising trend in improving Creative Performance over time. Despite the overall decline across all fields, Visual and Sound Design reach higher final values of 60 and 57 respectively, compared to the 50 of Interaction Design. Moreover, their rate of decline is somewhat slower compared to that of Interaction Design, suggesting more stability and potential for improvement." } ], "id_raw": 1127 }, { "id": "ByrQnLnEUYUKNw39", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_7.png", "conversations": [ { "from": "human", "value": "\nWhat's the range of values for \"Gradual Decline\" across all Design types?" }, { "from": "gpt", "value": "To calculate the range for \"Gradual Decline\", I would pinpoint the highest and lowest values across all Design types. So, I observe the {line points for Gradual Decline}[[0.114, 0.325], [0.517, 0.384], [0.920, 0.424]] and extract the corresponding values: 60, 57, 55. The maximum value is 60 at {Visual Design}[0.114, 0.227] and the minimum value is 55 at {Interaction Design}[0.920, 0.128]. Taking the difference between the maximum and minimum gives us the range.\nBased on the above thought, the answer is:\n5." } ], "id_raw": 1127 }, { "id": "hXXXIXEYDYj5ewbL", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_8.png", "conversations": [ { "from": "human", "value": "\nCould you extract the information about the investments and profits of the fintech companies from the line chart and tabulate the data?" }, { "from": "gpt", "value": "Based on the line chart, I can locate the {x-axis labels}[0.074, 0.763, 0.197, 0.843] for the names of the fintech companies. Then, I find the {coloured endpoints of the lines}[0.222, 0.261, 0.940, 0.743] corresponding to the investment and profit values for each company. I also note the {legend}[0.852, 0.044, 0.978, 0.304] to determine which colour corresponds to either 'Investment' or 'Profit'. By matching these all together, I can create a tabular representation of the line chart data.\nBased on the above thought, the answer is:\nCompany,Investment,Profit\nCoinBase,450,350\nDigiBank,500,450\nByteCash,900,700\nBankDigi,650,550\nBitWallet,400,350\nCoinSecure,550,400\nZ-Cash,650,500\nDigiDollar,500,400" } ], "id_raw": 1128 }, { "id": "Sr7GMV4xvQcIMuyv", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_8.png", "conversations": [ { "from": "human", "value": "\nAmong the eight fintech and digital currency companies, which one has the most significant difference between the amount of investment and profit? What does this suggest?" }, { "from": "gpt", "value": "To answer this, I need to calculate the difference between investment and profit for each company by looking at their respective {point values on the graph}[0.222, 0.261, 0.940, 0.743]. It seems like {ByteCash}[0.4, 0.763, 0.490, 0.988] has a significant gap between investment and profit. This indicates that the company's investment exceeded its profits by a considerable margin.\nBased on the above thought, the answer is:\nByteCash has the most significant difference between the amount of investment and profit. This suggests that the company might take on a high investment risk strategy, or it possibly has inefficiencies in generating profits from investments." } ], "id_raw": 1128 }, { "id": "FgG4mA0PtUjIHIFQ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the investment and profit figures for Z-Cash?" }, { "from": "gpt", "value": "To establish the difference between investment and profit for Z-Cash, I will find {the point for investment in Z-Cash}[0.813, 0.589] and decipher its value. I will also identify {the point for profit in Z-Cash}[0.813, 0.386] and ascertain its value. I will then subtract the profit from the investment to get the difference.\nBased on the above thought, the answer is:\n150." } ], "id_raw": 1128 }, { "id": "Ejok5T67jitpqYEm", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_9.png", "conversations": [ { "from": "human", "value": "\nCan the data from the line chart be presented in a table with Banks as rows and Attributes as columns?" }, { "from": "gpt", "value": "In order to convert the line chart data into a table, I firstly locate {the x-axis tick labels}[[0.062, 0.751, 0.166, 0.959], [0.195, 0.751, 0.266, 0.893], [0.315, 0.751, 0.378, 0.876], [0.412, 0.751, 0.514, 0.954], [0.556, 0.751, 0.601, 0.841], [0.659, 0.751, 0.731, 0.894], [0.756, 0.751, 0.866, 0.970], [0.875, 0.751, 0.980, 0.960]] as the row labels showing each bank. Next, the {the attribute legend}[0.420, 0.086, 0.684, 0.126] can be identified as the column headers exhibiting the return rate and outliers. For each bank, I locate {the line points for return rate}[[0.114, 0.185, 0.231, 0.185, 0.347, 0.324, 0.463, 0.602, 0.579, 0.408, 0.695, 0.491, 0.811, 0.463, 0.927, 0.604]] and {the line points for outliers}[[0.114, 0.324, 0.231, 0.324, 0.347, 0.435, 0.463, 0.741, 0.579, 0.435, 0.695, 0.380, 0.811, 0.380, 0.927, 0.435]] to extract the values for the Return Rate and Outliers columns of the corresponding banks.\nBased on the above thought, the answer is:\nBanks,Return Rate(%),Outliers\nGoldman_Sachs,77,74\nJP_Morgan,80,75\nBarclays,75,71\nDeutsche_Bank,65,60\nHSBC,72,66\nCiti_Group,69,65\nBank_of_America,70,67\nMorgan_Stanley,73,71" } ], "id_raw": 1129 }, { "id": "2Bvnuds75pd52Inh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_9.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which bank has the most stable Return Rate? Justify your response." }, { "from": "gpt", "value": "To identify which bank has the most stable Return Rate, I need to consider the variance in each banks return rate. For each bank, the {lines for Return Rates}[[0.114, 0.185], [0.231, 0.185], [0.347, 0.324], [0.463, 0.602], [0.579, 0.408], [0.695, 0.491], [0.811, 0.463], [0.927, 0.380]] show the trend. It is noticeable that the line for 'JP Morgan' demonstrates the least fluctuations, staying relatively constant through the period. Therefore, 'JP Morgan' appears to have the most stable growth in its return rates.\nBased on the above thought, the answer is:\nBased on the line chart, 'JP Morgan' has the most stable Return Rate. This is because the line corresponding to 'JP Morgan' exhibits the least fluctuations, indicating that their Return Rate remained very constant compared to other banks during the same period." } ], "id_raw": 1129 }, { "id": "8D7MlM6LlwEtJQCG", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_9.png", "conversations": [ { "from": "human", "value": "\nWhat's the average Return Rate for all banks?" }, { "from": "gpt", "value": "To find out the average Return Rate, we have to identify all {the line points for Return Rate}[[0.114, 0.185], [0.231, 0.185], [0.347, 0.324], [0.463, 0.602], [0.579, 0.408], [0.695, 0.491], [0.811, 0.463], [0.927, 0.380]]. The corresponding Return rates are: 77, 80, 75, 65, 72, 69, 70, 73. Add these together and divide by the total number of banks which is 8.\nBased on the above thought, the answer is:\n72.625." } ], "id_raw": 1129 }, { "id": "FkSlc1ds8jOEF85e", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_10.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the bar chart into a table format?" }, { "from": "gpt", "value": "To generate the tabular data from the bar chart, I first look at {the x-axis labels}[[0.136, 0.652, 0.297, 0.677], [0.136, 0.591, 0.322, 0.616], [0.136, 0.529, 0.276, 0.554], [0.136, 0.464, 0.318, 0.489], [0.136, 0.397, 0.325, 0.422], [0.136, 0.332, 0.259, 0.357], [0.136, 0.268, 0.296, 0.293], [0.136, 0.204, 0.298, 0.229]], which represent the row headers i.e. initiatives. Next, I look at {bar labels}[[0.313, 0.631, 0.359, 0.647], [0.313, 0.572, 0.358, 0.588], [0.313, 0.509, 0.362, 0.525], [0.313, 0.444, 0.365, 0.460], [0.313, 0.377, 0.362, 0.394], [0.313, 0.315, 0.346, 0.331], [0.313, 0.254, 0.342, 0.270], [0.313, 0.194, 0.346, 0.209]], which represent the percentages of effectiveness for each initiative. The table can be generated using these identified components.\nBased on the above thought, the answer is:\nInitiative, Effectiveness Rate\nGraduate Startups, 90.6%\nBusiness Incubation Centers, 76.2%\nR&D Investments, 69.4%\nPublic-Private Partnerships, 68.2%\nOpen Innovation Programs, 64.3%\nVenture Capital, 45.2%\nStartup Competitions, 25.6%\nCrowdfunding Platforms, 12.5%" } ], "id_raw": 1130 }, { "id": "39ZH0269ig8Kvoum", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage range of effectiveness between the most and least effective initiatives?" }, { "from": "gpt", "value": "To determine the percentage range, I need to identify the values for the most and least effective initiatives from the bar chart. The most effective initiative is {Graduate Startups}[0.136, 0.652, 0.297, 0.677] with a value of 90.6%, and the least effective initiative is {Crowdfunding Platforms}[0.136, 0.204, 0.298, 0.229] with a value of 12.5%. The difference can be calculated as 90.6% - 12.5% = 78.1%.\nBased on the above thought, the answer is:\n78.1%." } ], "id_raw": 1130 }, { "id": "h3GLRZeMrG6RGmGP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_11.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you extract the data into a table format, with the categories on the x-axis representing the different features, and the labels on the y-axis representing the index level?" }, { "from": "gpt", "value": "To extract the line chart data into a table, I first identify {the x-axis labels}[0.102, 0.698, 0.966, 0.795] corresponding to the different features. I also notice the legend text for {Global Impact}[0.437, 0.048, 0.529, 0.076] and {NewTown}[0.596, 0.048, 0.661, 0.076] which represent the different blocks. Now, I identify the data points for each block across different features. For example, {the points for GlobalImpact}[0.115, 0.181, 0.944, 0.371] and {points for NewTown}[0.115, 0.34, 0.944, 0.593]. Using these points, I can compile the data into a structured table.\nBased on the above thought, the answer is:\nBlock,All,Greenspace,Public Transportation,Bike Lanes,Commercial Areas,Residential Areas,Industries,Roads\nGlobalImpact,62,70,68,66,64,72,60,65\nNewTown,52,60,58,46,54,62,50,55" } ], "id_raw": 1131 }, { "id": "IxPOImSW5zNsZabq", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_11.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which block (either GlobalImpact or NewTown) appears more balanced in terms of the distribution of development index across different features? Please explain your reasoning." }, { "from": "gpt", "value": "To determine balance, we need to observe the variance in the Development Index across the different features for each block. Looking at the line for {GlobalImpact}[0.115, 0.181, 0.944, 0.371], there's a range of values between 60 and 72. This shows the variance is minor, indicating a relatively balanced distribution for GlobalImpact.\nContrarily, examining the trend for {NewTown}[0.115, 0.34, 0.944, 0.593], the Development Index values span from 46 to 62. This larger range shows that NewTown's distribution of development index across different features is less balanced compared to GlobalImpact.\nBased on the above thought, the answer is:\nBased on the line chart, GlobalImpact shows a more balanced distribution of development index across different features compared to NewTown. This is because GlobalImpact has a narrower range of values (between 60 and 72) across different features. In contrast, NewTown's Development Index values span between 46 and 62, indicating a more uneven distribution." } ], "id_raw": 1131 }, { "id": "D2eOZXaVYwftcZIQ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in the Development Index between GlobalImpact and NewTown for Bike Lanes?" }, { "from": "gpt", "value": "To determine the difference in the Development Index between GlobalImpact and NewTown for Bike Lanes, I would find the coordinates for {The GlobalImpact value for Bike Lanes}[0.470, 0.276], which extracts a value of 66. I would also locate the coordinates for {the NewTown value for Bike Lanes}[0.470, 0.593], which gives a value of 46. The difference between the two values is 66 - 46 = 20.\nBased on the above thought, the answer is:\n20" } ], "id_raw": 1131 }, { "id": "5pVTH7VS8tFlzzhK", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_12.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you convert the data into a tabular format where the columns are years and rows represent types of activities?" }, { "from": "gpt", "value": "To create the tabular data from the line chart, I'll start by identifying {the x-axis tick labels}[[0.120, 0.814, 0.178, 0.923], [0.204, 0.814, 0.293, 0.981], [0.321, 0.814, 0.376, 0.917], [0.421, 0.814, 0.475, 0.915]], which represent the column headers or years. Next, I pinpoint {the legend texts}[[0.068, 0.044, 0.153, 0.069], [0.224, 0.044, 0.367, 0.069]], as the row labels indicating the different activities. To populate the data values, I locate {the data points for each activity line} for 'Human Life'[[0.149, 0.571], [0.249, 0.461], [0.349, 0.366], [0.448, 0.271], [0.548, 0.192], [0.648, 0.555], [0.748, 0.461], [0.847, 0.366], [0.947, 0.508]] and for 'Living Environment' [[0.149, 0.650], [0.249, 0.524], [0.349, 0.492], [0.448, 0.571], [0.548, 0.650], [0.648, 0.587], [0.748, 0.618], [0.847, 0.587], [0.947, 0.666]] at the respective years and extract the corresponding values for years 2018, 2019, 2020, 2021.\nBased on the above thought, the answer is:\nActivities,2018,2019,2020,2021\nHuman Life,65,72,84,69\nLiving Environment,65,68,65,63" } ], "id_raw": 1132 }, { "id": "TfiapmQSxHKByBs6", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_12.png", "conversations": [ { "from": "human", "value": "\nWhich activity has seen more change over the 4-year period? Please explain your reasoning." }, { "from": "gpt", "value": "To determine the activity that has seen more change over the four-year period, I will examine the difference between the starting and ending values for both activities. I find {the starting and ending points for Human Life}[[0.149, 0.571], [0.947, 0.508]], which indicate a change from 65 to 69 in the Human Life activity. In contrast, by looking at {the starting and ending points for the Living Environment}[[0.149, 0.650], [0.947, 0.666]], I can see that the Living Environment activity experiencing a change from 65 to 63. As a result, the Living Environment activity shows a greater change, as it declines from 65 to 63, whereas the Human Life activity improves from 65 to 69.\nBased on the above thought, the answer is:\nOver the 4-year period, the Living Environment activity has seen more change. Even though the Human Life frequency increased from 65 to 69, the Living Environment frequency decreased from 65 to 63. This is larger in absolute terms, thus we can conclude that the Living Environment has seen more significant change." } ], "id_raw": 1132 }, { "id": "YdXAMclw3mFkQhTs", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage decrease in frequency for Gardening from 2018 to 2021?" }, { "from": "gpt", "value": "I can find the decrease by comparing the values of 2018 and 2021 for Gardening. The 2018 value for Gardening I can find at this point in coordinates {Living Environment in 2018} [0.149, 0.650] is 65. The 2021 value for Gardening is given at {Living Environment in 2021} [0.947, 0.666] as 63. So the difference is 65 - 63 = 2 and the percentage decrease would be (2 / 65 * 100)%.\nBased on the above thought, the answer is:\n3.08%." } ], "id_raw": 1132 }, { "id": "7tGKd0Craq1RUzKx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_13.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data showing the hours spent on exercise and socializing over months into a table format?" }, { "from": "gpt", "value": "Firstly, I determine the month's data by viewing {the x-axis labels}[0.101, 0.891, 0.136, 0.919] to [0.926, 0.891, 0.961, 0.919], which represent January to December. For socializing and exercise, I observe {the line for exercise}[0.118, 0.350, 0.944, 0.168] and {the line for socializing}[0.118, 0.426, 0.944, 0.398]. I then extract the specific hours by checking the respective points marked above these lines for each month.\nBased on the above thought, the answer is:\nMonth,Exercise,Socializing\nJanuary,30,20\nFebruary,35,25\nMarch,40,30\nApril,45,35\nMay,50,45\nJune,55,50\nJuly,60,55\nAugust,65,60\nSeptember,70,65\nOctober,40,45\nNovember,35,40\nDecember,30,35" } ], "id_raw": 1133 }, { "id": "s8RHkHSvPVsL2A31", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_13.png", "conversations": [ { "from": "human", "value": "\nWhat trend can be observed in the activities of exercise and socializing across the year based on the chart?" }, { "from": "gpt", "value": "Observing {the line for exercise from January to September}[0.118, 0.350, 0.737, 0.244] and {the line for socializing}[0.118, 0.426, 0.737, 0.380], there is a consistent rise in hours for both activities, indicating increased participation. Notably, {from October to December}[0.737, 0.380, 0.944, 0.289] the lines show a sharp decline, which could be attributed to the end of year festive period causing a drop in regular activities.\nBased on the above thought, the answer is:\nThe trend shows a steady increase in hours for both exercise and socializing from January to September, followed by a sudden decrease from October to December. This pattern suggests a seasonal effect where activities reduce towards the year-end, possibly due to festive distractions or weather changes." } ], "id_raw": 1133 }, { "id": "vlas74QQ5HQ3bJZr", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the total number of hours spent on exercise and socializing combined in January?" }, { "from": "gpt", "value": "To calculate the total hours spent, I'll look at both {the point for exercise in January}[0.118, 0.350] and {the point for socializing in January}[0.118, 0.426]. The hours noted are 30 for exercise and 20 for socializing. Adding these together provides the total hours.\nBased on the above thought, the answer is:\n50" } ], "id_raw": 1133 }, { "id": "ND7jT0cJYyp8L1Pi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_14.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured table format where each row represents a social welfare activity, and the columns are divided into Personal Donations, Corporate Donations, and Government Funding?" }, { "from": "gpt", "value": "To create a structured table, I first identify {the x-axis labels}[[0.045, 0.705, 0.153, 0.919], [0.133, 0.705, 0.231, 0.900], [0.216, 0.705, 0.316, 0.905], [0.302, 0.705, 0.395, 0.890]] which represent Youth Services, Homeless Support, Environmental Preservation, and Animal Rescue. For each category, I analyze the lines corresponding to {Personal Donations}[[0.102, 0.470], [0.218, 0.082], ..., [0.798, 0.243]], {Corporate Donations}[[0.099, 0.179, 0.848, 0.332]], and {Government Funding}[[0.099, 0.255, 0.848, 0.409]] to extract the funding values.\nBased on the above thought, the answer is:\nActivity,Personal Donations,Corporate Donations,Government Funding\nYouth Services,200,180,160\nHomeless Support,180,190,170\nEnvironmental Preservation,150,140,130\nAnimal Rescue,120,250,110" } ], "id_raw": 1134 }, { "id": "xfk8lfb0NKcucUXq", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_14.png", "conversations": [ { "from": "human", "value": "\nWhich social welfare activity shows the most stable funding across all sources and why?" }, { "from": "gpt", "value": "Analyzing the stability of funding across all sources, {the line for Homeless Support}[[0.102, 0.599], [0.218, 0.340], [0.334, 0.179]] exhibits minor variations between Personal Donations (180), Corporate Donations (190), and Government Funding (170). The differences between the maximum and minimum funding values are smaller relative to other activities, showcasing higher stability.\nBased on the above thought, the answer is:\nHomeless Support shows the most stable funding. This is deduced by observing the minor variations among the funding sources. The line representing Homeless Support does not show large fluctuations, indicating consistent support across personal, corporate, and governmental donations." } ], "id_raw": 1134 }, { "id": "1sbvAZ1QcQrWHuEX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the average funding for Environmental Preservation across all donation sources?" }, { "from": "gpt", "value": "To determine the average funding for Environmental Preservation, I locate the funding values from {Personal Donations}, {Corporate Donations}, and {Government Funding} for Environmental Preservation which are 150, 140, and 130 respectively. The average is calculated by (150 + 140 + 130) / 3.\nBased on the above thought, the answer is:\n140" } ], "id_raw": 1134 }, { "id": "pYsmCwoCqNbyorZD", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_16.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart and tabulate it with corresponding years as columns and food types as rows?" }, { "from": "gpt", "value": "From the line chart, first, I identify {the x-axis tick labels}[[0.108, 0.834, 0.150, 0.919],[0.923, 0.834, 0.966, 0.919]] which denote years that would serve as the column headers. Next, I pick out {the legend labels}[[0.831, 0.131, 0.965, 0.171],[0.831, 0.193, 0.964, 0.233],[0.831, 0.255, 0.959, 0.295]] which will serve as the food types or row labels. Now, to populate these rows, I locate {the line points}[[0.129, 0.335],[0.944, 0.384],[0.129, 0.213],[0.944, 0.164],[0.129, 0.702],[0.944, 0.653]] for each type at the respective years, and extract the corresponding values.\nBased on the above thought, the answer is:\nYear,Food Import,Food Export,Food Waste\n2020,250,300,100\n2021,230,320,120" } ], "id_raw": 1136 }, { "id": "EkWRA8nGlluA9U7x", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_16.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which sector in the Food and Agriculture Industry showed an increase from 2020 to 2021? Provide your analysis." }, { "from": "gpt", "value": "To identify which sector of the Food and Agriculture Industry has shown an increase from 2020 to 2021, I need to observe the slopes of {the lines representing the metrics}[[0.129, 0.335, 0.944, 0.384],[0.129, 0.213, 0.944, 0.164],[0.129, 0.702, 0.944, 0.653]]. \nThe line plot for {Food Import}[0.129, 0.335, 0.944, 0.384] depicts a decrease from 250 in 2020 to 230 in 2021.\nThe line plot for {Food Export}[0.129, 0.213, 0.944, 0.164] shows an increase from 300 in 2020 to 320 in 2021. \nThe line for {Food Waste}[0.129, 0.702, 0.944, 0.653] also exhibits an increase from 100 in 2020 to 120 in 2021.\nBased on the above thought, the answer is:\nAs per the line chart, the Food Export and Food Waste sectors of the Food and Agriculture Industry showed an increase from 2020 to 2021. The lines corresponding to these sectors showed positive slopes, indicating an upward trend or an increase for the given time period." } ], "id_raw": 1136 }, { "id": "Sa2ITHuK2RmKV7i5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the quantity of Food Waste generated from 2020 to 2021?" }, { "from": "gpt", "value": "To calculate the difference in Food Waste from 2020 to 2021, I need to locate {the points on the line representing Food Waste}[[0.129, 0.702],[0.944, 0.653]]. The values for these points are 100 corresponding to {Year 2020}[0.108, 0.834, 0.150, 0.919] and 120 corresponding to {Year 2021}[0.923, 0.834, 0.966, 0.919]. The difference between them is obtained by subtracting the 2020 value from the 2021 value, that is, 120 - 100 = 20.\nBased on the above thought, the answer is:\n20." } ], "id_raw": 1136 }, { "id": "YtHgh9Dio8V648aH", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_17.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart to a standard table format, using the category labels as row headers and groups as columns?" }, { "from": "gpt", "value": "To transform the data, I'll start by identifying {the x-axis labels}[0.063, 0.760, 0.167, 0.966], [0.268, 0.760, 0.373, 0.970], [0.478, 0.760, 0.574, 0.952], [0.681, 0.760, 0.783, 0.962], [0.896, 0.760, 0.979, 0.925] - they'll be our row headers. Next, I'll look at {the legend entries}[0.583, 0.086, 0.673, 0.126], [0.780, 0.086, 0.894, 0.126] that will act as column headers referring to different groups. For the values, I'll check {the line markers}[0.115, 0.328], [0.321, 0.445] and so on, extracting the corresponding assessment values for each group under different categories.\nBased on the above thought, the answer is:\nCategories,Reading,Exercising\nSlight decrease,78,70\nGradual decline,73,65\nPersistent rise,81,82\nRapid recovery,84,78\nAbrupt drop,79,72" } ], "id_raw": 1137 }, { "id": "pGM5lF6dXe9EFOqH", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_17.png", "conversations": [ { "from": "human", "value": "\nWhat can you tell about the behavior towards Exercising based on the trend line?" }, { "from": "gpt", "value": "Looking at {the trend line for Exercising}[0.115, 0.234, 0.938, 0.633], I see that the level of exercising encounters a steep rise from the 'Slight decrease' stage to the 'Persist rise' stage. After reaching a peak at 'Slight decrease' stage, the line starts a relatively modest descent before a brief recovery in the 'Abrupt drop' stage. This fluctuating pattern indicates a certain level of inconsistency in their exercising habits.\nBased on the above thought, the answer is:\nThe trend line for Exercising reveals an inconsistent behavior. It starts with a high level at the 'Slight decrease' stage, and after a persistent rise, it reaches the peak. After reaching its height, it then experiences a decline before slightly recovering in the 'Abrupt drop' stage. The fluctuations show that the habit of exercising is not consistent." } ], "id_raw": 1137 }, { "id": "n36RHqqBep7bQf9H", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_17.png", "conversations": [ { "from": "human", "value": "\nHow much has the frequency of Reading dropped from its peak to the Abrupt drop stage?" }, { "from": "gpt", "value": "To answer this question, I first need to locate the peak value of Reading. Looking at {the points for the Reading line}[0.115, 0.328], [0.321, 0.445], [0.526, 0.257], [0.732, 0.187], [0.938, 0.304], I observe that the highest value is 84 at the stage of Rapid recovery. Now, for the Abrupt drop stage, {the point corresponds}[0.938, 0.304] to the value of 79. Therefore, the drop in the frequency of Reading is 84 - 79 = 5.\nBased on the above thought, the answer is:\n5." } ], "id_raw": 1137 }, { "id": "7Zc9Bx01eZllQoAh", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_57_19.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into structured table format with the series of change as the column and the companies as rows?" }, { "from": "gpt", "value": "To form structured data, first I identify {the x-axis tick labels}[[0.064, 0.714, 0.192, 0.970],[0.347, 0.714, 0.447, 0.913],[0.612, 0.714, 0.718, 0.925],[0.887, 0.714, 0.980, 0.899]] as columns representing various series of changes. Then, the {legend}[[0.767, 0.070, 0.954, 0.110],[0.767, 0.132, 0.850, 0.172]] are identified as row labels which represent the different companies. The rest of the information, particularly each company's funding at different series, is captured in the {points of line items} [[0.128, 0.569],[0.397, 0.502], [0.665, 0.165], [0.934, 0.637], [0.128, 0.637], [0.397, 0.569], [0.665, 0.232], [0.934, 0.704]].\nBased on the above thought, the answer is:\nCompany,Pronounced decline,Inexorable rise,Abrupt increase,Steep decline\nInvestment Fund,90,95,120,85\nStartup,85,90,115,80" } ], "id_raw": 1139 }, { "id": "geEyGOepurmO7VAw", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_57_19.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which series of change demonstrates the largest difference in funding levels between the Investment Fund and the startup? Explain your reasoning." }, { "from": "gpt", "value": "To determine which series demonstrates the largest funding difference between the Investment Fund and the startup, I need to compare {the funding levels of Investment Fund across the series}[[0.128, 0.569],[0.397, 0.502], [0.665, 0.165], [0.934, 0.637]], with {the funding levels of the Startup across the same series}[[0.128, 0.637], [0.397, 0.569], [0.665, 0.232], [0.934, 0.704]]. The largest difference seems to be at the 'Abrupt Increase' series with Investment Fund at 120 and the Startup at 115.\nBased on the above thought, the answer is:\nThe 'Abrupt Increase' series shows the largest difference in funding levels between the Investment Fund and the Startup. This is because the Investment Fund's funding level at this series is 120, while the Startup's funding level is 115, resulting in a difference of 5, which is the most significant difference across the series." } ], "id_raw": 1139 }, { "id": "8K7X9h9m2iWPKKiV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_57_19.png", "conversations": [ { "from": "human", "value": "\nWhat is the average change in the funding level for the Investment fund across all series?" }, { "from": "gpt", "value": "To identify the average change in funding level for Investment Fund, I first locate the {points representing Investment Fund funding levels across series}[[0.128, 0.569],[0.397, 0.502], [0.665, 0.165], [0.934, 0.637]], and extract the corresponding values: 90, 95, 120, 85. Then, I calculate the average of these values.\nBased on the above thought, the answer is:\n97.5" } ], "id_raw": 1139 }, { "id": "vpqa3XYFwgYsUcYU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_0.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data into a structured table with years as rows and development types as columns?" }, { "from": "gpt", "value": "To transform the line chart data into a structured table, I locate {the x-axis labels}[0.055, 0.958, 0.073, 0.981] which enumerate the years from 2001 to 2010, the rows of our table. For the columns, I determine from {the legend}[0.062, 0.063, 0.220, 0.135] that we have two development types: Rural and Urban. Reviewing {the points on the line for Rural development}[0.098, 0.277, 0.945, 0.887] and {the points on the line for Urban development}[0.098, 0.813, 0.945, 0.887], I extract the development scores for each year to populate the table.\nBased on the above thought, the answer is:\nYear,Rural Development,Urban Development\n2001,32,35\n2002,33,36\n2003,34,37\n2004,35,38\n2005,36,39\n2006,37,40\n2007,55,41\n2008,38,42\n2009,39,43\n2010,40,44" } ], "id_raw": 1140 }, { "id": "MDAou1JeZzKccWSJ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_0.png", "conversations": [ { "from": "human", "value": "\nWhat year exhibited an outlier in Rural development according to the line chart, and what might explain this unusual spike?" }, { "from": "gpt", "value": "By examining {the line for Rural development}[0.098, 0.277, 0.945, 0.887] in detail, I identify a sharp peak represented between the usual trend line. Pinpointing this on the x-axis labels, the peak occurs at 2007. To understand this unusual spike in the context of the chart, I assess its relationship with other data points and note it deviates significantly from the norm. This outlier might be due to specific rural initiatives or unexpected economic influences that year, which merit further investigation beyond the chart's data.\nBased on the above thought, the answer is:\nThe year that exhibited an outlier in Rural development is 2007. This unusual spike is likely due to specific significant rural initiatives or unexpected economic factors that drastically differed from other years." } ], "id_raw": 1140 }, { "id": "sYI6TXJdxA2fvjJ5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_0.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall increase in Urban development from 2001 to 2010 as per the chart?" }, { "from": "gpt", "value": "To calculate the overall increase in Urban development from 2001 to 2010, I first examine {the points on the line for Urban development}[0.098, 0.813, 0.945, 0.887]. I identify the first and last data points of the Urban development line, which are at years 2001 and 2010 respectively. Extracting the values, the development score for 2001 is 35 and for 2010 it is 44. The overall increase is calculated by subtracting the 2001 score from the 2010 score.\nBased on the above thought, the answer is:\n9" } ], "id_raw": 1140 }, { "id": "7EMN31O6MOwFt9SZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_1.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured table with Year as rows and Index Values of Residential, Commercial, and Rental according to the line chart?" }, { "from": "gpt", "value": "To create the structured table, I'll first identify the {year labels on the x-axis}[0.059, 0.927], which will serve as the row headers. Next, I locate the data points for {the Residential Index line}[0.101, 0.546, 0.945, 0.897], {the Commercial Index line}[0.101, 0.371, 0.945, 0.887], and {the Rental Rate Index line}[0.101, 0.093, 0.945, 0.441]. I'll then extract these values by matching them with the corresponding year.\nBased on the above thought, the answer is:\nYear,Residential_Index,Commercial_Index,Rental_Rate_Index\n2005,100,105,95\n2006,102,110,100\n2007,104,120,103\n2008,105,125,105\n2009,100,90,100\n2010,102,95,102\n2011,105,100,105\n2012,110,105,110\n2013,115,110,115\n2014,120,115,120\n2015,125,120,125\n2016,127,125,127\n2017,130,130,130\n2018,135,135,135\n2019,140,140,140\n2020,145,145,145" } ], "id_raw": 1141 }, { "id": "5Kk2c9cGuLs0OP8m", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_1.png", "conversations": [ { "from": "human", "value": "\nWhich sector showed the highest growth from 2007 to 2008 and what was the rate of increase?" }, { "from": "gpt", "value": "Looking at the year 2007, I locate the values for {Residential}[0.334, 0.512], {Commercial}[0.334, 0.402], and {Rental}[0.334, 0.290], which are approximately 104, 120, and 103, respectively. For 2008, the values are {Residential}[0.405, 0.486] at 105, {Commercial}[0.405, 0.376] at 125, and {Rental}[0.405, 0.264] at 105. Comparing these, the Commercial sector shows the highest growth with an increase from 120 to 125, a 5-point increase or 4.17% growth rate.\nBased on the above thought, the answer is:\nThe Commercial sector showed the highest growth from 2007 to 2008. Its index increased from 120 to 125, marking a growth rate of approximately 4.17%." } ], "id_raw": 1141 }, { "id": "OzEJUDUgF1SqGNqb", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_1.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in Residential Price Index from 2005 to 2020?" }, { "from": "gpt", "value": "Initially, I locate the {value at 2005 for Residential Price Index}[0.101, 0.546] and observe it starts at 100. Next, I find the {value at 2020 for Residential Price Index}[0.945, 0.897] which is 145. The increase is calculated by subtracting the starting index from the final index, 145 - 100.\nBased on the above thought, the answer is:\n45" } ], "id_raw": 1141 }, { "id": "80IJE4ASDJ9GSqUF", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_2.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart showing the performance metrics over time for 'Deep Learning' and 'Neural Networks' into a structured table format?" }, { "from": "gpt", "value": "Observing the line chart, I identify the {x-axis labels}[0.059, 0.933, 0.066, 0.951] that outline the different time frames and {legend items}[0.739, 0.812, 0.981, 0.917] detailing the two methods 'Ours' and 'Saliency-based Sampling'. For each method, distinct {line plots} like {Ours for Deep Learning}[0.094, 0.569, 0.945, 0.728] and {Saliency-based Sampling for Neural Networks}[0.157, 0.283, 0.189, 0.315] denote performance values over time. Combining these details allows extraction of data point values for accurate table creation.\nBased on the above thought, the answer is:\nTime,Deep Learning - Ours,Deep Learning - Saliency-based Sampling,Neural Networks - Ours,Neural Networks - Saliency-based Sampling\n1,25,23,40,38\n2,30,28,42,39\n3,35,33,45,43\n4,38,34,47,44\n5,40,36,50,46\n6,42,38,52,48\n7,45,40,54,50\n8,47,42,55,52\n9,50,45,57,54\n10,55,50,60,56" } ], "id_raw": 1142 }, { "id": "B0mpem0zhIxajBYT", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_2.png", "conversations": [ { "from": "human", "value": "\nWhich method, 'Ours' or 'Saliency-based Sampling', demonstrates a more stable performance trend for 'Neural Networks'?" }, { "from": "gpt", "value": "By assessing the line trends in the chart, I observe {the line for 'Ours' method in Neural Networks}[0.094, 0.569, 0.945, 0.728] displaying a smooth and consistent growth. In contrast, {the line for 'Saliency-based Sampling' in Neural Networks}[0.157, 0.283, 0.189, 0.315] shows some volatility with noticeable fluctuations and dips. This comparison leads to a conclusion about the stability of each method's performance over time.\nBased on the above thought, the answer is:\nThe 'Ours' method shows a more stable performance trend for 'Neural Networks' compared to 'Saliency-based Sampling'. The line representing 'Ours' method is smoother, indicating fewer fluctuations and a more consistent growth in performance." } ], "id_raw": 1142 }, { "id": "MxMGo9zExZOezHmS", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in 'Deep Learning' performance using 'Ours' method from time 1 to time 10?" }, { "from": "gpt", "value": "To compute the increase, I refer to the {line for 'Ours' in Deep Learning}[0.094, 0.569, 0.945, 0.728]. At time 1, the performance percentage is {25}[0.094, 0.728] and at time 10 it's {55}[0.945, 0.569]. The percentage increase is calculated by ((55 - 25) / 25) * 100.\nBased on the above thought, the answer is:\n120%." } ], "id_raw": 1142 }, { "id": "TFZ3HDle6fPrnu01", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_3.png", "conversations": [ { "from": "human", "value": "\nCan you extract the information from the bar chart and arrange it into a table with civilization and age as the categories?" }, { "from": "gpt", "value": "To convert this information, I observe the bar chart and use {the x-axis labels}[0.108, 0.946, 0.733] that indicate the Industrial Age and the Digital Age, and {the y-axis labels}[0.108, 0.946, 0.093] indicating Western and Eastern civilizations. Extracting details from the bar lengths, I translate each bar's length into percentage growth.\nBased on the above thought, the answer is:\nCivilization,Age,Growth Percentage\nWestern,Industrial Age,70.40\nEastern,Industrial Age,63.60\nWestern,Digital Age,89.20\nEastern,Digital Age,80.10" } ], "id_raw": 1143 }, { "id": "COnqD9zsSGMqTvPQ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_3.png", "conversations": [ { "from": "human", "value": "\nHow does the growth trend of Western civilization compare to that of Eastern civilization across different ages based on the bar chart?" }, { "from": "gpt", "value": "To analyze the growth trend between Western and Eastern civilizations across different ages, I review the lengths of {the bars representing each civilization}[0.108, 0.946, 0.809] and[0.549, 0.848, 0.681]. In both the Industrial Age and the Digital Age, the bar for Western civilization is longer than that for Eastern civilization, indicating a higher growth percentage. Comparatively, Western civilization shows a consistent higher growth rate than Eastern civilization in both ages.\nBased on the above thought, the answer is:\nWestern civilization consistently shows a higher growth rate than Eastern civilization in both the Industrial Age and the Digital Age, as depicted by longer bars in the bar chart for Western civilization." } ], "id_raw": 1143 }, { "id": "binC8r7Y3MYQ1ob5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage growth difference between Western and Eastern civilizations in the Digital Age?" }, { "from": "gpt", "value": "I determine the percentage growth from {the bar representing Western civilization in the Digital Age}[0.108, 0.946, 0.809], which is 89.20%, and from {the bar representing Eastern civilization in the Digital Age}[0.549, 0.848, 0.681], which is 80.10%. The difference is calculated by subtracting the Eastern civilization growth percentage from the Western civilization growth percentage.\nBased on the above thought, the answer is:\n9.10" } ], "id_raw": 1143 }, { "id": "w1taawWYjOz4TNW2", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data displayed in the line chart showing service quality scores over the years into a tabular format based on the lines for Healthcare, Education, and Public Transit?" }, { "from": "gpt", "value": "To generate the table from the line chart, I first observe {the x-axis tick labels}[0.094, 0.927, 0.109, 0.951], {0.221, 0.927, 0.251, 0.951], [0.363, 0.927, 0.392, 0.951], [0.505, 0.927, 0.534, 0.951], [0.647, 0.927, 0.676, 0.951] which represent the years from 2011 to 2015. For the y-values, I analyze the positions and colors of {the line for Healthcare}[0.094, 0.416, 0.945, 0.581], {the line for Education}[0.094, 0.792, 0.945, 0.887], and {the line for Public Transit}[0.094, 0.274, 0.945, 0.235], extracting the annual scorer from each segment.\nBased on the above thought, the answer is:\nYear,Healthcare,Education,Public Transit\n2011,85,77,68\n2012,87,78,65\n2013,90,80,62\n2014,88,82,55\n2015,86,84,50" } ], "id_raw": 1144 }, { "id": "H08TeZcPFrJzNk3E", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_4.png", "conversations": [ { "from": "human", "value": "\nWhich service quality shows the most stability over the period from 2011 to 2015 according to the line chart?" }, { "from": "gpt", "value": "To assess the stability, I need to look at the variation in line plots. Observing {the line for Healthcare}[0.094, 0.416, 0.945, 0.581], it fluctuates but within a narrower range indicating relatively stable scoring. Comparatively, {the line for Education}[0.094, 0.792, 0.945, 0.887] shows a consistent upward trend with minor fluctuations, and {the line for Public Transit}[0.094, 0.274, 0.945, 0.235] indicates a significant downward trajectory, marking high instability. Among these, Education demonstrates the most stability with slight consistent growth over the years.\nBased on the above thought, the answer is:\nEducation shows the most stability over the period from 2011 to 2015, as its line shows a consistent and slight upward trend with minimal fluctuations compared to the more varied changes seen in Healthcare and the pronounced decline in Public Transit." } ], "id_raw": 1144 }, { "id": "Ntwyx9qoJiTgdN5M", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_4.png", "conversations": [ { "from": "human", "value": "\nWhat is the average score decrease for Public Transit from 2011 to 2015 based on the line chart?" }, { "from": "gpt", "value": "To calculate the average decrease for Public Transit, I need to determine the scores at the starting and ending year by examining {the line for Public Transit}[0.094, 0.274, 0.945, 0.235] where it starts at a score of 68 in 2011 and ends at a score of 50 in 2015. Over this 5 year period, the score decreased by 18 points divided by the 4 intervals gives an average decrease of 4.5 points per year.\nBased on the above thought, the answer is:\n4.5" } ], "id_raw": 1144 }, { "id": "3e0zD7tbUo0OeNuv", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_5.png", "conversations": [ { "from": "human", "value": "\nCan you tabulate the data represented in the bar chart, with 'Companies' and 'Market Share' as columns?" }, { "from": "gpt", "value": "To tabulate the data, I look at {the x-axis tick labels}[[0.075, 0.107, 0.150, 0.127], [0.226, 0.107, 0.270, 0.127], [0.377, 0.107, 0.420, 0.127],[0.527, 0.107, 0.571, 0.127], [0.678, 0.107, 0.721, 0.127], [0.828, 0.107, 0.872, 0.127], [0.978, 0.107, 1.023, 0.127]] representing the company names, and {the heights of the bars}[[0.097, 0.185, 0.133, 0.703], [0.247, 0.185, 0.284, 0.307], [0.398, 0.185, 0.435, 0.253], [0.549, 0.185, 0.586, 0.207],[0.699, 0.185, 0.737, 0.203], [0.850, 0.185, 0.887, 0.188], [1.000, 0.185, 1.038, 0.188]] for determining the market share percentages.\nBased on the above thought, the answer is:\nCompanies,Market Share\nTesla,54\nGM,20\nToyota,15\nFord,5\nHonda,4\nHyundai,1\nNissan,1" } ], "id_raw": 1145 }, { "id": "R629LUnthwUgZYxv", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_5.png", "conversations": [ { "from": "human", "value": "\nBased on the information in the bar chart, can you describe the competitive landscape of the market?" }, { "from": "gpt", "value": "According to the bar chart, the market is dominated by {Tesla}[[0.097, 0.185, 0.133, 0.703]], holding the largest market share at 54%. The second-largest share is {GM}[[0.247, 0.185, 0.284, 0.307]], but significantly less than Tesla at 20%. {Toyota}[[0.398, 0.185, 0.435, 0.253]] holds the third-largest share at 15%, followed by {Ford}[[0.549, 0.185, 0.586, 0.207]] at 5%, {Honda}[[0.699, 0.185, 0.737, 0.203]] at 4%, and both {Hyundai}[[0.850, 0.185, 0.887, 0.188]] and {Nissan}[[1.000, 0.185, 1.038, 0.188]] at the lowest share of 1% each.\nBased on the above thought, the answer is:\nThe automotive market, as depicted in the bar chart, is predominantly occupied by Tesla with a 54% share, highlighting its strong position within the industry. GM follows as the next largest competitor, but with a significantly smaller share of 20%. Other companies like Toyota, Ford, Honda, Hyundai, and Nissan hold much smaller portions of the market, indicating a largely monopolistic market dominated by Tesla, with minor competition amongst the remaining companies." } ], "id_raw": 1145 }, { "id": "HhasoeIpiEP0jiV7", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in market share between Tesla and GM?" }, { "from": "gpt", "value": "To find the difference in market share, I need to locate {the bar for Tesla}[[0.097, 0.185, 0.133, 0.703]] and {the bar for GM}[[0.247, 0.185, 0.284, 0.307]] and extract the corresponding values: Tesla at 54% and GM at 20%. The difference between the two is therefore 54 - 20 = 34%.\nBased on the above thought, the answer is:\n34%." } ], "id_raw": 1145 }, { "id": "5O6iSKlkjUdzgBg1", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_6.png", "conversations": [ { "from": "human", "value": "\nCan you convert the bar chart data into a structured table format with hotel names as observations and years as the attributes?" }, { "from": "gpt", "value": "To convert this into table format, first, I identify the {x-axis tick labels}[0.137, 0.891, 0.244, 0.915], [0.313, 0.891, 0.418, 0.915], [0.490, 0.891, 0.607, 0.915], [0.671, 0.891, 0.788, 0.915] as the row headers or observations, which represent the different hotels. Next, I recognize that there are two years, so there will be two columns, which will be '2020 Occupancy' and '2021 Occupancy' as indicated in the {legend}[0.035, 0.921, 0.274, 0.972]. To fill the table values representing the occupancy rate, I look at the height of the {bars representing 2020 occupancy}[0.137, 0.217, 0.208, 0.891], [0.313, 0.307, 0.384, 0.891], [0.490, 0.227, 0.561, 0.891], [0.671, 0.317, 0.742, 0.891] and the {bars representing 2021 occupancy}[0.221, 0.457, 0.292, 0.891], [0.397, 0.497, 0.467, 0.891], [0.573, 0.527, 0.644, 0.891], [0.753, 0.597, 0.824, 0.891]. The height of these bars can be translated to the occupancy rate at the respective hotels in the respective years.\nBased on the above thought, the answer is:\nHotel, 2020 Occupancy, 2021 Occupancy\nOcean View, 45, 32\nMountain Escape, 39, 32\nCity Central, 45, 31\nDesert Dream, 39, 31" } ], "id_raw": 1146 }, { "id": "tYyOoTVMOPif4eBJ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_6.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar graph, how has the occupancy rate evolved from 2020 to 2021 in general, and which hotel has witnessed the most significant growth?" }, { "from": "gpt", "value": "To analyze the evolution of the occupancy rate from 2020 to 2021, I need to compare the{bar heights for each hotel in 2020}[0.137, 0.217, 0.208, 0.891], [0.313, 0.307, 0.384, 0.891], [0.490, 0.227, 0.561, 0.891], [0.671, 0.317, 0.742, 0.891] with the {corresponding bar heights in 2021}[0.221, 0.457, 0.292, 0.891], [0.397, 0.497, 0.467, 0.891], [0.573, 0.527, 0.644, 0.891], [0.753, 0.597, 0.824, 0.891]. A general trend can be observed from comparing these bar heights in 2020 and 2021. To identify which hotel has witnessed the most significant growth, I would need to identify the pair of bars where the growth from 2020 to 2021 is the greatest.\nBased on the above thought, the answer is:\nAccording to the bar graph, the occupancy rate for all the hotels shows a general increase from 2020 to 2021, indicating a recovering market situation in the hospitality sector. The significant increase of the bar from 2020 to 2021 for 'Desert Dream' indicates that this hotel has witnessed the most significant growth in occupancy rate during this period." } ], "id_raw": 1146 }, { "id": "sDgHoQ4ZQq7fJ6Am", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in occupancy from 2020 to 2021 for 'Ocean View'?" }, { "from": "gpt", "value": "To determine the percentage increase in occupancy for 'Ocean View', I need to first identify the {bar representing 2020 occupancy for 'Ocean View'}[0.137, 0.217, 0.208, 0.456] and the {bar representing 2021 occupancy for 'Ocean View'}[0.221, 0.457, 0.292, 0.567]. The value for 2020 is 45, and the value for 2021 is 32. The percentage increase can be calculated as ((32 - 45) / 45) * 100.\nBased on the above thought, the answer is:\n-28.88%." } ], "id_raw": 1146 }, { "id": "CeoSlQLy3x2rf63j", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_7.png", "conversations": [ { "from": "human", "value": "\nCould you please create a table with months as columns and each Series as a different row?" }, { "from": "gpt", "value": "To create a table from the given line chart, every distinct {x-axis tick label}[0.017, 0.933, 0.024, 0.951],[0.171, 0.933, 0.178, 0.951],[0.325, 0.933, 0.332, 0.951],... become the column headers, which corresponds to the various months. The categories for each row can be acquired from the {legend}[0.062, 0.063, 0.177, 0.201] which represents each Series. The data values can be found by extracting the y-values of the plot points for each line: {Apple's line}[0.098, 0.093, 0.945, 0.298], {Samsung's line}[0.098, 0.504, 0.945, 0.586] and {Google's line}[0.098, 0.682, 0.945, 0.778].\nBased on the above thought, the answer is:\nMonth,Apple,Samsung,Google\nJan,0.47,0.3,0.1\nFeb,0.50,0.25,0.12\nMar,0.49,0.27,0.11\nApr,0.48,0.26,0.10\nMay,0.45,0.25,0.11\nJun,0.50,0.24,0.12\nJul,0.55,0.28,0.13\nAug,0.60,0.27,0.15\nSep,0.57,0.26,0.15\nOct,0.58,0.28,0.17\nNov,0.55,0.27,0.15\nDec,0.60,0.3,0.15" } ], "id_raw": 1147 }, { "id": "pEP7bKZWhQ1IHZ4A", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_7.png", "conversations": [ { "from": "human", "value": "\nBased on the trends from the line chart, which series showed the most stable trend over the year?" }, { "from": "gpt", "value": "To determine the most stable trend, I need to evaluate the volatility or changes in each line - {Apple's line}[0.098, 0.093, 0.945, 0.298], {Samsung's line}[0.098, 0.504, 0.945, 0.586], and {Google's line}[0.098, 0.682, 0.945, 0.778]. Looking at {Apple's line}, I notice several fluctuations with a noticeable increase in market share. Evaluating {Samsung's line}, it appears there is a steady decrease in market share. Lastly, for {Google's line}, Google\u2019s market share appears mildly fluctuating but generally around a constant value of 0.1 - 0.15.\nBased on the above thought, the answer is:\nBased on the trends from the line chart, Google showed the most stable trend over the year. While Samsung\u2019s line depicted a constant decline and Apple\u2019s line depicted growth with fluctuations, Google's line was relatively constant over time with mild fluctuations around a market share of 0.1 - 0.15." } ], "id_raw": 1147 }, { "id": "6QmfXJdcjnmEXnWx", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_7.png", "conversations": [ { "from": "human", "value": "\nWhat was the difference between the highest and lowest values for Apple?" }, { "from": "gpt", "value": "The highest and lowest values for Apple can be found by identifying the peaks and troughs in {Apple's line}[0.098, 0.093, 0.945, 0.298]. The highest point for Apple occurs in November, where its value reaches approximately 0.60. The lowest point occurs in May, where its value drops to approximately 0.45. The difference between the highest and lowest values for Apple is therefore 0.60 - 0.45 = 0.15.\nBased on the above thought, the answer is:\n0.15." } ], "id_raw": 1147 }, { "id": "46iukZtBgRoTgdjb", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the bar chart, can you extract the data into a table format with AI Applications as rows, and Score as columns?" }, { "from": "gpt", "value": "To generate the table from the bar chart, I first identify {the x-axis tick labels}[[0.047, 0.933, 0.161, 0.951], [0.127, 0.933, 0.264, 0.951], [0.229, 0.933, 0.344, 0.951], [0.320, 0.933, 0.436, 0.951], [0.443, 0.933, 0.495, 0.951], [0.520, 0.933, 0.602, 0.951], [0.627, 0.933, 0.677, 0.951], [0.703, 0.933, 0.784, 0.951], [0.781, 0.933, 0.889, 0.951], [0.871, 0.933, 0.982, 0.951]] as the row headers showing the AI applications. Next, my task is to read {the y-axis}[0.093, 0.060, 0.926, 0.660] to extract the corresponding score values for each AI application.\nBased on the above thought, the answer is:\nAI Applications, Score\nPattern Recognition, 0.35\nRecommender Systems, 0.56\nSpeech Recognition, 0.30\nAutonomous Robots, 0.45\nChatbots, 0.40\nDriverless Car, 0.60\nGame AI, 0.65\nCybersecurity, 0.54\nPredictive Analysis, 0.40\nMarket Forecasting, 0.55" } ], "id_raw": 1148 }, { "id": "OTERYXGyXG8iPS6c", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_8.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which area of AI Application appears to have the least case analysis score? Give your analysis." }, { "from": "gpt", "value": "To determine the area of AI application with the least case analysis score, I need to inspect {the line points}[[0.104, 0.774], [0.195, 0.297], [0.287, 0.887], [0.378, 0.547], [0.469, 0.660], [0.561, 0.206], [0.652, 0.093], [0.744, 0.342], [0.835, 0.660], [0.926, 0.320]]. Here, the highest point on the chart corresponds to the lowest score. Upon examination, it's clear that {Speech Recognition}[0.287, 0.887] is located at the highest y-coordinate, indicating the lowest score of 0.3.\nBased on the above thought, the answer is:\nBased on the chart, the area of AI Application \"Speech Recognition\" appears to have the least case analysis score, given its highest positioning on the y-axis and the corresponding score of 0.3." } ], "id_raw": 1148 }, { "id": "ArBciNEixhDZiZ8q", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_8.png", "conversations": [ { "from": "human", "value": "\nWhich AI application has the highest score on the chart?" }, { "from": "gpt", "value": "To find the AI application with the highest score, I have to analyze {the line points}[[0.104, 0.774], [0.195, 0.297], [0.287, 0.887], [0.378, 0.547], [0.469, 0.660], [0.561, 0.206], [0.652, 0.093], [0.744, 0.342], [0.835, 0.660], [0.926, 0.320]]. It appears that the line point corresponding {Game AI}[0.652, 0.093] is located at the lowest y-coordinate, indicating the highest score of 0.65.\nBased on the above thought, the answer is:\nGame AI." } ], "id_raw": 1148 }, { "id": "uagKMP4CDJYtoERj", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_9.png", "conversations": [ { "from": "human", "value": "\nCan you create a table displaying the active user counts each week for each social media platform according to the line chart data?" }, { "from": "gpt", "value": "From the 24 weeks line chart, I extracted the weekly user data by examining {the line points for Facebook}[0.101, 0.834], [0.138, 0.781], ..., [0.943, 0.093], {the line points for Twitter}[0.101, 0.569], [0.138, 0.516], ..., [0.943, 0.093], and {the line points for Instagram}[0.101, 0.887], [0.138, 0.861], ..., [0.943, 0.119]. Each set represents data for the respective social media platforms across 24 weeks.\nBased on the above thought, the answer is:\nWeek,Facebook,Twitter,Instagram\n1,100,150,90\n2,110,160,95\n3,120,155,100\n4,130,140,105\n5,140,160,110\n6,150,170,100\n7,155,175,95\n8,150,172,98\n9,160,164,102\n10,155,168,105\n11,200,190,180\n12,210,192,190\n13,220,195,195\n14,230,200,200\n15,235,203,202\n16,240,205,205\n17,240,210,209\n18,225,215,208\n19,230,220,215\n20,235,225,220\n21,235,220,225\n22,235,225,227\n23,235,230,230\n24,240,235,235" } ], "id_raw": 1149 }, { "id": "Pqrp1Jv0ADr3n78E", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_9.png", "conversations": [ { "from": "human", "value": "\nWhich social media platform displayed the most growth in active users from week 1 to week 24?" }, { "from": "gpt", "value": "Observing the line chart, I analyzed the start and end points for each social media platform to gauge growth. Notably, {Facebook\u2019s points}[0.101, 0.834], [0.943, 0.093] show substantial growth from 100 million to 240 million. {Twitter\u2019s trend}[0.101, 0.569], [0.943, 0.093] also shows a significant increase from 150 million to 235 million. {Instagram\u2019s progression}[0.101, 0.887], [0.943, 0.119], though growing, is from 90 million to 235 million, but with visible fluctuations.\nBased on the above thought, the answer is:\nFacebook displayed the most consistent and highest increase in active users, growing from 100 million to 240 million from week 1 to week 24, seeing the largest net growth compared to Twitter and Instagram." } ], "id_raw": 1149 }, { "id": "2IaCotXuTvfnneuD", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the total number of active users across all platforms by the end of week 24?" }, { "from": "gpt", "value": "To ascertain the cumulative active users by week 24, I reviewed {the final point values for Facebook}[0.943, 0.093], {Twitter}[0.943, 0.093], and {Instagram}[0.943, 0.119]. The values are 240, 235, and 235 million for Facebook, Twitter, and Instagram respectively. Adding these provides the total.\nBased on the above thought, the answer is:\n710 million." } ], "id_raw": 1149 }, { "id": "N6grfi6xco5dHJGy", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_10.png", "conversations": [ { "from": "human", "value": "\nCan you extract the trends of Research Funding and Technological Innovation over time from the line chart into a table format?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[0.033, 0.933, 0.051, 0.951], [0.146, 0.933, 0.164, 0.951], ..., [1.045, 0.933, 1.070, 0.951], which represent the Time Periods. The y-axis represents Normalized Value. The two lines represent trends of different factors: {Research Funding}[0.096, 0.072, 0.219, 0.094] and {Technological Innovation}[0.096, 0.105, 0.266, 0.127]. Corresponding to them, there are a series of {data points on the lines}[0.087, 0.440, 0.945, 0.887], [0.087, 0.093, 0.945, 0.887] denoting the values of these two factors over different time periods.\nBased on the above thought, the answer is:\nTime Period,Research Funding,Technological Innovation\n1,2.1,1.5\n2,2.2,1.7\n3,2.8,2.0\n4,2.0,2.5\n5,1.5,3.0\n6,1.7,3.6\n7,2.0,4.0\n8,2.5,4.2\n9,3.0,4.4\n10,3.2,5.0\n11,3.4,5.2\n12,3.7,6.0\n13,3.5,6.2\n14,4.0,6.6\n15,4.6,7.0\n16,5.0,7.5\n17,5.2,8.0\n18,5.4,8.4\n19,5.5,9.0\n20,6.0,9.5" } ], "id_raw": 1150 }, { "id": "JQ304cHfkkPgiZ8b", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_10.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which variable, Research Funding or Technological Innovation, shows a steady increase over time?" }, { "from": "gpt", "value": "To answer this question, I have to analyze the trend lines for {Research Funding}[0.096, 0.072, 0.219, 0.094] and {Technological Innovation}[0.096, 0.105, 0.266, 0.127]. Looking at {the line for Research Funding}[0.087, 0.440, 0.945, 0.887], the trend begins with a slight increase, followed by a sharp drop, and then a steady increase. However, there are fluctuations in between. For {Technological Innovation}[0.087, 0.093, 0.945, 0.887], begins at a lower point and shows a consistently upward trend over time, with no evident decrease at any point.\nBased on the above thought, the answer is:\nTechnological Innovation shows a steady increase over time without any evident decrease, in contrast to Research Funding, which experiences several fluctuations despite a general upward trend." } ], "id_raw": 1150 }, { "id": "n6LFAUXzfLSGBgdP", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_10.png", "conversations": [ { "from": "human", "value": "\nDuring which time period did the value for Research Funding first exceed 3?" }, { "from": "gpt", "value": "The plot line for {Research Funding}[0.096, 0.072, 0.219, 0.094] has various data points against time. We need to find the first one that's above 3. Looking at the {data points on the line}[0.087, 0.440, 0.945, 0.887], I see that the value first exceeds 3 at time period 9.\nBased on the above thought, the answer is:\nTime period 9." } ], "id_raw": 1150 }, { "id": "LW0b7LbgzlIb3MHz", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_12.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart to form a structured table with months as columns and media types as rows?" }, { "from": "gpt", "value": "To generate the table, I will first focus on {the x-axis tick labels}[0.028, 0.933, 0.036, 0.951], [0.181, 0.933, 0.188, 0.951], [0.333, 0.933, 0.340, 0.951], [0.485, 0.933, 0.493, 0.951], [0.637, 0.933, 0.645, 0.951], [0.786, 0.933, 0.801, 0.951], [0.938, 0.933, 0.953, 0.951], which represent the twelve months. Next, I look at {the legend}[0.073, 0.063, 0.235, 0.168]. This represents the different types of media, i.e. Email marketing, Social media and SEO. Now, to get the actual data values for each month and each media type, I need to examine the y-coordinates of certain points on {the lines}[0.108, 0.385, 0.946, 0.887], [0.108, 0.720, 0.184, 0.804], [0.108, 0.093, 0.946, 0.762]. The points where these lines intersect with the vertical lines representing each month would provide the values for the table.\nBased on the above thought, the answer is:\nMonths,EmailMarketing,SocialMedia,SEO\nJanuary,1000,1100,1200\nFebruary,1050,1200,1150\nMarch,1200,1250,1300\nApril,1150,1300,1400\nMay,1300,1400,1350\nJune,1250,1450,1500\nJuly,1400,1550,1600\nAugust,1350,1600,1650\nSeptember,1500,1700,1750\nOctober,1450,1750,1800\nNovember,1600,1850,1900\nDecember,1550,1900,1950" } ], "id_raw": 1152 }, { "id": "SWs4B0kmanJDwwoC", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_12.png", "conversations": [ { "from": "human", "value": "\nWhich type of media consistently improved its performance over the course of a year?" }, { "from": "gpt", "value": "To answer this question, I need to observe {the trends of each media type line}[0.108, 0.385, 0.946, 0.887], [0.108, 0.720, 0.946, 0.762], [0.108, 0.093, 0.946, 0.762]. {The Email Marketing line}[0.108, 0.385, 0.946, 0.887] initially went upwards but then underwent some fluctuations. However, the overall trend is positive. {The Social Media line}[0.108, 0.720, 0.946, 0.762] also went up and down, but it generally improved towards the end. {The SEO line}[0.108, 0.093, 0.946, 0.762] stands out as it shows a consistent increase from the start of the year to the end. Therefore, it can be concluded that SEO consistently improved its performance.\nBased on the above thought, the answer is:\nThe Search Engine Optimization (SEO) strategy consistently improved its performance over the year as evident by a steady and consistent increase in the number of leads from January to December." } ], "id_raw": 1152 }, { "id": "tylTfxYhlwybGVdV", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference between the maximum and minimum leads generated by SEO over the year?" }, { "from": "gpt", "value": "To figure out the maximum and minimum leads generated by SEO, I'll inspect {the SEO line's points}[0.108, 0.720], [0.184, 0.762], [0.261, 0.636], [0.337, 0.553], [0.413, 0.595], [0.489, 0.469], [0.565, 0.385], [0.641, 0.344], [0.717, 0.260], [0.793, 0.218], [0.870, 0.134], [0.946, 0.093]. The maximum corresponds to the peak point on the line, which is 1950 in December, and the minimum value corresponds to the lowest point on the line, which is 1150 in February. To get the difference, I should subtract the minimum from the maximum, so 1950 - 1150 = 800.\nBased on the above thought, the answer is:\n800" } ], "id_raw": 1152 }, { "id": "uJWUasbCZKhPzcVX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_13.png", "conversations": [ { "from": "human", "value": "\nCould you please format the line graph data into a structured table format using Weeks as rows and Sales Revenue, Advertising Expenditure, and Website Traffic as columns?" }, { "from": "gpt", "value": "To collate a structured data table for this line chart, we must identify the x-axis label as weeks represented by {the x-axis tick labels}[[0.081, 0.933, 0.034, 0.951], [0.214, 0.933, 0.217, 0.951], [0.397, 0.933, 0.400, 0.951], [0.580, 0.933, 0.584, 0.951], [0.763, 0.933, 0.767, 0.951], [0.946, 0.933, 0.954, 0.951]], and additionally, the legend denoting {Sales Revenue}[[0.132, 0.072, 0.234, 0.094]], {Advertising Expenditure}[[0.132, 0.105, 0.299, 0.127]] and {Website Traffic}[[0.132, 0.137, 0.234, 0.160]] as the column headers or variables. The corresponding values would be found within the line points for each category: {values for Sales Revenue}[[0.122, 0.246], [0.214, 0.229], ..., [0.946, 0.093]], {values for Advertising Expenditure}[[0.122, 0.887], [0.214, 0.887], ..., [0.946, 0.882]], and {values for Website Traffic}[[0.122, 0.802], [0.214, 0.800], ..., [0.946, 0.776]].\nBased on the above thought, the answer is:\nWeeks, Sales Revenue, Advertising Expenditure, Website Traffic\n1, 80000, 5000, 15000\n2, 82000, 5000, 15200\n3, 83000, 5200, 15500\n4, 84000, 5250, 16000\n5, 85000, 5500, 16000\n6, 86000, 5500, 16500\n7, 90000, 5200, 17000\n8, 92000, 5300, 17500\n9, 95000, 5500, 17700\n10, 98000, 5600, 18000" } ], "id_raw": 1153 }, { "id": "3KSDPQSDwYHyFAqK", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_13.png", "conversations": [ { "from": "human", "value": "\nWhich variable showed the most consistency in the given weeks and why do you say so?" }, { "from": "gpt", "value": "To judge which variable exhibited the most consistency over the weeks, I need to scrutinize the fluctuations in the lines representing each variable. Upon examining {the line for Sales Revenue}[0.122, 0.093, 0.946, 0.246], I notice a steady ascension over the weeks, indicating a uniform trend. However, looking at {Advertising Expenditure}[0.122, 0.882, 0.946, 0.887] and {Website Traffic}[0.122, 0.776, 0.946, 0.802], it's clear that their trend lines fluctuate less drastically, almost maintaining a consistent level throughout the ten weeks.\nBased on the above thought, the answer is:\nAdvertising Expenditure and Website Traffic appear to have shown the most consistency over the given weeks. Their trend lines showed very minimal fluctuations, nearly maintaining a constant level throughout, compared to Sales Revenue which displayed a steady increase over the same period. This reflects a consistent investment in advertising and stable website traffic, even as sales revenue continued to climb." } ], "id_raw": 1153 }, { "id": "0JRXHEy6zSKGsun5", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_13.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in sales revenue from week 1 to week 10?" }, { "from": "gpt", "value": "To find the percentage increase in sales revenue from week 1 to week 10, I need to locate {the line points for Sales Revenue}[[0.122, 0.246], ..., [0.946, 0.093]] and extract the values for week 1 (80000) and week 10 (98000). The percentage increase can be calculated as (98000 - 80000) / 80000 * 100.\nBased on the above thought, the answer is:\n22.5%." } ], "id_raw": 1153 }, { "id": "jp3s4KoQTfMXl3Ut", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_14.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart data showing GDP, Interest Rate, and Debt to GDP Ratio from 2005 to 2020 into a structured table format?" }, { "from": "gpt", "value": "To transform the line chart data into a structured table, I start by analyzing the x-axis to locate {the years}[0.053, 0.933, 0.961, 0.951] which range from 2005 to 2020. Each line on the chart, representing GDP (in billion USD), Interest Rate (%), and Debt to GDP Ratio (%), has points plotted for each of these years. I observe the respective y-axis points for each metric at specified {x-ticks coordinates}[0.067, 0.927], [0.946, 0.927]. The data for each year is linked to specific y-values that correspond to the performance measurements.\nBased on the above thought, the answer is:\nYear,GDP(Billion USD),Interest Rate(%),Debt to GDP Ratio(%)\n2005,466640.0,5.6,92.0\n2006,494503.0,5.9,94.0\n2007,516104.0,5.8,98.0\n2008,486527.0,0.1,108.0\n2009,470704.0,0.8,116.0\n2010,495430.0,0.1,139.0\n2011,513282.0,0.3,138.0\n2012,527073.0,0.2,148.0\n2013,541303.0,0.3,146.0\n2014,556495.0,0.3,146.0\n2015,574458.0,0.5,144.0\n2016,587719.0,1.2,142.0\n2017,605402.0,2.3,140.0\n2018,612064.0,2.9,138.0\n2019,625292.0,2.6,136.0\n2020,591238.0,1.9,144.0" } ], "id_raw": 1154 }, { "id": "PmDdlw7qRGhs0xrp", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_14.png", "conversations": [ { "from": "human", "value": "\nOver the observed period, which economic metric shows a consistent increase despite other fluctuations in the chart between 2005 and 2020?" }, { "from": "gpt", "value": "Observing the trends, the GDP line {the GDP line plot}[0.122, 0.294, 0.946, 0.136] mostly shows an upward trajectory despite some intermittent declines specifically around 2008 to 2010. Unlike the {Interest Rate line}[0.122, 0.887, 0.946, 0.887] which displays significant fluctuations and drops to near zero several times, and {the Debt to GDP ratio line}[0.122, 0.887, 0.946, 0.887] which although increases, also exhibits some fluctuations during similar periods.\nBased on the above thought, the answer is:\nThe GDP metric consistently shows an upward trend from 2005 to 2020, despite minor declines around the financial crisis period and afterwards. This is noticeable when compared to the more fluctuating trends of the Interest Rate and Debt to GDP ratio, making GDP the most consistently increasing economic metric in the chart." } ], "id_raw": 1154 }, { "id": "gSPO7PZnjN1OwmBL", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_14.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall percentage change in GDP from 2005 to 2020?" }, { "from": "gpt", "value": "First, I ascertain the GDP values for the years 2005 and 2020 by locating the data points on the GDP line {the GDP line points}[0.122, 0.294], [0.946, 0.136]. The value for 2005 is 466640.0 billion USD and for 2020, it is 591238.0 billion USD. Calculating the percentage change: ((591238 - 466640) / 466640) * 100.\nBased on the above thought, the answer is:\n26.7%" } ], "id_raw": 1154 }, { "id": "xKcFrgRMpE4GAu8g", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_15.png", "conversations": [ { "from": "human", "value": "\nCould you convert the line chart depicting Sustainability Index and Ecological Footprint into a structured table format, listing the categories and their respective values from both assessors' perspectives?" }, { "from": "gpt", "value": "Observing the chart, I examine the plot for the GPT4-as-a-judge on the left and the PaLM2-as-a-judge on the right. Both plots visualize trends across three categories: Energy, Water, and Waste. Each category has a Sustainability Index and an Ecological Footprint value for each assessor, denoted by distinct colors and a legend. By analyzing each line's points, I can derive the exact numerical values for these measures. I focus on {the lines representing the Sustainability Index and the Ecological Footprint for both subplots}[0.055, 0.053, 0.988, 0.927] and decipher the numbers based on their heights against the y-axis.\nBased on the above thought, the answer is:\nCategory,Assessor,Sustainability Index,Ecological Footprint\nEnergy,GPT4,85,70\nWater,GPT4,75,50\nWaste,GPT4,60,80\nEnergy,PaLM2,80,65\nWater,PaLM2,70,55\nWaste,PaLM2,65,75" } ], "id_raw": 1155 }, { "id": "QRL6TgTenlv3Lkst", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_15.png", "conversations": [ { "from": "human", "value": "\nWhich category exhibits the best overall performance in terms of sustainable ecological practices according to the chart?" }, { "from": "gpt", "value": "To ascertain the best performing category, I analyze the balance between the Sustainability Index and the Ecological Footprint from the line chart across the categories of Energy, Water, and Waste. It is necessary to assess which category has the highest Sustainability Index relative to its Ecological Footprint. Monitoring {the line charts for both subplots}[0.055, 0.053, 0.988, 0.927], it is evident that the Energy category consistently shows a higher Sustainability Index while maintaining lower Footprint values compared to others.\nBased on the above thought, the answer is:\nThe Energy category exhibits the best overall performance in terms of sustainable ecological practices as it has the highest Sustainability Index and lower Ecological Footprint across both assessors according to the chart." } ], "id_raw": 1155 }, { "id": "jjPB0WgPoN4raXJ4", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_15.png", "conversations": [ { "from": "human", "value": "\nWhat is the range of the Ecological Footprint for the Water category between both assessors?" }, { "from": "gpt", "value": "To calculate the range, I first need to identify and extract the exact values of the Ecological Footprint for the Water category from both assessors. These values are obtained by inspecting {the line points}[0.055, 0.053, 0.988, 0.927] on the chart, where each assessor's data is represented. By seeing their position relative to the y-axis scaled from 0 to 100, I note that GPT4 has a value of 50 and PaLM2 has 55.\nBased on the above thought, the answer is:\n5" } ], "id_raw": 1155 }, { "id": "CXxp4E2WIsYiyHjZ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_16.png", "conversations": [ { "from": "human", "value": "\nCould you convert the data about education sectors and job market demand from 2005 to 2020 into a structured table from the chart, where the years are the rows and sectors are the columns?" }, { "from": "gpt", "value": "To generate the table from the chart data, I first look at {the years on the x-axis}[0.038, 0.933, 0.067, 0.951], [0.149, 0.933, 0.179, 0.951], [0.261, 0.933, 0.290, 0.951], [0.373, 0.933, 0.402, 0.951], [0.484, 0.933, 0.514, 0.951], [0.596, 0.933, 0.625, 0.951], [0.708, 0.933, 0.737, 0.951], [0.819, 0.933, 0.849, 0.951], [0.931, 0.933, 0.960, 0.951] which represent the row labels or years. I then identify {the legend}[0.073, 0.063, 0.268, 0.168] as the column labels representing different sectors such as Online Education, Traditional Education, and Job Market Demand. To populate the values within the table, I analyze {the line points for each sector}[0.108, 0.887], [0.164, 0.853], [0.220, 0.818], [0.276, 0.784], [0.331, 0.749], [0.387, 0.715], [0.443, 0.680], [0.499, 0.645], [0.555, 0.611], [0.611, 0.576], [0.666, 0.542], [0.722, 0.542], [0.778, 0.507], [0.834, 0.473], [0.890, 0.438], [0.946, 0.404] and extract their corresponding values for each year.\nBased on the above thought, the answer is:\nYear,Online Education,Traditional Education,Job Market Demand\n2005,50,800,700\n2006,100,750,750\n2007,150,700,800\n2008,200,650,750\n2009,250,600,700\n2010,300,550,750\n2011,350,500,800\n2012,400,450,850\n2013,450,400,900\n2014,500,350,950\n2015,550,300,1000\n2016,550,270,1000\n2017,600,240,1050\n2018,650,210,1100\n2019,700,180,1150\n2020,750,150,1200" } ], "id_raw": 1156 }, { "id": "JT9ofe0eCJvfQx83", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_16.png", "conversations": [ { "from": "human", "value": "\nWhat does the trend data suggest about the traditional education sector between 2005 and 2020?" }, { "from": "gpt", "value": "By analyzing the progression of {the line for Traditional Education}[0.108, 0.404, 0.946, 0.818], it is evident there is a consistent downward trend. Starting in 2005 with approximately 800 {at the point}[0.108, 0.369], the enrollment figures sequentially decrease, reaching approximately 150 by 2020 {at the point}[0.946, 0.818]. This indicates a strong shift away from traditional educational settings over these years, possibly in favour of other forms of education like online platforms.\nBased on the above thought, the answer is:\nThe trend data for the traditional education sector exhibits a substantial decline from 800 in 2005 to 150 in 2020, reflecting a significant reduction in traditional education enrollment possibly due to the rising preference for online education methods over the years." } ], "id_raw": 1156 }, { "id": "P4eBMXqtfZGSKsuZ", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_16.png", "conversations": [ { "from": "human", "value": "\nHow much did the Job Market Demand increase from 2005 to 2020?" }, { "from": "gpt", "value": "To find the increase in Job Market Demand over the years, I trace {the line for Job Market Demand}[0.108, 0.438], [0.946, 0.093], noting the points corresponding to the years 2005 and 2020. The values start at 700 in 2005 from {the point}[0.108, 0.438] and ends at 1200 in 2020 from {the point}[0.946, 0.093]. Calculating the increase involves subtracting the initial value from the final value: 1200 - 700.\nBased on the above thought, the answer is:\n500" } ], "id_raw": 1156 }, { "id": "bZe1nb0r71DGxS30", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_18.png", "conversations": [ { "from": "human", "value": "\nCan you convert this chart into a structured table with Evaluation Criteria as rows and Innovations as columns?" }, { "from": "gpt", "value": "To generate the tabular data from the line chart, I first identify {the x-axis tick labels}[[0.069, 0.934, 0.125, 0.952], [0.178, 0.934, 0.204, 0.952], [0.247, 0.934, 0.324, 0.952], [0.350, 0.934, 0.410, 0.952], [0.448, 0.934, 0.499, 0.952], [0.497, 0.934, 0.639, 0.952], [0.599, 0.934, 0.725, 0.952], [0.722, 0.934, 0.791, 0.952], [0.812, 0.934, 0.890, 0.952], [0.935, 0.934, 0.955, 0.952]] as the row labels, representing the different Evaluation Criteria. Next, I locate {the legend texts}[[0.097, 0.065, 0.170, 0.082], [0.097, 0.091, 0.170, 0.108],[0.097, 0.117, 0.170, 0.135]] as the column headers representing different Innovations. Then, to populate the values, I examine {the line points for each innovation}[[0.097, 0.709],[0.191, 0.475]...] and {extract the corresponding values}[[0.45,0.62...]].\nBased on the above thought, the answer is:\nEvaluation_Criterion,Innovation_1,Innovation_2,Innovation_3\nEfficiency,0.45,0.78,0.35\nCost,0.62,0.5,0.77\nCollaboration,0.75,0.88,0.64\nScalability,0.52,0.6,0.83\nPotential,0.9,0.76,0.67\nEase_of_Implementation,0.85,0.68,0.45\nBenefit_to_Cost_Ratio,0.75,0.64,0.82\nProject_Risk,0.32,0.54,0.4\nSustainability,0.86,0.7,0.88\nROI,0.78,0.9,0.76" } ], "id_raw": 1158 }, { "id": "DGTU6MuNn96WfsK4", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_18.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, which Innovation project received the highest average score across all evaluation criteria and why?" }, { "from": "gpt", "value": "In order to answer this question, I need to compare the average scores for each innovation project across all evaluation criteria. Upon examining {the line points for Innovation 1}[[0.097, 0.709],[0.191, 0.475],[0.285, 0.296],[0.380, 0.613],[0.474, 0.089],[0.568, 0.158],[0.662, 0.296],[0.757, 0.888],[0.851, 0.144],[0.945, 0.254]], {Innovation 2}[[0.097, 0.254],[0.191, 0.640],...], and {Innovation 3}[[0.097, 0.847],[0.191, 0.268],...], I notice that Innovation 3 has generally higher values across most evaluation criteria compared to other innovations.\nBased on the above thought, the answer is:\nInnovation 3 appears to have received the highest average score across all evaluation criteria, as it has performed well across most measures, including the areas of Collaboration, Scalability, Benefit to Cost Ratio, Sustainability, and ROI. This suggests a strong overall performance, justifying its high average score." } ], "id_raw": 1158 }, { "id": "IOpASVyTyKo5jQwG", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the average score of Innovation 1 across all evaluation criteria?" }, { "from": "gpt", "value": "To find the average score of Innovation 1 across all evaluation criteria, I need to locate {the line points for Innovation 1} [[0.097, 0.709],[0.191, 0.475],[0.285, 0.296],[0.380, 0.613],[0.474, 0.089],[0.568, 0.158],[0.662, 0.296],[0.757, 0.888],[0.851, 0.144],[0.945, 0.254]] and extract the corresponding scores, then calculate the average of these scores.\nBased on the above thought, the answer is:\n0.65." } ], "id_raw": 1158 }, { "id": "hU1FWBIDW5EO2zP3", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_58_19.png", "conversations": [ { "from": "human", "value": "\nCan you depict the asset values data present in the lineplot into a structured table format? The table should have quarters as rows and asset types as columns." }, { "from": "gpt", "value": "For the desired table, I can label the quarters from the {x-axis}[0.125, 0.896, 0.873, 0.914] as rows. The asset types, namely Physical Assets and Digital Assets, can be taken from {the legend}[0.737, 0.130, 0.893, 0.203] as the column names. I can gather the asset values as per each quarter and respective asset type from their line plot points {Physical Assets line}[0.160, 0.445, 0.865, 0.855] and {Digital Assets line}[0.160, 0.155, 0.865, 0.831]. Each point corresponds to normalized asset value per quarter.\nBased on the above thought, the answer is:\nQuarter,Physical_Assets,Digital_Assets\nQ1,0.47,0.94\nQ2,0.62,0.83\nQ3,0.39,0.75\nQ4,0.57,0.68\nQ5,0.36,0.61\nQ6,0.44,0.58\nQ7,0.61,0.48\nQ8,0.70,0.38" } ], "id_raw": 1159 }, { "id": "76RLw7KA1NEshCq0", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_58_19.png", "conversations": [ { "from": "human", "value": "\nHow can you describe the trend of asset values observed across the quarters according to the line plots?" }, { "from": "gpt", "value": "The trend analysis can be done based on the data points of both asset types in the line plot. The plot of the line for {Physical Assets}[0.160, 0.722, 0.865, 0.445] shows a gradual rise, then a drop, and a rough steady increase towards the end. This indicates a mixed trend with some volatility, but an overall increase in value.\nIn contrast, the line for {Digital Assets}[0.160, 0.155, 0.865, 0.831] shows a pronounced downward trend, indicating a steady decrease in value from Q1 to Q8.\nBased on the above thought, the answer is:\nThe Physical Assets seem to follow a mixed trend with an initial increase, a drop, and then another increase, indicating some volatility but an overall upward path across the quarters. In contrast, the values for Digital Assets consistently decrease over the quarters, indicating a clear downward trend. This suggests that over the observed period, Physical Assets perform better than Digital Assets." } ], "id_raw": 1159 }, { "id": "IC0IUfvMbftVqWkF", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_58_19.png", "conversations": [ { "from": "human", "value": "\nBy how much are the ending values of both physical and digital assets different from the beginning values according to the line plot?" }, { "from": "gpt", "value": "To calculate this difference, we must compare the normalized asset value of Q1 and Q8 for both asset types. The line plots of {Physical Assets}[0.160, 0.722, 0.865, 0.445] and {Digital Assets}[0.160, 0.155, 0.865, 0.831] indicate the respective values. Physical Assets have decreased by 0.70 - 0.47 = 0.23 while Digital Assets have decreased by 0.94 - 0.38 = 0.56 from Q1 to Q8.\nBased on the above thought, the answer is:\nPhysical Assets: 0.23, Digital Assets: 0.56" } ], "id_raw": 1159 }, { "id": "7a4CDYWiPZY6WbNp", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_59_1.png", "conversations": [ { "from": "human", "value": "\nCan you provide the structured data of viewership trends from the line chart, organizing the years as rows and the type of viewership as columns?" }, { "from": "gpt", "value": "By examining the line chart, I analyze the plots representing each viewership type. {SVoD's line}[0.400, 0.171, 0.642, 0.828] shows upward trends and {Linear TV's line}[0.400, 0.480, 0.642, 0.828] shows a downward trend over the period from 2018 to 2020. Aligning with the x-axis labels {2018}[0.579, 0.879, 0.608, 0.914], {2019}[0.603, 0.879, 0.632, 0.914], {2020}[0.627, 0.879, 0.657, 0.914], I extract related data values for both viewership types.\nBased on the above thought, the answer is:\nYear,SVoD Viewership,Linear TV Viewership\n2018,Significant Growth,High but declining\n2019,Accelerated Growth,Moderately Declining\n2020,Rapid Growth,Lowest Point" } ], "id_raw": 1161 }, { "id": "OGOWwHXF7LBjJDZT", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_59_1.png", "conversations": [ { "from": "human", "value": "\nWhat does the comparison between the 'Subscription Video on Demand' and 'Linear TV' viewership trends suggest about consumer preferences over the years 2018 to 2020?" }, { "from": "gpt", "value": "To analyze consumer preferences, I review the representation of viewership trends on the line chart, tracking both {SVoD's line}[0.400, 0.171, 0.642, 0.828] and {Linear TV's line}[0.400, 0.480, 0.642, 0.828]. SVoD portrays a consistent upward trajectory, indicating increased adoption and preference among consumers, while the downward trend in Linear TV viewership hints at a declining favor over the same period.\nBased on the above thought, the answer is:\nThe comparison indicates a growing consumer preference for Subscription Video on Demand services over Linear TV from 2018 to 2020. The rising trend for SVoD coupled with the decline of Linear TV suggests a shift in how consumers opt to access media, favoring on-demand streaming over traditional television formats." } ], "id_raw": 1161 }, { "id": "vw3Jwc61a4ujAMnt", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_59_1.png", "conversations": [ { "from": "human", "value": "\nHow many years does the Persistent Growth phase for SVoD last, based on the chart?" }, { "from": "gpt", "value": "To find the duration of the Persistent Growth phase for SVoD, I observe the segment of the {SVoD line}[0.400, 0.171, 0.642, 0.828]. The trend starts from 2018 and continues to show a consistent rise through to 2020, as depicted in the x-axis {ticks}[0.579, 0.879, 0.657, 0.914]. Calculating the span from 2018 to 2020 gives a duration of 3 years.\nBased on the above thought, the answer is:\n3 years." } ], "id_raw": 1161 }, { "id": "986et2D4vUXYZ3ge", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_59_2.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured table showing the accuracy trends for Machine Learning and Deep Learning from 2018 to 2020 according to the data in the chart?" }, { "from": "gpt", "value": "To construct the table, I first identify {the x-axis tick labels}[0.056, 0.879, 0.085, 0.914; 0.177, 0.879, 0.206, 0.914; 0.298, 0.879, 0.327, 0.914] indicating the years 2018, 2019, and 2020. Then, I examine {the points on the lines for Machine Learning}[0.071, 0.351; 0.192, 0.196; 0.313, 0.145] and {Deep Learning}[0.071, 0.428; 0.192, 0.325; 0.313, 0.248] which gives the corresponding accuracy values for each year.\nBased on the above thought, the answer is:\nYear,Machine Learning, Deep Learning\n2018,0.9,0.87\n2019,0.96,0.91\n2020,0.98,0.94" } ], "id_raw": 1162 }, { "id": "aOiyW9WBeLuLXKAh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_59_2.png", "conversations": [ { "from": "human", "value": "\nHow does the performance of Machine Learning technologies compare to Deep Learning technologies over the analyzed period in terms of reliability and consistency based on the chart data?" }, { "from": "gpt", "value": "By evaluating {the line plot for Machine Learning}[0.071, 0.351; 0.192, 0.196; 0.313, 0.145], it shows a consistent upward trend in accuracy from 0.9 in 2018 to 0.98 in 2020 with relatively less fluctuation. Conversely, {the line plot for Deep Learning}[0.071, 0.428; 0.192, 0.325; 0.313, 0.248] also shows an increase from 0.87 in 2018 to 0.94 in 2020, but the increments each year are not as pronounced or steady as Machine Learning\u2019s, hence less consistent. Therefore, Machine Learning displays slightly better reliability and consistency in performance improvement over the analyzed period compared to Deep Learning.\nBased on the above thought, the answer is:\nMachine Learning is more consistent and reliable in terms of performance improvement over the analyzed period compared to Deep Learning." } ], "id_raw": 1162 }, { "id": "BGtg79DWOyKN2yqb", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_59_2.png", "conversations": [ { "from": "human", "value": "\nWhat is the trend in accuracy improvement from 2018 to 2020 for Machine Learning based on numerical values?" }, { "from": "gpt", "value": "Analyzing the data from {the points on the Machine Learning line}[0.071, 0.351; 0.192, 0.196; 0.313, 0.145], the accuracy starts at 0.9 in 2018, increases to 0.96 in 2019, and reaches 0.98 in 2020. The overall difference indicates an improvement of 0.08.\nBased on the above thought, the answer is:\n0.08" } ], "id_raw": 1162 }, { "id": "Cxp91B9mGqMTzb5V", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_59_3.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the bar chart showing the manufacturing efficiency of two products across two processes into a structured table format?" }, { "from": "gpt", "value": "Observing {the horizontal bars}[0.1, 0.35, 0.5, 0.6] and {the labels}[0.2, 0.4, 0.55, 0.65] within them, I can see the different processes for WidgetA and WidgetB. {Process1}[0.1, 0.35] is represented by light blue and {Process2}[0.5, 0.6] by dark blue, respectively. By translating these bars and their labels into a categorical format, we can tabulate the data appropriately.\nBased on the above thought, the answer is:\nProduct,Process1 (%),Process2 (%)\nWidgetA,70.5,74.3\nWidgetB,89.6,92.1" } ], "id_raw": 1163 }, { "id": "DIEzKbsXsGJX2yKw", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_59_3.png", "conversations": [ { "from": "human", "value": "\nWhich of the two processes, Process1 or Process2, demonstrates higher overall efficiency across the manufactured products?" }, { "from": "gpt", "value": "By analyzing the chart data for each product-process combination, it's evident {Process2}[0.5, 0.6] has higher values than {Process1}[0.1, 0.35] regardless of the product. Specifically, WidgetA's efficiency increased from 70.5% to 74.3% and WidgetB's from 89.6% to 92.1% as illustrated by {the efficiency labels inside the bars}[0.2, 0.4, 0.55, 0.65]. This indicates that Process2 is more effective in improving production efficiency for both products.\nBased on the above thought, the answer is:\nProcess2 demonstrates higher overall efficiency for both WidgetA and WidgetB according to the efficiency values shown in the chart." } ], "id_raw": 1163 }, { "id": "pvJaJWjHJx6lDyym", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_59_3.png", "conversations": [ { "from": "human", "value": "\nWhat percentage increase in production efficiency is observed for WidgetA moving from Process 1 to Process 2?" }, { "from": "gpt", "value": "Pinpointing the values for WidgetA, which are encapsulated within {the bars}[0.15, 0.25], the specific values I observe are 70.5% for {Process 1}[0.15, 0.25, 0.1, 0.2] and 74.3% for {Process 2}[0.55, 0.65, 0.5, 0.6]. Calculating the increase, the difference is 74.3% - 70.5% = 3.8%.\nBased on the above thought, the answer is:\n3.8%" } ], "id_raw": 1163 }, { "id": "ISWS5nwVMqHEM1DP", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_59_4.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart information into a table with web browsers as rows and risk types as columns?" }, { "from": "gpt", "value": "From the chart, the x-axis represents various browser names, which can be denoted as row headers in the table. Meanwhile, the legend describes 'High Risk' and 'Low Risk' categories, representing the two columns in the table. To populate the table, I locate the height of each bar which indicates the values corresponding to the risk category for each browser. The bars representing 'High Risk' values can be found in {High Risk bars}[[0.192, 0.123, 0.221, 0.569], [0.303, 0.123, 0.331, 0.354], [0.410, 0.123, 0.439, 0.412], [0.517, 0.123, 0.546, 0.387], [0.624, 0.123, 0.653, 0.533]] for each browser. Similarly, 'Low Risk' values are denoted by {Low Risk bars}[[0.230, 0.123, 0.259, 0.476], [0.341, 0.123, 0.369, 0.321], [0.449, 0.123, 0.477, 0.464], [0.556, 0.123, 0.584, 0.408], [0.662, 0.123, 0.691, 0.314]] for each browser.\nBased on the above thought, the answer is:\nBrowser,High Risk,Low Risk\nFirefox, 22.4, 17.7\nChrome, 11.6, 9.9\nSafari, 14.5, 17.1\nEdge, 13.2,14.2\nOpera, 20.5, 9.55" } ], "id_raw": 1164 }, { "id": "7EKM5moGedL7l6KE", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_59_4.png", "conversations": [ { "from": "human", "value": "\nWhat conclusions can you derive about the browsers' high and low-risk levels based on this chart?" }, { "from": "gpt", "value": "To derive conclusions, I need to look at the heights of the bar plot representing the High and Low risk in the chart. The 'High Risk' bars are {High Risk bars}[[0.192, 0.123, 0.221, 0.569], [0.303, 0.123, 0.331, 0.354], [0.410, 0.123, 0.439, 0.412], [0.517, 0.123, 0.546, 0.387], [0.624, 0.123, 0.653, 0.533]], and the 'Low Risk' bars are {Low Risk bars}[[0.230, 0.123, 0.259, 0.476], [0.341, 0.123, 0.369, 0.321], [0.449, 0.123, 0.477, 0.464], [0.556, 0.123, 0.584, 0.408], [0.662, 0.123, 0.691, 0.314]]. Upon examining the trends, it's clear that Firefox users are the most susceptible to high-risk threats, while Chrome users are the least likely to face these threats. Conversely, Safari users have a higher likelihood of encountering low-risk threats while Firefox users have the lowest risk in this category.\nBased on the above thought, the answer is:\nThe chart indicates that when users are choosing a browser aimed at minimizing high-risk privacy threats, Chrome is the safest choice. However, if users are more concerned with low-risk data privacy issues, Firefox is the safer option. Each browser has varying degrees of strengths and weaknesses when it comes to managing cybersecurity and privacy threats and users should opt according to their preference for low risk or high-risk threats mitigation." } ], "id_raw": 1164 }, { "id": "YpWgxm5oAmG5q4oM", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_59_4.png", "conversations": [ { "from": "human", "value": "\nBetween Firefox and Chrome, which web browser has a higher difference in high and low-risk levels?" }, { "from": "gpt", "value": "To know the difference in high and low-risk levels between Firefox and Chrome, I need to locate the respective bar heights in the chart. For Firefox, the heights of the high-risk and low-risk bars are located at {Firefox High Risk} [0.192, 0.123, 0.221, 0.569] and {Firefox Low Risk} [0.230, 0.123, 0.259, 0.476] respectively which show values of 22.4% and 17.7%. For Chrome, the high-risk and low-risk bars are located at {Chrome High Risk} [0.303, 0.123, 0.331, 0.354] and {Chrome Low Risk} [0.341, 0.123, 0.369, 0.321] respectively with values of 11.6% and 9.9%. The difference in high and low-risk levels for Firefox is 22.4% - 17.7% = 4.7% and for Chrome, it is 11.6% - 9.9% = 1.7%\nBased on the above thought, the answer is:\nFirefox." } ], "id_raw": 1164 }, { "id": "rxFj4Epbo390l9p7", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_59_5.png", "conversations": [ { "from": "human", "value": "\nCan you convert the data from the line chart showing the performance of different quarters over 5 years into a structured table?" }, { "from": "gpt", "value": "Analyzing the line chart, I start by identifying {the x-axis labels}[0.021, 0.962, 0.170, 0.975] which indicate the years from 2015 to 2020. These serve as the column headers for the table. Observing the lines for each quarter, I tag them with {the legend entries}[0.569, 0.074, 0.649, 0.116] such as Q1 through Q4, providing row headers. The values for each quarter per year are plotted as {data points}[0.369, 0.232, 0.639, 0.741], which are directly translated to the respective cells in the table.\nBased on the above thought, the answer is:\nYear,Q1,Q2,Q3,Q4\n2015,0.8,1.5,1.2,0.5\n2016,1.0,2.0,1.4,0.6\n2017,1.2,2.2,1.6,0.7\n2018,1.0,2.1,1.5,0.6\n2019,0.9,2.3,1.3,0.5\n2020,1.0,2.5,1.2,0.7" } ], "id_raw": 1165 }, { "id": "rNe7lFY4AH2La7ZK", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_59_5.png", "conversations": [ { "from": "human", "value": "\nHow does the trend in Q3 from 2015 to 2020 compare to the other quarters in terms of stability and variability?" }, { "from": "gpt", "value": "Observing trends, I note that {the line for Q3}[0.429, 0.402, 0.609, 0.402] shows moderate fluctuation, peaking in 2017 and dipping in 2020. In contrast, {the line for Q1}[0.369, 0.741, 0.549, 0.741] is relatively stable with minor peaks. {The line for Q2}[0.399, 0.628, 0.639, 0.458] shows a continuous elevation suggesting stable growth, while {the line for Q4}[0.549, 0.571, 0.639, 0.571] exhibits slight downward trends. Comparatively, Q3 possesses less stability and greater variability than Q1 and Q2 but is more stable than Q4.\nBased on the above thought, the answer is:\nQ3 exhibits medium stability with variable peaks and troughs when compared to the other quarters. It's more stable than Q4 but shows greater variability than Q1 and Q2, reflecting an inconsistent development pattern over the years." } ], "id_raw": 1165 }, { "id": "QA9s4L8QLyLsdoAl", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_59_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall average value for Q2 across all years?" }, { "from": "gpt", "value": "To determine the average for Q2, I collect values for Q2 from 2015 to 2020 from {data points for Q2}[0.399, 0.628, 0.639, 0.741], which are 1.5, 2.0, 2.2, 2.1, 2.3, 2.5 respectively. To find the average, I calculate (1.5+2.0+2.2+2.1+2.3+2.5)/6.\nBased on the above thought, the answer is:\n2.1" } ], "id_raw": 1165 }, { "id": "oi9ORtbSwSFbmTUp", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_59_7.png", "conversations": [ { "from": "human", "value": "\nCan you convert the information shown in the line chart into a structured table format keeping 'Area' as the row header and 'Average Income' and 'Average Age' as the column headers?" }, { "from": "gpt", "value": "To achieve a structured table form, I start by identifying the label {the x-axis tick labels}[0.201, 0.766, 0.334, 0.802], [0.402, 0.766, 0.534, 0.802], [0.605, 0.766, 0.737, 0.802] which indicates the rows as Urban, Suburban, and Rural. For the column headers, 'Average Income' and 'Average Age' are depicted through line plots. I locate the respective values by observing and pinpointing {the line for income}[0.201, 0.390, 0.737, 0.680] and {the line for age}[0.201, 0.245, 0.737, 0.560] across different areas.\nBased on the above thought, the answer is:\nArea,Average Income,Average Age\nUrban,45000,27\nSuburban,55000,35\nRural,35000,45" } ], "id_raw": 1167 }, { "id": "m9a13R67MvY0hJAX", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_59_7.png", "conversations": [ { "from": "human", "value": "\nWhat factors might contribute to the difference in average income between urban and rural areas as seen in the chart?" }, { "from": "gpt", "value": "Observing the given chart, {lines indicating average age and income}[0.201, 0.245, 0.737, 0.560] demonstrate that with increasing age, there appears to be a decrease in average income. Urban areas, with a younger average age, show a higher income compared to rural areas which have the oldest average age but the lowest average income. This suggests a correlation between age demographics & economic opportunities which are potentially more abundant in urban settings leading to higher incomes.\nBased on the above thought, the answer is:\nThe difference in average income between urban and rural areas as shown in the chart can be likely attributed to differing age demographics and access to economic opportunities. Urban areas have a younger population and potentially better access to higher-paying jobs and resources, whereas rural areas, with an older population, might face limitations on these fronts." } ], "id_raw": 1167 }, { "id": "rxweQR34MPjUVV9A", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_59_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in average income between Urban and Rural areas according to the chart?" }, { "from": "gpt", "value": "Utilizing the data plotted on the chart, I locate {the income feature at Urban}[0.201, 0.390] which shows an income of $45,000, and {the income feature at Rural}[0.737, 0.680] where it reads $35,000. Calculating the difference between these two values results in $45,000 - $35,000.\nBased on the above thought, the answer is:\n$10,000." } ], "id_raw": 1167 }, { "id": "o9r128KH1K6SHuQp", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_59_8.png", "conversations": [ { "from": "human", "value": "\nCan you convert the chart data showing different sustainable business indexes over five years into a table format?" }, { "from": "gpt", "value": "Observing {the x-axis tick labels}[0.071, 0.879, 0.127, 0.914], [0.172, 0.879, 0.230, 0.914], [0.238, 0.879, 0.303, 0.914], [0.312, 0.879, 0.381, 0.914], [0.401, 0.879, 0.471, 0.914] indicates the categories like Water Conservation, Energy Efficiency, etc. I then identify {the year labels in legends}[0.209, 0.734, 0.280, 0.849], [0.538, 0.734, 0.609, 0.849], [0.867, 0.734, 0.938, 0.849] which represents the years 2016 through 2020 respectively. Data points are observed from {the lines representing different years}[0.070, 0.306, 0.275, 0.619], [0.399, 0.271, 0.605, 0.627], [0.728, 0.271, 0.934, 0.627] associating with each category and year.\nBased on the above thought, the answer is:\nCategory,2016,2017,2018,2019,2020\nWater Conservation,32,30.5,31,30,29.8\nEnergy Efficiency,45,47,46.5,48,48.5\nWaste Management,53,52,52.5,51,50.5\nClimate Change,60,62.5,65,67,70\nRenewable Energy,72.5,74,77,80,83" } ], "id_raw": 1168 }, { "id": "3GGI66Mgdegmnlmy", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_59_8.png", "conversations": [ { "from": "human", "value": "\nHow does the trend between Energy Efficiency and Climate Change compare over the years based on the Sustainable Business Index?" }, { "from": "gpt", "value": "Analyzing the line trends for {Energy Efficiency}[0.070, 0.519, 0.275, 0.491] and {Climate Change}[0.070, 0.403, 0.275, 0.325] from the chart data, Energy Efficiency shows slight increases and maintains a relatively stable index, peaking in 2019. In contrast, Climate Change exhibits a clearer upward trend, increasing consistently from 2016, indicating a stronger focus and improvements relative to Energy Efficiency over the years.\nBased on the above thought, the answer is:\nThe trend for Climate Change is more positively progressive compared to Energy Efficiency over the years, with a clear upward trajectory and gradual improvement in its index whereas Energy Efficiency shows moderate fluctuations with minor improvements." } ], "id_raw": 1168 }, { "id": "75yodUG06CPwmfh2", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_59_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall percentage increase in the Sustainable Business Index for Renewable Energy from 2016 to 2020?" }, { "from": "gpt", "value": "To compute the percentage increase for Renewable Energy, I first locate the Sustainable Business Index values for 2016 and 2020 from {the line points for Renewable Energy}[0.275, 0.306], [0.934, 0.225] which are 72.5 in 2016 and 83 in 2020 respectively. Using the formula (New Value - Old Value) / Old Value * 100, we calculate (83 - 72.5) / 72.5 * 100.\nBased on the above thought, the answer is:\n14.48%." } ], "id_raw": 1168 }, { "id": "QkEqPSqGOJ6ML1Hz", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_59_9.png", "conversations": [ { "from": "human", "value": "\nCan you convert the trends for 'Faith', 'Steadiness', and 'Honesty' across different moods into a structured table format using the line chart data?" }, { "from": "gpt", "value": "I observe {the line chart's x-axis labels}[0.365, 0.919, 0.657, 0.942] showcasing the moods: Rapid increase, Dramatic surge, Slow recovery, Steady decline, Slow expansion, and Rapid growth. I analyze {the line chart data points for 'Faith'}[0.378, 0.162, 0.648, 0.262], {for 'Steadiness'}[0.378, 0.237, 0.648, 0.287], and {for 'Honesty'}[0.378, 0.312, 0.648, 0.337]. Each point represents the score for the respective behavior trait under a given mood, which can be tabulated.\nBased on the above thought, the answer is:\nMood,Faith,Steadiness,Honesty\nRapid Increase,1.5,1.2,0.8\nDramatic Surge,1.8,1.6,0.4\nSlow Recovery,1.2,1.4,0.7\nSteady Decline,1.0,1.1,0.6\nSlow Expansion,1.2,1.3,0.8\nRapid Growth,1.5,1.4,0.9" } ], "id_raw": 1169 }, { "id": "BTsEp1I7nBnSNZKh", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_59_9.png", "conversations": [ { "from": "human", "value": "\nWhich behavioral trait shows the least variation in scores across all moods?" }, { "from": "gpt", "value": "I need to examine the level of fluctuation within the line plots for each behavioral trait {for 'Faith'}[0.378, 0.162, 0.648, 0.262], {for 'Steadiness'}[0.378, 0.237, 0.648, 0.287], and {for 'Honesty'}[0.378, 0.312, 0.648, 0.337]. 'Faith' started high, dipped, and rose, 'Steadiness' showed moderate changes, whereas 'Honesty' consistently declined or held stable values across the trends. The line plots suggest 'Honesty' had the least variation when compared across all moods.\nBased on the above thought, the answer is:\n'Honesty' shows the least variation in scores across all moods. It maintains relatively stable or slightly decreasing scores throughout, indicating lesser fluctuations compared to 'Faith' and 'Steadiness'." } ], "id_raw": 1169 }, { "id": "RLNE5TUyTJg3g8bH", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_59_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the cumulative score increase for 'Faith' from 'Rapid Increase' to 'Rapid Growth'?" }, { "from": "gpt", "value": "To determine the cumulative score increase for 'Faith', I inspect {the line points on the plot for 'Faith'}[0.378, 0.162, 0.648, 0.262] corresponding to the moods from 'Rapid Increase' to 'Rapid Growth'. The scores increase from 1.5 to 1.8 and finally to 1.5 again. The net change from the starting point to the endpoint is computed as 1.5 (end) - 1.5 (start) = 0.\nBased on the above thought, the answer is:\n0." } ], "id_raw": 1169 }, { "id": "FgfQh38IFCecVHrX", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_59_11.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data of series from January to December into a structured table with months as columns and series as rows?" }, { "from": "gpt", "value": "To transform the data into a table, I first identify {the x-axis labels}[0.055, 0.093, 0.284, 0.866] which represent the months from January to December. Next, I focus on {the legend}[0.772, 0.111, 0.937, 0.226] to determine the series names as rows. Observing each {line plot}[0.724, 0.248, 0.932, 0.635], I sequentially capture the values for each series corresponding to each month to populate the table values.\nBased on the above thought, the answer is:\nMonth, Series A, Series B, Series C\nJanuary, 20, 85, 75\nFebruary, 25, 80, 80\nMarch, 30, 75, 85\nApril, 35, 70, 90\nMay, 40, 65, 95\nJune, 45, 60, 100\nJuly, 50, 55, 105\nAugust, 55, 50, 110\nSeptember, 60, 45, 115\nOctober, 65, 40, 120\nNovember, 80, 35, 125\nDecember, 85, 30, 130" } ], "id_raw": 1171 }, { "id": "wBl1mLYVv0pl4tnS", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_59_11.png", "conversations": [ { "from": "human", "value": "\nWhich month and series illustrate a sharp outlier and what might it indicate in the context of the data?" }, { "from": "gpt", "value": "Observing the dramatic surge in November for Series A, which I pinpoint on the chart at {November point for Series A}[0.863, 0.209], it clearly stands out in comparison to the other months. This indicates a significant event or occurrence that affected Series A specifically during November, causing a notable increase in its values.\nBased on the above thought, the answer is:\nThe sharp outlier is observed in November for Series A. This could possibly indicate an external event or significant change specific to the context of Series A, leading to an unexpected and distinct rise during this month." } ], "id_raw": 1171 }, { "id": "GkWSJXHBgPcNMQir", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_59_11.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in Series A from January to December?" }, { "from": "gpt", "value": "Initially, I locate {the line points for Series A}[0.724, 0.635, 0.932, 0.248] and identify the y-values corresponding to January and December, which are 20 and 85 respectively. The calculation for percentage increase is ((85 - 20) / 20) * 100.\nBased on the above thought, the answer is:\n325%" } ], "id_raw": 1171 }, { "id": "YV8GeHYRkoWuwmSS", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_59_12.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, how can the connectivity index data for social networks and interpersonal relationships be tabulated?" }, { "from": "gpt", "value": "As I analyze the line chart, I first notice {the x-axis labels}[0.671, 0.879, 0.776, 0.914] and [0.887, 0.879, 0.987, 0.914], indicating 'Social Networks' and 'Interpersonal Relationships' as columns. I then evaluate the y-values of the line points at {the corresponding x positions}[0.724, 0.415] and [0.937, 0.287], indicating the connectivity indexes.\nBased on the above thought, the answer is:\nCategory,Connectivity Index\nSocial Networks,0.75\nInterpersonal Relationships,0.95" } ], "id_raw": 1172 }, { "id": "w7e2JedgRwxbISLD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_59_12.png", "conversations": [ { "from": "human", "value": "\nWhy is the point associated with interpersonal relationships considered an outlier in the chart?" }, { "from": "gpt", "value": "Observing the line progression from {the point for Social Networks}[0.724, 0.415] to {the point for Interpersonal Relationships}[0.937, 0.287], there's a noticeable jump in the connectivity index, markedly higher than the gradual increase expected in the trend. Typically, an outlier in such a context is identified by a sudden and sizeable deviation from the general trend line, which is what is observed here.\nBased on the above thought, the answer is:\nThe point for interpersonal relationships is considered an outlier due to its significantly higher connectivity index that deviates sharply from the general increasing trend observed from social networks to interpersonal relationships, highlighting a sudden and considerable increase." } ], "id_raw": 1172 }, { "id": "KCQettXHW8ddyCux", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_59_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in the connectivity index from social networks to interpersonal relationships?" }, { "from": "gpt", "value": "To calculate the increase, I first identify the connectivity index for social networks at {the point for Social Networks}[0.724, 0.415], which is approximately 0.75. Then, I find the index for interpersonal relationships at {the point for Interpersonal Relationships}[0.937, 0.287], estimated to be 0.95. The difference is thus 0.95 - 0.75.\nBased on the above thought, the answer is:\n0.2" } ], "id_raw": 1172 }, { "id": "iyvjhyPHBYq7O8if", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_59_14.png", "conversations": [ { "from": "human", "value": "\nCan you compile the Market Style and Trend Level data from the line charts into a structured table?" }, { "from": "gpt", "value": "To generate the table, I first look at {the x-axis labels for both charts}[0.025, 0.879, 0.491, 0.914], [0.518, 0.879, 0.985, 0.914], which represent the Market Styles. Next, I locate the titles for Equity {Market charts}[0.212, 0.037, 0.315, 0.083] and Cryptocurrency {Market charts}[0.674, 0.037, 0.840, 0.083]. Trend levels are extracted from {line points of both charts}[0.077, 0.171, 0.450, 0.635], [0.571, 0.132, 0.944, 0.519].\nBased on the above thought, the answer is:\nMarket Style,Equity Markets,Cryptocurrency Markets\nUpward trajectory,1.8,0.9\nAbrupt decline,1.1,1.3\nIncremental growth,1.0,1.6\nErratic growth,0.6,1.9" } ], "id_raw": 1174 }, { "id": "4IZoC3Xs3eXYzWbF", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_59_14.png", "conversations": [ { "from": "human", "value": "\nJudging by the chart, which market seems to have a more stable trend from 'Upward trajectory' to 'Erratic growth'?" }, { "from": "gpt", "value": "To determine the more stable market, I will need to compare the line plots from 'Upward trajectory' to 'Erratic growth' for both markets. For {Equity Markets}[0.077, 0.171, 0.450, 0.635], the trend moves from 1.8 to 1.1 to 1.0 to 0.6. For {Cryptocurrency Markets}[0.571, 0.132, 0.944, 0.519], the trend moves from 0.9 to 1.3 to 1.6 to 1.9. The trend for Equity Markets demonstrates a decline whereas the trend for Cryptocurrency Markets demonstrates an increase.\nBased on the above thought, the answer is:\nFrom the chart, Equity Markets seem to have a more stable trend from 'Upward trajectory' to 'Erratic growth'. This market shows a general downward trend, with smaller fluctuations in trend levels compared to the Cryptocurrency Markets, which exhibits a sharper increase. Thus, despite the trend decline, Equity Markets are more stable in the context of this comparison." } ], "id_raw": 1174 }, { "id": "o6Up4mK2QZvEb1Ct", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_59_14.png", "conversations": [ { "from": "human", "value": "\nCan you provide the difference in trend levels between 'Upward trajectory' and 'Erratic growth' for Equity Markets?" }, { "from": "gpt", "value": "To calculate the difference in trend levels between 'Upward trajectory' and 'Erratic growth' for Equity Markets, I will locate {the corresponding points for 'Upward trajectory'}[0.077, 0.171] and for {'Erratic growth'}[0.450, 0.635] in the Equity Markets line plot. The trend level for 'Upward trajectory' is 1.8 and for 'Erratic growth' is 0.6. The difference can be calculated by subtracting the two: 1.8 - 0.6 = 1.2.\nBased on the above thought, the answer is:\n1.2" } ], "id_raw": 1174 }, { "id": "jzRWQhxY5uZs8y6J", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_59_16.png", "conversations": [ { "from": "human", "value": "\nCan you provide a structured data table showing the trends for AI Consumption and Human Interaction across the future living scenarios indicated in the chart?" }, { "from": "gpt", "value": "First, I'll locate the x-axis labels representing the future living scenarios, which include Habitual Living, Smart City, Personalized Services, Automated Infrastructure, and Sustainable Living. By analyzing the line graphs, I identify two distinct lines: the first line corresponds to AI Consumption and the second to Human Interaction. For each scenario, I extract the data points by pinpointing {the markers and lines for AI Consumption and Human Interaction}[0.047, 0.093, 0.328, 0.866] on the chart.\nBased on the above thought, the answer is:\nScenario, AI Consumption, Human Interaction\nHabitual Living, 24, 30\nSmart City, 50, 70\nPersonalized Services, 46, 55\nAutomated Infrastructure, 60, 62\nSustainable Living, 74, 70" } ], "id_raw": 1176 }, { "id": "j2FPpnAigUtgS3nQ", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_59_16.png", "conversations": [ { "from": "human", "value": "\nWhy do we observe a significant spike in Human Interaction in the Smart City scenario, as seen on the chart?" }, { "from": "gpt", "value": "To determine the reason behind the spike in Human Interaction in the Smart City scenario, I analyze {the trend observed in the line representing Human Interaction}[0.047, 0.093, 0.328, 0.866]. This line shows a significant spike when shifting from Habitual Living to Smart City. Typically, this suggests a major change or improvement in society\u2019s structure or technology, whereby Smart Cities, known for their integration of ICT to improve citizen welfare and city functions, greatly enhance human connectivity and interaction. The substantial difference seen on the chart signifies how Smart City infrastructure could be improving quality of life by increasing human interaction.\nBased on the above thought, the answer is:\nThe significant spike in Human Interaction in the Smart City scenario is likely indicative of the enhanced social connectivity and quality of life improvements associated with Smart City infrastructure. These urban areas often use advanced technology to foster greater human engagement and efficient urban management, thus explaining the observed spike." } ], "id_raw": 1176 }, { "id": "YCdBvhM70xWzXK9i", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_59_16.png", "conversations": [ { "from": "human", "value": "\nWhat is the increase in AI Consumption between the Habitual Living and Smart City scenarios?" }, { "from": "gpt", "value": "To calculate the increase, I need to compare the AI Consumption values in both scenarios. First, I locate {the points corresponding to AI Consumption in Habitual Living}[0.060, 0.866] and {Smart City}[0.124, 0.866]. The values are 24 for Habitual Living and 50 for Smart City. Then, I simply subtract the former value from the latter to find the difference.\nBased on the above thought, the answer is:\n26" } ], "id_raw": 1176 }, { "id": "1yrjfPschjNul5Dg", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_59_17.png", "conversations": [ { "from": "human", "value": "\nCan you create a table listing the utilization percentages of Telemedicine and Wearables technologies for both genders?" }, { "from": "gpt", "value": "To create the table listing the utilization percentages of Telemedicine and Wearables, I locate {the x-axis tick labels}[0.08, 0.12, 0.152, 0.18] and find two entries labeled as Telemedicine and Wearables. I then note {the line plot for wearables}[0.15, 0.4, 0.85, 0.6] and {the line plot for telemedicine}[0.15, 0.2, 0.85, 0.4], examining the points along these lines corresponding to each gender.\nBased on the above thought, the answer is:\nTechnology, Men, Women\nTelemedicine, 68%, 66%\nWearables, 47%, 50%" } ], "id_raw": 1177 }, { "id": "glzYHlI3Z9mKRWnz", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_59_17.png", "conversations": [ { "from": "human", "value": "\nHow does the trend in Remote Patient Monitoring (RPM) usage change between genders, and what might this indicate about gender preferences?" }, { "from": "gpt", "value": "Analyzing the trend line for RPM, labeled specifically for men, yields {the points along the line for men}[0.65, 0.75] showing a higher and declining progression. Meanwhile, {the points for women}[0.65, 0.7] reflect a milder and increasing tendency in the same category. This indicates that men's usage of RPM is initially high but declines over time, possibly due to saturation or changing interests, while women display a growing interest in RPM services.\nBased on the above thought, the answer is:\nThe RPM usage trend indicates a decline for men, possibly implying saturation or shifting preferences, and an increase for women, suggesting a rising interest or recognition of its benefits." } ], "id_raw": 1177 }, { "id": "gc4S5CWGu3Se6hcg", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_59_17.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in the usage percentage of Telehealth services between men and women?" }, { "from": "gpt", "value": "To find the difference in the usage percentage of Telehealth services between men and women, I examine {the point on the line plot corresponding to men}[0.25, 0.4] and {the point on the line plot corresponding to women}[0.25, 0.35]. I observe that men have a usage percentage annotated near these coordinates for Telehealth.\nBased on the above thought, the answer is:\n3%" } ], "id_raw": 1177 }, { "id": "YhwjnJJGwuGubjnU", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_59_18.png", "conversations": [ { "from": "human", "value": "\nCan you compile the AI Consumption and Human Interaction data into a table format with Scenarios as rows and their respective scenarios values?" }, { "from": "gpt", "value": "To create the table from the chart, I first look at {the x-axis tick labels}[0.061, 0.773, 0.144, 0.898], [0.178, 0.773, 0.259, 0.895], [0.291, 0.773, 0.378, 0.903], [0.397, 0.773, 0.504, 0.934], [0.525, 0.773, 0.607, 0.896] which represent the columns or Scenarios. I then identify {the legend texts}[0.884, 0.101, 0.982, 0.123], [0.884, 0.134, 0.933, 0.156] as the row labels representing AI Consumption and Human Interaction. Then, I extract the values associated with each point on the graph for these scenarios and their respective attributes.\nBased on the above thought, the answer is:\nScenario,AI Consumption,Human Interaction\nHabitual Living,0.6,0.4\nSmart City,0.92,0.85\nPersonalized Services,0.45,0.75\nAutomated Infrastructure,0.65,0.5\nSustainable Living,0.85,0.7" } ], "id_raw": 1178 }, { "id": "8uklJcR6hxX5hklr", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_59_18.png", "conversations": [ { "from": "human", "value": "\nWhy does the Human Interaction trend show a spike in the Smart City scenario based on the graph data?" }, { "from": "gpt", "value": "Observing the line graph for Human Interaction, I notice the significant spike at the Smart City scenario, depicted by {the peak point for Human Interaction in the Smart City scenario}[0.207, 0.010]. This suggests a substantial increase in human interaction within this scenario, possibly due to the implementation of smart technologies that enhance and require human involvement.\nBased on the above thought, the answer is:\nThe spike in the Human Interaction trend during the Smart City scenario suggests a strong emphasis on human-centric designs in technology implementation in urban environments. It indicates an increase in activities that involve human interaction, reflecting the adoption of smart technology solutions aimed at improving life quality and efficiency in densely populated areas." } ], "id_raw": 1178 }, { "id": "dM3RbzeJfkaW4h0c", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_59_18.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in AI Consumption between Habitual Living and Sustainable Living scenarios?" }, { "from": "gpt", "value": "To determine the difference, I locate {the points for Habitual Living and Sustainable Living under AI Consumption}[0.046, 0.403], [0.798, 0.155] to find their values as 0.6 and 0.85, respectively. The difference in AI Consumption between these scenarios is 0.85 - 0.6.\nBased on the above thought, the answer is:\n0.25" } ], "id_raw": 1178 }, { "id": "0u1YptMyyhn2aXN1", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_0.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line chart into a structured table with months as columns and Value Types as rows?" }, { "from": "gpt", "value": "To recreate the line chart data into a table, first I examine {the x-axis tick labels}[0.198, 0.919, 0.792, 0.942] that correspond to the months columns. Then I identify the {legend}[0.684, 0.037, 0.973, 0.113] as the row labels representing different types of values. To fill in the table, I extract the corresponding values from {line points for Tech Company Value}[0.123, 0.180, 0.940, 0.871] and {line points for Innovative Business Value}[0.123, 0.065, 0.940, 0.295].\nBased on the above thought, the answer is:\nMonth,Tech Company Value,Innovative Business Value\n1,100,150\n2,105,155\n3,110,150\n4,115,157\n5,120,160\n6,125,167\n7,130,170\n8,140,150\n9,145,155\n10,150,152\n11,155,158\n12,160,160" } ], "id_raw": 1180 }, { "id": "1URO1nerMObZlxOB", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_0.png", "conversations": [ { "from": "human", "value": "\nCan you describe the growth trend of each value type in the chart and compare them?" }, { "from": "gpt", "value": "To interpret the growth trend, I need to examine the slopes of the line charts for each value type. The {Tech Company Value line}[0.123, 0.180, 0.940, 0.871] exhibits a steady, incremental upward trend across the months, indicating consistent growth.\nThe {Innovative Business Value line}[0.123, 0.065, 0.940, 0.295] shows a steeper initial increase in value, levels off and then accelerates again around the eighth month. This may suggest a more volatile growth pattern. In comparison to the Tech Company Value, the Innovative Business Value starts higher, experiences more fluctuations, but the overall trend is also upward.\nBased on the above thought, the answer is:\nThe Tech Company Value shows a steady incremental growth throughout the year, indicating a relatively stable rate of value increase. On the other hand, the Innovative Business Value shows a more varied trend. It starts with a more significant value, then levels off to a slower growth rate before accelerating again. While both trends show an overall increase in value over time, the Innovative Business Value has more pronounced fluctuations suggesting a higher degree of volatility in its growth rate." } ], "id_raw": 1180 }, { "id": "tz0pM13qfSJZeLfr", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_0.png", "conversations": [ { "from": "human", "value": "\nHow many billions in value did the Innovative Business gain from the first to the sixth month?" }, { "from": "gpt", "value": "To calculate the gain in value, I need to identify {the starting value point at the first month for the Innovative Business}[0.123, 0.295] which corresponds to 150 billion, and {the ending value point at the sixth month for the Innovative Business}[0.495, 0.100] which corresponds to 167 billion. The difference between the two values is therefore 167 - 150 = 17 billion.\nBased on the above thought, the answer is:\n17." } ], "id_raw": 1180 }, { "id": "M7f1LhvFcQlyUZjv", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_1.png", "conversations": [ { "from": "human", "value": "\nCan you transform the line chart data into a structured table with years acting as rows and categories (Training Hours and Job Positions Created) as columns?" }, { "from": "gpt", "value": "Sure, first I identify {the x-axis ticks}[0.116, 0.893, 0.944, 0.893] representing the years. Now I'll locate {the legend texts}[0.126, 0.055, 0.273, 0.125] for 'Training Hours' and 'Job Positions Created' categories. Then, I analyze coordinates of {the line points for Training Hours}[0.116, 0.069, 0.944, 0.527] and {the line points for Job Positions Created}[0.116, 0.592, 0.944, 0.854]. Each point on these lines corresponds to the number of training hours and job positions created respectively for each year.\nBased on the above thought, the answer is:\nYear,Training_Hours,Job_Positions_Created\n2008,1000,500\n2009,1200,600\n2010,1400,700\n2011,1600,800\n2012,1500,750\n2013,1300,700\n2014,1400,700\n2015,1500,800\n2016,1600,850\n2017,1450,780\n2018,1530,820\n2019,1650,860\n2020,1700,900" } ], "id_raw": 1181 }, { "id": "1xPAvydvPEzLLYr5", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_1.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you identify any noticeable trends in Training Hours and Job Positions Created over the years?" }, { "from": "gpt", "value": "To determine any notable trends, I need to trace the change in line plots over time. Looking at {the line for Training Hours}[0.116, 0.069, 0.944, 0.527], I see an overall increase in training hours from 2008 to 2012, then a slight dip in 2013, another peak in 2016, another dip in 2017, and then a gradual increase through to 2020. This suggests a recurring pattern of increase and decrease in training hours, with a net increase over the years.\nLooking at {the line for Job Positions Created}[0.116, 0.592, 0.944, 0.854], I observe a steady increase from 2008 to 2011, then a mild dip till 2013, then somewhat stable at 700 through 2014, and a steady increase again from 2015 to 2020. This indicates a positive trend for job positions over these years despite some fluctuations.\nBased on the above thought, the answer is:\nThe line chart shows an overall increasing trend in both Training Hours and Job Positions Created from 2008 to 2020, albeit with some fluctuations. The Training Hours show a repeating pattern of increase then decrease, suggesting cyclic changes in training needs or resource allocation. Job Positions Created, on the other hand, exhibits a more steady growth with slight stability during 2014 and starts to increase again onwards, indicating an overall positive expansion of job opportunities over the years." } ], "id_raw": 1181 }, { "id": "eWdOJ67VzJ7j4eY7", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_1.png", "conversations": [ { "from": "human", "value": "\nHow much did the Training Hours increase from 2008 to 2020?" }, { "from": "gpt", "value": "To find the increase in Training Hours from 2008 to 2020, I need to locate the {line points for Training Hours}[0.116, 0.069, 0.944, 0.527] and get the values for both years. The value for 2008 is 1000 and for 2020 it is 1700. The difference between the two is therefore 1700 - 1000 = 700.\nBased on the above thought, the answer is:\n700." } ], "id_raw": 1181 }, { "id": "EASi18ymDOWNllCh", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_3.png", "conversations": [ { "from": "human", "value": "\nCould you turn the data in the chart into a CSV table where the years are columns and the indexes are rows?" }, { "from": "gpt", "value": "Sure, first I'll identify the columns of the data table, which are the years that can be seen on {the chart's x-axis tick labels}[0.206, 0.869, 0.276, 0.914]. The rows correspond to the two types of indexes shown in the chart: {Quality of Life Index}[0.204, 0.086, 0.437, 0.121] and {House Size Index}[0.204, 0.139, 0.402, 0.174]. Their respective data points can be found by following their lines on the chart [0.241, 0.524, 0.853, 0.685] and [0.241, 0.135, 0.853, 0.398].\nBased on the above thought, the answer is:\n,2000,2005,2010,2015,2020,2025,2030,2035,2040,2045,2050\nQuality of Life Index,70,72,75,77,80,85,87,90,92,95,98\nHouse Size Index,120,125,130,136,141,146,150,154,158,162,166" } ], "id_raw": 1183 }, { "id": "W4CgJbhtKYIbROse", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_3.png", "conversations": [ { "from": "human", "value": "\nWhich index, Quality of Life or House Size, has seen a greater rate of change from 2000 to 2050, based on the data in the chart?" }, { "from": "gpt", "value": "To answer this question, I need to calculate the change in each index from 2000 to 2050. Looking at {the data points for the Quality of Life Index}[0.241, 0.685, 0.853, 0.524], in 2000 the value was 70 and in 2050 the value was 98, for a change of 28 points. For {the House Size Index}[0.241, 0.398, 0.853, 0.135], the value in 2000 was 120 and in 2050 was 166, for a change of 46 points. Thus, the House Size Index has seen a larger rate of change.\nBased on the above thought, the answer is:\nThe House Size Index has seen a greater rate of change from 2000 to 2050. It increased by 46 points, compared to a 28-point increase in the Quality of Life Index." } ], "id_raw": 1183 }, { "id": "Db9Y4QdGLz6WSGah", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_3.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in the House Size Index between 2000 and 2050?" }, { "from": "gpt", "value": "To find the difference in the House Size Index between 2000 and 2050, I need to examine {the data points for House Size Index}[0.241, 0.398, 0.853, 0.135]. Specifically, I'll compare {the 2000 point}[0.241, 0.398] (120) and {the 2050 point}[0.853, 0.135](166). The difference is then 166 - 120 = 46.\nBased on the above thought, the answer is:\n46." } ], "id_raw": 1183 }, { "id": "UiZmZg0YOSvbT1Dx", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_4.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, can you convert the data into a table format where the columns represent the Startup Phases and the rows represent the different type of Ratings?" }, { "from": "gpt", "value": "To create a table from the line chart, I will start by identifying {the x-axis labels}[[0.197, 0.869, 0.297, 0.914], [0.303, 0.869, 0.482, 0.914], [0.404, 0.869, 0.672, 0.914], [0.612, 0.869, 0.755, 0.914], [0.781, 0.869, 0.878, 0.914]] which stand for the Startup Phases. I will then identify {the legend}[[0.752, 0.738, 0.913, 0.773], [0.752, 0.790, 0.954, 0.825]], as the row labels representing the Given Ratings and Expected Ratings. To populate the values, I trace the {points for Given Ratings}[[0.247, 0.189], [0.392, 0.144], [0.538, 0.278], [0.684, 0.100], [0.829, 0.233]] and {points for Expected Ratings}[[0.247, 0.233], [0.392, 0.189], [0.538, 0.233], [0.684, 0.144], [0.829, 0.278]] for every phase and then map these coordinates to the corresponding numeric values on the y-axis.\nBased on the above thought, the answer is:\nRatings,Launch,Early Growth,Established Growth,Expansion,Mature\nGiven Ratings,75,80,65,85,70\nExpected Ratings,70,75,70,80,65" } ], "id_raw": 1184 }, { "id": "cnZiHJX3DhvfCEix", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_4.png", "conversations": [ { "from": "human", "value": "\nBased on trends in the line chart, can you analyze the correlation between the Given Ratings and Expected Ratings?" }, { "from": "gpt", "value": "To assess the correlation, I must scrutinize the line plots for both {Given Ratings line}[[0.247, 0.189], [0.392, 0.144], [0.538, 0.278], [0.684, 0.100], [0.829, 0.233]] and {Expected Ratings line}[[0.247, 0.233], [0.392, 0.189], [0.538, 0.233], [0.684, 0.144], [0.829, 0.278]]. From an initial glance, a correlation seems to present - when the Given Ratings increase, the Expected Ratings also tend to increase, and when the Given Ratings decrease, the Expected Ratings also exhibit a decline.\nBased on the above thought, the answer is:\nThere appears to be a positive correlation between the Given Ratings and the Expected Ratings. This trends suggests that higher Given Ratings correspond to higher Expected Ratings, and conversely, lower Given Ratings associate with lower Expected Ratings. It indicates that the expectations are following very closely with the given ratings in each phase of the startup." } ], "id_raw": 1184 }, { "id": "h8zosiBfhY3oSRsy", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_4.png", "conversations": [ { "from": "human", "value": "\nHow much is the average innovation score across all startup phases for both Given Ratings and Expected Ratings?" }, { "from": "gpt", "value": "To find the average innovation score, we would locate the {points on Given Ratings line}[[0.247, 0.189], [0.392, 0.144], [0.538, 0.278], [0.684, 0.100], [0.829, 0.233]] and extract the corresponding values which are 75, 80, 65, 85, 70. The average can be calculated as (75+80+65+85+70)/5 = 75. The same needs to be done for {points on Expected Ratings line}[[0.247, 0.233], [0.392, 0.189], [0.538, 0.233], [0.684, 0.144], [0.829, 0.278]] which are 70, 75, 70, 80, 65. The average in this case will be (70+75+70+80+65)/5 = 72.\nBased on the above thought, the answer is:\nFor Given Ratings, the average is 75 and for Expected Ratings, the average is 72." } ], "id_raw": 1184 }, { "id": "zxWuLcSdgzpDLLgQ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_5.png", "conversations": [ { "from": "human", "value": "\nConsidering the line graphs on the chart representing each workout trend, could you please generate a table where rows indicate the specific workout activity and columns represent each season of the year?" }, { "from": "gpt", "value": "To commence the process of creating this table from the line graphs, I initially locate {the x-axis tick labels}[ [0.046, 0.919, 0.068, 0.942], [0.128, 0.919, 0.152, 0.942], [0.210, 0.919, 0.237, 0.942], [0.294, 0.919, 0.319, 0.942]]. Here, the labels represent each season: Spring, Summer, Autumn, and Winter.\nNext, I find {the legend texts}[[0.108, 0.046, 0.226, 0.069], [0.108, 0.081, 0.298, 0.104], [0.108, 0.116, 0.288, 0.139]], which are used as row labels for Singer Yoga, Zumba Classes, HIIT Sessions, Pilates Workouts and Morning Runs. \nIn order to populate the table, I identify specific {line points} such as [ [0.057, 0.126], [0.140, 0.379], [0.223, 0.379], [0.306, 0.468]], to extract the respective values associated with each form of workout for each season across the 4 line charts.\nBased on the above thought, the answer is:\nWorkout, Spring, Summer, Autumn, Winter\nSinger Yoga, 10, 20, 15, 10\nZumba Classes, 15, 18, 16, 12\nHIIT Sessions, 18, 30, 25, 15\nPilates Workouts, 10, 15, 30, 20\nMorning Runs, 20, 30, 24, 10" } ], "id_raw": 1185 }, { "id": "yp5z5WFQ8CzlzDPN", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_5.png", "conversations": [ { "from": "human", "value": "\nAccording to the line charts, which workout activity demonstrates a trend that deviates from the general pattern?" }, { "from": "gpt", "value": "The general pattern in the data indicates that workout interests peak in the summer season and decrease during the winter season. To identify deviations from this pattern, I examine each workout trend line. The activity that shows a significant spike in autumn, deviating from the typical pattern, is {Pilates Workouts}[0.390, 0.443, 0.722, 0.215]. While other workout routines decrease in popularity as it gets colder, Pilates Workouts peak during autumn, which is unusual and contradicts the common trend.\nBased on the above thought, the answer is:\nThe trend of Pilates Workouts deviates from the general pattern according to the line charts. While other workout routines follow a trend of peaking in the summer season and declining during the winter season, Pilates Workouts peaks in popularity during the autumn season, defying the common pattern." } ], "id_raw": 1185 }, { "id": "GIMkmrcVgqy1PeNg", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_5.png", "conversations": [ { "from": "human", "value": "\nWhat is the overall increase in the trend of Pilates Workouts from Spring to Winter?" }, { "from": "gpt", "value": "To calculate the overall increase in the trend of Pilates workouts, I must identify the values of Pilates workouts at Spring and Winter. Looking at the {line points for Pilates Workout}[ [0.057, 0.379], [0.972, 0.379]], I extract the values at Spring and Winter: 10 and 20 respectively. The overall increase is calculated by subtraction, which is 20 - 10 = 10.\nBased on the above thought, the answer is:\n10" } ], "id_raw": 1185 }, { "id": "r2El7dTaFbffHzyt", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_6.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data from the line chart into a structured table format?" }, { "from": "gpt", "value": "To convert the data from the line chart into a structured table format, I need to first identify {the x-axis tick labels}[[0.117, 0.919, 0.152, 0.942], [0.194, 0.919, 0.229, 0.942],..., [0.890, 0.919, 0.925, 0.942]], which will serve as the row labels or 'Years'. I then look at {the legend texts}[[0.759, 0.046, 0.973, 0.069],[0.759, 0.081, 0.791, 0.104]], which represent the column headers 'High Intensity Interval Training' and 'Yoga'. To populate the values, I locate {the line points for High Intensity Interval Training}[[0.134, 0.701], [0.212, 0.687],..., [0.908, 0.533]] and {the line points for Yoga}[[0.134, 0.323], [0.212, 0.309],..., [0.908, 0.057]] at each respective year and extract the values annotated.\nBased on the above thought, the answer is:\nYear,High Intensity Interval Training,Yoga\n2000,15,42\n2002,16,43\n2004,18,47\n2006,20,51\n2008,22,55\n2010,23,56\n2012,21,53\n2014,20,52\n2016,24,58\n2018,26,60\n2020,27,61" } ], "id_raw": 1186 }, { "id": "4NQWcN8qgzdAL6p9", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_6.png", "conversations": [ { "from": "human", "value": "\nBased on the trends shown in the line chart, which exercise trend has seen a consistent increase over the years?" }, { "from": "gpt", "value": "By looking at the line plots of both exercise trends, it can be noted that while {High Intensity Interval Training line}[0.134, 0.533, 0.908, 0.701] shows fluctuation over the years, {the line for Yoga}[0.134, 0.057, 0.908, 0.323] shows a steady increase from the year 2000 to the year 2020 without any decline. This is an important comparison, thus indicating that the exercise trend that has seen a consistent increase over the years is Yoga.\nBased on the above thought, the answer is:\nThe exercise trend that has seen a consistent increase over the years according to the line chart is Yoga." } ], "id_raw": 1186 }, { "id": "WhIBVuHjRd7zQX6I", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_6.png", "conversations": [ { "from": "human", "value": "\nWhat is the percentage increase in people doing High Intensity Interval Training from 2000 to 2020?" }, { "from": "gpt", "value": "To find the percentage increase of people doing High Intensity Interval Training from 2000 to 2020, I need to look at {the value for High Intensity Interval Training in 2000} at [0.134, 0.701] and {the value for High Intensity Interval Training in 2020} at [0.908, 0.533], indicated as 15 and 27 respectively. The percentage increase is then calculated by subtracting the initial value from the final value, dividing by the initial value, and then multiplying by 100. So, (27 - 15) / 15 * 100 = 80.\nBased on the above thought, the answer is:\n80%." } ], "id_raw": 1186 }, { "id": "Mz8d85mz6iz0Sdvp", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_7.png", "conversations": [ { "from": "human", "value": "\nCan you generate a table that shows the Hospital Admission Rate and Discharge Rate for each Age Group?" }, { "from": "gpt", "value": "I see that the {x-axis tick labels}[0.102, 0.879, 0.168, 0.914],[0.216, 0.879, 0.283, 0.914],[0.330, 0.879, 0.397, 0.914],[0.445, 0.879, 0.511, 0.914],[0.559, 0.879, 0.626, 0.914],[0.673, 0.879, 0.740, 0.914],[0.787, 0.879, 0.854, 0.914],[0.911, 0.879, 0.959, 0.914] represent the Age Groups. The data on Hospital Admission Rate and Discharge Rate can be found on the plotted {lines for Hospital Admission Rate and Discharge Rate}[0.135, 0.075, 0.935, 0.829],[0.135, 0.183, 0.935, 0.613].\nBased on the above thought, the answer is:\n\"Age Group\", \"Hospital Admission Rate\", \"Discharge Rate\"\n\"20-30\", 80, 70\n\"30-40\", 90, 86\n\"40-50\", 70, 85\n\"50-60\", 70, 75\n\"60-70\", 60, 80\n\"70-80\", 75, 74\n\"80-90\", 85, 88\n\"90+\", 95, 90" } ], "id_raw": 1187 }, { "id": "J6RTFIqUJbNItXGY", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_7.png", "conversations": [ { "from": "human", "value": "\nBased on the charts, do Hospital Admission Rates generally match the Discharge Rates across Age Groups?" }, { "from": "gpt", "value": "To answer this, I need to compare the {line for Hospital Admission Rate}[0.135, 0.075, 0.935, 0.829] and the {line for Discharge Rate}[0.135, 0.183, 0.935, 0.613] across all Age Groups represented by {x-tick labels}[0.102, 0.879, 0.168, 0.914],[0.216, 0.879, 0.283, 0.914],[0.330, 0.879, 0.397, 0.914],[0.445, 0.879, 0.511, 0.914],[0.559, 0.879, 0.626, 0.914],[0.673, 0.879, 0.740, 0.914],[0.787, 0.879, 0.854, 0.914],[0.911, 0.879, 0.959, 0.914]. It's noticeable that for some Age Groups the Admission and Discharge Rates are similar, while for others they differ significantly.\nBased on the above thought, the answer is:\nIt seems that Hospital Admission Rates and Discharge Rates do not generally match across Age Groups. For Age Group 20-30, the Admission Rate is higher than the Discharge Rate, while for Age Group 30-40, the pattern is reversed. This trend is alternating in different Age Groups, suggesting a fluctuation rather than a consistent match between Admission and Discharge Rates." } ], "id_raw": 1187 }, { "id": "D12AgpzsDNpyIctX", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_7.png", "conversations": [ { "from": "human", "value": "\nWhat is the Discharge Rate for the 50-60 Age Group?" }, { "from": "gpt", "value": "To find the Discharge Rate for the 50-60 Age Group, I need to look at the {Discharge Rate line}[0.135, 0.183, 0.935, 0.613] and locate the point corresponding to the Age Group 50-60, which is one of the {x-tick labels}[0.445, 0.879, 0.511, 0.914].\nBased on the above thought, the answer is:\n75" } ], "id_raw": 1187 }, { "id": "ZGMjkIFt9T1t4tGi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_8.png", "conversations": [ { "from": "human", "value": "\nCould you please provide the underlying data for the line chart in a tabular form where the columns correspond to 'Time Interval' and the rows represent 'Temperature Avg' and 'Sales Avg'?" }, { "from": "gpt", "value": "To organize the provided data, I'll consider {the x-axis tick labels}[[0.267, 0.869, 0.285, 0.914], [0.337, 0.869, 0.355, 0.914], [0.398, 0.869, 0.433, 0.914], [0.468, 0.869, 0.503, 0.914], [0.538, 0.869, 0.573, 0.914], [0.608, 0.869, 0.643, 0.914], [0.678, 0.869, 0.713, 0.914], [0.748, 0.869, 0.783, 0.914], [0.818, 0.869, 0.853, 0.914]] as defining the time intervals, which would be the columns of the table. Then, looking at the points on the 'Temperature Avg' line {line for Temperature Avg}[[0.276, 0.798, 0.835, 0.819]] and 'Sales Avg' line {line for Sales Avg}[[0.276, 0.075, 0.835, 0.691]], I'll extract the y-values. These y-values, corresponding to each time interval, will form the rows of the table.\nBased on the above thought, the answer is:\nTime_Interval, Temperature_Avg, Sales_Avg\n0, 10, 250\n5, 15, 350\n10, 20, 450\n15, 25, 600\n20, 30, 700\n25, 40, 1200\n30, 35, 800\n35, 45, 1300\n40, 50, 1400" } ], "id_raw": 1188 }, { "id": "rmjW4TwYwTgrnpmk", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_8.png", "conversations": [ { "from": "human", "value": "\nBased on the line plots in the chart, are there any time intervals where the temperature and sales averages increase simultaneously? Explain your reasoning." }, { "from": "gpt", "value": "To answer this, I need to analyze {the Temperature Avg line}[[0.276, 0.798, 0.835, 0.819]] and {the Sales Avg line}[[0.276, 0.075, 0.835, 0.691]]. On observing, it's clear that from the time intervals of 0 to 20, both the temperature and sales averages display an almost parallel upward trend. Therefore, these are the time intervals where both averages increase simultaneously.\nBased on the above thought, the answer is:\nYes, based on the line plots, there is an increase in both averages (temperature and sales) from the time intervals of 0 to 20. The lines trend upwards in a nearly parallel fashion, indicating that both the temperature and sales averages rise together during this period." } ], "id_raw": 1188 }, { "id": "UUfNo4C50ypHtghN", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_8.png", "conversations": [ { "from": "human", "value": "\nWhat is the highest Sales Avg achieved and at which time interval did it occur according to the line chart?" }, { "from": "gpt", "value": "To find the highest Sales Avg, I need to locate the line points of {the Sales Avg line}[[0.276, 0.075, 0.835, 0.691]] in order to identify the point with the highest y-value. Extracting the y-values from these points, I find that the value is highest (at 1400) for the point corresponding to {the time interval of 40} [0.818, 0.869, 0.853, 0.914].\nBased on the above thought, the answer is:\nThe Highest Sales Avg achieved was 1400 at the time interval of 40 months." } ], "id_raw": 1188 }, { "id": "uNNvDhnboRjZaCmp", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_9.png", "conversations": [ { "from": "human", "value": "\nCan you organize the information represented in the line graph about the estimated average annual household income into a structured table?" }, { "from": "gpt", "value": "Firstly, I identify {the x-axis labels}[[0.167, 0.869, 0.184, 0.914], [0.224, 0.869, 0.242, 0.914], [0.281, 0.869, 0.299, 0.914], [0.338, 0.869, 0.356, 0.914], [0.395, 0.869, 0.413, 0.914], [0.452, 0.869, 0.470, 0.914], [0.510, 0.869, 0.527, 0.914], [0.567, 0.869, 0.584, 0.914], [0.624, 0.869, 0.641, 0.914], [0.681, 0.869, 0.698, 0.914], [0.729, 0.869, 0.764, 0.914], [0.786, 0.869, 0.821, 0.914], [0.843, 0.869, 0.879, 0.914], [0.900, 0.869, 0.936, 0.914]] which represent the age groups. The single line in the line chart represents the estimated average annual household income and hence to populate the values, I look at each of {the line's points}[[0.176, 0.095], [0.233, 0.827], [0.290, 0.722], [0.347, 0.317], [0.404, 0.445], [0.461, 0.383], [0.518, 0.275], [0.575, 0.529], [0.632, 0.318], [0.690, 0.095], [0.747, 0.262], [0.804, 0.252], [0.861, 0.304]].\nBased on the above thought, the answer is:\nAge Group, Estimated Average Annual Household Income ($)\nUnder 5, 16595\n5-9, 16703\n10-14, 15768\n15-19, 16504\n20-24, 16130\n25-34, 16342\n35-44, 16202\n45-54, 16859\n55-64, 16542\n65 and older, 16241" } ], "id_raw": 1189 }, { "id": "PW6CTnpmYEpAb8R1", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_9.png", "conversations": [ { "from": "human", "value": "\nFrom the data represented in the line graph, which age group is observed to have the greatest increase in estimated average annual household income compared to the previous age group and why might that be the case?" }, { "from": "gpt", "value": "To find the age group with the greatest increase, I need to look at each point from {the line}[[0.176, 0.095, 0.861, 0.304]], compare it with the previous point, and identify where the largest increase occurs. Here, the largest increase is from '35-44' with a value of 16202 at {the point for '35-44'}[0.518, 0.275], to '45-54' with a value of 16859 at {the point for '45-54'}[0.575, 0.529].The probable cause of such an increase in income could be increased work experience and career growth, as this period typically represents a person's peak earning years.\nBased on the above thought, the answer is:\nThe largest increase in estimated average annual household income is observed in the age group '45-54' compared to the previous age group '35-44'. This might be attributed to the fact that individuals within the '45-54' group are typically at the peak of their careers with an increase in their experience and professional skills; hence, being rewarded with higher salaries or income." } ], "id_raw": 1189 }, { "id": "BwZSr3ukhra01eMl", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_9.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in estimated average household income between the age groups 'Under 5' and '65 and older'?" }, { "from": "gpt", "value": "To find the income difference between the 'Under 5' and '65 and older' age groups, I need to extract their respective estimated average household incomes. The point corresponding to 'Under 5' can be located at {the point for 'Under 5'}[0.176, 0.095] and its value is 16595. The point corresponding to '65 and older' can be located at {the point for '65 and older'}[0.861, 0.304] and its value is 16241. The difference between these two incomes is 16595 - 16241 = 354.\nBased on the above thought, the answer is:\n354" } ], "id_raw": 1189 }, { "id": "R9HXHtMYacPN8ddi", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_10.png", "conversations": [ { "from": "human", "value": "\nFrom the line chart, please extract the impact values for each educational field into a table?" }, { "from": "gpt", "value": "To extract data from the line chart to a table, first I identify the {x-axis labels}[{'label': 'AI', 'label_coordinates': '[0.165, 0.869, 0.202, 0.914]'}, {'label': 'Data Science', 'label_coordinates': '[0.251, 0.869, 0.316, 0.914]'}, {'label': 'Cloud Computing', 'label_coordinates': '[0.344, 0.869, 0.445, 0.914]'}, {'label': 'Cyber Security', 'label_coordinates': '[0.439, 0.869, 0.553, 0.914]'}, {'label': 'Robotics', 'label_coordinates': '[0.533, 0.869, 0.594, 0.914]'}, {'label': 'Quantum Computing', 'label_coordinates': '[0.628, 0.869, 0.753, 0.914]'}, {'label': 'Natural Language Processing', 'label_coordinates': '[0.817, 0.869, 0.983, 0.914]'}] which represents the fields of research. Next, Locate the line points [0.179, 0.410, 0.798, 0.632], [0.179, 0.510, 0.798, 0.662], and so on each representing a value of the impact level for that particular field.\nBased on the above thought, the answer is:\nFields, Impact\nArtificial Intelligence, 0.90\nData Science, 0.80\nCloud Computing, 0.70\nCyber Security, 0.55\nRobotics, 0.85\nQuantum Computing, 0.45\nNatural Language Processing, 0.35" } ], "id_raw": 1190 }, { "id": "y6CgUiqZWA8wqfBK", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_10.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, which field of study seems to be most impacted in the tech industry and why might that be?" }, { "from": "gpt", "value": "To answer this question, I need to look at the {line points}[0.179, 0.410, 0.798, 0.632] for each field and find the one with the highest impact value. {AI}[0.179, 0.410] seems to have the highest value indicating the highest impact. It is important to note that AI has been increasingly incorporated into many aspects of technology, from predictive analytics to automation, which could explain its high impact level.\nBased on the above thought, the answer is:\nAccording to the chart, the field of Artificial Intelligence seems to have the highest impact in the tech industry. This could be due to the increasing utilization of AI in various aspects of technology, including but not limited to, predictive analytics, automation, and machine learning which are critical components in today's technology-driven industries." } ], "id_raw": 1190 }, { "id": "ZAH8YrdMwgjXnC9o", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_10.png", "conversations": [ { "from": "human", "value": "\nWhat is the difference in impact value between the highest and lowest performing fields?" }, { "from": "gpt", "value": "To find the differences between the highest and lowest impact, I first identify the {line point for AI}[0.179, 0.410], which gives the highest value of impact, and the {line point for Natural Language Processing}[0.798, 0.632] which gives the lowest value of impact. The difference is therefore 0.90 - 0.35.\nBased on the above thought, the answer is:\n0.55" } ], "id_raw": 1190 }, { "id": "IXguj1IdvdJusbvs", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, can you create a table of the Number of Social Connections for Friends and Followers, by Year?" }, { "from": "gpt", "value": "To transform the data from the line chart into table format, I start by recognizing {the x-axis tick labels}[[0.083, 0.869, 0.154, 0.914], [0.190, 0.869, 0.261, 0.914], ..., [0.868, 0.856]] as the years. Next, I identify {the legend texts}[[0.756, 0.685, 0.839, 0.720], [0.756, 0.738, 0.865, 0.773]] as the categories representing Friends and Followers. For each category, I locate {the line points}[[0.118, 0.645], [0.225, 0.574], ..., [0.975, 0.292]], [[0.118, 0.461], [0.225, 0.363], ..., [0.975, 0.066]] corresponding to the respective years and extract the values.\nBased on the above thought, the answer is:\nYear,Friends,Followers\n2012,150,280\n2013,200,350\n2014,230,400\n2015,250,410\n2016,240,420\n2017,259,445\n2018,300,505\n2019,330,515\n2020,400,560" } ], "id_raw": 1192 }, { "id": "7UpNDgIBa41X7sVD", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_12.png", "conversations": [ { "from": "human", "value": "\nBased on the chart, who saw a more significant increase in social connections over the years? How can you tell?" }, { "from": "gpt", "value": "To establish who has seen a more significant increase in social connections over the years, I need to compare {the line for Friends}[0.118, 0.292, 0.975, 0.645] with {the line for Followers}[0.118, 0.066, 0.975, 0.461]. Friends had an increase from 150 to 400 connections. Followers also had an increase, but from a higher starting point of 280 to a higher end point of 560. Therefore, considering the start and end points, Followers saw a greater increase in social connections over the analyzed period.\nBased on the above thought, the answer is:\nIt is the Followers category that has seen a more significant increase in social connections over the years. From 2012 to 2020, the number of Followers has considerably grown from 280 to 560, a rise substantially higher than that of the Friends category, as seen from the chart." } ], "id_raw": 1192 }, { "id": "eE3jtmboqDFkzf9a", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_12.png", "conversations": [ { "from": "human", "value": "\nWhat is the period with the highest increase in the number of Friends?" }, { "from": "gpt", "value": "To determine the period with the highest increase in the number of Friends, I analyze {the line points for Friends}[[0.118, 0.645], [0.225, 0.574], ..., [0.975, 0.292]] and the corresponding values: 150, 200, 230, 250, 240, 259, 300, 330, 400. Here, the increase from 2019 to 2020 from 330 to 400 represents the most significant jump, looking at the data. So, this is the period with the highest increase in number of friends.\nBased on the above thought, the answer is:\n2019 to 2020." } ], "id_raw": 1192 }, { "id": "jDjE35SBcqBF033N", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_13.png", "conversations": [ { "from": "human", "value": "\nCan you extract all the depicted data from the chart and present it in a structured table format, with 'Trends' as the rows and 'Financial Management Aspects' as the columns?" }, { "from": "gpt", "value": "To produce the tabular data, I first identify {the x-axis tick labels}[0.200, 0.869, 0.271, 0.914], [0.283, 0.869, 0.353, 0.914], [0.365, 0.869, 0.435, 0.914], [0.447, 0.869, 0.517, 0.914], [0.529, 0.869, 0.600, 0.914], [0.611, 0.869, 0.682, 0.914], [0.693, 0.869, 0.764, 0.914], [0.776, 0.869, 0.846, 0.914]], demonstrating the column labels or years. Then, I observe {the legend texts}[0.239, 0.069, 0.365, 0.104], [0.239, 0.121, 0.352, 0.156], [0.239, 0.173, 0.495, 0.208] as the row labels naming the different aspects of Financial Management. To fill out the table, I locate the {bar values}[[0.318, 0.732], [0.400, 0.586], [0.482, 0.478], [0.564, 0.316], [0.646, 0.262], [0.729, 0.208], [0.811, 0.100], [0.893, 0.073]] and [0.318, 0.759], [0.400, 0.711], [0.482, 0.640], [0.564, 0.532], [0.646, 0.451], [0.729, 0.424], [0.811, 0.397], [0.893, 0.341]], for Blockchain and Ethereum respectively, from different years and extract the growth values.\nBased on the above thought, the answer is:\nYear, Blockchain, Ethereum\n2013, 1500, 1200\n2014, 2300, 1800\n2015, 5000, 2700\n2016, 7000, 4000\n2017, 10000, 6000\n2018, 11000, 7500\n2019, 12000, 8000\n2020, 14000, 8500\n2021, 14500, 9550" } ], "id_raw": 1193 }, { "id": "Pl5ZLVrfMQDRfZ7I", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_13.png", "conversations": [ { "from": "human", "value": "\nBy analyzing the line chart, which of the cryptocurrencies, Blockchain or Ethereum, demonstrates a steady and consistent growth trend?" }, { "from": "gpt", "value": "Evaluating the steadiness and consistency of growth for Blockchain and Ethereum involves examining {the line for Blockchain}[0.236, 0.773, 0.893, 0.073] and {the line for Ethereum}[0.236, 0.792, 0.893, 0.341]. Looking at Blockchain, despite some fluctuations, the line generally displays upward motion indicative of a consistent rising trend. Ethereum's growth line, on the other hand, though also showing increments over years, has experienced bumps and troughs and is less steady in comparison.\nBased on the above thought, the answer is:\nBy analyzing the line chart, it can be deduced that Blockchain demonstrates a more steady and consistent growth trend when compared to Ethereum. The line representing the growth of Blockchain depicts a more consistent upward trajectory with fewer fluctuations, validating steady growth. On the contrary, while Ethereum also shows growth, the line progression has more noticeable bumps and troughs, indicating less steadiness over the years." } ], "id_raw": 1193 }, { "id": "aGNdRowp4oKQjzEE", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_13.png", "conversations": [ { "from": "human", "value": "\nCould you compute the yearly average growth numbers for both Blockchain and Ethereum and determine which one has the higher average growth from 2013 to 2021 inclusive?" }, { "from": "gpt", "value": "To calculate the average growth for Blockchain and Ethereum, I have to find {the points on the Blockchain line}[0.236, 0.773, 0.893, 0.073] and {the points on the Ethereum line}[0.236, 0.792, 0.893, 0.341], which represent the yearly growth values for each. For Blockchain, the yearly growths are 1500, 2300, 5000, 7000, 10000, 11000, 12000, 14000, 14500. For Ethereum, the yearly growths are 1200, 1800, 2700, 4000, 6000, 7500, 8000, 8500, 9550. I calculate the mean of these yearly growth numbers for Blockchain and Ethereum to arrive at the averages.\nBased on the above thought, the answer is:\nBlockchain." } ], "id_raw": 1193 }, { "id": "YLbjzCv4I4nYpRmR", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_14.png", "conversations": [ { "from": "human", "value": "\nCan you organize the data from the chart explaining Preservation/Integration Levels of Heritage Sites, Ancient Architecture and World Cultures over time, into a table?" }, { "from": "gpt", "value": "In translating the line graph into a tabular format, I should first recognize the entities whose trends are shown. These are {Heritage Sites}[0.718, 0.685, 0.881, 0.720], {Ancient Architecture}[0.718, 0.738, 0.954, 0.773] and {World Cultures}[0.718, 0.790, 0.888, 0.825], which will function as row labels. The column labels 'Start' and 'End' are extracted from {the x-axis tick labels}[0.107, 0.869, 0.174, 0.914] and [0.909, 0.869, 0.961, 0.914]. The values for each entity at the given time points are located at {the line points} [0.140, 0.283] and [0.935, 0.660] for Heritage Sites, [0.140, 0.169] and [0.935, 0.234] for Ancient Architecture, and [0.140, 0.578] and [0.935, 0.791] for World Cultures. Their values are respectively 85 to 62 for Heritage Sites, 92 to 88 for Ancient Architecture, and 67 to 54 for World Cultures.\nBased on the above thought, the answer is:\nCategory,Start,End\nHeritage Sites,85,62\nAncient Architecture,92,88\nWorld Cultures,67,54" } ], "id_raw": 1194 }, { "id": "yqmoXpaR6vsuY4Ne", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_14.png", "conversations": [ { "from": "human", "value": "\nAccording to the chart, which category's Preservation/Integration Level has dropped the most during the assessed time period?" }, { "from": "gpt", "value": "To answer this question, I have to compare the difference in Preservation/Integration Levels from start to end for all categories - {Heritage Sites}[0.718, 0.685, 0.881, 0.720], {Ancient Architecture}[0.718, 0.738, 0.954, 0.773] and {World Cultures}[0.718, 0.790, 0.888, 0.825]. The levels for Heritage Sites decreased from 85 to 62 (a drop of 23 units), the levels for Ancient Architecture decreased from 92 to 88 (a drop of 4 units), and the levels for World Cultures decreased from 67 to 54(a drop of 13 units). Comparing these drops, it's clear that the drop for Heritage Sites is the greatest.\nBased on the above thought, the answer is:\nThe category whose Preservation/Integration Level has dropped the most during the assessed time period is Heritage Sites. Its Preservation/Integration Level fell by 23, which is the highest decrease amongst the three categories." } ], "id_raw": 1194 }, { "id": "BHWzWkGMDJusvpON", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_14.png", "conversations": [ { "from": "human", "value": "\nHow much has the Preservation/Integration Level for Heritage Sites decreased from the start to the end of the timeframe?" }, { "from": "gpt", "value": "To compute the decrease in Preservation/Integration Level of {Heritage Sites}[0.718, 0.685, 0.881, 0.720] from the start to the end of the timeframe, I examine the {line points for Heritage Sites} [0.140, 0.283] and [0.935, 0.660]. The level was 85 at the start and it decreased to 62 by the end. Therefore, the decrease in level is calculated as 85 minus 62.\nBased on the above thought, the answer is:\n23." } ], "id_raw": 1194 }, { "id": "nffMqoOuXkbXd1fO", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_15.png", "conversations": [ { "from": "human", "value": "\nCan you extract the data points of 'Product A' and 'Product B' across different quarters into a structured table format?" }, { "from": "gpt", "value": "To get the structured table from the line chart, I first identify the {x-axis tick labels}[[0.108, 0.861, 0.157, 0.942], [0.182, 0.861, 0.231, 0.942], [0.256, 0.861, 0.305, 0.942], [0.330, 0.861, 0.378, 0.942], [0.404, 0.861, 0.452, 0.942], [0.477, 0.861, 0.526, 0.942], [0.551, 0.861, 0.600, 0.942], [0.625, 0.861, 0.674, 0.942], [0.699, 0.861, 0.747, 0.942], [0.773, 0.861, 0.821, 0.942], [0.846, 0.861, 0.895, 0.942], [0.920, 0.861, 0.969, 0.942]] which represent the different quarters (Time Period). I then locate the points of {line for Product A}[0.133, 0.413, 0.944, 0.765] and {line for Product B}[0.133, 0.060, 0.944, 0.559] to extract the corresponding Sales (in thousands) for each quarter for 'Product A' and 'Product B' respectively.\nBased on the above thought, the answer is:\nPeriod,Product A,Product B\n2012Q1,12000,11000\n2012Q2,11800,11500\n2012Q3,11600,11000\n2012Q4,11300,11500\n2013Q1,11000,12000\n2013Q2,10800,12500\n2013Q3,10650,12800\n2013Q4,10400,13000\n2014Q1,10300,13500\n2014Q2,10000,14000\n2014Q3,9700,14200\n2014Q4,9600,14400" } ], "id_raw": 1195 }, { "id": "uXTMoAzh9SEPPZO6", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_15.png", "conversations": [ { "from": "human", "value": "\nBased on the sales trend depicted in the line chart, which product seems to have a more positive performance over time? Provide your rationale." }, { "from": "gpt", "value": "To determine which product has a more positive performance over time, I need to analyze the trends in the line plots for each product. Looking at {the line for Product A}[0.133, 0.413, 0.944, 0.765], it displays a continuous declining trend from 12000 to 9600 indicating a negative performance. On the other hand, {the line for Product B}[0.133, 0.060, 0.944, 0.559] shows an overall increase from 11000 to 14400 signaling an upward trend and a positive performance.\nBased on the above thought, the answer is:\nBased on the sales trends depicted in the line chart, 'Product B' seems to have a more positive performance over time. While 'Product A' indicates a downward trend with decreasing sales from 2012Q1 to 2014Q4, 'Product B' reflects an upward trend with an increase in sales over the same period. Hence, 'Product B' has shown a more positive progression in sales over time." } ], "id_raw": 1195 }, { "id": "LQiPLB0EUdxiby5s", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_15.png", "conversations": [ { "from": "human", "value": "\nHow much did the sales of 'Product B' increase from 2012Q1 to 2014Q4?" }, { "from": "gpt", "value": "To find the change in sales of 'Product B' from 2012Q1 to 2014Q4, I locate the points for 'Product B' in {2012Q1}[0.133, 0.559] and {2014Q4}[0.944, 0.060]. The sales in 2012Q1 and 2014Q4 are 11000 and 14400 respectively. The change in sales is therefore 14400 - 11000 = 3400 (in thousands).\nBased on the above thought, the answer is:\n3400." } ], "id_raw": 1195 }, { "id": "1XMLkzYYvxgwis2S", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_16.png", "conversations": [ { "from": "human", "value": "\nCan you convert the line graph data into a table format listing the years as rows and Technology as columns?" }, { "from": "gpt", "value": "To generate a structured table from the line graph, I start by identifying {the x-axis tick labels}[[0.135, 0.929, 0.177, 0.952], [0.210, 0.929, 0.252, 0.952], [0.285, 0.929, 0.328, 0.952], [0.361, 0.929, 0.403, 0.952], [0.436, 0.929, 0.478, 0.952], [0.512, 0.929, 0.554, 0.952], [0.587, 0.929, 0.629, 0.952], [0.662, 0.929, 0.704, 0.952], [0.738, 0.929, 0.780, 0.952], [0.813, 0.929, 0.855, 0.952], [0.888, 0.929, 0.931, 0.952]] which are the rows representing the years. Then I recognize {the legend texts}[[0.132, 0.064, 0.274, 0.081], [0.132, 0.090, 0.277, 0.107], [0.132, 0.116, 0.272, 0.133]] as the column headers representing the technologies. To fill the table, I track the {line points per technology}[[0.156, 0.100, 0.910, 0.170],[0.156, 0.057, 0.910, 0.267],[0.156, 0.092, 0.910, 0.267]] for each year and extract the related values.\nBased on the above thought, the answer is:\nYear,Artificial Intelligence,Quantum Computing,Prevision Tech Trend\n2000,0.90,0.75,0.75\n2001,0.92,0.80,0.78\n2002,0.91,0.83,0.80\n2003,0.94,0.85,0.81\n2004,0.93,0.86,0.83\n2005,0.92,0.87,0.85\n2006,0.90,0.88,0.88\n2007,0.88,0.89,0.90\n2008,0.86,0.95,0.92\n2009,0.88,0.97,0.94\n2010,0.87,0.99,0.95" } ], "id_raw": 1196 }, { "id": "81aC1cGlDJiYetNE", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_16.png", "conversations": [ { "from": "human", "value": "\nWhat technology shows the most unpredictable trend?" }, { "from": "gpt", "value": "To determine which technology had the most unpredictable trend I need to locate and observe all three lines for {Artificial Intelligence}[0.156, 0.100, 0.910, 0.170], {Quantum Computing}[0.156, 0.057, 0.910, 0.267], and {Prevision Tech Trend}[0.156, 0.092, 0.910, 0.267]. Artificial Intelligence shows a general decline over the years, Prevision Tech Trend shows a steady increase. Quantum Computing's line has maxima and minima at more varied points which indicates a less predictable development over time.\nBased on the above thought, the answer is:\nQuantum Computing exhibits the most unpredictable trend across the years. Its trend line has highs and lows that are not consistent, unlike the generally steady or downward patterns in Artificial Intelligence and Prevision Tech Trend." } ], "id_raw": 1196 }, { "id": "nEr9k6MGP2kyBET1", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_16.png", "conversations": [ { "from": "human", "value": "\nWhat was the maximum difference between any two years for the Prevision Tech Trend?" }, { "from": "gpt", "value": "I need to find out the maximum change in the line plot of {Prevision Tech Trend}[0.156, 0.092, 0.910, 0.267]. Looking at the coordinates of this line, I observe points making a steady upwards trend with values: 0.75, 0.78, 0.80, 0.81, 0.83, 0.85, 0.88, 0.90, 0.92, 0.94, 0.95. The greatest difference appears to be between 2006 and 2007, where the value goes from 0.88 to 0.90, resulting in a change of 0.02.\nBased on the above thought, the answer is:\n0.02" } ], "id_raw": 1196 }, { "id": "8lZixSHxSF5niEQJ", "Chart_type": "line", "QA_type": "Chart2table", "image": "png_line/line_60_17.png", "conversations": [ { "from": "human", "value": "\nBased on the line chart, can you structure the data of the Growth Rate of Knowledge Economy and Innovation Industry from 2000 to 2030 into a table?" }, { "from": "gpt", "value": "Considering the information provided by the chart structure, {the x-axis}[0.520, 0.952, 0.557, 0.975] contains labels that represent the Years from 2000 to 2030. On the other hand, {the legend}[0.729, 0.781, 0.973, 0.893] contains the names of the Knowledge Economy and Innovation Industry, which would serve as the different data categories. Now, I will map each point on these lines, represented by {the data points on the line for the Knowledge Economy}[[0.206, 0.820], [0.317, 0.736], [0.428, 0.652], [0.538, 0.568], [0.649, 0.484], [0.760, 0.399], [0.871, 0.315]] and {the data points on the line for the Innovation Industry}[[0.206, 0.736], [0.317, 0.652], [0.428, 0.568], [0.538, 0.399], [0.649, 0.231], [0.760, 0.147], [0.871, 0.063]], corresponding to their respective annual growth rate values.\nBased on the above thought, the answer is:\nYear,Knowledge Economy,Innovation Industry\n2000,0.55,0.60\n2005,0.60,0.65\n2010,0.65,0.70\n2015,0.70,0.80\n2020,0.75,0.90\n2025,0.80,0.95\n2030,0.85,1.00" } ], "id_raw": 1197 }, { "id": "2oc06MpARewYEiDg", "Chart_type": "line", "QA_type": "Open QA", "image": "png_line/line_60_17.png", "conversations": [ { "from": "human", "value": "\nWhich sector, the Knowledge Economy or Innovation Industry, had a higher growth rate overall from 2000 to 2030?" }, { "from": "gpt", "value": "To determine the sector with the higher growth rate, I need to compare the final values in 2030 for the Knowledge Economy and Innovation Industry. These values are represented by the data points at the end of their respective lines. On one hand, {the final data point for the Knowledge Economy}[[0.871, 0.315]] is 0.85. On the other hand, {the final data point for the Innovation Industry}[[0.871, 0.063]] is 1.00.\nBased on the above thought, the answer is:\nThe Innovation Industry had a higher overall growth rate from 2000 to 2030." } ], "id_raw": 1197 }, { "id": "noGSamrAjAA1Vv0q", "Chart_type": "line", "QA_type": "Numerical QA", "image": "png_line/line_60_17.png", "conversations": [ { "from": "human", "value": "\nHow much growth did the Innovation Industry observe from 2000 to 2030?" }, { "from": "gpt", "value": "The values for the Innovation Industry are represented by the data points on the corresponding line. The point in the year 2000 is at {2000, 0.60}[[0.206, 0.736]] and the point in 2030 is at {2030, 1.00}[[0.871, 0.063]]. By subtracting the growth rate in 2000 from that in 2030, I can calculate the amount of growth the Innovation Industry observed over this period.\nBased on the above thought, the answer is:\n0.40." } ], "id_raw": 1197 } ]